Reviewerly/exHarmony huggingface dataset:
Evo-Clocks: Software Evolution at a Glance

Understanding the evolution of a project is crucial in reverse-engineering, auditing and otherwise understanding existing software. Visualizing how software evolves can be challenging, as it typically abstracts a multi-dimensional graph structure where individual components undergo frequent but localized changes. Existing approaches typically consider either only a small number of revisions or they focus on one particular aspect, such as the evolution of code metrics or architecture. Approaches using a static view with a time axis (such as line charts) are limited in their expressiveness regarding structure, and approaches visualizing structure quickly become cluttered with an increasing number of revisions and components. We propose a novel trade-off between displaying global structure over a large time period with reduced accuracy and visualizing fine-grained changes of individual components with absolute accuracy. We demonstrate how our approach displays changes by blending redundant visual features (such as scales or repeating data points) where they are not expressive. We show how using this approach to explore software evolution can reveal ephemeral information when familiarizing oneself with a new project. We provide a working implementation as an extension to our open-source library for fine-grained evolution analysis, LISA.
Recognition of a Single Dynamic Gesture with the Segmentation Technique HS-ab and Principle Components Analysis (PCA)

A continuous path performed by the hand in a period of time is considered for the purpose of gesture recognition. Dynamic gestures recognition is a complex topic since it spans from the conventional method of separating the hand from surrounding environment to searching for the fingers and palm. This paper proposes a strategy of hand recognition using a PC webcam, a segmentation technique (HS-ab which means HSV and CIELab color space), pre-processing of images to reduce noise and a classifier such as Principle Components Analysis (PCA) for the detection and tracking of the hand of the user. The results show that the segmentation technique HS-ab and the method PCA are robust in the execution of the system, although there are various conditions such as illumination, speed and precision of the movements. It is for this reason that a suitable extraction and classification of features allows the location of the gesture. The system was tested with the database of the training images and has a 94.74% accuracy.
A Novel Stitching Method for Dust and Rock Analysis Based on Yutu Rover Panoramic Imagery

The distributions of lunar dust and rock can reflect a large amount of physical and geological information, which has contributed to analyze the environment of the Moon. China's Yutu rover collected large numbers of high-definition images of the lunar surface. In order to obtain a wide field of view and study the lunar environment at close quarters, this article proposes a novel image stitching method named as-least-loss-as-possible wrap to provide a reasonable wider overview of the studying area based on the Yutu rover. In the proposed scheme, a double-feature matching approach is designed to solve the matching error which is generated by the specific environment of the lunar surface. From the stitched image, it can be found that the proposed method can achieve well stitched results for scientific studies, the distribution of the lunar dust on the rock's surface associates with the shadow (temperature), roughness, and height of the rock, and a special ribbon-type distribution of the lunar rocks is demonstrated clearly in a wide stitched image. This ribbon band is caused by the density inhomogeneity of impact target. And the distribution of lunar dust on the rock has the same pattern, even on the whole Moon.
Memory system characterization of deep learning workloads

Deep neural networks (DNNs) have emerged as the prevalent approach for implementing learning tasks in many application domains. As DNN models become increasingly complex, the large amount of data generated during network computations exerts substantial pressure on the capacity and bandwidth of the memory subsystem. Consequently, memory hierarchy is quickly becoming a major bottleneck for DNN performance scaling.
HOG-based Hand Gesture Recognition Using Kinect

One of the keys to the success of interaction between people is communication. Communication can be done verbally or non-verbally. In this paper, we build interactions between humans and computers using hand gestures. The hand gesture is recognized by the palm of the hand which is obtained from the results of human skeleton segmentation through camera Kinect. Recognition of palm gestures is performed on a series of RGB Kinect output frames. Histogram of Oriented Gradient (HOG) is used to produce a palm frame per frame gesture feature which is arranged in 4 seconds as a gesture descriptor. Dynamic Time Warping (DTW) is used as a classifier that will compare the description of the input gesture with the template gesture description. Based on the results of the experiment, the performance of the hand gesture recognition system reached 76.7%.
Data Stream Statistics Over Sliding Windows: How to Summarize 150 Million Updates Per Second on a Single Node

Traditional data management systems map information using centralized and static data structures. Modern applications need to process in real time datasets much larger than system memory. To achieve this, they use dynamic entities that are updated with streaming input data over a sliding window. For efficient and high performance processing, approximate sketch synopses of input streams have been proposed as effective means for the summarization of streaming data over large sliding windows with probabilistic accuracy guarantees. This work presents a system-level solution to accelerate the Exponential Count-Min (ECM) sketch algorithm on reconfigurable technology. Different reconfigurable architectures for the sketch structure that correspond to different cost and performance tradeoffs are presented. We map the proposed system-level ECM sketch architectures to a high-end modern HPC platform to achieve guaranteed and best-effort update rates up to 150 and 180 million tuples per second respectively. We compare the performance of the implemented system against the best optimized multi-thread software alternative and show that our scalable full-system accelerators outperform software solutions by 5-7.5x for Virtex6 devices and in excess of 10x for current Ultrascale devices.
The Disappearing Computer Science in Healthcare VR applications

One growing area of research in the field of interaction design concerns new applications of filmed VR, or what is sometimes referred to as 360 video. We have studied this new semi-interactive medium in two applied use settings: in clinical psychotherapy sessions for agoraphobia and in elderly care. The systems used in both projects were effective and well received by patients and practitioner and are now taken into continued use at the studied organisations. As researchers in HCI, we were surprised that these small experiments could be so successful in the wild, while only loosely connected to the discourse of VR as we knew it. Based on our design process, and experiences from real use settings, we discuss the future role of the historically strong field of computer science in the domain of applied VR.
Learning Region Similarity over Spatial Knowledge Graphs with Hierarchical Types and Semantic Relations

A large number of spatial knowledge graphs (SKGs) are available from spatially enriched knowledge bases, e.g., DBpedia and YAGO2. This provides a great chance to understand valuable information about the regions surrounding us. However, it is hard to comprehend SKGs due to the explosively growing volume and the complication of the graph structures. Thus we study the problem of similar region search (SRS), which is an easy-to-use but effective way to explore spatial data. The effectiveness of SRS highly depends on how to measure the region similarity. However, existing approaches cannot make use of the rich information contained in SKGs thus may lead to incorrect results. In this paper, we propose a spatial knowledge representation learning method for region similarity, namely SKRL4RS. SKRL4RS firstly encodes the spatial entities of an SKG into a vector space to make it easier to extract useful features. Then regions are represented by 3-D tensors using the spatial entity embeddings together with geographical information. Finally, region tensors are fed into the conventional triplet network to learn the feature vectors of regions. The region similarity measure learned by SKRL4RS can capture the hierarchical types, semantic relatedness, and relative locations of spatial entities inside a region. Experimental results on two real-world datasets show that our SKRL4RS outperforms the state-of-the-art by a significant margin in terms of the accuracy of measuring region similarity.
Human-robot relationships and the development of responsible social robots

The contemporary development of social robots has been accompanied by concerns over their capacity to cause harm to humans. Our RoboTIPS study sets out to design and trial an innovative design feature that will advance the safe operation of social robots and foster societal trust. The Ethical Black Box (EBB) collects data about a robot's actions in real time and in context; when an incident occurs, this data can be used within a wider investigation process to determine what went wrong and prevent similar adverse events. In this paper we draw on Lucy Suchman's groundbreaking work on human-machine relationships to elucidate the goals, practices and potential impact of our study. We align with Suchman's positioning of safety as an accomplishment of situated action and draw on her analysis to describe the actions of the EBB-enhanced social robot as contingent on context and the robot's status as a social agent. We also describe shared priorities in our methodological approaches. We close with observations on how participatory design and an ethnomethodologically-informed stance towards data collection and analysis can contribute to the field of responsible innovation (RI), which seeks to ensure that innovations are undertaken in the public interest and provide societal value.
Bootstrapping Trust in a "Trusted" Virtualized Platform

The Trusted Platform Module (TPM) can be used to establish trust in the software configuration of a computer. Virtualizing the TPM is a logical next step towards building trusted cloud environments and providing a virtual TPM to a virtual machine promises a continuation of trusted computing concepts. The association between a virtual TPM and a virtual machine is a critical concern. We show that a "trusted'' virtualized platform may fall victim to a Goldeneye attack. In this work, we put forward a formal model for virtualization systems and trusted virtualized platforms. We pair this with a model for establishing trust in a virtualized platform following conventional reasoning over trusted computing systems. We show that if a Goldeneye attack is successful, it would allow a verifier to establish trust in an untrustworthy platform. We discuss attack vectors and possible solutions which would mitigate Goldeneye.
Evolutionary and Swarm-Based Feature Selection for Imbalanced Data Classification

Recently, feature selection task has gained more attention in classification of problems. This task aims to find the most important features in a large search space of potential solutions. Hence, a challenging problem is manifested to find the optimal solution. In this paper, we study a metaheuristic-based approach for feature selection in binary classification problems. The scenario deals with several highly imbalanced datasets. In an attempt to handle the problem of imbalanced data, the common fitness function based on the classification accuracy is replaced with two more effective fitness functions: the area under the ROC curve and the geometric mean. To evaluate the effectiveness of the developed approach, two popular metaheuristic approaches are experimented with the three fitness functions for classifying six imbalanced datasets. The chapter discusses the impact of the used fitness function on the final performance of the proposed methods. The proposed methods demonstrated that some fitness functions like the accuracy rate can mislead the identification process of the relevant features in imbalanced datasets.
Accuracy Rejection Normalized-Cost Curves (ARNCCs): A Novel 3-Dimensional Framework for Robust Classification

Machine learning (ML) offers several supervised learning algorithms to build classifiers for developing accurate decision support systems.However, the selection of robust classifier for reliable decision making in healthcare domain is three-fold: accuracy, refraining for low confidence decisions, and the cost of decisions.In the field of medical science, there are costs associated with the incorrect and the refraining from decisions, which can have negative implications in devising adequate therapeutic interventions.For example, it may be life threating if a cancer patient is declared as healthy one (misclassification cost) or decision remains pending for some time (rejection cost).In this work we proposed the concept of Accuracy Rejection Normalized-Cost Curves (ARNCCs), which is an extension of Accuracy Rejection Curves (ARCs); a threedimensional visualization technique to demonstrate the strengths and weaknesses of classification algorithms over different rejection regions and normalized-cost (NC) to select the robust classifier.ARNCCs method holds ARCs plot on two dimensions, in addition it computes NC at third dimension against ratio of false positive costs and ratio of rejection costs obtained at different rejection rates.The proposed three-dimensional graphs have the potential to answer a variety of questions regarding accuracy, rejection rate and NC of a classifier.Six publicly available cancer datasets (four breast cancer and two lung cancer) having clinical parameters obtained from ML repository of University of California, Irvine (UCI) were used to assess the performance of proposed ARNCCs in this study.Empirical results show that ARNCCs provide broad range of decisions to choose the desired parameters (accuracy, rejection rate and NC) for further necessary actions as compared to traditional ARCs method.ARNCCs framework has the ability to more logically compare the performances of classification algorithms in terms of accuracy, rejection rate and NC based scenarios.
Patch-Based Colour Transfer with Optimal Transport

This paper proposes a new colour transfer method with Optimal transport to transfer the colour of a source image to match the colour of a target image of the same scene. We propose to formulate the problem in higher dimensional spaces (than colour spaces) by encoding overlapping neighborhoods of pixels containing colour information as well as spatial information. Since several recoloured candidates are now generated for each pixel in the source image, we define an original procedure to efficiently merge these candidates which allows denoising and artifact removal as well as colour transfer. Experiments show quantitative and qualitative improvements over previous colour transfer methods. Our method can be applied to different contexts of colour transfer such as transferring colour between different camera models, camera settings, illumination conditions and colour retouch styles for photographs.
A High Performance Modified K-Means Algorithm for Dynamic Data Clustering in Multi-core CPUs Based Environments

K-means algorithm is one of the most widely used methods in data mining and statistical data analysis to partition several objects in K distinct groups, called clusters, on the basis of their similarities. The main problel and distributed clustering algorithms start to be designem of this algorithm is that it requires the number of clusters as an input data, but in the real life it is very difficult to fix in advance such value. In this work we propose a parallel modified K-means algorithm where the number of clusters is increased at run time in a iterative procedure until a given cluster quality metric is satisfied. To improve the performance of the procedure, at each iteration two new clusters are created, splitting only the cluster with the worst value of the quality metric. Furthermore, experiments in a multi-core CPUs based environment are presented.
Ranking Knowledge Graphs By Capturing Knowledge about Languages and Labels

Capturing knowledge about the mulitilinguality of a knowledge graph is of supreme importance to understand its applicability across multiple languages. Several metrics have been proposed for describing mulitilinguality at the level of a whole knowledge graph. Albeit enabling the understanding of the ecosystem of knowledge graphs in terms of the utilized languages, they are unable to capture a fine-grained description of the languages in which the different entities and properties of the knowledge graph are represented. This lack of representation prevents the comparison of existing knowledge graphs in order to decide which are the most appropriate for a multilingual application.
The Prevalence of Errors in Machine Learning Experiments

Context: Conducting experiments is central to research machine learning research to benchmark, evaluate and compare learning algorithms. Consequently it is important we conduct reliable, trustworthy experiments. Objective: We investigate the incidence of errors in a sample of machine learning experiments in the domain of software defect prediction. Our focus is simple arithmetical and statistical errors. Method: We analyse 49 papers describing 2456 individual experimental results from a previously undertaken systematic review comparing supervised and unsupervised defect prediction classifiers. We extract the confusion matrices and test for relevant constraints, e.g., the marginal probabilities must sum to one. We also check for multiple statistical significance testing errors. Results: We find that a total of 22 out of 49 papers contain demonstrable errors. Of these 7 were statistical and 16 related to confusion matrix inconsistency (one paper contained both classes of error). Conclusions: Whilst some errors may be of a relatively trivial nature, e.g., transcription errors their presence does not engender confidence. We strongly urge researchers to follow open science principles so errors can be more easily be detected and corrected, thus as a community reduce this worryingly high error rate with our computational experiments.
Adaptive Differential Evolution Algorithm Based on Restart Mechanism and Direction Information

Differential evolution is a competent algorithm for solving single objective real-parameter optimization problems.In order to enhance the performance of adaptive DE algorithms based on successful parameters, in this paper, a new DE algorithm, called adaptive differential evolution restart and direction, abbreviated ADERD, is proposed for solving global numerical optimization problems over continuous space.In the proposed algorithm, a novel mutation strategy based on the feasible descent direction is introduced.Only individuals of the population top ranked with smaller errors adopt this novel strategy to mutate.Additionally, the variable coefficient is used in the restart mechanism first to avoid stagnation and/or jump out of the local optima.Modified mechanism of crossover probability sorting is also introduced.In order to better understand the effectiveness of our proposed strategies, those are integrated into two representative adaptive DE variants, i.e.JADE_ rcr and JADE_ sort.Experimental results demonstrate that the our proposed strategies are capable of enhancing the performance of JADE_ rcr and JADE_ sort.Improved JADE_ sort is denoted as ADERD_ sort.Experiments have been conducted on 30 functions presented in CEC 2017 competition.Moreover, compared with recent adaptive DE algorithms, ADERD_ sort obtains better, or at least comparable, results in terms of the quality of final solutions.
The mobile phone revolution, the Internet and rural electricity: What are the implications for food security in Africa?

The theorized benefits of mobile phone and Internet penetration have not been sufficiently tested, especially on cross-country data. Using a dataset involving 48 regionally grouped African countries and a robust dynamic system GMM model, this study attempts to fill this gap. In carrying out this study, we adopted an empirical strategy that allowed us to estimate the specific and complementary impacts of mobile phone, Internet, and rural electricity on food security, from panel and regional comparative perspectives. The findings indicate that: the incremental and intra-temporal effects in food security situation linger awhile; the promotional effects of ICT development on food security are more induced by mobile phone development than by Internet development; mobile phone and Internet have a robust and stable synergistic effect on food security; contrary to expectations, there is no complementarity between ICT protocols and rural electricity, with the impact of the latter on food security being more distortive than promotional; and that the promotional benefits of ICT protocols on food security are dependent on a country’s capacity to resolve inherent deficiencies in rural electricity energy supply, climate conditions and credit challenges. Policy-wise, the findings confirm how the convergence and complementarity between mobile phone and Internet connectivity can conditionally be enhanced in the case of Africa; and how such convergence can potentially facilitate the catch-up between the lagging Internet access and the booming mobile phone subscription and to enhance the quality, quantity and relevance of the information contents of the ICT protocols.
Adjustable Autonomy for Human-UAVs Collaborative Searching Using Fuzzy Cognitive Maps

Collaborating with a team of UAVs remains a challenge for a human operator in the target searching task where the operator has to simultaneously monitor several video streams to search and verify the targets. In this paper, we propose an adjustable autonomy method that can dynamically change the levels of autonomy (LOA) for the UAVs according to the task complexity as well as the operator's cognitive workload. Specifically, we use a fuzzy cognitive map (FCM) to construct the framework of autonomy levels for unmanned systems (ALFUS). We have shown the effectiveness of the proposed method in an experiment that one human collaborates with four UAVs in a target searching task.
Security Organizing

In the knowledge economy, organizational information is one of a firm's most valuable assets. However, despite significant advances in information security (InfoSec) research, problems in the field persist. This paper contends that advances in reliability and safety can help inform the future of InfoSec research. Specifically, the paper presents a framework for security organizing that is based on previous research about the organizational mindfulness characteristics of high reliability organizations (HROs). This paper considers the similarities among reliability, safety, and InfoSec, provides an initial validation of a potential construct for measuring security organizing in the workplace, and discusses opportunities for future research.
Resource Bottleneck Analysis of the Blockchain Based on Tron’s TPS

As one of the well-known public chain projects, Tron has initially solved the throughput bottleneck. Compared with Bitcoin and Etherum, TRON can achieve 2000 TPS in terms of performance theoretically. However, due to the limitations of the users, the current peak TPS of the main network is 748, which is still far from the theoretical upper limit. But with the increase of Dapps and users, the capacity problem will be exposed. According to the expected TPS, this paper analyzes the resource bottlenecks that Tron will face from three dimensions: processing performance, transmission performance and storage capacity requirement.