Reviewerly/exHarmony huggingface dataset:
A study of refactorings during software change tasks

Abstract Developers frequently undertake software change tasks that could be partially or fully automated by refactoring tools. As has been reported by others, all too often, these refactoring steps are instead performed manually by developers. These missed opportunities are referred to as occasions of disuse of refactoring tools. We perform an observational study in which 17 developers with professional experience attempt to solve three change tasks with steps amenable to the use of refactoring tools. We found that the strategies developers use to approach these tasks shape their workflow, which, in turn, shape the opportunities for refactoring tool use. We report on a number of findings about developer strategies, demonstrating the difficulty of aligning the kind of refactoring steps that emerge during a change task based on the strategy with the tools available. We also report on findings about refactoring tools, such as the difficulties developers face in controlling the scope of application of the tools. Our findings can help inform the designers of refactoring tools.
HCI taking turns

research-article Share on HCI taking turns Authors: Verena Fuchsberger University of Salzburg University of SalzburgView Profile , Marta Dziabiola University of Salzburg and Salzburg University of Applied Sciences University of Salzburg and Salzburg University of Applied SciencesView Profile , Azur Mešić University of Salzburg and Salzburg University of Applied Sciences University of Salzburg and Salzburg University of Applied SciencesView Profile , Daniel Nørskov University of Salzburg and Salzburg University of Applied Sciences University of Salzburg and Salzburg University of Applied SciencesView Profile , Ralf Vetter University of Salzburg and the Salzburg University of Applied Sciences University of Salzburg and the Salzburg University of Applied SciencesView Profile Authors Info & Claims InteractionsVolume 28Issue 5September - October 2021 pp 38–43https://doi.org/10.1145/3477107Online:31 August 2021Publication History 0citation825DownloadsMetricsTotal Citations0Total Downloads825Last 12 Months825Last 6 weeks34 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Threat Intelligence Quality Dimensions for Research and Practice

As the adoption and diversity of threat intelligence solutions continue to grow, questions about their effectiveness, particularly in regards to the quality of the data they provide, remain unanswered. Several studies have highlighted data quality issues as one of the most common barriers to effective threat intelligence sharing. Furthermore, research and practice lack a common understanding of the expected quality of threat intelligence. To investigate these issues, our research utilised a systematic literature review followed by a modified Delphi study that involved 30 threat intelligence experts in Europe. We identified a set of threat intelligence quality dimensions along with revised definitions for threat data , information, and intelligence .
Sequential Recommendation through Graph Neural Networks and Transformer Encoder with Degree Encoding

Predicting users’ next behavior through learning users’ preferences according to the users’ historical behaviors is known as sequential recommendation. In this task, learning sequence representation by modeling the pairwise relationship between items in the sequence to capture their long-range dependencies is crucial. In this paper, we propose a novel deep neural network named graph convolutional network transformer recommender (GCNTRec). GCNTRec is capable of learning effective item representation in a user’s historical behaviors sequence, which involves extracting the correlation between the target node and multi-layer neighbor nodes on the graphs constructed under the heterogeneous information networks in an end-to-end fashion through a graph convolutional network (GCN) with degree encoding, while the capturing long-range dependencies of items in a sequence through the transformer encoder model. Using this multi-dimensional vector representation, items related to a user historical behavior sequence can be easily predicted. We empirically evaluated GCNTRec on multiple public datasets. The experimental results show that our approach can effectively predict subsequent relevant items and outperforms previous techniques.
A Robust IoT Device Identification Method with Unknown Traffic Detection

Internet of Things (IoT) device identification plays a basic and essential role in network management and cyberspace security. Previous device identification methods perform poorly in real-world networks where much unknown traffic exists, since they only focus on improving performance on closed datasets and rely on manual features that are difficult to generalize. To achieve robust IoT device identification, we propose a new approach with unknown traffic detection, which consists of general training and novel inference stage. In the first stage, a simple neural network is trained to extract spatial-temporal features automatically and explicitly form tight clusters of known devices through multi-task learning. Furthermore, we use extreme value theory (EVT) to model the boundary of each cluster in the feature space. Then the probability of unknown traffic is recalibrated in the inference stage. Experiments on a public IoT traffic dataset show that the accuracy and f1 score of our method are both over 92%, which significantly outperforms all compared methods under diverse experimental settings. In addition, the visualization of intermediate results illustrates the interpretability of our method.
Band Selection for Dehazing Algorithms Applied to Hyperspectral Images in the Visible Range

Images captured under bad weather conditions (e.g., fog, haze, mist, dust, etc.), suffer from poor contrast and visibility, and color distortions. The severity of this degradation depends on the distance, the density of the atmospheric particles and the wavelength. We analyzed eight single image dehazing algorithms representative of different strategies and originally developed for RGB images, over a database of hazy spectral images in the visible range. We carried out a brute force search to find the optimum three wavelengths according to a new combined image quality metric. The optimal triplet of monochromatic bands depends on the dehazing algorithm used and, in most cases, the different bands are quite close to each other. According to our proposed combined metric, the best method is the artificial multiple exposure image fusion (AMEF). If all wavelengths within the range 450–720 nm are used to build a sRGB renderization of the imagaes, the two best-performing methods are AMEF and the contrast limited adaptive histogram equalization (CLAHE), with very similar quality of the dehazed images. Our results show that the performance of the algorithms critically depends on the signal balance and the information present in the three channels of the input image. The capture time can be considerably shortened, and the capture device simplified by using a triplet of bands instead of the full wavelength range for dehazing purposes, although the selection of the bands must be performed specifically for a given algorithm.
Enhancing Medical Data Security on Public Cloud

Cloud computing, supported by advancements in virtualisation and distributed computing, became the default options for implementing the IT infrastructure of organisations. Medical data and in particular medical images have increasing storage space and remote access requirements. Cloud computing satisfies these requirements but unclear safeguards on data security can expose sensitive data to possible attacks. Furthermore, recent changes in legislation imposed additional security constraints in technology to ensure the privacy of individuals and the integrity of data when stored in the cloud. In contrast with this trend, current data security methods, based on encryption, create an additional overhead to the performance, and often they are not allowed in public cloud servers. Hence, this paper proposes a mechanism that combines data fragmentation to protect medical images on the public cloud servers, and a NoSQL database to secure an efficient organisation of such data. Results of this paper indicate that the latency of the proposed method is significantly lower if compared with AES, one of the most adopted data encryption mechanisms. Therefore, the proposed method is an optimal trade-off in environments with low latency requirements or limited resources.
Detection of Anomalous Behaviour in Online Exam towards Automated Proctoring

The improvement of e-learning and online evaluation frameworks is increasing rapidly. The Main Goal is to develop a model which is intended to distinguish the ordinary examples for activities of concern, for example, conversations during a test or the pivoting, processes more exactness and computes more accuracy. Certain presumptions about normal behaviour with regards to delegating tests are made. In the existing system, it takes more computational power and speed is less. Even though it computes not much more accuracy and with the system only able to manage one invigilator for twenty students. Thus, it is important to develop a framework which is high precision and less the manual force. Identification depends on highlights registered utilizing the textural features followed by a Haar Cascade classifier and ADA Boosting calculation and search through explained examples of pre-recorded clips to prepare the framework for train the system for behaviour, the framework is planned as a choice emotionally supportive network to work with programmed administering of tests and distinguishes misbehaviour or malpractice.
Radio frequency fingerprint recognition based on deep learning

In order to overcome the problems of low accuracy and environmental impact of radio frequency fingerprint identification method, this paper proposes a radio frequency fingerprint identification method based on improved resnet50. This method adds random noise to IQ signal to increase network robustness, and then obtains IQ with different sampling rate through hole convolution, and gives different weights to different feature vectors by using attention mechanism Finally, features are extracted and classified by resnet50 backbone. Experimental results show that the improved network achieves 91.70% recognition accuracy in RF fingerprint task.
HyperspherE: An Embedding Method for Knowledge Graph Completion Based on Hypersphere

Knowledge graph completion (KGC) aims to predict missing facts by mining information already present in a knowledge graph (KG). A general solution for KGC task is embedding facts in KG into a low-dimensional vector space. Recently, several embedding models focus on modeling isA relations (i.e., instanceOf and subclassOf), and produce some state-of-the-art performance. However, most of them encode instances as vectors for simplification, which neglects the uncertainty of instances. In this paper, we present a new knowledge graph completion model called HyperspherE to alleviate this problem. Specifically, HyperspherE encodes both instances and concepts as hyperspheres. Relations between instances are encoded as vectors in the same vector space. Afterwards, HyperspherE formulates isA relations by the relative positions between hyperspheres. Experimental results on dataset YAGO39K empirically show that HyperspherE outperforms some existing state-of-the-art baselines, and demonstrate the effectiveness of the penalty term in score function.
An optical beam deflection based MEMS biomimetic microphone for wide-range sound source localization

Miniature microphones inspired by the ears of the parasitic fly Ormia Ochracea are an important means to overcome the fundamental size constraint of microphone arrays for sound source localization (SSL). However, the signal demodulation performance and the SSL range of the biomimetic microphones need to be improved. This study aims to develop a miniature biomimetic microphone with good signal demodulation capability and wide SSL range. Specifically, in this paper, an optical beam deflection (OBD) demodulation system was designed and combined with a micro-electro-mechanical system omimetic diaphragm to form a miniature microphone for SSL. The OBD-based biomimetic microphone shows many merits in signal demodulation, including low noise floor (⩽−110 dB), high repeatability of sound pressure measurement, and linear temperature response. The theoretical and experimental results demonstrated that the two wings of the biomimetic microphone have the bi-directional polar patterns with different sizes of lobes at the rocking-mode eigenfrequency, and have omnidirectional response at the bending-mode eigenfrequency. Furthermore, a new SSL method was proposed, which effectively extends the SSL range of the biomimetic microphone at the rocking-mode eigenfrequency to ±90° by combining the interaural intensity difference between the two wings. The proposed OBD-based biomimetic microphone not only has the advantages of small volume and low power consumption, but also has the stable and wide-range SSL capability, which shows that it has a great prospect in the practical applications of SSL, such as wearable devices and rescue robots, etc.
Embedded YOLO

Object detection is a fundamental but very important task in computer vision. Most current algorithms require high computing resources, which hinders their deployment on embedded system. In this research, we propose a neural network model named Embedded YOLO to solve this problem. We propose the DSC_CSP module to replace the middle layers of YOLOv5s to reduce the number of model parameters. On the other hand, in order to avoid the decrease of performance due to the reduction of parameters, we utilize knowledge distillation to maintain performance. To make good use of the information provided by data augmentation, we propose a new method called Dynamic Interpolation Mosaic to improve the original Mosaic. Due to serious imbalance in the number of samples of different data types, we employ a two-stage training scheme to overcome the data imbalance problem. The proposed model achieved the best results in the ICMR2021 Grand Challenge PAIR Competition with 0.59 mAP and model size of 12MB and 41 FPS on the MediaTek's Dimensity 1000 platform. These results confirm that the proposed model is suitable for deployment in embedded systems for object detection task.
AutoML for Multilayer Perceptron and FPGA Co-design

Optimizing neural network architectures (NNA) is a difficult process in part because of the vast number of hyperparameter combinations that exist. The difficulty in designing performant neural networks has brought a recent surge in interest in the automatic design and optimization of neural networks. The focus of the existing body of research has been on optimizing NNA for accuracy [1] [2] with publications starting to address hardware optimizations [3]. Our focus is to close this gap by using evolutionary algorithms to search an entire design space, including NNA and reconfigurable hardware. Large data-centric companies such as Facebook[4] [5] and Google [6] have published data showing that MLP workloads are the majority of their application base. Facebook cites the use of MLP for tasks such as determining which ads to display, which stories matter to see in a news feed, and which results to present from a search. Park et al. stress the importance of these networks and the current limitations on standard hardware and the call for what this research aims to solve, i.e., software and hardware co-design in [7]. Our research aims to take advantage of the reconfigurable architecture of an FPGA device that is capable of molding to a specific workload and neural network structure. Leveraging evolutionary algorithms to search the entire design space of both MLP and target hardware simultaneously, we find unique solutions that achieve both top accuracy and optimal hardware performance.
Low-Hit-Zone Wide Gap Frequency Hopping Sequence Sets With Optimal Average Hamming Correlation

In frequency hopping (FH) sequence design, the wide gap is an important performance indicator. In this paper, the average Hamming correlation of wide gap FH sequence (WG-FH sequence) sets with low hit zone, which has not yet been reported previously, is studied. A lower bound on the average Hamming auto-correlation and the average Hamming cross-correlation of low-hit-zone FH sequence sets is firstly derived. The new lower bound includes the bound for conventional FH sequence sets derived by Peng et al. as special case. Then a construction of WG-FH sequence sets with multiple low hit zones is presented, which are optimal by the new bound for these low hit zones. Finally, another class of optimal WG-FH sequence sets with multiple low hit zones is presented, which have larger wide gap.
A Traffic Scene Object Detection Method Combining Deep Learning and Stereo Vision Algorithm

Object detection has been an important topic in the field of intelligent vehicle. The deep learning object detection is based on monocular vision without depth of the scene information and the detection of small objects in complex traffic scenes often has problems such as misdetection and omission, so the detection of small objects in traffic scenes is still a big challenge. In this paper, a method combining depth information with YOLOv5s object detection algorithm is proposed to improve the accuracy of object detection. Firstly, the depth information is obtained by the disparity images which generated by the end-to-end PSMNet network. Secondly, add an Attention Feature Fusion Module (AFFM) to YOLOv5s to improve the accuracy of small object detection. Finally, the depth information is fused with the object detection to reduce the probability of missed detection and false detection, and the distance information is also obtained. The experimental results show that the SUPER_YOLOv5s object detection algorithm combined with stereo vision can reduce the rate of missed detection and improve the detection accuracy.
The impact knowledge management processes on business performance via the role of accounting information quality as a mediating factor

Purpose This study aims to empirically investigate the role of accounting information quality (AIQ) as a mediating factor in the relationship between knowledge management (KM) processes and business performance (BP) of the financial institutions (FI) operating in Jordan. Design/methodology/approach Based on a literature review and knowledge-based theory, an integrated conceptual framework has been developed to guide the study. The study’s conceptual framework is constituted of three primary constructs, namely, KM processes (acquisition, integration and utilization), BP (financial indicators and non-financial indicators) and AIQ conceptualized using the International Accounting Standard Board’s (2010) framework fundamental qualitative characteristics (relevance, understandability, faith representation and comparability). Data has been collected through a self-administered questionnaire applied to 247 respondents. The targeted respondents have been FIs (commercial banks and insurance companies) in Jordan. Findings The main findings supported accounting information as a mediator factor in enhancing the relationship between the FIs’ KM process and BP (FI) operating in Jordan. Originality/value This study contributed to theory by filling a gap in the literature regarding the role AIQ as a mediator factor between the KM process and BP of the FI operating in Jordan as a developing country.
Point-Guided Contrastive Learning for Monocular 3-D Object Detection

3-D object detection is a fundamental task in the context of autonomous driving. In the literature, cheap monocular image-based methods show a significant performance drop compared to the expensive LiDAR and stereo-images-based algorithms. In this article, we aim to close this performance gap by bridging the representation capability between 2-D and 3-D domains. We propose a novel monocular 3-D object detection model using self-supervised learning and auxiliary learning, resorting to mimicking the representations over 3-D point clouds. Specifically, given a 2-D region proposal and the corresponding instance point cloud, we supervise the feature activation from our image-based convolution network to mimic the latent feature of a point-based neural network at the training stage. While state-of-the-art (SOTA) monocular 3-D detection algorithms typically convert images to pseudo-LiDAR with depth estimation and regress 3-D detection with LiDAR-based methods, our approach seeks the power of the 2-D neural network straightforwardly and essentially enhances the 2-D module capability with latent spatial-aware representations by contrastive learning. We empirically validate the performance improvement from the feature mimicking the KITTI and ApolloScape datasets and achieve the SOTA performance on the KITTI and ApolloScape leaderboard.
Boosting for Straggling and Flipping Classifiers

Boosting is a well-known method in machine learning for combining multiple weak classifiers into one strong classifier. When used in distributed setting, accuracy is hurt by classifiers that flip or straggle due to communication and/or computation unreliability. While unreliability in the form of noisy data is well-treated by the boosting literature, the unreliability of the classifier outputs has not been explicitly addressed. Protecting the classifier outputs with an error/erasure-correcting code requires reliable encoding of multiple classifier outputs, which is not feasible in common distributed settings. In this paper we address the problem of training boosted classifiers subject to straggling or flips at classification time. We propose two approaches: one based on minimizing the usual exponential loss but in expectation over the classifier errors, and one by defining and minimizing a new worst-case loss for a specified bound on the number of unreliable classifiers.
Similarity-Based Clustering For IoT Device Classification

Classifying devices connected to an enterprise network is a fundamental security control that is nevertheless challenging due to the limitations of fingerprint-based classification and black-box machine learning. In this paper, we address such limitations by proposing a similarity-based clustering method. We evaluate our solution and compare it to a state-of-the-art fingerprint-based classification engine using data from 20,000 devices. The results show that we can successfully classify around half of the unclassified devices with a high accuracy. We also validate our approach with domain experts to demonstrate its usability in producing new fingerprinting rules.
Research and Discussion on Key Technologies of Lightweight Smart Campus Based on LAPP

With the development and popularization of big data, artificial intelligence, and cloud computing, the digital and information development of campuses and education has become a common phenomenon. Smart campus is the development of traditional digital campus. At present, many colleges and universities are constructing and exploring smart campus. The lightweight “smart campus” based on LAPP is a unique campus system that combines manpower, equipment, environment, resources, and social factors on the basis of informatization to form a unique campus system. All information resources related to life services are fully integrated, interconnected and integrated, based on in-depth data mining and analysis, to provide comprehensive, effective and intelligent management and service process support for school work. Based on in-depth data mining and analysis, this paper analyzes and studies the lightweight “smart campus” system of LAPP from the perspective of construction and technology.