Reviewerly/exHarmony huggingface dataset:
Enterprise-Oriented Policy Push Algorithm

In the traditional collaborative filtering recommendation algorithm, the similarity calculation of users is only based on cosine similarity; in the rating prediction link, only the direct neighbors of users are used for prediction. Therefore, under the circumstance that the rating matrix of enterprises on policies is highly sparse, traditional collaborative filtering has the problem that it cannot accurately predict the attitudes of enterprises towards policies and implement policies to corresponding enterprises in a timely manner. This paper proposes an enterprise-oriented policy push algorithm, which incorporates the extreme attitudes and characteristics of enterprises into the similarity calculation process. When the rating matrix is highly sparse and cannot be predicted accurately by relying on direct neighbors, iterative prediction is performed by referring to indirect neighbors and using z-score to eliminate rating bias. The experiments are carried out on the enterprise-policy dataset collected in the article and the film-trust dataset commonly used in recommender systems. The experimental results show that the algorithm reduces the mean absolute error by 5.67% and 1.54% respectively compared with the iterative rating prediction algorithm, which shows that the algorithm has achieved good optimization in the recommendation accuracy.
Evaluating Software Diversity Based on Gadget Feature Analysis

Evaluating the security gains brought by software diversity is one key issue of software diversity research, but the existing software diversity evaluation methods are generally based on conventional code features and are relatively single, which are difficult to accurately reflect the security gains brought by software diversity. To solve these problems, from the perspective of return-oriented programming (ROP) attack, we present a software diversity evaluation method which integrates metrics for the quality and distribution of gadgets. Based on the proposed evaluation method and SpiderMonkey JavaScript engine, we implement a software diversity evaluation system for compiled languages and script languages. Diversity techniques with different granularities are used to test. The evaluation results show that the proposed evaluation method can accurately and comprehensively reflect the security gains brought by software diversity.
DDR-Defense: 3D Defense Network with a Detector, a Denoiser, and a Reformer

Recently, 3D deep neural networks have been fully developed and applied to many high-safety tasks. However, due to the uninterpretability of deep learning networks, adversarial examples can easily prompt a normally trained deep learning model to make wrong predictions. In this paper, we propose a new point cloud defense network named DDR-Defense, a framework for defending neural network classifiers against adversarial examples. DDR-Defense neither modifies the number of the points in the input samples nor the protected classifiers so that it can protect most classification models. DDR-Defense first distinguishes adversarial examples from normal examples through a reconstruction-based detector. The detector can prevent errors caused by processing the entire input samples, thereby improving the security of the defense network. For adversarial examples, we first use the statistical outlier removal (SOR) method for denoising, then use a reformer to rebuild them. In this paper, We design a new reformer based on FoldingNet and variational autoencoder, named Folding-VAE. We test DDR-Defense on the ModelNet40 dataset and find that it has a better defense effect than other existing 3D defense networks, especially in saliency maps attack and LG-GAN attack. The lightweight detector, denoiser, and reformer framework ensures the security and efficiency of 3D defense for most application scenarios. Our research will provide a basis for improving the robustness of deep learning models on 3D point clouds.
Multistage Multi-population Species Conservation Evolutionary Algorithm for Reversible Logic Circuit Synthesis

This paper aims to propose a multistage multi-population species conservation evolutionary algorithm for reversible logic circuit synthesis (MMSCEA_RLC), which substitutes the species updating schemes in our SCEA_RLC by a new population updating one. After deleting the species falling into evolutionary stagnation, SCEA_RLC reinitializes the species using random chromosome, whereas, MSSCEA_RLC redefines a new fitness function for a reinitialized population according to the seed of the deleting species. The population updating intends to conquer the side effect brought by parsimony pressure originally used to balance the object optimization and constraint violation decreasing in variable length representation, increase the feasible solution rate through a more detailed exploration and save the fitness evaluation. Dynamic tree-structured populations with species are formed through population updating. The solution composition is conducted from the root to a leaf. Experiments carried out over a series of benchmark problems hard for SCEA_RLC show the superior performance of MSSCEA_RLC.
Emotional Analysis and Comparison of Network Novel Review and Main Text

As the network grows in popularity, so does the popularity of network novels based on the network platform. Simultaneously, the network novel platform has amassed a huge number of network novels with unique network reviews-“Benzhangshuo”. The purpose of this article is to use text emotional analysis technology to extract and visualize emotional information in the main text and “Benzhangshuo”, as well as to assess their properties and their relationship. It can make recommendations for content development, retrieval channels, and network innovative recommendations. This study divides the emotion types in the network novel text and “Benzhangshuo” using an emotion analysis method based on an emotion dictionary. The experimental results demonstrate that the main text and “Benzhangshuo” in the popular novel Da Wang Rao Ming” have essentially the same emotion distribution principles, but the proportion of the two emotion distribution is different.
An Airport Surface Target Tracking Method Based on Improved Meanshif

To effectively solve the moving target losing problem of the change with background and tracking fast moving target in real-time airport surface video surveillance system, an automatic target tracking method based on improved Meanshift is proposed. Firstly, according to the analysis and research of the traditional Meanshift algorithm, motion vector is introduced to modify the candidate template center to lock target. In addition, color feature and edge feature is introduced to decrease the influence of changing surroundings or similar color. Finally, moving target is tracked accurately in real-time video surveillance system. The experiment on multiple targets (i.e. pedestrians and aircraft) in airport surface different surveillance scene shows that the proposed method can not only track moving target continuously and accurately, but also be applied in the foggy low visibility conditions.
Adaptive Track Fusion Method Based on Uncertainty

In the complex battlefield environment, the radar is subject to multiple types of interference, and the measurement accuracy fluctuates and changes abruptly. Because of the instability of radar measurement ability in the actual environment, an adaptive track fusion method based on uncertainty is proposed, which uses the measurement wave gate to judge the current measurement ability of each radar. When the radar accuracy decreases due to interference, the uncertainty is proposed to measure the real-time error level of the track, and the uncertainty is used as the weight to participate in the fusion, to effectively resist the interference in the complex environment. Simulation results show that the algorithm has good scene adaptability and can always maintain high track fusion accuracy in different interference environments.
Identity-Based Public Auditing with Tag Compression

The benefits of high efficiency and robust scalability are found in cloud storage. Users can share data with others and reduce the cost of local storage by using cloud storage. Users may lose physical control of the data once it is stored on a cloud server, so a method must be in place to guarantee the integrity of the data there. Shacham and Waters proposed a cloud auditing system in 2008 that cut the cost of tag storage in half. Susilo et al. recently put forth a new scheme for weighing tags and storage expenses that further condenses tag storage costs. In this paper, we present a tags-compressed identity-based cloud auditing scheme. The scheme can also be guaranteed to be secure under the random oracle model as it is based on a general construction. Additionally, the scheme uses an identity-based cryptosystem, which can help with the key escrow issue. Furthermore, we show how to retrofit current identity-based cloud auditing schemes to decrease their tag sizes and increase tag storage effectiveness. It primarily contains how to process generated and ungenerated tags to reduce tag sizes.
A High-Performance Hardware Accelerator for Sparse Convolutional Neural Network on FPGA

Recently convolutional neural networks (CNNs) have developed rapidly and shown excellent performance in a wide range of applications. The design of hardware accelerators for convolutional neural networks has attracted great attention. With the number of layers in a CNN network deepens, the amount of model computation increases. The high computation and memory requirements can limit the deployment of CNNs on resource-limited hardware accelerators such as FPGAs, and it is urgent to use pruning technology to compress network models for easy deployment on FPGAs. In this paper, we use a sparse way based on the combination of convolutional kernel pruning and weight pruning to compress CNN models, together with 16-bit fixed-point quantization, skip zero-value processing, and independent multiply-and-accumulate (MAC) modules to implement a FPGA-based high-performance hardware accelerator design for sparse convolutional neural networks. The LeNet-5 network is implemented as an example on the Xilinx Virtex VC-707 FPGA board, and the experimental result from inference of 10,000 images of the MNIST test set shows that when the network sparsity is 87.46%, the inference time is 82.43% less than the CPU, the energy efficiency ratio is 264.85 times that implemented on the CPU.
Efficient Two-Party Privacy-Preserving Protocols for Activation Functions

This paper investigates secure and efficient implementation of the activation functions - sigmoid and tanh in a two-party environment. Specifically, first, we propose a new two-party secure multiplication protocol, where the product of two multiplicative secret shares is re-shared as an additive secret shares. Next, we show that the communication complexity of the SecureNLP system is improved by our new protocol. Finally, experimental results show that our new protocol has lower communication rounds and less protocol runtime than the already existing ones in the literature.
A Framework for Context-Sensitive Control-Loops with Roles

Control loops for self-adaptation are one approach to reduce the complexity of information technology systems by splitting a system into a managing and a managed part. While the managing part comprises the actual domain-specific application logic, the managed part acts as its overseer by monitoring sensors, analyzing operational data, as well as planning and executing corrections in case the application logic does not function as expected. To date, two challenges remain in this area. First, in most cases, the managing part and the managed part were specifically designed to work together, leaving little room for replacing the managed part without redesigning and reimplementing the managing part. As a result, the managing functions are tied to single application domains and require a large amount of research and development to apply self-adaptation to new application domains. Second, even within the same application domain, small changes in application logic often prohibit the reuse of the same management logic. In this chapter, we propose a solution to both challenges. First, we introduce a framework for creating a control-loop based managing system that is capable of managing systems from different application domains. Second, by applying the concept of roles in our framework, we provide a basis for developers to flexibly add or adjust features in the existing code when small changes occur in the managed system, or to replace entire parts of the management functions when large parts of the managed system change. We discuss the implementation of our framework in different scenarios as well as the application of our framework to different use cases within different application domains.
Self-driving Cars

Self-Driving Cars, also so-called Unmanned Vehicles (UmV): what is about their appearance in mid-term? In February 2018, UNECE's Inland Transport Committee (ITC) acknowledged the importance of the Working Party 29 (GRVA) activities related to automated, autonomous and connected vehicles: Automated, Autonomous and Connected Vehicles. We would like to discuss technical, ethical and legal aspects of the technology which domineering and bold emerge in the era of Digital Transformation. The work contains a brief survey and some case studies.
RESERVE VENDING MACHINE FOOD WASTE SEBAGAI DEPOSITO MELALUI QRIS BANK SAMPAH RUMAH TANGGA

The problem of waste is one of the things that need to be considered by society, especially food waste. Household waste management has begun to be carried out using the Reduce, Reuse and Recycle (3R) method but has not yet used technology. By using Finite State Automata (FSA) which is the basic concept of the Vending Machine (VM), waste management can be more economically by converting waste into balances for Rubbish Bank customers which of course becomes income for the society. In the payment method, QRIS applies where the customer does not get cash but immediately gets the balance which will go into the customer's own account. This implementation certainly reduces the amount of waste because it applies paper less and cashless. In this Reserve Vending Machine Food Waste process using Non-Deterministic Finite Automata (NFA).
Pelatihan Pembuatan Video Pembelajaran Menggunakan Smartphone bagi Guru MTs Nurul Huda Paowan Situbondo

Training in Developing Instructional Videos for teachers at MTs Nurul Huda Paowan. During the pandemic, the learning process at MTS Nurul Huda experienced a decline in the quality of learning. This is because the learning system has changed from conventional to online. The limited online learning facilities owned by students make teachers at MTs Nurul Huda not optimal in carrying out learning. The use of learning media for online learning has never been carried out because teachers did not have a good understanding of teaching media and lack the skills to make instructional videos as appropriate teaching media during the pandemic. Therefore, we tried to offer solutions to overcome these problems by providing training on making learning videos using smartphones for MTS teachers Nurul Huda. The implementation of this activity was carried out for approximately a month with the following details: counseling, two training activities, and evaluation. This training utilized the Kinemaster application as an instructional video maker tool for teachers. The evaluation was carried out in two ways, with participants and with team members of the activity. The result of this activity is an increase in teachers' understanding of the importance of teaching media which is marked by their high interest and enthusiasm during the training. In addition, this activity also resulted in an increase in the quality and professionalism of teachers which was marked by the instructional videos they produced.
Pelatihan dan Pendampingan Penyusunan Penelitian Tindakan Kelas bagi Guru SDIT Bilingual Alma Asy Syauqi Kota Cirebon

TRAINING AND ASSISTANCE IN THE PREPARATION OF CLASSROOM ACTION RESEARCH FOR ALMA ASY SYAUQI BILINGUAL ISLAMIC ELEMENTARY SCHOOL TEACHERS, CIREBON CITY. Training and Assistance in the Preparation of Classroom Action Research for Bilingual IT Elementary School Teachers Alma Asy Syauqi, Cirebon City. The purpose of this activity is to provide teachers with an understanding of classroom action research and to train teachers to develop classroom action research. The number of participants in this activity was six teachers. This activity was carried out for approximately three weeks from November 5 to November 22, 2021 at the IT Bilingual Elementary School Alma Asy Syauqi, Cirebon City. The implementation of this activity has several steps including providing material on the basic concepts of classroom action research, models of classroom action research, the importance of classroom action research, and systematics of classroom action research. The next step is for the trainees to practice using the Mendeley application for technical citation and bibliography writing. Then practice compiling a class action research proposal. The methods used in this training activity are expository methods, lectures, questions and answers, discussions, and assignments to make a class action research proposal. The result of this activity is that the teachers have a good understanding of CAR and are able to prepare a class action research proposal well. The enthusiasm of the teachers in the implementation of the training activities was very good and this activity received a positive response from the head of the foundation, principals, and teachers who participated in the training.
EDGAR: Embedded Detection of Gunshots by AI in Real-time

Electronic shot counters allow armourers to perform preventive and predictive maintenance based on quantitative measurements, improving reliability, reducing the frequency of accidents, and reducing maintenance costs. To answer a market pressure for both low lead time to market and increased customisation, we aim to solve the shot detection and shot counting problem in a generic way through machine learning. In this study, we describe a method allowing one to construct a dataset with minimal labelling effort by only requiring the total number of shots fired in a time series. To our knowledge, this is the first study to propose a technique, based on learning from label proportions, that is able to exploit these weak labels to derive an instance-level classifier able to solve the counting problem and the more general discrimination problem. We also show that this technique can be deployed in heavily constrained microcontrollers while still providing hard real-time (<100 ms) inference. We evaluate our technique against a state-of-the-art unsupervised algorithm and show a sizeable improvement, suggesting that the information from the weak labels is successfully leveraged. Finally, we evaluate our technique against human-generated state-of-the-art algorithms and show that it provides comparable performance and significantly outperforms them in some offline and real-world benchmarks.
A Neuron Network Learning Algorithm for the Recognition of Fractal Image and the Restoration of Their Quality

The chapter discusses the results of a neural network learning algorithm for recognizing fractal images of the “Fractal dust” type (Kantor's set). This algorithm allows you to restore a fractal image with low quality. A generated set of matrices of the randomized system of iterative functions (RSIF), and fractals built on these matrices were used for training the neural network Mathematical formulas and schemes for calculating and constructing a fractal image and recognition are derived. This algorithm consists of finding RSIF for an existing fractal with different image quality. A learnt neural network makes it possible to compress an existing fractal image to a small set of coefficients. With their help you can reproduce an image of any format (resolution), as well as improve (distort) the quality of the image. The use of the algorithm in the future will make it possible to develop an automated library which can be integrated for graphic editors, data protection, information compression and improvement of display quality.
Not so fast! Data temporalities in law enforcement and border control

In this paper, we investigate the temporal implications of data in law enforcement and border control. We start from the assumption that the velocity of knowledge and action is defined by heterogeneous formations and interactions of various actors, sites, and materials. To analyze these formations and interactions, we introduce and unpack the concept of “data temporality.” Data temporality explicates how the speed of knowledge and action in datafied environments unfolds in close correspondence with (1) variegated social rhythms, (2) technological inscriptions, and (3) the balancing of speed with other priorities. Specifically, we use the notion of data temporality as a heuristic tool to explore the entanglements of data and time within two case studies: Frontex’ Joint Operation Reporting Application and the predictive policing software PRECOBS. The analysis identifies two key themes in the empirical constitution of data temporalities. The first one pertains to the creation of events as reference points for temporally situated knowledge and action. And the second one pertains to timing and actionability, that is, the question of when interventions based on data analysis should be triggered.
Feedback Analysis of Online Teaching Using SVM

Study examined how we can upgrade the quality of online teaching (Feng and Bienkowski in Enhancing teaching and learning through educational data mining and learning analytics. Department of Education, Office of Educational Technology [Feng M, Bienkowski M (2012) Enhancing teaching and learning through educational data mining and learning analytics. Department of Education, Office of Educational Technology]) in the upcoming years. Data were collected through the Google form which were filled up by the students. Feedback is the most important attribute of assessment as it provides students with a statement of their learning and advises how to improve. Result was helping to enhance the quality of online teaching in educational system and also provide the constituent which helps in improving the online teaching. We will use sentiment analysis (Kumar and Nezhurina in Sentiment analysis on tweets for trains using machine learning. Research Gate, p 10 [Kumar S, Nezhurina MI (2020) Sentiment analysis on tweets for trains using machine learning. Research Gate, p 10]) also.
Agenda-Based Automated Negotiation Through Utility Decomposition

The success of a negotiation depends mainly on the strategies of the negotiators and the problem domain. It is common for negotiators to rely on an agenda to simplify the process and reach better deals. This is particularly true when the negotiators’ preferences are defined over multiple issues. Using an agenda to explore and decompose the interdependencies between the issues is one way to address this problem. This paper applies the classical divide-and-conquer approach to automated negotiations through utility decomposition and bottom-up agenda construction. The approach does not impose an agenda from the top level of the negotiations but builds it bottom-up, given the individual utility functions of the agents and the relationships between the issues. We implemented our method in a novel protocol called the Decomposable Alternating Offers Protocol (DAOP). The protocol reduces the cost of exploring the utility spaces of the agents and the generation of optimal bids. As a result, the divide-and-conquer algorithm positively influences the global performance of an automated negotiation system.