Reviewerly/exHarmony huggingface dataset:
A Stochastic Algorithm Based on Reverse Sampling Technique to Fight Against the Cyberbullying

Cyberbullying has caused serious consequences especially for social network users in recent years. However, the challenge is how to fight against the cyberbullying effectively from the algorithmic perspective. In this article, we study the fighting against the cyberbullying problem, i.e., identify an initial witness set with a budget to spread the positive influence to protect the users in a specific target set such that the number of cybervictim users in the target set being activated by the seed set of cyberbullying is minimized. We first formulate this problem and show its NP-hardness. We further prove that the objective function is submodular with respect to the size of witnesses set when we convert the original problem into the maximal version. Then we propose a stochastic approach to solve this maximal version problem based on the Reverse Sampling Technique with a constant factor guarantee. In addition, we provide theoretical analysis and discuss the relationship between the optimal value and the value returned by the proposed algorithm. To evaluate the proposed approach, we implement extensive experiments on synthetic and real datasets. The experimental results show our approach is superior to the comparison methods.
Teaching Responsible Data Science: Charting New Pedagogical Territory

Although an increasing number of ethical data science and AI courses is available, with many focusing specifically on technology and computer ethics, pedagogical approaches employed in these courses rely exclusively on texts rather than on algorithmic development or data analysis. In this paper we recount a recent experience in developing and teaching a technical course focused on responsible data science, which tackles the issues of ethics in AI, legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, privacy, and data protection. Interpretability of machine-assisted decision-making is an important component of responsible data science that gives a good lens through which to see other responsible data science topics, including privacy and fairness. We provide emerging pedagogical best practices for teaching technical data science and AI courses that focus on interpretability, and tie responsible data science to current learning science and learning analytics research. We focus on a novel methodological notion of the object-to-interpret-with, a representation that helps students target metacognition involving interpretation and representation. In the context of interpreting machine learning models, we highlight the suitability of "nutritional labels"-a family of interpretability tools that are gaining popularity in responsible data science research and practice.
Digitalization Online Exam Cards in the Era of Disruption 5.0 using the DevOps Method

In the current 5.0 era, almost all documents are digitally packaged, and the university needs to adapt to ever-evolving technology. For students, the test card is essential as proof of the contest to carry out the final exam. However, in practice, some University Student Exam Card sheets still adopt the traditional nature of queues. This will cause problems if the exam card can be lost or damaged. Digitalization can disruption the world of education, especially Exam Cards that are digital and systematically stored on the website page. SiS + is a Student iLearning Services that can make it easier for Raharja University students to access all their lecture needs by digitizing them. The YII Framework-based SiS + website's development uses the DevOps method to get online exam cards because applicable regulations fulfill student attendance. This research is expected to transform the student management system efficiency, reduce queues, and implement physical distancing during a pandemic.
Convolutional neural networks-based valve internal leakage recognition model

The internal leakage signal of a valve is generally weak, and it is vulnerable to complex background noise. Due to the limitations of traditional internal leakage diagnosis methods and models, it is difficult to effectively evaluate a valve state under complex working conditions. Recognising this challenge, convolutional neural networks (CNN) is proposed to recognise valve internal leakage, which uses the power spectral density images of internal leakage and non-leakage signals under multiple working conditions as input. The experimental results show that the proposed models effectively recognise a internal leakage or non-leakage signal, and the maximum prediction error is less than 3%, which can be used a new method for valve leakage diagnosis.
Adaptive Fusion Techniques for Multimodal Data

Effective fusion of data from multiple modalities, such as video, speech, and text, is challenging due to the heterogeneous nature of multimodal data.In this paper, we propose adaptive fusion techniques that aim to model context from different modalities effectively.Instead of defining a deterministic fusion operation, such as concatenation, for the network, we let the network decide "how" to combine a given set of multimodal features more effectively.We propose two networks: 1) Auto-Fusion, which learns to compress information from different modalities while preserving the context, and 2) GAN-Fusion, which regularizes the learned latent space given context from complementing modalities.A quantitative evaluation on the tasks of multimodal machine translation and emotion recognition suggests that our lightweight, adaptive networks can better model context from other modalities than existing methods, many of which employ massive transformer-based networks. 1
Spiral Motion Mode Embedded Grasshopper Optimization Algorithm: Design and Analysis

A new enhanced grasshopper optimization algorithm (GOA) has been developed and successfully applied to feature selection. GOA, as a heuristic algorithm, is proposed by simulating the living habits of grasshoppers in nature. Although GOA has an excellent global optimization capability, it still faces the disadvantage of low efficiency of searching optimization due to its ease of falling into the local optimum. Hence, based on the original GOA, this study integrates new ideas to reduce the defects to obtain a better global optimization ability. Because of the continuous optimization problem, the features of pursuing the best possible individual of spiral motion have been considered. The spiral motion is integrated into the GOA exploitation search stage, which further expands the diversification and intensification trends' capacities and effectively balances the exploration and exploitation procedures. Intuitively speaking, GOA with spiral search method can find better solutions in the exploration movement process, which is more efficient than the original search method. In the experimental comparison, to verify the proposed SGOA's ability in dealing with global unconstrained and constrained optimization problems, we compared it with other 30 IEEE 2017 benchmark tasks in meta-heuristic algorithms. Then, it is adopted to optimize engineering design and feature selection problems. We can know that the proposed SGOA has a good optimization ability in practical application from the experimental results. Spiral motion mode can significantly improve the original GOA's exploitation and exploration ability, and the proposed SGOA is of great assistance in practical fields. More info about this paper can be found on the web services https://aliasgharheidari.com.
A Superquantile Approach to Federated Learning with Heterogeneous Devices

We present a federated learning framework that allows one to handle heterogeneous client devices that may not conform to the population data distribution. The proposed approach hinges upon a parameterized superquantile-based objective, where the parameter ranges over levels of conformity. We introduce a stochastic optimization algorithm compatible with secure aggregation, which interleaves device filtering steps with federated averaging steps. We conclude with numerical experiments with neural networks on computer vision and natural language processing data.
Edge computing based surveillance framework for real time activity recognition

Closed Circuit Television (CCTV) based Surveillance has become the fundamental part of the security Systems. In most cases, surveillance feeds are only used as evidence. The emergence of Edge Computing gives hope for enabling real time surveillance systems that focuses on prevention of crimes. The proposed architecture consists of a Convolutional Neural Network (CNN) enabled in an edge device, with reduced computational complexity, which classifies various actions like Pulling, pushing and other hand movements and locates the identified activities in the image frame using bounding boxes. The proposed architecture gives an alert whenever a suspicious activity is detected. The system was found efficient when validated against the Dataset taken from the SRM IST Campus.
Entangled Watermarks as a Defense against Model Extraction

Machine learning involves expensive data collection and training procedures. Model owners may be concerned that valuable intellectual property can be leaked if adversaries mount model extraction attacks. As it is difficult to defend against model extraction without sacrificing significant prediction accuracy, watermarking instead leverages unused model capacity to have the model overfit to outlier input-output pairs. Such pairs are watermarks, which are not sampled from the task distribution and are only known to the defender. The defender then demonstrates knowledge of the input-output pairs to claim ownership of the model at inference. The effectiveness of watermarks remains limited because they are distinct from the task distribution and can thus be easily removed through compression or other forms of knowledge transfer. We introduce Entangled Watermarking Embeddings (EWE). Our approach encourages the model to learn features for classifying data that is sampled from the task distribution and data that encodes watermarks. An adversary attempting to remove watermarks that are entangled with legitimate data is also forced to sacrifice performance on legitimate data. Experiments on MNIST, Fashion-MNIST, CIFAR-10, and Speech Commands validate that the defender can claim model ownership with 95\% confidence with less than 100 queries to the stolen copy, at a modest cost below 0.81 percentage points on average in the defended model's performance.
Code Structure–Guided Transformer for Source Code Summarization

Code summaries help developers comprehend programs and reduce their time to infer the program functionalities during software maintenance. Recent efforts resort to deep learning techniques such as sequence-to-sequence models for generating accurate code summaries, among which Transformer-based approaches have achieved promising performance. However, effectively integrating the code structure information into the Transformer is under-explored in this task domain. In this article, we propose a novel approach named SG-Trans to incorporate code structural properties into Transformer. Specifically, we inject the local symbolic information (e.g., code tokens and statements) and global syntactic structure (e.g., dataflow graph) into the self-attention module of Transformer as inductive bias. To further capture the hierarchical characteristics of code, the local information and global structure are designed to distribute in the attention heads of lower layers and high layers of Transformer. Extensive evaluation shows the superior performance of SG-Trans over the state-of-the-art approaches. Compared with the best-performing baseline, SG-Trans still improves 1.4% and 2.0% on two benchmark datasets, respectively, in terms of METEOR score, a metric widely used for measuring generation quality.
STaRFlow: A SpatioTemporal Recurrent Cell for Lightweight Multi-Frame Optical Flow Estimation

We present a new lightweight CNN-based algorithm for multi-frame optical flow estimation. Our solution introduces a double recurrence over spatial scale and time through repeated use of a generic “STaR” (SpatioTemporal Recurrent) cell. It includes (i) a temporal recurrence based on conveying learned features rather than optical flow estimates; (ii) an occlusion detection process which is coupled with optical flow estimation and therefore uses a very limited number of extra parameters. The resulting STaRFlow algorithm gives state-of-the-art performances on MPI Sintel and Kitti2015 and involves significantly less parameters than all other methods with comparable results.
A detailed survey of Turkish automatic speech recognition

Significant improvements have been made in automatic speech recognition(ASR)systems in terms of both the general technology and the software used. Despite these advancements, however, there is still an important difference between the recognition performance of humans and machines. This work focuses on the studies conducted in the field of Turkish speech recognition, the progress made in such studies in recent years, the language-specific constraints, the performance results achieved in the applications developed to date, and the development of a general scheme for researchers wishing to develop an ASR system for the Turkish language. A comprehensive study on the Turkish language, including all literature and speech recognition systems,wasprepared. There has been significant progress in the languages of developed countries in terms of ASR systems, but it was observed in this work that the opposite is true for the Turkish language. The lack of labor force and material resources remains the biggest obstacle to developing better systems. As a result, concrete studies are needed to achieve an ASR system with performance equivalent to the human level for the Turkish language.
influence: A partizan scoring game on graphs

We introduce the game influence, a scoring combinatorial game, played on a directed graph where each vertex is either colored black or white. The two players, Black and White, play alternately by taking a vertex of their color and all its successors (for Black) or all its predecessors (for White). The score of each player is the number of vertices he has taken. We prove that influence is a nonzugzwang game, meaning that no player has interest to pass at any step of the game, and thus belongs to Milnor's universe. We study this game in the particular class of paths where black and white vertices are alternated. We give an almost tight strategy for both players when there is one path. More precisely, we prove that the first player always gets a strictly better score than the second one, but that the difference between the scores is bounded by 5. Finally, we exhibit some graphs for which the initial proportion of vertices of the color of a player is as small as possible but where this player can get almost all the vertices.
A meta-analysis on educational technology in English language teaching

Abstract As more various types of computer-assisted language learning (CALL) programs have been incorporated into language classrooms over the recent decades, it has become more important to uncover whether, to what extent, and under which moderator variables CALL can be yield more effective outcomes than traditional language instruction. The issue of education is one of the most important materials addressed by technology. Instead, meta-analysis is a statistical and quantitative method that leads us to a general conclusion by integrating the results of different researches. In this study, researchers worked on the impact of educational technology in English language teaching by studying 67 articles and theses (from 1000 studies that were relevant in title and abstract). All articles and theses were included from 2009 to 2020 and 7 articles were excluded from this study due to insufficient information. Furthermore, two instruments, SPSS (mainly its sub-branch Kruskal-Wallis test) and CMA were used to calculate and evaluate data in this research. The total effect size calculated for studies under both fixed and random models was statistically significant and also the study of effects by year of publication, instruments used in research and research methods showed that their effect size was significant. Teaching English with the help of technology has an effective effect size and has shown the success of this technology in language learning.
Enhancement and validation of building integrated PV system: 3D modelling, techno-economics and environmental assessment

The incorporation of photovoltaic elements in buildings have been gaining more mileage in recent times. Building integrated photovoltaic (BIPV) technologies are on the rise in terms of efficiency and longevity, with a compound annual growth rate (CAGR) of 15.7 % since 2018. The costs of production and raw materials of BIPV have reached a level that is economically beneficial for building developers to adopt the technology. However, the lack of infrastructure as compared to traditional means of energy production has impeded the maturing of such technologies. The issue of conversion efficiency and module degradation have been addressed but not completely resolved by the scientific community. Although attractive governmental incentives such as net energy metering (NEM) and better returns of investments are shifting the tide as of late, what remains to be seen is the mass adoption of BIPV technology in residential and commercial infrastructure. This work aims to develop an optimal layout for photovoltaic panels in the university building precise 3D modelling and solar energy resource assessment. The method adopted is based on energy production capability of a 3D modelled BIPV system which will be carried out in three stages. They are i) Assessment of geographical location and meteorological data, ii) Development of 3D model and orientation analytics and (iii) Development of optimal PV layout. Three systems were considered for this study, which is the roof and two systems on the Southern Façade. The proposed rooftop BIPV design is expected to provide 49.27 % of the building's energy consumption while reducing CO2 emissions by 20155.32 tonnes throughout the lifespan of the system's deployment. This paper serves as a pioneering study on the feasibility of a BIPV system through the incorporation of building geometry for computations on incident solar irradiation. Through the reduction of electricity imported from the grid, the adoption of the BIPV system also serves as an incremental step towards achieving Net Zero Energy Building (NZEB) status for HWUM.
Label-Only Membership Inference Attacks

Membership inference attacks are one of the simplest forms of privacy leakage for machine learning models: given a data point and model, determine whether the point was used to train the model. Existing membership inference attacks exploit models' abnormal confidence when queried on their training data. These attacks do not apply if the adversary only gets access to models' predicted labels, without a confidence measure. In this paper, we introduce label-only membership inference attacks. Instead of relying on confidence scores, our attacks evaluate the robustness of a model's predicted labels under perturbations to obtain a fine-grained membership signal. These perturbations include common data augmentations or adversarial examples. We empirically show that our label-only membership inference attacks perform on par with prior attacks that required access to model confidences. We further demonstrate that label-only attacks break multiple defenses against membership inference attacks that (implicitly or explicitly) rely on a phenomenon we call confidence masking. These defenses modify a model's confidence scores in order to thwart attacks, but leave the model's predicted labels unchanged. Our label-only attacks demonstrate that confidence-masking is not a viable defense strategy against membership inference. Finally, we investigate worst-case label-only attacks, that infer membership for a small number of outlier data points. We show that label-only attacks also match confidence-based attacks in this setting. We find that training models with differential privacy and (strong) L2 regularization are the only known defense strategies that successfully prevents all attacks. This remains true even when the differential privacy budget is too high to offer meaningful provable guarantees.
Twenty-Four-Hour Ahead Probabilistic Global Horizontal Irradiance Forecasting Using Gaussian Process Regression

Probabilistic solar power forecasting has been critical in Southern Africa because of major shortages of power due to climatic changes and other factors over the past decade. This paper discusses Gaussian process regression (GPR) coupled with core vector regression for short-term hourly global horizontal irradiance (GHI) forecasting. GPR is a powerful Bayesian non-parametric regression method that works well for small data sets and quantifies the uncertainty in the predictions. The choice of a kernel that characterises the covariance function is a crucial issue in Gaussian process regression. In this study, we adopt the minimum enclosing ball (MEB) technique. The MEB improves the forecasting power of GPR because the smaller the ball is, the shorter the training time, hence performance is robust. Forecasting of real-time data was done on two South African radiometric stations, Stellenbosch University (SUN) in a coastal area of the Western Cape Province, and the University of Venda (UNV) station in the Limpopo Province. Variables were selected using the least absolute shrinkage and selection operator via hierarchical interactions. The Bayesian approach using informative priors was used for parameter estimation. Based on the root mean square error, mean absolute error and percentage bias the results showed that the GPR model gives the most accurate predictions compared to those from gradient boosting and support vector regression models, making this study a useful tool for decision-makers and system operators in power utility companies. The main contribution of this paper is in the use of a GPR model coupled with the core vector methodology which is used in forecasting GHI using South African data. This is the first application of GPR coupled with core vector regression in which the minimum enclosing ball is applied on GHI data, to the best of our knowledge.
The combination of gray level co-occurrence matrix and back propagation neural network for classifying stairs descent and floor

Smart wheelchairs (SW) technology is one of the solutions to help disabled people who do not have a hand or people to help them. Apart from able to move on its own, a smart wheelchair needs to be safe to use. One of the ways to increase SW safety is the ability to detect obstacles. In this study, we tried to create obstacle detection that can classify the stairs descent and floor based on image processing. To achieve our purpose, Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to increase image contrast. After that, Gray Level Co-occurrence Matrix (GLCM) is used to extract features from the image. Finally, Back Propagation Neural Network (BPNN) is used to classify the image. Based on the test result, BPNN achieves results with 95% Accuracy, 95% Sensitivity, 95% Specificity with an average computation time of 0.0035 s.