Reviewerly/exHarmony huggingface dataset:
Survey on Pains and Best Practices of Code Review

Despite widespread agreement on the benefits of code review, its outcomes may not be as expected. The complications can undermine the purpose of the development process and even destroy the entire development cycle. Both academia and the industrial communities have invested a great deal of time and effort into code reviews. When a project team adheres to the best practices and creates a conducive environment, it is likely that code reviews could be conducted effectively and efficiently. By reviewing peer-reviewed scientific publications and gray literature on code review best practices, we summarized 57 practices as well as 19 code review pains that they address. Our review has shown that following best practices can ease the process of code review considerably. Multiple actionable practices are needed to support code review pains at the same time. To enable the adoption of best practices, OSS and industrial communities alike invest in integrating automatic techniques with code review tools. We hope that this review will provide researchers and practitioners with a comprehensive understanding of code review practices, aiding them in conducting code reviews more successfully.
Unsupervised Spectrum Anomaly Detection Method for Unauthorized Bands

With the rapid development of wireless communication, spectrum plays increasingly important role in both military and civilian fields. Spectrum anomaly detection aims at detecting emerging anomaly signals and spectrum usage behavior in the environment, which is indispensable to secure safety and improve spectrum efficiency. However, spectrum anomaly detection faces many difficulties, especially for unauthorized frequency bands. In unauthorized bands, the composition of spectrum is complex and the anomaly usage patterns are unknown in prior. In this paper, a Variational Autoencoder- (VAE-) based method is proposed for spectrum anomaly detection in unauthorized bands. First of all, we theoretically prove that the anomalies in unauthorized bands will introduce Background Noise Enhancement (BNE) effect and Anomaly Signal Disappearance (ASD) effects after VAE reconstruction. Then, we introduce a novel anomaly metric termed as percentile (PER) score, which focuses on capturing the distribution variation of reconstruction error caused by ASD and BNE. In order to verify the effectiveness of our method, we developed an ISM Anomaly Detection (IAD) dataset. The proposed PER score achieves superior performance against different type of anomalies. For QPSK type anomaly, our method increases the recall rate from 80% to 93% while keeping a false alarm rate of 5%. The proposed method is beneficial to broadband spectrum sensing and massive spectrum data processing. The code will be released at git@github.com :QXSLAB/vae_ism_ano.git.
Multi-Robot Leader Grouping Consistent Formation Control Method Research with Low Convergence Time Based on Nonholonomic Constraints

Aiming at the formation and maintenance of the multiple formations of nonholonomic constrained multi-robots, a leader-follower formation control method under the grouping consistency is proposed on the trajectory tracking of a nonholonomic constrained mobile robots with the low convergence time. The distributed control structure in the leader-follower formation is adopted. The multi-robot cooperative formation is realized by using the consistency algorithm of graph theory. According to the graph theory, the communication topology matrixes are designed by the consistency algorithm. The mathematical model of nonholonomic constrained robot is established with the wheeled structure as the mobile structure under the nonholonomic constraints. Then the navigation following model is transformed into the error model of a local coordinate system through the global coordinate transformation. The formation control law of multi-robot cooperative motion is put forward based on the leader-follower model. Its convergence is proved by the Lyapunov function. By setting the reasonable communication protocol parameters, the MATLAB software (Natick, MA, USA, R2016b) is employed on the simulation verification and result comparison. Through the comparison of the two leader formation control methods, the convergence time of the algorithm in this article can be 25% less than that of PFC. The effectiveness and feasibility of the formation control law are verified under the leader-follower method. The proposed control method lays a foundation for reducing the convergence time to improve the multi-robot cooperative motion under nonholonomic constraints.
Lockdown lessons learned. Reconceptualising a Sociology of Education module in ITE using a TPACKframework, optimising pedagogy, enhancing student outcomes through the provision of inclusive and accessible opportunities for learning

(2022). Lockdown lessons learned. Reconceptualising a Sociology of Education module in ITE using a TPACKframework, optimising pedagogy, enhancing student outcomes through the provision of inclusive and accessible opportunities for learning. Irish Educational Studies: Vol. 41, Digital Education Futures: Design for Doing Education Differently, Guest Editors: Tony Hall, Rupert Wegerif, Suzanna Loper, Déirdre Ní Chróinín and Emma O'Brien, pp. 257-268.
Analysis and Implementation of Novel Keylogger Technique

Keystroke logging, also known as keystroke or keyboard scanning, is a way to record the keys that are being typed on the keyboard, by the user's computer or keyboard, so that the keyboard user does not know that their details are being tracked. Data can be gained by someone using the login system. Some programs themselves are legal, and are created to track the use of employer's computer. Key logging is also used to learn keystroke power or human-computer interconnection. Many methods for key chain are available, which are software or hardware based methods or acoustic crypt-analysis. In this paper, we have proposed a key logger that captures the keystrokes of the victim as well as the running applications on which the victim might be working, and stores it in an encrypted form in a file, which is further uploaded on the server after every hour. It also records extra information of the victim like it's IP address, MAC Address, and Username. The software runs on a stealth mode, that is it uses very low RAM. It re-launches itself as soon as the system starts. The keystrokes are stored in an encrypted form in the file and then uploaded on server, which is later downloaded and decrypted by the attacker.
Large Forests and Where to “Partially” Fit Them

The Artificial Intelligence of Things (AIoT) calls for on-site Machine Learning inference to overcome the instability in latency and availability of networks. Thus, hardware acceleration is paramount for reaching the Cloud's modeling performance within an embedded device's resources. In this paper, we propose Entree, the first automatic design flow for deploying the inference of Decision Tree (DT) ensembles over Field-Programmable Gate Arrays (FPGAs) at the network's edge. It exploits dynamic partial reconfiguration on modern FPGA-enabled Systems-on-a-Chip (SoCs) to accelerate arbitrarily large DT ensembles at a latency a hundred times stabler than software alternatives. Plus, given Entree's suitability for both hardware designers and non-hardware-savvy developers, we believe it has the potential of helping data scientists to develop a non-Cloud-centric AIoT.
Exploring Public Sentiment During COVID-19: A Cross Country Analysis

COVID-19 has spread all over the world, accounting for countless death and enormous economic loss. Since the World Health Organization (WHO) declared COVID-19 as a pandemic, governments from different countries have made various policies to prevent the pandemic from becoming worse. However, civilian reactions to the pandemic vary when they face similar situations. This behavioral variation creates a challenge when it comes to policy-making. Such differences are generally implicit, hidden in ones’ social lives. As a result, it is challenging to analyze such differences when the governments make policies. In this work, we investigate social media posts on Twitter and Weibo in order to effectively explore the difference in reactions across various countries, with the aim to understand national differences. To this end, we employ natural language processing (NLP) methods and Linguistic Inquiry and Word Count (LIWC) tools to process six languages in different countries, including the USA, Germany, France, Italy, the U.K., and China. We provide a comprehensive analysis of public reaction differences from the emotional perspective. Our findings verify that the reactions vary noticeably among various countries for some policies. Therefore, sentiment analysis can significantly influence policy-making. Our work sheds light on the mechanism of detecting the reaction differences in various countries, which can be utilized to conduct effective communication and make appropriate policy decisions.
Retrieving Black-box Optimal Images from External Databases

Suppose we have a black-box function (e.g., deep neural network) that takes an image as input and outputs a value that indicates preference. How can we retrieve optimal images with respect to this function from an external database on the Internet? Standard retrieval problems in the literature (e.g., item recommendations) assume that an algorithm has full access to the set of items. In other words, such algorithms are designed for service providers. In this paper, we consider the retrieval problem under different assumptions. Specifically, we consider how users with limited access to an image database can retrieve images using their own black-box functions. This formulation enables a flexible and finer-grained image search defined by each user. We assume the user can access the database through a search query with tight API limits. Therefore, a user needs to efficiently retrieve optimal images in terms of the number of queries. We propose an efficient retrieval algorithm Tiara for this problem. In the experiments, we confirm that our proposed method performs better than several baselines under various settings.
HealthCare EHR

Blockchain technology is currently playing a significant role in providing a secure and effective means to share information in a variety of domains, including the financial sector, supply chain management (SCM) in various domains, IoT, and the field of health care systems (HCS). The HCS application's interoperability and security allow patients and vendors to communicate information seamlessly. The absence of such traits reveals the patient's difficulties in gaining access to his or her own health status. As a result, incorporating blockchain technology will eliminate this disadvantage, allowing the HCS to become more effective and efficient. These potential benefits provide a foundation for blockchain technology to be used in various aspects of HCS, such as maintain the patient electronic health record (EHR) and electronic medical records (EMR) for various medical devices, billing, and telemedicine systems, and so on.
Co‐designing and building an expert‐elicited non‐parametric Bayesian network model: demonstrating a methodology using a<i>Bonamia Ostreae</i>spread risk case study

The development and use of probabilistic models, particularly Bayesian networks (BN), to support risk-based decision making is well established. Striking an efficient balance between satisfying model complexity and ease of development requires continuous compromise. Codesign, wherein the structural content of the model is developed hand-in-hand with the experts who will be accountable for the parameter estimates, shows promise, as do so-called nonparametric Bayesian networks (NPBNs), which provide a light-touch approach to capturing complex relationships among nodes. We describe and demonstrate the process of codesigning, building, quantifying, and validating an NPBN model for emerging risks and the consequences of potential management decisions using structured expert judgment (SEJ). We develop a case study of the local spread of a marine pathogen, namely, Bonamia ostreae. The BN was developed through a series of semistructured workshops that incorporated extensive feedback from many experts. The model was then quantified with a combination of field and expert-elicited data. The IDEA protocol for SEJ was used in its hybrid (remote and face-to-face) form to elicit information about more than 100 parameters. This article focuses on the modeling and quantification process, the methodological challenges, and the way these were addressed.
QUALIFIER: Question-Guided Self-Attentive Multimodal Fusion Network for Audio Visual Scene-Aware Dialog

Audio video scene-aware dialog (AVSD) is a new but more challenging visual question answering (VQA) task because of the higher complexity of feature extraction and fusion brought by the additional modalities. Although recent methods have achieved early success in improving feature extraction technique for AVSD, the technique of feature fusion still needs further investigation. In this paper, inspired by the success of self-attention mechanism and the importance of understanding questions for VQA answering, we propose a question-guided self-attentive multi-modal fusion network (QUALIFIER) to improve the AVSD practice in the stage of feature fusion and answer generation. Specifically, after extracting features and learning a comprehensive feature for each modality, we first use the designed self-attentive multi-modal fusion (SMF) module to aggregate each feature with the correlated information learned from others. Later, by prioritizing the question feature, we concatenate it with each fused feature to guide the generation of a natural language response to the question. As for experimental results, QUALIFIER shows better performance than other baseline methods in the large-scale AVSD dataset named DSTC7. Additionally, the human evaluation and ablation study results also demonstrate the effectiveness of our network architecture.
Identification of Lithology Using Sentinel-2A Through an Ensemble of Machine Learning Algorithms

Remotely sensed data has become an effective, operative and applicable tool that provide critical support for geological surveys and studies by reducing the costs and increasing the precision. Advances in remote-sensing data analysis methods, like machine learning algorithms, allow for easy and impartial geological mapping. This study aims to carry out a rigorous comparison of the performance of three supervised classification methods: Random Forest, k-Nearest Neighbor and maximum likelihood using remote sensing data and additional information in Souk El Had N’Befourna region. The enhancement of remote sensing geological classification by using geomorphometric features, principal component analysis, gray level co-occurrence matrix (GLCM) and multispectral data of the Sentinel-2A imagery was highlighted. The Random Forest algorithm showed reliable results and discriminated limestone, dolomite, conglomerate, sandstone and rhyolite, silt and Alluvium, ignimbrite, granodiorite, Lutite, granite, and quartzite. The best overall accuracy (~91%) was achieved by Random Forest algorithm.
Assamese Character Recognition Using Convolutional Neural Networks

In this article, we study recognition of handwritten Assamese alphabet when Convolutional Neural Networks (CNN) are applied. First, we generate a dataset, consisting of the 52 characters, with over 12k images in total. To this dataset we apply LeNet-5, ResNet-50, InceptionV3, and DenseNet-201 models. Transfer learning is used for faster training. Best accuracy of over 94.62% is obtained on the test data. This is, currently, state of the art performance in Assamese character recognition.
Severity and Trustworthy Evidence: Foundational Problems versus Misuses of Frequentist Testing

Abstract For model-based frequentist statistics, based on a parametric statistical model ${{\cal M}_\theta }({\bf{x}})$ , the trustworthiness of the ensuing evidence depends crucially on (i) the validity of the probabilistic assumptions comprising ${{\cal M}_\theta }({\bf{x}})$ , (ii) the optimality of the inference procedures employed, and (iii) the adequateness of the sample size ( n ) to learn from data by securing (i)–(ii). It is argued that the criticism of the postdata severity evaluation of testing results based on a small n by Rochefort-Maranda (2020) is meritless because it conflates [a] misuses of testing with [b] genuine foundational problems. Interrogating this criticism reveals several misconceptions about trustworthy evidence and estimation-based effect sizes, which are uncritically embraced by the replication crisis literature.
Movie Recommendation Models Using Machine Learning

A recommendation system is essentially a kind of data filtering. Recommendation systems are very important in today's world since they give users suggestions based on their interests and needs when they are looking for something. Different types of recommendation systems are utilized on numerous platforms and have become an integral feature of a variety of applications. Movie recommendation systems aim to assist movie lovers by recommending what movie to watch without requiring them to go through the time-consuming and complex process of selecting from a vast number of movies ranging from thousands to millions. The goal of this paper is to propose a hybrid movie recommendation system based on the MovieLens dataset. The aim is to combine the user's personalization with the movie's overall features, such as genre, popularity, and so on. A popularity model, a content-based model, a collaborative filtering model, and a model based on latent factors have been used. Each model's hyperparameter tuning, testing accuracy, and evaluation are meticulously carried out. A combined linear model is created by combining the predictions of latent factor based and collaborative filtering methods, which exhibits an improvement in the accuracy of anticipated ratings. To address the constraints of individual models, all the methods are combined to build a hybrid model to get the final suggestion list for users. In terms of quality and diversity of recommendations, the hybrid model performs better than the separate models. The study contains a thorough assessment of each model.
Analysis of Performance Measure in Q Learning with UCB Exploration

Compared to model-based Reinforcement Learning (RL) approaches, model-free RL algorithms, such as Q-learning, require less space and are more expressive, since specifying value functions or policies is more flexible than specifying the model for the environment. This makes model-free algorithms more prevalent in modern deep RL. However, model-based methods can more efficiently extract the information from available data. The Upper Confidence Bound (UCB) bandit can improve the exploration bonuses, and hence increase the data efficiency in the Q-learning framework. The cumulative regret of the Q-learning algorithm with an UCB exploration policy in the episodic Markov Decision Process has recently been explored in the underlying environment of finite state-action space. In this paper, we study the regret bound of the Q-learning algorithm with UCB exploration in the scenario of compact state-action metric space. We present an algorithm that adaptively discretizes the continuous state-action space and iteratively updates Q-values. The algorithm is able to efficiently optimize rewards and minimize cumulative regret.
Class-aware Object Counting

Estimating the correct number of objects in a given natural scene is a common challenge in computer vision. Natural scenes usually contain multiple object categories and varying object densities. Detection-based algorithms are well suited for class-aware object counting and low object counts. However, they underperform with high or varying numbers of objects. To address this challenge, we propose an end-to-end approach to enhance an existing detection based method with a multi-class density estimation branch. The results of both branches are fed into a successive count estimation network, which estimates object counts for each category. Although these numbers do not contain any 10-calization information, they can be used as a valuable indicator for verifying the exactness of the object detector results and improving its counting performance. In order to demonstrate the effectiveness, we evaluate our method on common object detection datasets.
A Systematic Approach for Malware URL Recognition

In today&#x0027;s world, new communication platforms have had a profound effect on business promotion and growth over a broad range of applications, comprising social networking, e-commerce, and online banking. Regrettably, technological advancements are accompanied by new sophisticated tactics that target people in order to defraud them. Finally, the user&#x0027;s device can be compromised by identity fraud, revenue, or malware. In order to initiate attacks, a malicious Uniform Resource Locator (URL) or malicious website hosts a different unsolicited material in the structure of email, drive-by downloads, or phishing. Unsuspecting people access those websites and become victims of a variety of hoaxes, which includes the personal information theft (credit card, identities, and so on), financial ruin, and the installation of malware. The blacklist approach is the most popular technique utilized by many antivirus companies for detecting malignant URLs. Blacklists are simply the directory of URLs that have already been determined to be malicious. However, keeping a complete list of malicious URLs was nearly not possible, particularly because new URLs are created on a daily basis. Machine Learning methods utilize a collection of URLs as training data and develop a predictive algorithm based on mathematical properties to characterize a URL as malicious or not. Unlike blacklisting tactics, this enables them to popularize new URLs. This survey reviews the work related to the detection of malicious URL research using in the machine learning part.
An Unsupervised Detection Framework for Chinese Jargons in the Darknet

With the continuous development of the darknet technology, the scale of darknet and have increased rapidly in recent years, leading to rampant crime in these anonymous trading markets. Monitoring these markets can effectively combat the criminal forces that hide behind them. One of the difficulties in understanding the darknet is that criminals usually use jargons to disguise transactions and thus avoid surveillance. These jargons usually distort the original meaning of innocent-looking words in obscure ways, posing significant challenges for crime tracking. Current research on Chinese jargon detection mainly adopts the method of keyword filtering, however, such methods have little effect on the complex and ever-changing structure of darknet jargons. We propose a Chinese jargon detection framework based on unsupervised learning. The main idea is to compare similarity with high-dimensional word embedding features from different corpus to find jargons. Firstly, we collect data from six Chinese Tor websites to build a dark corpus dataset. Afterwards, we build a word-based pre-training model called DC-BERT, which can generate high-quality contextual word embeddings. Finally, we construct a cross-corpus jargon detection framework based on similarity analysis, which can effectively detect Chinese jargons in the darknet. The experimental results show that the proposed framework is both innovative and practical, reaching a detection accuracy of 91.5%.