Reviewerly/exHarmony huggingface dataset:
Automatic Bug Triaging via Deep Reinforcement Learning

Software maintenance and evolution account for approximately 90% of the software development process (e.g., implementation, testing, and maintenance). Bug triaging refers to an activity where developers diagnose, fix, test, and document bug reports during software development and maintenance to improve the speed of bug repair and project progress. However, the large number of bug reports submitted daily increases the triaging workload, and open-source software has a long maintenance cycle. Meanwhile, the developer activity is not stable and changes significantly during software development. Hence, we propose a novel bug triaging model known as auto bug triaging via deep reinforcement learning (BT-RL), which comprises two models: a deep multi-semantic feature (DMSF) fusion model and an online dynamic matching (ODM) model. In the DMSF model, we extract relevant information from bug reports to obtain high-quality feature representation. In the ODM model, through bug report analysis and developer activities, we use a strategy based on the reinforcement learning framework, through which we perform training while learning and recommend developers for bug reports. Extensive experiments on open-source datasets show that the BT-RL method outperforms state-of-the-art methods in bug triaging.
The New Way Improve Learners' Speaking Skills: Picture and Picture Learning Media Based on Articulate Storyline

Success in the learning process in elementary schools can be seen from the active participation of students in conveying their ideas and feelings through good speaking skills. The low speaking ability of students in elementary schools hurts the quality of learning. Therefore, learning media is needed to improve students' speaking skills, using interactive picture and picture learning media based on storyline articulation. This study aims to create learning media that can improve the speaking skills of elementary school students. This research is a research and development (RnD) with the ADDIE model which consists of five stages, namely analysis, design, development, implementation, and evaluation. The subjects of this study were teachers and students of grade 3 elementary school. Data collection techniques were carried out using documentation, interviews, and questionnaires. The data analysis technique used is descriptive qualitative analysis technique and quantitative analysis. The results of the study obtained valid, practical. Effective learning media with an excellent validity level, namely 92% and 90.90% for materials and media, the practicality level of students and teachers was 82.5%; 85% and 82.5%; 90% with a convenient category and has an effect that can be seen from the Z count of -3.411 with Asymp. Sig 0.001 of 12 and 15 students from the limited group and the broad group who took the test and had a pretest average of 68.4 and got a post-test average of 83.4. Thus, this media can improve the speaking skills of elementary school children.
A sentiment analysis model of Agritech startup on Facebook comments using naive Bayes classifier

&lt;span&gt;Facebook page is a tool able to generate perceptions and acceptance, and support people and investors in making business decisions. Moreover, Facebook page plays a part in engaging people in the form of a community. People share experiences and opinions toward products, services, and trends in particular periods on the Facebook page community. Regarding sentiment analysis on Facebook pages, most education and other general topics in English have only been analyzed in English. However, sentiment analysis regarding Agritech startups topics in Thai language has not been done yet. This study analyzes opinions and categorizes positive and negative comments by using naive Bayes classifier to examine the sentiments and attitudes of people and investors. The results could possibly reflect the perception rate of Agritech startups in Thailand and could be applied to explain attentiveness and assess people’s engagement opinions. Furthermore, it could be applied in studying consumer behavior, marketing analysis, spread of information, and attitudes. The study's model is generic and could be applied in other contexts to provide insightful suggestions.&lt;/span&gt;
An Efficient Blockchain Based Data Access with Modified Hierarchical Attribute Access Structure with CP-ABE Using ECC Scheme for Patient Health Record

Secure patient health record (PHR) information exchange via cloud computing is a considerable security risk to user privacy. The fundamental reason of this issue is cloud computing’s reliance on trustworthy third parties to share data across it. To exchange data securely, many conventional cryptographic algorithms employ various keying approaches. However, relying on a trusted third party compromises the privacy of consumers’ data. To offer secure communication without the involvement of a third party, a distributed blockchain based (DBC) ciphertext–policy attribute-based encryption (CP-ABE) approach is employed in this study. Because of bilinear paring and simple scalar multiplication factors, the proposed CP-ABE system is entirely dependent on elliptic curve cryptography to reduce complexity. Furthermore, the data requester provides dynamic attributes, and a user-centric access policy is created, allowing multiple authorities to manage the attributes and provide data access. Data confidentiality, data authentication, user authentication, and tamper-proof data are all guaranteed by the suggested method. The DBC-CP-ABE method is used to provide user-centric access policies and effective key management.
State-Of-The-Art Solar Energy Forecasting Approaches: Critical Potentials and Challenges

OPINION article Front. Energy Res., 15 March 2022Sec.Process and Energy Systems Engineering https://doi.org/10.3389/fenrg.2022.875790
Prediction of Heavy Metal Concentrations in Contaminated Sites from Portable X-ray Fluorescence Spectrometer Data Using Machine Learning

Portable X-ray fluorescence (pXRF) spectrometers provide simple, rapid, nondestructive, and cost-effective analysis of the metal contents in soils. The current method for improving pXRF measurement accuracy is soil sample preparation, which inevitably consumes significant amounts of time. To eliminate the influence of sample preparation on PXRF measurements, this study evaluates the performance of pXRF measurements in the prediction of eight heavy metals’ contents through machine learning algorithm linear regression (LR) and multivariate adaptive regression spline (MARS) models. Soil samples were collected from five industrial sites and separated into high-value and low-value datasets with pXRF measurements above or below the background values. The results showed that for Cu and Cr, the MARS models were better than the LR models at prediction (the MARS-R2 values were 0.88 and 0.78; the MARS-RPD values were 2.89 and 2.11). For the pXRF low-value dataset, the multivariate MARS models improved the pXRF measurement accuracy, with the R2 values improved from 0.032 to 0.39 and the RPD values increased by 0.02 to 0.37. For the pXRF high-value dataset, the univariate MARS models predicted the content of Cu and Cr with less calculation. Our study reveals that machine learning methods can better predict the Cu and Cr of large samples from multiple contaminated sites.
Color Image Inpainting via Robust Pure Quaternion Matrix Completion: Error Bound and Weighted Loss

In this paper, we study color image inpainting as a pure quaternion matrix completion problem. In the literature, the theoretical guarantee for quaternion matrix completion is not well established. Our main aim is to propose a new minimization problem with an objective combining nuclear norm and a quadratic loss weighted among three channels. To fill the theoretical vacancy, we obtain the error bound in both clean and corrupted regimes, which relies on some new results of quaternion matrices. A general Gaussian noise is considered in robust completion where all observations are corrupted. Motivated by the error bound, we propose to handle unbalanced or correlated noise via a cross-channel weight in the quadratic loss, with the main purpose of rebalancing noise level or removing noise correlation. Extensive experimental results on synthetic and color image data are presented to confirm and demonstrate our theoretical findings.
Towards European standards for quantum technologies

Abstract The Second Quantum Revolution facilitates the engineering of new classes of sensors, communication technologies, and computers with unprecedented capabilities. Supply chains for quantum technologies are emerging, some focused on commercially available components for enabling technologies and/or quantum-technologies research infrastructures, others with already higher technology-readiness levels, near to the market. In 2018, the European Commission has launched its large-scale and long-term Quantum Flagship research initiative to support and foster the creation and development of a competitive European quantum technologies industry, as well as the consolidation and expansion of leadership and excellence in European quantum technology research. One of the measures to achieve an accelerated development and uptake has been identified by the Quantum Flagship in its Strategic Research Agenda: The promotion of coordinated, dedicated standardisation and certification efforts. Standardisation is indeed of paramount importance to facilitate the growth of new technologies, and the development of efficient and effective supply chains. The harmonisation of technologies, methodologies, and interfaces enables interoperable products, innovation, and competition, all leading to structuring and hence growth of markets. As quantum technologies mature, the time has come to start thinking about further standardisation needs. This article presents insights on standardisation for quantum technologies from the perspective of the CEN-CENELEC Focus Group on Quantum Technologies (FGQT), which was established in June 2020 to coordinate and support the development of standards relevant for European industry and research.
Multiauthority Attribute-Based Access Control for Supply Chain Information Sharing in Blockchain

Information sharing has become an important application in modern supply chain management systems with business technology development. Because traditional supply chain information systems have problems such as easy data tampering, low information transparency, and interaction delays, blockchain has been taken consideration into supply chain information sharing research. Furthermore, blockchain technology is expected to provide decentralized supply chain information sharing solutions to enhance security, availability, and transparency. However, with the in-depth study of the application of blockchain technology in supply chain information sharing, people have found that the data stored publicly in the blockchain are still threatened by privacy leakage. In addition, due to the openness and accessibility of the blockchain, the lack of fine-grained access control is also apparent. In order to improve the security of data, we propose a novel privacy-preserving multiauthority attribute-based access control scheme for secure blockchain-based information sharing in a supply chain. In this scheme, blockchain stores encrypted supply chain information on distributed nodes. Multiple attribute authorities manage different attributes of users to achieve fine-grained access control and flexible authorization. Even if some attribute authorities fail, the user’s private key will not be leaked. In user secret key generation, we adopt an anonymous key generation protocol to realize the secure distribution of user keys by the attribute authorities. Furthermore, in order to meet the protection of communication privacy between blockchain nodes, properties of policy hiding and identity hiding are considered. Finally, we design experiments to analyze the performance of our scheme, including secret key sizes and running time of encryption and decryption.
On the Black-Box Challenge for Fraud Detection Using Machine Learning (II): Nonlinear Analysis through Interpretable Autoencoders

Artificial intelligence (AI) has recently intensified in the global economy due to the great competence that it has demonstrated for analysis and modeling in many disciplines. This situation is accelerating the shift towards a more automated society, where these new techniques can be consolidated as a valid tool to face the difficult challenge of credit fraud detection (CFD). However, tight regulations do not make it easy for financial entities to comply with them while using modern techniques. From a methodological perspective, autoencoders have demonstrated their effectiveness in discovering nonlinear features across several problem domains. However, autoencoders are opaque and often seen as black boxes. In this work, we propose an interpretable and agnostic methodology for CFD. This type of approach allows a double advantage: on the one hand, it can be applied together with any machine learning (ML) technique, and on the other hand, it offers the necessary traceability between inputs and outputs, hence escaping from the black-box model. We first applied the state-of-the-art feature selection technique defined in the companion paper. Second, we proposed a novel technique, based on autoencoders, capable of evaluating the relationship among input and output of a sophisticated ML model for each and every one of the samples that are submitted to the analysis, through a single transaction-level explanation (STE) approach. This technique allows each instance to be analyzed individually by applying small fluctuations of the input space and evaluating how it is triggered in the output, thereby shedding light on the underlying dynamics of the model. Based on this, an individualized transaction ranking (ITR) can be formulated, leveraging on the contributions of each feature through STE. These rankings represent a close estimate of the most important features playing a role in the decision process. The results obtained in this work were consistent with previous published papers, and showed that certain features, such as living beyond means, lack or absence of transaction trail, and car loans, have strong influence on the model outcome. Additionally, this proposal using the latent space outperformed, in terms of accuracy, our previous results, which already improved prior published papers, by 5.5% and 1.5% for the datasets under study, from a baseline of 76% and 93%. The contribution of this paper is twofold, as far as a new outperforming CFD classification model is presented, and at the same time, we developed a novel methodology, applicable across classification techniques, that allows to breach black-box models, erasingthe dependencies and, eventually, undesirable biases. We conclude that it is possible to develop an effective, individualized, unbiased, and traceable ML technique, not only to comply with regulations, but also to be able to cope with transaction-level inquiries from clients and authorities.
Low-Dose CT Image Post-Processing Based on Learn-Type Sparse Transform

As a detection method, X-ray Computed Tomography (CT) technology has the advantages of clear imaging, short detection time, and low detection cost. This makes it more widely used in clinical disease screening, detection, and disease tracking. This study exploits the ability of sparse representation to learn sparse transformations of information and combines it with image decomposition theory. The structural information of low-dose CT images is separated from noise and artifact information, and the sparse expression of sparse transformation is used to improve the imaging effect. In this paper, two different learned sparse transformations are used. The first covers more organizational information about the scanned object. The other can cover more noise artifacts. Both methods can improve the ability to learn sparse transformations to express various image information. Experimental results show that the algorithm is effective.
Video Moment Retrieval from Text Queries via Single Frame Annotation

Video moment retrieval aims at finding the start and end timestamps of a moment (part of a video) described by a given natural language query. Fully supervised methods need complete temporal boundary annotations to achieve promising results, which is costly since the annotator needs to watch the whole moment. Weakly supervised methods only rely on the paired video and query, but the performance is relatively poor. In this paper, we look closer into the annotation process and propose a new paradigm called "glance annotation". This paradigm requires the timestamp of only one single random frame, which we refer to as a "glance", within the temporal boundary of the fully supervised counterpart. We argue this is beneficial because comparing to weak supervision, trivial cost is added yet more potential in performance is provided. Under the glance annotation setting, we propose a method named as Video moment retrieval via Glance Annotation (ViGA) based on contrastive learning. ViGA cuts the input video into clips and contrasts between clips and queries, in which glance guided Gaussian distributed weights are assigned to all clips. Our extensive experiments indicate that ViGA achieves better results than the state-of-the-art weakly supervised methods by a large margin, even comparable to fully supervised methods in some cases.
Morphological geodesic active contour algorithm for the segmentation of the histogram‐equalized welding bead image edges

Assessment and evaluation are the essential processes of industrially manufactured products for the determination of the quality and quantity of products. They give justifications in a practical way about whether the machine is perfect or imperfect, which can lead to a better or poorer production. In this study, the authors propose an algorithm that uses morphological geodesic active contour and image processing techniques to perform segmentation and assess the performance of a robot used to manufacture welding beads. The algorithm has four parameters which are pre-processed images, balloon force, smoothing parameter, and number of iterations. To pre-process the images, the algorithm uses an inverse Gaussian gradient operator for edge detection and applies the histogram equalization method to level the distribution. To detect the external contour of the bead, the level set is initialized as the region of interest whereby a balloon force can inflate or deflate towards the edges. To smoothen the contour, a smoothing parameter is applied to convert the jagged lines into a curve over a reasonable number of iterations. Based on the experimental results, the authors’ algorithm used a fixed balloon force of −2, a smoothing parameter value of 4, and 40 iterations to segment images obtained from three different environments. The computation time for the segmentation and evaluation of one image was 0.70, 0.61, and 0.67 s for datasets with high brightness, low brightness, and normal brightness, respectively. Additionally, the authors’ proposed algorithm achieved an outstanding performance of 0.9954, 0.9843, 0.9892, and 0.9435 in terms of recall, precision, F-measure, and IOU, respectively. To justify the performance of the authors’ proposed algorithm, the authors compared it with the existing algorithms and found that it worked better than all the others for segmentation, although it lagged behind the entropy-based algorithm in terms of speed.
SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialogues

Dialogue systems are usually categorized into two types, open-domain and task-oriented.The first one focuses on chatting with users and making them engage in the conversations, where selecting a proper topic to fit the dialogue context is essential for a successful dialogue.The other one focuses on a specific task instead of casual talks, e.g., finding a movie on Friday night, playing a song.These two directions have been studied separately due to their different purposes.However, how to smoothly transition from social chatting to task-oriented dialogues is important for triggering the business opportunities, and there is no any public data focusing on such scenarios.Hence, this paper focuses on investigating the conversations starting from open-domain social chatting and then gradually transitioning to taskoriented purposes, and releases a large-scale dataset with detailed annotations for encouraging this research direction.To achieve this goal, this paper proposes a framework to automatically generate many dialogues without human involvement, in which any powerful opendomain dialogue generation model can be easily leveraged.The human evaluation shows that our generated dialogue data has a natural flow at a reasonable quality, showing that our released data has a great potential of guiding future research directions and commercial activities.Furthermore, the released models allow researchers to automatically generate unlimited dialogues in the target scenarios, which can greatly benefit semi-supervised and unsupervised approaches. 1
Characterization and Costs of Integrating Blockchain and IoT for Agri-Food Traceability Systems

An increasing amount of research focuses on integrating the Internet of Things and blockchain technology to address the requirements of traceability applications for Industry 4.0. However, there has been little quantitative analysis of several aspects of these new blockchain-based traceability systems. For instance, very few works have studied blockchain’s impact on the resources of constrained IoT sensors. Similarly, the infrastructure costs of these blockchain-based systems are not widely understood. This paper characterizes the resources of low-cost IoT sensors and provides a monetary cost model for blockchain infrastructure to support blockchain-based traceability systems. First, we describe and implement a farm-to-fork case study using public and private blockchain networks. Then, we analyze the impact of blockchain in six different resource-limited IoT devices in terms of disk and memory footprint, processing time, and energy consumption. Next, we present an infrastructure cost model and use it to identify the costs for the public and private networks. Finally, we evaluate the traceability of a product in different scenarios. Our results showed that low-cost sensors could directly interact with both types of blockchains with minimal energy overhead. Furthermore, our cost model showed that setting a private blockchain infrastructure costs approximately the same as that managing 50 products on a public blockchain network.
Focusing on the Weakest Link: A Similarity Analysis on Phishing Campaigns Based on the ATT&amp;CK Matrix

In the past, phishing techniques were a common means of attack carried out by individuals or small groups via spam mail on a randomly selected target. However, in recent years, phishing techniques have been adopted by advanced persistent threat (APT) groups to attack organizations such as the Sony Pictures Enterprise and Korea Hydro &amp; Nuclear Power. As such, our study aims to analyze the past campaigns conducted by the APT groups. We aim to propose a countermeasure that corresponds to the phishing campaign by collecting datasets pertaining to the phishing techniques. Based on our past study, we collected private and public data from 16 different cases that utilize a phishing attack. Our research adopted MITRE’s ATT&amp;CK framework and tactic, techniques, and procedures (TTPs) to extract and examine the various campaigns. The framework proposed in this study makes considerable contributions to both the private and public sectors, as the framework aids the organizations in counteracting the malicious threats performed by the APT groups.