Reviewerly/exHarmony huggingface dataset:
Self-design in development of future specialist’s readiness for professional growth

The exclusive role of self-design enables life and professional planning in adolescence. The basis of the qualitative preparation of a future specialist for professional activity is a set of special tools and methods integrated into the system of formation of a person’s readiness to carry out professional development processes. Self-designing is a kind of self-education and self-realization program that has goals and contains a description of the means to achieve them. This allows to actively and consciously build a professional perspective career. The process of self-designing in relation to the construction of professional prospects is a circular sequence of interrelated stages. Among named components of the readiness for successful professional activity, there are theoretical readiness, psychological readiness, practical readiness. The readiness of the individual for self-improvement is fundamental for a professional career and further professional growth. We analyze results of self-design influence on the formation of students’ readiness for professional growth. The analysis present that the purposeful, systematic inclusion of students in self-design activities provides the practical experience and skills required to build professional prospects. It is concluded that self-design enables the individual to systematically carry out his life activity and is one of the components of the future specialist's readiness for professional growth.
Publishing Weighted Graph with Node Differential Privacy

At present, how to protect user privacy and security while publishing user data has become an increasingly important problem. Differential privacy is mainly divided into two directions in graph data publishing. One is to publish the statistical characteristics of the graph that meets the differential privacy, and the other is to publish the synthesis graph that meets the differential privacy. This paper proposes a weighted graph publishing method based on node difference privacy. First, this paper proposes a projection method that constrains the degree of nodes and the number of triangles and reduces the increase in noise by reducing the sensitivity. Afterward, select appropriate statistical characteristics of the weighted graph to form node attributes as the parameters of the syn-thesis weighted graph. The next part proposes a graph publishing method based on node attributes and weights. This method synthesizes the initial graph according to the degree in the node attribute. It then adds or deletes the edges of the initial graph according to the number of triangles in the node attribute to obtain the final synthesis graph. Finally, this paper verifies the weighted graph publishing method proposed on three data sets. The results show that the method proposed in this paper satisfies the different privacy conditions of nodes while maintaining certain utility.
Exploring Fine-Grained In-Memory Database Performance for Modern CPUs

Modern CPUs keep integrating more cores and large size cache, which is beneficial for in-memory databases to improve parallel processing power and cache locality. While state-of-the-art CPUs have diverse architectures and roadmaps such as large core count and large cache size (AMD x86), moderate core count and cache size (intel x86), large core count and moderate cache size (ARM), exploring in-memory databases performance characteristics for different CPU architectures is important for in-memory database designs and optimizations. In this paper, we develop a fine-grained in-memory database benchmark to evaluate the performance of each operator on different CPUs to explore how CPU hardware architectures influence performance. Different from well known conclusions that more cores and larger cache size can achieve higher performance, we find out that the micro cache architectures play an important role opposite to core count and cache size, the shared monolithic L3 cache with moderate size beats large disaggregated L3 cache. The experiments also show that predicting operator performance on different CPUs is difficult according to diverse CPU architectures and micro cache architectures, and different implementations of each operator are not always high or low with interleaved strong and weak performance regions influenced by CPU hardware architectures. Intel x86 CPUs represent cache-centric processor design, while AMD x86 and ARM CPUs represent computing-centric processor design, the OLAP benchmark experiments of SSB discover that OmniSciDB and OLAP Accelerator with vector-wise processing model performs well on intel x86 CPUs compared to AMD x86 CPUs and the JIT compliant based Hyper prefers to AMD x86 CPUs rather than intel x86 CPUs. The CPU roadmaps of increasing cores or improving cache locality should be considered for in-memory database algorithm design and platform selection.
Multi-Scale Pedestrian Detection Algorithm Based on YOLOv3

Deep learning has excellent achievements in computer vision such as pedestrian detection. Based on YOLOv3 algorithm, this paper introduces CBAM to improve the feature representation ability and improve the overall accuracy of target detection. Then the easy to implement CSPNet is used to reduce the computational workload while maintaining the original accuracy. The experimental results based on INRIA dataset show that after the introduction of CBAM attention module and CSPNet, the efficiency and accuracy of object detection are improved, so it has better performance.
An Investigation of Blockchain-Based Sharding

Nowadays, blockchain distributed ledger technology is becoming more and more prominent, and its decentralization, anonymization, and tampering obvious features have been widely recognized. These excellent technical features of blockchain have also made it a hot issue for global research. With the wide application of blockchain technology in various industries, some defects are gradually exposed, and more prominently, the blockchain system is unable to meet the current demand of explosive growth of data volume and frequent data interaction. As one of the key technologies to solve this problem, sharding technology is gaining attention. This article introduces common blockchain scaling schemes and focuses on an overview of blockchain sharding. Sharding technology is introduced from two perspectives of intra-slice consensus and inter-slice consensus. The current mainstream slicing technology is summarized according to three different slicing methods: network sharding, transaction sharding, and state sharding. Finally, the challenges faced by current blockchain sharding technology are analyzed and the full text is summarized.
Virtual Try-On Considering Temporal Consistency for Videoconferencing

Virtual fitting, in which a person’s image is changed to an arbitrary clothing image, is expected to be applied to shopping sites and videoconferencing. In real-time virtual fitting, image-based methods using a knowledge distillation technique can generate high-quality fitting images by inputting only the image of arbitrary clothing and a person without requiring the additional data like pose information. However, there are few studies that perform fast virtual fitting from arbitrary clothing images stably with real person images for situations such as videoconferencing considering temporal consistency. Therefore, the purpose of this demo is to perform robust virtual fitting with temporal consistency for videoconferencing. First, we created a virtual fitting system and verified how effective the existing fast image fitting method is for webcam video. The results showed that the existing methods do not adjust the dataset and do not consider temporal consistency, and thus are unstable for input images similar to videoconferencing. Therefore, we propose to train a model that adjusts the dataset to be similar to a videoconference and to add temporal consistency loss. Qualitative evaluation of the proposed model confirms that the model exhibits less flicker than the baseline. Figure 1 shows an example usage of our try-on system which is running on Zoom.
A Performance Evaluation of IPsec with Post-Quantum Cryptography

As Post-Quantum Cryptography (PQC) incurs higher costs in some metrics compared to conventional cryptosystems, performance evaluation to determine the trade-offs on circumstantial usage is essential. In this paper, we provide state-of-the-art performance evaluation results of PQC algorithms in the IPsec protocol. Specifically, we perform a deep dive into the performance of PQC-integrated IKEv2 in terms of the execution speed of each IKEv2 stage and packet size according to various PQC algorithms and their security levels. The evaluation is conducted with our implementation, constructed upon strongSwan, the most popular open-source IPsec implementation. As only the latest, but unstable version of strongSwan supports PQC, it is not straightforward to integrate the existing PQC implementations into strongSwan. We propose a well-established method to integrate PQC algorithmhe code base of strongSwan. Our evaluation targets a variety of PQC KEM algorithms, including NIST Round 3 finalists (i.e., Kyber, NTRU, and Saber) and algorithms developed in Korea and China. The performance evaluation shows the trade-offs between the security level and performance for individual PQC algorithms in the IPsec protocol.
Quantum in Consumer Technology

The new quantum technology is a rapidly developing field that has the potential to revolutionize a wide range of industries, including consumer technology. One of the most exciting current applications of quantum technology in the consumer industry is the area of quantum computing. Quantum computers use quantum bits, or qubits, instead of classical bits, which allows them to perform certain calculations much faster than traditional computers. This has the potential to greatly improve fields such as machine learning, drug discovery, financial modeling, and cryptography.
Heterogeneous System Data Storage and Retrieval Scheme Based on Blockchain

In the field of information interaction, when a project involves a large amount of heterogeneous information, it is difficult to transmit and update the required information timely, accurately, reliably and securely in such a complex environment to maintain synchronization. At present, blockchain itself has problems such as high storage pressure of nodes, low access efficiency, and simple query. Therefore, this paper takes this as a starting point and proposes a data mapping method of physical resources based on node attributes and heterogeneous nodes, which provides a general method for the data mapping from actual physical resources to information domain. This method can define the attributes of heterogeneous physical information nodes and support unified expression of various physical information resources on the same platform in the real physical world, then connect heterogeneous physical information nodes and improve the sharing ability of data resources between nodes. When accessing data, the corresponding content can be found in the off-chain database by obtaining the index information of the off-chain location stored on the chain. This method takes advantage of the large space and high access efficiency of the off-chain storage system to share the pressure of on-chain data storage. This paper expounds the design idea of the system, introduces the design objective and method in detail, gives the flow chart of the system operation, and carries out a simple software test and verification. Finally, this paper summarizes the work and prospects the development direction of future work, hoping to provide inspiration for solving such problems.
Design and Implementation of Deep Learning Real-Time Streaming Video Data Processing System

With the arrival of big data era and the rapid development of artificial intelligence, deep learning has made breakthroughs in many fields. However, although it has been widely used in many fields, there are still many challenges in itself, such as the slow reference speed of neural networks. In stream processing scenarios that integrate deep learning algorithms, data is often massive and generated with high-speed, requiring the system to respond in seconds or even milliseconds. If the response speed is too slow, the actual application requirements may not be met, and the user experience cannot be guaranteed. How to use stream processing technology to improve the speed and throughput of such systems has become an urgent problem to be solved. This paper used the popular real-time stream processing framework Flink to implement a complete data stream processing program, and integrated three algorithms of face detection, facial key points detection and face mosaic into the processing logic. Setting the operator parallelism realized parallel processing of video data, which improved the system throughput. The user can choose which algorithm to perform on the video, and can also choose the parallelism according to the performance of the machine. The system implemented the Flink framework to process video in parallel, achieved the effect of improving processing efficiency, and completely implemented the front-end interface and back-end program.
The Node Selection Strategy for Federated Learning in UAV-Assisted Edge Computing Environment

Recently, edge computing plays the important role in Internet of Things (IoT). However, for the emergency scenarios, the capacity of edge computing is limited. Thus, the unmanned aerial vehicle (UAV)-assisted edge computing is introduced for IoT. Meanwhile, it faces privacy disclosure issues that the data generated on user terminals (UTs) is transmitted to edge/cloud servers for processing. So, the federated learning (FL) is adopted to train the model. But due to the difference between UTs or data quality, and the dynamic wireless network, the FL faces the bottleneck of training efficiency and accuracy in a UAV-assisted edge computing environment. In this article, a three-layer FL architecture is proposed. The FL training process is optimized by jointly optimizing the UT selection, the UAV selection, the data set selection, and the wait delay selection of one-time training. The convergence gap optimization problem is built by analyzing model training convergence, and the node selection algorithm is designed. Finally, the extensive simulation experiments are conducted to verify the feasibility and efficiency of the proposed algorithm for FL.
Improvement of ERP Cost Accounting System with Big Data

With the rapid development of computer technology, ERP system arises at the right time as an advanced cost management tool. ERP is mainly implemented in enterprises with the purpose of optimizing resource allocation and giving full play to its functions. ERP system is an enterprise resource planning system with strategic management thought, which tries to reduce the consumption of resources. Cost management is an important part of enterprise management. ERP system provides a tool for the cost management of enterprises. It greatly enhances the comprehensive management ability of enterprises. This paper discusses the improvement of ERP system to enterprise cost. We proposes to combine enterprise cost management theory with big data, use digital technology to adjust the strategic cost plan of enterprises, and improve the cost accounting system of ERP + MES + SCADA industry finance deep integration. We build lean production management and control platform of cost information sharing and use digital technology to deeply integrate performance evaluation index.
Analysis of (U,U+V)-code Problem with Gramian over Binary and Ternary Fields

Debris-Alazard, Sendrier, and Tillich proposed SURF, which is a code-based signature scheme and enjoys efficient signature generation and verification (eprint in 2017). The security of this scheme is based on two problems: one is DOOM (Decoding One Out of Many), and the other is the plain (U,U+V)-code problem over $$\mathbb {F}_2$$ . There are many studies on the former one but few studies on the latter one. Later the security of SURF was broken because the hardness of the plain (U,U+V)-code problem does not hold with considering a notion of the hull. Then Debris-Alazard et al. proposed Wave as a successor of SURF, which is known as one of the most promising quantum-resistant signature schemes (ASIACRYPT 2019). Wave is based on similar problems used in SURF. Wave uses DOOM and the normalized generalized (U,U+V)-code problem over $$\mathbb {F}_3$$ . In this paper, we utilize a notion of the Gramian (the determinant of the Gram matrices) of public keys and analyze the plain (U,U+V)-code problem over $$\mathbb {F}_2$$ . For this purpose, we compute the asymptotic probability distribution of Gramians of random matrices. Furthermore, we also show a way to analyze the normalized generalized (U,U+V)-code problem over $$\mathbb {F}_2$$ . Finally, we apply our analysis to the normalized generalized (U,U+V)-code problem over $$\mathbb {F}_3$$ . By our analysis with Gramian, SURF is completely broken, however, Wave is not directly threatened.
Private Evaluation of a Decision Tree Based on Secret Sharing

There has been increasing interest in developing privacy-preserving algorithms for evaluating machine learning (ML) models. With the advancement of cloud computing, it is now possible for model owners to host their trained ML models on a cloud server and offer cloud computing solutions on different ML tasks to users (clients). Thus private evaluation of ML models is an attractive area of research as it allows solution providers to protect their propriety ML models and users to protect their sensitive data while using cloud computing solutions. In this work, we propose an algorithm to privately evaluate a decision tree. We examine current state-of-the-art private evaluation protocols and present a solution that is sublinear in tree size and linear in tree depth. The key feature of our proposal is that it is entirely based on secret sharing and thus there are no computational costs associated with heavy cryptographic primitives such as modular exponentiation. We propose a new method to privately index arrays that avoids the use of public/symmetric key cryptosystem, typically associated with private array indexing protocols. The results of our experiments show that our solution has a low communication cost compared to existing methods (lower by a factor of $$\approx $$ 10 in the online phase), and demonstrate a faster runtime at low network latency (such as LAN network). We conclude by suggesting improvement to our protocol and proposing potential areas of future research.
Individuals, Society, and AI: Online Communication

This chapter examines a current ethical issue in AI, using the example of online content moderation and consolidating previous material. The problem of online harms is in tension with values of freedom of expression. Freedom of expression is a perennial issue. We consider how the use of technology may impact and exacerbate the problems and examine the possibilities of using technology to address them, such as the challenges of using algorithms to detect nuanced meaning. The ethical issues are tightly connected to wider political, social, regulatory, and legal issues. Here, we focus on ethics while also discussing how wider interests from government and industry may skew debates and solutions. The issue of free speech is outlined, drawing on the claims of John Stuart Mill and the ‘harm principle’ as a limit to free speech. We also address issues in the philosophy of language, considering how meaning and intention are related and the critical importance of context, drawing on the work of H. P. Grice. We consider how communication online may impact views of self and others and consider how both the problems of online content and the attempts to find solutions may influence how we understand and address ethical questions.
EdgeMan: Ensuring Real-Time Service for Containerized Edge Systems

Containers have emerged as a popular technology for edge computing platforms. Although there are varieties of container orchestration frameworks, e.g., Kubernetes to provide high-reliable services for cloud infrastructure, ensuring realtime service at the containerized edge systems (CESs) remains a challenge. In this paper, we propose Edgeman,a holistic edge service management framework for CESs, which consists of (1) a model-assisted event-driven lightweight online scheduling algorithm to provide request-level execution plans; (2) a bottleneck-metric-aware progressive resource allocation mechanism to improve resource efficiency. We then build a testbed that installed three containerized services with different latency sensitivities for concrete evaluation. Besides, we adopt real-world data traces from Alibaba and Twitter for large-scale emulations. Extensive experiments demonstrate that the deadline miss ratio of Edgemanis reduced 85.9% on average compared with existing methods in both industry and academia.
Demons Hidden in the Light: Unrestricted Adversarial Illumination Attacks

As deep learning-based computer vision is widely used in IoT devices, it is especially critical to ensure its security. Among the attacks against deep neural networks, adversarial attacks are a stealthy means of attack, which can mislead model decisions during the testing phase. Therefore, the exploration of adversarial attacks can help to understand the vulnerability of models in advance and make targeted defense. Existing unrestricted adversarial attacks beyond the $$\ell _p$$ norm often require additional models to be both adversarial and imperceptible, which leads to a high computational cost and task-specific design. Inspired by the observation that models exhibit unexpected vulnerability to changes in illumination, we develop Adversarial Illumination Attack (AIA), an unrestricted adversarial attack that imposes large but imperceptible alterations to the image. The core of the attack lies in simulating adversarial illumination through Planckian jitter, of which the effectiveness comes from a causal chain where the attacker misleads the model by manipulating the confusion factor. We propose an efficient approach to generate adversarial samples without additional models by image gradient regularization. We validate the effectiveness of adversarial illumination in the face of black-box models, data preprocessing, and adversarially trained models through extensive experiments. Experiment results confirm that AIA can be both a lightweight unrestricted attack and a plug-in to boost the effectiveness of other attacks.
Research on Detection Algorithms of Weak Electromagnetic Spectrum Signal

The weak electromagnetic spectrum signal drowned in the noise is difficult to be detected and identified. To solve this problem, the frequency-domain superposition algorithm and the power-entropy algorithm are proposed respectively. Considering the actual electromagnetic spectrum signals detection scenario, three types of experimental scenarios in which the DS-BPSK signal invades the frequency bands of FM, QPSK and 16QAM signals are simulated. By comparing the advantages and disadvantages of two different algorithms, it is proved that the power-entropy algorithm is much more effective. Even there exists the strong electromagnetic spectrum signal, the weak electromagnetic spectrum signal can be easily distinguished and not be ignored.