Reviewerly/exHarmony huggingface dataset:
Cross-domain vulnerability detection using graph embedding and domain adaptation

Vulnerability detection is an effective means to maintain cyberspace security. Machine learning methods have risen much attention in software security due to their advantage of accuracy and automation. However, current researches mainly focus on in-domain vulnerability detection where the training data and test data belong to the same domain. Due to application scenarios, coding habits, and other factors, vulnerabilities in different software projects may obey different probability distributions. This discrepancy compromises the performance of machine learning methods when they are applied to a brand-new project. To address this cold start problem, we propose a cross-domain vulnerability detection framework using graph embedding and deep domain adaption (VulGDA). It works in a variety of cross-domain fashions, including the Zero-Shot fashion that no labeled data in the target domain is available for training. VulGDA is decomposed to graph embedding and domain adaptation. At the graph embedding stage, we transform the samples in source code into graph representations where elements are directly concatenated according to their syntactic and semantic relationships. Then, we aggregate information from neighbors and edges defined in the graph into real-valued vectors. By graph embedding, VulGDA extracts comprehensive vulnerability features and compromises the challenge of long-term dependency. Aiming at the discrepancy between training data and test data, domain adaption is used to train a feature generator. This feature generator maps the graph embedding to a “deep” feature that is discriminative for vulnerability detection, and invariant to the shift between domains. We perform a systematic experiment to validate the effectiveness of VulGDA. The results show that combining graph embedding and deep domain adaptation promotes VulGDA's performance in cross-domain vulnerability detection. Compared with the state-of-the-art methods, our method has better performance under the cold start condition.
Secure outsourced NB: Accurate and efficient privacy-preserving Naive Bayes classification

As an important network service in the big data era, cloud computing is widely used in storage and computing outsourcing tasks. The ensuing privacy issues make privacy protection the focus of our attention. In this paper, we propose a secure Naive Bayes classification scheme with better efficiency and accuracy than existing methods. The operations involved in Naive Bayes classification have multiplication, division, comparison, etc., and are accurately implemented by our secret-sharing protocols. Besides, we propose a secure count protocol to support count operation in Naive Bayes classification. The data between cloud servers are properly packaged to reduce communication rounds. We carefully analyze the accuracy and efficiency of our scheme. The experimental results show that our scheme has no accuracy loss and our scheme is more efficient than existing schemes without accuracy loss.
Performance Analysis of Saccades for Primary and Confirmatory Target Selection

In eye-gaze-based selection, dwell suffers from several issues, e.g., the Midas Touch problem. Here we investigate saccade-based selection techniques as an alternative to dwell. First, we designed a novel user interface (UI) for Actigaze and used it with (goal-crossing) saccades for confirming the selection of small targets (i.e., < 1.5-2°). We compared it with three other variants of Actigaze (with button press, dwell, and target reverse crossing) and two variants of target magnification (with button press and dwell). Magnification-dwell exhibited the most promising performance. For Actigaze, goal-crossing was the fastest option but suffered the most errors. We then evaluated goal-crossing as a primary selection technique for normal-sized targets (≥ 2°) and implemented a novel UI for such interaction. Results revealed that dwell achieved the best performance. Yet, we identified goal-crossing as a good compromise between dwell and button press. Our findings thus identify novel options for gaze-only interaction.
Bridging feature complementarity gap between encoder and decoder for salient object detection

Deep convolutional neural networks have pushed the resulting performances of salient object detection to the new state-of-the-art. However, most of the existing methods mainly focus on the calibration of the encoder and decoder but ignore the complementarity between them, which may lose some vital saliency areas or produce noises. Moreover, the research on the saliency structure-level loss function remains scarce. To address the above issues, we propose a network that BRIdgEs the Feature complementarity gap (BriefNet), which consists of a common encoder-decoder structure, a collaborative complementarity module (CCM), an encoder clustering module (ECM), and a feature aggregation module (FAM) optimized by a novel hybrid loss. Specifically, CCM and ECM are embedded after the encoder-decoder structure to recalibrate the features of the encoder and decoder, and FCM aggregates them. Firstly, CCM includes a feature supplement operation (FSO) and a feature generality extraction (FGE). FSO makes up the lost features of the decoder. FGE aims at exploring the generality features between encoder and decoder, aiming to constrain the saliency. Secondly, by clustering the local and global features of encoders, ECM enhances the connectivity and aggregates the relevancy semantics information. Thirdly, CCM and ECM are aggregated by FAM to predict the final results. Finally, we propose a structure recalibration loss (SRL) to further recalibrate the fuzzy structure features. The hybrid loss that consists of the pixel-level binary cross entropy loss (BCE), the structure-level loss SRL, and the map-level Dice loss is used to optimize the network. Experimental results on five prevailing datasets show that the proposed BriefNet achieves consistently superior performances under various evaluation metrics. In addition, we put forward a simplified BriefNet, which also achieves competitive results by only increasing a few parameters (10 M) compared with the baseline.
The mediating role of the management accounting information system in the relationship between innovation strategy and financial performance in the Jordanian industrial companies

This study aims to identify the mediating role of the management accounting information system (MAIS) in the relationship between innovation strategy and the financial performance of industrial companies in Jordan. To achieve the objectives of the study, the prepared a questionnaire, where the number of questionnaires was distributed (374) randomly. Where (358) questionnaires were retrieved by (95.7%) of the total questionnaires sent electronically, and they were distributed to (the general manager, financial manager, chief accountant, internal auditors, and accountants. The study reached several results, most notably: There is no significant mediating role of MAIS on the relationship between management’s value orientation towards innovation and financial performance in Jordanian industrial companies. Moreover, there is a significant mediating role of MAIS in the relationship between riskiness and financial performance in Jordanian industrial companies. In view of these results, the study recommends to the Jordanian industrial companies’ management the importance of applying the modern MAIS because it has become a critical component, and it is difficult to achieve a competitive advantage and stay in the market without relying on systems.
Has the concept of systems consolidation outlived its usefulness? Identification and evaluation of premises underlying systems consolidation

Systems consolidation has mostly been treated as a neural construct defined by the time-dependent change in memory representation from the hippocampus (HPC) to other structures, primarily the neocortex.Here, we identify and evaluate the explicit and implicit premises that underlie traditional or standard models and theories of systems consolidation based on evidence from research on humans and other animals.We use the principle that changes in neural representation over time and experience are accompanied by corresponding changes in psychological representations, and vice versa, to argue that each of the premises underlying traditional or standard models and theories of systems consolidation is found wanting.One solution is to modify or abandon the premises or theories and models.This is reflected in moderated models of systems consolidation that emphasize the early role of the HPC in training neocortical memories until they stabilize.The fault, however, may lie in the very concept of systems consolidation and its defining feature.We propose that the concept be replaced by one of memory systems reorganization, which does not carry the theoretical baggage of systems consolidation and is flexible enough to capture the dynamic nature of memory from inception to very long-term retention and retrieval at a psychological and neural level.The term "memory system reorganization" implies that memory traces are not fixed, even after they are presumably consolidated.Memories can continue to change as a result of experience and interactions among memory systems across the lifetime.As will become clear, hippocampal training of neocortical memories is only one type of such interaction, and not always the most important one, even at inception.We end by suggesting some principles of memory reorganization that can help guide research on dynamic memory processes that capture corresponding changes in memory at the psychological and neural levels.
Enabling zero knowledge proof by accelerating zk-SNARK kernels on GPU

As a recent cryptography protocol, Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) allows one party to prove that it possesses certain information without revealing information to these untrusted proof provers. This mechanism has the ability to provide the function for constructing and verifying information integrity without privacy leaking. However, the computation kernels of zk-SNARK consume too much computing power and produce a significant performance bottleneck with the growing data volume and security requirement. In this paper, we take advantage of Graphic Processing Unit (GPU) to enhance zk-SNARK efficiency by accelerating the most time-consuming computation kernels: modular multiplication and Number-Theoretic Transform (NTT)/Inverse Number-Theoretic Transform (INTT) in Elliptic Curve Cryptography (ECC) pairing with two major improvements: (1) Adopting interval limbs multiply-add quaternary operation to directly accelerate ECC pairing by making full advantage of information entropy within the limited hardware bit width; (2) Data layout and shuffle methods in GPU global memory and shared memory for data space consistency maintenance accelerating NTT/INTT which indirectly works on ECC pairing. To the best of our knowledge, our work would be the first exploration to accelerate these improvements on GPU. The measured results show that our methods are able to accelerate modular multiplication and NTT/INNT by 1.22× and 4.67× times respectively compared with the previous GPU implementation. With these accelerated kernels, we are able to achieve 3.14× speedup for Groth16, which is the most efficient zk-SNARK implementation working on BLS12-381 ECC field. With the bottleneck tackled, our work will expand the deployment scenarios of zk-SNARK in Zero Knowledge Proof (ZKP).
A customized residual neural network and bi-directional gated recurrent unit-based automatic speech recognition model

Speech recognition aims to convert human speech into text and has applications in security, healthcare, commerce, automobiles, and technology, just to name a few. Inserting residual neural networks before recurrent neural network cells improves accuracy and cuts training time by a good margin. Furthermore, layer normalization instead of batch normalization is more effective in model training and performance enhancement. Also, the size of the datasets presents tremendous influences in achieving the best performance. Leveraging these tricks, this article proposes an automatic speech recognition model with a stacked five layers of customized Residual Convolution Neural Network and seven layers of Bi-Directional Gated Recurrent Units, including a logarithmic softmax for the model output. Each of them incorporates a learnable per-element affine parameter-based layer normalization technique. The training and testing of the new model were conducted on the LibriSpeech corpus and LJ Speech dataset. The experimental results demonstrate a character error rate (CER) of 4.7 and 3.61% on the two datasets, respectively, with only 33 million parameters without the requirement of any external language model.
MuST: An interpretable multidimensional strain theory model for corporate misreporting prediction

Corporate misreporting has become a serious problem in both e-commerce and offline economies. Developing models to predict corporate misreporting involves challenges related to (1) the lack of interpretability in the past literature, (2) corporate fraud prevention, (3) the heterogeneity in both the corporate ability and complexity of regulatory requirements, and (4) the sparsity of misreporting observations. This study proposes a novel multidimensional strain theory (MuST) model to address these challenges. Inspired by strain theory, we quantify the incentive to misreport as the gap between corporate abilities and the complexity of regulatory requirements. Experiments show that our model achieves high accuracy in identifying various types of corporate misreporting indicated by comment letters from regulators. Furthermore, the interpretability and application analyses show that latent corporate abilities learned from the MuST model effectively reflect inherent corporate risks and can further be applied to the classified supervision of e-commerce companies. Our paper contributes to the emerging literature on misreporting prediction and provides a precise and interpretable prediction model for e-commerce supervision.
Breast Cancer Detection Using a PSO-ANN Machine Learning Technique

Machine learning is employed in all facets of life. Breast cancer has been known to be the second most severe cancer that leads to death among women globally. The use of dimensionality reduction to reduce noise and eliminate irrelevant features from dataset is of enormous significant on breast cancer detection. In this study, particle swarm optimization (PSO) algorithm was employed to select relevant features from the data with artificial neural network for classification purpose on a University of California Irvine machine learning database dataset. The study was evaluated with the findings revealing the performance of the study at 97.13% accuracy. Conclusively, the aim of this study is to improve machine learning approach for breast cancer detection. This paper will be of help to radiologists in taking accurate results and making proper decisions regarding breast cancer early diagnosis based on machine learning.
Instruments, agents, and artificial intelligence: novel epistemic categories of reliability

Deep learning (DL) has become increasingly central to science, primarily due to its capacity to quickly, efficiently, and accurately predict and classify phenomena of scientific interest. This paper seeks to understand the principles that underwrite scientists’ epistemic entitlement to rely on DL in the first place and argues that these principles are philosophically novel. The question of this paper is not whether scientists can be justified in trusting in the reliability of DL. While today’s artificial intelligence exhibits characteristics common to both scientific instruments and scientific experts, this paper argues that the familiar epistemic categories that justify belief in the reliability of instruments and experts are distinct, and that belief in the reliability of DL cannot be reduced to either. Understanding what can justify belief in AI reliability represents an occasion and opportunity for exciting, new philosophy of science.
An Improved U-Net for Watermark Removal

Convolutional neural networks (CNNs) with different layers have performed with excellent results in watermark removal. However, how to extract robust and effective features via CNNs of black box in watermark removal is very important. In this paper, we propose an improved watermark removal U-net (IWRU-net). Taking the robustness of obtained information into account, a serial architecture is designed to facilitate useful information for guaranteeing performance in watermark removal. Taking the problem of long-term dependency into account, U-nets based simple components are integrated into the serial architecture to extract more salient hierarchical information for addressing watermark removal problems. To increase the adaptability of IWRU-net to the real world, we use randomly distributed blind watermarks to implement a blind watermark removal model. The experiment results illustrate that the proposed method is superior to other popular watermark removal methods in terms of quantitative and qualitative evaluations.
Talking Face Generation via Facial Anatomy

To generate the corresponding talking face from a speech audio and a face image, it is essential to match the variations in the facial appearance with the speech audio in subtle movements of different face regions. Nevertheless, the facial movements generated by the existing methods lack detail and vividness, or the methods are only oriented toward a specific person. In this article, we propose a novel two-stage network to generate talking faces for any target identity through annotations of the action units (AUs). In the first stage, the relationship between the audio and the AUs in the audio-to-AU network is learned. The audio-to-AU network needs to produce the consistent AU group for the input audio. In the second stage, the AU group in the first stage and a face image are fed into the generation network to output the resulting talking face image. Various results confirm that, compared to state-of-the-art methods, our approach is able to produce more realistic and vivid talking faces for arbitrary targets with richer details of facial movements, such as the cheek motion and eyebrow motion.
More Gamification Is Not Always Better

Community Question Answering Websites (CQAs) like Stack Overflow rely on continuous user contributions to keep their services active. Nevertheless, they often undergo a sharp decline in their user participation during the holiday season, undermining their performance. To address this issue, some CQAs have developed their own special promotional gamification schemes to incentivize users to maintain their contributions throughout the holiday season. These promotional gamification schemes are often time-limited, optional, and run alongside the default gamification schemes of their websites. However, the impact of such promotional gamification schemes on user behavior remains largely unexplored in the existing literature. This paper takes the first steps toward filling this knowledge gap by conducting a large-scale empirical study of a particular promotional gamification scheme called Winter Bash (WB) on the CQA of Stack Overflow. According to our findings, promotional gamification schemes may not be the panacea they are portrayed to be. For example, in the case of WB, we find that the scheme is not effective for improving the collective engagement of all users. Only some particular user types (i.e., experienced and reputable users) are often provoked under WB. Most novice users, who comprise the majority of Stack Overflow website's user base, seem to be indifferent to such a gamification scheme. Our research also shows the importance of studying the quantity and quality of user engagement in unison to better understand the effectiveness of a gamification scheme. Previous gamification studies in the literature have focused predominantly on studying the quantity of user engagement alone. Last but not least, we conclude our paper by presenting some practical considerations for improving the design of future promotional gamification schemes in CQAs and similar platforms.
Artificial bee colony algorithm based on online fitness landscape analysis

As an effective optimization tool, artificial bee colony (ABC) algorithm has attracted increasing attention in recent years. However, ABC still shows unsatisfactory performance in solving complex optimization problems. Although many ABC variants have been developed, the structural features of problems have been rarely considered, which have a significant effect on algorithm performance. Therefore, a novel ABC variant, called FLABC, is proposed by designing an online fitness landscape analysis technique. In this technique, the population is considered as a sample of the fitness landscape, and the idea of dispersion metric is used to identify the landscape features. According to the identified features, the solution search equations with distinct characteristics can be adaptively used, which helps adapt the search to the fitness landscape. In addition, FLABC has another two modifications, i.e., the dynamic multiple subpopulations and modified scout bee phase. In the experiments, FLABC is verified on two well-known test suites (CEC2013 and CEC2015) and two real-world optimization problems. Seven well-established ABC variants and five non-ABC variants are included in the performance comparison, and the results verify that FLABC has very competitive performance, especially on the functions with rugged fitness landscapes.
Communication-efficient semi-synchronous hierarchical federated learning with balanced training in heterogeneous IoT edge environments

Federated Learning (FL) aims to train a globally shared model by employing local data samples generated by data sources. The inherent heterogeneity of IoT environments, in terms of hardware, network technology, network condition, and power supply, imposes challenges to original FL associated with model convergence speed, model accuracy, balanced training, and communication costs. In this paper, we propose a Hierarchical FL (HFL) framework based on semi-synchronous communications to address heterogeneity issues of original FL in intelligent IoT Edge environments. In particular, the proposed approach runs the aggregation process of local models in a hierarchal manner to generate a global model and uses semi-synchronous communications to reduce communication costs. Moreover, a weighted aggregation method is introduced to overcome the unbalanced training problem of original FL in heterogeneous environments. To evaluate the performance of the proposed framework, we implement a heterogeneous IoT use case related to anomaly detection in a milling machining and design a prediction-based anomaly detection method based on Convolutional Neural Network (CNN), which is trained based on proposed Semi-synchronous HFL (SHFL). The results of implementations demonstrate the effectiveness of the proposed approach in comparison to fully synchronous and fully asynchronous FL approaches.