Reviewerly/exHarmony huggingface dataset:
BONNIE: Building Online Narratives from Noteworthy Interaction Events

Nowadays, we have access to data of unprecedented volume, high dimensionality, and complexity. To extract novel insights from such complex and dynamic data, we need effective and efficient strategies. One such strategy is to combine data analysis and visualization techniques, which are the essence of visual analytics applications. After the knowledge discovery process, a major challenge is to filter the essential information that has led to a discovery and to communicate the findings to other people, explaining the decisions they may have made based on the data. We propose to record and use the trace left by the exploratory data analysis, in the form of user interaction history, to aid this process. With the trace, users can choose the desired interaction steps and create a narrative, sharing the acquired knowledge with readers. To achieve our goal, we have developed the BONNIE ( Building Online Narratives from Noteworthy Interaction Events ) framework. BONNIE comprises a log model to register the interaction events, auxiliary code to help developers instrument their own code, and an environment to view users’ own interaction history and build narratives. This article presents our proposal for communicating discoveries in visual analytics applications, the BONNIE framework, and the studies we conducted to evaluate our solution. After two user studies (the first one focused on history visualization and the second one focused on narrative creation), our solution has showed to be promising, with mostly positive feedback and results from a Technology Acceptance Model ( TAM ) questionnaire.
Detecting the Trend of a Product by Online Reviews Using the Supervised Machine Learning

Online reviews become helpful while designing a product, it motivates consumers to post a review for the product. The fact that customer is satisfied by the product purchased by him/her can be understood by the review posted by him/her. If the review posted is positive and recommend further to purchase the product to other users, then the customer is satisfied else if negative review or comment is made and a warning experience is posted that means the customer is dissatisfied with the product purchase. MLgis at fastest growing pace in area of computer science, having numerous applications in different types of fields. Machine learning tools have capacity to self-learning patterns which has the ability to adapt and learn. As the amount of data is increasing and becoming easily available, so it’s a better option to state that smart analysis of data that is often considered as a key element for progressing technology. Users frequently make errors throughout analysis, when relationships are established among multiple features. The process begins with the pre-processing techniques for feature selection which includes elimination of stop words, tokenization, stemming and lower casing. In this paper basically two techniques are used: Naïve Bayes (NB) algorithm and optimized feature selection using Naïve Bayes. The paper compares the accuracy, recall, precision and f-measure of the model of NB and optimized feature selection using NB text classification algorithms on online reviews of products to predict its trend in market. The optimized feature selection plays an important part when working with the data mining algorithms; it is capable of reducing the ambiguity of the processor as the vector space of features are reduced. The results are discussed in the detailed manner comparing the algorithms performance metrics.
Design and analysis of a fair, efficient and stable multi-server adaptive streaming protocol

Due to the surging bandwidth demand for applications like 360° video streaming, hypertext transfer protocol (HTTP) adaptive video streaming has been extended to fetch contents from a single server to multiple servers. However, when players share a bottleneck link, it fails to hold three critical properties: fairness, efficiency, and stability. Effective algorithms have been proposed to tackle the problem in the single-server case. We revisit the three properties and show that they degenerate in multi-server streaming. In this paper, we propose MultiFast, a protocol that offers the best user experience by achieving fair, efficient, and stable multi-server video streaming. MultiFast consists of (1) a bitrate selection algorithm that ensures fairness with concurrent connections; (2) a connection management algorithm that ensures efficiency and stability by managing connections based on network conditions. Our real-world experiments show that MultiFast achieves the best performance in fairness, efficiency, and stability metrics, outperforming its closest alternative.
Experimental Evaluations of Parallel Tempering on an Ising Machine

Ising machines have recently attracted much attention because they are expected to solve combinatorial optimization problems efficiently. We focus on an Ising machine whose algorithm is based on parallel tempering (PT), and experimentally evaluate the performance of the Ising machine for MIN-CUT problems. Experimental results show that the Ising machine outperforms a famous graph partitioning solver in terms of the quality of solution and the time-to-target-solution.
Research on Named Entity Recognition Based on Text Data of Beijing Subway Accident

Safety is an essential theme of urban rail transportation. For the safety of urban rail transportation, the relevant companies have developed and implemented a large number of safety-themed services, these services generated a lot of business information. This information is unstructured data such as text, voice, graphics and image, and most of this information is based on text. Extracting valuable information which from a great deal of text is an effective using of data. In this paper, based on the acquired text of urban rail traffic accidents, text analysis technology is used to extract features, and a BERT-BiLSTM-CRF model for entity recognition in the field is constructed. The experiment selected almost 1,200 open source Beijing subway accident texts obtained by the crawler, and identified four types of entities such as time, site, route, and the causes of the accident. The results show that the constructed model can effectively identify the entities in the accident text and achieve good results. The accuracy, recall, and F1 value of entity recognition are 93.18%, 92.68% and 92.93%, respectively. Compared with LSTM-CRF model and BiLSTM-CRF model, the proposed model has certain advantages.
Automatically Assessing Complexity of Contributions to Git Repositories

Lehman’s second law of software evolution suggests that under certain conditions software “becomes more difficult to evolve”. Similarly, Technical Debt (TD) is often considered as technical compromises that render future changes of software more costly. But how does one actually assess if modifying software becomes more difficult or costly? So far research studied this question indirectly by assessing internal structural complexity of successive software versions arguing that increasing internal complexity renders evolution tasks more difficult and costly too. Our goal is to assess complexity of evolution tasks directly. Therefore, we present an algorithm and tool that allows to automatically assess Contribution Complexity (CC), which is the complexity of a contribution respecting difficulty of integration work. Our initial evaluation suggests that our proposed algorithm and readily available tool are suitable to automatically assess complexity of contributions to software in Git repositories and the results of applying it on 8 686 contributions to two open-source systems indicate that evolution tasks actually become slightly more difficult.
Performance Evaluation of Multi-class Sentiment Classification Using Deep Neural Network Models Optimised for Binary Classification

This paper presents a comparative study on the performance of binary- and multi-class Deep Neural Network classification models that have been trained with the optimized hyperparameters. Four sequential models are developed using Tensorflow and Keras to perform sentiment classification of product reviews posted on Amazon, IMDb, Twitter and Yelp. Preprocessing is carried out to cleanse text reviews and to extract informative features. 5-fold cross evaluation results demonstrate that the final multiclass model perform with an accuracy of 74.7% and 72.4%, whereas the binary model achieves of 80.0% and 69.5% indicating better performance. The conclusion of this study is that an optimized binary model architecture can be used to train multiclass classification models and save significant amounts of computing time.
Research on AUV Integrated Navigation Method Based on Improved Particle Filter Algorithm

In view of the problem that GPS navigation equipment cannot be used underwater, the integrated navigation method which is based on the strap-down inertial navigation system (SINS) and long-base line (LBL) positioning system with buoys network are researched in this paper. The mathematical model of SINS/LBL integrated navigation system is established, and the integrated navigation method which is based on Rao—Blackwellised adaptive particle is researched, which dynamically reduces the particle number through the Euclidean distance and the cost function between particles. The simulation analysis of SINS/LBL integrated navigation shows that, compared with the standard particle filter algorithm, the Rao—Blackwellised adaptive particle filter algorithm is higher accuracy in position error estimation, and the number of particles is less, so it can be solved the state estimation problem of high-dimensional integrated navigation system.
Code Summarizer

Source Code Summarization implies generating summary in natural language from a given code snippet which can be helpful to developers for a platitude of reasons like Knowledge Training, to understand in brief about a newly imported project, to maintain precise summaries on the evolution of source code (using git history), etc. Instead of using state-of-art approaches like RNN and CNN, we propose an alternative approach that uses UAST (Universal Abstract Syntax Tree) of the source code to generate tokens and then use the Transformer model with self-attention mechanism that uses encoder-decoder, which unlike RNN method is helpful for capturing long-range dependencies. We have considered Java code snippets for generating the code summaries.
Performance Analysis of Modulation Classification Using Machine learning

Automatic modulation classification is used to identify the modulation scheme of the received signal, without prior knowledge of system parameters. In this work, we compare the performance of modulation classification in additive white gaussian noise channel using a conventional method and a deep learning-based method. Firstly, we classified the modulation schemes using a likelihood-based classifier. Another classifier is also implemented by exploiting the estimated probability density function. Next, a feature-based learning technique using a feedforward neural network was executed. We have analyzed this for digital modulation schemes like BPSK, QPSK, and 16-QAM. The performance of each modulation classification technique in different signal-to-noise ratios is tabulated.
Experimental Design of Artificial Neural-Network Solutions for Traffic Sign Recognition

Object recognition is a large application area of Machine Learning (ML) aiming to design solutions to autonomous driving capable to accurately identify traffic signs. Such applications have to minimise risks of making incorrect decisions and must have the highest recognition accuracy. In this paper we explore and compare ML methods such as Artificial Neural Networks and Convolutional Neural Networks on German Traffic Sign Recognition Benchmark. Based on the experiments we conclude that ML methods require massive experiments in order to maximise the performance and particularly in our case of study to achieve almost 100% recognition accuracy.
Volume adaptive under-actuated pickup based on self-tuning principle of trailer body

At present, the electromechanical system which is compatible with all kinds of ball picking has no practical application value and low flexibility, and gradually becomes the Short Board of the stadium management automation. This paper presents a volume adaptive under-actuated pickup device based on the self-tuning principle of trailer body. The volume is always selfa-daptively maintained at the minimum. Based on the semi-trailer steering principle, a 360 ° self-tuning steering can be achieved by dragging the pickup on the chassis to adjust the collection port orientation rapidly. The whole car only two steering gear control chassis rotation, small range of rotation to achieve direction adjustment, large range of rotation through screw nut to control the volume of ball pickup, the final realization can be deep into the gap to pick up different kinds of spheres.
Forward Selfies

Taking selfies is a common practice for smartphone users. Simultaneously capturing oneself and the desired background is not a trivial task, because it is often not possible to get a good view of both. Moreover, users often loose attention of their surroundings, thus taking a selfie also showed to lead to serious injuries. To ease the process of capturing selfies and to make it more safe, this work proposes forward selfies as a simple yet effective concept to account for both, risk and challenges. Forward selfies seamlessly combine images of the front-facing and the rear-facing smartphone camera. We propose a mobile app that builds on this concept and implements the selfie synthesis in a post-processing image composition stage. Thereby, we can take advantage of the commonly more advanced back-camera hardware, i.e., providing higher image resolutions, larger field of views, and different perspectives. Finally, we leverage built-in camera optimizations for independently (de-)focusing objects at different distances, such as for persons and backgrounds. We conclude that the concept of forward selfies can effectively address and solve certain challenges of capturing selfies, which we demonstrate by a simple app user interface.
Self-Adaptive Semantics Masking for Text Classification

Text classification is a typical problem in NLP, which is to classify a given text into a certain category. In this article, we propose a classification-based training method, which is mainly based on the input text adaptive masking, and then further pre-training and fine-tuning training. This training method makes it easier to obtain the semantic features of the text, and thus can improve the semantic learning ability of the model, and thus can improve the classification effect. Experiments show that compared with other classic methods, this method can now improve the classification performance.
Towards Financial Sentiment Analysis in a South African Landscape

Sentiment analysis as a sub-field of natural language processing has received increased attention in the past decade enabling organisations to more effectively manage their reputation through online media monitoring. Many drivers impact reputation, however, this thesis focuses only the aspect of financial performance and explores the gap with regards to financial sentiment analysis in a South African context. Results showed that pre-trained sentiment analysers are least effective for this task and that traditional lexicon-based and machine learning approaches are best suited to predict financial sentiment of news articles. The evaluated methods produced accuracies of 84%–94%. The predicted sentiments correlated quite well with share price and highlighted the potential use of sentiment as an indicator of financial performance. A main contribution of the study was updating an existing sentiment dictionary for financial sentiment analysis. Model generalisation was less acceptable due to the limited amount of training data used. Future work includes expanding the data set to improve general usability and contribute to an open-source financial sentiment analyser for South African data.
A Novel O2O E-commerce Model based on Blockchain

In order to improve the effectiveness of the O2O e-commerce model, the paper presents a novel O2O e-commerce model based on block chain. This paper analyzes the mechanism of blockchain technology to solve the information asymmetry between multiple parties, and gives the O2O e-commerce platform architecture based on blockchain technology, and deeply explores the application of blockchain technology in O2O e-commerce field. The validity of the new method is verified by simulation.
An Intelligent Speed-Suggestion Planner for Coverage Path with Multiple Constraints

In this paper, we consider various constraints to generate a recommended speed profile for the covered path of the mobile robot, and use the recommended speed profile as the control input to complete trajectory tracking. These speeds are affected by the constraints of the robot's kinematics and dynamics model and the constraints of the robot's environment. The speed planner is used in many scenarios of mobile robots, such as cleaning robots, disinfecting robots, etc., which can improve the safety and efficiency of tasks performed by robots. We introduce the commonly used traffic regulations in daily life into the generation of speed profile. In certain sections of highways or urban roads, there will be a road sign indicating the recommended speed of the section. And our paper is to generate the speed of each point for the given path as the recommended speed for the robot to drive for reference. We use a dynamic window method to update the current environment in real-time. And calculate the influence of the environment in the window on the current speed. Then we use a pure pursuit method to track the given path, and we define a safety cost function to express the safety of the trajectory. We demonstrated the efficiency and security of this method by using a simulation platform.
Comparison of Different Methods

BBasic information related to the artificial neural network method is shown in Fig. 4.1. The predictors in this case are neural networks, the number of test examples is 1221 and the number of training examples is 2442
Targeting Experiences

Shook (2007) informed how Human experience is the ultimate source and justification for all knowledge. Experience itself has accumulated in human memory and culture, gradually producing the methods of intelligence called “reason” and “science.” Having a focus on such experiences as opening theme for this work communicates the ‘human-at-center’ perspectives of this volume. Scholarly perspectives are presented that illustrate across disciplines and cultures.
A Reconfigurable Permutation Based Address Encryption Architecture for Memory Security

Most of the existing memory encryption techniques in IoT devices are based on data encryption. The level of security increases at the cost of the increased encryption algorithm complexity, resulting in large power consumption and area overhead for high-security devices. In this paper, we take a significantly different approach to encrypt the device memory through address encryption. A reconfigurable architecture called Permutation based Address Encryption (PAE) is proposed, for the first time, to encrypt the device memory with minor hardware overhead and much shorter processing time. The architecture is synthesized in SMIC 40nm standard CMOS technology. Compared with Data Encryption Standard (DES), the proposed PAE achieves 16x encryption speed and 1.4x effective key length. When combined with the DES, the PAE+DES encryption outperforms existing hardware Advanced Encryption Standard (AES) with almost 2x in power efficiency, more than 1.5x in area efficiency and better security, making it a promising hardware encryption technique for IoT devices.
The log rolls on: Hybrid search with same-category targets and distractors

In a “hybrid search”, observers search for any of multiple targets stored in memory. Reaction time (RT) is typically a linear function of visual set size (VSS) and a logarithmic function of memory set size (MSS) (Wolfe, 2012). In previous experiments, targets and distractors were dissimilar objects taken from different categories, and, hence, they could be coarsely discriminated. Would logarithmic search through memory survive if targets and distractors were similar, thus requiring finer discrimination in memory? We compared search patterns when targets and distractors came from unrelated basic object categories to search when targets and distractors were different exemplars from the same category. Observers memorized 4-32 items and performed a pure memory search (Experiment 1: VSS=1, i.e., no visual search) or a hybrid search (Experiment 2: VSS=4 or 8). In both tasks, we found that search was slower and that RTxMSS functions were steeper in the “exemplar” search. However, the results were inconclusive regarding the shape of the RTxMSS function, because of insufficient VSS (Experiment 1) or a high error rate with large MSS (Experiment 2). In Experiment 3, to decrease the error rate, we employed a more engaging hybrid novelty search. On each trial, observers clicked on the one novel target among distractor objects, that had been seen at least once in previous trials (VSS=4). In this paradigm, MSS increases from trial to trial. 107 observers were tested for MSS=4-64. This task kept the error rate at a reasonable level (≤25% even at the highest MSS). Again, we found that the search among similar exemplars was slower than search among unrelated items. Critically, the shapes of the RTxMSS functions were logarithmic in both conditions. We conclude that the “logarithmic” character of memory search is robust and generalizes to the fine-resolution, same-category search.
An Efficient Road Surveillance Approach to Detect, Recognize &amp; Tracking Vehicles Using Deep Learning Methods

In the current scenario on the increasing number of motor vehicles day by day, so traffic regulation faces many challenges on intelligent road surveillance and governance, this is one of the important research areas in the artificial intelligence or deep learning. Among various technologies, computer vision and machine learning algorithms have the most efficient, as a huge vehicles video or image data on road is available for study. In this paper, we proposed computer vision-based an efficient approach to vehicle detection, recognition and Tracking. We merge with one-stage (YOLOv4) and two-stage (R-FCN) detectors methods to improve vehicle detection accuracy and speed results. Two-stage object detection methods provide high localization and object recognition precision, even as one-stage detectors achieve high inference and test speed. Deep-SORT tracker method applied for detects bounding boxes to estimate trajectories. We analyze the performance of the Mask RCNN benchmark, YOLOv3 and Proposed YOLOv4 + R-FCN on the UA-DETRAC dataset and study with certain parameters like Mean Average Precisions (mAP), Precision recall.
Framework Design of Talent Evaluation and Selection System in Colleges and Universities

In order to overcome the limitations of the university talent evaluation and selection system and to optimise the existing university talent evaluation and selection system this paper proposes a novel framework design for a university talent evaluation and selection system. The design solution is able to incorporate advanced science and technology, uses the PHP language and adopts a B/S framework as the architecture. In addition, the design solution adds new technical tools such as responsive layout and ThikPHP architecture. On top of this, the design solution also meets the needs of university teachers and students in several dimensions, such as business requirements and functional requirements. The experimental structure shows that the design is advanced and can effectively improve the objectivity of talent evaluation results and the scientific nature of talent selection in universities.