Reviewerly/exHarmony huggingface dataset:
Improved recommender systems by denoising ratings in highly sparse datasets through individual rating confidence

Collaborative filtering (CF) is the most successful approach of Recommender Systems that has been applied in a wide range of applications. In this approach, historical rating data is exploited to calculate similarity between pairs of users and predict user preference over unseen items. The similarity is calculated using users' global preferences estimated from their previous interactions with items. To this end, commonly rated items by a given pair of users are utilized to calculate the similarity. In a sparse dataset, there are limited co-rated items. In addition, inconsistent behaviours of users in rating items may add some noise to data, which makes the ratings more untrustworthy leading to less accuraterecommendations. In this manuscript, we introduce an individual ratings confidence measure (IRC) to calculate the confidence of a given rating to each item by the target user. IRC consists of 5 factors that help to have more information about the interest and boredom of users. User ratings are denoised by prioritizing extreme and high-confidence ratings, and finally, the denoised ratings are used to obtain similarity values. This approach leads to a more accurate neighbourhood selection and rating prediction. We show the superiority of the proposed method when compared with state-of-the-art recommender algorithms over some well-known benchmark datasets.
TRINE: TELEPRESENCE ROBOTS IN EDUCATION

T. Wernbacher 1, A. Pfeiffer2, P. Häfner3, A. Buchar3, N. Denk1, N. König1, C. DeRaffaele4, A. Attard4, A.A. Economides5, M. Perifanou5 1Danube University Krems (AUSTRIA) 2B&P Emerging Technologies Consultancy Lab Ltd) (MALTA) 3Karlsruhe Institute of Technology (GERMANY) 4MCAST (MALTA) 5University of Macedonia (GREECE)
Entropy guided attention network for weakly-supervised action localization

One major challenge of Weakly-supervised Temporal Action Localization (WTAL) is to handle diverse backgrounds in videos. To model background frames, most existing methods treat them as an additional action class. However, because background frames usually do not share common semantics, squeezing all the different background frames into a single class hinders network optimization. Moreover, the network would be confused and tends to fail when tested on videos with unseen background frames. To address this problem, we propose an Entropy Guided Attention Network (EGA-Net) to treat background frames as out-of-domain samples. Specifically, we design a two-branch module, where a domain branch detects whether a frame is an action by learning a class-agnostic attention map, and an action branch recognizes the action category of the frame by learning a class-specific attention map. By aggregating the two attention maps to model the joint domain-class distribution of frames, our EGA-Net can handle varying backgrounds. To train the class-agnostic attention map with only the video-level class labels, we propose an Entropy Guided Loss (EGL), which employs entropy as the supervision signal to distinguish action and background. Moreover, we propose a Global Similarity Loss (GSL) to enhance the action-specific attention map via action class center. Extensive experiments on THUMOS14, ActivityNet1.2 and ActivityNet1.3 datasets demonstrate the effectiveness of our EGA-Net.
Reading Social Media Marketing Messages as Simulated Self Within a Metaverse: An Analysis of Gaze and Social Media Engagement Behaviors within a Metaverse Platform

The current paper discusses how individuals will process social media content within the metaverse world. Also, the current paper will propose an exploratory study that is designed to provide preliminary evidence regarding how individuals cognitively and emotionally process social media posts embedded in a metaverse platform where they experience the simulation of becoming their desired or positive &#x201C;future self&#x201D;. From data obtained from the gaze tracking and social media engagement metrics that measure users&#x0027; simulated attentional and engagement behaviors, the author will examine to what extent the different temporal distances of virtual or simulated self (present vs near- vs far-future self) and the actual self (lowly vs highly conscientious self) interactively influences the durations of attention (duration of viewing the posts) to and the engagement (i.e., clicking &#x201C;like&#x201D; button for the posts) in different social media posts (e.g., news feeds, positive vs negative dog pictures in native adverts, a health marketing post) seen in the virtual computer screen within the virtual room. In general, it is expected that the father the temporal distance, the longer the duration of the attention to the social media posts. This effect can be moderated by the actual self-views. That is, for participants with negative self-views, the farther the simulated temporal distance, the shorter the duration of attention to the social media posts relevant for the positive self within the metaverse environment. The opposite results are expected to be observed from individuals with positive self-views: the farther the simulated temporal distance, the longer the duration of attention to the social media posts seen within the non-real or metaverse world. The analysis and the plan for the presentation at 2022 IEEE VR workshop is provided in this paper.
Blockchain-Based Internet of Things Access Control Technology in Intelligent Manufacturing

The integration of information systems and physical systems is the development trend of today’s manufacturing industry. Intelligent manufacturing is a new model of manufacturing, based on advanced manufacturing technology with human–machine–material collaboration. Internet of Things technology is the core technology of intelligent manufacturing, and access control technology is one of the main measures to ensure the security of the IoT. In view of the problem that the existing IoT access control model does not support distributed and fine-grained dynamic access control, this paper uses the characteristics of blockchain, such as decentralization and non-tampering, combined with the attribute-based access control (ABAC) method, to propose a distributed access control method, applicable to the IoT environment in the process of intelligent manufacturing. This paper describes a fine-grained access control policy by defining the access control attribute values in a formal language, which supports complex logic operations in the policy and enhances the expressiveness of the model. Distributed access control decision making, using smart contracts for blockchain, improves the decision-making efficiency of the access control model, increases the post-facto audit of the access control behavior, and improves the overall security of IoT data protection. The paper concludes with proof of security and a performance analysis, and the experimental results, such as storage and computing overheads, show that this method can provide fine-grained, dynamic, and distributed access control for devices in intelligent manufacturing, ensuring the security and reliability of access control for IoT devices.
Autonomous quantum error correction in a four-photon Kerr parametric oscillator

Autonomous quantum error correction has gained considerable attention to avoid complicated measurements and feedback. Despite its simplicity compared with the conventional measurement-based quantum error correction, it is still a far from practical technique because of significant hardware overhead. We propose an autonomous quantum error correction scheme for a rotational symmetric bosonic code in a four-photon Kerr parametric oscillator. Our scheme is the simplest possible error correction scheme that can surpass the break-even point -- it requires only a single continuous microwave tone. We also introduce an unconditional reset scheme that requires one more continuous microwave tone in addition to that for the error correction. The key properties underlying this simplicity are protected quasienergy states of a four-photon Kerr parametric oscillator and the degeneracy in its quasienergy level structure. These properties eliminate the need for state-by-state correction in the Fock basis. Our schemes greatly reduce the complexity of autonomous quantum error correction and thus may accelerate the use of the bosonic code for practical quantum computation.
Towards an Interpretable Approach to Classify and Summarize Crisis Events from Microblogs

Microblogging platforms like Twitter have been heavily leveraged to report and exchange information about natural disasters. The real-time data on these sites is highly helpful in gaining situational awareness and planning aid efforts. However, disaster-related messages are immersed in a high volume of irrelevant information. The situational data of disaster events also vary greatly in terms of information types ranging from general situational awareness (caution, infrastructure damage, casualties) to individual needs or not related to the crisis. It thus requires efficient methods to handle data overload and prioritize various types of information. This paper proposes an interpretable classification-summarization framework that first classifies tweets into different disaster-related categories and then summarizes those tweets. Unlike existing work, our classification model can provide explanations or rationales for its decisions. In the summarization phase, we employ an Integer Linear Programming (ILP) based optimization technique along with the help of rationales to generate summaries of event categories. Extensive evaluation on large-scale disaster events shows (a). our model can classify tweets into disaster-related categories with an 85% Macro F1 score and high interpretability (b). the summarizer achieves (5-25%) improvement in terms of ROUGE-1 F-score over most state-of-the-art approaches.
Virtual Reality for Mind and Body: Does the Sense of Embodiment Towards a Virtual Body Affect Physical Body Awareness?

Mind-body therapies aim to improve health by combining physical and mental exercises. Recent developments tend to incorporate virtual reality (VR) into their design and execution, but there is a lack of research concerning the inclusion of virtual bodies and their effect on body awareness in these designs. In this study, 24 participants performed in-VR body awareness movement tasks in front of a virtual mirror while embodying a photorealistic, personalized avatar. Subsequently, they performed a heartbeat counting task and rated their perceived body awareness and sense of embodiment towards the avatar. We found a significant relationship between sense of embodiment and self-reported body awareness but not between sense of embodiment and heartbeat counting. Future work can build on these findings and further explore the relationship between avatar embodiment and body awareness.
Geospatial modeling for selection of optimum sites for hybrid solar-wind energy in Assiut Governorate, Egypt

Renewable energy resources are strategic solutions to develop remote urban areas, provide accessibility, meet the growing energy needs, prevent and mitigate adverse environmental impacts of anthropogenic activities. The purpose of the current research paper is to map and designate optimal sites for locating new hybrid solar and wind farms that fulfill suitability criteria for photovoltaic power plants and wind power in Assiut Governorate. SRTM digital elevation model was processed using the ESRI ArcGIS Area solar radiation tool to estimate and map insolation across the landscape on the topographic surface. Same data (SRTM DEM) was used together with the average wind speed map at elevation 50 m to calculate the wind power density. A geospatial model was designed based on three criteria sets covering three themes: first energy resource (climate and topography), second least cost (infrastructure and third, environmental constraints. The Analytic Hierarchy Process, Weighted Linear Combination and Binary overlay methods were applied, resulting in three sub-models. Such were further combined to produce the solar radiation and the wind power suitability indices separately. The solar radiation index values were reclassified; zones with a minimum threshold of 4 kw/m2/day were selected. From such zones, twenty optimum solar sites were selected based on a minimum area threshold. To identify potential wind zones, a minimum of wind power density equivalent to 220 W/m2 was considered. Results reveal that six potential solar sites exist within the potential wind energy zones; such sites were finally selected as candidates for allocation of hybrid solar-wind power stations.
Effects of Transparency on Perceived Humanness: Implications for Rendering Skin Tones Using Optical See-Through Displays

Current optical see-through displays in the field of augmented reality are limited in their ability to display colors with low lightness in the hue, saturation, lightness (HSL) color space, causing such colors to appear transparent. This hardware limitation may add unintended bias into scenarios with virtual humans. Humans have varying skin tones including HSL colors with low lightness. When virtual humans are displayed with optical see-through devices, people with low lightness skin tones may be displayed semi-transparently while those with high lightness skin tones will be displayed more opaquely. For example, a Black avatar may appear semi-transparent in the same scene as a White avatar who will appear more opaque. We present an exploratory user study ( N=160) investigating whether differing opacity levels result in dehumanizing avatar and human faces. Results support that dehumanization occurs as opacity decreases. This suggests that in similar lighting, low lightness skin tones (e.g., Black faces) will be viewed as less human than high lightness skin tones (e.g., White faces). Additionally, the perceived emotionality of virtual human faces also predicts perceived humanness. Angry faces were seen overall as less human, and at lower opacity levels happy faces were seen as more human. Our results suggest that additional research is needed to understand the effects and interactions of emotionality and opacity on dehumanization. Further, we provide evidence that unintentional racial bias may be added when developing for optical see-through devices using virtual humans. We highlight the potential bias and discuss implications and directions for future research.
Edge-Based Video Compression Texture Synthesis Using Generative Adversarial Network

It has been recognized that texture patterns with abundant high-frequency components, such as grass and water, produce visual masking effects, and the distortion in textures is hard to be perceived by human eyes than structure regions. However, modern video codecs in a rate-distortion optimized manner usually consume a lot of bits to encode textures, leading to the insufficiency in perceptual coding performance. Nowadays, with the rapid development of deep learning, learning based texture synthesis methods have been proposed to replace the coding process of prediction residuals to reduce the rate cost. In this paper, we present a deep texture synthesizer named edge-based texture synthesis framework (ETSF). At encoder side, the framework detects texture regions by semantic and fidelity classification criteria, and the detected regions are quantized coarsely by the hybrid coding framework. In texture characterization, ETSF extracts low-level edge features representing pixel intensity variation. Feature processing tools are developed to remove the spatiotemporal redundancy of edges. The processed edge information is compressed and transmitted. To effectively recover textures, we design an edge-based texture synthesis generative adversarial network (ETSGAN) at the decoder of ETSF, which can incorporate edge information into convolutional layers and generate realistic textures. Experimental results on a collected texture dataset show that the proposed ETSF can achieve an average of -12.8%, -14.2% and -9.6% MOS BD-rate under lowdelay_B, lowdelay_P and random_access configurations of VVC coding, respectively.
Weakly Supervised Visual Saliency Prediction

The success of current deep saliency models heavily depends on large amounts of annotated human fixation data to fit the highly non-linear mapping between the stimuli and visual saliency. Such fully supervised data-driven approaches are annotation-intensive and often fail to consider the underlying mechanisms of visual attention. In contrast, in this paper, we introduce a model based on various cognitive theories of visual saliency, which learns visual attention patterns in a weakly supervised manner. Our approach incorporates insights from cognitive science as differentiable submodules, resulting in a unified, end-to-end trainable framework. Specifically, our model encapsulates the following important components motivated from biological vision. (a) As scene semantics are closely related to visually attentive regions, our model encodes discriminative spatial information for scene understanding through spatial visual semantics embedding. (b) To model the objectness factors in visual attention deployment, we incorporate object-level semantics embedding and object relation information. (c) Considering the "winner-take-all" mechanism in visual stimuli processing, we model the competition mechanism among objects with softmax based neural attention. (d) Lastly, a conditional center prior is learned to mimic the spatial distribution bias of visual attention. Furthermore, we propose novel loss functions to utilize supervision cues from image-level semantics, saliency prior knowledge, and self-information compression. Experiments show that our method achieves promising results, and even outperforms many of its fully supervised counterparts. Overall, our weakly supervised saliency method makes an essential step towards reducing the annotation budget of current approaches, as well as providing a more comprehensive understanding of the visual attention mechanism. Our code is available at: https://github.com/ashleylqx/WeakFixation.git.
Sensing Machines

How we are tracked, surveilled, tantalized, and seduced by machines ranging from smart watches and Roombas to immersive art installations. Sensing machines are everywhere in our world. As we move through the day, electronic sensors and computers adjust our thermostats, guide our Roombas, count our steps, change the orientation of an image when we rotate our phones. There are more of these electronic devices in the world than there are people—in 2020, thirty to fifty billion of them (versus 7.8 billion people), with more than a trillion expected in the next decade. In Sensing Machines, Chris Salter examines how we are tracked, surveilled, tantalized, and seduced by machines ranging from smart watches and mood trackers to massive immersive art installations. Salter, an artist/scholar who has worked with sensors and computers for more than twenty years, explains that the quantification of bodies, senses, and experience did not begin with the surveillance capitalism practiced by Facebook, Amazon, Netflix, and Google but can be traced back to mathematical and statistical techniques of the nineteenth century. He describes the emergence of the “sensed self,” investigating how sensor technology has been deployed in music and gaming, programmable and immersive art environments, driving, and even eating, with e-tongues and e-noses that can taste and smell for us. Sensing technology turns our experience into data; but Salter's story isn't just about what these machines want from us, but what we want from them—new sensations, the thrill of the uncanny, and magic that will transport us from our daily grind.
On the dynamics of Zoom fatigue

The COVID-19 pandemic has made videoconferencing tools an essential part of our lives as these tools are what allowed us to keep in touch in a time of social distancing. Having said that, however, many people have found virtual interactions to be surprisingly exhausting. This has given rise to the concept of Zoom fatigue. The purpose of this article is to explore the dynamics that give rise to this peculiar phenomenon. The article first discusses the concept of Zoom fatigue and critiques the brain-centrism of current explanations. It then proposes a more embodied approach to interaction, discusses the mediating role of technology in videoconferencing, and proceeds to presents a list of five videoconferencing dynamics that may induce Zoom fatigue: Awkward turn-taking, inhibited spontaneity, restricted motility, lack of eye contact and increased self-awareness. Finally, it is argued that these dynamics should make us temper our collective expectations about the hybrid future.
A futuristic approach to generate random bit sequence using dynamic perturbed chaotic system

Most of the web applications require security which in turn requires random numbers. Pseudo-random numbers are required with good statistical properties and efficiency. Use of chaotic map to dynamically perturb another chaotic map that generates the random bit output is introduced in this work. Perturbance is introduced to improvise the chaotic behaviour of a base map and increase the periodicity. PRNG with this architecture is devised to generate random bit sequence from initial keyspace. The statistical properties of newly constructed PRNG are tested with NIST SP 800-22 statistical test suite and were shown to have good randomness. To ensure its usability in cryptographic applications, it has been analyzed for the size of its keyspace, key sensitivity and performance speed. The test results provide evidence that newly designed PRNG has a 3.6% increase in keyspace and a 5% increase in its performance speed compared to existing chaotic PRNGs. The novel PRNG can be used for cryptographic applications with a faster generation of keys and increased security.
Socio-realistic optimal path planning for indoor real-time autonomous mobile robot navigation

An autonomous mobile robotic navigation system consists of many modules which work co-ordinately and concurrently. The most important module is the realistic and optimal path planning algorithm (ROPPA) through which the overall system improves it performance. Many algorithms have been developed and deployed for data structures, and computer games are partially modified for use in real time robotic environments. The major drawback of such modified algorithms is they are designed for unconstrained artificial environments where the robot's collision with a static obstacle or moving object is partially allowed. Many researchers successfully developed the path planning algorithm through improving the basic A* algorithm, such as D* lite, theta*, any angle path planning, and jump point search. In real environments one should seek the optimal path along with maintaining uniform safer distance with the objects or in-path obstacles. This paper describes the implementation and evaluation of a new realistic optimal path planning algorithm which follows the safer distance rule through exploring minimum workspace arena. Along with less memory overhead, the algorithm explores shortest final path with fewer sub-paths if they exist. The experimentation with different map sizes and obstacle densities clearly defines improvement in ROPPA over the other path planning methods.