Reviewerly/exHarmony huggingface dataset:
Exploring folk theories of algorithmic news curation for explainable design

Algorithmic news curation determines users’ news exposure in the online environment. Despite its usefulness, it also comes along with the problem of algorithmic opacity. To combat this, explainable algorithmic news curation systems are necessary. One user-centered solution to design these systems can be achieved through the systematic exploration of user folk theories. For this, we conducted twelve in-depth semi-structured interviews to explore (1) the user preferences for explainable system design, and (2) folk theories of algorithmic news curation. By applying qualitative content analysis, we found a psychological trade-off between the desire for transparency and feelings of creepiness, thus a preference for explanations to be hidden. Furthermore, we identified eight assumptions of folk theories. The results are compared to previous folk theories and discussed in terms of the ‘sweet spot’ of system transparency. We conclude that exploring folk theories is a key requirement for designing explainable algorithmic news curation systems.
Distributed Recursive Projection Identification with Binary-Valued Observations

This paper investigates a distributed recursive projection identification problem with binary-valued observations built on a sensor network, where each sensor in the sensor network measures partial information of the unknown parameter only, but each sensor is allowed to communicate with its neighbors. A distributed recursive projection algorithm is proposed based on a specific projection operator and a diffusion strategy. The authors establish the upper bound of the accumulated regrets of the adaptive predictor without any requirement of excitation conditions. Moreover, the convergence of the algorithm is given under the bounded cooperative excitation condition, which is more general than the previously imposed independence or persistent excitations on the system regressors and maybe the weakest one under binary observations. A numerical example is supplied to demonstrate the theoretical results and the cooperative effect of the sensors, which shows that the whole network can still fulfill the estimation task through exchanging information between sensors even if any individual sensor cannot.
Predicting the Lossless Compression Ratio of Remote Sensing Images With Configurational Entropy

Compression of remote sensing images is beneficial to both storage and transmission.For lossless compression, the upper and lower limits of compression ratio are defined by Shannon's Source Coding Theorem with Shannon entropy as the metric, which measures the statistical information of a dataset.However, the calculation of the actual Shannon entropy of a large image is not an easy task, which limits the practicality of predicting the lossless compression ratio with Shannon entropy.On the other hand, most recently developed compression techniques take into consideration the configurational information of images to achieve a high compression ratio.This leads us to hypothesize that a metric capturing configurational information can be employed to build mathematical models for predicting compression ratios.To test this hypothesis, a two-step investigation was carried out, i.e., to find the most suitable metric through extensive experimental tests and to build a model upon this metric.A total of 1850 8-bit images with 15 compression techniques were used to form the experimental dataset.First, 29 metrics were analyzed in terms of correlation magnitude, distinctiveness, and model contribution.As a result, the configurational entropy outperformed the rest.Second, six configurational entropy-based prediction models for predicting the compression ratio were established and tested.Results illustrated that these models work well.The PolyRatio model with 9.0 as a numerator, which was in a similar form to Shannon's theorem, performed best and was thus recommended.This study provides a new direction for building a theoretical prediction model with configurational entropy.
An investigation of attention mechanisms in histopathology whole-slide-image analysis for regression objectives

Analysis of whole-slide-images (WSIs) of histopathology tissue sections remains challenging due to the gigapixel scale of these images, which often necessitates their division into smaller image tiles. Recently, attention mechanisms have been successfully applied to alleviate the tile-to-slide challenges for classification tasks based on WSIs. In this study, we explore the potential of attention mechanisms in regression settings, by comparing four modelling approaches, two of which use attention mechanisms. We evaluate these models both in a simulated experiment using the MNIST data set, and in real histopathology data sets focused on prediction of gene expression levels from WSIs, including an analysis of the local prediction performance using spatial transcriptomics. The MNIST simulation demonstrates that if only a small proportion of instances in a set of images contribute to the set-level regression label, attention mechanisms may be preferable to commonly applied weakly supervised models. When predicting gene expression from WSIs, the differences in performance between the models that we investigated were small. Nevertheless, we found some evidence that attention mechanisms may be more sensitive to domain shifts. In the regression-based task of gene expression prediction, the prediction performance in the present study appears to be limited by other factors rather than by the choice of modelling approach. Nevertheless, attention mechanisms appear promising for regression objectives and warrant further investigation.
A comparative study between PVO-based framework and multi-predictor mechanism in reversible data hiding

Sorting-based reversible data hiding (RDH) methods like pixel-value-ordering (PVO) can predict pixel values accurately and achieve an extremely low distortion on the embedded image. However, the excellent performance of these methods was not well explained in previous works, and there are unexploited common points among them. In this paper, we propose a general multi-predictor (GMP) framework to summarize PVO-based RDH methods and explain their high prediction accuracy. Moreover, by utilizing the proposed GMP framework, a more efficient sorting-based RDH method is given as an example to show the generality and applicability of our framework. Comparing with other PVO-based methods, the proposed example method can achieve significant improvement in embedding performance. It is hopeful that more efficient sorting-based RDH algorithms can be designed according to our proposed framework by designing better predictors and their combination methods.
Comparison of CNNs for Lung Biopsy Images Classification

Deep learning approaches are widely used in the processing of medical images, including histopathological images for cancer diagnosis. Therefore, the scientific and practical problem of automation of biopsy image analysis using convolutional neural networks is considered in the paper. The LC25000 dataset was used to compare the classification accuracy of different CNN architectures. To analyze the impact of image size, two more datasets were created from the initial dataset by slicing of the images. The correlation between the complexity of CNN structure, size of the images, and the resulted accuracy on test data was obtained. Results were compared with related researches on the LC25000 dataset. The theory of deep learning neural networks and mathematical statistics methods are used.
Behind the scenes: On the relationship between developer experience and refactoring

Abstract Refactoring is widely recognized as one of the efficient techniques to manage technical debt and maintain a healthy software project through enforcing best design practices, or coping with design defects. Previous refactoring surveys have shown that code refactoring activities are mainly executed by developers who have sufficient knowledge of the system's design, and disposing of leadership roles in their development teams. However, these surveys were mainly limited to specific projects and companies. In this paper, we explore the generalizability of the previous results by analyzing 800 open‐source projects. We mine their refactoring activities, and we identify their corresponding contributors. Then, we associate an experience score to each contributor in order to test various hypotheses related to whether developers with higher scores tend to (1) perform a higher number of refactoring operations, (2) exhibit different motivations behind their refactoring, and (3) better document their refactoring activity. We found that (1) although refactoring is not restricted to a subset of developers, those with higher contribution scores tend to perform more refactorings than others; (2) although there is no correlation between experience and motivation behind refactoring, top contributed developers are found to perform a wider variety of refactoring operations, regardless of their complexity; and (3) top contributed developer tend to document less their refactoring activity. Our qualitative analysis of three randomly sampled projects shows that the developers who are responsible for the majority of refactoring activities are typically in advanced positions in their development teams, demonstrating their extensive knowledge of the design of the systems they contribute to.
DataType-Aware Knowledge Graph Representation Learning in Hyperbolic Space

Knowledge Graph (KG) representation learning aims to encode both entities and relations into a continuous low-dimensional vector space. Most existing methods only concentrate on learning representations from structural triples in Euclidean space, which cannot well exploit the rich semantic information with hierarchical structure in KGs. In this paper, we propose a novel DataType-aware hyperbolic knowledge representation learning model called DT-GCN, which has the advantage of fully embedding attribute values of data types information. We refine data types into five primitive modalities, including integer, double, Boolean, temporal, and textual. For each modality, an encoder is specifically designed to learn its embedding. In addition, we define a unified space based on Euclidean, spherical, and hyperbolic space, which is a continuous curvature space that combines advantages of three different spaces. Extensive experiments on both synthetic and real-world datasets show that our model is consistently better than the state-of-the-art models. The average performance is improved by 2.19% and 3.46% than the optimal baseline model on node classification and link prediction tasks, respectively. The results of ablation experiments demonstrate the advantages of embedding data types information and leveraging the unified space.
Stock market forecasting using intrinsic time-scale decomposition in fusion with cluster based modified CSA optimized ELM

Stock price prediction is a significant index which helps to achieve maximum benefit with minimum risk by increasing the decision making capability of financial investigators and investors. However, the problem of short term stock price prediction is a complex task due to its uncertainty, discontinuity, and random nonlinear nature. In this paper, a prediction model is proposed to predict random nonlinear stock market price using Intrinsic Time-Scale Decomposition (ITD), Cluster based Modified Crow Search Algorithm (CMCSA), and Optimized Extreme Learning Machine (OELM). ITD is adopted to decompose the non-stationary stock price data into some Proper-Rotation-Components (PRCs) and a residual component. ITD is used to convert non-stationary stock price data to stationary data which are simpler and steady for analysis. The CMCSA is proposed by modifying Crow Search Algorithm (CSA) with better capability to select optimal weight and biases of ELM. Thereafter, the optimized ELM is used to predict the PRCs and residual component individually which are then incorporated to predict closing price of short term stocks. The effectiveness of proposed CMCSA is tested and validated by solving benchmark problems. The experimental study indicates that the proposed ITD-CMCSA-OELM model outperforms the CMCSA-OELM, CSA-OELM, DE-OELM (Differential Evolution optimized ELM) and ANN models.
MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution

Space-time video super-resolution (STVSR) aims to construct a high space-time resolution video sequence from the corresponding low-frame-rate, low-resolution video sequence. Inspired by the recent success to consider spatial-temporal information for space-time super-resolution, our main goal in this work is to take full considerations of spatial and temporal correlations within the video sequences of fast dynamic events. To this end, we propose a novel one-stage memory enhanced graph attention network (MEGAN) for space-time video super-resolution. Specifically, we build a novel long-range memory graph aggregation (LMGA) module to dynamically capture correlations along the channel dimensions of the feature maps and adaptively aggregate channel features to enhance the feature representations. We introduce a non-local residual block, which enables each channel-wise feature to attend global spatial hierarchical features. In addition, we adopt a progressive fusion module to further enhance the representation ability by extensively exploiting spatial-temporal correlations from multiple frames. Experiment results demonstrate that our method achieves better results compared with the state-of-the-art methods quantitatively and visually.
Psychological and physiological evidence for an initial ‘Rough Sketch’ calculation of personal space

Personal space has been defined as "the area individuals maintain around themselves into which others cannot intrude without arousing discomfort". However, the precise relationship between discomfort (or arousal) responses as a function of distance from an observer remains incompletely understood. Also the mechanisms involved in recognizing conspecifics and distinguishing them from other objects within personal space have not been identified. Accordingly, here we measured personal space preferences in response to real humans and human-like avatars (in virtual reality), using well-validated "stop distance" procedures. Based on threshold measurements of personal space, we examined within-subject variations in discomfort-related responses across multiple distances (spanning inside and outside each individual's personal space boundary), as reflected by psychological (ratings) and physiological (skin conductance) responses to both humans and avatars. We found that the discomfort-by-distance functions for both humans and avatars were closely fit by a power law. These results suggest that the brain computation of visually-defined personal space begins with a 'rough sketch' stage, which generates responses to a broad range of human-like stimuli, in addition to humans. Analogous processing mechanisms may underlie other brain functions which respond similarly to both real and simulated human body parts.
Path Planning of Mobile Robot Based on A* Algorithm

Path planning for mobile robots has always been A hot topic. A-star algorithm and its extensibility algorithm have been widely used to solve the optimal path of mobile robots. In this paper, in the study of robot path planning algorithm, detailed elaborated the basic principle of traditional A * algorithm, and through the grid method to partition the robot path planning area, using the MATLAB simulation platform to generate two-dimensional path the robot simulation map simulation experiment, and analyze the results and research, provide experience for the further research in the future.
Reliable UAV Monitoring System Using Deep Learning Approaches

In recent years, unmanned aerial vehicles (UAV) or drones have become ubiquitous in our daily lives, bringing great convenience to our lives and playing a pivotal role in future wireless networks and the Internet of things. One of the major problems associated with the UAV is the heterogeneous nature of such deployments; this heterogeneity poses many challenges, particularly in the areas of security and privacy. The key to solving these problems is to accurately identify and authenticate drones. In this article, a reliable UAV identify framework based on radio frequency fingerprint is proposed. First, we established a wireless signal label architecture and systematically collected, analyzed, and recorded the radio frequency signals of different UAVs in different flight modes and different distances in the telemetry link, and established UAV signal datasets. Then, the intelligent algorithm and anti-UAV system are designed by using the collected dataset, and the feasibility of the developed dataset for detecting and identifying UAVs is verified by using machine learning and deep learning. The simulation results show that under the condition of Gaussian white noise, the method based on deep learning achieves high reliability, and when the SNR is not less than 5dB, the model achieves more than 95% of the monitoring and recognition accuracy. Finally, we discussed the possible applications of the dataset in the future.
Constructing Expertise: Surmounting Performance Plateaus by Tasks, by Tools, and by Techniques

Acquiring expertise in a task is often thought of as an automatic process that follows inevitably with practice according to the log-log law (aka: power law) of learning. However, as Ericsson, Chase, and Faloon (1980) showed, this is not true for digit-span experts and, as we show, it is certainly not true for Tetris players at any level of expertise. Although some people may simply "twitch" faster than others, the limit to Tetris expertise is not raw keypress time but the techniques acquired by players that allow them to use the tools provided by the hardware and software to compensate for the game's relentlessly increasing drop speed. Unfortunately, these increases in drop speed between Tetris levels make performance plateaus very short and quickly followed by game death. Hence, a player's success at discovering, exploring, and practicing new techniques for the tasks of board preparation, board maintenance, optimal placement discovery, zoid rotation, lateral movement of zoids, and other tasks important to expertise in Tetris is limited. In this paper, we analyze data collected from 492 Tetris players to reveal the challenges they confronted while constructing expertise via the discovery of new techniques for gameplay at increasingly difficult levels of Tetris.
Ensemble learning via feature selection and multiple transformed subsets: Application to image classification

In the machine learning field, especially in classification tasks, the model’s design and construction are very important. Constructing the model via a limited set of features may sometimes bound the classification performance and lead to non-optimal performances that some algorithms can provide. To this end, Ensemble learning methods were proposed in the literature. These methods’ main goal is to learn a set of models that provide features or predictions whose joint use could lead to a performance better than that obtained by the single model. In this paper, we propose three variants of a new efficient ensemble learning approach that was able to enhance the classification performance of a linear discriminant embedding method. As a case study we consider the efficient “Inter-class sparsity discriminative least square regression” method. We seek the estimation of an enhanced data representation. Instead of deploying multiple classifiers on top of the transformed features, we target the estimation of multiple extracted feature subsets obtained by multiple learned linear embeddings. These are associated with subsets of ranked original features. Multiple feature subsets were used for estimating the transformations. The derived extracted feature subsets were concatenated to form a single data representation vector that is used in the classification process. Many factors were studied and investigated in this paper including (Parameter combinations, number of models, different training percentages, feature selection methods combinations, etc.). Our proposed approach has been benchmarked on different image datasets of various sizes and types (faces, objects and scenes). The proposed scheme achieved competitive performance on four face image datasets (Extended Yale B, LFW-a, Gorgia and FEI) as well as on the COIL20 object dataset and the Outdoor Scene dataset. We measured the performance of our proposed schemes in comparison to (the single model ICS_DLSR, RDA_GD, RSLDA, PCE, LDE, LDA, SVM as well as the KNN algorithm) The conducted experiments showed that the proposed approach can enhance the classification performance in an efficient manner compared to the single-model based learning and was able to outperform its competing methods.
BC-HRM: A Blockchain-Based Human Resource Management System Utilizing Smart Contracts

Blockchain technology offers several advantages to Human Resource Management (HRM); easier verification of job candidates’ education and skills, efficient recording of job candidates’ education, expertise, and training, easier payment of cross-border transfers, international expenses, and tax liabilities, elimination of fraud and improved cyber-security. An in-depth literature review was conducted followed by an extensive analysis of several blockchain HRM systems that resulted in the design of BC-HRM; a Blockchain-Based Human Resource Management system utilizing smart contracts. A test plan was devised for BC-HRM and 15 test cases were devised and tested by HRM employees to test both the functional and non-functional requirements of the proposed system. The evaluation results via the System Usability Scale (SUS) model showed an overall successes rate of 85%.
Generative Inverse Deep Reinforcement Learning for Online Recommendation

Deep reinforcement learning enables an agent to capture users' interest through dynamic interactions with the environment. It uses a reward function to learn user's interest and to control the learning process, attracting great interest in recommendation research. However, most reward functions are manually designed; they are either too unrealistic or imprecise to reflect the variety, dimensionality, and non-linearity of the recommendation problem. This impedes the agent from learning an optimal policy in highly dynamic online recommendation scenarios. To address the above issue, we propose a generative inverse reinforcement learning approach that avoids the need of defining an elaborative reward function. In particular, we model the recommendation problem as an automatic policy learning problem. We first generate policies based on observed users' preferences and then evaluate the learned policy by a measurement based on a discriminative actor-critic network. We conduct experiments on an online platform, VirtualTB, and demonstrate the feasibility and effectiveness of our proposed approach via comparisons with several state-of-the-art methods.