Reviewerly/exHarmony huggingface dataset:
A Preliminary Evaluation of CPDP Approaches on Just-in-Time Software Defect Prediction

CONTEXT: Just-in-Time defect prediction is to specify the suspicious code commits that might make a product cause defects. Building JIT defect prediction models require a commit history and their fixed defect records. The shortage of commits of new projects motivated research of JIT cross-project defect prediction (CPDP). CPDP approaches proposed for component-level defect prediction were barely evaluated under JIT CPDP. OBJECTIVE: To explore the effects of CPDP approaches for component-level defect prediction where JIT CPDP is adopted. METHOD: A case study was conducted through two commit dataset suites provided in past studies for JIT defect prediction. JIT defect predictions with and without 21 CPDP approaches were compared regarding the classification performance using AUC. The CPDP approaches were also compared with each other. RESULTS: Most CPDP approaches changed the prediction performance of a baseline that simply combined all CP data. A few CPDP approaches could improve the prediction performance significantly. Not a few approaches worsened the performance significantly. The results based on the two suites could specify two CPDP approaches safer than the baseline. The results were inconsistent with a previous study. CONCLUSIONS: CPDP approaches for component-level might be effective for JIT CPDP. Further evaluations were needed to bring a firm conclusion.
Exploring the similarity between Han’s and non-Han’s Yuan poetry: Resistance distance metrics over character co-occurrence networks

Abstract The Yuan dynasty is the first non-Han unified dynasty in ancient China. It is subtle to tell the similarities and differences between the classic Chinese poems in the Yuan dynasty written by the writers of Han nationality and non-Han minority nationalities. By graph theory and statistics, this article establishes a novel method for quantifying the similarity between the Han’s and non-Han’s Yuan poetry. First, we formulate the character co-occurrence networks (CCONs) of the Han’s and non-Han’s Yuan poem databases collected from ‘Database of Ancient Chinese Classics’. The resistance distance, a graph theory concept, is introduced to describe the lexical relation between a pair of characters in the CCONs. Then, we propose the similarity index as the coefficient of variation for the ratios of resistance distances in the CCONs of Han’s and non-Han’s poem databases, which measures the consistency between the usages of characters in the Han’s and non-Han’s poems. The calculation results indicate that the Han’s and non-Han’s poems share a high percentage of common high-frequency characters and there do not exist significantly different lexical features in terms of the usage of these high-frequency characters. The obtained results offer new insights into the quantitative features of this long-debated issue.
Trust Leadership: A Work-from-Home Scenario in IT Sector During the COVID-19 Pandemic

The study covers aspects of leadership trust among the information technology (IT) employees in India, with the help of data collected from a cross section of 205 IT employees. The article has assessed leadership trust factor from the employee’s (workforce) perspective with COVID19 pandemic and work-from-home situation. The outcome of data establishes a positive relationship between leadership trust and factors such as communication, decision-making, integrity, and motivation. The study also suggests that the IT sector and its leadership have created a positive trust among the employees during the COVID-19 work-from-home scenario. Findings suggest that in areas like integrity and making decisions quickly, the IT leadership has exhibited high trust levels, while in some other areas like communication and keeping the employees motivated (inspired), there have been few minor gaps that require attention from the leadership, which could preclude the wilting of established trust between the leader and his employees.
Mapping the State of the Art of Scientific Production on Requirements Engineering Research

Requirements engineering (RE) is gaining acceptance by industries and practitioners as a significant process in systems development. Similarly, the publications on the research related to the RE topic progresses. However, there is a lack of study on the progressions of this research based on a bibliographic portfolio. This paper presents the emergence of the RE field and mapping the state of the art of scientific production using a bibliometric survey approach. This study identifies the research trends based on the observations of scientific production over the past two decades. Quantitative and qualitative information in requirements engineering research through various bibliographic attributes are presented, such as the leading journals, top-cited articles, top authors, and top countries. Research trends in RE can be observed based on the number of research themes, and the involvement of various disciplines from RE begin to get acknowledged until this study was conducted. The results provide evidence and an overview of future directions in this research field.
On Sufficient Oracles for Secure Computation with Identifiable Abort

Identifiable abort is the strongest security guarantee that is achievable for secure multi-party computation in the dishonest majority setting. Protocols that achieve this level of security ensure that, in case of an abort, all honest parties agree on the identity of at least one corrupt party who can be held accountable for the abort. It is important to understand what computational primitives must be used to obtain secure computation with identifiable abort. This can be approached by asking which oracles can be used to build perfectly secure computation with identifiable abort. Ishai, Ostrovsky, and Zikas (Crypto 2014) show that an oracle that returns correlated randomness to all n parties is sufficient; however, they leave open the question of whether oracles that return output to fewer than n parties can be used. In this work, we show that for $$t \le n - 2$$ corruptions, oracles that return output to $$n - 1$$ parties are sufficient to obtain information-theoretically secure computation with identifiable abort. Using our construction recursively, we see that for $$t \le n - \ell - 2$$ and $$\ell \in \mathcal {O}(1)$$ , oracles that return output to $$n - \ell - 1$$ parties are sufficient. For our construction, we introduce a new kind of secret sharing scheme which we call unanimously identifiable secret sharing with public and private shares (UISSwPPS). In a UISSwPPS scheme, each share holder is given a public and a private share. Only the public shares are necessary for reconstruction, and the knowledge of a private share additionally enables the identification of at least one party who provided an incorrect share in case reconstruction fails. The important new property of UISSwPPS is that, even given all the public shares, an adversary should not be able to come up with a different public share that causes reconstruction of an incorrect message, or that avoids the identification of a cheater if reconstruction fails.
Density estimation via Bayesian inference engines

We explain how effective automatic probability density function estimates can be constructed using contemporary Bayesian inference engines such as those based on no-U-turn sampling and expectation propagation. Extensive simulation studies demonstrate that the proposed density estimates have excellent comparative performance and scale well to very large sample sizes due to a binning strategy. Moreover, the approach is fully Bayesian and all estimates are accompanied by point-wise credible intervals. An accompanying package in the R language facilitates easy use of the new density estimates.
MFC-Prov: Media forensics challenge image provenance evaluation and data analysis on large-scale datasets

With the development of storage, transmission, editing, and sharing tools, digital forgery images are propagating rapidly. The need for image provenance analysis has never been more timely. Typical applications are content tracking, copyright enforcement, and forensics reasoning. However, large-scale image provenance datasets, which contain diverse manipulation history graphs with various manipulation operations and rich metadata, are still needed to facilitate the research. It is one of the major factors that hinders the development of techniques for image provenance analysis. To address this issue, we introduce large-scale datasets for provenance analysis, namely Media Forensics Challenge-Image Provenance (MFC-Prov) datasets. Two provenance tasks are designed along with evaluation metrics. Furthermore, extensive analysis is conducted for system performance in terms of accuracy on our datasets.
An Anisotropic PDE for Multi-frame Super-Resolution Image Reconstruction

The principal idea of the multi-frame super-resolution (SR) is to generate a higher resolution (HR) image from a set of lower resolution (LR) ones. Since, SR techniques suffers in particular from restoration artifacts such as staircasing effect, noise and blurring of edges. In this work, we tried to enhances the performance of super-resolution by using an anisotropic PDE based a structure tensor. To define this tensor, we takes the benefit from the classical model of Perona-Malik that allow readily to select the image homogeneous regions and use nonlinear tensor of Weickert near edges. Then, the proposed equation can preserve more the image features (corners and edges) much better while avoiding restoration artifacts. The efficiency and robustness of the our approach are illustrated and compared to other methods through numerical simulations.
FinEAS: Financial Embedding Analysis of Sentiment

In this article, the authors introduce a new language representation model for sentiment analysis of financial text called <i>financial embedding analysis of sentiment</i> (FinEAS). The new approach is based on transformer language models that are explicitly developed for sentence-level analysis. By building upon Sentence-BERT, a sentence-level extension of vanilla BERT, the authors argue that the new approach produces sentence embeddings of higher quality that significantly improve sentence/document-level tasks such as financial sentiment analysis. Using a large-scale financial news dataset from RavenPack, they demonstrate that for financial sentiment analysis the new model outperforms several state-of-the-art models such as BERT, a bidirectional LSTM, and FinBERT, a financial-domain-specific BERT. The authors make the model code publicly available.
Edge Inference Engine for Deep &amp; Random Sparse Neural Networks with 4-bit Cartesian-Product MAC Array and Pipelined Activation Aligner

A 4b-quantized convolutional neural network (CNN) inference engine for edge-AI is presented featuring a Cartesian-product MAC array and pipelined activation aligners targeting deep-/random-pruned models. A 40nm prototype with 32x32 MACs and 5Mb SRAM runs at 534 MHz, 1.07 TOPS, 352 mW at 1.1V, and attains 5.30 dense TOPS/W, 234 MHz at 0.8V. Sparse TOPS/W reaches 26.5 when running a randomly pruned model (after 88% pruning). Training algorithms for obtaining highly efficient sparse/quantized models are also proposed.
Thinking in Patch: Towards Generalizable Forgery Detection with Patch Transformation

Nowadays, synthetic faces can completely trick human eyes, which raises social concerns for malicious dissemination of such fake content. As a result, face forgery detection has become a significant research topic. Due to the different distributions of synthetic data in different generation algorithms, it is a great challenge to improve the generalization ability of the face forgery detection algorithm. To address this challenge, we propose a general two-stream patch-based face forgery detection network (FDPT), which introduces a patch transformation to encourage the model to focus on stable information in different data. Specifically, a random transformation is designed to help CNN stream extract local subtle artifacts from images. Meanwhile, a sequence transformation is employed to enhance the global spatial representation ability of the image through the CNN-GRU stream. Finally, a fusion strategy is used to improve the detection accuracy. We conduct extensive experiments to show that FDPT achieves state-of-the-art performance on two popular benchmarks. Moreover, FDPT outperforms the recently proposed generalization methods when applied to forgery generated by unseen face manipulation techniques (e.g., 84.39% \(\rightarrow \) 95.53% on Face2Face dataset).
Construction and applications of knowledge graph of porphyry copper deposits

A knowledge graph is becoming popular due to its ability to describe the real world by using a graph language that can be understood by both humans and machines using computer technologies. A case study to construct the knowledge graph of porphyry copper deposits is presented in this paper. First of all, the raw text data is collected and integrated from selected porphyry copper deposits and porphyry-skarn copper deposits in the Qinzhou Bay – Hangzhou Bay metallogenic belt, South China. Second, the text's entities, relations, and attributes are labeled and extracted with reference to the conceptual model of porphyry copper deposits in the study area. The third, a knowledge graph of porphyry copper deposits, was constructed using Neo4j 4.3. The resulted knowledge graph of porphyry copper deposit has the basic functions of an application. Furthermore, as part of a planned integrated knowledge graph from a single deposit, through an upper-geared metallogenic series, to a high-top metallogenic province, the understanding from the present study may be extended to mineral resource prospectivity and assessment beyond today. The interrelationship between the earth system, the metallogenic system, the exploration system, and the prospectivity and assessment (ES-MS-ES-PS) should be completely understood, and a knowledge graph system for ES-MS-ES-PS is needed. The key scientific and technological problems for achieving the ES-MS-ES-PS knowledge graph system are included in the progressively relative system of the domain ontology and knowledge graph of ES-MS-ES-PS, the automatic construction technology of complicated ESMS-ES-PS domain ontology and knowledge graph, the self-evolution and complementary techniques for multi-modal correlation data embedding in the ES-MS-ES-PS knowledge graph, and the knowledge graph, big data mining and artificial intelligence based on ES-resource prospectivity, and assessment theory, and methods.
Augmenting GAIL with BC for sample efficient imitation learning

Imitation learning is the problem of recovering an expert policy without access to a reward signal. Behavior cloning and GAIL are two widely used methods for performing imitation learning. Behavior cloning converges in a few iterations but doesn't achieve peak performance due to its inherent iid assumption about the state-action distribution. GAIL addresses the issue by accounting for the temporal dependencies when performing a state distribution matching between the agent and the expert. Although GAIL is sample efficient in the number of expert trajectories required, it is still not very sample efficient in terms of the environment interactions needed for convergence of the policy. Given the complementary benefits of both methods, we present a simple and elegant method to combine both methods to enable stable and sample efficient learning. Our algorithm is very simple to implement and integrates with different policy gradient algorithms. We demonstrate the effectiveness of the algorithm in low dimensional control tasks, gridworlds and in high dimensional image-based tasks.
AI-based voice assistants for digital marketing

Artificial Intelligence-based Voice Assistants (VAs) may alter the decision-making process throughout the entire consumer journey. With the distinction between humans and machines becoming increasingly blurred, VAs introduce biases and heuristics in consumer behaviour with consequences for Digital Marketing and e-commerce practices. The authors explain ‘why’ and ‘how’ marketing managers need to explore the challenges and opportunities posed by voice technologies and prepare to employ a combination of marketing and commerce initiatives. This chapter defines Voice Marketing and Voice Commerce practices through relevant cases while collocating voice-based activities within the broader communication mix strategy. Furthermore, a conceptual framework shed light on the disruptive impact of AI-based VAs across product categories.
Noise2Stack: Improving Image Restoration by Learning from Volumetric Data

Biomedical images are noisy. The imaging equipment itself has physical limitations, and the consequent experimental trade-offs between signal-to-noise ratio, acquisition speed, and imaging depth exacerbate the problem. Denoising is, therefore, an essential part of any image processing pipeline, and convolutional neural networks are currently the method of choice for this task. One popular approach, Noise2Noise, does not require clean ground truth, and instead, uses a second noisy copy as a training target. Self-supervised methods, like Noise2Self and Noise2Void, learn the signal without an explicit target, but are limited by the lack of information in a single image. Here, we introduce Noise2Stack, an extension of the Noise2Noise method to image stacks that takes advantage of a shared signal between spatially neighboring planes. Our experiments on magnetic resonance brain scans and multiplane microscopy data show that learning only from image neighbors in a stack is sufficient to outperform Noise2Noise and Noise2Void and close the gap to supervised denoising methods. Our findings point to a low-cost, high-reward improvement in denoising pipelines of multiplane biomedical images.
IFC and Monitoring Database System Based on Graph Data Models

An efficient database management system that supports the integration and interoperability of different information models is a foundation on which the higher levels of cyber-physical systems are built. In this paper, we address the problem of integrating monitoring data with building information models through the use of the graph data management system and the IFC standard (Industry Foundation Classes) to support the need for interoperability and collaborative work. The proposed workflow describes the conversion of IFC models into a graph database and the connection with data from sensors, which is then validated using the example of a bridge monitoring system. The presented IFC and sensor graph data models are structurally flexible and scalable to meet the challenges of smart cities and big data.
An NCDP for developing a Blockchain based dynamic supply chain management with auto-generation of smart contract

Increased global competition and complex patterns of customer demand have made supply chain management (SCM) extremely challenging for any company. Traditional SCM systems with centralized data storage architecture lead to data access and privacy challenges across the diverse spectrum of stakeholders in supply chain management. Blockchain alleviates the problem of data access and privacy but the task of developing an SCM solution over a blockchain from scratch is challenging, complex, and time-consuming. In this paper, we present our Nocode Development Platform (NCDP) that allows users to build customized SCM applications using a user-friendly interface. Once a customized supply chain is built, our platform autogenerates and deploys the smart contract on the Ethereum blockchain, thus easing the deployment of an SCM solution on the blockchain. A product can be tracked at any point in the supply chain using different tracking mechanisms. Our platform leverages the blockchain concept to provide supply chain data transparency in a secure manner to diverse stakeholders in a decentralized ecosystem. Our platform significantly reduces the time and investment to migrate existing SCM systems on the blockchain.
Data-Driven Detection of General Chiasmi Using Lexical and Semantic Features

Automatic detection of stylistic devices is an important tool for literary studies, e.g., for stylometric analysis or argument mining.A particularly striking device is the rhetorical figure called chiasmus, which involves the inversion of semantically or syntactically related words.Existing works focus on a special case of chiasmi that involve identical words in an A B B A pattern, so-called antimetaboles.In contrast, we propose an approach targeting the more general and challenging case A B B' A', where the words A, A' and B, B' constituting the chiasmus do not need to be identical but just related in meaning.To this end, we generalize the established candidate phrase mining strategy from antimetaboles to general chiasmi and propose novel features based on word embeddings and lemmata for capturing both semantic and syntactic information.These features serve as input for a logistic regression classifier, which learns to distinguish between rhetorical chiasmi and coincidental chiastic word orders without special meaning.We evaluate our approach on two datasets consisting of classical German dramas, four texts with annotated chiasmi and 500 unannotated texts.Compared to previous methods for chiasmus detection, our novel features improve the average precision from 17% to 28% and the precision among the top 100 results from 13% to 35%.
Digital Transformation of Vocational Education: Challenges of Modern Society

The passage of time in all spheres of human activity has significantly accelerated—industry is developing rapidly, priorities in the economy are changing, and new requirements are being imposed on science and education. To a large extent, these requirements are implemented by universities, and that means they must restructure their work. The educational process as a dynamic, holistic pedagogical system is subject to constant transformation. This shows the developing properties of the educational process, which characterize the change of the educational paradigm, the introduction of new learning tools and technologies. The digital transformation of vocational education, according to scientists, is the answer to the global information challenges taking place in the world. It should be accompanied by a “synergistic” update of the content. This will improve the quality of education. The authors of this article consider and systematize the directions of digitalization of professional education, determine their impact on the content and results of graduate training, on the relations of participants in the educational environment.
Accurate stereo vision system calibration with chromatic concentric fringe patterns

Camera calibration is used to determine the intrinsic and extrinsic parameters of a 3D imaging system based on structured light. Traditional methods like chessboard and circular dots usually employ an intensity-based feature point detection procedure, and are susceptible to noise, image contrast, and image blur. To address these issues, we proposed an active calibration method to accurately detect the centers of chromatic concentric fringe patterns (CCFP). Specifically, we first acquired the circular phase using a phase analysis algorithm, then extracted nine phase contours from the circular phase for the corresponding subpixel center coordinates using an ellipse fitting algorithm, and precisely calculated the final center with their weighted sum. We ran a simulation and evaluated the impacts of different degrees of Gaussian blur and noise on the calibrated parameters. The simulation demonstrates that our approach is more robust to noise and blur than previous ones, and our approach yields a higher calibration accuracy. Moreover, we carried out a comparison experiment to evaluate the performance of our method. It showed that the reprojection error can be reduced by at least 10% in the out-of-focus condition (i.e., the target is beyond the working distance of the camera) and the 3D reconstruction accuracy can be improved by nearly 10%.