Reviewerly/exHarmony huggingface dataset:
Adversarial net and its variants

With the rapid development of computer science, generative adversarial networks (GAN) have become increasingly important in machine learning. GAN is a machine learning model consisting of a generative network and discriminative network and it is a two-player game proposed by Goodfellow. GAN plays a significant role in real-life application, especially in the image to image translation, image synthesis, data augmentation, and image editing. However, GANs have several issues that have not been well addressed: non-convergence problem, collapse problem, and so on. This article provides a comprehensive overview and analysis of GAN. Firstly, the theory, architecture, and training procedure of the original GAN are introduced in detail. Moreover, we present several variants of GAN, including DCGAN, BEGAN, LSGAN, CIAGAN, and WGAN. We discuss the motivation and networks structures of these variants and list and summarize their advantages and difference. Finally, we provide readers with several evaluation metrics for GAN and point out future open research challenges.
Cross-Linguistic Similarity Evaluation Techniques Based on Deep Learning

For the cross-linguistic similarity problem, a twin network model with ordered neuron long- and short-term memory neural network as a subnet is proposed. The model fuses bilingual word embeddings and encodes the representation of input sequences by ordered neuron long- and short-term memory neural networks. Based on this, the distributed semantic vector representation of the sentences is jointly constructed by using the global modelling capability of the fully connected network for higher-order semantic extraction. The final output part is the similarity of the bilingual sentences and is optimized by optimizing the parameters of each layer in the framework. Multiple experiments on the dataset show that the model achieves 81.05% accuracy, which effectively improves the accuracy of text similarity and converges faster and improves the semantic text analysis to some extent.
Trend and Identification Analysis of Anti-investigation Behavior in Crime by Machine Learning Fusion Algorithm

In recent years, people’s living standards have gradually improved, and informatization has brought convenience, but it has also led to many criminal cases. Because the criminals’ criminal methods are diverse and constantly renovated, this feature is very prominent, and the illegal activities on the Internet are becoming more and more intense. Therefore, it is necessary to strengthen the research on the trend and identification of anti-investigation behavior in crime. The purpose of this paper is to study how to use machine learning fusion algorithms in the trend and identification of anti-investigation behavior in crime. This paper proposes a machine learning fusion algorithm and the basic conceptual knowledge of anti-investigation behavior in crime. The experimental results of this paper show that with the increasing number of criminal incidents, criminals’ means of committing crimes have also been improved. The anti-investigation capabilities of criminals have also become more sophisticated, which makes the work of law enforcement officers more difficult. The anti-investigation behavior of criminals in crime has many characteristics, among which the concealment reaches 54%-68%, and the virtuality reaches 68%-79%. It can be seen that the characteristics of criminals’ anti-investigation behavior provide a wider space for criminals to commit crimes. The advantage of the machine fusion algorithm is that the classification rules generated are easy to understand and have high accuracy, so it is very suitable for the classification and identification of anti-investigation behaviors in crimes. Therefore, it is urgent to use machine fusion algorithms to identify them.
A New Case-Mix Classification Method for Medical Insurance Payment

Rapidly rising medical expenses can be controlled by a well-designed medical insurance payment system with the ability to ensure the stability and development of medical insurance funds. At present, China is in the stage of exploring the reform of the medical insurance payment system. One of the significant tasks is to establish an appropriate reimbursement model for disease treatment expenses, so as to meet the needs of patients for medical services. In this paper, we propose a case-mixed decision tree method that considers the homogeneity within the same case subgroup as well as the heterogeneity between different case subgroups. The optimal case mix is determined by maximizing the inter-group difference and minimizing the intra-group difference. In order to handle the instability of the tree-based method with a small amount of data, we propose a multi-model ensemble decision tree method. This method first extracts and merges the inherent rules of the data by the stacking-based ensemble learning method, then generates a new sample set by aggregating the original data with the additional samples obtained by applying these rules, and finally trains the case-mix decision tree with the augmented dataset. The proposed method ensures the interpretability of the grouping rules and the stability of the grouping at the same time. The experimental results on real-world data demonstrate that our case-mix method can provide reasonable medical insurance payment standards and the appropriate medical insurance compensation payment for different patient groups.
Multi-scale Fourier descriptor based on arch features for shape retrieval

Improving retrieval accuracy and matching efficiency are the two main goals of shape retrieval. In order to improve the accuracy of shape retrieval and the efficiency of matching, it is necessary to design a compact and discriminative shape descriptor. The Multi-Scale Arch Height (MSAH) shape descriptor has good retrieval performance, but the description of the shape is still insufficient. Therefore, a multi-scale Fourier shape descriptor based on arch features is proposed. The proposed shape descriptor fully describes the global characteristics and local details of the shape. While maintaining high retrieval accuracy, it also has high computational efficiency. We tested the proposed descriptor on MPEG-7 image library. The results demonstrate that the performance of the proposed descriptor is better than the Multi-Scale Arch Height (MSAH) shape descriptor and other classic Fourier descriptors.
Semantics column

After an extended hiatus, the Semantics column is returning with this issue of the Newsletter. The subject of this quarter's column is Call-By-Push-Value , written by Paul B. Levy. CBPV is Paul's invention, and it's an interesting and powerful calculus that incorporates both the Call-By-Value and Call-By-Name calculi. Moreover, it's also simpler to describe, as Paul's column demonstrates. After a short introduction to set the stage, and an outline of relevant components of the pure functional programming -- i.e., the simply-typed lambda calculus, Paul describes CBPV and how it incorporates a wide range of computational effects. He then indicates how CBN and CBV each can be translated into CBPV.
Modeling design of university laboratory based on virtual reality technology

In order to improve the teaching effect of college experimental courses, this paper puts forward the modeling design method of College Laboratory Based on virtual reality technology, constructs the virtual model of college laboratory combined with virtual reality technology, constructs the spatial distance simulation algorithm of college laboratory, and optimizes the modeling steps of college laboratory. Finally, it is confirmed by experiments, The university laboratory model based on virtual reality technology has better effect in the process of practical application.
The Rising Battle for the Planet of the Apps

This chapter introduces mobile learning for individuals, groups, and macro-level mLearning for personal and professional development. The chapter offers practical application of theories to be leveraged within pedagogical and andragogical approaches. There are multiple layers of considerations offered in terms of context, content, and collaboration to optimize mLearning. There are more mobile devices in the world than people, and many more of the world's population already has some type of mobile phone, making it the most wide-spread technology and most common electronic device in people's hands. Tapping into this ubiquitous technology creates a wide array of educational possibilities. Hence, a mobile first learning design is crucial in personal, organizational, leadership, and professional development contexts to help bridge the gap between personal lives, schools, colleges, and the workplace. The chapter illuminates how mobile learning brings to life that learning is everywhere as a natural segue for ownership of learning and ripe for dynamic, interactive, educational engagement.
Culture coding - a method for diversifying artefact associations in design ideation

It can be claimed that technological systems are, in some ways, reflections of the designers’ way of thinking. These designs affect the behavior of the users and contribute to the reproduction of future designs, thus strengthening the existing human-technology relations. To address these issues critically, there is a need to challenge design ideation processes for generating a larger variety of design proposals that can contribute to varied users and user behavior. For this purpose, we propose a new method- Culture Coding, that can complement design ideation processes where generating, developing, and elaborating ideas is crucial. In this qualitative study, we explore the value of the proposed method in design by using design activities and a research-through-design approach. The experimental setup consists of two Design Cases where study participants contributed by taking part in two co-design workshops. The findings of the study indicate that Culture Coding may help to guide attention to new perspectives and challenge assumptions of the design context.
Secure Privacy-Oriented Location-Aware Network

IoT is an amalgamation of diverse devices. The system aims to overcome the infrastructure of the devices. The instruments communicate with each other to accomplish a task. The previous contribution aims in preserving privacy among the communicating devices. It supports formation of cluster, where the device chosen as a cluster head mediates the communication. The user with the devices will be able to post their queries to the untrusted server by camouflaging themselves. The untrusted server responds to the queries which are communicated to the users through the cluster head. Security is vital to the network. The attacks if detected at an early stage can conserve large amount of energy. The current proposal works to enhance reliability to the network by 4.96%, 1.31% enhancement in detecting the attacks, and conserves energy by 6.13% compared to the previous contribution.
Monocular 3D object detection with Pseudo-LiDAR confidence sampling and hierarchical geometric feature extraction in 6G network

The high bandwidth and low latency of 6G network technology enable the successful application of monocular 3D object detection on vehicle platforms. Monocular 3D-object-detection-based Pseudo-LiDAR is a low-cost, low-power solution compared to LiDAR solutions in the field of autonomous driving. However, this technique has some problems, i.e., (1) the poor quality of generated Pseudo-LiDAR point clouds resulting from the nonlinear error distribution of monocular depth estimation and (2) the weak representation capability of point cloud features due to neglecting global geometric structure features of point clouds existing in LiDAR-based 3D detection networks. Therefore, we proposed a Pseudo-LiDAR confidence sampling strategy and a hierarchical geometric feature extraction module for monocular 3D object detection. We first designed a point cloud confidence sampling strategy based on a 3D Gaussian distribution to assign small confidence to the points with great error in depth estimation and filter them out according to the confidence. Then, we present a hierarchical geometric feature extraction module by aggregating the local neighborhood features and a dual transformer to capture the global geometric features in the point cloud. Finally, our detection framework is based on PV-RCNN with high-quality Pseudo-LiDAR and enriched geometric features as input. From the experimental results, our method achieves satisfactory results in monocular 3D object detection.
A multi-center clustering algorithm based on mutual nearest neighbors for arbitrarily distributed data

Multi-center clustering algorithms have attracted the attention of researchers because they can deal with complex data sets more effectively. However, the reasonable determination of cluster centers and their number as well as the final clusters is a challenging problem. In order to solve this problem, we propose a multi-center clustering algorithm based on mutual nearest neighbors (briefly MC-MNN). Firstly, we design a center-point discovery algorithm based on mutual nearest neighbors, which can adaptively find center points without any parameters for data sets with different density distributions. Then, a sub-cluster discovery algorithm is designed based on the connection of center points. This algorithm can effectively utilize the role of multiple center points, and can effectively cluster non-convex data sets. Finally, we design a merging algorithm, which can effectively obtain final clusters based on the degree of overlapping and distance between sub-clusters. Compared with existing algorithms, the MC-MNN has four advantages: (1) It can automatically obtain center points by using the mutual nearest neighbors; (2) It runs without any parameters; (3) It can adaptively find the final number of clusters; (4) It can effectively cluster arbitrarily distributed data sets. Experiments show the effectiveness of the MC-MNN and its superiority is verified by comparing with five related algorithms.
Face Identification based on Informative Knowledge Distillation using Morphological Model

This manuscript deals with face detection and recognition with the help of a morphological-based Informative knowledge distillation model. It suggests a face prototype compression technique via discriminatory knowledge distillation, that can thoroughly study human potential with two simultaneous tasks, age estimation as well as face verification, for facial pictures captured at varying ages with greatly reduced information. The detailed and rigorous experimental study helps to understand the involvement of many variables such as gender, age gap, race, and age group. We propose Informative Knowledge Distillation Using Morphological Model (IKDMM) which can draw from current face patterns the most discriminatory facial attributes, which may be utilized to oversee the training process for face models with low resolution; and complete extensive tests to demonstrate that the compressed pattern is able to obtain an extreme high identification rate with comparative precision with the latest high-resolution face patterns.
Work-in-Progress: The vector of Increasing the Attractiveness of Modern Master Degree Programmes in Engineering Education at a Regional University

Despite a tiered system of higher education was introduced in Russia quite a long time ago, there still exist some problems with the training of masters, which reduces the attractiveness of most engineering master degree programmes. The paper introduces an approach to identify and evaluate the factors influencing the attractiveness of the master degree programmes for the students. The main contribution of this research is providing ways to increase, first, the master degree programme attractiveness and, second, the students&#x2019; motivation.
Restricted Hamming–Huffman trees

We study a special case of Hamming–Huffman trees, in which both data compression and data error detection are tackled on the same structure. Given a hypercube Q n of dimension n , we are interested in some aspects of its vertex neighborhoods. For a subset L of vertices of Q n , the neighborhood of L is defined as the union of the neighborhoods of the vertices of L . The minimum neighborhood problem is that of determining the minimum neighborhood cardinality over all those sets L . This is a well-known problem that has already been solved. Our interest lies in determining optimal Hamming–Huffman trees, a problem that remains open and which is related to minimum neighborhoods in Q n . In this work, we consider a restricted version of Hamming–Huffman trees, called [ k ]-HHT s, which admit symbol leaves in at most k different levels. We present an algorithm to build optimal [2]-HHT s. For uniform frequencies, we prove that an optimal HHT is always a [5]-HHT and that there exists an optimal HHT which is a [4]-HHT . Also, considering experimental results, we conjecture that there exists an optimal tree which is a [3]-HHT .
A Multicriteria English Teaching Decision Model Based on Deep Learning

In any stage of the teaching process, the teaching behavior depends on the decision of teachers. For English teachers, the English teaching decision level directly determines the course quality and the mining of learner potential. The existing studies only focus on a single teaching stage and involve one decision maker. The scientific level of decision is yet to be examined. Therefore, this paper develops a multicriteria English teaching decision (MCETD) model based on deep learning. Specifically, the author summarized the internalization and generation of MCETDs and expounded the generation mechanism of such decisions. Next, the problem of MCETD was described and mathematically modeled. After that, a neural network was constructed to weigh decision criteria and decision makers. In addition, the author explained how to comprehensively rank and generate MCETD schemes. Through experiments, the author obtained the information of decision matrix and preference ranking of decision schemes from decision makers and validated the effectiveness of the proposed model.
State-of-the-art on writing a literature review: An overview of types and components

In many academic fields, literature review has become an established research method of technical writing. In this process, it serves as a method for identifying relevant findings in a research area by synthesizing existing data, identifying knowledge gaps, and critically evaluating results. We systematically reviewed the literature on writing literature reviews and found that a number of papers on that topic has been published, but they do not include suggestions and guidelines for all typical components of a literature review. Therefore, this paper deals with the research question, what are the typical components of a literature review and what should they contain to achieve a high-quality literature review. This paper first explains the goals of literature reviews and then introduces the most common types of literature reviews. Afterwards, the main components are described, and methodological approaches of different authors are brought in. In addition, the goal-oriented process of individual components is presented. Thereby, the paper does not focus on a specific research area but takes an interdisciplinary approach to the given topic.
JSON SPARQL Application Profile for Linked Data

This paper presents the JSON SPARQL Application Profile for Linked Data which is a JSON-LD compliant file that can be used to describe applications powered by the SPARQL Event Processing Architecture. Thanks to a publish-subscribe broker built on top of a generic SPARQL endpoint, the architecture allows the development of distributed and context-aware applications based on interoperable microservices. The SPARQL Application Profile includes all the information needed to describe an application, like the SPARQL queries and updates used by each microservice as well as the parameters needed to connect to a broker instance. The paper presents the ontology that has been designed to represent the SPARQL Application Profile from which the JSON-LD context has been eventually extracted.
The SELENE Deep Learning Acceleration Framework for Safety-related Applications

The goal of the H2020 SELENE project is the development of a flexible computing platform for autonomous applications that includes built-in hardware support for safety. The SELENE computing platform is an open-source RISC-V heterogeneous multicore system-on-chip (SoC) that includes 6 NOEL-V RISC-V cores and artificial intelligence accelerators. In this paper, we describe the approach followed in the SELENE project to accelerate neural network inference processes. Our intermediate results show that both the FPGA and ASIC accel-erators provide real-time inference performance for the analyzed network models at a reasonable implementation cost.
Smart Classroom College English Listening Teaching System Based on Virtual Environment Technology

This article mainly studies the design and realization of English listening teaching system based on virtual environment technology. This paper designs and builds a virtual experimental operating platform to assist students in experimental learning. At the same time, according to database design methods, steps and database specifications, the database is designed, functional modules are divided, and the main interface of system operation is introduced in detail. The system adopts B/S architecture, interacts with the server through the browser, and the computing and processing functions are mainly completed by the server. This system uses black box testing to test and verify system functions. Complete the performance test of the system by checking the monitoring points in the performance test cases. Experimental data shows that more than 70% of students believe that they can learn and read English independently in the Cypris Club virtual language learning community.
XTENSTORE: Fast Shielded In-memory Key-Value Store on a Hybrid x86-FPGA System

We propose XtenStore, a system that extends the existing SGX-based secure in-memory key-value store with an external hardware accelerator in order to ensure comparable security guarantees with lower performance degradation. The accelerator is implemented on a commodity FPGA card that is readily connected with the x86 CPU via PCIe interconnect to form a hybrid x86-FPGA system. In comparison to the prior SGX-based work, XtenStore improves the throughput by 4–33x, and exhibits considerably shorter tail latency (>23x, 99th-percentile).