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Are depression, anxiety, and loneliness associated with visual hallucinations in younger adults with Charles Bonnet Syndrome?
Get PDFBackgroundCharles Bonnet Syndrome (CBS) refers to the presence of visual hallucinations experienced by people, without cognitive, or psychiatric deficits that are related to sight loss. This study surveyed younger adults (18-60 years) with visual impairments, to assess the impact of anxiety, depression, loneliness and the Covid-19 lockdowns on their visual hallucinations.Objectives To examine the association between depression, anxiety, loneliness, and the Covid-19 lockdowns, and visual hallucinations in younger adults with CBS.DesignAn on-line survey was used with an opportunistic sample of people with sight loss.Methods A survey assessed the frequency, duration and valence of visual hallucinations using a 5-point Likert scale, assessed anxiety, depression and loneliness using the Hospital Anxiety and Depression Scale and UCLA loneliness scale, respectively.Results29 young adults (21 female), aged 22-59 years with vision loss from a range of causes, who experience visual hallucinations, were included in the survey. The majority (76%) of participants had experienced hallucinations within the past week, with 83% stating they occurred frequently or very frequently. For 59% of participants the hallucinations were of short duration (<2 mins), but 34% experienced them continuously. Hallucinations were regarded as being unpleasant by 34% of participants, while 59% rated them as being neutral. The incidence of depression and anxiety were high in the sample (48% and 65% respectively) and 65% experienced loneliness. Participants with scores indicating anxiety or borderline anxiety had significantly more frequent hallucinations than other participants and a similar trend was found for depression. The Covid-19 lockdowns exacerbated hallucinations in 24% of cases, but for 68% they remained unchanged. ConclusionThe study demonstrated that Charles Bonnet Syndrome is observed in people of all ages, with sight-loss arising from a wide range of underlying causes. Depression, anxiety and loneliness are observed in many cases of CBS. While there was some indication that high anxiety, and to some extent depression, was associated with frequent hallucinations, no other relationships were found between the psychosocial factors (depression, anxiety and loneliness) and the frequency, duration, or valence of their visual hallucinations
Dynamic deep graph convolution with enhanced transformer networks for time series anomaly detection in IoT
Get PDFAnomaly detection of multi-time series data during the working process of Internet of Things systems that utilize sensors is one of the key aspects to prevent accidents in industrial information systems. The key challenge is to discover generalized normal patterns by capturing spatio-temporal correlations in multi-sensor data. However, most of the existing studies face the following challenges: (1) Complex topologies and nonlinear connectivity among sensors lack effective characterization methods. (2) Sophisticated correlations among time series need to be mined deeply. Therefore, we propose a novel dynamic deep graph convolution with enhanced transformer networks (DDGCT) for time series anomaly detection. We first construct a dynamic deep graph convolutional network to automatically learn the complex spatial dependencies of sensor data, which introduces norm with Hard Concrete distribution to further guide the optimization of graph structure in graph learning. Meanwhile, we devise a new transformer model to deeply mine temporal dependencies from time-series data by designing a new positional encoding coupled with patch design as well as channel independence constraint. Then, DDGCT fuses and optimizes the captured temporal and deep spatial features using attention networks. Finally, anomaly scores are efficiently computed by prediction methods with threshold-based approaches to detect anomalies. Extensive experiments on real datasets show that DDGCT outperforms several state-of-the-art methods
From Cellhouses to the Parliament: Kurdish Hizbullah’s Transformation and the AKP Governance in Turkey
Get PDFThis chapter investigates the ethnoreligious dimensions of the Kurdish question in Turkey. It focuses on a prominent Kurdish Islamist group, Hizbullah, and its transformation from an underground armed organization in the 1990s to a multiplicity of legal entities and a political party, the Free Cause Party(Hür Dava Partisi, Hüda-Par), under the rule of the Justice and Development Party (AKP) since 2002. After introducing a short historical account, it examines the conditions of Hizbullah’s emergence and its reconfiguration in the context of the Kurdish national struggle and the Islamist mobilization underthe AKP’s governmentality in Turkey. It explores the power configurations and shifting alliances among the Islamist political elds following the failed peace negotiations between the Turkish state and the PKK in 2015 and the military coup attempt in 2016. It suggests that by thoroughly examining these shifting alliances and paying scholarly attention to the developments in the ethnoreligious political fields, we can understand the ways in which Kurdish Islamist groups navigate and adaptwithin the complex socio-political landscape of Turkey, shedding light on the intricate interplay between ethnicity, religion, and politics in the region
Steady State – co-created by Alexander Schubert, Zubin Kanga, Felina Levits, Alexander Trattler and Serafeim Perdikis
Get PDFSteady State is a performative piece that integrates neuroscience and technology to create a feedback loop between the performer’s brain activity and computer-generated stimuli. Using an EEG cap, the performer’s dominant brain frequencies are measured as they interact with strobing video projections. The performer’s gaze and brain activity influence the visuals, creating a continuous loop. The piece blurs the lines between installation and performance, with the performer turning into a transhuman processing unit, revolving around the body, computation, hallucination, and transcendence.It is a collaboration between: Alexander Schubert: composerZubin Kanga: project manager and lead performerSerafeim Perdikis: neuro-programmingAlexander Trattler: video designFelina Levits: costumesANT Neuro: brain sensorsNote that the score, tech rider and brain-computer interaction software are provided in links below. Further materials for the work are available on request. <br/
Evaluation of Entity Trustworthiness Based on Public and Private Data
Get PDFThis paper presents an analysis of how reconciliation of public and private data can be used to evaluate the trustworthiness of an entity. The TE (Trustworthy Ecosystem) knowledge graph is used as a basis. We demonstrate how public and private data can be used to add instance data to a TE graph, and how a trustworthiness evaluation policy can be extended to make use of this data during evaluation. It is argued and demonstrated that reconciliation of independent information about an entity can be used in evaluating the entity’s trustworthiness
Distribution-aware fairness test generation
Get PDFEnsuring that all classes of objects are detected with equal accuracy is essential in AI systems. For instance, being unable to identify any one class of objects could have fatal consequences in autonomous driving systems. Hence, ensuring the reliability of image recognition systems is crucial. This work addresses how to validate group fairness in image recognition software. We propose a distribution-aware fairness testing approach (called DISTROFAIR) that systematically exposes class-level fairness violations in image classifiers via a synergistic combination of out-of-distribution (OOD) testing and semantic-preserving image mutation. DISTROFAIR automatically learns the distribution (e.g., number/orientation) of objects in a set of images. Then it systematically mutates objects in the images to become OOD using three semantic-preserving image mutations – object deletion, object insertion and object rotation. We evaluate DISTROFAIR using two well-known datasets (CityScapes and MS-COCO) and three major, commercial image recognition software (namely, Amazon Rekognition, Google Cloud Vision and Azure Computer Vision). Results show that about 21% of images generated by DISTROFAIR reveal class-level fairness violations using either ground truth or metamorphic oracles. DISTROFAIR is up to 2.3× more effective than two main baselines, i.e., (a) an approach which focuses on generating images only within the distribution (ID) and (b) fairness analysis using only the original image dataset. We further observed that DISTROFAIR is efficient, it generates 460 images per hour, on average. Finally, we evaluate the semantic validity of our approach via a user study with 81 participants, using 30 real images and 30 corresponding mutated images generated by DISTROFAIR. We found that images generated by DISTROFAIR are 80% as realistic as real-world images
The authentic virtual influencer:authenticity manifestations in the metaverse
Get PDFVirtual influencers (VI) are fictional entities operated by third parties (freelance creators, digital agencies, or brands). Despite their increasing popularity, the way people approach these often human-looking yet entirely fictitious creations, and whether they view them as ‘authentic’, remains unclear. Existing conceptualizations of authenticity in the VI literature do not offer sufficient depth and richness to understand this complex phenomenon. Building on the Entity-Referent Correspondence Framework of Authenticity, this paper aims to explore different manifestations of authenticity in the context of VIs. We draw on interviews with consumers (64) and industry experts (11) to unveil different perspectives. Our findings demonstrate how the three types of authenticity—true-to-ideal (TTI), true-to-fact (TTF) and true-to-self (TTS)—apply to and manifest in a virtual influencer context. We conclude with theoretical contributions, with particular attention to the uncanny valley theory, managerial recommendations, and areas for future research
A Convolutional Recurrent Neural Network with Spatial Feature Fusion for Environmental Sound Classification
Get PDFThis research proposes a new Convolutional Recurrent Neural Network (CRNN) model with spatial feature fusion for environmental sound classification. Besides data preprocessing such as spectrogram transformation and data augmentation, customized deep networks, i.e. VGG19, ResNet152, and EfficientNetB0, with additional layers, are also proposed for audio classification. Specifically, the proposed CRNN model embeds ResNet152 and EfficientNetB0 in the encoder where spatial features extracted by both networks are concatenated. A Long Short-Term Memory (LSTM) component is used as the decoder in the proposed CRNN for temporal feature extraction. Evaluated using the ESC-50 dataset, the proposed CRNN model with a multi-channel spatial feature fusion, outperforms the customized VGG19, ResNet152, EfficientNetB0 networks as well as existing studies, significantly. The spatial feature fusion in conjunction with LSTM-based sequential feature extraction accounts for the superiority of the proposed CRNN model for environmental sound classification
Evaluation of Entity Trustworthiness Based on Public and Private Data
Get PDFThis paper presents an analysis of how reconciliation of public and private data can be used to evaluate the trustworthiness of an entity. The TE (Trustworthy Ecosystem) knowledge graph is used as a basis. We demonstrate how public and private data can be used to add instance data to a TE graph, and how a trustworthiness evaluation policy can be extended to make use of this data during evaluation. It is argued and demonstrated that reconciliation of independent information about an entity can be used in evaluating the entity’s trustworthiness
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