Reviewerly/exHarmony huggingface dataset:
EMShepherd: Detecting Adversarial Samples via Side-channel Leakage

Deep Neural Networks (DNN) are vulnerable to adversarial perturbations — small changes crafted deliberately on the input to mislead the model for wrong predictions. Adversarial attacks have disastrous consequences for deep learning empowered critical applications. Existing defense and detection techniques both require extensive knowledge of the model, testing inputs and even execution details. They are not viable for general deep learning implementations where the model internal is unknown, a common ‘black-box’ scenario for model users. Inspired by the fact that electromagnetic (EM) emanations of a model inference are dependent on both operations and data and may contain footprints of different input classes, we propose a framework, EMShepherd, to capture EM traces of model execution, perform processing on traces and exploit them for adversarial detection. Only benign samples and their EM traces are used to train the adversarial detector: a set of EM classifiers and class-specific unsupervised anomaly detectors. When the victim model system is under attack by an adversarial example, the model execution will be different from executions for the known classes, and the EM trace will be different. We demonstrate that our air-gapped EMShepherd can effectively detect different adversarial attacks on a commonly used FPGA deep learning accelerator for both Fashion MNIST and CIFAR-10 datasets. It achieves a detection rate on most types of adversarial samples, which is comparable to the state-of-the-art ‘white-box’ software-based detectors.
POSTER: Performance Characterization of Binarized Neural Networks in Traffic Fingerprinting

Traffic fingerprinting allows making inferences about encrypted traffic flows through passive observation. They have been used for tasks such as network performance management and analytics and in attacker settings such as censorship and surveillance. A key challenge when implementing traffic fingerprinting in real-time settings is how the state-of-the-art traffic fingerprint models can be ported into programmable in-network computing devices with limited computing resources. Towards this, in this work, we characterize the performance of binarized traffic fingerprinting neural networks that are efficient and well-suited for in-network computing devices and propose a new data encoding method that is better suited for network traffic. Overall, we show that the proposed binary neural network with first-layer binarization and last-layer quantization reduces the performance requirement of hardware equipment while retaining the accuracies of those models of binary datasets over 70%. Furthermore, when combined with our proposed encoding algorithm, accuracies of binarized models of numeric datasets show further improvements to achieve over 65% accuracy.
Masked Language Model Based Textual Adversarial Example Detection

Adversarial attacks are a serious threat to the reliable deployment of machine learning models in safety-critical applications. They can misguide current models to predict incorrectly by slightly modifying the inputs. Recently, substantial work has shown that adversarial examples tend to deviate from the underlying data manifold of normal examples, whereas pre-trained masked language models can fit the manifold of normal NLP data. To explore how to use the masked language model in adversarial detection, we propose a novel textual adversarial example detection method, namely Masked Language Model-based Detection (MLMD), which can produce clearly distinguishable signals between normal examples and adversarial examples by exploring the changes in manifolds induced by the masked language model. MLMD features a plug and play usage (i.e., no need to retrain the victim model) for adversarial defense and it is agnostic to classification tasks, victim model’s architectures, and to-be-defended attack methods. We evaluate MLMD on various benchmark textual datasets, widely studied machine learning models, and state-of-the-art (SOTA) adversarial attacks (in total 3*4*4 = 48 settings). Experimental results show that MLMD can achieve strong performance, with detection accuracy up to 0.984, 0.967, and 0.901 on AG-NEWS, IMDB, and SST-2 datasets, respectively. Additionally, MLMD is superior, or at least comparable to, the SOTA detection defenses in detection accuracy and F1 score. Among many defenses based on the off-manifold assumption of adversarial examples, this work offers a new angle for capturing the manifold change. The code for this work is openly accessible at https://github.com/mlmddetection/MLMDdetection.
SoK: Systematizing Attack Studies in Federated Learning – From Sparseness to Completeness

Federated Learning (FL) is a machine learning technique that enables multiple parties to collaboratively train a model using their private datasets. Given its decentralized nature, FL has inherent vulnerabilities that make it susceptible to adversarial attacks. The success of an attack on FL depends upon several (latent) factors, including the adversary’s strength, the chosen attack strategy, and the effectiveness of the defense measures in place. There is a growing body of literature on empirical attack studies on FL, but no systematic way to compare and evaluate the completeness of these studies, which raises questions about their validity. To address this problem, we introduce a causal model that captures the relationship between the different (latent) factors, and their reflexive indicators, that can impact the success of an attack on FL. The proposed model, inspired by structural equation modeling, helps systematize the existing literature on FL attack studies and provides a way to compare and contrast their completeness. We validate the model and demonstrate its utility through experimental evaluation of select attack studies. Our aim is to help researchers in the FL domain design more complete attack studies and improve the understanding of FL vulnerabilities.
Investigating Traffic Analysis Attacks on Apple iCloud Private Relay

The iCloud Private Relay (PR) is a new feature introduced by Apple in June 2021 that aims to enhance online privacy by protecting a subset of web traffic from both local eavesdroppers and websites that use IP-based tracking. The service is integrated into Apple’s latest operating systems and uses a two-hop architecture where a user’s web traffic is relayed through two proxies run by disjoint entities.
New Characterization of the Factor Refinement Algorithm with Applications

We propose a new, simple characterization of the output of the Factor Refinement Algorithm, formally introduced and analyzed in [3]. Our characterization relies only on basic linear algebra. As applications, we obtain a non-adaptive algorithm that computes the square-free part of an integer, given oracle access to Euler’s Totient function, and identify a class of multiplicative functions which is polynomial-time equivalent to computing radicals.
Transcendence Certificates for D-finite Functions

Although in theory we can decide whether a given D-finite function is transcendental, transcendence proofs remain a challenge in practice. Typically, transcendence is certified by checking certain incomplete sufficient conditions. In this paper we propose an additional such condition which catches some cases on which other tests fail.
The role of learning in complex problem solving using MicroDYN

It is still an open question which cognitive and non-cognitive personality traits are useful for describing and explaining behaviour and performance in complex problems. During complex problem solving (CPS), problem solvers have to interact with the task in a way in which learning ability might be beneficial for successful task completion. By investigating the relationship between learning ability and CPS, while accounting for interactions between complex system characteristics and person characteristics, this paper aims to understand the role of learning processes in CPS more closely. In a sample of N = 241 participants, we performed a preregistered analysis to investigate the relationship between knowledge acquisition performance in a CPS test (MicroDYN) and learning test performance (ADAFI) with a multilevel modeling approach across 10 CPS systems with various characteristics. In line with our expectations, we replicated previous findings on a relationship between learning test and MicroDYN performance and found this relationship to be more pronounced in systems with (vs. without) autonomous changes. Further system and person characteristics also showed effects as expected, with better performance in systems with lower complexity, with more experience with the task, and with more strategic exploration behaviour. Our results provide further evidence for the notion that learning is an important component for the successful completion of CPS tasks.
Developing visualization and visual analytics skills in informatics course

The article explores theoretical and practical issues of developing skills in data visualization and visual analytics in informatics course (including examples of constructing analytical, tabular and visual models using diagrams).This study includes a review of bibliographic sources on the problems of developing visualization and visual analytics skills in the course of educational and scientific activities. It also includes development of a visualization and visual analysis competency structure. In the course of the study were applied methods of modeling (analytical and graphical models), methods of visualization and data systematization. In particular, such components of the competency of visualization and visual analysis as analytical, critical, abstract-logical, visual-figurative, spatial-figurative, associative, systemic, algorithmic were proposed and analyzed. The methods of forming this competency in the course of informatics were also considered.The article not only describes but also analyzes the problems, connected with formation of visualization and visual analysis competency. The study also presents methodological guidelines for the laboratory practical work, developed to teach the skills of graphic analytics in the informatics course, intended for students of the Faculty of Economics of the Bashkir State Agrarian University.The article is aimed at a wide range of specialists in the field of data analytics and visualization, including IT teachers, researchers, etc.
Digital transformation of the quality management process of intermediate certification of students

The article proposes a technology for the digital transformation of the quality management process of intermediate certification.The analysis of experience in creating and implementing models, technologies, and systems that implement the tasks of information support of the process of intermediate certification is carried out. Based on the analysis, the authors present their version of the problem of quality management of the process of intermediate certification in an educational institution in the context of digital transformation.The digital transformation of the quality management process of the intermediate certification is interpreted as the transformation of the entire process of intermediate certification, the technology of its organization and conduction, aimed at design and implementation of an information management system in the framework of digital policy of educational institutions which solve the problem of quality information support of intermediate certification (ensuring relevance, reliability, integrity, legal correctness, addressability, information protection) and the problem of managing of the process of intermediate certification (planning, monitoring, control, decision-making to improve), minimizing corruption risks and forming a digital trail of students.On the example of the Siberian State University of Geosystems and Technologies, the experience of implementing the technology of information support of intermediate certification and the formation of a student’s digital footprint is presented. The technology is distinguished by the presence of the function of “self-control” of the quality of information, which minimizes various risks of loss of information quality, including corruption.Based on the experience of automating the quality management process of interim certification in various educational institutions under different learning models (linear, credit, mixed), the authors presented a number of recommendations that can be useful in the process of digital transformation of educational institutions.
Online Hate Speech and the Representations of Refugees in #VatanimdaMülteci (#RefugeeinMyCountry)

In Turkey, a dramatic event involving Syrian refugees happened because of allegations that Syrian men had harassed Turkish women. Following the case, Turkish citizens generated a popular hashtag of #VatanimdaMülteci (#RefugeeinMyCountry) to share negative opinions, feelings, and ideologies towards Syrian refugees. This study is an examination of how Twitter was used to produce and spread hate speech discourse directed at refugees and focus on the representations of refugees through the online environment to provide information about anti-refugee rhetoric for specific nations. A quantitative and qualitative content analysis was carried out of the tweets under the hashtag #VatanimdaMülteci. The results demonstrate that a significant number of tweets contained hate speech comments designed to criticise Turkish government policies regarding refugees, such as the Turkish citizenship provided to refugees and their ability to open businesses in Turkey. The study shows that the hospitality of Turkish citizens turned into hostility over time.
Are There Hate Speeches on Spanish Television?

Few academic studies focus on hate speech on television. That is partly due to the difficulty of obtaining and analyzing broadcast content. However, the spread of those hate messages implies a high reach. For this study, the researchers propose an experimental methodology to analyze the content broadcast on the 24 hours of the five Spanish free-to-air television channels over one year (2020). The authors examined the presence of abusive or hurtful vocabulary and quantified the insults aired. They extracted and studied a sample through content analysis to detect if those insults were accompanied, in any way, by expressions of hatred. Although the messages the researchers studied for this article cannot be considered speeches or hate crimes, there are some offensive comments related to gender, race, or religion, mainly on fictional products.
The Semiotics of Xenophobia and Misogyny on Digital Media

This chapter raises three questions: a) concerns a synthesis of the classic contributions of the reference semiotic authors that are considered when analysing hate speech in social media; b) entails presenting a case study that is analysed precisely with that analysis synthesis; c) shows the usefulness and interest of this type of analysis in investigations of hate speech. It offers a semiotic model for analysing misogynistic and xenophobic hate speech from digital news media on Twitter. The case study comprises the news published by El Mundo (Spain) from its users on social media, and the 33 comments generated, as a reason for this publication, during January 2021. This serves as the basis of semiotic analysis for understanding the phenomenon. The results visualise the semiotic analyses for understanding the dissemination of expressions. This approach thus helps reveal the levels of intensity, the denotative and connotative differences, the destructive-constructive and intertextual nature of messages, and sheds light on the different symbolic structures associated with hate speech.
Dynamic Programming With Python

Algorithms are at the heart of computer programming. They form the basis of all software applications and help to solve complex computational problems. Various problem-solving strategies involve divide and conquer, greedy, recursion, dynamic programming, backtracking, etc. This can be used to solve optimization problems that have overlapping subproblems. It aims to find an optimal solution by breaking down a problem into sub-problems in order to manage the complexity of the problem-solving and remembering their solutions in order to avoid repeating computation time in future steps. Mathematical optimization is a crucial component of dynamic programming, and it allows us to efficiently solve a wide range of problems that involve making decisions over time. This chapter discusses dynamic programming's relevance, mathematical optimization, ability to solve a wide range of issues, important qualities, and top-down and bottom-up problem-solving methodologies. Dynamic programming solves some typical computational problems effectively, and Python code is supplied for reference.
The Interaction Between Offensive and Hate Speech on Twitter and Relevant Social Events in Spain

Hate speech is one of the major concerns of Europe. Different studies, mainly in English language, have been carried out to analyze hate speech, many of them from a theoretical perspective. Here, it is presented an observational study about hate speech poured on Twitter in Spanish regarding to five social important events: Women´s Day, International LGTBQ+ Pride Day, Spain National Day, national elections, and regional elections. Three different experiments were carried out; two used deep learning algorithms to automatically classify tweets, meanwhile, the latest tweets were classified by a human. Results showed that these events significantly triggered hate speech, yet results differed between experiments, and also depending on the nature of the events. A better understanding of the mechanisms of hate speech propagation can help improve policies in Spain or in countries with similar characteristics, and thus help law enforcement and other institutions to address the scourge of hate crimes.
Hate Speech or Hate Shot?

Nowadays, the Muslim community is one of the most discriminated groups in Europe. Anti-Islam hate speeches circulate online and offline especially through the intense use social media, fake news, bots, and click-baiting practices. However even if Muslim discriminatory practices have been gaining more media relevance in the recent years, anti-Muslim stereotypes date back far beyond our times. Using the theoretical frameworks developed by Said and Moscovici this research aims to analyze 31 semi-structured interviews conducted with the volunteers of the Amnesty International's Hate Speech Task Force, to investigate which are the most persistent anti-Muslim representations in Europe and Italy today. In bringing the theory into practice this work will explore the dynamics occurring on Facebook among users which show polarized and intolerant positions while engaging an Islam-related conversation. This specific case study will allow to show how and why old anti-Islam stereotypes persist almost unchanged from an offline to an online world.
Methods to compute interesting consequences

In modern deductive analysis, the main tasks include the following: finding proof of a given statement using axioms and rules of inference and checking the correctness of a given consequence from certain premises. Little is currently known about inference problems with predetermined properties (problems with interesting consequences). There are no clear answers to the following questions: what properties are inherent in an interesting consequence and how to calculate an interesting consequence? To solve these problems, it is proposed to use the methods of n-tuple algebra (NTA) based on properties of the Cartesian product of sets. The objects of NTA are arbitrary n-ary relations, which can be interpreted as the formulas of mathematical logic. They are matrix-like structures which cells do not contain elements, but subsets of the corresponding attributes. In NTA, operations (addition, generalized intersection and generalized union) in the tuple algebra correspond to logical connectives of mathematical logic (negation, conjunction, disjunction), and the generalized inclusion relation corresponds to the derivability relation. The calculation of quantifier operations is performed using operations on attributes (adding a dummy attribute, which corresponds to the generalization rule in predicate calculus, and eliminating an attribute). For two of the four types of NTA structures, the elimination of attributes corresponds to computing the projection of a relation. To derive interesting consequences in NTA, a structure called the minimal consequence is proposed, which is equal to the generalized intersection of premises. Interesting consequences are calculated as projections of the minimal consequence. As a result of calculations and checks, consequences are obtained with a reduced or a given composition of variables, as well as with a reduced amount of notation.
FORMATION OF ANTI-CORRUPTION COMPETENCE STUDENTS OF ANGARSK STATE UNIVERSITY TECHNICAL UNIVERSITY

The article discusses measures aimed at the formation of anti-corruption competence of students. The directions of work on the prevention of corruption in the country's universities are summarized and listed. The practice of anti-corruption work among students at the Angarsk State Technical University is given. It is concluded that it is necessary to carry out a set of measures aimed at strengthening the perception of corruption as a negative social phenomenon by the student youth. It is proved that the goal of the higher school system in the Russian Federation in modern conditions is to graduate students with their own open, pronounced civic position, with a developed rejection and aversion to corruption
ICT IN EDUCATION AND VOCATIONAL TRAININ

The article discusses interesting examples of the use of models of education and vocational training
Towards enhancing ecological validity in user studies: a systematic review of guidelines and implications for QoE research

Abstract The concept of conducting ecologically valid user studies is gaining traction in the field of Quality of Experience (QoE). However, despite previous research exploring this concept, the increasing volume of studies has made it challenging to obtain a comprehensive overview of existing guidelines and the key aspects to consider when designing ecologically valid studies. Therefor this paper aims to provide a systematic review of research articles published between 2011 and 2021 that offer insight into conducting ecologically valid user studies. From an initial count of 782 retrieved studies, a final count of 12 studies met the predefined criteria and were included in the final review. The systematic review resulted in the extraction of 55 guidelines that provide guidance towards conducting ecologically valid user studies. These guidelines have been grouped within 8 categories ( Environment, Technology, Content, Participant Recruitment, User Behavior, Study Design, Task and data collection ) overarching the three main dimensions (Setting, Users and Research Methodology). Furthermore, the review discusses: the flip side of ecological validity, the implications for QoE research, as well as provides a basic visualisation model for assessing the ecological validity of a study. In conclusion, the current review indicates that future research should address more in detail how and when research approaches characterized by high ecological validity (and correspondingly, low internal validity) and those characterized by low ecological validity (and normally high internal validity) can best complement each other in order to better understand the key factors influencing QoE for various types of applications, user segments, settings. Further, we argue that more transparency around the (sub)dimensions of ecological validity with respect to a particular study or set of studies is necessary.