Reviewerly/exHarmony huggingface dataset:
Dispelling Myths on Superposition Attacks: Formal Security Model and Attack Analyses

With the emergence of quantum communication, it is of folkloric belief that the security of classical cryptographic protocols is automatically broken if the Adversary is allowed to perform superposition queries and the honest players forced to perform actions coherently on quantum states. Another widely held intuition is that enforcing measurements on the exchanged messages is enough to protect protocols from these attacks. However, the reality is much more complex. Security models dealing with superposition attacks only consider unconditional security. Conversely, security models considering computational security assume that all supposedly classical messages are measured, which forbids by construction the analysis of superposition attacks. To fill in the gap between those models, Boneh and Zhandry have started to study the quantum computational security for classical primitives in their seminal work at Crypto’13, but only in the single-party setting. To the best of our knowledge, an equivalent model in the multiparty setting is still missing. In this work, we propose the first computational security model considering superposition attacks for multiparty protocols. We show that our new security model is satisfiable by proving the security of the well-known One-Time-Pad protocol and give an attack on a variant of the equally reputable Yao Protocol for Secure Two-Party Computations. The post-mortem of this attack reveals the precise points of failure, yielding highly counter-intuitive results: Adding extra classical communication, which is harmless for classical security, can make the protocol become subject to superposition attacks. We use this newly imparted knowledge to construct the first concrete protocol for Secure Two-Party Computation that is resistant to superposition attacks. Our results show that there is no straightforward answer to provide for either the vulnerabilities of classical protocols to superposition attacks or the adapted countermeasures.
Redesigning Technical Services for the 21st Century: A Case Study from the University of Alabama Libraries

The University of Alabama Libraries began the process of workflow analysis over a decade ago. Primarily focused on the traditional technical services areas, this process has been iterative and has evolved from looking for efficiencies to a broader change in the culture and an acceptance of an ongoing process of improvement. This article distills lessons learned from workflow analysis in the areas of acquisitions, electronic resources, and cataloging/metadata but also examines how these changes impacted the broader library and philosophies of collection development and management.
Methods of mathematical modeling for predicting university entry into TOP-100 world university rankings

Subject. The article focuses on mathematical methods for predicting university entry into TOP-100 World University Rankings. Objectives. The purpose of the study is to develop mathematical modeling techniques for predicting university entry into TOP-100 World University Rankings. Methods. We consider two approaches in mathematical simulation for the said purpose. The first approach employs population dynamics equations, including the Verhulst and the Lotka-Volterra equations. The second approach uses linear algebraic equation in three variables together with the variables constraints. Results. Population dynamics equations enable to model improvement in university performance expressed by Total Score or Overall Score indicator, to define intra-university competition and competition between universities of the World University Rankings. Linear algebraic equations provide projections of a university's specified position in the world rankings. We made clear mathematical statement of the problems; solved the problem that relates to entering TOP-100 of three global rankings (ARWU, THE, QS) by the Moscow State University and St. Petersburg University. Conclusions. We assume that the offered approaches will be useful for university managers who monitor and guide their university positioning.
Professional Competences in STEM Education

The ongoing changes in the modern world - represented with nano-, bio-, info- and cognitive technological development - requires a certain skill set, mastering of which is necessary for any professional and above all - a STEM-specialist. Thus, alongside the technological advancement, leading global corporations seek to employ candidates exhibiting particular soft-skills, among which leadership and emotional intellect are in especially high demand. The purpose of the article is to formulate a comprehensive STEM portfolio, consisting of soft, emotional and leadership skills (SELS). Consequently, a new method of developing leadership skills in the STEM educational process is offered. As opposed to traditional methods of management, the new approach is based on the principle of emotional intellect, thus allowing to influence employees on a level that engages them personally, hence more effectively. The offered methodology is applicable in both educational and corporal spheres of professional STEM activity.
Salp Swarm Algorithm: Tutorial

Salp swarm algorithm (SSA) is a new method of optimization, created to overcome multiple problems with optimizing. SSA belongs to the swarm intelligence algorithms family. Swarm Intelligence (SI) Swarm Intelligence is referred to the social conduct emerging within decentralized and self-organization of swarms. These swarms are summarized as the well-known organisms such as bird groups, fish schools, and the most social in insects species for instance bees, termites, and ants. In this paper, an introduction to the SSA algorithm was provided. The traditional version of the SSA algorithm pseudo-code form and its source code were presented in two programming language (Matlab, C++) and numerical examples were also provided, which could help researchers to understand SSA work and process.
Salp Swarm Algorithm: Modification and Application

Swarm Intelligence (SI) is referred to the social conduct emerging within decentralized and self-organization of swarms. These swarms are summarized as the well-known examples such as bird groups, fish schools, and the most social in insects species for instance bees, termites, and ants. Among those, salp Swarm Algorithm (SSA), that has been successfully utilized and held in different fields of optimization optimization, engineering practice, and real-world problem.
Resource Burning for Permissionless Systems (Invited Paper)

Proof-of-work puzzles and CAPTCHAS consume enormous amounts of energy and time. These techniques are examples of resource burning: verifiable consumption of resources solely to convey information. Can these costs be eliminated? It seems unlikely since resource burning shares similarities with “money burning” and “costly signaling”, which are foundational to game theory, biology, and economics. Can these costs be reduced? Yes, research shows we can significantly lower the asymptotic costs of resource burning in many different settings. In this paper, we survey the literature on resource burning; take positions based on predictions of how the tool is likely to evolve; and propose several open problems targeted at the theoretical distributed-computing research community.
Get lost, troll: How accusations of trolling in newspaper comment sections affect the debate

This qualitative study explores instances where someone is accused of being a troll or a bot in newspaper comment sections. Trolls have been known to create a hostile environment in comment sections, often motivated by attention seeking and amusement. In recent years, following the Brexit vote and the U.S. presidential election of 2016, trolls have also been accused of actively undermining the Western political climate by using social media to divide political opponents. Furthermore, technological development has led to the possibility of automated software, known as bots, playing a role in online debates. As social media users and participants of online comment sections become more digitally literate, the awareness of trolls and bots will hopefully make people less susceptible to online manipulation. But this awareness could also cause commenters to discredit and delegitimize opposing arguments in comment sections by accusing others of being a troll or a bot, without considering the merits of the argument itself. If this is the case, it constitutes a challenge in creating a democratically valuable debate in comment sections. In this study, comments from three U.S. news sites were sampled and analyzed to investigate how accusations of trolling are made, and how debates are affected by such accusations. The results showed that right-wing commenters were more likely to be accused of trolling, and that these accusations seem to have been motivated by political differences. Accusers would either challenge the suspected troll, critique the effectiveness of the perceived trolling, make fun of the suspected troll, or simply warn other commenters about their presence. Finally, while debates often continued after an accusation of trolling had been made, the accuser and the accused rarely participated further. The results suggest that accusations of trolling do not have any major impact on the debate. It is, however, problematic that such accusations seem to be used as a rhetorical tool to discredit opposing arguments, which could lower the deliberative quality of debates in comment sections.
A Comprehensive Study of DNS-over-HTTPS Downgrade Attack

Author(s): Huang, Qing | Advisor(s): Zhou, Li | Abstract: DNS-over-HTTPS (DoH) is one major effort to protect DNS confidentiality and integrity, which has been deployed by most of the popular browsers. However, we found this effort could be tainted by the downgrade attack, which exposes the content of DNS communications to attackers like censors. Specifically, we examined 6 browsers with 4 attack vectors that are relevant to our attack model and found all combinations that lead to successful attacks. The fundamental reason is that all browsers enable Opportunistic Privacy profile by default, which allows DoH fall backs to DNS when DoH is not usable. However, it is still concerning that none of the browsers attempt to notify users when such a change happens and some browsers take a long time to recover to DoH. At the end of the paper, we propose some countermeasures and we call for discussions from the Internet community to revisit the standards and implementations about DoH and usage profiles.
A Text Classification Survey: From Shallow to Deep Learning

Text classification is the most fundamental and essential task in natural language processing. The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the gap by reviewing the state of the art approaches from 1961 to 2020, focusing on models from shallow to deep learning. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification. We then discuss each of these categories in detail, dealing with both the technical developments and benchmark datasets that support tests of predictions. A comprehensive comparison between different techniques, as well as identifying the pros and cons of various evaluation metrics are also provided in this survey. Finally, we conclude by summarizing key implications, future research directions, and the challenges facing the research area.
Plants Classification Using Neural Shifted Legendre-Fourier Moments

Plants are the primary food source of humans. They are the raw material of most medicines. Therefore, it was necessary to use artificial intelligence to help those interested in plants to classify and identify various plants types quickly and accurately. In this article we present Neural Shifted Legendre-Fourier Moments, we used shifted Legendre-Fourier moments to extract features from leaves images and build descriptor vectors. These vectors are the inputs of the artificial neural network. We tested this model on MalayaKew (MK) Leaf dataset and we got important results. The validity of this proposed method has been provided under different transformations.
Automation and Presentation of Word Document Using Speech Recognition

Speech recognition system has the ability to recognize and interpret lexis in a spoken language and transcript the same. With all the available uses of such system, in this paper, light is shed on another use in automating the applications that manage documents and presentations and a solution is proposed for implementing the same, developed in python programming language that can benefit the regular users as well as the elderly and visually-impaired.
Three trillion lines: infrastructure for mining GitHub in the classroom

The increasing interest in collaborative software development on platforms like GitHub has led to the availability of large amounts of data about development activities. The GHTorrent project has recorded a significant proportion of GitHub’s public event stream and hosts the currently largest public dataset of meta-data about open-source development. We describe our infrastructure that makes this data locally available to researchers and students, examples for research activities carried out on this infrastructure, and what we learned from building the system. We identify a need for domain-specific tools, especially databases, that can deal with large-scale code repositories and associated meta-data and outline open challenges to use them more effectively for research and machine learning settings.
Bias in confidence: A critical test for discrete-state models of change detection.

Ongoing discussions on the nature of storage in visual working memory have mostly focused on 2 theoretical accounts: On one hand we have a discrete-state account, postulating that information in working memory is supported with high fidelity for a limited number of discrete items by a given number of slots, with no information being retained beyond these. In contrast with this all-or-nothing view, we have a continuous account arguing that information can be degraded in a continuous manner, reflecting the amount of resources dedicated to each item. It turns out that the core tenets of this discrete-state account constrain the way individuals can express confidence in their judgments, excluding the possibility of biased confidence judgments. Importantly, these biased judgments are expected when assuming a continuous degradation of information. We report 2 studies showing that biased confidence judgments can be reliably observed, a behavioral signature that rejects a large number of discrete-state models. Finally, complementary modeling analyses support the notion of a mixture account, according to which memory-based confidence judgments (in contrast with guesses) are based on a comparison between graded, fallible representations, and response criteria. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Utilization of Technology for Early Warning of Natural Disasters in Indonesia

Abstract This research aims to determine the signs of a natural disaster that may occur in Indonesia. Indonesia is a country that is very prone to natural disasters, for example, volcanoes and tsunamis, which caused by earthquakes under the sea. This research used a qualitative research method. The respondent involved in this research is several government agencies that are authorized for natural disasters in Indonesia. The results of this study show a high level of knowledge of early warning of natural disasters, especially in prone areas to natural disasters to reduce casualties when natural disasters occur. The comparison when an area does not have an early warning system to know the good and bad impacts. This study concludes that many people do not understand the code given when the natural disaster early warning device sounds. Besides, a lot of them do not understand the signs intended in natural disaster detection devices. Therefore, there is a need for socialization from the government so that people understand the warning technology early.
Author Correction: The role of mPFC and MTL neurons in human choice under goal-conflict

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
A New Method to Improve the Quality of Embolic Ultrasound Signal Detection

Emboli detection and denoising have been the focus of attention from many years. Emboli can be occurred because of the air bubbles, concentrated fat particles or blood clotted in a vessel. The detection of this issue can be possible by employing Doppler ultrasonic transducers. However, this method would be very sensitive to peripheral noise. In this paper for emboli denoising, CEEMDAN as one of the newest and effective methods is introduced. Then, an algorithm for emboli detection based on Bayesian estimation is proposed. The results are compared with a recent method, showing that the proposed method yields an SNR that this improved by 45% and an accuracy rate of 94%.
KIMS: Kernel Integrity Measuring System based on TrustZone

Android operating system has been getting widely used in these years. At the meantime, lots of security issues have been exposed. Not only the softwares running on it are suffering great losses, but the operating system itself is also becoming a target for attackers. Especially kernel-level rootkits have a bad influence on the system security. Some attackers can use this method to get the root privilege by exploiting these vulnerabilities. It's high time we should take measures to enhance protection for Android kernel. In this paper, we propose a kernel integrity measuring system named KIMS on the TrustZone Architecture. The key idea is to build a safe and isolated environment for kernel measuring provided by the secure world in the TrustZone. And with the high privilege of secure world, KIMS can monitor the operating system kernel and detect whether it is destroyed. Then we build the prototype using the Hikey960 board to evaluate our design. The experiments show that our system can execute kernel integrity measurement when a malicious behaviour detected. And it has a relatively small performance overhead.
Dance Training Movement Depth Information Recognition Based on Artificial Intelligence

The motion capture system under artificial intelligence can avoid the deviation caused by artificial feature extraction. Human motion is composed of complex motion sequences, and capturing the time series is essential for motion recognition. The purpose of this paper is to provide a more perfect design for artificial intelligence movement recognition through the research on the recognition of movement depth information in dance training based on depth information. This paper firstly proposes a classification framework based on characteristics and data types, and comprehensively reviews the methods proposed in recent years. Subsequently, the performance of the algorithms described in the literature was compared and analyzed. Through the experiment of dance movement recognition, the quoted public test data set was summarized, and the accurate positioning rate was above 95%. Finally, the author discusses the future research direction and gives some Suggestions.
Deploying VR in a Science Museum: Lessons Learned

The low cost and relative simplicity of consumer VR headsets has made them an attractive choice for education. However, informal education settings, like museums and science centers, face additional challenges not present in the home or classroom. The Fort Worth Museum of Science and History has deployed several kinds of VR activities over the last two years. We report our firsthand observations about the physical and mental assistance and affordances required for successful use of this technology in informal education.
SlideFusion

For mobility-impaired people, the wheelchair is considered as a main navigation and accessibility device. However, due to the inherited design of the chair, the user is restricted to utilize his hand all the time during navigation resulting in a dual impairment. Our proposed system, SlideFusion, expands on previous work of collaborative and assistive technologies for accessibility scenarios. SlideFusion is focused on the remote collaboration of the mobility impaired person, that an operator can remotely access an avatar embedded into the wheelchair. To reduce the physical and cognitive overload on the wheelchair user, we propose the use of eye gaze modality sharing with the remote operator. The eye gaze modality enables implicit interactions that do not require the user to point or verbally say, thus leveraging indirect communication. By using this, not only accessibility can be provided to wheelchair users and their caregivers, but also hearing impairments and pronunciation disorders are applicable.
A Study on Factors Influencing Young and Matured Adult Learners to Continue Their Education

Distance learning is one convenient form of education for adult learners who aspire to advance their career as well as to meet the demand at workplace. Thus, this study aims to investigate factors encouraging adult learners to continue their education and their coping strategies with challenges they encounter as adult learners. This group of adult learners furthers their studies through a distance learning programme via a system called ‘ilearn’, which is offered by the institution. This study adopted quantitative methodology to elicit information and substantiated with structured interview responses to add credibility to the findings. Eighty-two adult learners from Bachelor of Finance, UiTM Shah Alam participated in this study. The results indicated that the main factors motivating adult learners to pursue their education are their aspiration for professional advancement, monetary gains and improving their quality of lifestyle. The results also revealed that financial constraints and time factor are the most significant barriers affecting adult learners’ mindset and they strive to overcome these challenges by approaching banks for study loans and opting out for distance learning programme offered during weekends. These two options reduce their hurdles slightly and they are able to cope with their choice of furthering their education.
Autonomous Driving Path Planning based on Sarsa-Dyna Algorithm

In autonomous driving path planning, ensuring the computational efficiency and safety of planning is an important issue.The Dyna framework in reinforcement learning can solve the problem of planning efficiency.At the same time, the Sarsa algorithm in reinforcement learning can be effective in guaranteeing the safety of path planning.This paper proposes a path planning algorithm based on Sarsa-Dyna for autonomous driving, which effectively guarantees the efficiency and safety of path planning.The results show that the number of steps planned in advance is proportional to the convergence speed of the reinforcement learning algorithm.The Sarsa-Dyna will be proposed.The analysis of convergence speed and collision times has been done between the proposed Sarsa-Dyna, Q-learning, Sarsa and Dyna-Q algorithm.The proposed Sarsa-Dyna algorithm can reduce the number of collisions effectively, ensure safety during driving, and at the same time ensure convergence speed.
DrawmaticAR

DrawmaticAR is an AI-AR app capable of creating interactive 3D animated experiences from story words written in the designated section on an AR marker (Magic Paper).