Reviewerly/exHarmony huggingface dataset:
CHIEv

Researchers and practitioners in the fields of testing, security assessment and web development seeking to evaluate a given web application often have to rely on the existence of a model of the respective system, which is then used as input to task-specific tools. Such models may include information on HTTP endpoints and their parameters, available user actions/event listeners and required assets. Unfortunately, this data is often unavailable in practice, as only rigorous development practices or manual analysis guarantee their existence and correctness. Crawlers based on static analysis have traditionally been used to extract required information from existing sites. Regrettably, these tools can not accurately account for the dynamic behavior introduced by technologies such as JavaScript that are prevalent on modern sites. While methods based on dynamic analysis exist, they are often not fully capable of identifying event listeners and their effects. In an earlier work, we presented XIEv, an approach for dynamic analysis of web applications that produces an execution trace usable for the extraction of navigation graphs, identification of bugs at runtime and enumeration of resources. It offers improved recognition and selection of event listeners as well as a greater range of observed effects compared to existing approaches. While the evaluation of our research prototype implementation confirmed the capabilities of XIEv, it was generally out-performed by static crawlers in terms of speed. This work introduces CHIEv, an approach that augments XIEv by enabling concurrent processing as well as incorporating the results of a static crawler in real-time. Our results indicate a significant increase in performance, particularly when applied to larger sites.
Special Issue on “Natural Language Processing: Emerging Neural Approaches and Applications”

Nowadays, systems based on artificial intelligence are being developed, leading to impressive achievements in a variety of complex cognitive tasks, matching or even beating humans [...]
Distributed Mining of High Utility Time Interval Sequential Patterns with Multiple Minimum Utility Thresholds

In this paper, the problem of mining high utility time interval sequential patterns with multiple utility thresholds in a distributed environment is considered. Mining high utility sequential patterns (HUSP) is an emerging issue and the existing HUSP algorithms can mine the order of items and they do not consider the time interval between the successive items. In real-world applications, time interval patterns provide more useful information than the conventional HUSPs. Recently, we proposed distributed high utility time interval sequential pattern mining (DHUTISP) algorithm using MapReduce in support of the BigData environment. The algorithm has been designed considering a single minimum utility threshold. It is not convincing to use the same utility threshold for all the items in the sequence, which means that all the items are given the same importance. Hence, in this paper, a new distributed framework is proposed to efficiently mine high utility time interval sequential patterns with multiple minimum utility thresholds (DHUTISP-MMU) using the MapReduce approach. The experimental results show that the proposed approach can efficiently mine HUTISPs with multiple minimum utility thresholds.
An Efficient Transformer-Based Model for Vietnamese Punctuation Prediction

In both formal and informal texts, missing punctuation marks make the texts confusing and challenging to read. This paper aims to conduct exhaustive experiments to investigate the benefits of the pre-trained Transformer-based models on two Vietnamese punctuation datasets. The experimental results show our models can achieve encouraging results, and adding Bi-LSTM or/and CRF layers on top of the proposed models can also boost model performance. Finally, our best model can significantly bypass state-of-the-art approaches on both the novel and news datasets for the Vietnamese language. It can gain the corresponding performance up to $$21.45\%$$ and $$18.27\%$$ in the overall F1-scores.
Speeding Up Data Manipulation Tasks with Alternative Implementations

As data volume and complexity grow at an unprecedented rate, the performance of data manipulation programs is becoming a major concern for developers. In this article, we study how alternative API choices could improve data manipulation performance while preserving task-specific input/output equivalence. We propose a lightweight approach that leverages the comparative structures in Q&amp;A sites to extracting alternative implementations. On a large dataset of Stack Overflow posts, our approach extracts 5,080 pairs of alternative implementations that invoke different data manipulation APIs to solve the same tasks, with an accuracy of 86%. Experiments show that for 15% of the extracted pairs, the faster implementation achieved &gt;10x speedup over its slower alternative. We also characterize 68 recurring alternative API pairs from the extraction results to understand the type of APIs that can be used alternatively. To put these findings into practice, we implement a tool, AlterApi7 , to automatically optimize real-world data manipulation programs. In the 1,267 optimization attempts on the Kaggle dataset, 76% achieved desirable performance improvements with up to orders-of-magnitude speedup. Finally, we discuss notable challenges of using alternative APIs for optimizing data manipulation programs. We hope that our study offers a new perspective on API recommendation and automatic performance optimization.
Improving Adversarial Attacks on Face Recognition Using a Modified Image Translation Model

Deep learning models have been widely applied in various computer vision tasks because of their remarkable achievements. However the appearance of adversarial examples reveals vulnerability of these deep neural networks (DNNs) and brings security problems into researchers’ concern. Strategies on generating adversarial examples with unlimited perturbation are extensively explored, but how to acquire a higher success rate with limited perturbation is still unsolved and is comparably challenging. In this paper, to motivate more research on defence against adversarial attacks and mechanisms inside deep neural networks, we propose an application of our BiasGAN scheme to face images in face recognition tasks. BiasGAN can be inserted as a preprocesser prior to conducting adversarial attacks on face recognition models to get better attack performance. Experimental results demonstrate that our method can make improvements at different perturbation levels and achieve even better performance in challenges in a low perturbation range.
Andragogic Technologies Of Supporting Lifelong Education Of Adults

The article describes the set of original adult education technologies developed by the authors; it is based on the general theory of andragogic support, patterns of adult personality development, and the features of this process in the information environment.The mechanisms of predicting and tracking the technologies' effectiveness with the help of a level approach to the development of competencies are presented.The design of new technologies for educating adults involves the potential of the information society and digital environments; new professional and socio-cultural activities; use of personal experience and its transformation in regards to new life and professional situations; integrative coordination of new knowledge and skills with already established ideas and beliefs; increasing requirements for social professional competencies and personal mobility; expanding network and distance interaction in an open educational environment.These technologies are based on the subject-activity theory, which is using the basic provisions of the theory of personality development, the concept of activity mediation and the laws of professional and creative self-development.
Content Dependent Representation Selection Model for Systems Based on MPEG DASH

A standard called MPEG Dynamic Adaptive Streaming over HTTP (MPEG DASH) ensures the interoperability between different streaming services and the highest possible video quality in changing network conditions. The solutions described in the available literature that focus on video segmentation are mostly proprietary, use a high amount of computational power, lack the methodology, model notation, information needed for reproduction, or do not consider the spatial and temporal activity of video sequences. This paper presents a new model for selecting optimal parameters and number of representations for video encoding and segmentation, based on a measure of the spatial and temporal activity of the video content. The model was developed for the H.264 encoder, using Structural Similarity Index Measure (SSIM) objective metrics as well as Spatial Information (SI) and Temporal Information (TI) as measures of video spatial and temporal activity. The methodology that we used to develop the mathematical model is also presented in detail so that it can be applied to adapt the mathematical model to another type of an encoder or a set of encoding parameters. The efficiency of the segmentation made by the proposed model was tested using the Basic Adaptation algorithm (BAA) and Segment Aware Rate Adaptation (SARA) algorithm as well as two different network scenarios. In comparison to the segmentation available in the relevant literature, the segmentation based on the proposed model obtains better SSIM values in 92% of cases and subjective testing showed that it achieves better results in 83.3% of cases.
Interpolative decisions in the fuzzy signature based image classification for liver CT

In computer aided diagnostics image processing and classification plays an essential role. Image processing experts have been developing solutions for different types of problems, that can be related to image processing, however, due to the sensitivity of the data and the high cost of medical experts, these experimental methods usually have very limited use in real medical practice. The databases that are available are very limited, thus the elsewhere usual and extremely effective convolutional neural network or other automated learning methods are not so easy to adjust for medical image processing. To overcome this difficulty, this paper presents an expert knowledge based method which describes the decision structures by fuzzy signatures. Values of various properties of Computer Tomography images as e.g. density or homogeneity are being considered in these signatures that are different in all case of liver diseases. Because of the low number of samples available, fuzzy sets that describes the leafs of the signatures leads to sparse systems, hence interpolation is needed. However further investigations of other interpolation methods are planned, Stabilized Koczy-Hirota interpolation seems to be appropriate.
Risk Averse Bayesian Reward Learning for Autonomous Navigation from Human Demonstration

Traditional imitation learning provides a set of methods and algorithms to learn a reward function or policy from expert demonstrations. Learning from demonstration has been shown to be advantageous for navigation tasks as it allows for machine learning non-experts to quickly provide information needed to learn complex traversal behaviors. However, a minimal set of demonstrations is unlikely to capture all relevant information needed to achieve the desired behavior in every possible future operational environment. Due to distributional shift among environments, a robot may encounter features that were rarely or never observed during training for which the appropriate reward value is uncertain, leading to undesired outcomes. This paper proposes a Bayesian technique which quantifies uncertainty over the weights of a linear reward function given a dataset of minimal human demonstrations to operate safely in dynamic environments. This uncertainty is quantified and incorporated into a risk averse set of weights used to generate cost maps for planning. Experiments in a 3-D environment with a simulated robot show that our proposed algorithm enables a robot to avoid dangerous terrain completely in two out of three test scenarios and accumulates a lower amount of risk than related approaches in all scenarios without requiring any additional demonstrations.
Doctoral Students' Educational Needs in Research Data Management: Perceived Importance and Current Competencies

&#x0D; Sound research data management (RDM) competencies are elementary tools used by researchers to ensure integrated, reliable, and re-usable data, and to produce high quality research results. In this study, 35 doctoral students and faculty members were asked to self-rate or rate doctoral studentsâ€™ current RDM competencies and rate the importance of these competencies. Structured interviews were conducted, using close-ended and open-ended questions, covering research data lifecycle phases such as collection, storing, organization, documentation, processing, analysis, preservation, and data sharing. The quantitative analysis of the respondentsâ€™ answers indicated a wide gap between doctoral studentsâ€™ rated/self-rated current competencies and the rated importance of these competencies. In conclusion, two major educational needs were identified in the qualitative analysis of the interviews: to improve and standardize data management planning, including awareness of the intellectual property and agreements issues affecting data processing and sharing; and to improve and standardize data documenting and describing, not only for the researcher themself but especially for data preservation, sharing, and re-using. Hence the study informs the development of RDM education for doctoral students.&#x0D;
Collaborative Coverage for a Network of Vacuum Cleaner Robots

Coordination of mobile robot teams has attracted significant attention in the area of robotics research. As one of the most important techniques used in the multi-robot systems, coverage has shown great potential to be applied to many real-world applications. In this paper, we aim to provide a novel path planning method for multi-robot coverage with applications to cooperative autonomous vacuum cleaning. Some preliminary results are presented using an open-source simulator Webots, which lay the foundation for more in-depth theoretical analysis and practical implementation in the subsequent research.
A no-reference 3D virtual reality image quality assessment algorithm based on saliency statistics

In this work, we propose a supervised no-reference (NR) Image Quality Assessment (IQA) model for the objective evaluation of the perceptual quality of 3D virtual reality (VR) images. To achieve such practical algorithm, we first study the scene statistics of saliency maps of the individual left and right views of VR images, and empirically model these statistics with Univariate Generalized Gaussian Distribution (UGGD). We compute the UGGD model parameters at multi-scale and multi-orient steerable subband decomposition, and introduce these features as distortion discriminables. This is followed by the computation of the entropy and normalized root mean square scores of each subband and then these values are utilized as weights to pool the individual view features. We apply the popular 2D supervised BRISQUE model on the individual views to estimate the overall spatial quality of VR images. As the last step of the algorithm, the predicted saliency score and the spatial BRISQUE score are pooled to derive the final quality score of VR images. The performance of the proposed model is evaluated on the popular LIVE 3D VR IQA dataset. The results indicate robust and competitive performance against the off-the-shelf 2D full-reference and NR supervised algorithms.
Imitative Computer-Aided Musical Orchestration with Biologically Inspired Algorithms

Musical orchestration is an empirical art form based on tradition and heritage whose lack of formalism hinders the development of assisstive computational tools. Computer-aided musical orchestration (CAMO) systems aim to assist the composer in several steps of the orchestration procedure. Particularly, imitative CAMO focuses on instrumentation by aiding the composer in creating timbral mixtures as instrument combinations. Imitative CAMO allows composers to specify a reference sound and replicate it with a predetermined orchestra [51].
DIESEL+: Accelerating Distributed Deep Learning Tasks on Image Datasets

We observe that data access and processing takes a significant amount of time in large-scale deep learning training tasks (DLTs) on image datasets. Three factors contribute to this problem: (1) the massive and recurrent accesses to large numbers of small files; (2) the repeated, expensive decoding computation on each image, and (3) the frequent communication between computation nodes and storage nodes. Existing work has addressed some aspects of these problems; however, no end-to-end solutions have been proposed. In this article, we propose DIESEL+, an all-in-one system which accelerates the entire I/O pipeline of deep learning training tasks. DIESEL+ contains several components: (1) local metadata snapshot; (2) per-task distributed caching; (3) chunk-wise shuffling; (4) GPU-assisted image decoding and (5) online region-of-interest (ROI) decoding. The metadata snapshot removes the bottleneck on metadata access in frequent reading of large numbers of files. The per-task distributed cache across the worker nodes of a DLT task to reduce the I/O pressure on the underlying storage. The chunk-based shuffle method converts small file reads into large chunk reads, so that the performance is improved without sacrificing the training accuracy. The GPU-assisted image decoding and the online ROI method minimize the image decoding workloads and reduce the cost of data movement between nodes. These techniques are seamlessly integrated into the system. In our experiments, DIESEL+ outperforms existing systems by a factor of two to three times on the overall training time.
Towards Systematic Design Considerations for Visualizing Cross-View Data Relationships

Due to the scale of data and the complexity of analysis tasks, insight discovery often requires coordinating multiple visualizations (views), with each view displaying different parts of data or the same data from different perspectives. For example, to analyze car sales records, a marketing analyst uses a line chart to visualize the trend of car sales, a scatterplot to inspect the price and horsepower of different cars, and a matrix to compare the transaction amounts in types of deals. To explore related information across multiple views, current visual analysis tools heavily rely on brushing and linking techniques, which may require a significant amount of user effort (e.g., many trial-and-error attempts). There may be other efficient and effective ways of displaying cross-view data relationships to support data analysis with multiple views, but currently there are no guidelines to address this design challenge. In this article, we present systematic design considerations for visualizing cross-view data relationships, which leverages descriptive aspects of relationships and usable visual context of multi-view visualizations. We discuss pros and cons of different designs for showing cross-view data relationships, and provide a set of recommendations for helping practitioners make design decisions.
Near-Optimal Coverage Path Planning of Distributed Regions for Aerial Robots with Energy Constraint

Unmanned Aircraft Vehicles (UAVs) have gained immense popularity for area coverage having applications such as environmental monitoring, demining, search and rescue, among others. Most of the existing studies exploring area coverage have considered only a single region, however, few recent studies have considered coverage of multiple distributed regions. One of the limitations which UAV suffers while covering distributed multiple regions is energy constraints where complete area coverage is not possible. From a strategical point of view, we propose a novel algorithm which solves a variant of area coverage problem where the UAV aims to achieve near-optimal area coverage due to path length limitation caused by the energy constraint. In this paper, a preliminary study is conducted by first formulating the problem and later on presenting a solution. The solution has been partitioned into two inter-dependent sub-problems: i) inter-region coverage, ii) intra-region coverage. The performance of the algorithm has been evaluated by analysing its properties over an exhaustive set of test case scenarios and comparing it against two state-of-the-art area coverage approaches.
Datacenter-Scale Analysis and Optimization of GPU Machine Learning Workloads

In this article, we present a system to collectively optimize efficiency in a very large scale deployment of GPU servers for machine learning workloads at Facebook. Our system 1) measures and stores system-wide efficiency metrics for every executed workflow; 2) aggregates data from across the execution stack to identify optimization opportunities that maximize fleet-wide efficiency improvements; 3) provides periodic and on-demand whole-system profiling for workflows; and 4) automatically analyzes traces for common antipatterns. We present each component of the stack and show case studies demonstrating the use of the tools to significantly improve performance. To our knowledge, our system is the most complete and effective solution for identifying and addressing efficiency problems in datacenter-scale GPU deployments.
Language resources and technology in Russia: state and prospects. (Review)

The author determines the concept of linguistic information resources, gives an overview of their classifications. Describes the most significant Russian catalogs of linguistic information resources and the country's leading organizations in the field of computational linguistics. Discusses the priority tasks of creating a Russian infrastructure for linguistic information resources.
Developing a data collection application for following up the small-scale dairy farms’ performance in rural areas

The mobile application is an effective tool for data collection of performance in agriculture, particularly for small and geographically scattered farms. This study aimed to develop an economical and straightforward application to monitor small dairy herds performance with the flexibility to collect data remotely. The developed mobile application was designed using the MIT App Inventor. Where, the application explores icons of different farm management practices that are reported by farmers through touching the screen. Each icon is linked with an informative SMS, which was received by the mobile phone of the collection technician. We randomly selected and followed 18 farmers to test the application for one year. The main results were recognized the false SMS decreased as farmers took enough time for training in the software. Furthermore, most of sent SMS were for milk yield event. Age, level of education and land size had no significant effect on correct SMS sent by farmers, while herd size showed a significant effect. In Conclusion, mobile application could be developed for continuous data collection and following up small dairy farms in rural areas. Also, the validity test for the developed mobile application confirmed that farmers were able to use the new technology regardless their age and educational level.