Reviewerly/exHarmony huggingface dataset:
ADEPT: A Socio-Technical Theory of Continuous Integration

Continuous practices that rely on automation in the software development workflow have been widely adopted by industry for over a decade. Despite this widespread use, software development remains a primarily human-driven activity that is highly creative and collaborative. There has been extensive research on how continuous practices rely on automation and its impact on software quality and development velocity, but relatively little has been done to understand how automation impacts developer behavior and collaboration. In this paper, we introduce a socio-technical theory about continuous practices. The ADEPT theory combines constructs that include humans, processes, documentation, automation and the project environment, and describes propositions that relate these constructs. The theory was derived from phenomena observed in previous empirical studies. We show how the ADEPT theory can explain and describe existing continuous practices in software development, and how it can be used to generate new propositions for future studies to understand continuous practices and their impact on the social and technical aspects of software development.
On the Information of Feature Maps and Pruning of Deep Neural Networks

A technique for compressing deep neural models achieving competitive performance to state-of-the-art methods is proposed. The approach utilizes the mutual information between the feature maps and the output of the model in order to prune the redundant layers of the network. Extensive numerical experiments on both CIFAR-10, CIFAR-100, and Tiny ImageNet data sets demonstrate that the proposed method can be effective in compressing deep models, both in terms of the numbers of parameters and operations. For instance, by applying the proposed approach to DenseNet model with 0.77 million parameters and 293 million operations for classification of CIFAR-10 data set, a reduction of 62.66% and 41.00% in the number of parameters and the number of operations are respectively achieved, while increasing the test error only by less than 1%.
Equation Attention Relationship Network (EARN) : A Geometric Deep Metric Framework for Learning Similar Math Expression Embedding

Representational Learning in the form of high dimensional embeddings have been used for multiple pattern recognition applications. There has been a significant interest in building embedding based systems for learning representations in the mathematical domain. At the same time, retrieval of structured information such as mathematical expressions is an important need for modern IR systems. In this work, our motivation is to introduce a robust framework for learning representations for similarity based retrieval of mathematical expressions. Given a query by example, the embedding can find the closest matching expression as a function of euclidean distance between them. We leverage recent advancements in image-based and graph-based deep learning algorithms to learn our similarity embeddings. We do this first, by using unimodal encoders in graph space and image space and then, a multi-modal combination of the same. To overcome the lack of training data, we force the networks to learn a deep metric using triplets generated with a heuristic scoring function. We also adopt a custom strategy for mining hard samples to train our neural networks. Our system produces rankings similar to those generated by the original scoring function, but using only a fraction of the time. Our results establish the viability of using such a multi-modal embedding for this task.
The Cognitive Eye: Indexing Oculomotor Functions for Mental Workload Assessment in Cognition-Aware Systems

Cognitive load assessment has become an integral part of human-centered design as it enables cognition-aware systems to adapt to users’ needs. Among the range of physiological metrics, task-evoked pupillary responses have been implemented successfully to determine mental workload. Only recently, smooth pursuit eye-movements as another oculomotor indicator was suggested to index cognitive processing demands. The current study provides a comparison of both parameters with regard to the capacity to differentiate task load in an auditory n-back task. Results replicate earlier findings, suggesting pupil diameter to display the level of mental workload. However, task loads during high levels of difficulty are not distinguishable. Task difficulty also influences gaze behaviour during smooth pursuit eye-movements, although far less sensitive. A clear distinction is achieved only between low and high workloads. In contrast, a combination of control measures (reaction times, accuracy rates) is suited to distinguish between all n-back task stages.
The multi-task learning with an application of Pareto improvement

Multi-task learning is a promising field in machine learning, which aims to improve the performance of multiple related learning tasks by taking advantage of useful information between them. Multi-task learning is essentially equivalent to multi-objective optimization problem, the purpose is to find the most appropriate weight, and because the performance of many deep learning systems based on multi-task learning largely depends on the relative weight of each task loss. It's a problem that we need to study how to calculate the weight value under some constraint conditions by reasonable method. Therefore, this paper employs a powerful method based on convex optimization theory, whose purpose is to find the Pareto optimal solution and get the specific task loss weight. The optimization process is closely related to the gradient in deep learning. In addition, to improve the accuracy, we add the modules of gradient normalization and weight standardization. The experimental results show that the performance of our method is better than that of single task experiment or multi-task experiment under fixed weight, and multi-task experiment based on uncertainty based adaptive learning, and the accuracy is further improved after adding the above modules.
Thermal Face Verification through Identification

This paper reports on a new approach to face verification in long-wavelength infrared radiation. Two face images were combined into one double image, which was then used as an input for a classification based on neural networks. For testing, we exploited two external and one homemade thermal face databases acquired in various variants. The method is reported to achieve a true acceptance rate of about 83%. We proved that the proposed method outperforms other studied baseline methods by about 20 percentage points. We also analyzed the issue of extending the performance of algorithms. We believe that the proposed double image method can also be applied to other spectral ranges and modalities different than the face.
UCCTGAN: Unsupervised Clothing Color Transformation Generative Adversarial Network

Clothing color transformation refers to changing the clothes color in an original image to the clothes color in a target image. In this paper, we propose an Unsupervised Clothing Color Transformation Generative Adversarial Network (UCCTGAN) for the task. UCCTGAN adopts the color histogram of a target clothes as color guidance and an improved U-net architecture called AntennaNet is put forward to fuse the extracted color information with the original image. Meanwhile, to accomplish unsupervised learning, the loss function is carefully designed according to color moment, which evaluates the chromatic aberration between the target clothing and the generated clothing. Experimental results show that our network has the ability to generate convincing color transformation results.
Streaming cascade-based speech translation leveraged by a direct segmentation model

The cascade approach to Speech Translation (ST) is based on a pipeline that concatenates an Automatic Speech Recognition (ASR) system followed by a Machine Translation (MT) system. Nowadays, state-of-the-art ST systems are populated with deep neural networks that are conceived to work in an offline setup in which the audio input to be translated is fully available in advance. However, a streaming setup defines a completely different picture, in which an unbounded audio input gradually becomes available and at the same time the translation needs to be generated under real-time constraints. In this work, we present a state-of-the-art streaming ST system in which neural-based models integrated in the ASR and MT components are carefully adapted in terms of their training and decoding procedures in order to run under a streaming setup. In addition, a direct segmentation model that adapts the continuous ASR output to the capacity of simultaneous MT systems trained at the sentence level is introduced to guarantee low latency while preserving the translation quality of the complete ST system. The resulting ST system is thoroughly evaluated on the real-life streaming Europarl-ST benchmark to gauge the trade-off between quality and latency for each component individually as well as for the complete ST system.
Model Decay in Long-Term Tracking

To account for appearance variations, tracking models need to be updated during the course of inference. However, updating the tracker model with adverse bounding box predictions adds an unavoidable bias term to the learning. This bias term, which we refer to as model decay, offsets the learning and causes tracking drift. While its adverse affect might not be visible in short-term tracking, accumulation of this bias over a long-term can eventually lead to a permanent loss of the target. In this paper, we look at the problem of model bias from a mathematical perspective. Further, we briefly examine the effect of various sources of tracking error on model decay, using a correlation filter (ECO) and a Siamese (SINT) tracker. Based on observations and insights, we propose simple additions that help to reduce model decay in long-term tracking. The proposed tracker is evaluated on four long-term and one short-term tracking benchmarks, demonstrating superior accuracy and robustness, even on 30 minute long videos.
A Novel Structure-based Feature Extraction Approach for Financial Fraud Detection

Abstract In finance and economic area, financial fraud detection plays an important role for both corporate management and capital market system. Feature extraction is one of the most important procedure in fraudulent firm detection. Current feature extraction approaches pay large amounts of attention on their financial attributes, which have explicitly limited the representation of ‘normal’ pictures of firms, and furthermore, reduced the financial fraud detection performance. Hence it is necessary to search for a better set of functions as features to represent firms more accurately. In this work, notice that the imitation behaviors among firms often happen in business management, while this structure patterns have not been utilized in f inancial fraud detection so far, we extract features under the constraint of both financial characters and structure patterns of firms. We also design three measurements to quantify the structure patterns. Experimental results have shown a great performance of the proposed approach.
Implementing Virtual Reality Based Competence Recognition

In this paper, we discuss using VR (virtual reality) in competence recognition. In education, VR is an increasingly used technology, and competence recognition is an essential task, for example, in vocational education as well as in the evaluation of learning. In vocational settings, effective competence recognition is also increasingly significant for employers since many potential employees, such as immigrants, are without formal degrees or education but have adequate competencies. For identifying relevant competencies in this setting, we present two novel competence recognition models suitable for VR based competence recognition. In addition, we discuss two types of relevant VR implementations and provide examples of them using Oculus Quest VR technology with Unity and 3DVista development platforms. Furthermore, we evaluate the applicability and significance of these models and the user experience of the VR implementations.
Modular neural network via exploring category hierarchy

Modular is a powerful and inherently hierarchical concept in the human brain to process a large variety of complex tasks. Converging evidence has shown several advantages to hierarchically modular network organizations in the human brain such as interpretability and evolvability of network function. Inspired by previous neuroscience studies, we propose MNN-CH, a novel modular neural network that is constructed with explored category hierarchy. The basic idea is learning to learn an optimized category hierarchy to decompose complex patterns. And specific patterns are imposed into corresponding modules to realize a transparent design of the neural network. Specifically, for a given classification task, each class or superclass is first represented as a prototype. Afterward, the category hierarchy is initially determined by investigating class similarity and gather similar ones to train each branch neural network (i.e., modular) separately. Finally, an error-driven prototype learning is introduced to refine the category hierarchy by updating the class-superclass affiliation. Experiment results on several image classification datasets show that our model has a good performance, especially in complex tasks. Beyond, we conduct an analysis to illustrate the tree-manner interpretability of the modular neural network.
Intelligent Decision-Making System for Martial Arts Competition Using Deep Learning

In the field of martial arts, athletes can win the initiative in the competition if they can correctly and timely acquire the field knowledge, evaluate the situation efficiently, and formulate a suitable strategy. In this paper, we use fuzzy mathematics, mathematical statistics, and artificial intelligence learning algorithms to carry out systematic and in-depth research on the selection of Wushu competition scene decision-making. The fuzzy mathematics theory is combined with the intelligent design theory for decision-making based on a multiagent, case-based reasoning selection, and adaptability evaluation analysis. The Wushu competition scene decision system is constructed based on artificial intelligence learning algorithms. Our approach outperforms the existing approaches in terms of accuracy, sensitivity, specificity, and Matthew’s correlation coefficient (MCC). The results of our proposed model can be anticipated to have the potential for better flexibility and scalability in martial arts competition.
Review on Deep based Object Detection

Object detection aims to detect and recognize all the salient targets in the whole image, which is one of the most fundamental and significant problems in computer vision. With the rapid development of deep learning-based detection algorithms, the performance of object detectors has been greatly improved. Thus, based on this period of rapid development, the purpose of this paper is to provide a brief survey of the latest achievements and gives people a quick overview of the latest achievements in this field brought about by deep learning techniques. In this survey, deep based object detection is categorized, covering some well-known one-stage and two-stage detectors. Moreover, the mainstream object detection datasets are listed, and the evaluation metrics are also provided for them. A novel branch of the object detection dataset (MaSTr1325) is analyzed as well. This survey also gives an in-depth perspective on future research.
Analysis of (<i>n, n</i>)-Functions Obtained From the Maiorana-McFarland Class

Pott et al. (2018) showed that F(x) = x 2 r Tr n m (x), n = 2m, r ≥ 1, is a nontrivial example of a vectorial function with the maximal possible number 2 n -2 m of bent components.Mesnager et al. (2019) generalized this result by showing conditions onTr n m (Λ(x)) has the maximal possible number of bent components.We simplify these conditions and further analyse this class of functions.For all related vectorial bent functions F (x) = Tr n m (γF(x)), γ ∈ F2n \ F2m , which as we will point out belong to the Maiorana-McFarland class, we describe the collection of the solution spaces for the linear equations DaF (x) = F (x) + F (x + a) + F (a) = 0, which forms a spread of F2n .Analysing these spreads, we can infer neat conditions for functions H(x) = (F (x), G(x)) from F2n to F2m × F2m to exhibit small differential uniformity (for instance for Λ(x) = x and r = 0 this fact is used in the construction of Carlet's, Pott-Zhou's, Taniguchi's APN-function).For some classes of H(x) we determine differential uniformity and with a method based on Bezout's theorem nonlineariy.
Weakly Supervised Learning through Rank-based Contextual Measures

Machine learning approaches have achieved remarkable advances over the last decades, especially in supervised learning tasks such as classification. Meanwhile, multimedia data and applications experienced an explosive growth, becoming ubiquitous in diverse domains. Due to the huge increase in multimedia data collections and the lack of labeled data in several scenarios, creating methods capable of exploiting the unlabeled data and operating under weakly supervision is imperative. In this work, we propose a rank-based model to exploit contextual information encoded in the unlabeled data in order to perform weakly supervised classification. We employ different rank-based correlation measures for identifying strong similarities relationships and expanding the labeled set in an unsupervised way. Subsequently, the extended labeled set is used by a classifier to achieve better accuracy results. The proposed weakly supervised approach was evaluated on multimedia classification tasks, considering several combinations of rank correlation measures and classifiers. An experimental evaluation was conducted on 4 public image datasets and different features. Very positive gains were achieved in comparison with various semi-supervised and supervised classifiers taken as baselines when considering the same amount of labeled data.
An Improved Distributed Access Control Model in Cloud Computing by Blockchain

With the ever-expanding digital communications and the need for advanced interoperability and collaboration, organizations and entities need to share their digital assets. Cloud computing is now widely used for managing and storing resources. Access control is a critical issue, facing many challenges in distributed environments, including clouds. In this paper, we present a model of the cloud access control system. Our distributed model utilizes a role-based access control to enable the management of resources and the parties' access securely. We provide interoperability between multiple organizations to access shared resources using Ethereum Blockchain smart contracts and access levels for available resources. Roles define access permissions; however, unlike the traditional role-based access control model, the roles are determined according to the organizations involved' collaborative project, sometimes may not exist in any organization. They can only be created in their interactions. Finally, for evaluating its cost and time parameters. We use Ethereum smart contracts and deploy them in the Ethereum test network called Rinkby,.
MSR4ML: Reconstructing Artifact Traceability in Machine Learning Repositories

The increasing popularity of Machine Learning (ML) is generating challenges also for developers. The multitude of programming languages, libraries and available resources allow them to easily build their own models or algorithms. However, ML models are tightly connected to their data implying a different development process from other types of software. Software projects often rely on version control platforms, such as GitHub, but these platforms have not yet been extended to support ML projects. There is poor support for data versioning and no link between ML and software artifacts. Thus, traceability and model evolution can become challenging for developers. While some specific ML platforms exist, they still require considerable manual specification of ML artifacts and links between them. In this work, we propose a framework for automatic identification and traceability of links between data, code and ML model through Mining Software Repositories (MSR) techniques. Our tool combines static code analysis and mining commit data to identify ML, code and data artifacts, reconstruct links between them and retrieve commits that affect each end of the link. The objective is to increase productivity and the developers' awareness of their project through the recovered traceability.
Secure and Efficient Data-Privacy-Preserving Scheme for Mobile Cyber–Physical Systems

Research on mobile cyber–physical systems (MCPSs) that have the superiorities of cyber–physical systems (CPSs) and expand their application range has become a trend in recent years. The applications of MCPS in fields, such as intelligent transportation systems, smart home appliances, and mobile education, have also become increasingly mature. However, MCPS also has some shortcomings that need to be solved urgently. Sensors carried on mobile devices collect data and upload huge amounts of data to the cloud for statistics and analysis. Mobile devices need to directly interact with the cloud, causing both parties to bear huge computing and communication costs. Since the interaction process includes data sharing and storage, it is particularly important to protect the data itself and the security of sharing. Searchable encryption ensures the safety of communication among the MPEs and the cloud, while protecting the privacy of data on the cloud. However, the existing searchable encryption technology cannot provide efficient and reliable data storage and sharing services for MPEs in MCPS under the premise of ensuring security. In this article, we present a secure and efficient data sharing and privacy protection scheme in MCPS on the basis of the edge computing model (NESPS). Meanwhile, the introduction of edge computing (EC) significantly reduces the communication consumption between the device and the cloud. Furthermore, attribute-based encryption (ABE) enables the NESPS to achieve fine-grained management of device authorities. Moreover, we have carried out security analysis and simulation on the NESPS, the results demonstrate that the NESPS meets the proposed requirements.
Improving Event Detection by Exploiting Label Hierarchy

Event types are hierarchical, yet most existing methods for event detection classify candidate triggers into fine-grained event types directly, without considering the rich semantic correlations in the hierarchy of event types. To fully utilize such information to improve the detection of fine-grained event types, we propose a three-layer label hierarchy and introduce the detection of two coarser-grained types as auxiliary classification tasks. In particular, we leverage the supplementary supervision information from label hierarchy by a novel Logits Mapping (LM) strategy, which generates logits (the intermediate representations fed into classifier) for coarser-grained types by heuristic mapping of logits for fine-grained types. In this way, training signals provided by auxiliary tasks can help the encoder produce more precise logits via back propagation, thus providing a simple (no extra parameter needed) yet effective way to improve the target task. Results of extensive experiments on the ACE 2005 show that LM can not only be easily integrated into the state-of-the-art methods and achieve significant improvement over them, but also can effectively alleviate the data sparseness problem.