Reviewerly/exHarmony huggingface dataset:
Embodiment and Performance in the Supernumerary Hand Illusion in Augmented Reality

In teleoperations, robots are generally used because related tasks are too dangerous, uncomfortable or impossible for humans to perform. When using augmented reality to control robotic limbs in such teleoperations, it is essential to understand how these extra virtual limbs are experienced. In particular, the relationship between the embodiment experience of the user and relevant outcomes such as task performance must be examined. In this article, we study the relationship between experienced embodiment of a supernumerary virtual arm that acts alongside a user’s two real arms, and their task performance in augmented reality. Specifically, we compare how well users can trace a virtual half ring placed just outside of personal space using their virtual arm in a condition where there is expected to be low embodiment (a floating disconnected hand) and a condition where there is expected to be high embodiment (a connected arm and hand). Embodiment is measured quantitatively through skin conductance response and qualitatively through ownership, agency, and self-location questionnaires. Performance is measured in terms of tracing precision. The results show positive correlations between subjective ownership and agency, and agency and performance, but no correlation between subjective or objective ownership and performance. Also, ownership ratings were low overall, while the agency ratings were significantly higher for the disconnected hand condition than the connected arm condition, as was performance. Notably, the presence of the virtual arm evoked incorrect expectations of the movement capabilities of the arm, which may have contributed to an overall preference for the unrealistic disconnected hand over the more realistic connected arm in this particular task. Our results imply that methods to increase performance in various teleoperations can indeed be found in the experience of embodiment: not necessarily directly through ownership, but through ownership mediated by agency.
A Robust Distance Regularized Potential Function for Level Set Image Segmentation

The level set is a classical image segmentation method, but during the evolution of the level set, it can produce evolutionary problems such as local spikes and deep valleys, or overly flat regions, making the iterative process of final segmentation unstable and segmentation results inaccurate. In order to ensure the stability and validity of the level set evolution during the evolution process, the level set function must be periodically initialized so that the level set is always kept as a signed distance function. We construct a new distance regularization potential function based on logarithmic and power function and give a specific analysis. During the evolution process, the level set function always approximates the signed distance function, which is stable and efficient for level set image segmentation. Experimental analyses are conducted to compare the segmentation performance of various distance regularization potential functions when combining with the classical Chan Vese model.
A novel user trend‐based priority assigner and <scp>URL</scp> scheduler for dynamic incremental crawling

Summary An efficient search engine needs to be designed in such a way that is able to provide relevant and accurate information in accordance with user needs and interests. The quality of downloaded records can be guaranteed only when website pages of high pertinence are downloaded by the crawlers in accordance with the current topics or user trends. Earlier Focused Crawlers were used to download topic specific pages but these crawlers were not able to adapt to the changing interest of the users. Therefore, there is a need to design crawlers that are able to naturally track the present pattern points and download site pages that meet client's present need. In this paper, a priority assigner and scheduler method for organizing Uniform Resource Locators (URLs) is being proposed that helps the crawler in tracking user's interest and prioritize downloading documents that are relevant to the user's choice in addition to current trends. The experimental results conforms that the proposed priority assigner and URL scheduler‐based crawling outshines conventional crawling strategies based on Change‐history or Site‐Map‐based methods in terms of quality of downloaded web pages and reducing network traffic over the Internet.
Classification approaches in unmanned aerial vehicle (UAV) intrusion detection data set by using big data analysis

The Multilayer Perceptron produces the high level of accuracy i.e., 72.7% accuracy level. The Logistic approach produces the 81.45% of precision value. This is the highest precision value compare with other model. Rest of the algorithms namely Gaussian Processes, Linear Regression, Multilayer Perceptron and SGD have produce the precision values from 71 to 81%. The Multilayer Perceptron approach takes low time consumption like 0.09 s to build the model, the Next SGD approach takes 0.12 s to build the model, the Next Logistic approach takes the 0.18 s, the Next Gaussian Processes approach takes the 0.2 s and finally Linear Regression approach takes more time around 0.21 s to build the model. The Linear Regression, Multilayer Perceptron have produces the recall value most nearly with Gaussian Processes. Rest of the approaches like SGD results vast difference with our optimal results and Logistic has very low and vast difference with our optimal approaches. AI strategies are used in an ever-increasing number of true applications including limitations on computational spending plans, the precise incorporation of such imperatives into the cycle of model choice, and model improvement is needed to an expanding degree.
Psychometric Mapping of Audio Features to Perceived Physical Characteristics of Virtual Objects

Physically-based sound synthesis can simulate virtual sound sources whose audio features reflect the physical characteristics of corresponding objects displayed in a virtual environment, allowing for real-time generation of content without relying on pre-existing audio samples. This, however, requires efficient control strategies for sound synthesis models that, depending on the nature of the sounding objects, require to be mapped to varying physical characteristics displayed through visual information. In this experiment, participants were asked to adjust a set of sound synthesis parameters based on varying physical characteristics of a virtual bouncing ball: distance, elasticity and radius. Statistical analysis of recorded subject responses shows that object radius influences evaluation of pitch and amplitude for the object's representation. Similarly, distance influences user evaluation of both reverb and amplitude whilst elasticity doesn't influence user evaluation of the feature distributions. This result is consistent across user groups evaluated: audio experts and naïve listeners. Models are produced that encode these observations using linear regression, enabling automatic parameterisation of this feature space for audio synthesis engines.
How Much is Too Much: The Difficulties of Social Media Content Moderation

Recent events, including the 2020 Presidential Election and the Insurrection of the U.S. Capitol, have shown us that social media can be used for darker purposes. Hate speech, fake news, and content inciting violence have become the unfortunate norm when scrolling through one’s newsfeed. Platforms have had to face the issue of dealing with objectionable content such as this. Should they leave it there? Should they get rid of it? How do they differentiate between what’s acceptable and what’s not? Are these decisions made consistently and accurately? The bigger questions have become whether social media platforms are removing enough material or removing too much.This Article address the two major methods that social media platforms have used to moderate objectionable content, including the many flaws associated with each. External legal factors including Section 230 and FOSTA-SESTA are discussed as potential motivators for the evolving social media moderating techniques. Additionally, this Article discusses the strengthening hold that app markets such as Apple, Amazon, and Google have over social media platforms and how these relationships directly influence how platforms police content. Finally, alternative methods of moderation are proposed and discussed in relation to the current moderating norms.
NS-Hunter: BERT-Cloze Based Semantic Denoising for Distantly Supervised Relation Classification

Distant supervision can generate large-scale relation classification data quickly and economically. However, a great number of noise sentences are introduced which can not express their labeled relations. By means of pre-trained language model BERT’s powerful function, in this paper, we propose a BERT-based semantic denoising approach for distantly supervised relation classification. In detail, we define an entity pair as a source entity and a target entity. For the specific sentences whose target entities in BERT-vocabulary (one-token word), we present the differences of dependency between two entities for noise and non-noise sentences. For general sentences whose target entity is multi-token word, we further present the differences of last hidden states of [MASK]-entity (MASK-lhs for short) in BERT for noise and non-noise sentences. We regard the dependency and MASK-lhs in BERT as two semantic features of sentences. With BERT, we capture the dependency feature to discriminate specific sentences first, then capture the MASK-lhs feature to denoise distant supervision datasets. We propose NS-Hunter, a novel denoising model which leverages BERT-cloze ability to capture the two semantic features and integrates above functions. According to the experiment on NYT data, our NS-Hunter model achieves the best results in distant supervision denoising and sentence-level relation classification.
Implementation of a Reconfigurable Architecture to Compute Linear Transformations Used in Signal/Image Processing

Computationally intensive linear transformations used in signal or image processing applications are considered. The flow graph is a common way of describing these types of algorithms. Using graph theoretic approach, it is deduced that a completely connected bipartite graph will be able to replicate all possible paths of any arbitrary size flow graph representing any such transformation. Next, a reconfigurable architecture is proposed imitating a completely connected bipartite graph. The overall architecture and the processing element architecture are discussed in detail. The instruction set for implementing routing, data in/out, data move, arithmetic operations, etc. are implemented by generating appropriate control signals. One representative FDCT algorithm is selected, and the longest sequence of operations of that FDCT algorithm is implemented using the instructions from the proposed instruction set. Entire architecture is implemented using VHDL, and synthesized report is presented.
Single frame shadow segmentation based on image enhancement for video SAR

As Video synthetic aperture radar (SAR) technology has been developing rapidly in recent years, moving target detection and tracking has gradually become a research hotspot in the field of SAR. Since moving targets in Video SAR produce relatively clear shadows at their real locations, the shadow-based approach provides a new method for ground moving target detection. In this paper, a new approach based on image fusion enhancement is proposed to improve the extraction effect of target shadow in single frame Video SAR image, and the process of shadow segmentation is studied accordingly. First, we use Median Filter to denoise the image, and then use a variety of image enhancement methods to improve the contrast between shadows and background, including piecewise linear stretching, histogram specification, and S-curve enhancement, then use adaptive threshold segmentation algorithm to realize the separation of background and target shadow, finally use morphological processing method to further highlight the target shadow. The effectiveness of the proposed approach is verified on the Video SAR dataset published by Sandia Lab.
A Universal Cycle for Strings with Fixed-Content (Which Are Also Known as Multiset Permutations)

We develop the first universal cycle construction for strings with fixed-content (also known as multiset permutations) using shorthand representation. The construction runs a necklace concatenation algorithm on cool-lex order for fixed-content strings, and is implemented to generate the universal cycle in amortized O(1)-time per symbol. This generalizes two previous results: a universal cycle for shorthand permutations by Ruskey, Holroyd, and Williams [Algorithmica 64 (2012)] and a universal cycle for shorthand fixed-weight binary strings by Ruskey, Sawada, and Williams [SIAM J. on Disc. Math. 26 (2012)]. A consequence of our construction is the first shift Gray code for fixed-content necklaces.
Processing of global solar irradiance and ground-based infrared sky images for solar nowcasting and intra-hour forecasting applications

Shadows from moving clouds in the troposphere impact the energy generated by photovoltaic systems. intra-hour solar forecast can be used to regulate solar energy dispatch. This investigation develops a data processing method for cloud dynamic feature extraction from raw sky images and Global Solar Irradiance (GSI) measurements that can be integrated into solar forecasting algorithms to reduce the operational supervision of hardware. Sky images and GSI measurements are acquired from a low-cost long-wave infrared radiometric camera and a pyranometer. This sky imager is mounted on a solar tracker that maintains the Sun in the center of sky images throughout the day. Multiple processing methods are proposed here that take advantage of a hybrid approach to approximate the optimal parameters of physical models using computationally inexpensive machine learning models. A signal processing method removes cyclostationary biases in high-resolution clear sky index values found when detrending GSI measurements using the clear sky GSI. Image processing methods are then used to remove the effects of atmospheric radiation and the Sun's direct radiation from infrared sky images, plus the radiation effect emitted by debris on the sky imager's germanium outdoor lens. The result is an adaptive solar forecasting algorithm that can reduce the operational cost of power grids with the high participation of solar energy in the generation mix.
Tournament Topology Particle Swarm Optimization

Particle swarm optimization (PSO) has become a popular algorithm for performing global numerical optimization; however, it is known that the topology of PSO has a large influence on its performance. Topologies with high connectivity can have fast convergence, but they are also susceptible to convergence to local minima. Topologies with low connectivity may avoid converging to local minima and achieve high quality solutions, but they tend to have slow convergence. In this paper, we propose a novel PSO topology based on a single-elimination tournament. In the proposed tournament topology, particles move up a tree structure through a fitness-based tournament. PSO updates then propagate information about the global best position from the top of the tree to the bottom. Experimental results on eleven benchmark functions show that the proposed topology can achieve both the high quality solutions of low-connectivity topologies and the fast convergence of high-connectivity topologies.
Server-Side Segment Selection for Low-Latency Streaming—S4S

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Over-the-top (OTT) streaming growth is driven by the increasing number of wireless connected devices such as tablets and phones. An enabler for using these devices is adaptive bitrate (ABR) streaming that allows devices to select at each moment the stream quality that best fits the available bandwidth. The overall experience is highly dependent on the bandwidth estimation. The current approach to bandwidth estimation in HTTP-based ABR players is challenged by the evolution toward low-latency protocols (DASH CTE—Dynamic Adaptive Streaming over HTTP—Chunked Transfer Encoding) or (HLS LL—HTTP Live Streaming—Low-Latency), which result in micro-burst of traffic. Indeed, estimation at the client side, at the HTTP level, relies on the assumption that a relatively large segment of data is available and can be sent at the link speed, which is not the case anymore with low-latency protocols. To address this issue, we propose a novel scheme Server-Side Segment Selection for streaming (S4S), which allows players to interact with an S4S-enabled server to improve the overall experience. First, bandwidth estimation is done at the server side, using transport’s congestion control sender-side stats, leading to more precise estimates even in the presence of small burst, especially when using modern congestion control algorithms such as Bottleneck Bandwidth and Round-trip propagation time (BBR) or Performance-orientated Congestion Control (PCC). Second, we define a protocol for allowing the network to control the bandwidth versus quality tradeoff</i> .
Perceptions About English as Second Language Teachers’ Technology Based English Language Teaching in Pakistan: Attitudes, Uses of Technology and Challenges

This study aims to find out the perceptions of English as a second language (henceforth, ESL) teachers’ attitudes towards the use of technology for teaching of English to the undergraduate students in Pakistan. The study exposes the uses of information and communication technologies (henceforth, ICT) in teaching, and the difficulties faced in integrating technology for teaching of English. Data is collected through survey questionnaire and interview protocol from ESL teachers teaching at college level in Abbottabad, Pakistan. Survey data is analyzed through SPSS 22 while interview questions have a thematic analysis. Findings of survey and interview protocol used in the study reveal positive attitudes on the part of teachers towards the use of technology. No significant difference is found among teachers’ use of technology due to gender or academic qualification. However, the difference between teachers’ technology use can be ascribed to their competence. The use of technology is beneficial for teaching and learning environment, but its’ use is not optimal in educational environment in Pakistan. The findings highlight limited use of technology due to shortage of resources, unavailability of tools and lack of training. The study puts forward some recommendations for the effective use of technology in language teaching and the removal of the potential barriers in its use by teachers in Pakistan.
Cloud platform load balancing mechanism for microservice architecture

In response to the increase in request response latency under the microservice architecture, from the perspective of cloud platform load balancing, the average request latency and host load on the microservice chain are used as metrics to formalize the latency and problem environment. A request load balancing algorithm perceived by the microservice chain is proposed as the load balancing strategy of the load balancer. Simulation experiments prove that the algorithm in this paper can effectively reduce request latency in a complex microservice chain environment, and it can also maintain relatively good performance in an environment where instances are unevenly distributed, and for workloads between hosts.
The Curvelet Compressed Sensing Denoising Algorithm for Tobacco Insect Images

In tobacco insect image denoising, since the uniform threshold in iterative thresholding algorithm cant separate the image from noise completely in each step scale causes much noise is still presented in the reconstructed image, an improved curvelet thresholding compressed sensing thresholding algorithm is proposed. In the proposed algorithm, a curvelet wiener filtering denoising operator designed is used in tobacco insect image sparse transform for removing most noise information. Then an improved curvelet thresholding is proposed to remove remaining noise for further improving the reconstructed image quality. The experimental results on all kinds of tobacco insect images show that the proposed algorithm can remove noise from the noisy tobacco insect image effectively, preserves mores detailed features. Even when compression ratio is low, it still has a higher peak signal to noise ratio.
A Color Volume Mapping System for Perception- Accurate Reproduction of HDR Imagery in SDR Production Workflows

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The use of wide color volumes in cameras during image acquisition is common nowadays. However, these color volumes are mostly manufacturer-specific and often get minimized into a standardized color system, such as International Telecommunication Union – Radiocommunication (ITU-R) Recommendation BT.709. The outstanding color-capture characteristics of prevailing image sensors therefore do not get used enough yet. Most image reproduction devices will not be able to display high-dynamic-range/wide-color-gamut (HDR/WCG) content accurately in the near future. This justifies the current development of products that convert HDR/WCG imagery to standard-dynamic-range/standard-color-gamut (SDR/SCG) imagery with the aim of keeping the original look. The often overlooked aspect of color volume transformation as a conversion process is of particular importance. Patented or trademarked colors (e.g. Coca-Cola Red) should cause the same visual impression on all reproduction devices. Visual artifacts, such as hue shifts, banding, or constant color regions instead of differentiated colors, caused by clipped or incorrectly converted colors, could violate this rule. Tone mapping is usually performed to transform HDR to SDR content. The difference in brightness between HDR and SDR is most visible. Rarely, an accurate color volume transformation is performed. A novel HDR/WCG-SDR/SCG transformation algorithm is presented in this paper. It can be used on images after tone mapping, or as a static conversion method between HDR/WCG and SDR/SCG. The aim of the algorithm is to convert colors from a large source color volume into a smaller target color volume so that converted colors stay distinguishable and no clipping is necessary</i> .
TOWARD DATA-DRIVEN FILTERS IN PARAVIEW

Scientific visualization is a crucial part of many computational workflows in science and engineering. A given visualization is obtained by applying a series of filters or transformations to input data and finally mapping the filtered data to a graphical representation. So far, such filters mostly use deterministic algorithms to process the data. In this work, we aim at extending this methodology toward data-driven filters, filters that expose the abilities of pretrained machine learning models to the visualization system. The use of such data-driven filters can be of particular interest in fields like segmentation, classification, and surrogate models, where machine learning models often outperform existing algorithmic approaches. The objective of this work is to give a starting point for developments toward data-driven filters in multipurpose visualization tools. To this end, we propose, develop, and provide a first software prototype that combines the standard visualization tool ParaView with the state-of-the-art deep learning framework PyTorch in order to expose machine learning models as data-driven filters. Several simple use cases for segmentation and classification filters are presented, showing the technical applicability of our approach. The resulting prototype is provided as open source to facilitate the future development of data-driven filters.