Reviewerly/exHarmony huggingface dataset:
Brightness Preserving Enhancement for Dental Digital X-ray Images Based on Entropy and Histogram Analysis

ABSTRACTEnhancing dental x-ray images could be of great importance in helping the dentist for proper diagnosis. Image enhancement by histogram equalization, log transform, gamma correction for multiple gamma values, and contrast limited adaptive histogram equalization (CLAHE) were considered. Matlab 2015 was used to perform all the enhancement tasks. Dental x-rays images (periapical images) were obtained from a dataset published on the internet. It is preferred to preserve the x-ray images brightness so that the dentist can still use it for diagnosis with gray levels as close as possible to the originals. The CLAHE image enhancement, with eight rows and eight columns with clip limit of 0.01, proves to be the best. Two metrics were used for deciding the best enhancement, the entropy, looking for the highest obtained entropy, and the flatness of the resulted histogram. Eye inspection agrees with the metrics used.
Predicting Motor Vehicle Theft in Santiago de Chile using Graph-Convolutional LSTM

Vehicle theft represents one of the most frequent crimes in Chile and in the world. In this work, we propose an application of the GCLSTM (Graph-Convolutional Long Short Term Memory) neural network that combines a graph convolutional model with a LSTM for the prediction of vehicle thefts in the metropolitan region of Chile. The graph architecture considers the characteristics found in the neighbors to an area, assuming that the thefts of vehicles in nearby municipalities have similar patterns. For implementing the GCLSTM, first a smoothing technique based on LOESS regression was used for denoising the number of theft events for day, then the smoothed series of the last 30 days was considered as the input of the GCLSTM neural network for predicting the number of thefts in the following day. The results provided a better performance of the GCLSTM compared to a traditional LSTM, achieving an R2 of 0.86.
Comparison of processing speed of typical wavefront reconstruction methods for lateral shearing interferometry

Lateral shearing interferometry is widely applied in wavefront sensing, optical components testing, and defect inspection. The procedure of reconstructing the wavefront is the most specific difference between lateral shearing interferometry and other classical methods such as the Fizeau and Twyman interferometers. The speed and accuracy are two main features to evaluate the performance of one wavefront reconstruction method. In this work, optimized procedures for three typical wavefront reconstruction methods—the iterative FFT wavefront reconstruction method (FFT method), the partial differential least-squares method (LSQ method), and the difference Zernike polynomial fitting method (DZF method)—are designed. The calculation speeds of the three wavefront reconstruction methods are evaluated with different GPUs and CPUs. According to the test results, the DZF method is the fastest method both in the GPUs and CPUs. The shortest processing times of the DZF, FFT, and LSQ methods are 100, 449, and 494 ms, respectively, with the wavefront size of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>1024</mml:mn> </mml:mrow> <mml:mo>×<!-- × --></mml:mo> <mml:mn>1024</mml:mn> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">p</mml:mi> <mml:mi mathvariant="normal">i</mml:mi> <mml:mi mathvariant="normal">x</mml:mi> <mml:mi mathvariant="normal">e</mml:mi> <mml:mi mathvariant="normal">l</mml:mi> <mml:mi mathvariant="normal">s</mml:mi> </mml:mrow> </mml:math> . The calculation speeds of the FFT method and the LSQ method are similar in the CPUs, and the FFT method is faster in the GPUs. The relationship between the consumed time and the wavefront size is an exponential function in the CPUs and a power function in the GPUs. Generally speaking, GPUs’ processing speeds are faster than CPUs’. But CPUs can be faster than GPUs when the test wavefront sizes are smaller than <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>64</mml:mn> </mml:mrow> <mml:mo>×<!-- × --></mml:mo> <mml:mn>64</mml:mn> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">p</mml:mi> <mml:mi mathvariant="normal">i</mml:mi> <mml:mi mathvariant="normal">x</mml:mi> <mml:mi mathvariant="normal">e</mml:mi> <mml:mi mathvariant="normal">l</mml:mi> <mml:mi mathvariant="normal">s</mml:mi> </mml:mrow> </mml:math> . Besides, the differences between the consumed times of different CPUs are relatively smaller than those of the GPUs.
Unsupervised Adversarially Robust Representation Learning on Graphs

Unsupervised/self-supervised pre-training methods for graph representation learning have recently attracted increasing research interests, and they are shown to be able to generalize to various downstream applications. Yet, the adversarial robustness of such pre-trained graph learning models remains largely unexplored. More importantly, most existing defense techniques designed for end-to-end graph representation learning methods require pre-specified label definitions, and thus cannot be directly applied to the pre-training methods. In this paper, we propose an unsupervised defense technique to robustify pre-trained deep graph models, so that the perturbations on the input graph can be successfully identified and blocked before the model is applied to different downstream tasks. Specifically, we introduce a mutual information-based measure, graph representation vulnerability (GRV), to quantify the robustness of graph encoders on the representation space. We then formulate an optimization problem to learn the graph representation by carefully balancing the trade-off between the expressive power and the robustness (i.e., GRV) of the graph encoder. The discrete nature of graph topology and the joint space of graph data make the optimization problem intractable to solve. To handle the above difficulty and to reduce computational expense, we further relax the problem and thus provide an approximate solution. Additionally, we explore a provable connection between the robustness of the unsupervised graph encoder and that of models on downstream tasks. Extensive experiments demonstrate that even without access to labels and tasks, our model is still able to enhance robustness against adversarial attacks on three downstream tasks (node classification, link prediction, and community detection) by an average of +16.5% compared with existing methods.
Learning Unsupervised and Supervised Representations via General Covariance

Component analysis (CA) is a powerful technique for learning discriminative representations in various computer vision tasks. Typical CA methods are essentially based on the covariance matrix of training data. But, the covariance matrix has obvious disadvantages such as failing to model complex relationship among features and singularity in small sample size cases. In this letter, we propose a general covariance measure to achieve better data representations. The proposed covariance is characterized by a nonlinear mapping determined by domain-specific applications, thus leading to more advantages, flexibility, and applicability in practice. With general covariance, we further present two novel CA methods for learning compact representations and discuss their differences from conventional methods. A series of experimental results on nine benchmark data sets demonstrate the effectiveness of the proposed methods in terms of accuracy.
Finite-time fuzzy output-feedback control for $ p $-norm stochastic nonlinear systems with output constraints

&lt;abstract&gt; &lt;p&gt;This paper investigates the finite-time control problem of $ p $-norm stochastic nonlinear systems subject to output constraint. Combining a tan-type barrier Lyapunov function (BLF) with the adding a power integrator technique, a fuzzy state-feedback controller is constructed. Then, an output-feedback controller design scheme is developed by the constructed state-feedback controller and a reduce-order observer. Finally, both the rigorous analysis and the simulation results demonstrate that the designed output-feedback controller not only guarantees that the output constraint is not violated, but also ensures that the system is semi-global finite-time stable in probability (SGFSP).&lt;/p&gt; &lt;/abstract&gt;
An Expert System for Classifying Harmful Content on the Dark Web

In this research, we examine and develop an expert system with a mechanism to automate crime category classification and threat level assessment, using the information collected by crawling the dark web. We have constructed a bag of words from 250 posts on the dark web and developed an expert system which takes the frequency of terms as an input and classifies sample posts into 6 criminal category dealing with drugs, stolen credit card, passwords, counterfeit products, child porn and others, and 3 threat levels (high, middle, low). Contrary to prior expectations, our simple and explainable expert system can perform competitively with other existing systems. For short, our experimental result with 1500 posts on the dark web shows 76.4% of recall rate for 6 criminal category classification and 83% of recall rate for 3 threat level discrimination for 100 random-sampled posts.
From Statistics to Data Mining: A Brief Review

With the popularity of the Internet and the development of information technology, hundreds of millions of information and data that are so close to our human kind are recorded. In recent decades, as an interdisciplinary, data science has had a vigorous development. This paper mainly discusses two core problems in data science, that are data analysis and data mining. Beginning with the data analysis based on statistics, this paper discusses and reviews data mining based on traditional machine learning and that based on neural network. Then it discusses the common problems in data mining. Each part summarizes the corresponding application and research. Through the discussion of the basic contents in data analysis and data mining, this paper provides a powerful help for the future research in this field.
THE COMPETENCE PHENOMENON: OPEN QUESTIONS

Relevance. Historically formed scientific traditions, approaches, concepts (including those called the “competency-based approach” — the K-approach) are formed in certain social conditions to solve certain social problems, beyond them they discover their limitations. Identification of the possibilities and limitations of the K-approach contributes to both its development and the refinement of the conditions under which its implementation will be more constructive and effective. Methods: historical and theoretical analysis. Results. “Open questions” of the problem are highlighted: inconsistencies in the judgments of specialists in listing the qualities that determine the success of the subject; the number of competencies “necessary and sufficient”; non-realization of holistic, integrated approach; the place of competencies in the structure of professionalism, their role in career success and professional longevity of people; the issue of “universal” / “specific competencies”; the question about the place of the phenomenon of “competence” and the K-approach in the system of psychological knowledge; the question about the validity of the methods and the measure of the predictive value of the estimates. Conclusions. The interpretation of competencies and the K-approach proposed by specialists are unsatisfactory and poorly coordinated with each other. The K-approach and the established earlier approach of professionally important qualities (PVC-approach) are historically transient scientific concepts (falling into the scale of “small theories”). Both approaches have their own limitations as well as opportunities for development and integration with others. The PVC approach is characterized by an initial extremely broad statement of tasks; the K-approach — by the pragmatism of the original goals and objectives, empiricism in assessing the qualities of the subject. It is possible and necessary to search and find areas of their mutual complementarity, taking into account their ultimate goals and conditions of formation. Ignoring the merits and limitations of any scientific approach is not the best solution. The transition to an open discussion of all complex issues is in demand.
APLICACIÓN REMOTA DE TEST: RIESGOS Y RECOMENDACIONES

espanolLa necesidad de continuar ofreciendo servicios profesionales de evaluacion en situaciones de pandemia ha favorecido la aplicacion remota de test disenados para un uso presencial. Esta practica de tele-evaluacion modifica las condiciones en las que el test fue construido, estandarizado y validado, y por consiguiente conviene que vaya acompanada de un analisis de sus potenciales riesgos. En este trabajo se describen las amenazas asociadas con el uso remoto de test, y se apuntan algunas recomendaciones para mitigarlas. Cuando un profesional opte por la aplicacion remota de test que han sido construidos para ser utilizados en modo presencial, conviene que sea consciente de los riesgos y beneficios asociados a esta practica, y una vez evaluados estos, actue en consecuencia. EnglishThe need to continue to offer professional assessment services in pandemic situations has given rise to the remote use of tests designed for face-to-face administration. This practice of tele-assessment modifies the original conditions in which the test was constructed, standardized and validated, and therefore should be accompanied by an analysis of the potential risks. This paper describes the threats associated with remote test use, and suggests several recommendations to mitigate them. When considering the remote administration of tests that have been constructed to be used in person, the professional should be aware of the risks and benefits associated with this practice, and once these have been evaluated, he or she should act accordingly.
REM-Net: Recursive Erasure Memory Network for Commonsense Evidence Refinement

When answering a question, people often draw upon their rich world knowledge in addition to the particular context. While recent works retrieve supporting facts/evidence from commonsense knowledge bases to supply additional information to each question, there is still ample opportunity to advance it on the quality of the evidence. It is crucial since the quality of the evidence is the key to answering common- sense questions, and even determines the upper bound on the QA systems’ performance. In this paper, we propose a recursive erasure memory network (REM-Net) to cope with the quality improvement of evidence. To address this, REM-Net is equipped with a module to refine the evidence by recursively erasing the low-quality evidence that does not explain the question answering. Besides, instead of retrieving evidence from existing knowledge bases, REM-Net leverages a pre-trained generative model to generate candidate evidence customized for the question. We conduct experiments on two commonsense question answering datasets, WIQA and CosmosQA. The results demonstrate the performance of REM- Net and show that the refined evidence is explainable.
Reasoning Requirements for Indirect Speech Act Interpretation

We perform a corpus analysis to develop a representation of the knowledge and reasoning used to interpret indirect speech acts.An indirect speech act (ISA) is an utterance whose intended meaning is different from its literal meaning.We focus on those speech acts in which slight changes in situational or contextual information can switch the dominant intended meaning of an utterance from direct to indirect or vice-versa.We computationalize how various contextual features can influence a speaker's beliefs, and how these beliefs can influence the intended meaning and choice of the surface form of an utterance.We axiomatize the domain-general patterns of reasoning involved, and implement a proof-of-concept architecture using Answer Set Programming.Our model is presented as a contribution to cognitive science and psycholinguistics, so representational decisions are justified by existing theoretical work.
SOTPM: Software One-Time Programmable Memory to Protect Shared Memory on ARM Trustzone

In ARM TrustZone-based architecture, shared memory is one of the most useful schemes to enable isolated execution environments supported by TrustZone to communicate between environments.However, it is already known that shared memory is vulnerable to man-in-the-middle attacks since mechanisms to check integrity or authenticate callers for the shared memory payload are not supported in TrustZone.While an encryption-based method that resolves this limitation does exist, there are some architectural limitations.Indeed, even with key protection countermeasures applied, there is a risk that encryption keys may be leaked, as they are placed in insecure user memory during communication.Moreover, countermeasures for key leakage cause system performance overhead.In this paper, we propose a lightweight and secure scheme for shared memory, called Software One-Time Programmable Memory (SOTPM).SOTPM is a software-implemented, one-time programmable shared memory.It is based on the idea that payload encryption in the shared memory layer is unnecessary because sensitive data is already encrypted in the application layer before being written to the shared memory.SOTPM is set to read-only after data is written into SOTPM due to the one-time programmable characteristic.Therefore, attackers are unable to manipulate content in SOTPM during communication.Since it is not necessary for SOTPM to encrypt the payload in order to prevent malicious payload manipulation, it is possible to remove the risk of key leakage posed in previous studies.Additionally, in contrast with the existing method, our method can dramatically reduce system performance overhead.We implemented our prototype on an open-source hardware board with an Armv8-A processor and performed a security analysis and performance evaluation.The results show that SOTPM provides a sufficient level of security and less than 1% performance overhead, implying that SOTPM is a reasonable solution for current commercial products.
Communicating Uncertainty and Risk in Air Quality Maps

Environmental sensors provide crucial data for understanding our surroundings. For example, air quality maps based on sensor readings help users make decisions to mitigate the effects of pollution on their health. Standard maps show readings from individual sensors or colored contours indicating estimated pollution levels. However, showing a single estimate may conceal uncertainty and lead to underestimation of risk, while showing sensor data yields varied interpretations. We present several visualizations of uncertainty in air quality maps, including a frequency-framing “dotmap” and small multiples, and we compare them with standard contour and sensor-based maps. In a user study, we find that including uncertainty in maps has a significant effect on how much users would choose to reduce physical activity, and that people make more cautious decisions when using uncertainty-aware maps. Additionally, we analyze think-aloud transcriptions from the experiment to understand more about how the representation of uncertainty influences people’s decision-making. Our results suggest ways to design maps of sensor data that can encourage certain types of reasoning, yield more consistent responses, and convey risk better than standard maps.
Improved cryptanalysis of a ElGamal Cryptosystem Based on Matrices Over Group Rings

Abstract ElGamal cryptosystem has emerged as one of the most important construction in Public Key Cryptography (PKC) since Diffie-Hellman key exchange protocol was proposed. However, public key schemes which are based on number theoretic problems such as discrete logarithm problem (DLP) are at risk because of the evolution of quantum computers. As a result, other non-number theoretic alternatives are a dire need of entire cryptographic community. In 2016, Saba Inam and Rashid Ali proposed a ElGamal-like cryptosystem based on matrices over group rings in ‘Neural Computing &amp; Applications’. Using linear algebra approach, Jia et al. provided a cryptanalysis for the cryptosystem in 2019 and claimed that their attack could recover all the equivalent keys. However, this is not the case and we have improved their cryptanalysis approach and derived all equivalent key pairs that can be used to totally break the ElGamal-like cryptosystem proposed by Saba and Rashid. Using the decomposition of matrices over group rings to larger size matrices over rings, we have made the cryptanalysing algorithm more practical and efficient. We have also proved that the ElGamal cryptosystem proposed by Saba and Rashid does not achieve the security of IND-CPA and IND-CCA.
Digital literacy as a prerequisite for transforming the modern Russian education system

The article studies the necessity and features of transformation of the educational system under conditions of accelerated development in the digital economy. Particular attention in the context of this transformation is given to significance and possibilities of digital literacy, which forms the whole complex of fundamentally new requirements for all participants in the education system. The authors argue that the current stage of development of the digital economy requires the education system not only to digitalize its individual elements and links, but to apply a fundamentally new integrated approach that would transform the education system while taking into account new goals, structure and content of the educational process. The authors define digital literacy as the ability to form and apply educational content through digital technologies. The article gives particular emphasis on the significance and potential of individualizing the educational trajectory and the concept of continuing education. The authors consider the main factors in the development and achievement of the level of digital literacy, considering the requirements that the digital economy is currently imposing on the educational system.
Research on multi-feature fusion text classification model based on self-attention mechanism

Abstract For complex long texts, critical semantic information will be weakened and non-critical features will be forgotten. This paper proposes a multi-feature fusion text classification model based on autoattention mechanism. The preprocessed text data is represented by character-level vectors through the Bert model. First of all, self attention mechanism (Self-Attention) is used to learn the dependence of text words to capture the internal structural information of the text, Secondly, according to Deep superposition convolutional neural network (DSCNN) and Bi-directional Gated Recurrent Unit (BiGRU) based on soft attention mechanism (Soft-Attention), the semantic features of text data are extracted separately, and two different feature extraction results are combined. Finally, the Softmax layer is used to classify the deep-extracted features, and the accuracy of the classification model is improved by adding a uniform distribution item to the cross-entropy loss function. The text is verified in the medical consultation data set, and the results show that the F1 value of the text classification of the model is as high as 88.91%, Text semantic understanding is better than mainstream models.