Reviewerly/exHarmony huggingface dataset:
Are Multi-Language Design Smells Fault-Prone? An Empirical Study

Nowadays, modern applications are developed using components written in different programming languages and technologies. The cost benefits of reuse and the advantages of each programming language are two main incentives behind the proliferation of such systems. However, as the number of languages increases, so do the challenges related to the development and maintenance of these systems. In such situations, developers may introduce design smells (i.e., anti-patterns and code smells) which are symptoms of poor design and implementation choices. Design smells are defined as poor design and coding choices that can negatively impact the quality of a software program despite satisfying functional requirements. Studies on mono-language systems suggest that the presence of design smells may indicate a higher risk of future bugs and affects code comprehension, thus making systems harder to maintain. However, the impact of multi-language design smells on software quality such as fault-proneness is yet to be investigated. In this article, we present an approach to detect multi-language design smells in the context of JNI systems. We then investigate the prevalence of those design smells and their impacts on fault-proneness. Specifically, we detect 15 design smells in 98 releases of 9 open-source JNI projects. Our results show that the design smells are prevalent in the selected projects and persist throughout the releases of the systems. We observe that, in the analyzed systems, 33.95% of the files involving communications between Java and C/C++ contain occurrences of multi-language design smells. Some kinds of smells are more prevalent than others, e.g., Unused Parameters , Too Much Scattering , and Unused Method Declaration . Our results suggest that files with multi-language design smells can often be more associated with bugs than files without these smells, and that specific smells are more correlated to fault-proneness than others. From analyzing fault-inducing commit messages, we also extracted activities that are more likely to introduce bugs in smelly files. We believe that our findings are important for practitioners as it can help them prioritize design smells during the maintenance of multi-language systems.
Evolutionary Shallowing Deep Neural Networks at Block Levels

Neural networks have been demonstrated to be trainable even with hundreds of layers, which exhibit remarkable improvement on expressive power and provide significant performance gains in a variety of tasks. However, the prohibitive computational cost has become a severe challenge for deploying them on resource-constrained platforms. Meanwhile, widely adopted deep neural network architectures, for example, ResNets or DenseNets, are manually crafted on benchmark datasets, which hamper their generalization ability to other domains. To cope with these issues, we propose an evolutionary algorithm-based method for shallowing deep neural networks (DNNs) at block levels, which is termed as ESNB. Different from existing studies, ESNB utilizes the ensemble view of block-wise DNNs and employs the multiobjective optimization paradigm to reduce the number of blocks while avoiding performance degradation. It automatically discovers shallower network architectures by pruning less informative blocks, and employs knowledge distillation to recover the performance. Moreover, a novel prior knowledge incorporation strategy is proposed to improve the exploration ability of the evolutionary search process, and a correctness-aware knowledge distillation strategy is designed for better knowledge transferring. Experimental results show that the proposed method can effectively accelerate the inference of DNNs while achieving superior performance when compared with the state-of-the-art competing methods.
A Video Steganography Method Based on Transform Block Decision for H.265/HEVC

High definition video application has drawn a lot of interest both from academy and industry. The relevant latest video coding technology, H.265/HEVC has been a promising area for video steganography. In this paper, we present a novel and efficient video steganography method based on transform block decision for H.265. In order to improve the visual quality of carrier video, we analyze the embedding error of data hiding with modifying partitioning parameters of CB, PB and TB, and modify the transform block decision to embed secret message and update corresponding residuals synchronously. In order to limit embedding error, we utilize an efficient embedding mapping rule which can embed N (N>1) bits message and at most modify one bit transform partitioning flag. Our experimental results show that the proposed method can achieve better visual quality, larger embedding capacity and less bit-rate increase than state-of-the-art researches.
QPBFT: Practical Byzantine Fault Tolerance Consensus Algorithm Based on Quantified-Role

Practical Byzantine Fault Tolerance (PBFT) is an optional consensus protocol for consortium blockchains scenarios where strong consistency is required. However, it also inevitably incurs high energy consumption, low efficiency and poor scalability. What is more, the reliability of the consensus node cannot be guaranteed by itself. For addressing these problems, this paper proposes practical byzantine consensus algorithm based on quantified-role (QPBFT), which can achieve the following advantages: (1) Improving the security and reliability of the blockchain. The reliability attributes of nodes are quantified based on analytic hierarchy process (AHP), those nodes with high reliability evaluation scores are more likely to participate in block production by introduction of the quantified-role, which can ensure the reliability of blockchain network; (2) Realizing high efficiency and low energy consumption. Voting mechanism is adopted to simplify and optimize the PBFT consensus process; (3) Implementing adaptation to dynamic network environments. Management nodes, voting nodes, candidate nodes, and ordinary nodes are dynamically adjusted according to node reliability evaluation score for optimizing consensus performance. The paper demonstrates the security feature including reliability and fault tolerance. Meanwhile, simulation experiments are conducted to validate the higher efficiency and less resource consumption of QPBFT compared with PBFT.
PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving

In autonomous driving, accurately estimating the state of surrounding obstacles is critical for safe and robust path planning. However, this perception task is difficult, particularly for generic obstacles/objects, due to appearance and occlusion changes. To tackle this problem, we propose an end-to-end deep learning framework for LIDAR-based flow estimation in bird's eye view (BeV). Our method takes consecutive point cloud pairs as input and produces a 2-D BeV flow grid describing the dynamic state of each cell. The experimental results show that the proposed method not only estimates 2-D BeV flow accurately but also improves tracking performance of both dynamic and static objects.
Improved A-star algorithm for robot path planning in static environment

Abstract A-star algorithm is a kind of simple path planning algorithm without solving the calculus, which has a high application. However, compared with other path planning algorithms, it occupies a large memory space. To solve this problem, this paper proposes three new concepts such as the bidirectional search, a guide line and a list of key points. A-star algorithm is optimized and rasterize on indoor environment modeling method, finally through the MATLAB simulation experiments prove that the optimized algorithm feasible experimental results show that the improved algorithm in target different under the two kinds of experiment are able to reduce memory footprint by more than 60%.
Application of virtual machine consolidation in cloud computing systems

Cloud systems play a vital and significant role in our daily lives due to various internet services. For instance, email services, social networks, and others. Consequently, their energy consumption has also become an increasingly essential concern in Cloud Computing Systems (CCSs). Virtualization technologies are widely used to facilitate the management of CCSs and reduce their energy consumption. Virtualization enables live migration of Virtual Machines (VMs) where several VMs can be loaded on some Physical machines (PMs) called VM consolidation. A VM consolidation algorithm can be an effective technique for reducing energy consumption, operational cost, hardware cost, Service Level Agreements (SLAs) compliance/violation, CO2 emissions, and enhancing the hardware and service reliability, performance, and hardware lifetime, load balancing, and utilization in CCSs. Essentially, VM consolidation must minimize energy utilization and service quality in the cloud system. This study presents a taxonomy comprising resource assignment method, metrics, objective functions, migration methods, algorithmic methods, co-location criteria of VMs, architectures, workload dataset, and evaluation approaches in VM consolidation CCSs. Also, we reviewed related work regarding the resources of PMs, algorithm methods, metrics, architectures, and the objectives in static/dynamic VM consolidation.
Radar Trajectory-based Air-Writing Recognition using Temporal Convolutional Network

Air-writing systems offer users a virtual board to write characters or words in free space using fingers or hand movements. Several works have been proposed in literature that aim to use different sensors to enable such a system as an alternative to the keyboard and click form of human-machine interfaces. The advancement of miniature radar sensors and deep learning has enabled precise estimation and tracking of finger or marker movement followed by character recognition to offer an effective air-writing solution. However, deviating from earlier works in literature that make use of a network of radars to effectively track and recognize characters, in this paper, we propose to use only one or two radars to sense the local hand trajectory. We propose to use 1D temporal convolutional network (TCN) for simultaneous feature extraction and temporal modeling to recognize the drawn character from the local target trajectory. A dataset with 3750 character instances has been recorded using a 60-GHz millimeter-wave frequency-modulated continuous wave radar (FMCW) radar. We demonstrate the proposed end to end solution achieves a mean accuracy of 99.11% and 91.33% for two radar and one radar-based solution respectively outperforming other deep architectures.
Cryptocurrency Solutions to Enable Micropayments in Consumer IoT

The successful amalgamation of cryptocurrency and consumer Internet-of-Things (IoT) devices can pave the way for novel applications in machine-to-machine economy. However, the lack of scalability and heavy resource requirements of initial blockchain designs hinder the integration, and it is unclear how consumer devices will be adopting cryptocurrency. Therefore, in this article, we critically review the existing integration approaches and cryptocurrency designs that strive to enable micropayments among consumer devices. We identify and discuss solutions under three main categories; direct integration, payment channel network, and new cryptocurrency design. The first approach utilizes a full node to interact with the payment system. Offline channel payment is suggested as a second-layer solution to solve the scalability issue and enable instant payment with low fee. New designs converge to semicentralized scheme and focus on lightweight consensus protocol that does not require high computation power. We evaluate the pros and cons of each of these approaches and then point out future research challenges. Our goal is to help researchers and practitioners to better focus their efforts to facilitate micropayment adoptions.
Real time wearable speech recognition system for deaf persons

Numerous people around the world experience varying degrees of hearing difficulties. A sense of sound is critical for quality of life for people with hearing difficulties, including deaf people. Wearable devices have substantial potential for voice recognition applications owing to their low cost and light weight. They can assist deaf people perform their daily activities more easily without requiring assistance from others. I have designed and developed a wearable, vibration-based system that will enable deaf people to distinguish important sentences, thereby improving their quality of life. The device consists of a microphone and vibration motor and is mounted on the user’s back. A study was conducted with the device in which speech was detected using Mel Frequency Cepstral Coefficients (MFCC), pre-processing, and Dynamic Time Warping (DTW). Tests were conducted in three different signal-to-noise ratio environments in real time with a typical computer setup. The results had 95% accuracy, 97% specificity and 76% sensitivity.
Defining Cyber Security and Cyber Security Risk within a Multidisciplinary Context using Expert Elicitation

It is important to have and use standardized terminology and develop a comprehensive common understanding of what is meant by cyber security and cyber security risk given the multidisciplinary nature of cyber security and the pervasiveness of cyber security concerns throughout society. Using expert elicitation methods, collaborating cyber researchers from multiple disciplines and two sectors (academia, government-military) were individually interviewed and asked to define cyber security and cyber security risk. Data-driven thematic analysis was used to identify the most salient themes within each definition, sector, and cyber expert group as a whole with results compared to current standards definitions. Network analysis was employed to visualize the interconnection of salient themes within and across sectors and disciplines. When examined as a whole group, "context-driven," "resilient system functionality," and "maintenance of CIA (confidentiality, integrity, availability)" were the most salient themes and influential network nodes for the definition of cyber security, while "impacts of CIA vulnerabilities," "probabilities of outcomes," and "context-driven" were the most salient themes for cyber security risk. We used this expert elicitation process to develop comprehensive definitions of cyber security (cybersecurity) and cyber security risk that encompass the contextual frameworks of all the disciplines represented in the collaboration and explicitly incorporates human factors as significant cyber security risk factors.
Training AI-Based Feature Extraction Algorithms, for Micro CT Images, Using Synthesized Data

Abstract X-ray computed tomography (CT) is a powerful technique for non-destructive volumetric inspection of objects and is widely used for studying internal structures of a large variety of sample types. The raw data obtained through an X-ray CT practice is a gray-scale 3D array of voxels. This data must undergo a geometric feature extraction process before it can be used for interpretation purposes. Such feature extraction process is conventionally done manually, but with the ever-increasing trend of image data sizes and the interest in identifying more miniature features, automated feature extraction methods are sought. Given the fact that conventional computer-vision-based methods, which attempt to segment images into partitions using techniques such as thresholding, are often only useful for aiding the manual feature extraction process, machine-learning based algorithms are becoming popular to develop fully automated feature extraction processes. Nevertheless, the machine-learning algorithms require a huge pool of labeled data for proper training, which is often unavailable. We propose to address this shortage, through a data synthesis procedure. We will do so by fabricating miniature features, with known geometry, position and orientation on thin silicon wafer layers using a femtosecond laser machining system, followed by stacking these layers to construct a 3D object with internal features, and finally obtaining the X-ray CT image of the resulting 3D object. Given that the exact geometry, position and orientation of the fabricated features are known, the X-ray CT image is inherently labeled and is ready to be used for training the machine learning algorithms for automated feature extraction. Through several examples, we will showcase: (1) the capability of synthesizing features of arbitrary geometries and their corresponding labeled images; and (2) use of the synthesized data for training machine-learning based shape classifiers and features parameter extractors.
CPP-Net: Context-Aware Polygon Proposal Network for Nucleus Segmentation

Nucleus segmentation is a challenging task due to the crowded distribution and blurry boundaries of nuclei. Recent approaches represent nuclei by means of polygons to differentiate between touching and overlapping nuclei and have accordingly achieved promising performance. Each polygon is represented by a set of centroid-to-boundary distances, which are in turn predicted by features of the centroid pixel for a single nucleus. However, using the centroid pixel alone does not provide sufficient contextual information for robust prediction and thus degrades the segmentation accuracy. To handle this problem, we propose a Context-aware Polygon Proposal Network (CPP-Net) for nucleus segmentation. First, we sample a point set rather than one single pixel within each cell for distance prediction. This strategy substantially enhances contextual information and thereby improves the robustness of the prediction. Second, we propose a Confidence-based Weighting Module, which adaptively fuses the predictions from the sampled point set. Third, we introduce a novel Shape-Aware Perceptual (SAP) loss that constrains the shape of the predicted polygons. Here, the SAP loss is based on an additional network that is pre-trained by means of mapping the centroid probability map and the pixel-to-boundary distance maps to a different nucleus representation. Extensive experiments justify the effectiveness of each component in the proposed CPP-Net. Finally, CPP-Net is found to achieve state-of-the-art performance on three publicly available databases, namely DSB2018, BBBC06, and PanNuke. Code of this paper is available at \url{https://github.com/csccsccsccsc/cpp-net
Shape Changing Fabric Samples for Interactive Fashion Design

As technology is integrated into all aspects of our lives, researchers are exploring emerging technologies in the field of fashion design. The substantial growth in the field of functional apparel design, such as the use of smart textiles, encourages researchers and fashion designers to incorporate technology within their designs. In much previous work, e-textiles have required interdisciplinary knowledge such as electrical engineering and computer science to be successful. To help with this we created ready-to-use shape changing fabric samples for fashion designers. We explored this research gap through a preliminary user case study with seven experienced designers. Our results suggest design approaches for shape changing fabric samples that would assist non-technically skilled designers incorporating technology in their designs.
Sentiment Analysis of the New Indonesian Government Policy (Omnibus Law) on Social Media Twitter

In this era of modern technology, people are always connected to the internet. Twitter is one of the most developed social media technologies. Countries that adhere to democratic governments usually need opinions from various sources to determine the level of satisfaction and level of acceptance of policies for decision makers, one source that can be used is Twitter. The quality of community satisfaction and the level of acceptance of good policies carried out by the government are important and become benchmarks for maintaining the harmony of state life in Indonesia. In this research study, the level of satisfaction quality and level of acceptance of policies from public reviews will be measured using sentiment analysis, targeting people on Twitter who mention new government policies (omnibus law) in Indonesia. To determine the level of quality of satisfaction and level of acceptance, the Support Vector Machine (SVM) methodology and sentiment analysis were used to classify reviews for the following 8 policy topics in the omnibus law; Increase SMEs, Administration, Area and Land, Employment, Licensing and Investment, Punishment, Research and Innovation, and Taxation. The results showed that topics related to employment were the topics that received the most reviews and negative sentiment from the public, while research and innovation were the topics that were the least reviewed by the public.
Learning Multi-Label Aerial Image Classification Under Label Noise: A Regularization Approach Using Word Embeddings

Training deep neural networks requires well-annotated datasets. However, real world datasets are often noisy, especially in a multi-label scenario, i.e. where each data point can be attributed to more than one class. To this end, we propose a regularization method to learn multi-label classification networks from noisy data. This regularization is based on the assumption that semantically close classes are more likely to appear together in a given image. Hereby, we encode label correlations with prior knowledge and regularize noisy network predictions using label correlations. To evaluate its effectiveness, we perform experiments on a mutli-label aerial image dataset contaminated with controlled levels of label noise. Results indicate that networks trained using the proposed method outperform those directly learned from noisy labels and that the benefits increase proportionally to the amount of noise present.
FedMes: Speeding Up Federated Learning With Multiple Edge Servers

We consider federated learning (FL) with multiple wireless edge servers having their own local coverage. We focus on speeding up training in this increasingly practical setup. Our key idea is to utilize the clients located in the overlapping coverage areas among adjacent edge servers (ESs); in the model-downloading stage, the clients in the overlapping areas receive multiple models from different ESs, take the average of the received models, and then update the averaged model with their local data. These clients send their updated model to multiple ESs by broadcasting, which acts as bridges for sharing the trained models between servers. Even when some ESs are given biased datasets within their coverage regions, their training processes can be assisted by adjacent servers through the clients in their overlapping regions. As a result, the proposed scheme does not require costly communications with the central cloud server (located at the higher tier of edge servers) for model synchronization, significantly reducing the overall training time compared to the conventional cloud-based FL systems. Extensive experimental results show remarkable performance gains of our scheme compared to existing methods. Our design targets latency-sensitive applications where edge-based FL is essential, e.g., when a number of connected cars/drones must cooperate (via FL) to quickly adapt to dynamically changing environments.