Reviewerly/exHarmony huggingface dataset:
Trajectory Planning Algorithms in Two-Dimensional Environment with Obstacles

This article proposes algorithms for planning and controlling the movement of a mobile robot in a two-dimensional stationary environment with obstacles. The task is to reduce the length of the planned path, take into account the dynamic constraints of the robot and obtain a smooth trajectory. To take into account the dynamic constraints of the mobile robot, virtual obstacles are added to the map to cover the unfeasible sectors of the movement. This way of accounting for dynamic constraints allows the use of map-oriented methods without increasing their complexity. An improved version of the rapidly exploring random tree algorithm (multi-parent nodes RRT – MPN-RRT) is proposed as a global planning algorithm. Several parent nodes decrease the length of the planned path in comprise with the original one-node version of RRT. The shortest path on the constructed graph is found using the ant colony optimization algorithm. It is shown that the use of two-parent nodes can reduce the average path length for an urban environment with a low building density. To solve the problem of slow convergence of algorithms based on random search and path smoothing, the RRT algorithm is supplemented with a local optimization algorithm. The RRT algorithm searches for a global path, which is smoothed and optimized by an iterative local algorithm. The lower-level control algorithms developed in this article automatically decrease the robot’s velocity when approaching obstacles or turning. The overall efficiency of the developed algorithms is demonstrated by numerical simulation methods using a large number of experiments.
Review of Video Analytics Method for Video Surveillance

To alleviate increase rate of crime, security personnel, IP camera, CCTV (Closed-Circuit Television) is traditionally employed. But human vigilance leads to errors. Hence, automated video surveillance and analytics are implemented to minimize these errors. Human behaviour recognition algorithms can be used reactively to prevent issues or proactively for post-incident inquiry. There is increasing demand in automated video surveillance, which involves face detection, object detection, license plate detection. This study provides the most recent state-of-the-art image processing algorithms for automatic behaviour detection approaches, with an emphasis on human activity monitoring.
Real-time head-based deep-learning model for gaze probability regions in collaborative VR

Eye behavior has gained much interest in the VR research community as an interactive input and support for collaboration. Researchers used head behavior and saliency to implement gaze inference models when eye-tracking is missing. However, these solutions are resource-demanding and thus unfit for untethered devices, and their angle accuracy is around 7°, which can be a problem in high-density informative areas. To address this issue, we propose a lightweight deep learning model that generates the probability density function of the gaze as a percentile contour. This solution allows us to introduce a visual attention representation based on a region rather than a point. In this way, we manage the trade-off between the ambiguity of a region and the error of a point. We tested our model in untethered devices with real-time performances; we evaluated its accuracy, outperforming our identified baselines (average fixation map and head direction).
EmbER: A System for Transfer of Interoceptive Sensations to Improve Social Perception

Remote social interactions suffer from a loss of nonverbal cues used to build affiliation and connection. We propose the use of novel sensory channels for sharing social cues from interoceptive data through wearable devices that simulate the breathing and heartbeat patterns of another person, known to be linked to emotional perception and affect. We conducted a study with 16 participants testing the sharing of either heart rate or breathing rate through haptic or audio sensations. Participants experienced these sensations while watching videos of narrators describing personal experiences. We assessed the subjects' feelings of affiliation and synchrony toward a narrator through surveys, interviews, and correlated physiological data. Our findings show that sensory devices that transfer interoceptive sensations, especially those below the level of conscious perception, can have a positive impact on feelings of connectedness. This has implications for the application of physiological channels in remote interactions to improve social connection.
The Informatization of Small and Medium-Sized Enterprises Accounting System Based on Sensor Monitoring and Cloud Computing

Small and medium-sized enterprises (SMEs) are an indispensable part of the development of the market economy, and they occupy a major position in the national economic system. Nowadays, the information construction of SMEs is becoming more and more important. Having an informationized accounting system can speed up the economic development of SMEs. So, this article designs a new type of accounting system, mainly for SMEs. Therefore, this article is based on sensor monitoring and cloud computing to optimize the informatization construction of the accounting system of SMEs. This paper proposes a cloud computing SOA architecture to build a cloud computing-based accounting system, and then combines the wireless sensor network routing protocol in the wireless sensor network system and the method of measuring the distance of the sensor monitoring node, and the wireless sensor network is applied to the cloud computing-based accounting system. Then, designed the enterprise information construction investigation experiment to formulate the rules applicable to SMEs, and then tested the data detection ability of the new accounting system by testing the performance of the sensor network protocol. Finally, the data obtained from the analysis of the weight of the enterprise cloud service is used to optimize the new accounting system, and the performance of the final optimized accounting system is compared with the traditional system. Experiments show that the accuracy of data monitoring by an accounting system based on sensor monitoring and cloud computing has increased by 13.84% compared to traditional accounting systems; compared with the traditional accounting system, the data processing efficiency of the accounting system based on sensor monitoring and cloud computing has increased by 14.63%.
The Zipf-Polylog distribution: Modeling human interactions through social networks

The Zipf distribution attracts considerable attention because it helps describe data from natural as well as man-made systems. Nevertheless, in most of the cases the Zipf is only appropriate to fit data in the upper tail. This is why it is important to dispose of Zipf extensions that allow to fit the data in its entire range. In this paper, we introduce the Zipf-Polylog family of distributions as a two-parameter generalization of the Zipf. The extended family contains the Zipf, the geometric, the logarithmic series and the shifted negative binomial with two successes, as particular distributions. We deduce important properties of the new family and demonstrate its suitability by analyzing the degree sequence of two real networks in all its range.
A portrait of academic literacy in mid-adolescence: a computational longitudinal account of cognitive academic language proficiency during secondary school

This study describes academic literacy development during mid-adolescence, when learners need to adjust to the demands of academic discourse as a gateway to linguistic adulthood. Unlike most research to date, which is cross-sectional and detached from disciplinary content, this study provides a two-year longitudinal description of academic language in the form of history essays. During the two-year study, a group of 20 students (whose first language was Spanish) produced a corpus of history essays in relation to topics covered in the official curriculum. These essays were processed with MultiAzterTest, a state-of-the-art computational tool that produces linguistic and discursive representations of texts. A statistical analysis of the metrics shows a significant evolution of key measures, which may contribute to charting academic language literacy. More precisely, the results point to a statistically significant evolution of the length of language units (i.e. the amount of words, sentences and paragraphs), in syntactic complexity (i.e. the proportion of nouns and the complexity of noun phrases), in lexical richness (i.e. the frequency and precision of lexis) and in cohesion metrics (i.e. the overall use of connectives), hence exploring the boundaries between basic child speech and adult discourse.
Multicore Photonic Complex-Valued Neural Network with Transformation Layer

Photonic neural network chips have been widely studied because of their low power consumption, high speed and large bandwidth. Using amplitude and phase to encode, photonic chips can accelerate complex-valued neural network computations. In this article, a photonic complex-valued neural network (PCNN) chip is designed. The scale of the single-core PCNN chip is limited because of optical losses, and the multicore architecture of the chip is used to improve computing capability. Further, for improving the performance of the PCNN, we propose the transformation layer, which can be implemented by the designed photonic chip to transform real-valued encoding to complex-valued encoding, which has richer information. Compared with real-valued input, the transformation layer can effectively improve the classification accuracy from 93.14% to 97.51% of a 64-dimensional input on the MNIST test set. Finally, we analyze the multicore computation of the PCNN. Compared with the single-core architecture, the multicore architecture can improve the classification accuracy by implementing larger neural networks and has better phase noise robustness. The proposed architecture and algorithms are beneficial to promote the accelerated computing of photonic chips for complex-valued neural networks and are promising for use in many applications, such as image recognition and signal processing.
Detection of bulk feed volume based on binocular stereo vision

The volume detection of medical mice feed is crucial to understand the food intake requirements of mice at different growth stages and to grasp their growth, development, and health status. Aiming at the problem of volume calculation in the way of feed bulk in mice, a method for detecting the bulk volume of feed in mice based on binocular stereo vision was proposed. Firstly, the three-dimensional point coordinates of the feed's surface were calculated using the binocular stereo vision three-dimensional reconstruction technology. The coordinates of these dense points formed a point cloud, and then the projection method was used to calculate the volume of the point cloud; and finally, the volume of the mice feed was obtained. We use the stereo matching data set provided by the Middlebury evaluation platform to conduct experimental verification. The results show that our method effectively improves the matching degree of stereo matching and makes the three-dimensional point coordinates of the obtained feed's surface more accurate. The point cloud is then denoised and Delaunay triangulated, and the volume of the tetrahedron obtained after the triangulation is calculated and summed to obtain the total volume. We used different sizes of wood instead of feed for multiple volume calculations, and the average error between the calculated volume and the real volume was 7.12%. The experimental results show that the volume of the remaining feed of mice can be calculated by binocular stereo vision.
Efficient ROI-based compression of mammography images

Medical imaging and telemedicine are progressively employed on a large scale these days. The utilization of lossless compression techniques for medical image storage and transmission may not provide significant benefits in terms of data reduction. On the other hand, the utilization of lossy compression techniques may lead to loss of essential data, which affects the diagnosis process. The critical data in the region of interest (ROI) of a medical image should be compressed with high-quality compression with no data lost or destroyed, regardless of the remaining parts of the medical image (non-ROI). Generally, mammography is used for investigating breast malignancies and localization of small tumors. Therefore, to increase the compression efficiency of mammogram images without losing any essential data, we present a hybrid technique based on lossless compression for the critical data in the ROI and lossy compression for the remaining parts of the medical image (non-ROI). In this work, edge-directed prediction lossless compression is adopted for the ROI, while fractal lossy compression is proposed for the non-ROI of mammogram images. Mammogram images from the mammographic image analysis society (MIAS) database are used to test the proposed hybrid technique. Encoding time, decoded image quality in terms of peak signal-to-noise ratio (PSNR), and compression ratio are the metrics used to assess the quality of compression. The obtained results prove that the proposed technique achieves a high compression ratio, while maintaining an acceptable quality of the reconstructed images compared with other recent mammogram image compression techniques.
A Measurement Study of Wechat Mini-Apps

A new mobile computing paradigm, dubbed mini-app, has been growing rapidly over the past few years since being introduced by WeChat in 2017. In this paradigm, a host app allows its end-users to install and run mini-apps inside itself, enabling the host app to build an ecosystem around (much like Google Play and Apple AppStore), enrich the host's functionalities, and offer mobile users elevated convenience without leaving the host app. It has been reported that there are over millions of mini-apps in WeChat. However, little information is known about these mini-apps at an aggregated level. In this paper, we present MiniCrawler, the first scalable and open-source WeChat mini-app crawler that has indexed over 1,333,308 mini-apps. It leverages a number of reverse engineering techniques to uncover the interfaces and APIs in WeChat for crawling the mini-apps. With the crawled mini-apps, we then measure the resource consumption, API usage, library usage, obfuscation rate, app categorization, and app ratings at an aggregated level in this work.
Dynamic graph dropout for subgraph-based relation prediction

Relation prediction for knowledge graphs aims at predicting missing relationships between entities. Inductive relation prediction methods have received increasing attention because of their capability of handling unseen entities. Among the inductive methods, the subgraph-based algorithms have emerged, which inductively predict relations using the subgraph surrounding the target entities. Despite the effectiveness, prior subgraph-based studies rarely focus on the explainability of subgraph reasoning, with which is critical for humans to understand and trust the prediction from GNNs. One of the reasons for the weak explainability of subgraph-based methods is the existence of noisy nodes and edges, which also hinders having higher performance of the model. In this paper, we present a dynamic graph dropout algorithm that prunes irrelevant nodes and edges to find the minimum sufficient subgraph for relation prediction. By this means, the proposed algorithm provides an explanation for which parts of the subgraph the model derives its results from. Specifically, we design an estimation function to evaluate the importance of the nodes and edges. Two dropout functions, i.e., soft and hard dropouts, are elaborately designed to filter out noisy nodes and edges based on their importance. Moreover, multiple restriction losses, including topological loss and penalty loss, are proposed to regularize the generation of the pruned subgraph. In this way, our model seeks to preserve the topology information in the subgraph and meanwhile maximally eliminate the redundant information. By removing noisy information, the proposed algorithm outperforms state-of-the-art models on eight inductive datasets.
Evaluating Code Improvements in Software Quality Course Projects

Software quality sits at the core of software engineering as a discipline. Yet, although each university software-engineering and the software-development course covers software quality to some extent, practitioners still lament on graduates’ readiness for practise for this very reason—poor quality of their code. As a result, we have engaged university industrial partners in designing a master-degree Software Quality course that puts the key software quality topics in one place.
Towards a Reliable Text Summarization Evaluation Metric Using Predictive Models

With an increasing number of new summarization systems proposed in recent years, an automatic text evaluation metric that can accurately and reliably rate the performance of summarization systems has been a pressing need. However, current automatic text evaluation metrics can only measure one or certain aspects of the quality between two summary texts and do not agree with human judgments consistently. In this paper, we show that combining multiple well-chosen evaluation metrics and training predictive models using human annotated datasets can lead to more reliable evaluation scores than using any individual automatic metric. Our predictive models trained on a human annotated subset of the CNN/DailyMail corpus demonstrate significant improvements (e.g. approximately 25% along coherence dimension) over selected individual metrics. Furthermore, a concise meta-evaluation on automatic metrics is provided along with an analysis of the performance of 12 predictive models. We also investigate the sensitivity of automatic metrics when mixed together for training these models. We have made the code, the instructions for experiment setup, and the trained models available as a tool for comparing and evaluating text summarization systems. a a https://github.com/bzhao2718/ReliableSummEvalReg .
A Novel Fbit_sort Algorithm for digital encoding process

Managing and arranging of voluminous junk of facts can be a great task for computer sciences’ and data sciences’ domain. The data consists majorly of two types-numeric data and binary floating type data. The arrangement of humongous numeric data is done currently by the researcher community using various sorting based algorithmic procedures like insertion’s sort, bubble’s sort, merge’s sort, selection’s sort. Till now, to the best of our knowledge, there is no work available in literature which deals with the efficient management and arrangement of binary floating data. In this research paper, a novel Fbit_sort algorithm is proposed which can efficiently manage and sort the binary floating data. Binary data (two-digit number system) is used in digital encoding process on which computer language and programming is based on. In addition to this, the application of binary numbers in Boolean Algebra, a branch of mathematics has been determined.
Numeric Lyndon-based feature embedding of sequencing reads for machine learning approaches

Feature embedding methods have been proposed in literature to represent sequences as numeric vectors to be used in some bioinformatics investigations, such as family classification and protein structure prediction. Recent theoretical results showed that the well-known Lyndon factorization preserves common factors in overlapping strings. Surprisingly, the fingerprint of a sequencing read, which is the sequence of lengths of consecutive factors in variants of the Lyndon factorization of the read, is effective in preserving sequence similarities, suggesting it as basis for the definition of novels representations of sequencing reads. We propose a novel feature embedding method for Next-Generation Sequencing (NGS) data using the notion of fingerprint. We provide a theoretical and experimental framework to estimate the behaviour of fingerprints and of the $k$-mers extracted from it, called $k$-fingers, as possible feature embeddings for sequencing reads. As a case study to assess the effectiveness of such embeddings, we use fingerprints to represent RNA-Seq reads and to assign them to the most likely gene from which they were originated as fragments of transcripts of the gene. We provide an implementation of the proposed method in the tool lyn2vec, which produces Lyndon-based feature embeddings of sequencing reads.
A Study on Different Deep Learning Architectures on Image Captioning

In the recent era Deep learning is showing exemplary results in many application areas. Researches from different disciplines have incorporated deep learning in to their research to solve different interdisciplinary problems. Deep learning applications areas include Speech recognition, Natural Language Processing, Computer vision, Networking, Healthcare, IoT, Robotics, Agriculture, Remote sensing and many other areas. Thus, this paper contributes a review on various deep learning approaches which include Convolution Neural Network (CNN), Deep Neural Network (DNN), Auto-Encoder (AE), Recurrent Neural Network (RNN) enclosed with Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) and ConvLSTM, Deep Reinforcement Learning (DRL), Generative based Adversarial Network (GAN) and Deep Belief Network (DBN). For extraction of the most important features of an image various data extraction procedures have designed. CNN&#x0027;s tremendous learning capacity is based on the utilisation of several feature extraction stages that continuously learn from data. Some of the popular CNN architectures name as LeNet, AlexNet, ZFNet / Clarifai, VGGNET, GoogLeNet, ResNet, DenseNet, FractalNet and CapsuleNet. Recent works extended to a combination of two CNN models like Inception, ResNetV2 to attained different existing approaches consequences in deep learning. Identification of the significant objects, their parameters and relationships of an object images are required for image captioning. Syntactically and semantically correct sentences need to be generated. The intricacies and problems of picture captioning may be handled using deep learning approaches. A comparison between these models was also presented. We also discussed about different standard datasets which are utilized for executing and estimating the deep learning approaches.
A Robust, Lightweight Deep Learning Approach for Detection and Mitigation of DDoS Attacks in SDN

SDNs are considered as a new chance against malicious activities due to their better control and management capabilities. Despite this, SDNs suffer from a significant weakness. A central control plane in the SDN architecture is described as a single point of failure, and the controller can be a target of interest to attackers. Many defense frameworks have been introduced based on different methods to deal with DDoS attacks in SDNs, while deep learning models have shown good performance. However, there are two concerns about deep learning-based IDSs: speed of action and robustness against adversarial examples. It is essential that the IDS has a small processing overhead for the network and performs retrain and predict quickly. In this paper, we investigate two above discussed challenges. We consider two different scenarios; the IDS model training with a produced native dataset and the public CICIDS2018 dataset. For robustness, we first generated adversarial examples from the original dataset using white-box methods such as FGSM, FGSM-RS, Carlini-Wagner, and Black-Box methods such as WGANs. Then, we retrained the IDS model using this data. We considered a simple architecture for the IDS model for better performance. Furthermore, we consider a threshold that limits the algorithm running time according to the number of PACKET-IN messages. The results show that we have reached a good balance between speed of action, robustness, and accuracy.