Reviewerly/exHarmony huggingface dataset:
Design of Crane Virtual Simulation Teaching Assessment System

With the rapid development of cranes, crane teaching aids cannot follow up with new products in real time, and the teaching cost is high and the space utilization rate is low. In order to solve these problems, we used SolidWorks and 3ds Max to carry out mechanical modeling in the virtual environment. Based on the Unity3D development engine, we designed a virtual simulation teaching evaluation system for cranes, which completed the design of modules for cognitive teaching, maintenance, fault repairing and teaching assessment. The system takes advantage of virtual technology's features of immersion, interaction and imagination. Therefore, it solves the problem of the slow update of teaching aids in the teaching process of cranes effectively, saves teaching costs greatly, overcomes the traditional teaching boring shortcomings, and improves the efficiency of port machinery education. And It plays an important role in promoting the application of virtual reality technology in education and training.
QueryCrumbs search query history visualization – Usability, transparency and long-term usage

Models of human information seeking reveal that search, in particular ad-hoc retrieval, is non-linear and iterative. Despite these findings, today’s search user interfaces do not support non-linear navigation, like for example backtracking in time. We propose QueryCrumbs, a compact and easy-to-understand visualization for navigating the search query history supporting iterative query refinement. We apply a multi-layered interface design to support novices and first-time users as well as intermediate and expert users. The visualization is evaluated with novice users in a formative user study, with experts in a think aloud test and its usage in a long-term study with software logging. The formative evaluation showed that the interactions can be easily performed, and the visual encodings were well understood without instructions. Results indicate that QueryCrumbs can support users when searching for information in an iterative manner. The evaluation with experts showed that expert users can gain valuable insights into the back-end search engine by identifying specific patterns in the visualization. In a long-term usage study, we observed an uptake of the visualization, indicating that users deem QueryCrumbs beneficial for their search interactions.
Evaluating of Word Embeddings Hyper-parameters of the Master Data in Russian-Language Information Systems

Evaluating of word embeddings hyper-parameters for master data quality support task is conducted in this work. We introduce the structure and management of master data. We also describe a method of training the embeddings model for elements and methods for estimating the resulting vectors. Using a corpus of training and validation data sets – 264 thousand records, we have conducted an experiment on models with a different set of parameters and get results. Our vectors give good results in mapping and classification problems on specific industry texts in comparison with standard approaches. In conclusion, we present the main recommendations for the hyper-parameters setting in the task of management of master data for industry conditions. Our methods successfully using in the RPA-systems and in the Data Warehouse like the text analysis module.
Emerging regimes of value in distributed autonomous systems

Abstract This position article brings together perspectives from social sciences, computer science and economy to interrogate the emerging meanings of value produced by Distributed Autonomous Organizations (DAO). We explore this process in the context of the wider political economy enabled by Distributed Ledger Technologies (DLT) and Smart Contracts (SC). The article then questions the ways in which the current implementations of DAO reflect the various regimes of value and the emergent possibilities to rethink the social contract.
Parallelism as a universal principle of structuring information flows

The article is aimed at highlighting the fact of the presence of common features inherent in various types of creative conscious activity of people. It is exemplified by the notion of parallelism in their intellectual work, in the way of creating new educational products. In live communication, the reflection of the process parallelism is a powerful universal mechanism to facilitate the perception of information of an unfamiliar nature based on experience in the field of one's own professional interests. Using the ability to parallelize various information flows with an emphasis on general patterns is a means of streamlining a relatively large amount of information in order to ensure its understanding. The article discusses examples of parallelism inherent in human literary activity. Syntactic parallelism or concurrency, a stylistic device for figurative speech, refers to imaginative means in literature. Syntactic parallelism as manifested in similar construction of related phrases, poetic lines, etc., is illustrated by the works of English and American classical literature, e.g. those of William Shakespeare, Charles Dickens, John Fowles, Oscar Wilde, Edgar Poe, Robert Frost. In a natural connection with poetry is music, which is characterized by obvious manifestations of parallelism in various forms. Some of them allow algebraic formalization with the subsequent possibility of using the achievements of modern computer science including neural networks. In modern information society, an idea is considered useful if it can be adapted for computer implementation. This with the help of neural networks is presented on the example of recognition of musical fragments taking into account the parallelism of tonalities. The importance of a methodological approach to education, the components of which are logical and emotional principles, is emphasized.
VizCertify: A Framework for Secure Visual Data Exploration

Recently, there have been several proposals to develop visual recommendation systems. The most advanced systems aim to recommend visualizations, which help users to find new correlations or identify an interesting deviation based on the current context of the user's analysis. However, when recommending a visualization to a user, there is an inherent risk to visualize random fluctuations rather than solely true patterns: a problem largely ignored by current techniques. In this paper, we present VizCertify, a novel framework to improve the performance of visual recommendation systems by quantifying the statistical significance of recommended visualizations. The proposed methodology allows to control the probability of misleading visual recommendations using both classical statistical testing procedures and a novel application of the Vapnik Chervonenkis (VC) dimension towards visualization recommendation which results in an effective criterion to decide whether a recommendation corresponds to a true phenomenon or not.
«Perché noi s'ha da parlare toscano?». Note linguistiche sulle "Letture graduate" di Giulio Tarra

The article makes some comments on Giulio Tarra’s Letture graduate (published in 1862 and reissued in several editions in the following decades) and demonstrates his contributions in the field of spoken Italian didactics. Firstly, analyzing the teaching methodology of the author underlines the idea of a simple writing style, modeled on the «good language spoken in Tuscany». The study then focuses on the third volume of the textbook: the comparison between two different editions (1868 and 1886) allows the frequency of spoken expressions in the section Conversazioni e corrispondenze famigliari to be highlighted. Finally, the relationship between tradition and innovation in the other sections is also viewed from the same linguistic perspective. The article ends with an appendix dedicated to the probable influence of Tarra’s writings on Edmondo De Amicis’s Cuore.
Determining Crucial Factors for the Popularity of Scientific Articles

Using a set of over 70.000 records from PLOS One journal consisting of 37 lexical, sentiment and bibliographic variables we perform analysis backed with machine learning methods to predict the class of popularity of scientific papers defined by the number of times they have been viewed.Our study shows correlations among the features and recovers a threshold for the number of views that results in the best prediction outcomes in terms of Matthew's correlation coefficient.Moreover, by creating a variable importance plot for random forest classifier, we are able to reduce the number of features while keeping similar predictability and determine crucial factors responsible for the popularity.
Extended pointers for memory protection in single address space systems

With reference to a single address space paradigm of memory addressing, we identify a set of mechanisms aimed at preserving the integrity of the virtual space of a given process from erroneous or fraudulent access attempts originated from other processes. These mechanisms guarantee a level of protection that is, in many respects, superior to that of a traditional multiple address space environment. We introduce extended pointers as a generalization of the concept of a memory address, which includes a password and an access authorization. A universally known, parametric one-way function is used to assign passwords. A specialized address translation circuitry supports memory reference and protection at the hardware level. A small set of protection primitives forms the process interface of the protection system. An indication of the flexibility of the proposed protection paradigm is given by applying extended pointers to the solution of a variety of protection problems.
Your Voice Assistant Will See You Now: Reducing Complexity in Human and Artificial System Collaboration Using Voice as an Operating System

There is something about smart artificial systems that fascinates us. In recent decades we have seen researchers, technology experts, large technology companies and governments across the world investing significant time in getting artificial systems to be smarter, more interactive and easier to communicate with. Smart assistants are slowly becoming an integral part of our lives, but they are far from perfect. There are many concerns and questions that must be addressed about these assistants. How can we create the ideal version of this partnership? How can we break the barriers to communication with these systems? This paper will highlight how companies are addressing the issues with artificial systems by designing systems that are safer, more accurate and trustworthy.
Target Classification System Based on Target Detection for Students' Classroom Assessment

Classroom assessment is an evaluation system which combines deep learning techniques with traditional teaching theories. Deep learning is a kind of representation learning, which is used to extract feature. In this paper, the convolutional neural network (CNN) is used to extract the features of an image. The careful and careless states of the students are identified. All faces are located by a rectangular frame. This paper uses a real-time camera to collect training data and test data. The target detection model mainly includes YOLO (You Only Look Once) and Mask R-CNN. The former is mainly used for classification and calibration targets, and the latter is mainly used for semantic segmentation. This paper also uses a cross-platform software architecture, which includes the Android and the PC.
Design of Electrical Equipment Integration System for LNG Single-Fuel Bulk Carrier

This paper mainly designed the electrical equipment integrated system for LNG (Liquefied Natural Gas) single fuel bulk carrier, aiming at providing a solution scheme for realizing real-time, reliable and expandable communication between the ship and the shore. The core communication layer of the system adopts star-structure to communicate with the monitored equipment. According to the actual communication ability of electrical equipment, different communication configurations are made. Data access is accomplished by centralized communication protocol conversion through NX control components, which can solve the problem of communication matching under different communication protocols and transmission rates. In addition, for ship-shore communication, the 4G mobile communication links is configured. Under the special sections without mobile signal coverage, the network communication monitoring and data replenishment functions are designed to ensure the integrity and reliability of data remote transmission.
Joint Neural Collaborative Filtering with Basic Side Information

Nowadays, deep neural network has been greatly developed and widely used in many areas. However, the research of the deep neural network on recommendation system is inadequate. Most research focuses on analyzing the textual descriptions of items and comments of users, making use of the neural network to get feature vectors from texts or pictures. In this paper, we directly adopt the deep neural network to better fit the non-linear relationship of users and items and effectively integrate some side information (basic information and statistical information) into the neural network. Utilizing deep neural network, we explore the impact of some basic information on neural collaborative filtering. To the best of our knowledge, it is the first time to combine the basic information, statistical information and rating matrix by the deep neural network. Finally, we use the benchmark data set (MovieLens) to demonstrate the effectiveness of the proposed deep neural network model with side information.
Electric Bicycle Violation Automatic Detection in Unconstrained Scenarios

Object detection technology develops rapidly and has broad application prospects. There are few relevant researches on the detection of electric bicycle violation. It is of great practical significance to apply the object detection technology to the detection of electric bicycle violation. The main violations of electric bicycle are not wearing safety helmet, not install license plate, overload and so on. Use YOLOv3 to train the datasets of riding electric bike, safety helmet and license plate to detect electric bicycle violations; The technology of chineseocr is used to identify the electric bicycle license plate number. The experiment proves that the method presented in this paper has a high detection accuracy for the objects of riding electric bike, safety helmet and license plate, but the recognition accuracy for license plate number is a little less.
A Hessian Free Neural Networks Training Algorithm with Curvature Scaled Adaptive Momentum

In this paper we propose an algorithm for training neural network architectures, called Hessian Free algorithm with Curvature Scaled Adaptive Momentum (HF-CSAM). The algorithm’s weight update rule is similar to SGD with momentum but with two main differences arising from the formulation of the training task as a constrained optimization problem: (i) the momentum term is scaled with curvature information (in the form of the Hessian); (ii) the coefficients for the learning rate and the scaled momentum term are adaptively determined. The implementation of the algorithm requires minimal additional computations compared to a classical SGD with momentum iteration since no actual computation of the Hessian is needed, due to the algorithm’s requirement for computing only a Hessian-vector product. This product can be computed exactly and very efficiently within any modern computational graph framework such as, for example, Tensorflow. We report experiments with different neural network architectures trained on standard neural network benchmarks which demonstrate the efficiency of the proposed method.
TLS Certificates of the Tor Network and Their Distinctive Features

This article presents the results of an experimental study of the properties of SSL/TLS certificates of an anonymous Tor network, based on which it is concluded that there are several features that differ from other SSL/TLS certificates. At present, in the scientific literature and in the documentation of U.S. National Security Agency, and the U.K. Government Communications Headquarters devoted to the identification of Tor network traffic, two signs of SSL/TLS certificates are indicated - the name of the certificate subject, as well as the port of the certificate transmission and network connection. The results of an experimental study allow the authors to state with a high degree of probability that Tor network certificates can be identified in the data stream between the client and server of the specified network by their size, which is between 400 and 600 bytes. The list of features of the Tor network certificates is intended to develop software or add-ons to existing ones, which is used to block access of Internet users to Darknet resources or to limit the use of the Tor anonymous network service. Based on data on the distinguishing features of Tor network certificates, an algorithm is proposed for blocking access to the Internet for users of the Tor Bundle.
A New Signcryption Scheme without Hash or Redundant functions

Based on the intractable problem of discrete logarithm in ECC, this paper presents a new signcryption scheme without using Hash or redundant functions. This scheme has the public verifiability and forward security. Moreover, we analysis its correctness and security. Compared with Zhou scheme, the number of model index is reached the minimum zero time. This makes the scheme have higher security and wider applications.
Multi-Head Bidirectional Attention for MRC

Machine reading comprehension is a challenging task for natural language processing problem that usually requires both an in-depth understanding of complex interactions between the given context and the query and knowledge of the world. Reading comprehension problems require analysis of interconnected representations between context and query. SQuAD offers a large number of questions and their answers providing a testbed for evaluating machine comprehension algorithms. As the answers lie within the given context, we believe it is limiting and unnatural to represent the question or context with a single vector or apply single attention to model the interaction between the context and query. We propose an end-to-end neural multi-head bidirectional attention encoder architecture to understand the complex dependency between query and context. our experiments show a significant performance increase and faster convergence from the baseline model.
Computer-based Large-scale Construction Project Health Diagnosis System

A computer-based Large-scale Construction Project Health Diagnosis System (LCPHDS) was developed under the direction of diagnosis theory which is based on scientific evidence for diagnosing implementation status in construction projects. First, a brief introduction was given into diagnosis thought in medicine field; then, the health diagnosis theory was illustrated from four aspects of how to extract characteristics and relevant parameter variables, how to build a system of indexes and relevant weights, how to provide a set of judgment standards as references and how to illustrate the process of diagnostic computations. Programming technologies and developing tools like C#, SQL Server 2005 and C/S architecture were elaborated for a better understanding of the development environment of LCPHDS. In the end, the user interface (UI) and functional performance were demonstrated to exhibit the wholeness of the system. LCPHDS mainly helps managers or directors to analyze and supervise implementation status, especially quality, over the life-cycle. The proposed system has already been implemented by a construction company in Guangzhou for a trial run, and has made some effects.
Usability Analysis of an Academic Faculty Website: A Case Study of the Selected University

Abstract Website constitutes a pivotal element in an academic institution. It is useful to provide academic information for stakeholder requirements like final scores, academic calendar, scholarship, student competition, etc. This paper presents an investigation about the level of usability of the website at the selected faculty of the university in Indonesia based on 5 dimensions, namely learnability, efficiency, memorability, error, and satisfaction. It is noteworthy that the faculty should supply the information in detail and accurately. However, as a matter of fact, few of a faculty website in some certain universities sometimes experiencing matters for the user. Thus, it is crucial to do the analysis of the usability of the website at the faculty level. A qualitative study was conducted by distributing some Likert-scale questionnaire to stakeholders that consisted of students, academic staff, student’s parents, and lecturers. The questionnaire contains 25 statements divided into five groups of elements by referring five dimensions above with each group having 5 questions or statements. The non-parametric statistic was conducted to test the hypothesis. The result of this study shows that the website of the selected faculty is classified into a moderate level with 68% of usability grade. Thus, this study recommends that the faculty improve its website in the layout of menu, content, add some features to ease login and increase usability level.
NCTF Control Performance Analysis on Rehabilitation Robot

Abstract The need for rehabilitation robots is increasing, while the current price is too high. One reason for the high price of rehabilitation robots is the complicated controller design. For this reason, there is a need for a simpler controller design to be produced at an affordable price. In this study, the NCTF control system tested its performance on a rehabilitation robot prototype with a horizontal linear motion system. The tests are carried out using point to point and set point tracking commands. Point to point testing is carried out at a distance of 50 mm, 150 mm and 230 mm. Meanwhile, set point tracking testing was carried out on the sinusoidal wave with a 100 mm amplitude and a frequency of 0.25 Hz. Both tests were carried out by giving a load of 0 Kg, 0.82 Kg, and 1.64 Kg. The results of this study indicate that the NCTF control provides a good response. The steady-state error value for all point to point motion commands below 2%. As for set point tracking commands, the system can follow orders properly, but there is a 0.24-second delay.
Global and Local Deep Feature Representation Fusion for Vehicle Re-Identification

This paper introduces our submission to the Grand Challenges on Vehicle Re-Identification (ReID) held in the VCIP 2019. Vehicle Re-Identification, which aims to retrieve images of a query vehicle from a large-scale vehicle database, is of great significance to the urban security and city management. Although significant progress has been made in the last decade, vehicle ReID in the wild remains a very challenging problem due to the large intra-class variations of one vehicle instance from viewpoint, illumination, occlusion patterns, and the possibly small inter-class differentiation of different vehicle instances (e.g., two different vehicles of the same model and color). To deal with these challenges, we in this work present a vehicle ReID framework that integrates both the global visual cues along with the local part-based cues to learn discriminative feature representations. In addition, the proposed framework also makes use of the extra information such as brands, models and colors to further improve the performance. Experimental results are performed on the VCIP 2019 VehicleReID dataset and the proposed framework achieved the second place in the competition.