60 research outputs found
The emerging legal framework for private sector development in Viet Nam's transitional economy
No full textA major objective of Viet Nam's transition to a market economy has been to reactivate the private sector in a mixed economy. Several new laws have been introduced in the past five years to implement this policy and to create an enabling environment for the private sector. The author reviews some of the more important laws and regulations that affect Viet Nam's private sector activities, including laws on real property, intellectual property, companies, domestic investment, foreign investment, bankruptcy, contracts, and dispute resolution. Anti-monopoly law has not yet been introduced in Viet Nam. The issue of competition is addressed in the context of trade law, the relative roles of the state and private sector, and restrictions in company law. These areas all establish the foundation of a legal framework for a market economy. The author concludes that Viet Nam's legal framework, like China's, is still influenced by ideology, which causes problems in such areas as private ownership of real property and with such fundamental legal concepts as"due process of law."It is noted that the private sector is constrained by the lack of an independent judiciary, the absence of private land ownership, other uncertainties in property law that limit the develpoment of financial markets, and the inherent bias of the system in favor of the state sector (and collective ownership). Also noted is a law-abiding attitude, equally important to development has been slow to develop. The author goes on to point out that the foreign investment process is too complicated, and its company law too restrictive. A first priority should be to strreamline regulations, as well as liberalize trade policy and increase efforts in privatization of state enterprises. In this respect the author notes that export processing zones may be a useful interim instrument to attract foreign investment but should be phased out over time. More important in the long term is a good investment climate resting on a strong legal foundation.Legal Products,Environmental Economics&Policies,Banks&Banking Reform,Municipal Housing and Land,Municipal Financial Management,Environmental Economics&Policies,Banks&Banking Reform,Municipal Housing and Land,Legal Products,Municipal Financial Management
No effect of whole-hand water flow stimulation on skill acquisition and retention during sensorimotor adaptation
No full text新潟医療福祉大学Niigata University of Health and Welfare博士(保健学)application/pdfFrontiers in human neuroscience. 2024, 18, p. 1398164-1398164.甲第121号doctoral thesi
Effect of Surface Roughness on Friction of CoCrMo-on-UHMWPE Bearing in Total Hip Arthroplasty Under Lubrication of Bovine Serum Albumin
No full textTHE NEW ENTRANCE BUILDING TO THE FORMER VETERINARIA CAMPUS
No full textLAUREA MAGISTRALE“Questo libro descrive solo le mie opere e i miei progetti, perché una struttura svela la sua natura e gli aspetti più interessanti del suo comportamento al suo ideatore, progettista e costruttore. Dice ben poco all’uomo che la guarda dall’esterno o la esamina attraverso le fotografie.” (Nervi, 1956)
Nonostante Nervi affermi “opere mie”, questo progetto nasce da una meravigliosa collaborazione tra noi, professori e colleghi, ai quali desideriamo ringraziare di cuore.
L’edificio 22010 è l’edificio principale del campus Veterinaria prospiciente via Celoria, da dove avviene l’accesso al campus principale. Secondo il piano del 1910, la sua funzione originaria era l’edificio della Direzione. Oggi è inutilizzato e necessita di una seria ristrutturazione. Si prevede di trasformarlo in biblioteca, demolendo le superfetazioni nella parte ovest dove si inizierà la costruzione del nuovo edificio con nuovo ingresso. È stato costruito tra il 1920 e il 1930 come l’intero campus ed è composto da seminterrato, piano terra, primo piano, secondo piano e mansarda sul tetto. La struttura principale è in muratura perimetrale (spessore per lo più 50 cm), colonne in ca all’interno, pavimentazione in latero-cemento, travi in ca (successivamente aggiunte), volte nel seminterrato, gallerie e atri principali, capriata in legno per la struttura del tetto. L’edificio ha una struttura mista, a cui si aggiungono aggiunte nel corso della sua vita, come nuove scale, ascensori e impianti tecnici per i servizi dell’edificio.
Il Nuovo Edificio d’Ingresso è un’aggiunta su Via Celoria accanto all’attuale Edificio 22010, ora è in fase di trasformazione in biblioteca. Funzionerà come un cancello di apertura al campus. L’approccio principale è quello di lasciare il terreno il più libero possibile, per stimolare il flusso pedonale all’interno del campus, che ci siamo resi conto si è chiuso negli anni al mondo esterno, il che può essere un altro motivo del suo livello di degrado odierno. Il nuovo edificio migliora il flusso pubblico, fornendo anche interessanti elementi architettonici, come la tettoia galleggiante e la massa a sbalzo di 12 metri. Per rendere possibile il cantilever, abbiamo dovuto inventare una soluzione strutturale. L’edificio è composto da due parti: il sistema in ca del seminterrato e del piano terra e la struttura reticolare in acciaio, che funge da scatola rigida, che viene “posta” sul nucleo in ca. In questo modo è possibile avere spazi per le aule senza colonne, un piano terra senza colonne, trasparente e aperto al flusso umano. Il seminterrato ha l’impronta più ampia dell’edificio, intenzionalmente realizzato per creare un divario tra il vecchio e il nuovo aggiungendo più funzioni nel sottosuolo. I tagli al piano terra forniscono luce naturale ai laboratori didattici del piano interrato, portando il più possibile il mondo esterno nel sottosuolo, attraverso l’integrazione della natura. L’edificio mira ad avere il minimo contatto con il campus storico, oltre ad essere reversibile, poiché non è in collegamento fisico con l’edificio esistente 22010.“This book describes only my own works and designs, because a structure unveils its nature and the most interesting aspects of its behavior to its creator, designer, and builder. It tells very little to the man who watches it from the outside or examines it through photographs.” (Nervi, 1956)
Despite Nervi saying, “my own works”, this project is born out of a wonderful collaboration of us, professors and colleagues, whom we would like to give our sincere thanks.
Building 22010 is the main building of the Veterinaria campus facing Via Celoria, where access to the main campus is done through. Based on the 1910 plan, its prior function was the Directorate building. Today it is unused and in need of serious refurbishment. It is expected to be transformed into a library, while demolishing the additions on the west part where the construction of new building with new entrance is started. It had been built between 1920-30 as the whole campus was, and is composed of a basement, ground floor, first floor, second floor, and rooftop attic. The main structure is masonry on perimeter (50cm thickness mostly), RC columns inside, latero-cemento flooring, RC beams (later added), vaults in basement, galleries and main atriums, wooden truss for the roof structure. The building has mixed structure, together with additions throughout its lifespan, such as new staircases, elevators, and technical plants for building services.
The New Entrance Building is an addition on Via Celoria next to today’s Building 22010, is being transformed into a library now. It will function as an opening gate to the campus. The main approach is to leave the ground free as much as possible, to stimulate the pedestrian flow into the campus, which we realized has closed itself to the outer world in years, which can be another reason for its level of degradation today. The new building enhances the public flow, also by providing interesting architectural features, such as floating canopy and 12-meter cantilevered mass. To make the cantilever possible, we had to invent a structural solution. The building is composed of two parts: RC system of basement and ground floor, and steel truss structure, which acts as a rigid box, that is “put” on the RC core. In this way, it is possible to have column-free spaces for the classrooms, column-free ground floor that is transparent and open to human flow. The basement has the largest footprint of the building that is intentionally done to create a gap between the old and the new by adding more functions underground. Cuts on the ground level provide natural light to the didactic labs in the basement, bringing the outer world to the underground as much as possible, through integration of nature. The building aims to have the minimum touch to the historical campus, as well as being reversible, since it is not in physical connection with the existing Building 22010
INTELLIGENT CONTROL SYSTEMS SUPPORTING ENVIRONMENTAL MONITORING: A TECHNICAL FRAMEWORK FOR EVIDENCE BASED ENVIRONMENTAL GOVERNANCE
No full textEnvironmental monitoring is increasingly expected to do more than “observe” ecological conditions: it must also sustain lawful enforcement, support defensible administrative decisions, and enable credible public accountability. Yet many monitoring programs still rely on fragmented sampling routines, manual calibration cycles, and opaque data handling practices that can undermine evidentiary reliability—especially when measurements are challenged by regulated entities, communities, or courts. This paper develops an interdisciplinary framework that connects intelligent control systems with environmental governance needs. We conceptualize monitoring as a socio technical “evidence infrastructure” and show how control oriented functions—state estimation, adaptive sampling, fault detection, and disturbance rejection—can be designed to improve data continuity, uncertainty management, traceability, and responsiveness. Drawing on literature in wireless sensor networks, environmental sensor networks, industrial control, and governance by disclosure, we propose a reference architecture that integrates sensor/edge layers with an auditable data pipeline and governance aligned performance indicators. A simulation based case study for urban air quality monitoring illustrates how adaptive sampling and estimation can reduce missingness, shorten detection delay for exceedance events, and improve robustness to sensor drift while maintaining energy constraints. The discussion translates technical design choices into legal policy implications, including chain of custody practices, transparency and contestability, cybersecurity requirements, and institutional capacity building. The paper contributes a practical roadmap for agencies seeking to modernize monitoring under budgetary pressure without weakening evidentiary standards
New Cytotoxic 1,2,4-Thiadiazole Alkaloids from the Ascidian Polycarpa aurata
No full textTwo new alkaloids, polycarpathiamines A and B (1 and 2), were isolated from the ascidian Polycarpa aurata. Their structures were unambiguously determined by 1D, 2D NMR, and HRESIMS measurements and further confirmed by comparison with a closely related analogue, 3-dimethylamino-5-benzoy1-1,2,4-thiadiazole (4), that was prepared by chemical synthesis. Compounds 1 and 2 both feature an uncommon 1,2,4-thiadiazole ring whose biosynthetic origin is proposed. Compound 1 showed significant cytotoxic activity against L5178Y murine lymphoma cells (IC50 0.41 mu M).Chemistry, OrganicSCI(E)4ARTICLE92230-22331
Inferring Properties of Graph Neural Networks
No full textWe propose GNNInfer, the first automatic property inference technique for
GNNs. To tackle the challenge of varying input structures in GNNs, GNNInfer
first identifies a set of representative influential structures that contribute
significantly towards the prediction of a GNN. Using these structures, GNNInfer
converts each pair of an influential structure and the GNN to their equivalent
FNN and then leverages existing property inference techniques to effectively
capture properties of the GNN that are specific to the influential structures.
GNNINfer then generalizes the captured properties to any input graphs that
contain the influential structures. Finally, GNNInfer improves the correctness
of the inferred properties by building a model (either a decision tree or
linear regression) that estimates the deviation of GNN output from the inferred
properties given full input graphs. The learned model helps GNNInfer extend the
inferred properties with constraints to the input and output of the GNN,
obtaining stronger properties that hold on full input graphs.
Our experiments show that GNNInfer is effective in inferring likely
properties of popular real-world GNNs, and more importantly, these inferred
properties help effectively defend against GNNs' backdoor attacks. In
particular, out of the 13 ground truth properties, GNNInfer re-discovered 8
correct properties and discovered likely correct properties that approximate
the remaining 5 ground truth properties. Using properties inferred by GNNInfer
to defend against the state-of-the-art backdoor attack technique on GNNs,
namely UGBA, experiments show that GNNInfer's defense success rate is up to 30
times better than existing baselines.Comment: 20 pages main paper, 10 pages for appendi
Behavior-based User Authentication Dataset
No full textDescription:
The behavior-based user authentication dataset is collected from the smart user authentication system through daily activities leveraging commodity WiFi. The dataset contains the extracted CSI features from 8 walking activities and 9 stationary activities from 11 and 5 volunteers, respectively. The experiments are conducted in 2 different environments, including a university office and an apartment. We hope this dataset will help researchers to reproduce the former work of user authentication through WiFi sensing.
Dataset Format:
.dat files
Section 1: Device Configuration:
Transmitter: Intel 5300 NIC with a Dell E6430 laptop for control.
Receiver: Intel 5300 NIC with a Lenovo T61 laptop for control.
Run with a Linux 14.04 operating system with 4.2.0 kernel. Equipped with 3 MINI PCI-E internal antennas.
Intel 5300 network interface card (NIC) for CSI collection. The detail information regarding the CSI tool can be found at https://dhalperi.github.io/linux-80211n-csitool/faq.html.
WiFi Packet Rate: 1000 pkts/s
Section 2: Data Format
We provide raw data received by the CSI tool. The data files are saved in the dat format. The details are shown in the following:
8 walking activities and 8 stationary activities are collected from 11 and 5 participants are from two different experiments.
Each data file contains 30 rounds of one type of activity from each participant.
The dataset file name is presented as " Day_Channel_User_Action ". The detailed information as:
Day: The exact date this data was collected.
User: The participants that CSI was collected from.
Channel: The specific WiFi channel data was collected from.
Action: The specific activity performed.
Note: we select these data specifically to form the dataset to make it efficent, we did not publish every data that we have collected during paper writing. If you have any question regarding the dataset, please contact us for detail information.
Section 3: Experimental Setups
There are 2 different experiment setups, including a university office and an apartment environment, for our data collection. The detailed setups are shown in the paper. For the activities, we involve 8 walking activities and 8 stationary activities. An image of the experimental setup and the illustration of activities from two different environments is included in the dataset.
Environments:
2 different environments are involved, including an office environment with the size of 26 ft × 14 ft and an apartment with the size of 36 ft × 22 ft.
Activity description:
A total of 8 walking activities and 8 stationary activities (30 rounds for each) are performed by 11 and 5 volunteers.
The walking activities include 8 different trajectories of walking.
The stationary activities include 8 daily activities, such as typing on the keyboard, turning on the light, opening the cabinet, fetching documents, eating, opening the oven, opening the fridge and opening the door.
Detailed daily activities performed
Code
Walking activity
Code
Stationary activity
A
Entrance ⇒ Seat
a
Working (i.e., typing keyboard)
B
Seat ⇒ Entrance
b
Turning on the light
C
Seat ⇒ Light Switch
c
Opening the cabinet
D
Light Switch ⇒ Seat
d
Fetching documents
E
Seat ⇒ Cabinet
e
Eating at the table
F
Cabinet ⇒ Seat
f
Opening the microwave oven
G
Entrance ⇒ Kitchen
g
Opening the refrigerator
H
Kitchen ⇒ Entrance
h
Opening the door
Number of data samples:
In total, 3335 activity segments are performed by 11 subjects in the office. 834 activity segments are performed by 5 subjects in the apartment.
Section 4: Data Description
We separate our raw data into different folders based on different environment types. In each environment type, data are further distributed in terms of date. Each file includes all data from three internal antennas. All data files are in .dat format. We also provide Matlab scripts for CSI analysis and visualization. The following variables can be revealed from the codes:
CSI: This is the Channel State Information (CSI) received from one receiver antenna. It describes the signal propagation from the transmitter to the receiver, and it is very sensitive to the impact of environmental changes. Each data reveals CSI from 30 subcarriers.
Relative Phase: Relative Phase is a measurement to describe the degree of synchronization between data received from different antennas. It can be used to determine the phase offset for further signal preprocessing.
Time: This is the time interval in which the data file contains. It measures time by the number of seconds. It can be used to determine how long the signal has been received.
Section 5: Codes
analysis_spectrogram.m: load a .dat file and extract all data based on the “Data description” (I.e, CSI, and Relative Phase).
Section 6: Citations
If your work is related to our work, please cite our papers as follows.
https://dl.acm.org/doi/10.1145/3084041.3084061
Cong Shi, Jian Liu, Hongbo Liu, and Yingying Chen. 2017. Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT. In Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing (Mobihoc '17). Association for Computing Machinery, New York, NY, USA, Article 5, 1–10.
Bibtex:
@inproceedings{shi2017smart,
title={Smart user authentication through actuation of daily activities leveraging WiFi-enabled IoT},
author={Shi, Cong and Liu, Jian and Liu, Hongbo and Chen, Yingying},
booktitle={Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing},
pages={1--10},
year={2017}
Behavior-based User Authentication Dataset
No full textDescription:
The behavior-based user authentication dataset is collected from the smart user authentication system through daily activities leveraging commodity WiFi. The dataset contains the extracted CSI features from 8 walking activities and 9 stationary activities from 11 and 5 volunteers, respectively. The experiments are conducted in 2 different environments, including a university office and an apartment. We hope this dataset will help researchers to reproduce the former work of user authentication through WiFi sensing.
Dataset Format:
.dat files
Section 1: Device Configuration:
Transmitter: Intel 5300 NIC with a Dell E6430 laptop for control.
Receiver: Intel 5300 NIC with a Lenovo T61 laptop for control.
Run with a Linux 14.04 operating system with 4.2.0 kernel. Equipped with 3 MINI PCI-E internal antennas.
Intel 5300 network interface card (NIC) for CSI collection. The detail information regarding the CSI tool can be found at https://dhalperi.github.io/linux-80211n-csitool/faq.html.
WiFi Packet Rate: 1000 pkts/s
Section 2: Data Format
We provide raw data received by the CSI tool. The data files are saved in the dat format. The details are shown in the following:
8 walking activities and 8 stationary activities are collected from 11 and 5 participants are from two different experiments.
Each data file contains 30 rounds of one type of activity from each participant.
The dataset file name is presented as " Day_Channel_User_Action ". The detailed information as:
Day: The exact date this data was collected.
User: The participants that CSI was collected from.
Channel: The specific WiFi channel data was collected from.
Action: The specific activity performed.
Section 3: Experimental Setups
There are 2 different experiment setups, including a university office and an apartment environment, for our data collection. The detailed setups are shown in the paper. For the activities, we involve 8 walking activities and 8 stationary activities. An image of the experimental setup and the illustration of activities from two different environments is included in the dataset.
Environments:
2 different environments are involved, including an office environment with the size of 26 ft × 14 ft and an apartment with the size of 36 ft × 22 ft.
Activity description:
A total of 8 walking activities and 8 stationary activities (30 rounds for each) are performed by 11 and 5 volunteers.
The walking activities include 8 different trajectories of walking.
The stationary activities include 8 daily activities, such as typing on the keyboard, turning on the light, opening the cabinet, fetching documents, eating, opening the oven, opening the fridge and opening the door.
Detailed daily activities performed
Code
Walking activity
Code
Stationary activity
A
Entrance ⇒ Seat
a
Working (i.e., typing keyboard)
B
Seat ⇒ Entrance
b
Turning on the light
C
Seat ⇒ Light Switch
c
Opening the cabinet
D
Light Switch ⇒ Seat
d
Fetching documents
E
Seat ⇒ Cabinet
e
Eating at the table
F
Cabinet ⇒ Seat
f
Opening the microwave oven
G
Entrance ⇒ Kitchen
g
Opening the refrigerator
H
Kitchen ⇒ Entrance
h
Opening the door
Number of data samples:
In total, 3335 activity segments are performed by 11 subjects in the office. 834 activity segments are performed by 5 subjects in the apartment.
Section 4: Data Description
We separate our raw data into different folders based on different environment types. In each environment type, data are further distributed in terms of date. Each file includes all data from three internal antennas. All data files are in .dat format. We also provide Matlab scripts for CSI analysis and visualization. The following variables can be revealed from the codes:
CSI: This is the Channel State Information (CSI) received from one receiver antenna. It describes the signal propagation from the transmitter to the receiver, and it is very sensitive to the impact of environmental changes. Each data reveals CSI from 30 subcarriers.
Relative Phase: Relative Phase is a measurement to describe the degree of synchronization between data received from different antennas. It can be used to determine the phase offset for further signal preprocessing.
Time: This is the time interval in which the data file contains. It measures time by the number of seconds. It can be used to determine how long the signal has been received.
Section 5: Codes
analysis_spectrogram.m: load a .dat file and extract all data based on the “Data description” (I.e, CSI, and Relative Phase).
Section 6: Citations
If your work is related to our work, please cite our papers as follows.
https://dl.acm.org/doi/10.1145/3084041.3084061
Cong Shi, Jian Liu, Hongbo Liu, and Yingying Chen. 2017. Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT. In Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing (Mobihoc '17). Association for Computing Machinery, New York, NY, USA, Article 5, 1–10.
Bibtex:
@inproceedings{shi2017smart,
title={Smart user authentication through actuation of daily activities leveraging WiFi-enabled IoT},
author={Shi, Cong and Liu, Jian and Liu, Hongbo and Chen, Yingying},
booktitle={Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing},
pages={1--10},
year={2017}
Behavior-based User Authentication Dataset
No full textDescription:
The behavior-based user authentication dataset is collected from the smart user authentication system through daily activities leveraging commodity WiFi. The dataset contains the extracted CSI features from 8 walking activities and 9 stationary activities from 11 and 5 volunteers, respectively. The experiments are conducted in 2 different environments, including a university office and an apartment. We hope this dataset will help researchers to reproduce the former work of user authentication through WiFi sensing.
Dataset Format:
.dat files
Section 1: Device Configuration:
Transmitter: Intel 5300 NIC with a Dell E6430 laptop for control.
Receiver: Intel 5300 NIC with a Lenovo T61 laptop for control.
Run with a Linux 14.04 operating system with 4.2.0 kernel. Equipped with 3 MINI PCI-E internal antennas.
Intel 5300 network interface card (NIC) for CSI collection. The detail information regarding the CSI tool can be found at https://dhalperi.github.io/linux-80211n-csitool/faq.html.
WiFi Packet Rate: 1000 pkts/s
Section 2: Data Format
We provide raw data received by the CSI tool. The data files are saved in the dat format. The details are shown in the following:
8 walking activities and 8 stationary activities are collected from 11 and 5 participants are from two different experiments.
Each data file contains 30 rounds of one type of activity from each participant.
The dataset file name is presented as " Day_Channel_User_Action ". The detailed information as:
Day: The exact date this data was collected.
User: The participants that CSI was collected from.
Channel: The specific WiFi channel data was collected from.
Action: The specific activity performed.
Section 3: Experimental Setups
There are 2 different experiment setups, including a university office and an apartment environment, for our data collection. The detailed setups are shown in the paper. For the activities, we involve 8 walking activities and 8 stationary activities. An image of the experimental setup and the illustration of activities from two different environments is included in the dataset.
Environments:
2 different environments are involved, including an office environment with the size of 26 ft × 14 ft and an apartment with the size of 36 ft × 22 ft.
Activity description:
A total of 8 walking activities and 8 stationary activities (30 rounds for each) are performed by 11 and 5 volunteers.
The walking activities include 8 different trajectories of walking.
The stationary activities include 8 daily activities, such as typing on the keyboard, turning on the light, opening the cabinet, fetching documents, eating, opening the oven, opening the fridge and opening the door.
Detailed daily activities performed
Code
Walking activity
Code
Stationary activity
A
Entrance ⇒ Seat
a
Working (i.e., typing keyboard)
B
Seat ⇒ Entrance
b
Turning on the light
C
Seat ⇒ Light Switch
c
Opening the cabinet
D
Light Switch ⇒ Seat
d
Fetching documents
E
Seat ⇒ Cabinet
e
Eating at the table
F
Cabinet ⇒ Seat
f
Opening the microwave oven
G
Entrance ⇒ Kitchen
g
Opening the refrigerator
H
Kitchen ⇒ Entrance
h
Opening the door
Number of data samples:
In total, 3335 activity segments are performed by 11 subjects in the office. 834 activity segments are performed by 5 subjects in the apartment.
Section 4: Data Description
We separate our raw data into different folders based on different environment types. In each environment type, data are further distributed in terms of date. Each file includes all data from three internal antennas. All data files are in .dat format. We also provide Matlab scripts for CSI analysis and visualization. The following variables can be revealed from the codes:
CSI: This is the Channel State Information (CSI) received from one receiver antenna. It describes the signal propagation from the transmitter to the receiver, and it is very sensitive to the impact of environmental changes. Each data reveals CSI from 30 subcarriers.
Relative Phase: Relative Phase is a measurement to describe the degree of synchronization between data received from different antennas. It can be used to determine the phase offset for further signal preprocessing.
Time: This is the time interval in which the data file contains. It measures time by the number of seconds. It can be used to determine how long the signal has been received.
Section 5: Codes
analysis_spectrogram.m: load a .dat file and extract all data based on the “Data description” (I.e, CSI, and Relative Phase).
Section 6: Citations
If your work is related to our work, please cite our papers as follows.
https://dl.acm.org/doi/10.1145/3084041.3084061
Cong Shi, Jian Liu, Hongbo Liu, and Yingying Chen. 2017. Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT. In Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing (Mobihoc '17). Association for Computing Machinery, New York, NY, USA, Article 5, 1–10.
Bibtex:
@inproceedings{shi2017smart,
title={Smart user authentication through actuation of daily activities leveraging WiFi-enabled IoT},
author={Shi, Cong and Liu, Jian and Liu, Hongbo and Chen, Yingying},
booktitle={Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing},
pages={1--10},
year={2017}
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