Reviewerly/exHarmony huggingface dataset:
Bivariate Hermitian Polynomial Coding for Efficient Distributed Matrix Multiplication

Coded distributed computing is an effective framework to improve the speed of distributed computing systems by mitigating stragglers (temporarily slow workers). In essence, coded computing allows replacing the computation assigned to a straggling worker by that at a faster worker by assigning redundant computations. Coded computing techniques proposed so far are mostly based on univariate polynomial coding. These codes are not very effective if storage and computation capacity across workers are heterogeneous and lose completely the work done by the straggling workers. For the particular problem of distributed matrix-matrix multiplication, we show how bivariate polynomial coding addresses these two issues.
Measurement and Analysis of Depth Resolution Using Active Stereo Cameras

We propose a novel method to measure and analyze depth resolution in near range. Depth resolution is the smallest depth difference that can be detected by a depth camera, which is an important parameter but is hard to be measured accurately. Intuitively, a stair-shaped target is preferred to measure depth resolution but has many limitations. To overcome the difficulties, we use a flat target and move a depth camera at specified steps to form a stair-shaped depth map. Then, the depth map is compressed to one-dimensional profile by an average cross-section method. The 68-95-99.7 rule is applied for identifying the depth resolution. In order to obtain accurate data, a computer-controlled positioning platform is used to move or rotate a depth camera and a target in high precision. Three types of targets are made and tested, including a stair-shaped metal plate, a flat acrylic board, and a tensioned electric screen. A procedure is provided for aligning the optical plane of depth camera to the target before measuring. The Intel RealSense D400 series depth cameras are adopted for verification. Their theoretical depth resolution is derived and compared to the measured depth resolution. The effect of image resolution (x, y) on the depth resolution (z) is also analyzed. The experimental results offer useful information for researchers to design a depth camera and verify its depth resolution.
Scalable Gaussian Process Classification With Additive Noise for Non-Gaussian Likelihoods

Gaussian process classification (GPC) provides a flexible and powerful statistical framework describing joint distributions over function space. Conventional GPCs, however, suffer from: 1) poor scalability for big data due to the full kernel matrix and 2) intractable inference due to the non-Gaussian likelihoods. Hence, various scalable GPCs have been proposed through: 1) the sparse approximation built upon a small inducing set to reduce the time complexity and 2) the approximate inference to derive analytical evidence lower bound (ELBO). However, these scalable GPCs equipped with analytical ELBO are limited to specific likelihoods or additional assumptions. In this work, we present a unifying framework that accommodates scalable GPCs using various likelihoods. Analogous to GP regression (GPR), we introduce additive noises to augment the probability space for: 1) the GPCs with step, (multinomial) probit, and logit likelihoods via the internal variables and 2) particularly, the GPC using softmax likelihood via the noise variables themselves. This leads to unified scalable GPCs with analytical ELBO by using variational inference. Empirically, our GPCs showcase superiority on extensive binary/multiclass classification tasks with up to two million data points.
Foreign Aid Complementarities and Inclusive Human Development in Africa

This study complements existing literature by assessing how various types of foreign aid complement each other in boosting inclusive human development in Africa. (a) When ‘aid to social infrastructure’ is moderated with other aid types, ‘action on debts’ is substitutive whereas ‘aid to the production sector’, ‘aid for program assistance’ and humanitarian assistance are complementary. (b) ‘Aid to the production sector’ (‘action on debt’) is complementary (substitutive) to ‘aid for economic infrastructure’. (c) Whereas ‘action on debt’ is a substitute to ‘aid to the production sector’, ‘aid for social infrastructure’ and ‘aid for economic infrastructure’ are complementary. (d) ‘Action on debt’ is a substitute for ‘aid to the multi-sector’. (e) While ‘aid for social infrastructure’ and ‘action on debt’ are substitutive to ‘aid for program assistance’; humanitarian assistance is complementary. (f) The following are substitutes to ‘action on debt’: ‘aid for economic infrastructure’, ‘aid to the production sector’, ‘aid to the multi-sector’ and ‘program assistance’. (g) ‘Aid for social infrastructure’ and ‘program assistance’ are complementary to humanitarian assistance. The findings reveal various patterns that inform policy makers on the relevance of sequencing aid types to enhance inclusive development. Future research should focus on country-specific studies.
Characterization of QUBO reformulations for the maximum k-colorable subgraph problem

Quantum devices can be used to solve constrained combinatorial optimization (COPT) problems thanks to the use of penalization methods to embed the COPT problem’s constraints in its objective to obtain a quadratic unconstrained binary optimization (QUBO) reformulation of the COPT. However, the particular way in which this penalization is carried out affects the value of the penalty parameters, as well as the number of additional binary variables that are needed to obtain the desired QUBO reformulation. In turn, these factors substantially affect the ability of quantum computers to efficiently solve these constrained COPT problems. This efficiency is the key toward the goal of using quantum computers to solve constrained COPT problems more efficiently than with classical computers. Along these lines, we consider an important constrained COPT problem, namely the maximum k-colorable subgraph (MkCS) problem, in which the aim is to find an induced k-colorable subgraph with maximum cardinality in a given graph. This problem arises in channel assignment in spectrum sharing networks, VLSI design, human genetic research, and cybersecurity. We derive two QUBO reformulations for the MkCS problem and fully characterize the range of the penalty parameters that can be used in the QUBO reformulations. Further, one of the QUBO reformulations of the MkCS problem is obtained without the need to introduce additional binary variables. To illustrate the benefits of obtaining and characterizing these QUBO reformulations, we benchmark different QUBO reformulations of the MkCS problem by performing numerical tests on D-Wave’s quantum annealing devices. These tests also illustrate the numerical power gained by using the latest D-Wave’s quantum annealing device.
Abel’s integral operator: sparse representation based on multiwavelets

In this work, Abel’s integral operator is represented based on Alpert’s multiwavelets as a sparse matrix and then a non-linear Abel’s integral equation of the second kind is solved by multiwavelets Galerkin method. Nonlinearity and singularity make the numerical procedure more challenging. But the proposed scheme overcomes these problems. Convergence analysis is investigated and some numerical examples validated this analysis.
Minor-embedding heuristics for large-scale annealing processors with sparse hardware graphs of up to 102,400 nodes

Minor-embedding heuristics have become an indispensable tool for compiling problems in quadratically unconstrained binary optimization (QUBO) into the hardware graphs of quantum and CMOS annealing processors. While recent embedding heuristics have been developed for annealers of moderate size (about 2000 nodes), the size of the latest CMOS annealing processor (with 102,400 nodes) poses entirely new demands on the embedding heuristic. This raises the question, if recent embedding heuristics can maintain meaningful embedding performance on hardware graphs of increasing size. Here, we develop an improved version of the probabilistic-swap-shift-annealing (PSSA) embedding heuristic [which has recently been demonstrated to outperform the standard embedding heuristic by D-Wave Systems (Cai et al. in http://arxiv.org/abs/1406.2741 , 2014)] and evaluate its embedding performance on hardware graphs of increasing size. For random cubic and Barábasi–Albert graphs we find the embedding performance of improved PSSA to consistently exceed the threshold of the best known complete graph embedding by a factor of 3.2 and 2.8, respectively, up to hardware graphs with 102,400 nodes. On the other hand, for random graphs with constant edge density not even improved PSSA can overcome the deterministic threshold guaranteed by the existence of the best known complete graph embedding. Finally, we prove a new upper bound on the maximal embeddable size of complete graphs into hardware graphs of CMOS annealers and show that the embedding performance of its currently best known complete graph embedding has optimal order for hardware graphs with fixed coordination number.
Somewhere over the Rainbow: Exploring the Sense for Relevance in Children

We explore the facets of relevance that guide children when assessing materials retrieved by search engines when looking for information in the classroom. We involved children in a collaborative exercise and asked them to design innovative icons to point their peers towards useful results. We also asked them to complete a survey meant to capture explicit motivators guiding their design. This resulted in a rich set of metaphors. Analysis of the emerging metaphors is what allowed us to identify and discuss the many interpretations of relevance children naturally assign to resources they find in response to school-related information discovery tasks.
Distance Learning During the Corona-Lockdown: Some Psychological and Pedagogical Aspects

The study analyses distance learning experiences in enforced isolation during the COVID-19 pandemic. The aim was to summarize and analyze factors that, to a certain extent, influenced the effectiveness of distance learning for educators. In addition to the individual psychological characteristics of respondents, the study used an author’s questionnaire aimed at collecting information on the teaching characteristics in coronavirus. A valid sample of lecturers from Moscow and St. Petersburg leading universities took part in the research. Profiles were compiled in four age groups: novice teachers - 20–30 years old; experienced teachers - 30–40 years old, professionals - 40–50 years old and teachers before the retirement age - over 50. The data were analyzed through mathematical and statistical methods and SPSS 25. Significant links were identified between professional burnout constructs, distance-learning factors in COVID-19 conditions, procrastination and age. Factors that had a significant impact on distance learning were technical communication problems, the unexpected complete transition to distance learning, lack of live communication and the dramatically increased time teachers had to spend at the computer. Two age groups were the most exposed to professional burnout: teachers between the ages of 40 and 50 and over 50.
High‐degree cubature Kalman filter for nonlinear state estimation with missing measurements

Abstract This paper proposes high‐degree cubature Kalman filter for nonlinear systems with missing measurements. We derive out the explicit formulas for the prediction and update in the filtering. To fulfill the numerical computation, especially the numerical integrals, of these formulas, the fifth‐degree spherical‐radial cubature rule is adopted to give a high‐degree cubature Kalman filtering algorithm. Through numerical example, it is shown that the fifth‐degree cubature Kalman filter has better precision and stability than the extended Kalman filter, the unscented Kalman filter, and the fifth‐degree unscented Kalman filter.
A wavelet filter comparison on multiple datasets for signal compression and denoising

In this paper, we explicitly analyze the performance effects of several orthogonal and bi-orthogonal wavelet families. For each family, we explore the impact of the filter order (length) and the decomposition depth in the multiresolution representation. In particular, two contexts of use are examined: compression and denoising. In both cases, the experiments are carried out on a large dataset of different signal kinds, including various image sets and 1D signals (audio, electrocardiogram and seismic). Results for all the considered wavelets are shown on each dataset. Collectively, the study suggests that a meticulous choice of wavelet parameters significantly alters the performance of the above mentioned tasks. To the best of authors’ knowledge, this work represents the most complete analysis and comparison between wavelet filters. Therefore, it represents a valuable benchmark for future works.
Secure Deduplication-Based Storage Systems With Resistance to Side-Channel Attacks via Fog Computing

Data deduplication technique could greatly save the storage overhead of the cloud by eliminating duplicated data and retaining one copy. In order to ensure the privacy of the data against an untrusted cloud, many cryptographic schemes have been proposed to make deduplication feasible in ciphertext. A typical scheme is Message-Locked Encryption (MLE), which takes cryptographic hash values of messages as encryption keys. However, MLE is vulnerable to side-channel attacks. Current solutions trying to mitigate these attacks raise either expensive overhead or security drawbacks. In this paper, we propose a secure data deduplication system against an untrusted cloud with resistance to two typical side-channel attacks, namely probe attack and key-cache attack. Our system uses fog computing to devise two new techniques to solve the two side-channel attacks with new security and efficiency trade-offs. The analysis and evaluation show that our system achieves better trade-offs compared with previous works.
Weakly-supervised action localization via embedding-modeling iterative optimization

Action recognition and localization in untrimmed videos in weakly supervised scenario is a challenging problem of great application prospects. Limited by the information available in video-level labels, it is a promising attempt to fully leverage the instructive knowledge learned on trimmed videos to facilitate analysis of untrimmed videos, considering that there are abundant trimmed videos which are publicly available and well segmented with semantic descriptions. In order to enforce effective trimmed-untrimmed augmentation, this paper presents a novel framework of embedding-modeling iterative optimization network, referred to as IONet. In the proposed method, action classification modeling and shared subspace embedding are learned jointly in an iterative way, so that robust cross-domain knowledge transfer is achieved. With a carefully designed two-stage self-attentive representation learning workflow for untrimmed videos, irrelevant backgrounds are eliminated and fine-grained temporal relevance can be robustly explored. Extensive experiments are conducted on two benchmark datasets, i.e., THUMOS14 and ActivityNet1.3, and experimental results clearly corroborate the efficacy of our method. Source code is available on GitHub.2.
A study of Turkish emotion classification with pretrained language models

Emotion classification is a research field that aims to detect the emotions in a text using machine learning methods. In traditional machine learning (TML) methods, feature engineering processes cause the loss of some meaningful information, and classification performance is negatively affected. In addition, the success of modelling using deep learning (DL) approaches depends on the sample size. More samples are needed for Turkish due to the unique characteristics of the language. However, emotion classification data sets in Turkish are quite limited. In this study, the pretrained language model approach was used to create a stronger emotion classification model for Turkish. Well-known pretrained language models were fine-tuned for this purpose. The performances of these fine-tuned models for Turkish emotion classification were comprehensively compared with the performances of TML and DL methods in experimental studies. The proposed approach provides state-of-the-art performance for Turkish emotion classification.
Wavelength attack on atmospheric continuous-variable quantum key distribution

Recently, several practical wavelength attacks on continuous-variable quantum key distribution (CVQKD) were proposed based on the wavelength-dependent property of a beam splitter in Bob's station. These attack schemes are limited to the fiber channel systems with a relatively stable transmittance. However, unlike the fiber channel, the transmittance of the atmospheric channel is complex and fluctuates dramatically in time, which may invalidate the attack equations that must be satisfied in the optical fiber channel. In this paper, we propose two wavelength attack schemes against complex atmospheric CVQKD systems and formulize the two schemes based on the parameter estimation method. To verify the feasibility and availability of this attack, we deduce the success criteria for the two attack schemes. By numerical analysis, our attack schemes are proved to be feasible in the permitted parameter regimes. The secret key rate and propagation distance of CVQKD can be further overestimated by selecting the condition parameters effectively. Moreover, we discuss the possible prevention countermeasures to resist these attack schemes. The proposed method will fill the gap of wavelength attack for the implementation of practical atmospheric CVQKD.
Influence of Hand Representation Design on Presence and Embodiment in Virtual Environment

Previous research results have emphasized the influence of avatar representations on user perception in virtual environments, including presence and embodiment. It has been reported that realistic hands present a strong sense of presence and body ownership, while there is a controversy about the sense of agency. This paper investigates the influence of virtual hand representation on user perception and the association between the sense of body ownership and agency. An experiment based on virtual reality was designed with hand representations of different levels of realism to collect users' perception data through questionnaires, and the Spearman correlation was adopted to analyze the relationship between body ownership and agency. The results show that realistic hand induces the higher sense of presence and body ownership, but there is no significant difference in the sense of agency. Moreover, a positive correlation between body ownership and agency in virtual environments was found.
Understanding Heavy Drinking at Night through Smartphone Sensing and Active Human Engagement

Heavy alcohol consumption can lead to many severe consequences. In this paper, we study the phenomenon of heavy drinking at night (4+ drinks for women or 5+ for men on a single evening), using a smartphone sensing dataset depicting about nightlife and drinking behaviors for 240 young adult participants. Our work has three contributions. First, we segment nights into moving and static episodes as anchors to aggregate mobile sensing features. Second, we show that young adults tend to be more mobile, have more activities, and attend more crowded areas outside home on heavy drinking nights compared to other nights. Third, we develop a machine learning framework to classify a given weekend night as involving heavy or non-heavy drinking, comparing automatically captured sensor features versus manually contributed contextual cues and images provided over the course of the night. Results show that a fully automatic approach with phone sensors results in an accuracy of 71%. In contrast, manual input of context of drinking events results in an accuracy of 70%; and visual features of manually contributed images produce an accuracy of 72%. This suggests that automatic sensing is a competitive approach.
More than FAQ! Chatbot Taxonomy for Business-to-Business Customer Services

Chatbots are becoming increasingly important in the customer service sector due to their service automation, cost saving opportunities and broad customer satisfaction. Similarly, in the business-to-business (B2B) sector, more and more companies use chatbots on their websites and social media channels, to establish sales team contact, to provide information about their products and services or to help customers with their requests and claims. Customer relations in the B2B environment are especially characterized by a high level of personal contact service and support through expert explanations due to the complexity of the products and service offerings. In order to support these efforts, chatbots can be used to assist buying centers along the purchase decision process. However, B2B chatbots have so far only been marginally addressed in the scientific human-computer interaction and information systems literature. To provide both researchers and practitioners with knowledge about the characteristics and archetypal patterns of chatbots currently existing in B2B customer services, we develop and discuss a 17-dimensional chatbot taxonomy for B2B customer services based on Nickerson et al. [1]. By classifying 40 chatbots in a cluster analysis, this study has identified three archetypal structures prevailing in B2B customer service chatbot usage.
On the Impact of Biceps Muscle Fatigue in Human Activity Recognition

Nowadays, Human Activity Recognition (HAR) systems, which use wearables and smart systems, are a part of our daily life. Despite the abundance of literature in the area, little is known about the impact of muscle fatigue on these systems’ performance. In this work, we use the biceps concentration curls exercise as an example of a HAR activity to observe the impact of fatigue impact on such systems. Our dataset consists of 3000 biceps concentration curls performed and collected from 20 volunteers aged between 20–35. Our findings indicate that fatigue often occurs in later sets of an exercise and extends the completion time of later sets by up to 31% and decreases muscular endurance by 4.1%. Another finding shows that changes in data patterns are often occurring during fatigue presence, causing seven features to become statistically insignificant. Further findings indicate that fatigue can cause a substantial decrease in performance in both subject-specific and cross-subject models. Finally, we observed that a Feedforward Neural Network (FNN) showed the best performance in both cross-subject and subject-specific models in all our evaluations.