Reviewerly/exHarmony huggingface dataset:
Indexing source code and clone detection

Searching source code is a common task in code recommendation systems as well as in many other areas. Clone detection is used in software maintenance and bug detection. The paper introduces an algorithm for building the index structure of abstract syntax trees. When the index structure is built, a pattern tree can be found in time linear in the length of the pattern. Furthermore, the paper describes DrDup2 and DrDupLex, two open-source tools that use the index structure to find Type-2 clones. The index structure presented in this paper is based on the trie, which is a fundamental data structure in computer science. Evaluation of the presented clone detectors is done on BigCloneBench, which is a well-established benchmark for clone detection. Comparison with three state-of-the-art clone detectors (NiCad, CloneWorks and SourcererCC) shows that DrDup2 and DrDupLex are able to beat them in precision, recall and running time. The presented index structure can be used for example to speed up searching for code fragments in code recommendation systems. It is also shown that it can be used to detect Type-2 clones with high precision and recall.
Efficient Computer Aided Diagnosis System for Hepatic Tumors Using Computed Tomography Scans

One of the leading causes of mortality worldwide is liver cancer. The earlier the detection of hepatic tumors, the lower the mortality rate. This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors. Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range, intensity values overlap between the liver and neighboring organs, high noise from computed tomography scanner, and large variance in tumors shapes. The proposed method consists of three main stages; liver segmentation using Fast Generalized Fuzzy C-Means, tumor segmentation using dynamic thresholding, and the tumor's classification into malignant/benign using support vector machines classifier. The performance of the proposed system was evaluated using three liver benchmark datasets, which are MICCAI-Sliver07, LiTS17, and 3Dircadb. The proposed computer adided diagnosis system achieved an average accuracy of 96.75%, sensetivity of 96.38%, specificity of 95.20% and Dice similarity coefficient of 95.13%.
Generation of True Random Numbers using Entropy Sources Present within Portable Computers

Random numbers have wide-ranging applications in various domains such as cryptography, randomization of initial weights in machine learning and AI-simulation, Monte Carlo computation, industrial testing, computer games, gambling. The generation of random numbers is only possible from a sourceof entropy. A true random number generator (TRNG) uses a physical source of entropy to generate random numbers. The randomness of a TRNG can be scientifically characterized, and measured. A drawback of TRNGs is that they usually need an external hardware device containing the physical source of entropy. This necessity can be eliminated by attempting to use sources that are already part of the device's environment. This work attempts to identify such sources and analyze their entropy levels. The identified sources of entropy are then used to build a model that can be used to generate truly random numbers.
Integration of AI and IoT Approaches for Evaluating Cybersecurity Risk on Smart City

Cities have adopted the smart city model based on decision-making to maintain their sustainability and resilience. The decision-making process on a smart city is based on data generated in real-time for the city’s senzorization layer of physical components. For this goal, the digital abstraction of the physical aspects of city using digital twin to simulate scenarios to understand behaviors of a particular event. This study analyzes the use of artificial intelligence techniques and the IoT used in digital twin approaches to analyze cybersecurity risks in the smart city environment.
Diagnosing the Interference on CPU-GPU Synchronization Caused by CPU Sharing in Multi-Tenant GPU Clouds

The GPU-accelerated cloud, enabled by maturing GPU virtualization techniques, has become the most attractive platform for high-performance computing and machine learning workloads. However, it is notoriously challenging to build the multi-tenant GPU cloud where resources, like CPUs and GPUs, can be shared. One well-known and heavily studied reason is that workloads suffer from poor performance isolation and low GPU utilization when GPUs are shared. But little attention has been paid to another fundamental yet under studied problem: how sharing CPUs among GPU instances could affect the workload performance?Targeting this problem, the paper conducts experiments to measure the performance slowdown and vGPU utilization decrease under interference from CPU sharing. The results show that GPU workloads suffer from poor and unpredictable performance and heavy vGPU under-utilization because of CPU sharing. We find that such interference is the result of the complex interplay between the characteristics of CPU-GPU interactions and the special behavior of shared vCPUs: vCPU discontinuity. To diagnose how vCPU discontinuity causes the interference, the paper leverages NVIDIA Nsight Systems for fine-grained profiling and has the following findings: 1) vCPU discontinuity causes inefficient CPU-GPU synchronizations; 2) vCPU discontinuity delays task offloading to the vGPU; 3) Polling-based CPU-GPU synchronization suffers from interference more than blocking-based CPU-GPU synchronization; 4) GPU workloads with frequent task offloads and synchronizations are more vulnerable. Based on the findings, the paper proposes a novel polling-then-blocking CPU-GPU synchronization primitive. Evaluation shows that it can improve the performance by 4.2x.
Customer Intent Prediction using Sentiment Analysis Techniques

Analysing the voice of the customer (VoC) through the customer intent has many applications ranging from personalised marketing to behaviour study. Individuals express their feelings in a language that is frequently accompanied by ambiguity and figure of speech, making it difficult even for humans to understand. Customer feedback is crucial as part of the customer experience (CX) management in customer retention and improves the sales strategy. Modern research has been using machine learning and word embedding technique for sentiment analysis, and it is focused on the predictive model without further context. In this study, the customer feedback comes in the form of Net Promoter Score (NPS) with a text box for written feedback. We analyse the data and demonstrate a hybrid representation that has resulted in the accuracy improvement of the sentiment classification task and predicting customer intent. The datasets were first trained using Word2Vec with the previous dataset and then fit into the Random Forest classifier, tested as the best configuration to prevent overfitting. The hybrid representation is compared against the baseline sentiment polarity tool through few experiments; the results have shown that the hybrid model has improved accuracy for the sentiment classification task. Lastly, we performed customer intent prediction by using the Power BI influencer module. The outcome of the result can be used as a reference for IT management in decision making.
Development of GERAS DANcing for Cognition and Exercise (DANCE): a feasibility study

Dance is a mind-body activity of purposeful rhythmic movement to music. There is growing interest in using dance as a form of cognitive and physical rehabilitation. This manuscript describes the development of GERAS DANcing for Cognition and Exercise (DANCE) and evaluates its feasibility in older adults with cognitive and mobility impairments.The progressive dance curricula were delivered for 15 weeks (1-h class; twice weekly). Participants were eligible if they were community-dwelling older adults aged 60+ with early cognitive or mobility impairment able to follow three-step commands and move independently. Feasibility outcomes included recruitment/retention, adherence, participant satisfaction, safety, and adverse events.Twenty-five older adults (mean (standard deviation [SD]) age = 77.55 (6.10) years, range 68-90 years) with early cognitive (Montreal Cognitive Assessment score (SD) = 21.77 (4.05)) and mobility (92% were pre-frail/frail as indicated on the Fried Frailty Phenotype) impairments were recruited from a geriatric out-patient clinic or within the community. A total of 20/25 (80%) participants completed the study. Average class attendance was 72%, and self-reported homework adherence "most-days / every day" was 89%. A stepwise progression in the dance curricula was observed with increases in motor complexity and balance demands, and 95% of participants rated the program as a "just-right" challenge. Ninety percent of participants rated GERAS DANCE as excellent, and 100% would recommend the program to a friend or family member. Over 50% of participants connected outside of class time for a self-initiated coffee club. Adverse events of falls and fractures were reported for 2 participants, which occurred at home unrelated to the dance intervention during the study period. Pre-determined thresholds for feasibility were met for all outcomes.GERAS DANCE is a feasible and enjoyable program for older adults with early cognitive or mobility impairments. GERAS DANCE curriculum grading (duration; sequence; instructions) and motor complexity increases in agility, balance, and coordination appear appropriately tailored for this population. Future work will explore the feasibility of GERAS DANCE in new settings (i.e., virtually online, community centers, or retirement homes) and the mind-body-social benefits of dance.
Deep hash image retrieval method based on anti-autoencoder

Although the supervised deep hash image retrieval method has achieved good performance, its hash function learning process relies on the annotation information of the data set. In reality, most data is very lack of annotation information, so unsupervised deep hash The method can solve this problem well; an unsupervised deep image hash retrieval method based on the anti-self-encoder is proposed. The model is composed of an encoder, a discriminator and a decoder, and a continuous output part is introduced for the encoder to compensate for the hash layer band. For the information loss, the discriminator is used to make the generated hash code more compact. Experiments on single-label dataset MNIST, CIFAR-10, COCO show that the method in this paper has achieved better retrieval performance improvement.
MAIDRL: Semi-centralized Multi-Agent Reinforcement Learning using Agent Influence

In recent years, reinforcement learning algorithms have been used in the field of multi-agent systems to help the agents with interactions and cooperation on a variety of tasks. Controlling multiple agents simultaneously is extremely challenging as the complexity increases drastically with the number of agents in the system. In this study, we propose a novel semi-centralized deep reinforcement learning algorithm, MAIDRL, for mixed cooperative and competitive multi-agent environments. Specifically, we design a robust DenseNet-style actor-critic structured deep neural network for controlling multiple agents based on the combination of local observation and abstracted global information to compete with opponent agents. We extract common knowledge through influence maps considering both enemy and friendly agents for unit positioning and decision-making in combat. Compared to the centralized method, our design promotes a thorough understanding of the potential influence that a unit has without the need for a complete view of the global state. In addition, this design enables multiagent understanding of common goals, unlike fully decentralized methods. The proposed method has been evaluated on StarCraft Multi-Agent Challenge scenarios in the real-time strategy game, StarCraft II, and the results show that, statistically, the agents controlled by MAIDRL perform better than or as well as those controlled by centralized and decentralized methods.
A unifying framework for the systematic analysis of Git workflows

Git is a popular distributed version control system that provides flexibility and robustness for software development projects. Several workflows have been proposed to codify the way project contributors work collaboratively with Git. Some workflows are highly prescriptive while others allow more leeway but do not provide the same level of code quality assurance, thus, preventing their comparison to determine the most suitable for a specific set of requirements, or to ascertain if a workflow is being properly followed. In this paper, we propose a novel feature-based framework for describing Git workflows, based on a study of 26 existing instances. The framework enables workflows' comparison, to discern how, and to what extent, they exploit Git capabilities for collaborative software development. The framework uses feature-based modelling to map Git capabilities, regularly expressed as contribution guidelines, and a set of features that can be impartially applied to all the workflows considered. Through this framework, each workflow was characterised based on their publicly available descriptions. The characterisations were then vectorised and processed using hierarchical clustering to determine workflows' similarities and to identify which features are most popular, and more relevant for discriminatory purposes. Comparative analysis evidenced that some workflows claiming to be closely related, when described and then characterised, turned out to have more differences than similarities. The analysis also showed that most workflows focus on the branching and code integration strategies, whilst others emphasise subtle differences from other popular workflows or describe a specific development route and are, thus, widely reused. The characterisation and clustering analysis demonstrated that our framework can be used to compare and analyse Git workflows.
Research on Intelligent Acceleration Algorithm for Big Data Mining in Communication Network Based on Support Vector Machine

As a budding technology, big data’s technical implementation and commercial application are in the exploratory stage. With the increasing development of network and communication technology, a large amount of information is pouring in. How to effectively select the required information has become a more and more prominent problem. Data mining is a data processing technology developed to meet this need. Support vector machine is a new technology in data mining. It is a new tool to solve machine learning problems with the help of optimization methods. Among them, it focuses on the support vector machine, including the development history and present situation of support vector machine, the main basic concepts and research contents. On this basis, it studies various training algorithms of support vector machine which are relatively common at present, and compares their advantages and disadvantages. Big data has various data types, forming a data stream with various attributes. As we all know, data source classification based on batch processing can improve the query speed, but it still can’t meet the demand of real-time query. Therefore, feature selection mechanism is usually introduced in the process of data mining modeling to reduce its load. However, when faced with the query of high-dimensional data, the query space grows exponentially, which is difficult to realize. Therefore, this paper proposes the efficiency of an intelligent acceleration algorithm for big data mining based on vector machine communication network.
A Scalable Hierarchical Path Planning technique for Autonomous Inspections with multicopter drones

Multicopter drones equipped with cameras can perform rapid inspections of large buildings, including those with hard to reach features, like bridge pylons.Drones can be made autonomous by providing them with a method to choose a path that maximizes the collected information during the limited flight time allowed by the battery.It is therefore crucial to optimize the trajectories to minimize inspection time.The problem of finding an approximately optimal path passing through a series of desired inspection points in a three-dimensional environment with obstacles is considered.A hierarchical approach is proposed, where the space containing the inspection points is partitioned into different regions and multiple instances of the TSP (Travelling Salesman Problem) are solved, decreasing the overall complexity.An extended graph is used in the TSP, in order to tackle the problem of collision avoidance while planning the trajectory between point pairs.This approach leads to an efficient and scalable method capable of avoiding obstacles, and significantly reduces the time needed to find an optimal path with respect to non-hierarchical methods.Simulation results highlight these features.
Restoring the Executability of Jupyter Notebooks by Automatic Upgrade of Deprecated APIs

Data scientists typically practice exploratory programming using computational notebooks, to comprehend new data and extract insights. To do this they iteratively refine their code, actively trying to re-use and re-purpose solutions created by other data scientists, in real time. However, recent studies have shown that a vast majority of publicly available notebooks cannot be executed out of the box. One of the prominent reasons is the deprecation of data science APIs used in such notebooks, due to the rapid evolution of data science libraries. In this work we propose RELANCER, an automatic technique that restores the executability of broken Jupyter Notebooks, in near real time, by upgrading deprecated APIs. RELANCER employs an iterative runtime-error-driven approach to identify and fix one API issue at a time. This is supported by a machine-learned model which uses the runtime error message to predict the kind of API repair needed - an update in the API or package name, a parameter, or a parameter value. Then RELANCER creates a search space of candidate repairs by combining knowledge from API migration examples on GitHub as well as the API documentation and employs a second machine-learned model to rank this space of candidate mappings. An evaluation of RELANCER on a curated dataset of 255 un-executable Jupyter Notebooks from Kaggle shows that RELANCER can successfully restore the executability of 56% of the subjects, while baselines relying on just GitHub examples and just API documentation can only fix 38% and 36% of the subjects respectively. Further, pursuant to its real-time use case, RELANCER can restore execution to 49% of subjects, within a 5 minute time limit, while a baseline lacking its machine learning models can only fix 24%.
Label embedding semantic-guided hashing

Hashing technologies have been widely used for information retrieval tasks due to their efficient retrieval and storage capabilities. Generally, most of the current supervised learning only utilizes labels to construct a binary similarity matrix of instance pairs and ignores the rich semantic information contained in the labels. Indeed, the reason why supervised hashing is better than unsupervised hashing is that the labels itself has strong discriminative information. Therefore, how to effectively explore the label information is one of the ways to improve the performance of retrieval tasks. In addition, existing hashing methods have the problems of high time consumption and weak scalability when facing large-scale data. To remedy these problems, in this paper, we present a flexible two-step label embedding hashing method named Label Embedding Semantic-Guided Hashing (LESGH). In the first step, LESGH leverages an asymmetric discrete learning framework to learn discriminative compact hash codes only from label information, and adds the constraints of bit-balance and bit-decorrelation to boost the quality of the hash code generation. In the second step, LESGH learns the hash projection function through the generated hash codes in the first step. Moreover, an effective and fast iterative discrete optimization algorithm is presented to solve the discrete problem instead of using the relaxation-based scheme. In doing so, we can not only simplify the optimization process, but also easily scale to large-scale data. We conduct several experiments on three public datasets, i.e., WIKI, MIRFlickr and NUS-WIDE, demonstrate that LESGH can improve the retrieval performance over the compared state-of-the-art baselines.
Fast Monocular Depth Estimation via Side Prediction Aggregation with Continuous Spatial Refinement

Recent works have validated the benefit of integrating spatial information into deep networks to improve pixel-level prediction tasks such as monocular depth estimation. However, how to efficiently and robustly integrate spatial cues retains as an open problem. In this paper, we introduce the Side Prediction Aggregation (termed SPA) method to enhance the embedding of scene structural information from low-level to high-level layers. To improve the estimation accuracy, the proposed method is further equipped with continuous Spatial Refinement Loss (termed SRL) at multiple resolutions with negligible extra computation. Besides, the proposed sequential network can further perform adversarial learning at multiple resolutions. Such an adversarial refinement strategy greatly improves the accuracy of estimated depth with a little extra computation. Without using any pre-trained models, our network achieves the the-state-of-art accuracy on KITTI, NYUD V2, and Cityscapes datasets, which has achieved real-time depth estimation online.
Second Wave Content Moderation Institutional Design: From Rights To Regulatory Thinking

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Cyber Trends in Industrial Control Systems

The development of technologies reaches new heights, but with them come new security issues. With some of the biggest data breaches in history made in 2019 alone, it is clear that cyber-attacks will not go away any time soon. In the recent past, security concerns for control systems have focused on limiting physical attacks. Industrial systems today use the Internet and cloud technologies, making them vulnerable to modern attacks. With the COVID-19 pandemic many people moved to work from home, which led to an increase in cyber-attacks. This article presents a study of current threats to industrial systems and some perspectives on them.
Smooth Zero-Knowledge Hash Functions

We define smooth zero-knowledge hash functions (SZKHFs) as smooth projective hash functions (SPHFs) for which the completeness holds even when the language parameter \(\mathtt {lpar}\) and the projection key \(\mathsf {hp}\) were maliciously generated. We prove that blackbox SZKHF in the plain model is impossible even if \(\mathtt {lpar}\) was honestly generated. We then define SZKHF in the registered public key (RPK) model, where both \(\mathtt {lpar}\) and \(\mathsf {hp}\) are possibly maliciously generated but accepted by an RPK server, and show that the CRS-model trapdoor SPHFs of Benhamouda et al. are also secure in the weaker RPK model. Then, we define and instantiate subversion-zero knowledge SZKHF in the plain model. In this case, both \(\mathtt {lpar}\) and \(\mathsf {hp}\) are completely untrusted, but one uses non-blackbox techniques in the security proof.