Reviewerly/exHarmony huggingface dataset:
Contribution of G.N. Alexandrov in Pedagogical Theory and Practice

The article is devoted to the scientific and pedagogical activity of the outstanding scientist, teacher, mentor G.N. Alexandrov. The whole life and work of Gleb Nikolaevich Alexandrov, an outstanding person, a talented scientist, a wonderful teacher, is a clear example of serving the high ideals of science, caring for the problems of pedagogy. Gleb Nikolaevich Alexandrov – retired colonel, doctor of pedagogical sciences, professor, full member of the Academy of Pedagogical and Social Sciences (APSN), full member of the International Academy of Sciences of Pedagogical Education (IANPO), corresponding member of the Academy of Natural Sciences, Honored Scientist of the Russian Federation, Bashkortostan, North Ossetia-Alania, Deputy Chairman of the Dissertation Council on Pedagogy, North Ossetian State University named after K.L. Khetagurov. The areas of Alexandrov’s scientific interests are the didactics of high school, cybernetics and pedagogy, the problems of managing the cognitive activity of students, pedagogical qualimetry, the interaction of types of cognitive activity with the leading role of productive, a systematic approach and cognitive activity of students, the foundations of the theory and practice of problem-based and programmed learning, the ratio of algorithmic and heuristic processes, etc.
What Makes Problem-Solving Practice Effective? Comparing Paper and AI Tutoring

In numerous studies, intelligent tutoring systems (ITSs) have proven effective in helping students learn mathematics. Prior work posits that their effectiveness derives from efficiently providing eventually-correct practice opportunities. Yet, there is little empirical evidence on how learning processes with ITSs compare to other forms of instruction. The current study compares problem-solving with an ITS versus solving the same problems on paper. We analyze the learning process and pre-post gain data from N = 97 middle school students practicing linear graphs in three curricular units. We find that (i) working with the ITS, students had more than twice the number of eventually-correct practice opportunities than when working on paper and (ii) omission errors on paper were associated with lower learning gains. Yet, contrary to our hypothesis, tutor practice did not yield greater learning gains, with tutor and paper comparing differently across curricular units. These findings align with tutoring allowing students to grapple with challenging steps through tutor assistance but not with eventually-correct opportunities driving learning gains. Gaming-the-system, lack of transfer to an unfamiliar test format, potentially ineffective tutor design, and learning affordances of paper can help explain this gap. This study provides first-of-its-kind quantitative evidence that ITSs yield more learning opportunities than equivalent paper-and-pencil practice and reveals that the relation between opportunities and learning gains emerges only when the instruction is effective.
Evaluation of the Type 2 Errors of Testing Methods for Path Coefficients in Structural Equation Model

When performing data analysis using a structural equation model, the main concern is the estimation and testing of path coefficients. Testing methods for path coefficients include the t-value method, Satorra-Bentler's scaling testing statistic method, and Bollen-Stine's bootstrap method. All of these methods are asymptotic testing methods that assume a normal distribution of data, and may provide different testing results depending on the type of given data and model. In this study, the testing power (1-type 2 errors) was calculated under various circumstances using simulations for the testing methods that are mainly used, and the performance of these testing methods was evaluated through the results. The ML method was found to have robust properties in deviation from normal distribution in terms of estimation and power. Also, in most situations, the power of the Satorra-Bentler scaling testing was greater than that of the ML method. And in some situations, when the sample size is small, the power of the bootstrap method based on WLS was slightly smaller than that of other methods. Although the results of this study cannot be generalized in all situations, it is thought that they can be referenced in various fields of research using structural equation models.
Digital Signal Processing, Control and Hybrid Circuits

In this chapter we make some remarks on digital and hybrid signal processing. As stated in the preface, digital signal processing is not the main topic of this book. Here, we make a “gentle introduction” to sampled data and digital signal processing. The main tool used in digital and discrete-time control and signal processing is the Z-transform. We recall here the properties and applications of this tool. However, no advanced theoretical considerations about the Z-transform are provided. We mention only the minimal amount of Z-transform properties, necessary to understand the sampled-data and digital signal processing. Practical applications in audio circuits are described, including analog and hybrid delay lines.
The influence of college students' self-differentiation on interpersonal problem - mediating effect of basic psychological needs -

This research examines the effect of interpersonal problem influenced by self-differentiation and aim on verifying mediating effect of basic psychological needs between them. For this research, the total of 327 data was collected by conducting on/off-line survey to 4-year-course university students on both genders, and the data was analyzed by SPSS 21.0 and Amos 18.0. Also, bootstrapping examination technique was conducted to verify the significance evaluation of mediating effect based on verified research model. Summarizing the research result, Firstly, self-differentiation and interpersonal problem showed negative corelation as the interpersonal problem reduce as self-differentiation level is high. Secondly, self-differentiation and basic psychological needs showed static corelation as the basic psychological needs are fulfilled as the self-differentiation level is high. Thirdly, the research showed the basic psychological needs between self-differentiation and interpersonal problem are partially mediated. Therefore, the research can be interpreted as the basic psychological needs are satisfied when the self-differentiation level is high, leading to reduce interpersonal problem, accordingly.
Integrated programmable spectral filter for frequency-multiplexed neuromorphic computers

Artificial neural networks (ANN) are a groundbreaking technology massively employed in a plethora of fields. Currently, ANNs are mostly implemented through electronic digital computers, but analog photonic implementations are very interesting mainly because of low power consumption and high bandwidth. We recently demonstrated a photonic neuromorphic computing system based on frequency multiplexing that executes ANNs algorithms as reservoir computing and Extreme Learning Machines. Neuron signals are encoded in the amplitude of the lines of a frequency comb, and neuron interconnections are realized through frequency-domain interference. Here we present an integrated programmable spectral filter designed to manipulate the optical frequency comb in our frequency multiplexing neuromorphic computing platform. The programmable filter controls the attenuation of 16 independent wavelength channels with a 20 GHz spacing. We discuss the design and the results of the chip characterization, and we preliminary demonstrate, through a numerical simulation, that the produced chip is suitable for the envisioned neuromorphic computing application.
Social Media as a News Source: An Analysis of Facebook

Given study “Facebook as a News Source: An Analysis” examines the role of Facebook as a source of information and news for new generation as most of the consumers are relying on social media for getting news. Keeping in view the above topic, the researcher explored in depth use of social media, especially Facebook as a source of news and how it shapes the opinion by providing easy access to the masses. The purpose of the study was to examine Facebook as a news source, and to investigate what users do to evaluate the trustworthiness of the information provided by Facebook. The research method used in the study is survey method, and the questionnaire comprised of close ended questions. The universe of the present research is users of facebook. Sample size was 100 and the respondents’ data was further examined with the help of SPSS and the results were generated in tables and pie charts.
AHP based Learning Model of General IT Classes by an Analysis for Student’s Needs

학생들의 니즈를 분석하고 반영하는 강좌를 개발하는 것은 매우 중요하다. 이것은 교양 IT강좌에 학생들이 몰입하여 수강함으로서 학습의 효과를 높이는데 매우 중요한 요인이 되기 때문이다. 본 연구에서는 학생들이 학습하기 원하는 주제나 내용을 조사하여 텍스트 마이닝을 통해 분석하고, 대학의 교양 IT강좌에서 중요한 요인들을 학생들의 설문내용으로부터 추출하여 AHP분석을 통해 조사하였다. 이것을 기준으로 학생들의 니즈를 반영하는 교양 IT과목 개발을 위한 교수 학습 모형을 제시하였다. 이러한 연구결과는 교사에게 학생들이 원하는 학습 내용과 주제를 분석하고, 학습 콘텐츠를 개발할 때 중요한 요인들을 분석함으로서 학습자 지향의 학습 콘텐츠를 제공할 수 있는 구체적인 방법과 절차를 제시할 수 있었다. It is very important to develop classes with analyzing and applying students" needs. This is because it is important to increase the learning effect by immersing students in IT classes in general education. In this research, the topics or contents that students wanted to learn were analyzed through text mining, and important factors of the university liberal arts IT process were extracted from the contents of the students" survey and investigated through AHP analysis. Consequently, this research shows a teaching and learning model was presented for the development of IT classes in general education that apply the students" needs. These research result could provide teachers with specific methods and procedures to provide student-oriented learning content by analyzing the learning content and topics what they want and figure out what are important factors in learning when teacher develop learning content.
Indonesian EFL Teachers’ Use of Code Mixing in Classroom Interactions

Code Mixing (CM) is generally a communication strategy that is often used by teachers in the process of teaching English as a Foreign Language (EFL). This study aims to describe the types and categories of CM used by teachers in the context of teaching EFL in Indonesia, as well as investigate the perspectives of teachers and students regarding the use of code mixing in classroom interactions. This research uses case studies with a qualitative approach to analyze data. Data collection was carried out through recorded interviews with three teachers and seven students as well as observing the teaching and learning process during six meetings at one of the senior high schools in Medan, North Sumatera. The results of the study show the teachers use three types of CM, namely Insertion, Alternation and Congruent Lexicalization. There are 177 mixed code data found. There are 188 data classified as insertion type, 49 data classified as alternation type and 10 data classified as congruent lexicalization type. Then from the results of the interviews, the teacher considered that the functions of CM in students were 1) Improving student’ comprehension, 2) Encouraging students' independence in learning new vocabulary. Furthermore, students agreed that code mixing was an effective and effective learning strategy in increasing their understanding of English. This happened because 71.5% of students used English most often with some Indonesian language CM and 67.2% said the use of CM can help understand topic discussions in the learning process.
Adaptive mining difficulty for blockchain to resist selfish mining attack

Aim: A blockchain provides data consistency and builds a fair mining environment for a network by using a consensus mechanism such as proof of work (PoW) and proof of stake. However, selfish mining is a well-known mining attack. It can reduce the fairness and destabilize the network, especially for a PoW-based blockchain. Therefore, in this paper, we propose a new approach, named the adaptive mining difficulty adjustment protocol, which can deter a selfish attack. Methods: We propose using the unit profit as an improved version of the relative revenue, because it is more flexible for calculating the miners’ profits in different periods and can be used to analyze repeated mining games. Based on the unit profit, we propose using the adaptive mining-difficulty adjustment protocol to reduce an attacker’s profit. Our protocol evaluates the effective hash power in a network more accurately and corrects the mining difficulty. Moreover, we introduce the discount factor and model the long-term profit to analyze the impact of a miner’s patience on its future profit. Results: We used an open-source simulator to simulate the competition between a selfish miner and an honest miner and determined their profits under different protocols. Our experimental results show that our protocol can effectively raise the attack threshold, but it reduces the total network profit if the attacker still attacks. However, the long-term profit model shows that a more patient attacker needs to invest more hash power and pay increased mining costs to maintain its attack. Conclusion: We conclude that, under our protocol, selfish attackers will tend to become honest miners if their hash power does not exceed the threshold, which means that our protocol can effectively deter selfish attacks and some variants of selfish attacks. Briefly, our protocol can correct the mining difficulty and leads to a more stable and fairer mining environment for a PoW-based blockchain.
Business Ethics in E-Commerce – Legal Challenges and Opportunities

Background: This paper deals with the ethical limitations of e-commerce. The aim is to discover areas where the protection is not granted as users would expect and to make proposals for improvement. The authors will begin the paper by proposing the market of e-commerce and how it is directly linked to society’s daily life. The method adopted for the legal perspective is case studies, where the Kingdom of Saudi Arabia’s (KSA) legal context will be explored. In the end, the paper will answer two main questions: What are the ethical challenges facing the issue of data protection in electronic commerce? What are essential legal frameworks that regulate the subject of data protection in Saudi Arabia? Methods: The normative method is applied to identify the main legislations used in e-commerce and data protection, especially regarding big data regulations. A case study analysis is also used where KSA legislation is investigated. Results and Conclusions: The authors saw that e-commerce is an insecure place to protect customer data. This data is stored electronically, so it is very easy to steal it in addition to the use of this data by companies without the permission of the customer. Research proves that laws are unable to keep pace with technological developments and are unable to provide effective protection for data stored in the cloud.
Eavesdropping on the Vibrations of Earth’s Magnetic Bubble

A NASA-funded crowdsourced science project has converted the unheard sounds resonating inside Earth’s magnetic shield into audible tracks, revealing an orchestra of whistles, wooshes, and chirps.
Revizor: Testing Black-Box CPUs Against Speculation Contracts

Speculative execution attacks such as Spectre and Meltdown exploit microarchitectural optimizations to leak information across security domains. These vulnerabilities often stay undetected for years because we lack the tools for systematic analysis of CPUs to find them. In this article, we introduce such a tool, called Revizor, which automatically detects microarchitectural leakage in black-box CPUs. The key idea is to employ speculation contracts to model the expected information leaks, and then to use randomized testing to compare the CPU’s leakage against the model and thus detect unexpected leaks. We showcase the effectiveness of this approach on Intel CPUs, where we demonstrate that Revizor is capable of detecting both known and previously unknown speculative leaks.
Efficient Top-K Feature Selection Using Coordinate Descent Method

Sparse learning based feature selection has been widely investigated in recent years. In this study, we focus on the l2,0-norm based feature selection, which is effective for exact top-k feature selection but challenging to optimize. To solve the general l2,0-norm constrained problems, we novelly develop a parameter-free optimization framework based on the coordinate descend (CD) method, termed CD-LSR. Specifically, we devise a skillful conversion from the original problem to solving one continuous matrix and one discrete selection matrix. Then the nontrivial l2,0-norm constraint can be solved efficiently by solving the selection matrix with CD method. We impose the l2,0-norm on a vanilla least square regression (LSR) model for feature selection and optimize it with CD-LSR. Extensive experiments exhibit the efficiency of CD-LSR, as well as the discrimination ability of l2,0-norm to identify informative features. More importantly, the versatility of CD-LSR facilitates the applications of the l2,0-norm in more sophisticated models. Based on the competitive performance of l2,0-norm on the baseline LSR model, the satisfactory performance of its applications is reasonably expected. The source MATLAB code are available at: https://github.com/solerxl/Code_For_AAAI_2023.
Language models are weak learners

A central notion in practical and theoretical machine learning is that of a $\textit{weak learner}$, classifiers that achieve better-than-random performance (on any given distribution over data), even by a small margin. Such weak learners form the practical basis for canonical machine learning methods such as boosting. In this work, we illustrate that prompt-based large language models can operate effectively as said weak learners. Specifically, we illustrate the use of a large language model (LLM) as a weak learner in a boosting algorithm applied to tabular data. We show that by providing (properly sampled according to the distribution of interest) text descriptions of tabular data samples, LLMs can produce a summary of the samples that serves as a template for classification and achieves the aim of acting as a weak learner on this task. We incorporate these models into a boosting approach, which in some settings can leverage the knowledge within the LLM to outperform traditional tree-based boosting. The model outperforms both few-shot learning and occasionally even more involved fine-tuning procedures, particularly for tasks involving small numbers of data points. The results illustrate the potential for prompt-based LLMs to function not just as few-shot learners themselves, but as components of larger machine learning pipelines.
Cumulative Memory Lower Bounds for Randomized and Quantum Computation

Cumulative memory -- the sum of space used per step over the duration of a computation -- is a fine-grained measure of time-space complexity that was introduced to analyze cryptographic applications like password hashing. It is a more accurate cost measure for algorithms that have infrequent spikes in memory usage and are run in environments such as cloud computing that allow dynamic allocation and de-allocation of resources during execution, or when many multiple instances of an algorithm are interleaved in parallel. We prove the first lower bounds on cumulative memory complexity for both sequential classical computation and quantum circuits. Moreover, we develop general paradigms for bounding cumulative memory complexity inspired by the standard paradigms for proving time-space tradeoff lower bounds that can only lower bound the maximum space used during an execution. The resulting lower bounds on cumulative memory that we obtain are just as strong as the best time-space tradeoff lower bounds, which are very often known to be tight. Although previous results for pebbling and random oracle models have yielded time-space tradeoff lower bounds larger than the cumulative memory complexity, our results show that in general computational models such separations cannot follow from known lower bound techniques and are not true for many functions. Among many possible applications of our general methods, we show that any classical sorting algorithm with success probability at least $1/\text{poly}(n)$ requires cumulative memory $\tilde \Omega(n^2)$, any classical matrix multiplication algorithm requires cumulative memory $\Omega(n^6/T)$, any quantum sorting circuit requires cumulative memory $\Omega(n^3/T)$, and any quantum circuit that finds $k$ disjoint collisions in a random function requires cumulative memory $\Omega(k^3n/T^2)$.
Diagnostic Accuracy of Lymph Nodes Fine Needle Aspiration Biopsy Based on The Sydney System for Reporting Lymph Node Cytology

To evaluate the diagnostic accuracy and malignancy risk of The Sydney System Reporting for Lymph Nodes Cytology.This study utilized secondary data from 156 cases to conduct a retrospective analysis of a diagnostic test method. During 2019-2021, data were collected at Dr. Wahidin Sudirohusodo's Anatomical Pathology Laboratory in Makassar, Indonesia. The cytology slides of each case were split into five diagnostic groups using the Sydney method, which were then compared with the results of the histopathological diagnosis.There were six cases in the L1 category, thirty-two cases in the L2 category, thirteen patients in the L3 category, seventeen cases in the L4 category, and ninety-one cases in the L5 class. The malignant probability (MP) is computed for each diagnostic classification. L1 MP value is 66.7%, L2 MP value is 15.6%, L3 MP value is 76.9%, L4 MP value is 94.0%, and L5 MP value is 98.9%. The diagnostic value of the FNAB examination is as follows: 89.9% sensitivity, 92.9% specificity, 98.2% positive predictive value, 68.4% negative predictive value, and 90.47% diagnostic accuracy.The FNAB examination provides high sensitivity, specificity, and accuracy in diagnosing lymph node tumors. Using a classification based on the Sydney system promotes communication between laboratories and clinicians.<br />.
On the Density of Codes over Finite Chain Rings

We determine the asymptotic proportion (or density) of free modules over finite chain rings with good distance properties and treat the asymptotics in the code length n and the residue field size q separately. We then specialize and apply our technique to rank metric codes and to Hamming metric codes.
The Integrated Project of an Energy and Sustainable Community as an Input for Urban Regeneration and Development

Today the ways in which urban regeneration and development planning is tackled are an important aspect to reflect on to manage a sustainable urban growth scenario. European impulses to counter the effects of climate change and recent Local Authorities reforms have paved the way for the definition of flexible tools to foster a multi-level governance. This paper presents the policymaking process for the Sustainable Development Agenda of the Metropolitan City of Cagliari (Italy), as a continuous process of contextualisation of the SDGs and discusses the institutional context for promoting integrated projects for sustainability as an opportunity to agree mutual commitments and obligations for their implementation. A decision support tool based on the cognitive mapping technique together with specific selection criteria allow to build several clusters, representative of embryonic forms of integrated projects for sustainability. An integrated project for an energy and sustainable community will be started to test the method, as a starting point for the activation of initiatives of an aggregative nature, starting from subjects who have the ability to operate independently in the field of production and more rationale use of energy: They also can make an important contribution to initiating a broader change in behaviour in the use of spaces and in the management of services.