Reviewerly/exHarmony huggingface dataset:
Updatable Public Key Encryption from DCR

Forward-secure encryption (FS-PKE) is a key-evolving public-key paradigm that preserves the confidentiality of past encryptions in case of key exposure. Updatable public-key encryption (UPKE) is a natural relaxation of FS-PKE, introduced by Jost et al. (Eurocrypt'19), which is motivated by applications to secure messaging. In UPKE, key updates can be triggered by any sender -- via special update ciphertexts -- willing to enforce the forward secrecy of its encrypted messages. So far, the only truly efficient UPKE candidates (which rely on the random oracle idealization) only provide rather weak security guarantees against passive adversaries as they are malleable. Also, they offer no protection against malicious senders willing to hinder the decryption capability of honest users. A recent work of Dodis et al. (TCC'21) described UPKE systems in the standard model that also hedge against maliciously generated update messages in the chosen-ciphertext setting (where adversaries are equipped with a decryption oracle). While important feasibility results, their constructions lag behind random-oracle candidates in terms of efficiency. In this paper, we first provide a drastically more efficient UPKE realization in the standard model using Paillier's Composite Residuosity (DCR) assumption. In the random oracle model, we then extend our initial scheme so as to achieve chosen-ciphertext security, even in a model that accounts for maliciously generated update ciphertexts. Under the DCR and Strong RSA assumptions, we thus obtain the first practical UPKE systems that satisfy the strongest security notions put forth by Dodis et al.
Training of personnel for the internal affairs bodies of the russian federation in the context of digital transformation.

The article deals with the issues of personnel training for internal affairs bodies in the conditions of digitalization of the educational process and digital transformation of society and education. The main purpose of the work is to identify problems and clarify the main ways and pedagogical conditions for improving the effectiveness of professional training of law enforcement officers in the conditions of digital transformation.&#x0D; The author, on the basis of a learner-centered- social approach, carries out research on the theory and practice of the question posed through content analysis of scientific sources, monitoring of the work and analysis of the results of the activities of the teaching staff and heads of educational groups of territorial internal affairs bodies of the Russian Federation included and substantiates possible ways to improve the professional training of employees of territorial internal affairs bodies.
Efficient Zero-Knowledge Proofs on Signed Data with Applications to Verifiable Computation on Data Streams

We study the problem of privacy-preserving proofs on streamed authenticated data. In this setting, a server receives a continuous stream of data from a trusted data provider, and is requested to prove computations over the data to third parties in a correct and private way. In particular, the third party learns no information on the data beyond the validity of claimed results. A challenging requirement here, is that the third party verifies the validity with respect to the specific data authenticated by the provider, while communicating only with the server. This problem is motivated by various application areas, ranging from stock-market monitoring and prediction services; to the publication of government-ran statistics on large healthcare databases. All of these applications require a reliable and scalable solution, in order to see practical adoption.
Inventing the dark Web

This paper examines how the deep Web, i.e., Web sites that are not indexed and thus are not accessible through Web search engines, was described and represented in British newspapers. Through an extensive content analysis conducted on 833 articles about the deep Web published between 2001 and 2017 by six British newspapers, we demonstrate that these technologies were predominantly associated with crime, crypto markets and immoral content, while positive uses of this technology, such as protecting privacy and freedom of speech, were largely disregarded. The consistent association by the British press between the deep Web and criminal and antisocial behaviors is exemplary of a recent “apocalyptic turn” in the imaginary of the Web, whereby Web-related technologies are perceived and portrayed in more negative ways within the public sphere. We argue that the use of such negative concepts, definitions and associations engender distrust about uses of the deep Web, propagating user stereotypes that reflect what we argue to be an overall criminalization of privacy.
Poster  Towards Authorship Obfuscation with Language Models

Authorship obfuscation is the process of making changes to text such that identifying attributes (style, common words and phrases, tone) are masked. The goal of obfuscation is to retain the semantics of the text (i.e., the meaning) but rewrite it in such a way that the author cannot be identified. In this work, we investigate the effectiveness of language models for authorship obfuscation. More specifically, we examine the application of document summarization (a task where we learn to generate the summary of a text) as an authorship obfuscation method. Since summaries are shorter versions of text but which retain the significant points made in it, we hypothesize that summaries will be stripped off any stylistic identifying features of the text. Our experiments show that this is indeed the case; we were able to fool authorship classifiers and degrade their performance by as much as 70% However, this also significantly affected the semantics; there was a non-trivial loss of information and the produced text was not an accurate representation of the original.
Whether academic and teaching staff are universal

Academic personnel routinely carry out a wide range of teaching, research, administrative, and expert functions. However, many of them do not focus on universality, but in fact specialize in performing one or several academic functions (profiles). The article deals with the discrepancy between the traditional distribution of professional duties of academic and teaching staff (ATS) and their actual performance. The research methodology is based on the positive agency theory, which allows explaining the peculiarities of social and labour relations in the academic sphere. The research methods of descriptive, frequency and regression analysis are used, including the method of logistic regression. The empirical basis includes the results of a survey conducted in 2022 of 207 academic and teaching staff members of the Southern Federal University, Russia. The questionnaire was compiled of the aspects of academic, teaching, expert and administrative profiles, as well as the questions about the socio-economic characteristics of respondents, the professional role they prefer, labour productivity and salary satisfaction. The results of studying the structure and the ATS’s choice of professional roles (profiles) can be used to solve problems in the field of social and labour relations at the university. According to the findings, the share of employees choosing specialization and the share of ‘universal’ employees were distributed equally; a discrepancy was found between respondents’ preference for performing one or another function and the actual work they do in the academic field. It was revealed that the specialization of work in a certain profile has a significant impact on labour productivity, and an increase in the number of work profiles reduces the likelihood of being in a group of employees satisfied with the correlation between their salary and the volume and complexity of the work performed.
Cryptographic Standards in the Post-Quantum Era

If large-scale quantum computers are ever built, they will compromise the security of many commonly used cryptographic algorithms. In response, the National Institute of Standards and Technology is in the process of standardizing new cryptographic algorithms to replace the vulnerable ones.
A Meta-analysis of Research Trends and Effectiveness of Academic Support Programs in Korean College

Objectives The purposes of this study were to review the trends in research related to academic programs conducted at domestic universities and meta-analyze the effects of these programs, which can provide the data for improving of the quality of the academic program development. Methods For this purpose we searched for keywords such as ‘college student’, ‘study’, ‘program’, ‘academic probation’, and ‘college student underachievement’ in the RISS, and collected 103 papers of ‘college students’ academic support programs’ published from 2006 to 2021 in KCI and KCI-candidate journals. We analyzed the research trends of the collected studies, and conducted a meta-analysis on the effects of academic intervention program using 15 studies that applied a control group experimental design by using the R statistical program. Results The papers of academic programs for college students have been published since 2006, and their number has increased rapidly over the past decade. The target audience of the program was diverse, including program applicants, students with academic difficulties, and students from specific departments. For the operation form of the program, learning community, group education, and group counseling were in order. In addition to this, we analyzed the purpose of the programs, research methods, measurement variables and tools, and presented the results. Lastly, the meta-analysis result showed that the average effect size of the program was 0.5226, which was a medium effect size, and the significant moderator variables were the operation type and the purpose of the program. Conclusions We suggested how Korean colleges could design and operate the academic support programs for undergraduate students based on the results of this study and also provided some implications for the development of related research.
Construction of Innovation and Entrepreneurship MOOC Education Resource Library based on Distributed Information Storage and Retrieval Intelligence

An improved algorithm of the topic relevance model is designed, and the cosine value of the angle between the space vectors is used to measure the similarity between the block documents. By analyzing the development status of innovation and entrepreneurship education, this paper proposes innovative and entrepreneurial talents that integrate MOOCs Cultivating new ideas for curriculum reform, expounding from the aspects of curriculum system, integration mechanism, resource platform construction, and innovation and entrepreneurship mentor team building. In addition, the efficiency of distributed file storage and retrieval system based on MapReduce is much better than standalone processing and MPI parallel system. However, there is still a threshold problem in order to comprehensively improve the quality of innovative and entrepreneurial talent training.
Randomly Flipped Chip based signal power authentication for GNSS civilian signals

The navigation signal authentication schemes protect GNSS services from spoofing attacks by attaching unforgeable information to signals. In this study, Randomly Flipped Chip based Signal Power Authentication (RFC-SPA), is proposed to jointly authenticate the navigation message and spreading code of GNSS civilian signals. Designed to support two modes of authentication (fast channel and slow channel), the proposed scheme employs a fixed amount of flipped chips, which are randomly distributed in some areas of the spreading code to form a relative fluctuation in the correlation result of the signal. The fast channel is a spreading code authentication (SCA) of these flipped chips, which is the same as the Chimera. In the slow channel, since the correlation results between the local periodical spreading code and the signal in areas with flipped chips will be smaller than those results in areas without flipped chips, receivers in the tracking correlation can extract this fluctuation. Since the fluctuation modulates a digital signature, receivers can verify the authenticity of both the navigation message and spreading code through the authentication of this digital signature. Compared with mainstream schemes, RFC-SPA requires no extra tracking channel and has no impact on the navigation message. Besides, the proposed scheme maintains a similar robustness and security performance while significantly reducing the storage requirement compared with SCA. The scheme is demonstrated in a proposed implementation for Beidou B1C civilian signal, and the performance metrics are analysed. In the proposed implementation, the detection possibility is higher than 99.7% at 35dBHz of C/N0, and only 33.6% of the storage is required compared with the suggested configuration of the Chimera scheme for the GPS L1C civilian signal.
A fuzzy decision tree reasoning method for network forensics analysis

As an important branch of computer forensics, network forensics technology, whether abroad or at home, is in its infancy. It mainly focuses on the research on the framework of some forensics systems or some local problems, and has not formed a systematic theory, method and system. In order to improve the network forensics sys-tem, have a relatively stable and correct model for refer-ence, ensure the authenticity and credibility of network fo-rensics from the forensics steps, provide professional and non professional personnel with a standard to measure the availability of computer network crime investigation, guide the current network forensics process, and promote the gradual maturity of network forensics theories and methods, This paper presents a fuzzy decision tree reason-ing method for network forensics analysis.
GRIMGEP: Learning Progress for Robust Goal Sampling in Visual Deep Reinforcement Learning

Autotelic reinforcement learning (RL) agents sample their own goals, and try to reach them. They often prioritize goal sampling according to some intrinsic reward, ex. novelty or absolute learning progress (ALPs). Novelty-based approaches work robustly in unsupervised image-based environments when there are no distractors. However, they construct simple curricula that do not take the agent’s performance into account: in complex environments, they often get attracted by impossible tasks. ALP-based approaches, which are often combined with a clustering mechanism, construct complex curricula tuned to the agent’s current capabilities. Such curricula sample goals on which the agent is currently learning the most, and do not get attracted by impossible tasks. However, ALP approaches have not so far been applied to DRL agents perceiving complex environments directly in the image space. Goal regions guided intrinsically motivated goal exploration process (GRIMGEP), without using any expert knowledge, combines the ALP clustering approaches with novelty-based approaches and extends them to those complex scenarios. We experiment on a rich 3-D image-based environment with distractors using novelty-based exploration approaches: Skewfit and CountBased. We show that wrapping them with GRIMGEP—using them only in the cluster sampled by ALP—creates a better curriculum. The wrapped approaches are attracted less by the distractors, and achieve drastically better performances.
Data quality issues in online reinforcement learning for self-adaptive systems (keynote)

Online reinforcement learning is an emerging machine learning approach that addresses the challenge of design-time uncertainty faced when building self-adaptive systems. Online reinforcement learning means that the self-adaptive system can learn from data only available at run time. After introducing the fundamentals of self-adaptive systems and reinforcement learning, the keynote discusses three relevant issues and recent solutions related to data quality in online reinforcement learning for self-adaptive systems.
The best of both worlds

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings This paper identified the use of the metaverse in enhancing the customer experience within the hospitality industry. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.
Poster

MagNet is a defense method that adopts autoencoders to detect and purify adversarial examples. Although MagNet is robust against grey-box and black-box attacks, it is vulnerable to white-box attacks. Despite this prior knowledge, the fundamental reason for and mitigation of the vulnerability of MagNet have not been discussed. We suggest that the challenge of MagNet is the generalization of the data manifold. To explain this, in this work, we leverage deep learning coverage for the reformer of MagNet. We mutate training images through image transformation algorithms and then train the reformer using mutants with new coverage information. The selected mutants provide an interesting data manifold, that cannot be handled by the random noise of MagNet, to the reformer. In grey-box settings, our defense method classified adversarial examples for various perturbation sizes much more accurately than MagNet even with the same architecture. Based on the preliminary result of this work, we consider future work to identify whether the generalization power of deep learning coverage is effective for stronger adversaries and different architectures.
SCORED '22

Recent attacks on the software supply chain have shed light on the fragility and importance of ensuring the security and integrity of this vital ecosystem. Addressing the technical and social challenges to building trustworthy software for deployment in sensitive and/or large-scale enterprise or governmental settings requires innovative solutions and an interdisciplinary approach. The Workshop on Software Supply Chain Offensive Research and Ecosystem Defenses (SCORED) is a venue that brings together industry practitioners, academics, and policymakers to present and discuss security vulnerabilities, novel defenses against attacks, project demos, adoption requirements and best practices in the software supply chain. The complete SCORED'22 workshop proceedings are available at: https://dl.acm.org/doi/proceedings/10.1145/3560835
Poster

Relying party implementations are an important component of RPKI: they fetch and validate the signed authorizations mapping prefixes to their owners. Border routers use this information to check which Autonomous Systems (ASes) are authorized to originate given prefixes and to enforce Route Origin Validation (ROV) in order to block bogus BGP announcements, preventing accidental and malicious prefix hijacks. In 2021 the RPKI relying party implementations were patched against attacks by malicious publication points. In such attacks the relying parties are stalled processing malformed RPKI objects. In this work we perform a black-box analysis of the patched relying party implementations and find that out of five popular relying parties, two major implementations (Routinator and OctoRPKI) have vulnerabilities that can be exploited to cause large scale blackouts in the RPKI ecosystem. We show that the vulnerabilities we found apply to 84.9% of the networks supporting RPKI. We analyze the code to understand the factors causing the bugs. We show that these vulnerabilities can be exploited to crash the deployed relying parties, disabling RPKI validation and exposing the networks to prefix hijack attacks.
Poster

In Autonomous Driving (AD) systems, perception is both security and safety-critical. Among different attacks on AD perception, object-hiding adversarial attack is one of the most critical ones due to the direct impact on safety-critical driving decisions such as collision avoidance. However, all of the prior works on physical object-hiding adversarial attacks only study the security of the AI component alone rather than with the entire AD system pipeline with closed-loop control. This thus inevitably raises a critical research question: can these prior works actually achieve system-level effects (e.g., vehicle collisions, traffic rule violation) under real-world AD settings with closed-loop control?
User Anonymous Authentication Key Exchange Protocol Based on Biometrics and Password

The concept of “sharing economy” has become even more popular rapidly among people around the world nowadays, based on which, a new thing of “car-sharing” shows up in front the people. Though the problems related to security have been well studied, the protection of user data privacy in such type of protocols is still pending. In order to effectively improve the security performance of remote authentication protocol, the standard challenge/response mechanism is introduced in this study to propose a novel identity authentication key-exchange protocol, which is designed based on password and biologic features by two times of modular exponentiation operations and some certain lightweight symmetric operation, as well as biological feature authentication achieved by smart phones. In fact, the security performance of the above-mentioned protocol relies on Hash function unidirectionality and biological features. Meanwhile, the protocol protects users' anonymity to the maximum extent. Even if the smart phone is compromised by malicious code or gets lost, the protocol can also protect user's privacy data. The security proof and performance analysis under the security model show that the proposed protocol can resist dictionary attack, man-in-the-middle attack, replay attack, impersonation attack, insider attack, etc.
A message push method for iOS mobile terminals

This paper mainly proposes a message push method suitable for iOS mobile terminals. In this method, APNS is used for offline iOS terminal message push and MQTT is used for online iOS terminal message push. This method can reduce the number of messages sent through APNS. It can also solve the disability of storage-and-forward and the dissatisfaction of delivery rate under large amount of concurrent messages or over extent of message length. At the same time, different push strategies can be selected according to the online state of the target terminals. The experimental results about bandwidth and message length show that when the concurrency of pushing messages is large or network resources are insufficient, this strategy can dynamically balance the load or expand the bandwidth of network devices and servers to increase throughput. This strengthens the data processing capability for message push and improve resource utilization.
A survey on social image semantic analysis

多媒体分享网站使得网络用户可以与他人联系、分享他们的多媒体数据. 社交网络图像语义分析是多媒体、模式识别与计算机视觉领域的一个重要的基础研究任务, 通过分析用户提供的标签、用户信息、描述等社交网络上下文信息识别出图像的语义内容. 因此, 社交网络图像智能分析近年来得到了广泛的研究, 并取得了显著进展. 鉴于此, 本文对基于浅层模型和深度模型的社交网络图像语义分析方法进行了全面的综述, 将所要解决的任务整合到社交网络图像检索和社交网络图像标注两个广泛研究的领域进行了讨论, 并讨论了公开可用的基准数据集以及在每项研究任务上的性能比较和分析. 此外, 本文讨论了几个具有前景的研究方向和可能会引起广泛关注的开放问题.
Vision system for automatic recognition of selected road users

In this article, a system for automatic recognition of selected road users using artificial neural networks is proposed. Five models of neural networks were tested with various configurations. With the special prepared image database, training, validation and tests were conducted to identify five classes of road users: cyclists, people on electric scooters, roller skates, pedestrians and people using personal transport devices. A web application that recognize these classes of road users was also prepared.