Reviewerly/exHarmony huggingface dataset:
A promotion method for generation error-based video anomaly detection

Surveillance video anomaly detection is to detect events that rarely or never happened in a certain scene. The generation error (GE)-based methods exhibit excellent performances on this task. They firstly train a generative neural network (GNN) to generate normal samples, then judge the samples with large GEs as anomalies. Almost all the GE-based methods utilize frame-level GEs to detect anomalies. However, anomalies generally occur in local areas, the frame-level GE introduces GEs of normal areas to anomaly detection, that brings two problems: i) The GEs of normal areas reduce the anomaly saliency of the anomalous frame. ii) Different videos have different normal-GE-levels, thus it is hard to set a uniform threshold for different videos to detect anomalies. To address these problems, we propose a promotion method: utilize the maximum of block-level GEs on the frame to detect anomalies. Firstly, we calculate the block-level GEs at each position on the frame. Then, we utilize the maximum of the block-level GEs on the frame to detect anomalies. Based on the existing GNN models, we carry out experiments on multiple datasets. The results demonstrate the effectiveness of the proposed method and achieve state-of-the-art performance.
On the Relationship Between Developer Experience and Refactoring

Refactoring is one of the means of managing technical debt and maintaining a healthy software structure through enforcing best design practices, or coping with design defects. Previous refactoring surveys have shown that these code restructurings are mainly executed by developers who have sufficient knowledge of the system's design, and disposing of leadership roles in their development teams. However, these surveys were mainly limited to specific projects and companies. In this paper, we explore the generalizability of the previous results though analyzing 800 open-source projects. We mine their refactoring activities, and we identify their corresponding contributors. Then, we associate an expertise score to each contributor in order to test the hypothesis of whether developers with higher scores tend to perform a higher number of refactoring operations. We found that (1) although refactoring is not restricted to a subset of developers, those with higher experiences score tend to perform more refactorings than others; (2) our qualitative analysis of three randomly sampled projects show that the developers who are responsible for the majority of refactoring activities are typically on advanced positions in their development teams, demonstrating their extensive knowledge of the design of the systems they contribute to.
DDFL: A Deep Dual Function Learning-Based Model for Recommender Systems

Over the last two decades, latent-based collaborative filtering (CF) has been extensively studied in recommender systems to match users with appropriate items. In general, CF can be categorized into two types: matching function learning-based CF and representation learning-based CF. Matching function-based CF uses a multi-layer perceptron to learn the complex matching function that maps user-item pairs to matching scores, while representation learning-based CF maps users and items into a common latent space and adopts dot product to learn their relationship. However, the dot product is prone to overfitting and does not satisfy the triangular inequality. Different from latent based CF, metric learning represents user and item into a low dimensional space, measures their explicit closeness by Euclidean distance and satisfies the triangular inequality. In this paper, inspired by the success of metric learning, we supercharge metric learning with non-linearities and propose a Metric Function Learning (MeFL) model to learn the function that maps user-item pairs to predictive scores in the metric space. Moreover, to learn the mapping more comprehensively, we further combine MeFL with a matching function learning model into a unified framework and name this new model Deep Dual Function Learning (DDFL). Extensive empirical results on four benchmark datasets are conducted and the results verify the effectiveness of MeFL and DDFL over state-of-the-art models for implicit feedback prediction.
Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic World

The COVID-19 pandemic has made it paramount to maintain social distance to limit the viral transmission probability. At the same time, local businesses (e.g., restaurants, cafes, stores, malls) need to operate to ensure their economic sustainability. Considering the wide usage of local recommendation platforms like Google Local and Yelp by customers to choose local businesses, we propose to design local recommendation systems which can help in achieving both safety and sustainability goals. Our investigation of existing local recommendation systems shows that they can lead to overcrowding at some businesses compromising customer safety, and very low footfall at other places threatening their economic sustainability. On the other hand, naive ways of ensuring safety and sustainability can cause significant loss in recommendation utility for the customers. Thus, we formally express the problem as a multi-objective optimization problem and solve by innovatively mapping it to a bipartite matching problem with polynomial time solutions. Extensive experiments over multiple real-world datasets reveal the efficacy of our approach along with the three-way control over sustainability, safety, and utility goals.
GPU Accelerated Feature Engineering and Training for Recommender Systems

In this paper we present our 1st place solution of the RecSys Challenge 2020 which focused on the prediction of user behavior, specifically the interaction with content, on this year’s dataset from competition host Twitter. Our approach achieved the highest score in seven of the eight metrics used to calculate the final leaderboard position. The 200 million tweet dataset required significant computation to do feature engineering and prepare the dataset for modelling, and our winning solution leveraged several key tools in order to accelerate our training pipeline. Within the paper we describe our exploratory data analysis (EDA) and training, the final features and models used, and the acceleration of the pipeline. Our final implementation runs entirely on GPU including feature engineering, preprocessing, and training the models. From our initial single threaded efforts in Pandas which took over ten hours we were able to accelerate feature engineering, preprocessing and training to two minutes and eighteen seconds, an end to end speedup of over 280x, using a combination of RAPIDS cuDF, Dask, UCX and XGBoost on a single machine with four NVIDIA V100 GPUs. Even when compared to heavily optimized code written later using Dask and Pandas on a 20 core CPU, our solution was still 25x faster. The acceleration of our pipeline was critical in our ability to quickly perform EDA which led to the discovery of a range of effective features used in the final solution, which is provided as open source [16].
User Experiences with a Virtual Alcohol Prevention Simulation for Danish Adolescents

This pilot study explores 31 Danish adolescent user experiences for the newly developed virtual party simulation app—Virtual Reality (VR) FestLab. The main objective of this study was to investigate usability for VR FestLab, which aims to improve alcohol resistance skills for Danish adolescents. A secondary objective was to understand gameplay experiences. The study is a mixed method study that draws on questionnaire data (n = 31) and focus group interviews (n = 10) of boarding school students participating in the pilot study. Descriptive statistics were used to examine quantitative data, and qualitative data were analyzed thematically. Quantitative findings indicated that gameplay experiences of the VR simulation were positive, and all User Experience Questionnaire (UEQ) items were answered positively. The focus group interviews showed that adolescents found the simulation to be realistic. Feedback indicated that the group pressure experienced in the simulation was regarded to be less than in real life. Adolescents had varying approaches to playing the VR simulation, they thought the quality of the simulation was good, and only a few users experienced technical difficulties. These initial study findings indicate that VR FestLab is a promising tool for the prevention of alcohol use among adolescents.
Automated Cheating Detection in Exams using Posture and Emotion Analysis

Cheating in exams has become a serious issue these days. Exams play an important role in every student's life. Cheating in exams has been a widespread phenomenon all over the world regardless of the levels of development. Detection of manual cheating detection methods may no longer be wholly successful, to fully prevent cheating during examinations. There is a need to make this process automated and efficient. The suggested method makes the process of invigilation and cheating detection totally automated. Hence there is no need to rely on manual methods.
Recommendation of Move Method Refactoring Using Path-Based Representation of Code

Software refactoring plays an important role in increasing code quality. One of the most popular refactoring types is the Move Method refactoring. It is usually applied when a method depends more on members of other classes than on its own original class. Several approaches have been proposed to recommend Move Method refactoring automatically. Most of them are based on heuristics and have certain limitations (e.g., they depend on the selection of metrics and manually-defined thresholds). In this paper, we propose an approach to recommend Move Method refactoring based on a path-based representation of code called code2vec that is able to capture the syntactic structure and semantic information of a code fragment. We use this code representation to train a machine learning classifier suggesting to move methods to more appropriate classes. We evaluate the approach on two publicly available datasets: a manually compiled dataset of well-known open-source projects and a synthetic dataset with automatically injected code smell instances. The results show that our approach is capable of recommending accurate refactoring opportunities and outperforms JDeodorant and JMove, which are state of the art tools in this field.
Software engineering for 'quantum advantage'

Software is a critical factor in the reliability of computer systems. While the development of hardware is assisted by mature science and engineering disciplines, software science is still in its infancy. This situation is likely to worsen in the future with quantum computer systems. Actually, if quantum computing is quickly coming of age, with potential groundbreaking impacts on many different fields, such benefits come at a price: quantum programming is hard and finding new quantum algorithms is far from straightforward. Thus, the need for suitable formal techniques in quantum software development is even bigger than in classical computation. A lack of reliable approaches to quantum computer programming will put at risk the expected quantum advantage of the new hardware. This position paper argues for the need for a proper quantum software engineering discipline benefiting from precise foundations and calculi, capable of supporting algorithm development and analysis.
Representative null space LDA for discriminative dimensionality reduction

Null space Linear Discriminant Analysis (NLDA) was proposed twenty years ago to overcome the singularity problem of LDA in practical applications. With two decades of technique development, many Discriminative Dimensionality Reduction (DDR) methods that outperform NLDA have been proposed. This paper provides new insight into NLDA and illustrates that NLDA is much more powerful after solving its inherent problem. The main problem of NLDA is the intrinsic overfitting problem. An ideal NLDA model is proposed to analyze its overfitting problem. Based on the ideal NLDA model, a more reasonable Representative NLDA (RNLDA) method is proposed to prevent overfitting. Two simple but efficient RNLDA algorithms are proposed to implement the RNLDA method with a theoretical proof. This study theoretically analyzed and indicated that applying the classical but simple hold-out pretraining method can automatically set the only parameter to achieve high performance. Extensive experiments with eight databases demonstrate the superior performance of the RNLDA method over state-of-the-art DDR methods.
Recognition of car parts in automotive supply chains by combining synthetically generated training data with classical and deep learning based image processing

In the automotive industry, the ”completely knocked down” (CKD) business requires the identification and verification of a large number of unmarked parts. This is often a manual activity with a high risk of failure. Deep learning methods offer flexibility and a useful distinction between object classes for the automatic classification. However, these methods require large amounts of labeled training data and are sometimes inaccurate. In comparison, classical algorithms can achieve higher accuracy, but are inflexible and therefore unsuitable. This paper presents an approach to increase process reliability while alleviating the data labeling effort by using synthetic training data from spatially scanned and virtualized parts and combining deep learning and classical algorithms. The result is a flexible, sensor-independent, and autonomously trained classification system.
Detecting innovative companies via their website

Producing an overview of innovative companies in a country is a challenging task. Traditionally, this is done by sending a questionnaire to a sample of companies. This approach, however, usually only focuses on large companies. We therefore investigated an alternative approach: determining if a company is innovative by studying the text on its website. For this task a model was developed based on the texts of the websites of companies included in the Community Innovation Survey of the Netherlands. The latter is a survey carried out every two years that focusses on the detection of innovative companies with 10 or more working persons. We found that the text-based model developed was able to reproduce the result from the Community Innovation Survey and was also able to detect innovative companies with less than 10 employees, such as startups. Model stability, model bias, the minimal number of words extracted from a website and companies without a website were found to be important issues in producing high quality results. How these issues were dealt with and the findings on the number of innovative companies with large and small numbers of employees are discussed in the paper.
Experiences Building an Answer Bot for Gitter

Software developers use modern chat platforms to communicate about the status of a project and to coordinate development and release efforts, among other things. Developers also use chat platforms to ask technical questions to other developers. While some questions are project-specific and require an experienced developer familiar with the system to answer, many questions are rather general and may have been already answered by other developers on platforms such as the Q&A site StackOverflow.
A computing resources prediction approach based on ensemble learning for complex system simulation in cloud environment

Cloud computing provides a new infrastructure for the research of complex system simulation (CSS). However, insufficient computing resource allocation results in lower performance of a CSS application. On the other hand, excessive computing resource allocation will lead to the increase of simulation communication overhead and simulation synchronous computing. Therefore, accurate computing resource prediction is important to achieve optimal scheduling for CSS applications in the cloud environment. In this paper, a computing resource prediction approach based on ensemble learning has been proposed, which includes a cloud computing resource prediction framework and an intelligent ensemble algorithm. The framework with three–level architecture (simulation as a service, cloud computing resource predictor, and cloud computing resource pool) can provide computing resources to deal with the demands of the simulation applications. The intelligent ensemble algorithm uses an Accuracy and Relative Error-based Pruning algorithm to ensure the effective ensemble of base models (support vector machine, decision tree, and k-nearest neighbor). To improve the performance of the intelligent ensemble algorithm, a Feature Capability-based forward search Feature Selection algorithm is introduced to reduce redundancy between features. The experiments are presented to demonstrate that the intelligent ensemble algorithm can achieve higher accuracy by 4%-20% when compared with existing resource prediction models such as Regressive Ensemble Approach for Prediction, Bayesian, Linear Regression, Random Forest, and Fuzzy Neural Network.
Combining Rating and Review Data by Initializing Latent Factor Models with Topic Models for Top-N Recommendation

Nowadays we commonly have multiple sources of data associated with items. Users may provide numerical ratings, or implicit interactions, but may also provide textual reviews. Although many algorithms have been proposed to jointly learn a model over both interactions and textual data, there is room to improve the many factorization models that are proven to work well on interactions data, but are not designed to exploit textual information. Our focus in this work is to propose a simple, yet easily applicable and effective, method to incorporate review data into such factorization models. In particular, we propose to build the user and item embeddings within the topic space of a topic model learned from the review data. This has several advantages: we observe that initializing the user and item embeddings in topic space leads to faster convergence of the factorization algorithm to a model that out-performs models initialized randomly, or with other state-of-the-art initialization strategies. Moreover, constraining user and item factors to topic space allows for the learning of an interpretable model that users can visualise.
Modified three-wavelength phase unwrapping algorithm for dynamic three-dimensional shape measurement

Digital fringe projection (DFP) method has been widely used in three-dimensional (3D) shape measurement. Lots of studies have been conducted to enhance the measurement accuracy and speed by modulating the patterns. However, it is still hard to acquire enough number of 3D results to ensure the continuity of dynamic 3D shape measurement when phase shifting profilometry and temporal phase unwrapping algorithm is adopted. Besides, object motion can introduce phase error and thus measurement error for phase shifting profilometry. To this end, a dynamic 3D reconstruction framework based on modified three-wavelength phase unwrapping algorithm and phase error compensation method is proposed to solve this problem. By redefining the order of fringe patterns projected by the digital light processing (DLP) projector, the number of required patterns for 3D reconstruction can be reduced and the speed of dynamic 3D shape measurement can be improved. And using phase error compensation method based on Hilbert transform, the accuracy of 3D reconstruction can also be improved. Experiments were carried out to demonstrated the effectiveness of the proposed framework. And the modified algorithm successfully improved the speed of dynamic 3D shape measurement by 1∕3.
Polycyclic codes as invariant subspaces

Polycyclic codes are a powerful generalization of cyclic and constacyclic codes. Their algebraic structure is studied here by the theory of invariant subspaces from linear algebra. As an application, a bound on the minimum distance of these codes is derived which outperforms, in some cases, the natural analogue of the BCH bound.
CD-LEAK: Leaking Secrets from Audioless Air-Gapped Computers Using Covert Acoustic Signals from CD/DVD Drives

Air-gapped networks are isolated from the Internet, since they store and process sensitive information. It has been shown that attackers can exfiltrate data from air-gapped networks by sending acoustic signals generated by computer speakers, however this type of covert channel relies on the existence of loudspeakers in the air-gapped environment. In this paper, we present CD-LEAK - a novel acoustic covert channel that works in constrained environments where loudspeakers are not available to the attacker. Malware installed on a compromised computer can maliciously generate acoustic signals via the optical CD/DVD drives. Binary information can then be modulated over the acoustic signals and be picked up by a nearby Internet connected receiver (e.g., a workstation, hidden microphone, smartphone, laptop, etc.). We examine CD/DVD drives and discuss their acoustical characteristics. We also present signal generation and detection, and data modulation and demodulation algorithms. Based on our proposed method, we developed a transmitter and receiver for PCs and smartphones, and provide the design and implementation details. We examine the channel and evaluate it on various optical drives. We also provide a set of countermeasures against this threat - which has been overlooked.
Blockchain and AI-Based Natural Gas Industrial IoT System: Architecture and Design Issues

With the development of energy interconnection and increasing consumption of natural gas, the distributed energy supply and trusted-transaction- based natural gas Industrial Internet of Things (IIoT) has become one of the hottest research spots in the energy industry. This article puts forward natural gas IIoT architecture based on blockchain and AI, in order to address the defects of centralized energy supply architecture. The proposed architecture is introduced in detail from three aspects of infrastructure, side-chain of natural gas block based on data dimension, and backbone of natural gas block based on value dimension. Then the design issues on the integration existence of blockchain and AI in an actual natural gas IIoT scenario are discussed, including data association and interaction of blockchains, trusted identity authentication and management, energy source and virtual currency transformation, and so on. Next, an LSTM-based natural gas load prediction model is proposed by virtue of AI technology. The transaction model based on natural gas value and supply-demand relationship is also proposed by choosing the common natural gas application scenarios and taking advantage of blockchain technology. Experiments show that the proposed models can predict demand and output load of natural gas, while achieving the balance of interests of natural gas suppliers, users, and market in the transaction scenarios.
DeepSolar for Germany: A deep learning framework for PV system mapping from aerial imagery

The increasing availability of high-resolution aerial imagery and the recent deep learning-based advances in computer vision have made it possible to automatically map energy systems remotely at a large scale. In this paper, we focus on optimizing the existing DeepSolar framework for photovoltaics (PV) system classification. Specifically, we propose an efficient dataset creation methodology for aerial imagery which allows us to achieve state-of-the-art results, improving the previous model's recall score by more than eight percentage points to 98% while keeping its precision almost constant at 92%. Furthermore, we show that our optimized model extends its superior classification performance to lower image resolutions. After re-training our optimized model on lower resolution imagery, we apply it to Germany's most-populous state, North-Rhine Westphalia, and deliver a proof of concept for automatically validating, updating, and creating databases of renewable energy systems at a large scale. We conclude with a brief analysis of socio-economic factors correlating with PV system adoption.