Reviewerly/exHarmony huggingface dataset:
Leveraging Interpretability: Concept-based Pedestrian Detection with Deep Neural Networks

The automation of driving systems relies on proof of the correct functioning of perception. Arguing the safety of deep neural networks (DNNs) must involve quantifiable evidence. Currently, the application of DNNs suffers from an incomprehensible behavior. It is still an open question if post-hoc methods mitigate the safety concerns of trained DNNs. Our work proposes a method for inherently interpretable and concept-based pedestrian detection (CPD). CPD explicitly structures the latent space with concept vectors that learn features for body parts as predefined concepts. The distance-based clustering and separation of latent representations build an interpretable reasoning process. Hence, CPD predicts a body part segmentation based on distances of latent representations to concept vectors. A non-interpretable 2d bounding box prediction for pedestrians complements the segmentation. The proposed CPD generates additional information that can be of great value in a safety argumentation of a DNN for pedestrian detection. We report competitive performance for the task of pedestrian detection. Finally, CPD enables concept-based tests to quantify evidence of a safe perception in automated driving systems.
Quantum Computing: Challenges and Opportunities

Quantum computing is considered as good mixture of quantum physics, computer science and information theories. This paper introduces fundamentals needed to understand different layers associated with quantum computing some of them are superposition, entanglement, quantum gates and circuit models like Grover’s algorithm, Shor’s algorithm. The detailed discussion along with matrix representation of quantum gates and fault tolerant Calculation and quantum error correction (QEC) technique, followed by steps involved in simulator design is introduced. Thereafter, it will elaborate hurdles encounter in constructing a quantum computer like Constructing, verification, architecture, quantum error correction, compiler and programming issues and special focus is given on the different layers involved in construction of Q-Algorithm. Finally some of the applications like quantum cryptography is discussed.
BKW Meets Fourier New Algorithms for LPN with Sparse Parities

We consider the Learning Parity with Noise (LPN) problem with sparse secret, where the secret vector \(\mathbf {s}\) of dimension n has Hamming weight at most k. We are interested in algorithms with asymptotic improvement in the exponent beyond the state of the art. Prior work in this setting presented algorithms with runtime \(n^{c \cdot k}\) for constant \(c < 1\), obtaining a constant factor improvement over brute force search, which runs in time \({n \atopwithdelims ()k}\). We obtain the following results: We first consider the constant error rate setting, and in this case present a new algorithm that leverages a subroutine from the acclaimed BKW algorithm [Blum, Kalai, Wasserman, J. ACM ’03] as well as techniques from Fourier analysis for p-biased distributions. Our algorithm achieves asymptotic improvement in the exponent compared to prior work, when the sparsity \(k = k(n) = \frac{n}{\log ^{1+ 1/c}(n)}\), where \(c \in o(\log \log (n))\) and \(c \in \omega (1)\). The runtime and sample complexity of this algorithm are approximately the same. We next consider the low noise setting, where the error is subconstant. We present a new algorithm in this setting that requires only a polynomial number of samples and achieves asymptotic improvement in the exponent compared to prior work, when the sparsity \(k = \frac{1}{\eta } \cdot \frac{\log (n)}{\log (f(n))}\) and noise rate of \(\eta \ne 1/2\) and \(\eta ^2 = \left( \frac{\log (n)}{n} \cdot f(n)\right) \), for \(f(n) \in \omega (1) \cap n^{o(1)}\). To obtain the improvement in sample complexity, we create subsets of samples using the design of Nisan and Wigderson [J. Comput. Syst. Sci. ’94], so that any two subsets have a small intersection, while the number of subsets is large. Each of these subsets is used to generate a single p-biased sample for the Fourier analysis step. We then show that this allows us to bound the covariance of pairs of samples, which is sufficient for the Fourier analysis. Finally, we show that our first algorithm extends to the setting where the noise rate is very high \(1/2 - o(1)\), and in this case can be used as a subroutine to obtain new algorithms for learning DNFs and Juntas. Our algorithms achieve asymptotic improvement in the exponent for certain regimes. For DNFs of size s with approximation factor \(\epsilon \) this regime is when \(\log \frac{s}{\epsilon } \in \omega \left( \frac{c}{\log n \log \log c}\right) \), and \(\log \frac{s}{\epsilon } \in n^{1 - o(1)}\), for \(c \in n^{1 - o(1)}\). For Juntas of k the regime is when \(k \in \omega \left( \frac{c}{\log n \log \log c}\right) \), and \(k \in n^{1 - o(1)}\), for \(c \in n^{1 - o(1)}\).
A Descriptive Analytics of the Occurrence and Predictive Analytics of Cyber Attacks During the Pandemic

The SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) universally and commonly known as COVIT-19 and the across-the-board lockdown measures are having complex and unforeseen effects on intricate social domains, which includes opportunities for offline and online crimes. For some months since March 2020, most countries in the world if not all has been in one lockdown or the other. The lockdown measures have brought increased fear, anxiety, and depression. As if that is not enough, it has also forced the entire world populace to embrace the emergency use of technology to carry out their job functions which was unplanned for in a sit-at-home and work-from-home situation. All these have rendered vast majority of individuals and general populace vulnerable to cyber enabled and cyber dependent crimes. Anytime there is spike in number of crimes committed within a specific period, analysists and researchers seek to know the factor responsible for the increase. In this research, attempt is made to establish the relationship between the COVIT-19 pandemic and the increase in cybercrime. To achieve this, Regression Analysis is being used to study the relationship between the target class, attack class and the cybercrime occurrence. Furthermore, projection is also made using Time Series to see how the situation will look like if this lockdown that some have accepted to be a new-normal, continues like this (God forbids that).
The Impact of Ensemble Learning in Sentiment Analysis under Domain Shift

Domain adaptation is a well-studied field of research that aims at adapting machine learning models trained on specific source domains into different target domains. However, the case of multi-classifier systems is relatively less studied particularly in terms of experimentation. In this study, we investigate the impact of combining classifiers trained on different sources by simple averaging techniques, namely, arithmetic mean and normalized geometric mean, followed by a rich set of experiments to examine to what extent accuracies obtained in target domains benefit specifically from bagging. In this respect, we also propose a distance- based scheme to find a proper value for the number of weak classifiers to train. Our experiments have been conducted on the conventional Amazon product review dataset for a binary sentiment classification task. As a result, applying ensemble methods on multi-source datasets empirically stands out as a simple yet effective strategy to obtain robust results in case of domain shift. Thus, it is recommended as a strong baseline for evaluating more sophisticated domain adaptation algorithms. Provided that appropriate values are selected for the number of weak classifiers as well as for sample size, bagging-based models are resistant to domain shift and able to perform as accurately as in-domain classifiers.
Data Integration Models for Heterogeneous Industrial Systems: A Conceptual Analysis

In the industrial automation domain, data integration between heterogeneous systems and architectures is a challenge. Companies and factories are impatient with the selection of technical solutions that are beneficial to local requirements, therefore facing problems of data integration and incompatibility between other systems. There is a need to bridge heterogeneous industrial systems to provide data integration. This article aims to provide guidelines to industrial managers and solution designers to achieve data integration between heterogeneous industrial systems according to their requirements. For this purpose, we have explored the implementation of two data integration models that can be selected based on the state and needs of industrial systems. The first data integration model is based on event-driven architecture (EDA), where Message Queuing Telemetry Transport (MQTT) is deployed as an event broker. The second model is based on the Arrowhead Framework, which is designed by using service-oriented architecture (SOA) principles, where EventHandler provides SOA-based event-driven communication. We conceptually compare and analyze them based on selected criteria essential in an industrial environment. The qualitative analysis of these models is based on proof-of-concept implementations made available for our evaluation.
Classification of Texts Using a Vocabulary of Synonyms

In this work we try to classify 5,063 blog texts written by 68 authors using XLM Roberta Large Xnli model (from Facebook AI) and a vocabulary of Russian synonyms.
Detecting Hate Speech on Memes using FixEfficientNet-L2

In Indonesia, there were 3,325 cases of hate speech spreading in 2017, based on the popularity of memes and the number of hate speech cases. There are studies that have investigated this topic and get an accuracy of 50-52%. This study will create an image classification model using the FixEfficientNet-L2 architecture with the aim of being able to identify memes that contain hate speech and memes that do not contain hate speech. hate speech. Our research had used a dataset provided by the Facebook AI team, totaling 9,540 data of meme image, and will be divided into three parts of training, validation, and data testing. Our model will use 15 different configurations on the train and test image size. Previously, preprocessing stages were carried out on the data so that the data could be studied by the model. The highest accuracy results obtained from the FixEfficientNet-L2 model with several different configurations in classifying memes are 54.8% for validation and 63% for testing, the accuracy has increased from previous studies of 2% for validation and 10% for testing.
The Impact of Blockchain Technology on Tax and Accounting Practices

Introduced to the agenda of the world in 2008, blockchain has been researched and reviewed in both the public and the private sectors, interpretations have been made regarding the future of the technology, and it has been endeavored to determine potential areas of application. Blockchain is predicted to be applied in many areas such as the financial sector, smart contracts, and the public sector, and its benefits are expressed. The application introduces accounting and taxation based on blockchain technology, transparency, convenience in transactions, time saving, concurrent taxation, effective and continuous auditing. By applying this technology in the area of accounting and tax auditing, errors and particularly frauds shall be prevented accordingly. Within this framework, at the outset of this study, the blockchain technology is introduced, and the impacts thereof on the accounting and taxation are addressed accordingly. Blockchain technology is expected to have impact on the accounting entry process, tax practices, accounting profession, accounting professionals, and the taxpayers.
Identifying difficulties in teaching foreign language speaking in the online environment

The digital transformation of education has been causing dramatic changes in the organization and curriculum of the educational process. The problem of mastering foreign language speaking skills by university students in distance learning format has been attracting growing attention in academic circles worldwide. In addition to the technical difficulties faced by the stakeholders of the online learning process, there has been an increasing focus on other related factors, including psychological barriers and emerging interaction patterns in both student-student communication and teacher-student communication. In this paper, the authors will identify the difficulties faced by the students who learn speaking skills online. A survey was conducted among students of technical specialties of the NUST MISIS University. The survey involved 16 students who were studying English online. The conducted experiment revealed several types of psychological difficulties that arise while learning to speak online. The results of the study will help improve the quality of academic programs in terms of identifying the existing gaps in designing and managing speaking classes online.
Computer Network Information Security Analysis and Management Research Based on Neural Network

The rapid progress of computer network technology has significantly changed industrial production and residents' daily lives. However, we should clearly recognize the advantages and disadvantages of network technology, which not only promotes the progress of human civilization, but also causes great hidden dangers to people's lives and life safety. In addition to conventional vulnerabilities and viruses, there are also some targeted intrusions, and different factors are interrelated, which makes the security evaluation more difficult. In order to effectively avoid Internet risks and ensure the security of users' personal data, relevant researchers have begun to increase their research efforts on the use of neural networks in the field of computer information security. The application principle of neural network is to form a nonlinear adaptive dynamic system through neuron connection, and realize process operation, identification and control. The unique feature of neural network is that it can efficiently analyze and deal with the problems of computer information security management, and establish the evaluation standard of computer information security. In this paper, we will do research to understand the application performance of neural network in safety inspection.
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In this paper, we present two constructions of irreducible (primitive) polynomials over Fq of degree rm from irreducible (primitive) polynomial over Fqm of degree r, and we show that these two constructions coincide. The first construction is based on the Frobenius automorphism of Fqm over Fq. The second one comes from a generalization of a construction of primitive polynomials over Fq which uses the companion matrix. From this generalization, given an irreducible (resp. primitive) polynomial over Fqm of degree r, we generate multiple (resp. all) irreducible polynomials over Fq of degree rm. As an application, a characterization of the generator polynomial of a BCH code over Fq is given. Then, we show how two BCH codes over Fq and Fqm, respectively, and their generator polynomials are related.
Updates to LUCI: A New Fitting Paradigm Using Mixture Density Networks

Abstract LUCI is an general-purpose spectral line-fitting pipeline which natively integrates machine learning algorithms to initialize fit functions. LUCI currently uses point-estimates obtained from a convolutional neural network (CNN) to inform optimization algorithms; this methodology has shown great promise by reducing computation time and reducing the chance of falling into a local minimum using convex optimization methods. In this update to LUCI, we expand upon the CNN developed in Rhea et al. so that it outputs Gaussian posterior distributions of the fit parameters of interest (the velocity and broadening) rather than simple point-estimates. Moreover, these posteriors are then used to inform the priors in a Bayesian inference scheme, either emcee or dynesty . The code is publicly available at crhea93:LUCI ( https://github.com/crhea93/LUCI ).
A Preprocessing Approach to Improve the Performance of Inception v3-based Face Shape Classification

Face shape classification is considered one of the trending topics in the artificial intelligence research field. Face shape classification can be employed in many broad-scoped projects, such as hairstyle recommendation systems in the beauty and fashion industry. In this paper, the inception v3 model was employed to reach the highest possible performance for classifying the different face shapes. The model was re-trained after applying a proposed sequence of preprocessing techniques, including image straightening, cropping, resizing, and normalization. The model was re-trained on different data sizes of females' images. Comparing the proposed model to the other state-of-art previous work showed an outperforming testing accuracy of 94.3%.
Implementation of the NewHope Protocol for Post-Quantum Cryptography

Abstract In this paper, we propose a faster and less resource intensive implementation of the NewHope protocol to address the problems of resource-intensive random number generators and slow running NTT modules in NewHope implementations. In the random number generation module, choose the lightweight pseudo-random number generator (PRNG) Trivium instead of the SHAKE function to reduce the resource usage in the random number generation module. In the NTT transformation module, a pipeline structure is used in combination with a parallel structure of four butterfly units to shorten the cycle time of butterfly operations and achieve acceleration. The experimental results show that, compared with Kuo’s design, the overall time for key exchange in this paper is reduced by 25.3%, and the consumption of FFs and LUTs is reduced by 20.2% and 38.0%, respectively.
Multi-Level Relation Learning with Confidence Evaluation for Few-Shot Learning

Few-shot learning is developed to classify unknown categories through limited training samples. In this paper, a multi-level relation learning model with confidence evaluation (MLR-CE) is proposed in area of few-shot learning. In the proposed framework, multi-level features are extracted that contain semantic information of different depth, and multi-level relation pairs are built by stacking feature maps of support images and query images. To expand the support set, confidence evaluation by a Gaussian mixture model is developed to select samples with high confidences. Experiments on two data demonstrate that the proposed method can yield superior few-shot classification results.
Joint Model Using Character and Word Embeddings for Detecting Internet Slang Words

The language style on social media platforms is informal and many Internet slang words are used. The presence of such out-of-vocabulary words significantly degrades the performance of language models used for linguistic analysis. This paper presents a novel corpus of Japanese Internet slang words in context and partitions them into two major types and 10 subcategories according to their definitions. The existing word-level or character-level embedding models have shown remarkable improvement with a variety of natural-language processing tasks but often struggle with out-of-vocabulary words such as slang words. We therefore propose a joint model that combines word-level and character-level embeddings as token representations of the text. We have tested our model against other language models with respect to type/subcategory recognition. With fine-grained subcategories, it is possible to analyze the performance of each model in more detail according to the word formation of Internet slang categories. Our experimental results show that our joint model achieves state-of-the-art performance when dealing with Internet slang words, detecting semantic changes accurately while also locating another type of novel combinations of characters.
A Case Study on the Self-directed English Learning Experiences of Korean College Students Using Self-coaching

The purpose of this study is to analyze the English learning experience of college students using Colaizzi's method, which is one of the qualitative research methods, using self-coaching technology as a tool to improve self-directed learning ability. In-depth interview were conducted to collect data, and 9 significant statements, 4 theme clusters, and 2 categories were derived.&lt;br/&gt;The results are as follows. First, it showed that the participants clearly set the reasons and goals for learning English. Second, it was confirmed that they had time to return to the positive motivation they experienced in the past and strengthened their will to overcome difficulties caused by self-coaching. Third, it was reported that they had a valuable experience of asking the questions presented for the first time, and they recollected the feelings of when they happily learned English, thereby gaining new vitality for learning English. Finally, it also showed that they experienced risk factors such as a desire to procrastinate, a desire to give up, and a rush to complete a task due to difficulty in self-control, which could lead to poor learning.&lt;br/&gt;Based on these results, it was suggested that self-coaching contributed to the improvement of learning English, the importance of questions, necessary factors when expanding to regular classes, and the expansion of coaching research.
On-line Evaluation in Graphic Engineering Disciplines

The transition to on-line learning required rigors not only in the use of new technologies, but also in the organization of courses in schools and universities. It was necessary to revise the content of the course, as well as the methods of teaching and assessment. The evaluation of students in the graphic disciplines is of increased complexity. The article proposes a solution for testing students based on the cognitive levels of Bloom's Taxonomy and using the tools provided by the MOODLE platform.
Leveraging the Information in In-Domain Datasets for Transformer-Based Intent Detection

In this paper, we improve the intent detection performance by leveraging the datasets which are collected in the same domain but have different intent categories. In the baseline system, we use the state-of-the-art pre-trained transformer models with an intent detection classification layer. Our proposed method has two fine-tuning stages. In the first stage, the parameters of the transformer and classifier layers are adapted using an in-domain dataset to the target set. Then, we keep the updated transformer parameters but replace the classifier with a new one for the target task. In the second pass, the network is fine-tuned end-to-end using the target set. In the experiments, we observe that fine-tuning the transformer with a matched dataset in the first pass substantially improves the intent detection accuracy. The absolute improvement is approximately %1 over a strong baseline.
Type-safe generation of modules in applicative and generative styles

The MetaML approach for multi-stage programming provides the static guarantee of type safety and scope safety for generated code, regardless of the values of static parameters. Modules are indispensable to build large-scale programs in ML-like languages, however, they have a performance problem. To solve this problem, several languages proposed recently allow one to generate ML-style modules. Unfortunately, those languages had the problems of limited expressiveness, incomplete proofs, and code explosion.