Reviewerly/exHarmony huggingface dataset:
Listening to interruptions: sonic dominance and sonic norms

Interruption is a simple, recognisable, and affectively charged phenomenon. Interruption also has complex, patterned implications. In the aftermath of ethnographic investigations into music’s uses for violence, this essay seeks general principles according to which interruption does help and harm. An account of sonic normativity results, based on the principle that sound is difficult to escape. While epistemically useful, susceptibility to sound is also a vulnerability that social frames variously exploit – or protect by cultivating resilience. “Sonic interruptivity” – the propensity of sound to interrupt – gives dramatic import to styles of performing, recording, and audiencing, and provides a point of interdisciplinary convergence around which schools of thought from theatre performance praxis, culture critique, music theory, and soundscape ecology possess shared insight. Anticipating crises of interruption with rituals of interruption, performing extremes of immunity to interruption, or dominating discourses, invading niches, and projecting styles through frequent interruption, creative practitioners engage interruption as a widely understood communicative device whose very insensitivity constitutes part of its meaning. If this essay takes a normative stance, it is that students of sound should not neglect sonic interruptivity – from subtle elision to aggressive blare, the very uncouthness of interruption signals the attention it deserves.
Teaching redundant residue number system for electronics and computer students

This paper describes a study for teaching number system in a computer arithmetic course and addresses the existing gap in the current course by focusing on the characteristics of applications. A redundant residue number system (RRNS) is an efficient and innovative number system that inherits the interesting properties of both residue number system (RNS) and redundant number systems. It breaks the operands into residues by considering a moduli-set and then signifies the residues by a redundant representation. So, by limiting the carry propagation chain inside and outside the moduli, RRNS proposes more efficient arithmetic units which could be employed in digital signal processing (DSP) applications. Despite the applicability of RRNS, there is not well-organized teaching on RRNS covering the recent achievements. Besides, there is an important question for students and researchers what is the most appropriate number representation for each application category? In this paper, we present a step-by-step education process for RRNS that includes basic concepts of RNS and the redundant number system. Then we address characteristics of different applications that RRNS is suitable to be employed for them. So, the method gives a well-organized proceeding to computer arithmetic designers and students.
On Modal Opposition Within Some Modal Discussive Logics

We consider the issue of the square of opposition for two modal extensions of the discussive logic D 2. To this aim we recall some basic information on discussive logic, but also mention some facts concerning the mentioned extensions of D 2. Our idea is to extend the discussive language with modalities, which although are considered in the context of the discussive logic, but are used only auxiliarily and are absent from its object language.
Positive Dependency Graphs Revisited

Abstract Theory of stable models is the mathematical basis of answer set programming. Several results in that theory refer to the concept of the positive dependency graph of a logic program. We describe a modification of that concept and show that the new understanding of positive dependency makes it possible to strengthen some of these results.
Development of tasks on the discipline “Informatics and Computer Engineering” for students at the Faculty of History

In the process of changing the priorities of the educational paradigm, many tasks arise, one of which is the formation of information technology competence at all levels of education. The current Federal State Educational Standards are based on a competency-based approach. In this regard, it becomes necessary to develop students’ competencies in the field of information technology, which graduates of the Faculty of History should have. The article actualizes the need to develop adapted tasks in the discipline “Informatics and Computer Engineering” for the specialties of the training areas 46.03.01 “History” and 46.03.02 “Documentation Science and Archival Science”. The objectives of the article are to develop tasks that cover the competencies that a graduate should have as a result of mastering a bachelor’s program, to conduct a comparative analysis of informatics textbooks used in the Russian Federation, to identify sections of the course that should be improved based on the results obtained during the experiment and the interests of students, as well as analysis of the level of formation of information technology competence in first-year students of training areas 46.03.01 “History” and 46.03.02 “Document science and archive science” using testing. The purpose of the article is to develop tasks that cover the competencies that a graduate should have as a result of mastering the bachelor’s program, as well as an analysis of the level of formation of information technology competence among students of the first course of training areas 46.03.01 “History” and 46.03.02 “Documentation Science and Archival Science” using testing. As a result of the study, it turned out that the majority of students have an average level of knowledge of the school curriculum in the subject “Informatics”. It is concluded that the study of informatics at school does not have a significant impact on the general level of knowledge of students in the field of information and communication technologies.
A User-Friendly Interface for an Educational Travel English Game Supported with Virtual Reality

In this research, it is aimed to develop a virtual reality application which has the right interface and lets users experience the travel processes and as well as improve their speaking skills for travel English. For this purpose, the first module of the application that is the airport module for Oculus Go was developed to experience with a head-mounted display. On the developed module, a usability test was applied with the eye-tracking technique with 8 participants. User reviews were taken with this test simultaneously. Based on the findings, a user-friendly virtual reality interface was created in the field of education by updating the airport module. As a result of the update, a prototype module has been developed in which the feeling of being presence and immersion can be achieved, immersive and motivating features were revealed.
Watermarking of EEG Data to Provide Security Based on DWT-SVD and Optimized by Firefly Algorithm

A watermark embedding into digital media or signal is called digital watermarking for the purpose of enhancing security from copyright encroachment. In this paper, an optimized and advanced watermarking technique has been proposed, which is based on singular value decomposition (SVD) in the discrete wavelet transform (DWT) domain using the firefly algorithm (FA). In this, a watermark logo is embedded into electroencephalogram (EEG) data. To optimize the scaling factor, robustness and imperceptibility have been considered. Further, the performance of the proposed algorithm is also analyzed against various attacks. The results show the adequacy of the proposed algorithm and indicate a higher value of NCC of 0.95 as robustness and PSNR 51.83 as imperceptibility in contrast with the related existing method.
Proximal stochastic recursive momentum algorithm for nonsmooth nonconvex optimization problems

In this paper, we mainly consider a class of nonconvex nonsmooth optimization problems, whose objective function is the sum of a smooth function with a Lipschitz continuous gradient and a convex nonsmooth function. We first propose a proximal stochastic recursive momentum algorithm(ProxSTORM) with mini-batch for solving the optimization problems and consider its convergence behaviour. Then, based on the Polyak–Łojasiewicz inequality, we establish the global linear convergence rate of ProxSTORM. Finally, some numerical experiments have been conducted to illustrate the efficiency of our method.
Groundwater Classification by Using Fourier Analysis

The article illustrates a statistical technique for the visual representation of geochemical data. Quaternary and Pre-Quaternary groundwater samples from Northern Sinai Peninsula, Egypt, were interpreted statistically using Andrews plots, which use Fourier analysis to transform and represent a set of multivariate data by a waveform pattern. The resulting waveform patterns were classified into low, middle, and high amplitudes, following up the increase in the total dissolved solids of the samples. Comparison with the traditional hydrochemical polygonal Stiff diagrams resulted in a complete matching. The proposed mixing between the Quaternary and Pre-Quaternary aquifers has been proved via the similarity of waveform patterns of the mixed water. The application of Andrews plots is investigated by comparison with the Stiff conventional diagrams. The correlation between different amplitudes and the TDS value of every sample indicates that the amplitude increases with the increase in the salinity.
Programming Smart Contract with Solidity

In the previous chapter, we discussed Ethereum architecture, ecosystem, and decentralized applications. We also described development tools such as MetaMask, Remix, Truffle, and Geth. In this chapter, we are going to learn detailed Solidity programming skills for smart contract and decentralized application development.
Modern Cryptography for ADS-B Systems

Modern cryptography has been proven to be a mature technology to protect the security of wireless communication, so it should be workable to secure ADS-B wireless broadcasts. Specifically, by evaluating the practicability and effectiveness of cryptographic methods in the aviation industry, where technology is complex and cost-saving, it can be concluded that cryptography can indeed safeguard the ADS-B system. In this chapter, we first briefly introduce modern cryptography and then discuss some cryptographic primitives that are proper for securing ADS-B.
Developing Leadership Teams to Improve Human Capacity and Student Learning in Underperforming High-Needs Schools

Schools across the nation have experienced a significant increase in teacher shortages as a result of declining interest in the teacher profession, an escalating student enrollment, and a rising teacher attrition rate.This reduced human capacity of schools is particularly problematic in high poverty and minority schools that are often underperforming academically, resulting in their labeling as "hard-to-staff" or "high-needs" schools (Sutcher, Darling-Hammond, Carver-Thomas, 2016).Studies have consistently reported that the greatest predictor of teachers' employment decisions, especially as it relates to turnover, is their perceptions of the support that they receive from school administration (Author, 2019a;Burkhauser 2017;Boyd et al. 2011;Ladd 2011).This type of support has been found to be particularly critical for retaining new teachers in high-needs schools (Haynes, 2014).However, given the myriad of situations and events that require an administrators' attention on a daily basis, school leaders are often unable to attend to the needs of each teacher, while simultaneously maintaining their other duties, within the time limitations of the day.Indeed, the question of how to build capacity to support teachers , especially in underresourced hard-to-staff schools, is a conundrum.To exacerbate the problem, the working conditions associated with hard-to-staff schools is often correlated with not only higher levels of repeated teacher turnover, but also principal turnover as well, and both have been linked to each other (Jacob et al., 2015) and to lower student academic performance, with the effects being particularly harmful for lower performing Black students (Ronfeldt et al., 2013;Miller, 2009;Author, 2019b).Given these challenges, in recent years, many scholars have encouraged various
Repetitive, Oblivious, and Unlinkable SkNN Over Encrypted-and-Updated Data on Cloud

Location-Based Services (LBSs) depend on a Service Provider (SP) to store data owners' geospatial data and to process data users' queries. For example, a Yelp user queries the SP to retrieve the k nearest Starbucks by submitting her/his current location. It is well-acknowledged that location privacy is vital to users and several prominent Secure k Nearest Neighbor (SkNN) query processing schemes are proposed. We observe that no prior work addresses the requirement of repetitive query after index update and its privacy issue, i.e., how to match a data item from the cloud repetitively in an oblivious and unlinkable manner. Meanwhile, a malicious SP may skip some data items and recommend others due to unfair competition. In this work, we formally define the repetitive query and its privacy objectives and present an Repetitive, Oblivious, and Unlinkable SkNN scheme ROU. Specifically, we design a multi-level structure to organize locations to further improve search efficiency. Second, we integrate data item identity into the framework of existing SkNN query processing. Data owners encrypt their data item identity and location information into a secure index, and data users encrypt a customized identity range of a previously retrieved data item and location information into a token. Next, the SP uses the token to query the secure index to find the specific data item via privacy-preserving range querying. We formally prove the privacy of ROU in the random oracle model. We build a prototype based on a server to evaluate the performance with a real-world dataset. Experimental results show that ROU is efficient and practical in terms of computational cost, communication overhead, and result verification.
Deep Learning Based Webshell Detection Coping with Long Text and Lexical Ambiguity

Webshell is a web page used by hackers for communicating with the web server and network intrusion. To detect webshell, deep learning methods are proposed to automatically extract features and mine semantics from PHP script. Although deep learning methods show a promising perspective, challenges still exist including the challenge of text selection when coping with long PHP script, the challenge of coping with lexical ambiguity in programming language and the challenge of decline of generalization ability to unseen PHPs if training samples are not treated reasonably. To resolve these challenges, we propose a two-stage deep learning webshell detection method. In stage one, a TextRank based sentence-level text selection model is proposed to preserve code semantic via extracting high-value code lines. While in stage two, after the tokenization of selected code lines, a CodeBert based token embedding model is utilized to resolve lexical ambiguity and generate token representation vectors. Down-stream task specified classifier is further utilized to detect webshell and to fine-tune the token embedding model. In training procedure, we split training/validation data set more reasonably to prevent data leakage. Besides, for the additional man labour of disposing false alarm in practical application, an extra large benign PHP data set is utilized for more solid validation. Experiments demonstrate the effectiveness of our method.
A Note on the Security Framework of Two-key DbHtS MACs

Double-block Hash-then-Sum (DbHtS) MACs are a class of MACs that achieve beyond-birthday-bound (BBB) security, including SUM-ECBC, PMAC_Plus, 3kf9, LightMAC_Plus, etc. Recently, Shen et al. (CRYPTO 2021) proposed a security framework for two-key DbHtS MACs in the multi-user setting, stating that when the underlying block cipher is ideal and the universal hash functions are regular and almost universal, the two-key DbHtS MACs achieve 2n/3-bit security. Unfortunately, the regular and universal properties can not guarantee the BBB security of two-key DbHtS MACs. We propose three counter-examples which are proved to be 2n/3-bit secure in the multi-user setting by the framework, but can be broken with probability 1 using only $$\mathcal {O}(2^{n/2})$$ queries even in the single-user setting. We also point out the miscalculation in their proof leading to such a flaw. However, we haven't found attacks against 2k-SUM-ECBC, 2k-PMAC_Plus, and 2k-LightMAC_Plus proved 2n/3-bit security in their paper.
Maliciously Secure Multi-party PSI with Lower Bandwidth and Faster Computation

Private Set Intersection (PSI) allows a set of mutually distrustful parties, each holds a private data set, to compute the intersection of all sets, such that no information is revealed except for the intersection. The state-of-the-art PSI protocol (Garimella et al., CRYPTO'21) in the multi-party setting tolerating any number of malicious corruptions requires the communication bandwidth of $$O(n\ell |\mathbb F |)$$ bits for the central party $$P_0$$ due to the star architecture, where n is the number of parties, $$\ell $$ is the size of each set and $$|\mathbb F |$$ is the size of an exponentially large field $$\mathbb F $$ . When n and $$\ell $$ are large, this forms an efficiency bottleneck (especially for networks with restricted bandwidthes). In this paper, we present a new multi-party PSI protocol in dishonest-majority malicious setting, which reduces the communication bandwidth of the central party $$P_0$$ from $$O(n\ell |\mathbb F |)$$ bits to $$O(\ell |\mathbb F |)$$ bits using a tree architecture. Furthermore, our PSI protocol reduces the expensive LPN encoding operations performed by $$P_0$$ by a factor of n as well as the computational cost by $$2n\ell $$ hash operations in total. Additionally, while the multi-party PSI protocol (Garimella et al., CRYPTO'21) with a single output is secure, we present a simple attack against its multi-output extension, which allows an adversary to learn more information on the sets of honest parties beyond the intersection of all sets.
Query-Efficient Black-Box Adversarial Attack with Random Pattern Noises

Adversarial examples are one of the largest vulnerability of deep neural networks. An attacker can deceive the classifiers easily with the malicious inputs (called adversarial examples), which perturbations are slightly added to benign inputs. Various attack methods have been studied in both white-box and black-box settings, and some methods achieve high attack success rates even in the black-box settings; that is, the attacker is restricted to only query accesses to the target network. In this paper, we propose a simple hyperparameter-free score-based black-box $$\ell _\infty $$ -adversarial attack using local uniform noises and a random search. Specifically, we construct adversarial perturbations by combining local uniform noises such as vertical-wise and horizontal-wise, and incorporate this idea into the random search method to update the perturbation sequentially. We evaluate our method in terms of attack success rates and query efficiency using models that classify common datasets CIFAR-10 and ImageNet. We show that our method achieves higher attack success rates and query efficiency than previous attack methods, especially in low-query budgets on both untargeted and targeted attack settings. We also examine attacks to adversarially trained models and discuss the effect of local uniform noises on these models. Furthermore, we show that our method achieves relatively high attack success rates and query efficiency on average against input-transformation-based defense methods, and is virtually unaffected by these defense methods.
ODDITY: An Ensemble Framework Leverages Contrastive Representation Learning for Superior Anomaly Detection

Ensemble approaches are promising for anomaly detection due to the heterogeneity of network traffic. However, existing ensemble approaches lack applicability and efficiency. We propose ODDITY, a new end-to-end data-driven ensemble framework. ODDITY use Diverse Autoencoders trained on a pre-clustered subset with contrastive representation learning to encourage base-leaners to give distinct predictions. Then, ODDITY combines the extracted features with a supervised gradient boosting meta-learner. Experiments using benchmarking and real-world network traffic datasets demonstrate that ODDITY is superior in terms of efficiency and precision. ODDITY averages 0.8350 AUPRC on benchmarking datasets (10% better than traditional machine learning algorithms and 6% better than the state-of-the-art semi-supervised ensemble method). ODDITY also outperforms the state-of-the-art on real-world datasets regarding better detection accuracy and speed. Moreover, ODDITY is more resilient to evasion attacks and has a promising potential for unsupervised anomaly detection.
New Results of Breaking the CLS Scheme from ACM-CCS 2014

At ACM-CCS 2014, Cheon, Lee and Seo introduced a particularly fast additively homomorphic encryption scheme (CLS scheme) based on a new number theoretic assumption, the co-Approximate Common Divisor (co-ACD) assumption. However, at Crypto 2015, Fouque et al. presented several lattice-based attacks that effectively devastated this scheme. They proved that a few known plaintexts are sufficient to break both the symmetric-key and the public-key variants, and they gave a heuristic lattice method for solving the search co-ACD problem. In this paper, we mainly improve in terms of the number of samples, and propose a new key-retrieval attack. We first give an effective attack by Coppersmith's method to break the co-ACD problem with $$N=p_1\cdots p_n$$ is known. If n is within a certain range, our work is theoretically valid for a wider range of parameters. When $$n=2$$ , we can successfully solve it with only two samples, that is the smallest number of needed samples to the best of our knowledge. A known plaintext attack on the CLS scheme can be simply converted to solving the co-ACD problem with a known N, again requiring fewer samples than before to retrieve the private key. Finally, we show a ciphertext-only attack with a hybrid approach of direct lattice and Coppersmith's method that can recover the key with a smaller number of ciphertexts and without any restriction on the plaintext size, but N is needed. All of our attacks are heuristic, but we have experimentally verified that these attacks work efficiently for the parameters proposed in the CLS scheme, which can be broken in seconds by experiments.
Conditional Cube Attacks on Full Members of KNOT-AEAD Family

KNOT is a family of permutation-based lightweight AEAD and hashing algorithms, which is submitted to the NIST Lightweight Cryptography Standardization process and becomes one of the 32 candidates in the second round. In this paper, we focus on the security of the initialization phase of full members of KNOT-AEAD family against conditional cube attacks in the nonce-respecting setting. To be specific, we introduce a conditional cube attack framework by exploiting linearization technique and division property based degree evaluation. With this framework, we can use a conditional cube set including only one public variable to construct a conditional cube distinguisher, and then to recovery one key bit with a negligible time and data. As a result, we present the 8-, 8-, 8-, and 9-round practical full key-recovery attacks on KNOT-AEAD (128, 256, 64), KNOT-AEAD (128, 384, 192), KNOT-AEAD (192, 384, 96) and KNOT-AEAD (256, 512, 128), and a 9-round theoretical full key-recovery attack on KNOT-AEAD (128, 384, 192) with a complexity of $$2^{51.72}$$ and a negligible data complexity. To the best of our knowledge, this is the first time to achieve the practical full key-recovery attacks on all members of round-reduced KNOT-AEAD.