Reviewerly/exHarmony huggingface dataset:
How Human and Organizational Factors Influence Software Teams Productivity in COVID-19 Pandemic

Software companies have adopted remote work to maintain practitioners safe during the COVID-19 outbreak worldwide. The remote environment in pandemic time brings some challenges, mainly for practitioners with limited experience with this work modality. Moreover, there are several factors in the environment that together with the change in the work routine can directly impact the teams productivity, such as human and organizational factors. To investigate this problem, we conducted a survey applied to software teams in Brazil to analyze how such factors can influence their productivity in the remote environment during the COVID-19 pandemic. The survey was guided by human and organizational factors identified in the literature that affect the productivity of software teams, such as: work environment, collaboration and organization of teams, communication, motivation, among others. Our survey obtained 58 participants from all Brazilian regions. As main conclusions, we identified that 74.1% of the participants consider their productivity remains good or excellent, and 84.5% feel motivated and have easy communication with their co-workers. Furthermore, the main factors influencing the productivity are external interruption, environment adaptation, and emotional issues.
FedSCR: Structure-based Communication Reduction for Federated Learning

Federated Learning allows edge devices to collaboratively train a shared model on their local data without leaking user privacy. The non-independent-and-identically-distributed (Non-IID) property of data distribution, which leads to severe accuracy degradation, and enormous communication overhead for aggregating parameters should be tackled in federated learning. In this article, we conduct a detailed analysis of parameter updates on the Non-IID datasets and compare the difference with the IID setting. Experimental results exhibit that parameter update matrices are structure-sparse and show that more gradients could be identified as negligible updates on the Non-IID data. As a result, we propose a structure-based communication reduction algorithm, called FedSCR, that reduces the number of parameters transported through the network while maintaining the model accuracy. FedSCR aggregates the parameter updates over channels and filters, identifies and removes the redundant updates by comparing the aggregated values with a threshold. Unlike the traditional structured pruning methods, FedSCR retains the complete model that does not require to be retrained and fine-tuned. The local loss and weight divergence on each device vary a lot because of the unbalanced data distribution. We further propose an adaptive FedSCR, that dynamically changes the bounded threshold, to enhance the model robustness on the Non-IID data. Evaluation results show that our proposed strategies achieve almost 50 percent upstream communication reduction without loss of accuracy. FedSCR can be integrated into state-of-the-art federated learning algorithms to dramatically reduce the number of parameters pushed to the global server with a tolerable accuracy reduction.
Fault estimation and fault tolerant control for discrete-time nonlinear systems with perturbation by a mixed design scheme

This paper focuses on the observer-based fault-tolerant control problem for the discrete-time nonlinear systems with the perturbation and the fault signals. First, the nonlinear term with perturbation is put into the local nonlinear part so that the nonlinear system with perturbation can be described as an interval type-1 (IT1) T-S fuzzy system. Then, based on the unknown input observer technology, the IT1 T-S fuzzy fault estimation (FE) observer scheme is presented to obtain the real-time FE information and decouple the local nonlinear part from the estimation error system, where the design complexity and the computational burden are reduced simultaneously. Second, based on the real-time FE information, an FE-based interval type-2 (IT2) T-S fuzzy fault-tolerant control scheme is presented to achieve the compensation for the influence of the fault signal and the stabilization for the system. Different from the traditional methods, a mixed design scheme, which is based on the IT1 T-S fuzzy fault estimation observer method and the IT2 T-S fuzzy fault-tolerant controller method, is proposed in this paper. This strategy can not only reduce the computational burden, but also obtain a less conservative result. Finally, the effectiveness of the mixed design approach is illustrated by an example.
Criminological Problems of the Digital World (Digital Criminology)

The development of information society and the corresponding technologies raises to a new level the tasks of counteracting crimes committed using such technologies, and of minimizing damage from them. The growth in the scale of new types of crime is a cause of worry for the society and the authorities, and especially for criminologists, as the penetration of criminals into the virtual environment and their mastery of new technologies acquire dangerous forms, change criminal motivation and, at the same time, to some extent stimulate the development of information and telecommunication technologies. The growing sophistication of the tasks of preventing and counteracting hi tech crimes makes it necessary to critically assess the current criminological methods and to make an attempt to go beyond the known «common» methods of neo-classical criminology. The development of the digital criminology concept cannot be reduced to an aggregate of pioneer technological methods developed on the basis of mathematical modeling, i.e. computer processing of quantitative and qualitative parameters of crimes, mathematical detection of different dependencies (on time, place and other variables), it could and should be understood in a wider sense: on the one hand, it should influence the new criminological paradigm, and on the other - it should develop within its boundaries. The modern information-analytical sphere in the work of law enforcement bodes includes the use of digital criminological instruments within the programs of crime prevention, mathematical methods of analyzing crimes, profiling, etc. Their aggregate is generally applicable to criminological analysis and prediction, however, it does not have the most cutting edge theoretical basis that corresponds to the tasks of counteracting crimes of the digital world; it is now being formed on the basis of criminological neo-classics, the advances of the social sciences and the humanities, digital criminology. The predictions of new industrial revolutions include a rapid acceleration of the pace of technological development, a systemic transformation of production and management, which will not only stimulate a global rise in the living standards, but will also increase inequality and, consequently, will provide an impetus to crime. These aspects should be taken into consideration when predicting future development of digital criminology, whose theories should be based on the conceptual models of social development of the near future. Social consequences of the predicted new industrial revolutions will inevitably become new common determiners of the crimes of the future, as it always happened in the past.
Linear-Time Arguments with Sublinear Verification from Tensor Codes

Minimizing the computational cost of the prover is a central goal in the area of succinct arguments. In particular, it remains a challenging open problem to construct a succinct argument where the prover runs in linear time and the verifier runs in polylogarithmic time. We make progress towards this goal by presenting a new linear-time probabilistic proof. For any fixed $$\epsilon > 0$$ , we construct an interactive oracle proof (IOP) that, when used for the satisfiability of an N-gate arithmetic circuit, has a prover that uses O(N) field operations and a verifier that uses $$O(N^{\epsilon })$$ field operations. The sublinear verifier time is achieved in the holographic setting for every circuit (the verifier has oracle access to a linear-size encoding of the circuit that is computable in linear time). When combined with a linear-time collision-resistant hash function, our IOP immediately leads to an argument system where the prover performs O(N) field operations and hash computations, and the verifier performs $$O(N^{\epsilon })$$ field operations and hash computations (given a short digest of the N-gate circuit).
Robust Poisson Multi-Bernoulli Mixture Filter With Unknown Detection Probability

This paper proposes a robust Poisson multi-Bernoulli mixture (R-PMBM) filter immune to the unknown detection probability. In a majority of multi-object scenarios, the prior knowledge of detection probability is usually uncertain, which is often estimated offline from the training data. In such cases, online filtering is always unfeasible or unrealistic, otherwise, significant parameter mismatches will result in biased estimates (e.g., state and cardinality of objects). As a consequence, the ability of adaptively estimating the detection probability for a sensor is essential in practice. Based on the analysis, we detail how the detection probability can be estimated accompanied with the state estimates. Besides, the closed-form solutions to the proposed method are derived by means of approximating the intensity of Poisson random finite set (RFS) to a Beta-Gaussian mixture (BGM) form and density of Bernoulli RFS to a single Beta-Gaussian form, named BGM-PMBM filter. Simulation results demonstrate the effectiveness and robustness of the proposed method.
Paradoxes of freedom. An archaeological analysis of educational online platform interfaces

(2021). Paradoxes of freedom. An archaeological analysis of educational online platform interfaces. Critical Studies in Education: Vol. 62, Critical studies of digital education platforms, pp. 114-129.
Orienting supersingular isogeny graphs

Abstract We introduce a category of 𝓞-oriented supersingular elliptic curves and derive properties of the associated oriented and nonoriented ℓ -isogeny supersingular isogeny graphs. As an application we introduce an oriented supersingular isogeny Diffie-Hellman protocol (OSIDH), analogous to the supersingular isogeny Diffie-Hellman (SIDH) protocol and generalizing the commutative supersingular isogeny Diffie-Hellman (CSIDH) protocol.
Learning Dynamic Embeddings for Temporal Knowledge Graphs

Representation learning for temporal knowledge graphs has attracted increasing attention in recent years. In this paper, we study the problem of learning dynamic embeddings for temporal knowledge graphs. We address this problem by proposing a Dynamic Bayesian Knowledge Graphs Embedding model (DBKGE), which is able to dynamically track the semantic representations of entities over time in a joint metric space and make predictions for the future. Unlike other temporal knowledge graph embedding methods, DBKGE is a novel probabilistic representation learning method that aims at inferring dynamic embeddings of entities in a streaming scenario. To obtain high-quality embeddings and model their uncertainty, our DBKGE embeds entities with means and variances of Gaussian distributions. Based on amortized inference, an online inference algorithm is proposed to jointly learn the latent representations of entities and smooth their changes across time. Experiments on Yago and Wiki datasets demonstrate that our proposed algorithm outperforms the state-of-the-art static and temporal knowledge graph embedding models.
Evolving Deep Convolutional Variational Autoencoders for Image Classification

Variational autoencoders (VAEs) have demonstrated their superiority in unsupervised learning for image processing in recent years. The performance of the VAEs highly depends on their architectures, which are often handcrafted by the human expertise in deep neural networks (DNNs). However, such expertise is not necessarily available to each of the end users interested. In this article, we propose a novel method to automatically design optimal architectures of VAEs for image classification, called evolving deep convolutional VAE (EvoVAE), based on a genetic algorithm (GA). In the proposed EvoVAE algorithm, the traditional VAEs are first generalized to a more generic and asymmetrical one with four different blocks, and then a variable-length gene encoding mechanism of the GA is presented to search for the optimal network depth. Furthermore, an effective genetic operator is designed to adapt to the proposed variable-length gene encoding strategy. To verify the performance of the proposed algorithm, nine variants of AEs and VAEs are chosen as the peer competitors to perform the comparisons on MNIST, street view house numbers, and CIFAR-10 benchmark datasets. The experiments reveal the superiority of the proposed EvoVAE algorithm, which wins 21 times out of the 24 comparisons and outperforms the best competitors by 1.39%, 14.21%, and 13.03% on the three benchmark datasets, respectively.
Using confidence intervals to determine adequate item sample sizes for vocabulary tests: An essential but overlooked practice

The last three decades have seen an increase of tests aimed at measuring an individual’s vocabulary level or size. The target words used in these tests are typically sampled from word frequency lists, which are in turn based on language corpora. Conventionally, test developers sample items from frequency bands of 1000 words; different tests employ different sampling ratios. Some have as few as 5 or 10 items representing the underlying population of words, whereas other tests feature a larger number of items, such as 24, 30, or 40. However, very rarely are the sampling size choices supported by clear empirical evidence. Here, using a bootstrapping approach, we illustrate the effect that a sample-size increase has on confidence intervals of individual learner vocabulary knowledge estimates, and on the inferences that can safely be made from test scores. We draw on a unique dataset consisting of adult L1 Japanese test takers’ performance on two English vocabulary test formats, each featuring 1000 words. Our analysis shows that there are few purposes and settings where as few as 5 to 10 sampled items from a 1000-word frequency band (1K) are sufficient. The use of 30 or more items per 1000-word frequency band and tests consisting of fewer bands is recommended.
mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level Estimation

This work presents mEBAL, a multimodal database for eye blink detection and attention level estimation. The eye blink frequency is related to the cognitive activity and automatic detectors of eye blinks have been proposed for many tasks including attention level estimation, analysis of neuro-degenerative diseases, deception recognition, drive fatigue detection, or face anti-spoofing. However, most existing databases and algorithms in this area are limited to experiments involving only a few hundred samples and individual sensors like face cameras. The proposed mEBAL improves previous databases in terms of acquisition sensors and samples. In particular, three different sensors are simultaneously considered: Near Infrared (NIR) and RGB cameras to capture the face gestures and an Electroencephalography (EEG) band to capture the cognitive activity of the user and blinking events. Regarding the size of mEBAL, it comprises 6,000 samples and the corresponding attention level from 38 different students while conducting a number of e-learning tasks of varying difficulty. In addition to presenting mEBAL, we also include preliminary experiments on: i) eye blink detection using Convolutional Neural Networks (CNN) with the facial images, and ii) attention level estimation of the students based on their eye blink frequency.
Spatiotemporal Trident Networks: Detection and Localization of Object Removal Tampering in Video Passive Forensics

With the development of video and image processing technology, the field of video tampering forensics is facing enormous challenges. Specifically, as the fundamental basis of judicial forensics, passive forensics for object removal video forgery is particularly essential. To extract tampering traces in video more sufficiently, the author proposed a spatiotemporal trident network based on the spatial rich model (SRM) and 3D convolution (C3D), which provides three branches and can theoretically improve the detection and localization accuracy of tampered regions. Based on the spatiotemporal trident network, a temporal detector and a spatial locator were designed to detect and locate the tampered regions in the temporal and spatial domains of videos. For the temporal detector, 3D CNNs were employed in three branches as the encoders and a bidirectional long short-term memory (BiLSTM) as the decoder. For the spatial locator, a backbone network named C3D-ResNet12 was designed as the encoder of the three branches, and the region proposal networks (RPNs) were employed as the decoders in three branches. In addition, we optimized the loss functions of the above two algorithms based on focal loss and GIoU loss. The experimental results revealed the effectiveness of spatiotemporal detection and localization algorithms: for temporal forgery detection, the accuracy of the frame classification increased to 99+%; for spatial forgery localization, the successful localization rate of the tampered regions in forged frames reached 96+%, and the mean intersection over union of the located tampered regions and the real tampered regions reached 62+%.
Solar Photovoltaic Power Forecasting

The management of clean energy is usually the key for environmental, economic, and sustainable developments. In the meantime, the energy management system (EMS) ensures the clean energy which includes many sources grouped in a small power plant such as microgrid (MG). In this case, the forecasting methods are used for helping the EMS and allow the high efficiency to the clean energy. The aim of this review paper is providing the necessary data about the basic principles and standards of photovoltaic (PV) power forecasting by stating numerous research studies carried out on the PV power forecasting topic specifically in the short-term time horizon which is advantageous for the EMS and grid operator. At the same time, this contribution can offer a state of the art in different methods and approaches used for PV power forecasting along with a careful study of different time and spatial horizons. Furthermore, this current review paper can support the tenders in the PV power forecasting.
A Mixed-Pruning Based Framework for Embedded Convolutional Neural Network Acceleration

Convolutional neural networks (CNN) have been proved to be an effective method in the field of artificial intelligence (AI), and large-scale deploying CNN to embedded devices, no doubt, will greatly promote the development and application of AI into the practical industry. However, mainly due to the space-time complexity of CNN, computing power, memory bandwidth and flexibility are performance bottlenecks. In this paper, a framework containing model compression and hardware acceleration is proposed to solve the above problems. This framework consists of a mixed pruning method, data storage optimization for efficient memory utilization and an accelerator for mapping CNN on field programmable gate array (FPGA). The mixed pruning method is used to compress the model, and data bit-width is reduced to 8-bit by data quantization. Accelerator based on FPGA makes it flexible, configurable and efficient for CNN implementation. The model compression is evaluated on NVIDIA RTX2080Ti, and the results illustrate that the VGG16 is compressed by 30× and the fully convolutional network (FCN) is compressed by 11× within 1% accuracy loss. The compressed model is deployed and accelerated on ZCU102, which is up to 1.7× and 24.5× better in energy efficiency compared with RTX2080Ti and Intel i7 7700.
BPT Scheme: Establishing Trusted Vehicular Fog Computing Service for Rural Area Based on Blockchain Approach

Passing through a rural area with a limited network infrastructure may disrupt fog computing support for vehicles. As a result, some applications on vehicles may turn off and bother the performance of the vehicular systems. In order to escape from this kind of situation, vehicular fog computing is discussed in recent times as an alternative of fog computing support while passing through a blank spot of network infrastructure. However, it is not feasible to establish a trusted vehicular fog computing service among vehicles without mutual trust. To deal with this situation, this paper proposes a method called Bidding-Price-based Transaction (BPT) for vehicular fog computing service in rural areas. This method is composed of bidding-price-based mutual trust establishment between client vehicle and server vehicle and also payoff assignment based on transaction evaluation. By applying this method, trusted fog computing service transactions between two vehicles can be achieved without the direct assistance of any trusted third party as a validating entity. The simulation results and feasibility analysis then validate the performance of the BPT scheme in rural areas. Based on feasibility analysis, we claim that the BPT scheme can be realized by adjusting vehicle speed and transmission range with respect to the size of offloaded data.
Two-hand Global 3D Pose Estimation using Monocular RGB

We tackle the challenging task of estimating global 3D joint locations for both hands via only monocular RGB input images. We propose a novel multi-stage convolutional neural network based pipeline that accurately segments and locates the hands despite occlusion between two hands and complex background noise and estimates the 2D and 3D canonical joint locations without any depth information. Global joint locations with respect to the camera origin are computed using the hand pose estimations and the actual length of the key bone with a novel projection algorithm. To train the CNNs for this new task, we introduce a large-scale synthetic 3D hand pose dataset. We demonstrate that our system outperforms previous works on 3D canonical hand pose estimation benchmark datasets with RGB-only information. Additionally, we present the first work that achieves accurate global 3D hand tracking on both hands using RGB-only inputs and provide extensive quantitative and qualitative evaluation.
Virtual-Goal-Guided RRT for Visual Servoing of Mobile Robots With FOV Constraint

In this article, a virtual-goal-guided rapidly exploring random tree (RRT)-based visual servoing approach is proposed for nonholonomic mobile robots to simultaneously satisfy the field-of-view (FOV) constraint and the velocity constraints during the motion toward the desired pose. The presented approach contains two parts: 1) trajectory planning in the scaled Euclidean space and 2) trajectory tracking control. For the trajectory planning part, a new virtual-goal-guided RRT algorithm is designed to guarantee the FOV constraint and the velocity constraints by iteratively exploring the scaled Euclidean space in the presence of unknown image depth. Specifically, a virtual goal directly behind the desired pose is set to guide the tree to extend laterally into the area wherein the robot is easier to satisfy the FOV constraint. In addition, the lateral extension of the tree also helps decrease the lateral error of the robot as much as possible. Following each successful extension toward the virtual goal node, a greedy extension from the newly explored node to the desired pose is attempted using a polar stabilization controller, so that the planned trajectory can accurately arrive at the desired pose. Each newly explored edge in the scaled space is projected into the image space to check for the FOV limit. For the visual tracking part, the final searched trajectory in the scaled space is first transformed into image feature trajectories, which are then tracked by an image-based visual tracking controller. Experiments validate the effectiveness of the proposed approach.