Reviewerly/exHarmony huggingface dataset:
NeurIPS Conference Papers Classification Based on Topic Modeling

Paper illustrates the process of topic modeling and text classification. Specifically, the dataset used is a corpus consisting of scientific publications published by Neural Information Systems Processing Conference. Topic modeling itself is performed using Latent Dirichlet Allocation model. It is followed by optimization of a number of topics on the basis of topic coherence, a quality measure of human interpretability. Results of topic modeling are used for labeling data prior to text classification. Labels are determined based on the distribution of assigned papers' topics over time. Specifically, peak changes used for differentiating between time periods dominated by specific topics are calculated as a Kullback-Leibler divergence. Finally, transforming data into the feature vectors, several different text classification approaches are evaluated. As observed, the greatest accuracy score is recorded for the use of extreme gradient boosting classifier being 77.1%.
Long Short-Term Sample Distillation

In the past decade, there has been substantial progress at training increasingly deep neural networks. Recent advances within the teacher–student training paradigm have established that information about past training updates show promise as a source of guidance during subsequent training steps. Based on this notion, in this paper, we propose Long Short-Term Sample Distillation, a novel training policy that simultaneously leverages multiple phases of the previous training process to guide the later training updates to a neural network, while efficiently proceeding in just one single generation pass. With Long Short-Term Sample Distillation, the supervision signal for each sample is decomposed into two parts: a long-term signal and a short-term one. The long-term teacher draws on snapshots from several epochs ago in order to provide steadfast guidance and to guarantee teacher–student differences, while the short-term one yields more up-to-date cues with the goal of enabling higher-quality updates. Moreover, the teachers for each sample are unique, such that, overall, the model learns from a very diverse set of teachers. Comprehensive experimental results across a range of vision and NLP tasks demonstrate the effectiveness of this new training method.
WSCC 2019: The World Speed Chess Championship

WSCC 2019 was ICGA's continuation of its investigation of top chess engines playing at Blitz tempo with the consequential loss of accuracy. It was held in Macau in parallel with the 'WCCC' classical tempo and 'WCSC' neutral-hardware events. The ICGA demonstrated that, even so, the engines play at super-GM level. On this occasion, JONNY proved incisive to win while KOMODO sustained two losses.
A Quantum Safe User Authentication Protocol for the Internet of Things

The forthcoming of the Internet of Things has opened the gates for numerous applications in several domains. Unfortunately, it also has brought along with it several security challenges. IoT devices, being compact in size has several constraints. Therefore, it becomes a challenging task to define security protocols suitable for such constrained devices. Also, the significant strides made towards the development of Quantum computers pose a huge threat to traditionally used cryptosystems. It is known that a sufficiently large Quantum Computer running Shorâ€™s Algorithm can solve the integer factorization problem and the discrete logarithm problem. Thus, in our research, we provide an authentication protocol that utilizes Gate Way Node for high-end processing and suggest the usage of NTRU cryptosystem as the cipher suite. We suggest our authentication protocol in terms of cloud computing, as the number of IoT devices would be immense and cloud computing is better suited for processing and storing such large volumes of data. We also suggest the use of One Time Password, for adding another layer of security on top of the public key cryptosystem. We analyze our authentication protocol and find that it is safe.
Riverine Sediment Geochemistry as Provenance Fingerprints along the Eastern Coast of China: Constraint, Approach, and Application

Sediment geochemistry is affected by sediment granularity and chemical partition, which may greatly influence the accuracy of sediment source identification. In this study, we analyzed Ca, Co, Zr, V, Cr, Ti, Sc, Th, and Al in the sediments of major rivers and a Holocene core along the eastern coast of China to reveal the constraints on sediment geochemistry related to granularity and chemical partition and to try and identify the sources of the sediments present in the core. The results reveal that the element concentrations have a significant positive correlation with Al concentration in all these riverine sediments. There are significant differences in the element contents of the bulk sample and the residual sediment leached with acid, including in their ratios. The ratios of Cr/Th–Sc/Th, which is often used for provenance discrimination, reveal that uncertainty of provenance discrimination will increase if the impact of sediment granularity and chemical phase on the index system is not considered. We applied this geochemical approach for provenance on a Holocene core of the East China Sea using Cr/Th–Al, Sc/Th–Al, Ti/Zr–Al, and Cr/Th–Sc/Th with the same granularity as the residue sediments. Based on this approach, we identified the core sediments to have had a large component derived from the western Taiwanese rivers since the mid-Holocene. This study sheds light on the sediment geochemistry used to identify the provenance of marginal seas with multiple rivers entering them.
Game-Theoretic Model of the Dynamic Problem of Labor Resources Optimal Assignment

The paper considers a game-theoretic model of a dynamic optimal-purpose problem using the example of the labor market functioning. A deterministic model of the workers optimal distribution among enterprises is described taking into account changing conditions over a period of time. At each moment of time, the state of the employee and the enterprise are determined. Moments of time are moments of the system stationary states. In each stationary state, a game in normal form is determined. In the game there is a compromise situation, optimal policy, and the system's income from appointments is calculated as the sum of the payoff functions of all players. The functioning of the labor market as a system for some periods of time is presented as a multi-step game on a tree. In a one-step game based on the principle of compromise set optimality, there is a compromise situation and the corresponding compromise control vector. On the multi-step game tree, there is a compromise income of the system in a few steps, when a sequence of games was realized, and a compromise path corresponding to a sequence of compromise control vectors. The compromise system income and the sequence of compromise controls are found using dynamic programming recurrence relationships. Thus, it is possible to indicate the optimal behavior of all participants in the labor market at any given time.
Klm-PPSA: Klm-based profiling and preventing security attacks for cloud environments: Invited Paper

Cloud computing is the newly emerged technology adopted by many organizations due to its different benefits. Unfortunately, despite all the benefits offered by the cloud, there are certain concerns regarding the security issues related to the cloud platform which can threaten its widespread adoption. In this study, we suggest a scalable model to profile and prevent security attacks in the application layer of a cloud environment using an accurate and interpretable machine learning algorithm called regularized class association rules. The proposed model is based, first, on three additional security factors (k, l and m), second, on the traditional authentication methods such as passwords and biometrics including keystroke to grant access to the cloud services/resources for an authorized user. Moreover, a case study of the proposal is given in order to validate the model and its usefulness. Eventually, a simulation was done to test the model performances.
Uncertain Context: Uncertainty Quantification in Machine Learning

Machine learning and artificial intelligence will be deeply embedded in the intelligent systems humans use to automate tasking, optimize planning, and support decision‐making. However, many of these methods can be challenged by dynamic computational contexts, resulting in uncertainty in prediction errors and overall system outputs. Therefore, it will be increasingly important for uncertainties in underlying learning‐related computer models to be quantified and communicated. The goal of this article is to provide an accessible overview of computational context and its relationship to uncertainty quantification for machine learning, as well as to provide general suggestions on how to implement uncertainty quantification when doing statistical learning. Specifically, we will discuss the challenge of quantifying uncertainty in predictions using popular machine learning models. We present several sources of uncertainty and their implications on statistical models and subsequent machine learning predictions.
Research of the Model of Territorial Distribution of Labor Resources

The paper considers a game model of spatial competition of workers of various professional qualifications, taking into consideration the interests of employers. It is assumed that workers and employers are interested in maximizing their profits and acting on the basis of their interests. To simulate this situation, you need to use the mathematical apparatus of game theory. The article presents a solution to the problem of locating a finite number of employees with a known finite number of employers on a network defined on a flat torus. Winning employees is defined as the salary offered to them, taking into consideration all their merits. Employers choose workers, not only on the basis of the cost function specified for each employer when moving workers, but also focusing on the minimum cost of employees' professional qualifications. Compromise solutions serve as a criterion of optimality. A compromise solution and equilibrium in the network are found according to Stackelberg.
Preference corrections: capturing student and instructor perceptions in educational recommendations

Abstract Recommender systems (RS) have been applied in the area of educations to recommend formal and informal learning materials, after-school programs or online courses. In the traditional RS, the receiver of the recommendations is the only stakeholder, but other stakeholders may be involved in the environment. Take educations for example, not only the preference of the student, but also the perspective of other stakeholders (e.g., instructors, parents, publishers, etc) may be important in the process of recommendations. Multi-stakeholder recommender systems (MSRS) were recently proposed to balance the needs of multiple stakeholders in the recommender systems. We use course project recommendations as a case study, and the perspectives of both students and instructors will be considered in our work. However, students and instructors may have different perceptions on the technical difficulty of the projects. In this paper, we particularly focus on the solution of preference corrections which can be used to capture different perceptions of students and instructors in the multi-stakeholder educational recommendations.
Input and Weight Space Smoothing for Semi-Supervised Learning

We propose regularizing the empirical loss for semi-supervised learning by acting on both the input (data) space, and the weight (parameter) space. We propose a method to perform such smoothing, which combines known input-space smoothing with a novel weight-space smoothing, based on a min-max (adversarial) optimization. The resulting Adversarial Block Coordinate Descent (ABCD) algorithm performs gradient ascent with a small learning rate for a random subset of the weights, and standard gradient descent on the remaining weights in the same mini-batch. It is simple to implement and achieves state-of-the-art performance.
The promise of open survey questions—The validation of text-based job satisfaction measures

Recent advances in computer-aided text analysis (CATA) have allowed organizational scientists to construct reliable and convenient measures from open texts. As yet, there is a lack of research into using CATA to analyze responses to open survey questions and constructing text-based measures of psychological constructs. In our study, we demonstrated the potential of CATA methods for the construction of text-based job satisfaction measures based on responses to a completely open and semi-open question. To do this, we employed three sentiment analysis techniques: Linguistic Inquiry and Word Count 2015, SentimentR and SentiStrength, and quantified the forms of measurement error they introduced: specific factor error, algorithm error and transient error. We conducted an initial test of the text-based measures’ validity, assessing their convergence with closed-question job satisfaction measures. We adopted a time-lagged survey design (Nwave 1 = 996; Nwave 2 = 116) to test our hypotheses. In line with our hypotheses, we found that specific factor error is higher in the open question text-based measure than in the semi-open question text-based measure. As expected, algorithm error was substantial for both the open and semi-open question text-based measures. Transient error in the text-based measures was higher than expected, as it generally exceeded the transient error in the human-coded and the closed job satisfaction question measures. Our initial test of convergent and discriminant validity indicated that the semi-open question text-based measure is especially suitable for measuring job satisfaction. Our article ends with a discussion of limitations and an agenda for future research.
Against Malicious SSL/TLS Encryption: Identify Malicious Traffic Based on Random Forest

It has become a significant research direction to resist cyberattacks through traffic identification technology. Traditional traffic identification technology is often based on network port or feature matching, which has become inefficient in the increasingly complex network environment. Nowadays, the malicious cyberattacks usually encrypt their traffic to escape the traditional traffic identification, and the most common encryption method is the SSL/TLS encryption. In response to this phenomenon, this paper proposes an encrypted malicious traffic identification method based on the random forest, which uses features based on packet information, time, TCP Flags field, and application layer payload information. We designed the technology and application framework to ensure the success of the experiment and collected a large amount of SSL/TLS encrypted traffic as datasets. Benefit from model optimization by parameter adjusting, the experimental results showed that final model had highly accurate and predictive ability.
Feature Variance Regularization: A Simple Way to Improve the Generalizability of Neural Networks

To improve the generalization ability of neural networks, we propose a novel regularization method that regularizes the empirical risk using a penalty on the empirical variance of the features. Intuitively, our approach introduces confusion into feature extraction and prevents the models from learning features that may relate to specific training samples. According to our theoretical analysis, our method encourages models to generate closer feature distributions for the training set and unobservable true data and minimize the expected risk as well, which allows the model to adapt to new samples better. We provide a thorough empirical justification of our approach, and achieves a greater improvement than other regularization methods. The experimental results show the effectiveness of our method on multiple visual tasks, including classification (CIFAR100, ImageNet, fine-grained datasets) and semantic segmentation (Cityscapes).
Local Peaks-Based Clustering Algorithm in Symmetric Neighborhood Graph

Density-based clustering methods have achieved many applications in data mining, whereas most of them still likely suffer poor performances on data sets with extremely uneven distributions, like the manifold or ring data.The paper proposes a novel method for clustering with local peaks in the symmetric neighborhood.Local peaks are points with maximum densities at the local level.During the searching of local peaks, all data, except those outliers, can be easily divided into a number of small clusters in accordance with the local peaks in each point's neighborhood.Especially, a graph-based scheme is adopted here to merge similar clusters based on their similarity in the symmetric neighborhood graph, followed by assigning each outlier to the closest cluster.A variety of artificial, real data sets and a real building data set have been tested for clustering by the proposed method and compared against other popular density-based methods and other algorithms.
Tutors’ Support For Students Of Educational Intensive In Novsu

Tutoring support of the individualization of education and its quality assessment based on the growth of a student’s competencies in a classical university is a response to both external and internal factors from subjects of education. The problem with introducing flexible educational models is the lack of trained personnel and the inertia of the classical education system. The first experience of tutoring students is described on the basis of the competency model of the NTI University 20.35 in educational intensive at Novgorod State University named after Yaroslav the Wise in 2018. The model of tutorial support for adult students of a digital University has been adapted for the students of a classical university. Employees external to UNTI broadcast its digital competency model through the framework management in project teams heterogeneous in age and direction of preparation. The task of tutorials was: identification of individual requests of students in the educational process; supporting the development of an individual educational trajectory (IET) and fixing its development; organization of reflection of the IET implementation results; interaction with a mentor. The focus of the tutor IET was formed by students on the educational deficit actualized by the project activity through a wide selection of online courses and offline activities. The article analyzes the dynamics of team and individual tutorials attendance, the reasons for its changes during the educational intensive. Recommendations and limitations based on the results of analysis of the NovSU tutorial team activities are presented.
Deep Neural Network Acceleration With Sparse Prediction Layers

The ever-increasing computation cost of Convolutional Neural Network (CNN) makes it imperative for real-world applications to accelerate the key steps especially the inference. In this work, we propose an efficient yet general scheme called Sparse Prediction Layer (SPL) which can predict and skip the trivial elements in the CNN layer. Pruned weights are used to predict the locations of maximum values in max-pooling kernels and those of positive values before Rectified Linear Units (ReLUs). Thereafter, the precise values of these predicted important elements are calculated selectively and the complete outputs are restored from them. Our experiments on ImageNet Large Scale Visual Recognition Competition (ILSVRC) 2012 show that SPL can reduce 68.3%, 58.6% and 59.5% Floating-point Operations (FLOPs) on AlexNet, VGG-16 and ResNet-50, respectively, within an accuracy loss of less than 1% without retraining. The proposed SPL scheme can further accelerate these networks pruned by other pruning-based methods, such as a FLOP reduction of 50.2% on the ResNet-50 which has been pruned by Channel Pruning (CP) before being applied with SPLs. A special matrix multiplication called Sparse Result Matrix Multiplication (SRMM) is proposed to support the implementation of SPL, and its acceleration effect is in line with expectations.
Automated dilemmas generation in simulations

Our ultimate purpose is to train individuals, in virtual environments, to handle critical situations. One of these critical situations is dilemmas. They refer to situations that lead to negative consequences whichever is the choice made by the protagonist. In critical contexts, it is crucial to know how to handle this kind of situations to prevent disastrous consequences from happening. Thus, people need to be exposed to various training situations in which they put in play and develop the appropriate skills. However, in complex domains, it is difficult—sometimes impossible—to write all the possible training scenarios. To address this problem, an automated generation approach is considered. In this article, we present KOBA, a scenario engine that automatically generates dilemma situations without having to write them beforehand. This engine uses knowledge models to extract the necessary properties for dilemmas to emerge. In this article, we present this approach and expose a proof of concept of the generation process.
Accurate angle estimation based on moment for multirotation computation imaging

In a multirotation computation imaging system, the fidelity of the reconstructed result is limited by the accuracy of the estimated rotation angles. Here, an accurate angle detection method using image moment is proposed to estimate angles of diffraction images. The second moment of a digital image is adopted as the rotational inertia in order to estimate angles of diffraction images. Compared with previous versions based on Radon/Hough transform, it has higher accuracy and is simultaneously time-saving, which is verified in both simulation and experiment. The angle error of moment method is narrowed down within 0.1°, or even less, and it also can perform well in sample diversity or when slightly out of focus.
Parametric Probabilistic Quantum Memory

Probabilistic Quantum Memory (PQM) is a data structure that computes the distance from a binary input to all binary patterns stored in superposition on the memory. This data structure allows the development of heuristics to speed up artificial neural networks architecture selection. In this work, we propose an improved parametric version of the PQM to perform pattern classification, and we also present a PQM quantum circuit suitable for Noisy Intermediate Scale Quantum (NISQ) computers. We present a classical evaluation of a parametric PQM network classifier on public benchmark datasets. We also perform experiments to verify the viability of PQM on a 5-qubit quantum computer.
News - Comment. View from Taipei: Industry - Taiwan demonstrates energetic response to 'interesting times'

Taiwan is on the front line of global trade tensions and a shifting technology landscape. Its reaction offers useful lessons. Engineering & Technology July 2019 www.EandTmagazine.com.
Novel Performance Analysis of DCT, DWT and Fractal Coding in Image Compression

In digital image processing, image compression has prominent significance to improve the storage capacity, transmission bandwidth and transmission time. Basically, in image compression, the redundant information is reduced from the image by using proper compression techniques. The vital information in image is mined using different transformation techniques so that these transforms are used to restore image data without loss of information. In this paper, the compression of an image is done by three transform methods which are DCT, DWT and fractal coding using quadtree decomposition. Comparative analysis among these three transform methods is evaluated by finding performance evaluation parameters which are PSNR, MSE, CR and SSIM. Among the three different transform methods, DWT transform gives better PSNR, high compression ratio and low mean square error compared to other methods. Each transformation has its own merit and demerits. According to the application used for compression, the suitable method is used.