Reviewerly/exHarmony huggingface dataset:
A goal-reference-point decision-making method based on normal cloud model and its application in distribution network planning evaluation

Scientific experiments show that specific and difficult goals can lead to higher performance than do vague and simple goals. Therefore, we study a goal-reference-point decision-making method that considers multiple expert-reference-points and the difficulty grade in a single-stage decision-making problem. First, based on the prior information set, an optimization model is built to obtain the distance range between the attribute value and the goal-reference-point. Next, based on the principle of minimizing the distance between the attribute value and its difficulty grade, an optimization model is built to divide the difficulty grades into several distance ranges. Moreover, based on the principle of maximum entropy, an optimization model that considers the given difficulty grade is built to obtain the weights of experts. Afterward, the ranking values of alternatives are calculated according to the goal-reference-point and prospect theory. Finally, a real-world case of the distribution network planning evaluation and a method comparison illustrate the feasibility and validity of the proposed method.
A Novel Positioning Module and Fusion Algorithm for Unmanned Aerial Vehicle Monitoring

Aiming to solve the monitoring problem of unmanned aerial vehicles (UAVs), a novel positioning module is designed and implemented. The hardware design meets the prerequisites of miniaturization and low-cost. Then, a calibration procedure based on six-position calibration and ellipsoid fitting is executed to eliminate the bad influence on the system’s accuracy and stability caused by deterministic errors. Also, a stochastic error modeling procedure based on Allan variance is performed to identify stochastic errors’ model type and model parameters. Finally, data from inertial measurement units (IMUs), global navigation satellite system (GNSS), magnetometers, and a barometer are fused by an extended Kalman filter (EKF) based sensor fusion algorithm. This algorithm is applied to a UAV flight test and comparison result using calibrated data and uncalibrated data respectively are shown. Besides, the proposed EKF algorithm is compared with unscented Kalman filter (UKF) and unscented quaternion estimator (USQUE) to show its practicability in the application. The main contribution of this research is the design of the positioning module and the validation of the sensor fusion algorithm in this hardware. It is found that, under the constraint of low-cost sensors, the accuracy of positioning can achieve around 7.5m horizontally, and 2m vertically. The frequency of positioning increases to 50Hz after sensor fusion, a large improvement over the previous 1Hz GNSS positioning frequency.
SAGAN: Skip-Attention GAN For Anomaly Detection

Generative Adversarial Networks (GANs) have been used recently for anomaly detection from images, where the anomaly scores are obtained by comparing the global difference between the input and generated image. However, the anomalies often appear in local areas of an image scene, and ignoring such information can lead to unreliable detection of anomalies. In this paper, we propose an efficient anomaly detection network Skip-Attention GAN (SAGAN), which adds attention modules to capture local information to improve the accuracy of latent representation of images, and uses depth-wise separable convolutions to reduce the number of parameters in the model. We evaluate the proposed method on the CIFAR-10 dataset and the LBOT dataset (built by ourselves), and show that the performance of our method in terms of area under curve (AUC) on both datasets is improved by more than 10% on average, as compared with three recent baseline methods.
Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness

Recommendation algorithms are susceptible to popularity bias: a tendency to recommend popular items even when they fail to meet user needs. A related issue is that the recommendation quality can vary by demographic groups. Marginalized groups or groups that are under-represented in the training data may receive less relevant recommendations from these algorithms compared to others. In a recent study, Ekstrand et al. [15] investigate how recommender performance varies according to popularity and demographics, and find statistically significant differences in recommendation utility between binary genders on two datasets, and significant effects based on age on one dataset. Here we reproduce those results and extend them with additional analyses. We find statistically significant differences in recommender performance by both age and gender. We observe that recommendation utility steadily degrades for older users, and is lower for women than men. We also find that the utility is higher for users from countries with more representation in the dataset. In addition, we find that total usage and the popularity of consumed content are strong predictors of recommender performance and also vary significantly across demographic groups.
AMC-Net: Attentive modality-consistent network for visible-infrared person re-identification

Visible-infrared person re-identification (VI-ReID) aims at matching people across images from RGB and infrared modality. Existing methods tend to utilize the readily available models to extract features, ignoring mining spatial and channel information of features. In this paper, we propose an attentive modality-consistent network (AMC-Net) for VI-ReID. Firstly, to avoid the damage to discriminability of features caused by overfitting of local regions in the network, a context-aware attention block (CAB) is designed to mine the spatial information of the whole person region by magnifying the perception scope of convolution layers. Secondly, the attentive channel aggregation block (ACB) is adopted to mine the channel information with richer semantic cues by considering local cross-channel interactive information. Thirdly, we propose a modality-consistent regularizer to narrow down the discrepancy of high order features between heterogeneous images. Extensive experiments on two datasets indicate that the proposed method outperforms the state-of-the-art methods.
Formation of Computational Thinking Skills Using Computer Games in Teaching Mathematics

The research is relevant as educational computer games are included in students' mathematical activity and form additional opportunities to improve the quality of teaching mathematics in a digital school, to support the formation of the demanded professional competence - computational thinking. To form the appropriate skills that determine the essence of special computational thinking, the authors propose to include game educational spaces based on digital gamification resources into students' mathematical activities. The research aims to resolve the contradiction between requirements of the modern economy for specialists' high level of computational thinking and an insufficiently developed methodological base for training graduates that meets these requirements. The purpose of this research is to study the features of using gamification technologies in teaching mathematics to form the skills and abilities that make up the essence of computational thinking. The article describes directions of educational and cognitive mathematical activity based on the principles of gamification. The authors clarify the concept of "computational thinking", which includes a system of actions for activating patterns, connections between them from human memory, and compile an effective algorithm for solving them: to obtain relevant information on advanced technological developments; to state the problem and model; to use a software product with mathematical content.
Hypergraph wavelet neural networks for 3D object classification

Recently, hypergraph learning has shown great potential in a variety of classification tasks. However, existing hypergraph neural networks lack flexibility in modeling and extracting high-order relationships among data. To solve this problem, we propose a novel framework called hypergraph wavelet neural networks (HGWNN) to explore the high-order correlation in 3D data. Firstly, considering the non-uniformity of most data sets in the real world, we propose a “data-driven” hypergraph construction scheme, which is more efficient than some commonly used hypergraph construction methods. Secondly, in order to efficiently learn deep embeddings from the constructed hypergraph, we propose a hypergraph wavelet convolution operator. It enables efficient information aggregation by fully exploiting the localization property of wavelets. This convolution operator is suitable for both non-uniform and uniform hypergraphs. Finally, we design a new hypergraph regularizer based on the sparse prior of wavelet coefficients to promote local smoothness and avoid network overfitting. We have conducted experiments on object classification tasks on two 3D benchmark datasets: the National Taiwan University (NTU) 3D model dataset and the ModelNet40 dataset. Experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods.
Efficient algorithms for mining closed high utility itemsets in dynamic profit databases

The problem of discovering high-utility itemsets (HUIs) in transaction databases, which is an extension of Frequent Itemset Mining, is a commonly encountered mining task. Researchers have proposed algorithms to efficiently mine highly profitable itemsets in customer transaction databases, in which the unit profits of items are fixed. However, this assumption does not reflect the true nature of the utility measure of items in real-life transaction databases, which might vary over time. Moreover, since this important characteristic is ignored by all the current HUI mining algorithms, they are either not applicable to this type of database, or they generate inaccurate results. In addition, the HUI mining algorithms’ traditional limitation is that they produce a huge number of HUIs for users. In this paper, we define the problem of mining a lossless, concise and compact representation of HUIs, called closed HUIs (CHUIs), in dynamic unit profit databases. Based on newly defined of utility measure, a novel algorithm, called iEFIM-Closed, is introduced. This relies on this new utility measure, a novel compact database format to reduce the cost of database scans and increase the efficiency of the mining process. Experimental evaluations show that iEFIM-Closed significantly outperforms state-of-the-art algorithms for mining CHUIs on sparse databases with dynamic profit, and has competitive performance in dense databases in terms of runtime, the cost of database scans and scalability.
Improving mobile learning in secondary mathematics and science: Listening to students

Abstract Background Mobile learning studies often focus on teachers' perspectives. This study instead considers students' experiences of learning with mobile devices (i.e., m‐learning) in secondary school mathematics and science. Objectives The research aims to describe the m‐learning experiences of secondary mathematics and science students, and to determine the extent to which distinctive pedagogical dimensions impact students' perceived learning. Methods A survey instrument using the iPAC mobile pedagogical framework as a theoretical lens is developed and validated. This framework highlights three pedagogical dimensions: personalization, authenticity, and collaboration. Structural equation modelling is used to investigate how each dimension predicts students' perceived improvement in learning, whilst accounting for usage context, among a sample of students in schools where mobile devices are used extensively. Results and Conclusions Students were in agreement that personalization was a characteristic of their m‐learning experiences, but authentic and collaborative learning were not as strongly featured. M‐learning activities fostering personalization were most important for improving perceived learning; authenticity and collaboration were also significant, but no differences in perceived learning improvement due to location were found. Authentic m‐learning and perceived improvement in learning with mobile devices were significantly higher in science than mathematics subjects. Implications When teachers design m‐learning tasks that enhance personalization, collaboration, and authenticity, students are predicted to perceive improvements in their learning. The findings suggest that teachers should consider designing technology‐enhanced tasks that improve students' experiences of collaborative and authentic learning, particularly in mathematics for greatest gains in students' perceived improvement in learning.
Comparison of Tensorflow and PyTorch in Convolutional Neural Network - based Applications

In this paper, we present a comparison between the PyTorch and TensorFlow environments, used in defining neural networks. The purpose is to find whether the choice of a library affects the overall performance of the system both during training and design. To do so, our approach involves analysis of the processes involved when creating a neural network, as well as taking measurements and monitoring its evolution over the epochs. Advantages and disadvantages are then extracted from the results and are then used to draw conclusions.
Counterfeit detection with QR codes

Serialized QR Codes applied to product packaging have received considerable interest as a potential solution to the problem of industrial counterfeiting. Compared to traditional security solutions (e.g. taggants, holograms, security inks), they are indeed simpler to integrate in existing production workflow, easier to verify, and more cost-effective at scale. In addition, they allow to convey a product digital identity and history, and are used to connect brand owners with consumers. However, by itself a QR Code offers no protection at all against copy by cloning. Various schemes have been proposed to add a copy-sensitive layer to QR Codes, but most techniques in the state of the art have not been applied to real production environments, where QR Codes are printed at mass scale and products are scanned in a non-controlled environment, typically by consumers with their smartphones. This paper presents a system based on integrating copy detection patterns into QR Codes, which has been deployed for a number of years on the market.
Typing on Midair Virtual Keyboards: Exploring Visual Designs and Interaction Styles

We investigate typing on a QWERTY keyboard rendered in virtual reality. Our system tracks users' hands in the virtual environment via a Leap Motion mounted on the front of a head mounted display. This allows typing on an auto-correcting midair keyboard without the need for auxiliary input devices such as gloves or handheld controllers. It supports input via the index fingers of one or both hands. We compare two keyboard designs: a normal QWERTY layout and a split layout. We found users typed at around 16 words-per-minute using one or both index fingers on the normal layout, and about 15 words-per-minute using both index fingers on the split layout. Users had a corrected error rate below 2% in all cases. To explore midair typing with limited or no visual feedback, we had users type on an invisible keyboard. Users typed on this keyboard at 11 words-per-minute at an error rate of 3.3% despite the keyboard providing almost no visual feedback.
RevFRF: Enabling Cross-Domain Random Forest Training With Revocable Federated Learning

Random forest is one of the most heated machine learning tools in a wide range of industrial scenarios. Recently, federated learning enables efficient distributed machine learning without direct revealing of private participant data. In this article, we present a novel framework of federated random forest (RevFRF), and further emphatically discuss the participant revocation problem of federated learning based on RevFRF. Specifically, RevFRF first introduces a suite of homomorphic encryption based secure protocols to implement federated random forest (RF). The protocols cover the whole lifecycle of an RF model, including construction, prediction and participant revocation. Then, referring to the practical application scenarios of RevFRF, the existing federated learning frameworks ignore a fact that even every participant in federated learning cannot maintain the cooperation with others forever. In company-level cooperation, allowing the remaining companies to use a trained model that contains the memories from an off-lying company potentially leads to a significant conflict of interest. Therefore, we propose the revocable federated learning concept and illustrate how RevFRF implements participant revocation in applications. Through theoretical analysis and experiments, we show that the protocols can efficiently implement federated RF and ensure the memories of a revoked participant in the trained RF to be securely removed.
SoK: Money Laundering in Cryptocurrencies

Money laundering using cryptocurrencies has become increasingly prevalent, and global and national regulatory authorities have announced plans to implement stringent anti-money laundering regulations. In this paper, we examine current anti-money laundering (AML) mechanisms in cryptocurrencies and payment networks from a technical and policy perspective, and point out practical challenges in implementing and enforcing them. We first discuss blacklisting, a recently proposed technique to combat money laundering, which seems appealing, but leaves several unanswered questions and challenges with regard to its enforcement. We then discuss payment networks and find that there are unique problems in the payment network domain that might require custom-designed AML solutions, as opposed to general cryptocurrency AML techniques. Finally, we examine the regulatory guidelines and recommendations as laid out by the global Financial Action Task Force (FATF), and the U.S. based Financial Crimes Enforcement Network (FinCEN), and find that there are several ambiguities in their interpretation and implementation. To quantify the effects of money laundering, we conduct experiments on real-world transaction datasets. Our goal in this paper is to survey the landscape of existing AML mechanisms, and focus the attention of the research community on this issue. Our findings indicate the community must endeavor to treat AML regulations and technical methods as an integral part of the systems they build and must strive to design solutions from the ground up that respect AML regulatory frameworks. We hope that this paper will serve as a point of reference for researchers that wish to build systems with AML mechanisms, and will help them understand the challenges that lie ahead.
On (− 1)-differential uniformity of ternary APN power functions

Very recently, a new concept called multiplicative differential and the corresponding c-differential uniformity were introduced by Ellingsen et al. (IEEE Trans. Inform. Theory 66(9), 5781–5789 2020). A function F(x) over finite field GF(pn) to itself is said to have c-differential uniformity δ, or equivalent, F(x) is differentially (c,δ)-uniform, when the maximum number of solutions x ∈GF(pn) of F(x + a) − cF(x) = b, a,b,c ∈GF(pn), c≠ 1 if a = 0, is equal to δ. The objective of this paper is to study the (− 1)-differential uniformity of some ternary APN power functions F(x) = xd over GF(3n). We obtain ternary power functions with low (− 1)-differential uniformity, and some of them are almost perfect (− 1)-nonlinear.
A Techno-Economic Viability Analysis of the Two-Axis Tracking Grid-Connected Photovoltaic Power System for 25 Selected Coastal Mediterranean Cities

Generating energy from renewable sources, particularly solar energy, offers significant benefits and achieves a more clean and sustainable development. In the present paper, the potential of developing a 4.2kW grid-connected rooftop two-axis tracking PV system in 25 selected coastal Mediterranean cities located in different Arabic countries is evaluated using RETScreen software. The proposed system is serving the basic household energy needs according to the load profile from monthly electrical bills. It is found that the proposed system produces about 8824kW annually, which helps to reduce CO2 emissions. Also, the average energy production cost is assumed to range from 0.0337 to 00.0475$/kWh. It is concluded that the proposed system can provide an effective solution for energy poverty in developing regions with a very positive socio-economic and environmental impact. The small-scale grid-connected PV system will provide the domestic energy needs at a lower energy production cost than the electricity price grid-connected consumers pay. This study demonstrated that generating electricity from solar energy will help reduce the electricity tariff rates and the dependence on fossil fuels.
Long memory and efficiency of Bitcoin during COVID-19

The COVID-19 pandemic has raised great attention to the study of its impacts on Bitcoin. We focus on the impacts of the COVID-19 pandemic on the long memory and efficiency of Bitcoin. There exist a few studies on this topic. These studies all ignore the issues of heavy tails and extreme events during COVID-19, which are obstacles to obtaining the reliable continuous time-varying results of long memory and efficiency. After considering the two issues, we first obtain the reliable continuous time-varying results during COVID-19 via sliding window and estimation of Hurst exponent. The other four markets (Ethereum, Binance Coin, S&P 500, and gold spot) are also analysed for comparison. Bitcoin results show that the Bitcoin market keeps efficient during the pandemic and the heavy tails become weaker after the onset of the pandemic. Results of the comparison study show that Bitcoin has similar efficiency with spot gold and is more efficient than Ethereum, Binance Coin, and S&P 500 during the pandemic. This study contributes to current rare literature on the long memory and efficiency of cryptocurrency during COVID-19.
Recurrent Attention and Semantic Gate for Remote Sensing Image Captioning

The remote sensing image captioning has attracted wide spread attention in remote sensing field due to its application potentiality. However, most existing approaches model limited interactions between image content and sentence and fail to exploit special characteristics of the remote sensing images. We introduce a novel recurrent attention and semantic gate (RASG) framework to facilitate the remote sensing image captioning in this article, which integrates competitive visual features and a recurrent attention mechanism to generate a better context vector for the images every time as well as enhances the representations of the current word state. Specifically, we first project each image into competitive visual features by taking the advantage of both static visual features and multiscale features. Then, a novel recurrent attention mechanism is developed to extract the high-level attentive maps from encoded features and nonvisual features, which can help the decoder recognize and focus on the effective information for understanding the complex content of the remote sensing images. Finally, the hidden states from the long short-term memory (LSTM) and other semantic references are incorporated into a semantic gate, which contributes to more comprehensive and precise semantic understanding. Comprehensive experiments on three widely used datasets, Sydney-Captions, UCM-Captions, and Remote Sensing Image Captioning Dataset, have demonstrated the superiority of the proposed RASG over a series of attentive models based on image captioning methods.