Reviewerly/exHarmony huggingface dataset:
Distance Estimation with Social Distancing: A Mobile Augmented Reality Study

Although Augmented Reality (AR) can be easily implemented with most smartphones and tablets today, the investigation of distance perception with these types of devices has been limited. In this paper, we question whether the distance of a virtual human, e.g., avatar, seen through a smartphone or tablet display is perceived accurately. We also investigate, due to the Covid-19 pandemic and increased sensitivity to distances to others, whether a coughing avatar that either does or does not have a mask on affects distance estimates compared to a static avatar. We performed an experiment in which all participants estimated the distances to avatars that were either static or coughing, with and without masks on. Avatars were placed at a range of distances that would be typical for interaction, i.e., action space. Data on judgments of distance to the varying avatars was collected in a distributed manner by deploying an app for smartphones. Results showed that participants were fairly accurate in estimating the distance to all avatars, regardless of coughing condition or mask condition. Such findings suggest that mobile AR applications can be used to obtain accurate estimations of distances to virtual others "in the wild," which is promising for using AR for simulations and training applications that require precise distance estimates.
A semiparametric Bayesian approach to binomial distribution logistic mixed-effects models for longitudinal data

Logistic mixed-effects models are widely used to study the relationship between the binary response and covariates for longitudinal data analysis, where the random effects are typically assumed to have a fully parametric distribution. As this assumption is likely limited or unreasonable in a multitude of practical researches, a semiparametric Bayesian approach for relaxing it is developed in this paper. In the context of binomial distribution logistic mixed-effects models, a general Bayesian framework is presented in which a semiparametric hierarchical modelling with an approximate truncated Dirichlet process prior distribution is specified for the random effects. The stick-breaking prior and the blocked Gibbs sampler using Pólya-Gamma mixture are employed to efficiently sample in the posterior analysis. Besides, a procedure calculating DIC for Bayesian model comparison is addressed. The methodology is demonstrated through simulation studies and a real example.
Adaptive optimal multi-features learning based representation for face hallucination

Face hallucination (FH) is a classical problem to reconstruct a high-resolution (HR) face image for an observed low-resolution (LR) one. The existing methods represent LR facial images though the spatial pixel domain or by combining confined image features with this spatial pixel information. However, the uncertainty in stipulating the optimal proportion for such multiple image features may lead to unexpected results as the optimal proportion for each LR input face image may vary for obtaining the desired HR result. Additionally, they suffer from degraded performance when the observed LR images are contaminated with higher noise. For addressing such problems, this paper proposes an adaptive optimal multi-features proportion learning (OMFPL) scheme, which adopts the Grey Wolf Optimization (GWO) approach for determining the optimum proportion of each feature to represent a particular LR face image. Moreover, an appropriate threshold is applied on different feature samples in the training data for representing the LR patches with their nearest examples. The optimal proportion of these relevant features helps to reconstruct the high-quality HR faces for both noise-free and noisy LR faces. The performance of OMFPL is validated on widely used public databases, real-world images, and surveillance faces, where it achieves the superior results concerning the several competitive state-of-the-art FH methods.
Solver-Aided Constant-Time Hardware Verification

We present Xenon, a solver-aided, interactive method for formally verifying that Verilog hardware executes in constant-time. Xenon scales to realistic hardware designs by drastically reducing the effort needed to localize the root cause of verification failures via a new notion of constant-time counterexamples, which Xenon uses to synthesize a minimal set of secrecy assumptions in an interactive verification loop. To reduce verification time Xenon exploits modularity in Verilog code via module summaries, thereby avoiding duplicate work across multiple module instantiations. We show how Xenon's assumption synthesis and summaries enable us to verify different kinds of circuits, including a highly modular AES- 256 implementation where modularity cuts verification from six hours to under three seconds, and the ScarVside-channel hardened RISC-V micro-controller whose size exceeds previously verified designs by an order of magnitude. In a small study, we also find that Xenon helps non-expert users complete verification tasks correctly and faster than previous state-of-art tools.
Structural and Statistical Analysis of Multidimensional Linear Approximations of Random Functions and Permutations

The goal of this paper is to investigate linear approximations of random functions and permutations. Our motivation is twofold. First, before the distinguishability of a practical cipher from an ideal one can be analysed, the cryptanalyst must have an accurate understanding of the statistical behaviour of the ideal cipher. Secondly, this issue has been neglected both in old and in more recent studies, particularly when multiple linear approximations are being used simultaneously. Traditional models have been based on the average behaviour and simplified using other assumptions such as independence of the linear approximations. Multidimensional cryptanalysis was introduced to avoid making artificial assumptions about statistical independence of linear approximations. On the other hand, it has the drawback of including many trivial approximations that do not contribute to the attack but just cause a waste of time and memory. We show for the first time in this paper that the trivial approximations reduce the degree of freedom of the related χ2 distribution. Previously, the affine linear cryptanalysis was proposed to allow removing trivial approximations and, at the same time, admitting a solid statistical model. In this paper, we identify another type of multidimensional linear approximation, called Davies-Meyer approximation, which has similar advantages, and present full statistical models for both the affine and the Davies-Meyer type of multidimensional linear approximations. The new models given in this paper are realistic, accurate and easy to use. They are backed up by standard statistical tools such as Pearson’s χ2 test and finite population correction and demonstrated to work accurately using practical examples.
Person Re-Identification by Low-Dimensional Features and Metric Learning

Person re-identification (Re-ID) has attracted attention due to its wide range of applications. Most recent studies have focused on the extraction of deep features, while ignoring color features that can remain stable, even for illumination variations and the variation in person pose. There are also few studies that combine the powerful learning capabilities of deep learning with color features. Therefore, we hope to use the advantages of both to design a model with low computational resource consumption and excellent performance to solve the task of person re-identification. In this paper, we designed a color feature containing relative spatial information, namely the color feature with spatial information. Then, bidirectional long short-term memory (BLSTM) networks with an attention mechanism are used to obtain the contextual relationship contained in the hand-crafted color features. Finally, experiments demonstrate that the proposed model can improve the recognition performance compared with traditional methods. At the same time, hand-crafted features based on human prior knowledge not only reduce computational consumption compared with deep learning methods but also make the model more interpretable.
Performance Analysis of Machine Learning Techniques for Sentiment Analysis

Sentiment analysis determines the sentiment or opinion of a given text. A sentiment analysis model can classify whether a given text data is positive or negative by extracting meaning from the natural language. The growth of social media such as Twitter, forum discussions and reviews, contributed to the huge data repository in digital form. Analyzing these huge data manually is very time consuming and challenging. Thus, applying machine learning techniques can automatically classify the sentiment effectively. This research compares the performance of five popular machine learning techniques for sentiment analysis namely, Support Vector Machine (SVM), Logistic Regression, Naïve Bayes, Random Forest and K-Nearest Neighbor using a publicly available dataset from kaggle.com. Their classification performances are compared based on accuracy and training time where fine tuning of some of the hyperparameters are per-formed to improve the accuracy. Experimental analysis indicates that SVM with linear kernel function produces the highest accuracy but a slower training time. On the other hand, Naïve Bayes requires the shortest training time but with a slightly lower accuracy compared to SVM.
Texture-Enhanced Light Field Super-Resolution With Spatio-Angular Decomposition Kernels

Despite the recent progress in light field super-resolution (LFSR) achieved by convolutional neural networks, the correlation information of LF images has not been sufficiently studied and exploited due to the complexity of 4-D LF data. To cope with such high-dimensional LF data, most of the existing LFSR methods resorted to decomposing it into lower dimensions and subsequently performing optimization on the decomposed subspaces. However, these methods are inherently limited as they neglected the characteristics of the decomposition operations and only utilized a limited set of LF subspaces ending up failing to sufficiently extract spatio-angular features and leading to a performance bottleneck. To overcome these limitations, in this article, we comprehensively discover the potentials of LF decomposition and propose a novel concept of decomposition kernels. In particular, we systematically unify the decomposition operations of various subspaces into a series of such decomposition kernels, which are incorporated into our proposed decomposition kernel network (DKNet) for comprehensive spatio-angular feature extraction. The proposed DKNet is experimentally verified to achieve considerable improvements compared with state-of-the-art methods. To further improve DKNet in producing more visually pleasing LFSR results, based on the VGG network, we propose a light field VGG (LFVGG) loss to guide the texture-enhanced DKNet (TE-DKNet) to generate rich authentic textures and enhance LF images’ visual quality significantly. We also propose an indirect evaluation metric by taking advantage of LF material recognition to objectively assess the perceptual enhancement brought by the LFVGG loss.
Application of Blockchain in Marketing

Blockchain is one of the most popular upcoming technologies which has the potential to grow and disrupt the ongoing business model. Its scope is not only limited to finance but has great potential in the field of tourism, health care, supply chain, real estate, etc. Existing studies have explored its potential in financial industry and cryptocurrency. There is a great potential to explore the application of blockchain in other areas. This paper attempts to explain the application of blockchain in marketing.
Video Forgery Detection using CNN

With the widespread use of digitally interactive multimedia such as audio, images, and video, there has been a significant increase in the mode and motivation to create digital forgeries. The widespread availability of video information and services, as well as the low cost of devices such as cameras, camcorders, and CCTVs, has led to widespread use of video information and services in our society for a variety of purposes such as video surveillance, forensics investigation, and entertainment. Previously, video editing techniques were mostly employed to improve digital information. However, as the popularity of low-cost, easy-to-use video editing software has grown, so has the number of negative repercussions and risks associated with such editing procedures. By merging, changing, or synthesising new footage, video forgery is a technique for creating changed or fraudulent videos. A method based on deep learning is given in the proposed system for classifying videos as tampered or original. The video clip that is used as input is divided into two categories: original and modified. The video is segmented into non-overlapping frames, and the authenticity of the movie is determined by whether or not all of the frames are genuine. The suggested method uses a deep CNN model that has two types of layers: (1) CNN layers which involve convolutional, pooling and fully connected layers and (2) Parasitic layers.
High-spatiotemporal-resolution estimation of solar energy component in the United States using a new satellite-based model

Diffuse solar radiation (Rd), known as an important component of global solar radiation (Rg), is a key parameter for solar energy related applications and ecosystem photosynthesis. Some meteorological models have been developed to estimate Rd with acceptable accuracy, but their spatial scales are often small due to the limited meteorological station number. Satellite-based models provide accurate and large-scale Rg estimates. However, remote sensing estimations of Rd are often with low spatial resolutions and large uncertainties, because their methods were based on inaccurate surface and atmospheric parameters. To address these challenges, the high-spatiotemporal-resolution (half-hourly and 1-km) Rd in the United States was estimated using the top-of-atmosphere (TOA) data from the new-generation geostationary satellite Geostationary Operational Environmental Satellites (GOES-16) and the method iterative random forest (RF). The results showed that the iterative RF model had higher accuracy than the simple RF and artificial neural network (ANN) models, and using TIR (thermal infrared) bands can improve models' accuracy. The best model can estimate half-hourly Rd with the accuracy R2 = 0.88, RMSE = 37.81 W/m2, and MBE = 0.01 W/m2. Compared with the previous 0.01-degree (∼11-km) Rd product Earth Polychromatic Imaging Camera (EPIC), the GOES-16 estimated 1-km Rd had similar spatial patterns. Moreover, based on the GOES-16 estimated half-hourly and 1-km Rd in the United States, the spatiotemporal heterogeneity of Rd was quantitatively observed. The proposed approach can be used to produce more high-spatiotemporal-resolution Rd products, and these products are very helpful for many solar-related research topics and industrial applications.
Device or User

We are witnessing a trend of users owning multiple data-generating wearable and IoT devices that continuously capture sensor data pertaining to a user's activities and context. Federated Learning is a potential technique to derive meaningful insights from this sensor data in a privacy-preserving way without revealing the raw sensor data to a central server. In this paper, we introduce a new problem setting in this multi-device context called Federated Learning in Multi-Device Local Networks (FL-MDLN). We identify core challenges for FL-MDLN in relation to its federation architecture, and statistical and systems heterogeneity across multiple users and multiple devices. Then, we introduce a new user-as-client (UAC) federation architecture, and propose various device selection strategies to counter statistical and systems heterogeneity in FL-MDLN. Early empirical findings show that our proposed techniques improve model test accuracy as well as battery power efficiency in FL. Based on these findings, we elucidate open research questions and future work in FL-MDLN.
The Impact of Unsystematic Factors on Bitcoin Value

The results of empirical analyses confirm that analysed unsystematic factors, the Stock-to-Flow index (S2F), and information on the Bitcoin (BTC) are directly correlated with BTC values. These results are expected and in line with the economic theory; however, this research paper aimed to investigate the impact of unsystematic factors on the value of decentralised virtual cryptocurrency BTC. Its aim was also to analyse the reasons for significant oscillations of market values in relation to the S2F and S2FX model and thus confirm the reliability of these models in the estimation of BTC value. The research further confirms the strong influence of non-technical information directly linked with the BTC. The limitations of this paper are the lack of possibilities for examining the impact of non-technical information affecting the Bitcoin price deviation regarding the S2F model. In addition to all mentioned limitations, the research results indicate the relevance of the S2F and S2FX models and show a strong impact of (half) the information on the value of cryptocurrencies.
Analysis Of Examination Process During Covid And Post Covid In Indian Context

COVID-19 impacted almost all areas of life. Education sector is one area that faces various challenges. Live classes, examinations, lab conduction etc. we're burning issues. One of the important processes in the education cycle is examination. In many of the academic institution's examinations could not be conducted and students were promoted on the basis of their previous performance in half yearly or internal exams. It was a big challenge to switch the entire offline system (teaching learning process and examination system) into online mode. It defines the quality of education and filters the right one for the future. In this paper we are focusing on the examination process during COVID and Post COVID in INDIAN Context and collected data samples and process structure of different universities. Our purpose is to discuss and conclude pros and cons of online examination & offline examination. We are comparing offline and online examination processes to extract research insights. To ensure that the examination process is carried out quickly and that the evaluation process is carried out properly in a timely manner in the best interests of the student.
An API Learning Service for Inexperienced Developers Based on API Knowledge Graph

Software development kits (SDKs) including application programming interfaces (APIs) are always required by developers who need to learn how to use the APIs. However, inexperienced developers may face two problems when learning APIs. Firstly, API-related learning resources cannot be easily obtained. Secondly, inexperienced developers often cannot find proper learning entries and paths to learn APIs, either. To solve these problems, we design an API learning service for inexperienced developers. Firstly, we propose an API link method to find learning resources about APIs in Stack Overflow (SO). Secondly, we construct an API knowledge graph which contains APIs and API-related Q&A threads from SO. Thirdly, by mining how APIs are discussed together in SO, we propose a learning entry recommendation method. At last, we propose an API learning service using the methods above to help inexperienced developers learn APIs. We conduct experiments and results demonstrate the feasibility of our methods and service.
Zero Knowledge Static Program Analysis

Static program analysis tools can automatically prove many useful properties of programs. However, using static analysis to prove to a third party that a program satisfies a property requires revealing the program's source code. We introduce the concept of zero-knowledge static analysis, in which the prover constructs a zero-knowledge proof about the outcome of the static analysis without revealing the program. We present novel zero-knowledge proof schemes for intra- and inter-procedural abstract interpretation. Our schemes are significantly more efficient than the naive translation of the corresponding static analysis algorithms using existing schemes. We evaluate our approach empirically on real and synthetic programs; with a pairing-based zero knowledge proof scheme as the backend, we are able to prove the control flow analysis on a 2,000-line program in 1,738s. The proof is only 128 bytes and the verification time is 1.4ms. With a transparent zero knowledge proof scheme based on discrete-log, we generate the proof for the tainting analysis on a 12,800-line program in 406 seconds, the proof size is 282 kilobytes, and the verification time is 66 seconds.
The effect of personal pronouns on users and the social role of conversational agents

There is a growing tendency for users to expect conversational agents (CAs) to recognise social cues and follow interpersonal communication principles to enhance their subjective evaluation. Therefore, this paper studies how personal pronouns should be used by CAs in response to users. We conducted a 3 (CAs’ personal pronoun) × 3 (users’ personal pronoun) × 2 (participants’gender) mixed design. this study used mixed methods based on an experimental design, including ratings, forced choices and interviews, for mutual confirmation. The findings indicate that first, users prefer that CAs use second-person pronouns. Second, there is also turn-taking and convergence tendency between users and CAs in personal pronoun use. Third, there are gender differences in personal pronoun preferences and relationship positions toward CAs. These results can inform personalised voice interaction and humanlike design and help build closer relationships between users and CAs in future human–computer interactions.
CEBS update: curated toxicology database with enhanced tools for data integration

The Chemical Effects in Biological Systems database (CEBS) contains extensive toxicology study results and metadata from the Division of the National Toxicology Program (NTP) and other studies of environmental health interest. This resource grants public access to search and collate data from over 10 250 studies for 12 750 test articles (chemicals, environmental agents). CEBS has made considerable strides over the last 5 years to integrate growing internal data repositories into data warehouses and data marts to better serve the public with high quality curated datasets. This effort includes harmonizing legacy terms and metadata to current standards, mapping test articles to external identifiers, and aligning terms to OBO (Open Biological and Biomedical Ontology) Foundry ontologies. The data are made available through the CEBS Homepage (https://cebs.niehs.nih.gov/cebs/), guided search applications, flat files on FTP (file transfer protocol), and APIs (application programming interface) for user access and to provide a bridge for computational tools. The user interface is intuitive with a single search bar to query keywords related to study metadata, publications, and data availability. Results are consolidated to single pages for each test article with NTP conclusions, publications, individual studies, data collections, and links to related test articles and projects available together.