Reviewerly/exHarmony huggingface dataset:
Intrinsic Resiliency of S-Boxes Against Side-Channel Attacks–Best and Worst Scenarios

Constructing S-boxes that are inherently resistant against side-channel attacks is an important problem in cryptography. By using an optimal distinguisher under an additive Gaussian noise assumption, we clarify how a defender (resp., an attacker) can make side-channel attacks as difficult (resp., easy) as possible, in relation with the auto-correlation spectrum of Boolean functions. We then construct balanced Boolean functions that are optimal for each of these two scenarios. Generalizing the objectives for an S-box, we analyze the auto-correlation spectra of some well-known S-box constructions in dimensions at most 8 and compare their intrinsic resiliency against side-channel attacks. Finally, we perform several simulations of side-channel attacks against the aforementioned constructions, which confirm our theoretical approach.
Binary Particle Swarm Optimization Based Feature Selection (BPSO-FS) for Improving Breast Cancer Prediction

Breast cancer is currently one of the leading causes of cancer-related deaths among women around the world. Although the severity of the disease is undeniable, an efficient early diagnosis of the disease can lead to a much higher chance of survival for the patients. Effective clinical decision support systems (CDSS) could potentially be of very high utility for medical practitioners, in this regard. In this paper, a binary Particle Swarm Optimization (BPSO) based feature selection approach is presented, which can be used to improve the performance of automatic breast cancer prediction CDSS. The key idea is to formulate the problem of feature selection in terms of a discrete optimization problem, with appropriate data-driven objective function. The average cost of bad feature selection is considered as the objective function to minimize, in this work. Multiple evaluation metrics like average prediction accuracy, sensitivity, specificity and area under the curve (AUC) of the receiver operating characteristics (ROC) curve have been considered in this work, to evaluate the performance of the prediction system. Average prediction accuracies of 80.83% and 98.24% have been observed respectively for the Breast Cancer Coimbra Dataset (BCCD) and Breast Cancer Wisconsin Diagnostic Dataset (BCWDD) after feature selection is performed. When pre-feature selection and post feature selection performances are compared, overall improvements of 4–8% and 1–4% have been observed across all the evaluation metrics for BCCD and BCWDD respectively, suggesting the potential applicability of the proposed approach in real diagnostic settings.
FUTURE TEACHERS RESOURCE-BASED LEARNING SYSTEM: EXPERIENCE OF HIGHER EDUCATION INSTITUTIONS IN POLTAVA CITY, UKRAINE

The article explores the problem of introducing resource-based learning (RBL) in higher education institutions as a holistic dynamic process of organizing and stimulating the independent cognitive activity of students for mastering the skills of the active transformation of the information environment, which involves the optimal use of the triad "student-teacher-librarian" of the consolidated personnel, logistical, methodological, financial and information resources.The effectiveness of the RBL system of future teachers, which consists of 5 subsystems (conceptual-target, structural-content, process-technological, motivational, diagnostic and productive) is theoretically substantiated and experimentally verified.The RBL system was implemented in the higher educational establishments of Poltava region.The organizational and pedagogical conditions of the implementation of the RBL system of future teachers as a set of interconnected circumstances, measures ensuring the purposeful management of the process of mixed learning, and the rules established in the system for ensuring high quality of professional training in higher educational establishments are identified.On the basis of pedagogical experiment was made the Implementation of future teachers in the RBL of higher educational establishments in Poltava region (Poltava V.
Modified BBO-Based Multivariate Time-Series Prediction System With Feature Subset Selection and Model Parameter Optimization

Multivariate time-series prediction is a challenging research topic in the field of time-series analysis and modeling, and is continually under research. The echo state network (ESN), a type of efficient recurrent neural network, has been widely used in time-series prediction, but when using ESN, two crucial problems have to be confronted: 1) how to select the optimal subset of input features and 2) how to set the suitable parameters of the model. To solve this problem, the modified biogeography-based optimization ESN (MBBO-ESN) system is proposed for system modeling and multivariate time-series prediction, which can simultaneously achieve feature subset selection and model parameter optimization. The proposed MBBO algorithm is an improved evolutionary algorithm based on biogeography-based optimization (BBO), which utilizes an S -type population migration rate model, a covariance matrix migration strategy, and a Lévy distribution mutation strategy to enhance the rotation invariance and exploration ability. Furthermore, the MBBO algorithm cannot only optimize the key parameters of the ESN model but also uses a hybrid-metric feature selection method to remove the redundancies and distinguish the importance of the input features. Compared with the traditional methods, the proposed MBBO-ESN system can discover the relationship between the input features and the model parameters automatically and make the prediction more accurate. The experimental results on the benchmark and real-world datasets demonstrate that MBBO outperforms the other traditional evolutionary algorithms, and the MBBO-ESN system is more competitive in multivariate time-series prediction than other classic machine-learning models.
A NIS Directive Compliant Cybersecurity Maturity Assessment Framework

The EU NIS Directive introduces obligations related to the security of the network and information systems for Operators of Essential Services and for Digital Service Providers.Moreover, National Competent Authorities for cybersecurity are required to assess compliance with these obligations.This paper describes a novel Cybersecurity Maturity Assessment Framework (CMAF) that is tailored to the NIS Directive requirements.CMAF can be used either as a self-assessment tool from Operators of Essential Services and Digital Service Providers or as an audit tool from the National Competent Authorities for cybersecurity.
Approximation Schemes for ReLU Regression

We consider the fundamental problem of ReLU regression, where the goal is to output the best fitting ReLU with respect to square loss given access to draws from some unknown distribution. We give the first efficient, constant-factor approximation algorithm for this problem assuming the underlying distribution satisfies some weak concentration and anti-concentration conditions (and includes, for example, all log-concave distributions). This solves the main open problem of Goel et al., who proved hardness results for any exact algorithm for ReLU regression (up to an additive $\epsilon$). Using more sophisticated techniques, we can improve our results and obtain a polynomial-time approximation scheme for any subgaussian distribution. Given the aforementioned hardness results, these guarantees can not be substantially improved.\n\nOur main insight is a new characterization of {\em surrogate losses} for nonconvex activations. While prior work had established the existence of convex surrogates for monotone activations, we show that properties of the underlying distribution actually induce strong convexity for the loss, allowing us to relate the global minimum to the activation's {\em Chow parameters.}
Staged selective parser combinators

Parser combinators are a middle ground between the fine control of hand-rolled parsers and the high-level almost grammar-like appearance of parsers created via parser generators. They also promote a cleaner, compositional design for parsers. Historically, however, they cannot match the performance of their counterparts. This paper describes how to compile parser combinators into parsers of hand-written quality. This is done by leveraging the static information present in the grammar by representing it as a tree. However, in order to exploit this information, it will be necessary to drop support for monadic computation since this generates dynamic structure. Selective functors can help recover lost functionality in the absence of monads, and the parser tree can be partially evaluated with staging. This is implemented in a library called Parsley.
A Bio-Inspired Reservoir-Computer for Real-Time Stress Detection From ECG Signal

This letter presents the first on-chip bio-inspired reservoir computer (RC) prototype implemented in a 65-nm CMOS. The RC comprises 50 time-multiplexed neurons, and each neuron embeds a strong nonlinearity in a feedback loop. The RC applies a nonlinear transformation to the input and projects it to high-dimensional space, thus allowing linear separation by a simple logistic-regression (LR) layer implemented off-chip. We demonstrate real-time stress detection from electrocardiogram (ECG) signals using the RC. The RC achieves 93% classification accuracy which is 6% better than the state-of-the-art digital classifiers. Operating at 40 kHz, the prototype consumes 27.5 nJ/classification which is $7\times $ lower than the state-of-the-art ECG processors performing similar complexity classification task.
Cache-Based Privacy Preserving Solution for Location and Content Protection in Location-Based Services

Location-Based Services (LBSs) are playing an increasingly important role in people’s daily activities nowadays. While enjoying the convenience provided by LBSs, users may lose privacy since they report their personal information to the untrusted LBS server. Although many approaches have been proposed to preserve users’ privacy, most of them just focus on the user’s location privacy, but do not consider the query privacy. Moreover, many existing approaches rely heavily on a trusted third-party (TTP) server, which may suffer from a single point of failure. To solve the problems above, in this paper we propose a Cache-Based Privacy-Preserving (CBPP) solution for users in LBSs. Different from the previous approaches, the proposed CBPP solution protects location privacy and query privacy simultaneously, while avoiding the problem of TTP server by having users collaborating with each other in a mobile peer-to-peer (P2P) environment. In the CBPP solution, each user keeps a buffer in his mobile device (e.g., smartphone) to record service data and acts as a micro TTP server. When a user needs LBSs, he sends a query to his neighbors first to seek for an answer. The user only contacts the LBS server when he cannot obtain the required service data from his neighbors. In this way, the user reduces the number of queries sent to the LBS server. We argue that the fewer queries are submitted to the LBS server, the less the user’s privacy is exposed. To users who have to send live queries to the LBS server, we employ the l-diversity, a powerful privacy protection definition that can guarantee the user’s privacy against attackers using background knowledge, to further protect their privacy. Evaluation results show that the proposed CBPP solution can effectively protect users’ location and query privacy with a lower communication cost and better quality of service.
Authorized Attribute-Based Encryption Multi-Keywords Search with Policy Updating

Attribute-based encryption is cryptographic techniques that provide flexible data access control to encrypted data content in cloud storage. Each trusted authority needs proper management and distribution of secret keys to the user’s to only authorized user’s attributes. However existing schemes cannot be applied multiple authority that supports only a single keywords search compare to multi keywords search high computational burden or inefficient attribute’s revocation. In this paper, a ciphertext policy attribute-based encryption (CP-ABE) scheme has been proposed which focuses on multi-keyword search and attribute revocation by new policy updating feathers under multiple authorities and central authority. The data owner encrypts the keywords index under the initial access policy. Moreover, this paper addresses further issues such as data access, search policy, and confidentiality against unauthorized users. Finally, we provide the correctness analysis, performance analysis and security proof for chosen keywords attack and search trapdoor in general group model using DBDH and DLIN assumption.
Pronoun interpretation in Mandarin Chinese follows principles of Bayesian inference

Successful natural language understanding requires that comprehenders be able to resolve uncertainty in language. One source of potential uncertainty emerges from a speaker’s choice to use a pronoun (e.g., he, she, they), since pronouns often do not fully specify the speaker’s intended referent. Nevertheless, comprehenders are typically able to interpret pronouns rapidly despite having limited cognitive resources. Here we report three pronoun interpretation experiments that investigate whether comprehenders reverse-engineer a speaker’s referential intentions based on Bayesian principles, as documented in previous studies for English. Using Mandarin Chinese, we test the generality of the Bayesian pronoun interpretation theory, and further evaluate the predictions of the theory in ways that are not possible in English. Our results lend both qualitative and quantitative support to a cross-linguistically general Bayesian theory of pronoun interpretation.
A recent renaissance in privacy law

Considering the recent increased attention to privacy law issues amid the typically slow pace of legal change.
Single-photon switch controlled by a qubit embedded in an engineered electromagnetic environment

A single-photon switch is an important element for the building of scalable quantum networks. In this paper, we propose a feasible scheme for efficient single-photon switching. The proposed switch is controlled by a state of a qubit formed by the pair of the lowest levels of a three-level system (qutrit) coupled to a resonator. This resonator-qutrit system comprises a switching unit of the considered setup. For suppression of the Purcell relaxation of the control qubit, the switching unit is embedded into a coupled-resonator array serving as an engineered electromagnetic environment with a band gap on a qubit transition frequency. We discuss the possible implementation of the considered single-photon switch on the microwave circuit QED architecture. We demonstrate that high switching contrasts can be attained for the parameters achievable for the state-of-the-art superconducting circuit QED setups.
An Image Encryption Algorithm Based on Multichaotic System and DNA Coding

In this paper, an improved two-dimensional logistic-sine coupling map (N2D-LSCM) and an improved Henon map (NHenon) are proposed. Furthermore, by combining N2D-LSCM and NHenon map, an image encryption algorithm is proposed based on these two chaotic systems and DNA coding. The chaotic sequences generated by N2D-LSCM are used as the parameters of NHenon. In the scrambling stage, DNA encoding is carried out for pixels after scrambling by two chaotic sequences generated by N2D-LSCM; in the stage of diffusion, DNA random coding acts on random matrix obtained by two chaotic sequences generated by NHenon, and DNA XOR operation is carried out with the image obtained in the scrambling stage to diffuse. Compared with other 2D map for image encryption algorithm, this algorithm exhibits good security and holds high efficiency.
Identity management using permissioned blockchain

Authenticating a person's identity has always been a challenge. While attempts are being made by government agencies to address this challenge, the citizens are being exposed to a new age problem of Identity management. The sharing of photocopies of identity cards in order to prove our identity is a common sight. From score-card to Aadhar-card, the details of our identity has reached many unauthorized hands during the years. In India the identity thefts accounts for 77% [1] of the fraud cases, and the threats are trending. Programs like e-Residency by Estonia[2], Bitnation using Ethereum[3] are being devised for an efficient Identity Management. Even the US Home Land Security is funding a research with an objective of “Design information security and privacy concepts on the Blockchain to support identity management capabilities that increase security and productivity while decreasing costs and security risks for the Homeland Security Enterprise (HSE).” [4] This paper will discuss the challenges specific to India around Identity Management, and the possible solution that the Distributed ledger, hashing algorithms and smart contracts can offer. The logic of hashing the personal data, and controlling the distribution of identity using public-private keys with Blockchain technology will be discussed in this paper.
An improved machine learning and artificial intelligence algorithm for classroom management of English distance education

As an inevitable trend in the development of English teaching, English distance education needs to use artificial intelligence to control the classroom, so as to improve the degree of control of teacher over the classroom. Based on the machine learning algorithm, according to the needs of English distance education classroom management, this paper builds an English distance education classroom management system based on improved machine learning artificial intelligence algorithms. Moreover, this research constructs the system function module through requirement analysis, and combines the positioning algorithm to locate students in real time. In addition, this study analyzes the students’ status through intelligent database processing to grasp the students’ learning status in a timely and effective manner. In order to verify the performance of this system, this study verifies the performance of the model by means of comparative experiments. The research results show that the system constructed in this paper has a certain effect.
A Novel GPU Overdrive Fault Attack

Graphics processing units (GPUs) are widely used to accelerate applications including cryptographic operations. The reliability and security of GPUs have become a concern. Prior work reported power and timing side-channel attacks on GPUs. In this paper, we present the first-ever overdrive fault attack targeting modern GPUs. This attack exploits voltage-frequency scaling features present on most commercial GPUs to introduce random faults during kernel execution. We demonstrate an effective fault-based attack on an AMD GPU, recovering the AES keys in minutes. Such software-controlled fault injections also pose serious threats to data integrity and service availability in the cloud.
An Automated Self-Healing Cloud Computing Framework for Resource Scheduling

In cloud computing, applications, administrations, and assets have a place with various associations with various goals. Elements in the cloud are self-sufficient and self-adjusting. In such a collaborative environment, the scheduling decision on available resources is a challenge given the decentralized nature of the environment. Fault tolerance is an utmost challenge in the task scheduling of available resources. In this paper, self-healing fault tolerance techniques have been introducing to detect the faulty resources and measured the best resource value through CPU, RAM, and bandwidth utilization of each resource. Through the self-healing method, less than threshold values have been considering as a faulty resource and separate from the resource pool. The workloads submitted by the user have been assigned to the available best resource. The proposed method has been simulated in cloudsim and compared the multi-objective performance metrics with existing methods, and it is observed that the proposed method performs utmost.
Hybridized Machine Learning based Fractal Analysis Techniques for Breast Cancer Classification

The usefulness of Fractal Analysis (FA) is not limited to a particular area.It is applied in variety of fields and has shown its efficiency towards irregular objects.Fractal dimension is the best measure of the roughness for natural elements and hence, it can be treated as a feature of the natural object.Breast masses are irregular and divers from a malignant tumor to benign; hence breast can be treated as one of the best areas where fractal geometry can be applied.It gives a scope where fractal geometry concept can be used as a feature extraction technique in mammogram.On the other hand, the support vector machine is an emerging technique for classification.The survey shows that few works have done on breast mass classification using support vector machine.In our work two most effective techniques are used in separate operations, FA: Box Count Method (BCM) and Support Vector Machine (SVM) that result well in their fields.Feature extraction is done through Box Count Method.The extracted feature, "fractal dimension", measures the complexity of the input data set of 42 images.For the next segment, the resulting Fractal Dimensions (FD) are processed under the support vector machine classifier to classify benign and malignant cells.The result analysis shows that the combination of SVM and FD yielded the highest with 98.13% accuracy.
Masta: An HE-Friendly Cipher Using Modular Arithmetic

The Rasta cipher, proposed by Dobraunig et al. (CRYPTO 2018), is an HE-friendly cipher enjoying the fewest ANDs per bit and the lowest ANDdepth among the existing ciphers. A novel feature of Rasta is that its affine layers are freshly and randomly generated for every encryption. In this paper, we propose a new variant of Rasta, dubbed Masta. Similarly to Rasta, Masta takes as input a (master) secret key and a nonce, and generates a keystream block for each counter. On the other hand, Masta has two main differences from Rasta: Masta uses modular arithmetic to support HE schemes over a non-binary plaintext space, and it uses a smaller number of random bits in the affine layers by defining them with finite field multiplication. In this way, Masta outperforms Rasta in a transciphering framework with BGV/FVstyle HE schemes. Our implementation shows that Masta is 505 to 592 times faster in terms of the throughput on the client-side, while 4792 to 6986 times faster on the server-side.
An Effective End-to-End Modeling Approach for Mispronunciation Detection

Recently, end-to-end (E2E) automatic speech recognition (ASR) systems have garnered tremendous attention because of their great success and unified modeling paradigms in comparison to conventional hybrid DNN-HMM ASR systems. Despite the widespread adoption of E2E modeling frameworks on ASR, there still is a dearth of work on investigating the E2E frameworks for use in computer-assisted pronunciation learning (CAPT), particularly for Mispronunciation detection (MD). In response, we first present a novel use of hybrid CTCAttention approach to the MD task, taking advantage of the strengths of both CTC and the attention-based model meanwhile getting around the need for phone-level forced alignment. Second, we perform input augmentation with text prompt information to make the resulting E2E model more tailored for the MD task. On the other hand, we adopt two MD decision methods so as to better cooperate with the proposed framework: 1) decision-making based on a recognition confidence measure or 2) simply based on speech recognition results. A series of Mandarin MD experiments demonstrate that our approach not only simplifies the processing pipeline of existing hybrid DNN-HMM systems but also brings about systematic and substantial performance improvements. Furthermore, input augmentation with text prompts seems to hold excellent promise for the E2E-based MD approach.