Reviewerly/exHarmony huggingface dataset:
Boosting Guided Probabilistic Ensemble-based Approach For Phishing Website Detection

Phishing is a cyber crime that targets naive online users to disclose their sensitive and personal information such as username, pin, password, social insurance numbers, credit card numbers, etc. To prevent such alarming event from taking place, an early detection of Phishing websites is significantly important and may keep users’ sensitive information safe. Thanks to the Machine Learning (ML) based techniques, there have already been automated methods available for detecting phishing websites. Although traditional ML approaches achieve success to some extent, there are still possibilities to improve the performance by introducing probabilistic approach in the solution. In this paper, we propose a probabilistic ensemble-based integrated solution that combines individual prediction probabilities from different classifiers and enhances the overall performance by introducing probabilistic estimation guided boosting classifier in the end. Additionally, we performed state-of-the-art data preprocessing methods to eliminate unwanted outliers and also, select statistically significant features from out data. Being evaluated with a benchmark dataset, we performed intensive experiments to verify the effectiveness of our proposed solution from which we obtained potential preliminary results.
Design of Higher Vocational Student Management System Based on Intelligent Optimization Algorithm

With the continuous development of China's social economy, the number of students in higher vocational colleges is increasing year by year. The traditional management model can no longer meet the requirements of modern education for talent training. In order to achieve this goal, it is necessary to establish an intelligent optimization algorithm to solve this problem. This paper mainly introduces the system design and analysis, model construction and related programs based on intelligent optimization algorithm. Firstly, it expounds the current situation and existing problems of management system in higher vocational colleges; Secondly, each module of the system is proposed from the perspective of software development, and the performance and function of the system are tested. Finally, the test results show that all modules of the system run normally, the ratio of using CPU is small, the response time is fast, and the amount of memory is small.
Easy-to-Implement Two-Server based Anonymous Communication with Simulation Security

Anonymous communication, that is secure end-to-end and unlinkable, plays a critical role in protecting user privacy by preventing service providers from using message metadata to discover communication links between any two users. Techniques, such as Mix-net, DC-net, time delay, cover traffic, Secure Multiparty Computation (SMC) and Private Information Retrieval, can be used to achieve anonymous communication. SMC-based approach generally offers stronger simulation based security guarantee. In this paper, we propose a simple and novel SMC approach to establishing anonymous communication, easily implementable with two non-colluding servers which have only communication and storage related capabilities. Our approach offers stronger security guarantee against malicious adversaries without incurring a great deal of extra computation. To show its practicality, we implemented our solutions using Chameleon Cloud to simulate the interactions among a million users, and extensive simulations were conducted to show message latency with various group sizes. Our approach is efficient for smaller group sizes and sub-group communication while preserving message integrity. Also, it does not have the message collision problem.
No-Reference Image Quality Assessment via Degenerate Decision Logic

In this study, a no-reference (NR) IQA method based on degenerate decision logic (DDL) is proposed. DDL uses wavelet transform to extract the wavelet sub-band features of images and then maps these features to higher- level features through a hybrid network, thereby achieving effective extraction of mixing characteristics and avoiding the redundant information of low level features. DDL also adds decision logic to the assessment, combining decision logic differentiation training and fine-tuning training to allow the assessment result to approach that of human subjective cognition as closely as possible. The performance of the proposed DDL method is evaluated on the LIVE database.
S2FCM: A Two-Stage Fine Classification Model for Students’ Attention Analysis

As the school’s surveillance cameras generate a large amount of video data every day, analyzing these video images to promote the development of intelligent campuses has become a research hot spot. By classifying the body postures, we can analyze the attention of students during the learning process. Many researchers have proposed methods to identify the posture of the human body in images. However, due to the occlusion and angle of the video data, these methods are not suitable for classroom images. In this paper, we propose S2FCM, a two-stage fine classification model to analyze the attention of students. We first propose a two-branch network to classify body posture. To deal with the occlusion in the image, we use the skeleton heat map as the input of the graph convolutional structure. In the second stage, we propose a position-robust network to classify the head posture. To solve the problems caused by the position difference, we design a position correction module. Experiments show that the macro-f1-score of our classification model is at least 20% higher than that of the other two general methods.
Research on Network White Traffic Filtering Based on Machine Learning

The Internet facilitates all aspects of people&#x2019;s lives, but due to the digitization of a large amount of important information, the network carries too much important information, and malicious people are using this to attack the Internet, resulting in increasingly serious security problems on the Internet. Although the detection technology based on full traffic can achieve full traffic collection and storage, full behavior analysis and full traffic backtracking, and extract network metadata and upload it to the big data analysis platform to achieve richer functions, in actual scenarios, a large number of unpacking actions are worthless. The study uses a multi-layer integrated learning model to study the filtering of white traffic, optimizes the model accuracy rate under the premise of ensuring performance, and through a large number of data experiments, the model accuracy reaches more than 98%, and shows a high processing efficiency.
Implementation of Cloud Component for Security Monitoring and Comprehensive Guarantee of Identifier Resolution System

Aiming at the fact that the existing security protection technology cannot effectively solve the security risks faced by enterprises accessing the identifier resolution system, the security monitoring comprehensive guarantee cloud component is developed to realize the functions of security protection, performance monitoring and high availability guarantee suitable for the identifier resolution system. Firstly, encapsulate the cloud component parameter configuration and management process. Then improve the security protection management mechanism. Next, design four modules which include service proxy, aggregation request, response analysis and log processing to monitor the system status. Finally, realize the service degradation technology based on business indicators. The experimental result indicates that the cloud component exhibits competitive performances in improving the security, observability and high availability, so that provide a strong guarantee for the stable and reliable operation of the identifier resolution system.
Stabilize the scale by geometry consistency in self-supervised monocular depth estimation

Limited by the difficulty to gather per-pixel ground truth depth data, self-supervised depth estimation suggests an alternative approach to take advantage of large-scale dataset of autonomous driving scenes. Compared to binocular or multi-view stereo methods, depth estimation on monocular videos appears easy to implement. However, the scale-ambiguity of monocular videos prevents the metric-scale outputs of depth value and causes scale-shift that limits further application such as SLAM. In this paper we attempt to stabilize the scale of depth map by geometry consistency. Experiments on KITTI dataset illustrate that our method gains progress both on the depth and the visual odometry tasks.
Lures for Money

YouTube hosts a wide variety of user-generated videos accessible to a global population of users. The potential of videos to persuade users by engaging them with the experience of the content creator makes them an attractive medium for promoting various kinds of products and services, including apps and websites. The Youtubers promoting these products online may not necessarily be aware of the harms to which they might expose potential users. In fact, they may neither have the incentive nor the technical expertise to look into potential harms.
Fashion Meets Bot: What Should the Bot Wear?

Intelligent bots are evolving with the development of artificial intelligence, especially the deep learning method. Many skills like semantic judgment, speech recognition, and text generation have been added, making bots more like real persons. The latest ones, such as Microsoft XiaoIce, Amazon Alexa, and Apple Siri, focus on enhancing general functionalities but still overlook the personality of the bot itself nevertheless, e.g., unchanging name and its virtual appearance. To further personalize the user experience, we desire to make the appearance of intelligent bots more diverse, i.e., appearing capable of autonomously changing its characteristic appearance according to users’ contexts like the changing geolocation. In this paper, we designe a personalized appearance transformation framework for the next generation intelligent bots. Specifically, Multi-modal crowd-intelligence technology is used for differential analysis of various regions, and generative adversarial network (GAN) is customized to render the bot appearance target domain. We also collecte new region-specific data sets from social media platforms, implement a fully-fledged prototype, and demonstratedthe effectiveness of our proposed framework.
EPID with Efficient Proof of Non-Revocation

EPID systems are anonymous authentication mechanisms which are standardized by ISO/IEC and massively deployed in Intel processors. They are related to the large family of privacy-preserving signatures but differ in that they provide a very pragmatic way of revoking members. Concretely, a member ℘ can be revoked by simply placing one of its signatures in a so-called signature revocation list SRL. Once this is done, every signer will have to include in its future signatures a proof that it has not generated any element of SRL, which implicitly revokes ℘.
Archival Visualization Based on Time Characteristics and Associations

With the rapid growth of information technology, the number of digital archives in various fields of social life is also growth. Since archive data contains a lot of information, how to mine hidden information and exhibition the patterns underlying the documents in computer vision way has become a key problems. The archival visualization technology based on the combination of time characteristics and association relationships can dynamically link the digital archives with the same theme in the archive library. This paper proposes a visualization method for the analysis results of archives associations, which visualized the archives according to time characteristics and associations through interactive multi-level visualization, and displays the specific content of the archives from the perspective of the whole and individual. In experiment result, the actual case shows that the visualization method can improves the efficiency and quality of archives editing and studying.
IOAS: A Discrete Path Planning Algorithm for Mobile Robot

For the path planning problem of mobile robots in static environment, there are some problems that the planned path is not optimized enough and it is difficult to solve the environment models with narrow channels or traps. To solve these problems, this paper proposes an Iterative Obstacle Avoidance Search (IOAS) algorithm, which can quickly plan the optimal path and can be applied to various environment models. Firstly, a path network is established by IOAS algorithm, and the nature that the path network must contain the shortest path is proved, and then the Dijkstra algorithm is used to plan the shortest path. In the process of establishing the path network, for reducing the number of the edges, the algorithm uses the positions of the start point and the target point of the mobile robot and the obstacles information to construct the path network, and uses a method to bypass the obstacle when the path collides with them. Experimental results show that by using the path network, the IOAS algorithm can solve the shortest path, and it takes less time to solve the shortest path than using the full path network on large scale problems. At the same time, the IOAS algorithm is better than two state-of-the-art algorithms in the literature on the solving quality.
GouDa - generation of universal data sets

Data preparation is necessary to ensure data quality in machine learning-based decisions and data-driven systems. A variety of different tools exist to simplify this process. However, there is often a lack of suitable data sets to evaluate and compare existing tools and new research approaches. For this reason, we implemented GouDa, a tool for generating universal data sets. GouDa can be used to create data sets with arbitrary error types at arbitrary error rates. In addition to the data sets with automatically generated errors, ground truth is provided. Thus, GouDa can be used for the extensive analysis and evaluation of data preparation pipelines.
Adaptive Feature Fusion Based Cooperative 3D Object Detection for Autonomous Driving

In this paper, we focus on the collaborative 3D object detection problem in autonomous vehicle systems in which autonomous vehicles can improve their detection accuracy by aggregating the information received from spatially diverse sensors through wireless links. We propose a novel adaptive feature fusion based cooperative 3D object detection framework, which consists of feature transformation networks and an improved region proposal network. The framework learns to fuse features from different views to improve object detection accuracy on the autonomous vehicle. To evaluate the proposed method, we build a new synthetic dataset created in two driving scenarios (a Roundabout and a T-junction). Experiment analysis and results demonstrate that the proposed adaptive feature fusion approach performs better than two baseline approaches in terms of detection accuracy.
Certificateless Identification Schemes Using Supersingular Isogenies

Certificateless public key cryptography (CL-PKC) is a cryptosystem that lies between traditional public key cryptography (PKC) and identity-based cryptography (IBC). It not only removes the inherited key escrow property in IBC, but also is free from the problem of certificate management in PKC. In this paper, we propose a new certificateless identification (CLI) scheme that is based on isogenies between supersingular elliptic curves. Since supersingular isogeny problems are believed to be quantum-resistant, our scheme is post-quantum secure assuming hardness of some computational problems related to supersingular isogenies. Our scheme combines De Feo-Jao-Plût identification scheme and SQISign. Note that compared to all other post-quantum signatures, SQISign has the smallest signature and public key sizes combined, this feature benefits our scheme as well. To our best knowledge, our scheme is the first certificateless identification scheme that is based on supersingular isogenies problems. Moreover, it achieves trust level 3 of publickey cryptosystem.
Defrost Period Allocation with Flexible Intervals via Group Role Assignment

Timing hot gas defrosting is one of the commonly used methods of cold storage defrosting, it only needs to determine the time period interval and duration. However, this method is difficult to arrange a reasonable defrost time period according to the different power of the air cooler and the type of industrial power time period, resulting in a waste of electricity. Thereby, this paper tackle this issue using the Group Role Assignment (GRA) model to formalize the defrosting period allocation with flexible intervals problem (DPFI) of the air cooler. On the one hand, it innovatively uses periods instead of discrete individuals as agents for the allocation of defrosting periods. On the other hand, according to the actual frosting situation of the air cooler, different period intervals are adopted to defrost the air cooler. The focus of this article is to express the defrosting period interval constraint linearly. The formalization of GRA makes it easy to find a solution through integer programming (IP) using the PuLP package of Python. Large-scale simulation experiments verify the robustness and practicability of the proposed method. In addition, by comparing the power cost of defrosting at different period intervals and the same period interval, simulation experiments show that using flexible period intervals to defrost the air cooler can significantly reduce the power cost of defrosting.
Assessing Students' Speaking Ability Using Communicative Language Testing Approach on Secondary Level in the Pandemic Era

The objectives of this study are: (1) to find out what types of speaking skills in the communicative language testing approach do the teachers use on the secondary level; (2) and to find out which of the five types of speaking skills on the communication language test were most used by teachers during the pandemic. This study used the descriptive qualitative method. Respondents of this study were English teachers at the secondary level in Jakarta, which consisted of 20 teachers. The results of this study indicate that: first, the English teacher only uses performance to assess students' speaking ability in this pandemic era. Second, Assessing Students' Speaking Ability Using Communicative Language Testing Approach on a Secondary Level in the Pandemic Era is only carried out during the initial assessment stage and formative test. The implementation of the assessment of students' speaking abilities using a Communicative Language Testing Approach was applied when the teacher asked students to build a response or performance that is not just providing information. The teacher asks students to solve specific case problems (problem-solving). The teacher asks students to select the correct response that requires information and concepts so that they are more active in class because they must be able to bring information to solve problems based on the learning material that has been previously delivered. Third, teachers' perceptions are assessment using Communicative Language Testing is very good for improving students' speaking ability because students clearly show their work. Students can convey their ideas without fear. Students not only understand the theory but practice it in real life. On the other hand, the use of authentic assessment takes a long time.
An Improved Practical Byzantine Fault Tolerance Algorithm Based on Vague Sets and Random Numbers

Aiming at the problems of improper election of master nodes of the improved PBFT algorithm and the tendency of system centralization, an improved PBFT consensus algorithm based on vague sets and random numbers is proposed in this paper. First of all, vague sets are used not only considering the promotion effect brought by the positive vote to the node, but also considering the bad impact of the negative vote on the node, which can distinguish the reputation value of the node better. Then, we simplify the consistency protocol of the PBFT, this can reduce the complexity of communication. Finally, the master node is determined by the random number generation based on Proof of Vote (PoV). The random number is established with the signature and timestamp of the node, which increases the unpredictability of the master node number and ensures the security of the system. Through experiments on systems of different sizes, it is proved that the accuracy of distinguishing node reputation values is improved by 8% compared with the previous algorithms. It can also ensure that the nodes in all consensus node sets have the same probability of becoming master nodes.
Reimagining Places, Reconstructing Histories: Augmented Reality Art and Heritage Apps as an Ultimate Display

Abstract This article employs Ivan Sutherland’s 1965 concept of the Ultimate Display to discuss the experience generated by augmented reality art and cultural heritage. Analyzing projects that reimagine or reconstruct reality, the author discusses the social impact and cultural workings of these apps. The article shows how such apps can facilitate kinesthetic performances that intervene in established cultural narratives and open a window to new social realities. In this context, the author also introduces her project CHAR, an online collection of AR art and cultural heritage apps, which illustrates how Sutherland’s vision has been materialized through the mapping of AR-reconstructed realities.
The Social Composition of Students at Kharkov Imperial University in the Second Half of the 19th century

This paper examines an official document originally entitled 'The List of Students at Kharkov Imperial University' to provide an insight into the size, social composition, and religious affiliation of students and auditors at Kharkov Imperial University in the period 1840-1899.A glimpse is provided of the evolution of the title of this historical source through the period of its publication.An insight is also provided into the substantive and formal characteristics of this historical document, including its structure, format, and the nature of the data contained therein.A quantitative analysis of the data in the Lists helped determine the total number and composition of students enrolled in Kharkov Imperial University in the period under review.It was established that, depending on the date of publication, different versions of the List varied in the kind of data available on students and auditors at the University.The paper provides trend data on the composition of students within different departments and across the University as a whole.It was established that by the late 19 th century the estate composition of the student body at Kharkov Imperial University had changed, with similar processes taking place in universities across the Russian Empire.This trend was associated with certain common processes that were taking place across the Russian Empire.The paper also describes the way education in the Russian Empire transformed from an estate-based vocational education system to an estateless general education system.