Reviewerly/exHarmony huggingface dataset:
Attribute-aware Pedestrian Detection in a Crowd

Pedestrian detection is an initial step to perform outdoor scene analysis, which plays an essential role in many real-world applications. Although having enjoyed the merits of deep learning frameworks from the generic object detectors, pedestrian detection is still a very challenging task due to heavy occlusion and highly crowded group. Generally, the conventional detectors are unable to differentiate individuals from each other effectively under such a dense environment. To tackle this critical problem, we propose an attribute-aware pedestrian detector to explicitly model people's semantic attributes in a high-level feature detection fashion. Besides the typical semantic features, center position, target's scale and offset, we introduce a pedestrian-oriented attribute feature to encode the high-level semantic differences among the crowd. Moreover, a novel attribute-feature-based Non-Maximum Suppression~(NMS) is proposed to distinguish the person from a highly overlapped group by adaptively rejecting the false-positive results in a very crowd settings. Furthermore, a novel ground truth target is designed to alleviate the difficulties caused by the attribute configuration and extremely class imbalance issues during training. Finally, we evaluate our proposed attribute-aware pedestrian detector on two benchmark datasets including CityPersons and CrowdHuman. The experimental results show that our approach outperforms state-of-the-art methods at a large margin on pedestrian detection.
Robust Real-time UAV Replanning Using Guided Gradient-based Optimization and Topological Paths

Gradient-based trajectory optimization (GTO) has gained wide popularity for quadrotor trajectory replanning. However, it suffers from local minima, which is not only fatal to safety but also unfavorable for smooth navigation. In this paper, we propose a replanning method based on GTO addressing this issue systematically. A path-guided optimization (PGO) approach is devised to tackle infeasible local minima, which improves the replanning success rate significantly. A topological path searching algorithm is developed to capture a collection of distinct useful paths in 3-D environments, each of which then guides an independent trajectory optimization. It activates a more comprehensive exploration of the solution space and output superior replanned trajectories. Benchmark evaluation shows that our method outplays state-of-the-art methods regarding replanning success rate and optimality. Challenging experiments of aggressive autonomous flight are presented to demonstrate the robustness of our method. We will release our implementation as an open-source package.
Selection of Evaluation Metrics for Dialogue Breakdown Detection in Dialogue Breakdown Detection Challenge 3

The task of detecting dialogue breakdown, the aim of which is to detect whether a system utterance causes dialogue breakdown in a given dialogue context, has been actively researched in recent years. However, currently, it is not clear which evaluation metrics should be used to evaluate dialogue breakdown detectors, hindering progress in dialogue breakdown detection. In this paper, we propose finding appropriate metrics for evaluating the detectors in dialogue breakdown detection challenge 3. In our approach, we first enumerate possible evaluation metrics and then rank them on the basis of system ranking stability and discriminative power. By using the submitted runs (results of dialogue breakdown detection of participants) of dialogue breakdown detection challenge 3, we experimentally found that RSNOD(NB,PB,B) is an appropriate metric for dialogue breakdown detection in dialogue breakdown detection challenge 3 for English and Japanese, although NMD(NB,PB,B) and MSE(NB,PB,B) were found appropriate specifically for English and Japanese, respectively.
CsiNet-Plus Model with Truncation and Noise on CSI Feedback

For the frequency-division-duplex (FDD)-based massive multiple-input multiple-output (MIMO) systems, channel state information (CSI) feedback plays a critical role. Although deep learning has been used to compress the CSI feedback, some issues like truncation and noise still need further investigation. Facing these practical concerns, we propose an improved model (called CsiNet-Plus), which includes a truncation process and a channel noise process. Simulation results demonstrate that the CsiNet-Plus outperforms the existing CsiNet. The performance interchangeability between truncated decimal digits and the signal-to-noise-ratio helps support flexible configuration.
Securing Private Key using New Transposition Cipher Technique

The security of any public key cryptosystem depends on the private key thus, it is important that only an authorized person can have access to the private key. The paper presents a new algorithm that protects the private key using the transposition cipher technique. The performance of the proposed technique is evaluated by applying it in the RSA algorithm's generated private keys using 512-bit, 1024-bit, and 2048-bit, respectively. The result shows that the technique is practical and efficient in securing private keys while in storage as it produced high avalanche effect.
Computing Superior Counter-Examples for Conformant Planning

In a counter-example based approach to conformant planning, choosing the right counter-example can improve performance. We formalise this observation by introducing the notion of “superiority” of a counter-example over another one, that holds whenever the superior counter-example exhibits more tags than the latter. We provide a theoretical explanation that supports the strategy of searching for maximally superior counter-examples, and we show how this strategy can be implemented. The empirical experiments validate our approach.
Machine Learning-Based Detection of Credit Card Fraud: A Comparative Study

One of the fastest-growing problems with a high impact on the financial sector is financial fraud.Recently, data mining has been identified as one of the effective ways of detecting fraudulent credit card transactions.As a data mining problem, the detection of fraudulent credit card transaction is a challenging task due to the following reasons: (i) The frequent changes in the patterns of normal and fraudulent activities and (ii) the high level of skewness related with credit card fraud datasets.The aim of this article is to review the existing techniques for fraudulent transactions detection in credit cards, with more focus on the techniques that are Machine Learning (ML) based and nature inspired-based.The recent trend in the detection of credit card fraud was also presented in this article.Furthermore, the limitations and usefulness of the existing techniques for fraudulent transaction detection in credit cards were also outlined.The necessary fundamental information for further studies in this area was also provided.This review will also guide individuals and financial institutions seeking for effective techniques for credit card fraud detection, especially those that are based on ML and nature-inspired algorithms.
Successful Systems in Production Graduate Teaching

This paper presents our experience in coordinating and teaching a novel graduate systems and computing course named “Successful Systems in Production” (SSP). The course targets graduate students of different research interests in Computer Science. The course aims at giving a breadth knowledge on cutting-edge well-known systems in production, and exploring the potential synergies across different areas of research. Having its roots in Distributed Computing, SSP addresses those systems that overlap with other research areas like Computational Systems, Parallel Computing, Databases, Cloud Computing, Artificial Intelligence, Security, etc. SSP exhibits an agile topic selection model that fits several students’ backgrounds in each academic year. The topics focus on the practical aspects of each selected system that is considered “successful”, i.e., based on its worldwide impact and technical significance. This is important for graduate students to acquire best practices in industry and academia, necessary to build practical computing systems. In the same vein, the assessment method includes a project that is based on one of the presented systems and also intersects with the student’s own research plan. Based on our teaching experience and the excellent feedback of the students, we strongly recommend this graduate course to be taught at other universities.
An Aeroengine Gas Path Anomaly Detection Method in The Case of Sample Imbalance

In the process of aeroengine anomaly detection, there is always an unbalance distribution among the samples of gas path performance parameters, that is, the number of normal samples is much larger than the number of abnormal samples. In addition, this imbalance will worsen with time, which leads to the classifier paying too much attention to normal samples in the process of model training. Thus, the recognition rate of abnormal samples will reduce significantly. To solve the above problems, an adaptive decision threshold support vector machine (ADT-SVM) is proposed and applied to the anomaly detection of aeroengine. Firstly, this paper analyzes the influence of the unbalanced training data on the performance of the traditional classification model. Then the concept of decision threshold is introduced and introduced into support vector machine for anomaly detection. Finally, an adaptive method is proposed to calculate the decision threshold based on the equal expected number of samples, and the performance of the adaptive threshold and the traditional default threshold SVM is compared through experiments, which show that the adaptive threshold is effective in solving the problem of the classification performance degradation of unbalanced gas path performance parameters.
Stochastic Recursive Variance Reduction for Efficient Smooth Non-Convex Compositional Optimization

Stochastic compositional optimization arises in many important machine learning tasks such as value function evaluation in reinforcement learning and portfolio management. The objective function is the composition of two expectations of stochastic functions, and is more challenging to optimize than vanilla stochastic optimization problems. In this paper, we investigate the stochastic compositional optimization in the general smooth non-convex setting. We employ a recently developed idea of \textit{Stochastic Recursive Gradient Descent} to design a novel algorithm named SARAH-Compositional, and prove a sharp Incremental First-order Oracle (IFO) complexity upper bound for stochastic compositional optimization: $\mathcal{O}((n+m)^{1/2} \varepsilon^{-2})$ in the finite-sum case and $\mathcal{O}(\varepsilon^{-3})$ in the online case. Such a complexity is known to be the best one among IFO complexity results for non-convex stochastic compositional optimization, and is believed to be optimal. Our experiments validate the theoretical performance of our algorithm.
Project Management Of Publication Activity At The Modern University

The principles and methods of project management of publication activity are considered, the role of scientometrics in research work is determined, the importance of the most important scientometric databases is identified, the modern requirements for the formation and characterization of the author’s personal publication career, its role and place in scientific research are examined, the value of information and management competencies of a modern author is determined. The analysis of the main scientometric indicators on three scientometric databases main for the Russian scientist was carried out (RISC, Scopus, WebofScienceCoreCollection). Based on the analysis of scientometric indicators, the directions of improving the management of publication activity are proposed: the formation of a scientific policy in order to increase the effectiveness of the organizations scientific activity, taking into account modern requirements for the publication activity of a university; development of the university’s publication strategy based on an analysis of strategies to increase publication activity and scientific trends; the use of effective scientometric tools to manage the research activities of the university; the formation of information and management skills of a modern author: management of personal knowledge and publication career; active application of the principles, methods and indicators for assessing the publication activity of authors; stimulation of the publication activity of authors and research teams; creating an environment conducive to increasing the number of publications in high-rated journals and increasing the citation of articles of scientists; increasing the visibility of the results of research activities of the university.
A Health Indicator Construction Method based on Deep Belief Network for Remaining Useful Life Prediction

Remaining useful life (RUL) prediction is of great importance in a successful prognostics and health management system. The performance of RUL prediction is mainly decided by the development of an appropriate health indicator (HI), which can accurately indicate the degree of degradation of the equipment. Therefore, we proposed an unsupervised method for HI construction based on deep belief network (DBN) by using multisensory historical data. Firstly, DBN is employed to describe the hidden representation corresponding to the healthy state. With the running of the system, its performance will decrease over time and the corresponding potential characteristics tend to be different. The deviation degree of degraded state can be used to establish HI so as to estimate the RUL. Finally, a case study is conducted to validate the effectiveness of the new method, where it can be seen that the new approach achieves better performance compared to traditional methods.
Adaptive Financial Fraud Detection in Imbalanced Data with Time-Varying Poisson Processes

This paper discusses financial fraud detection in imbalanced dataset using homogeneous and non-homogeneous Poisson processes.The probability of predicting fraud on the financial transaction is derived.Applying our methodology to financial datasets with different fraud profiles shows a better predicting power than a baseline approach, especially in the case of higher imbalanced data.
Integrating Linguistic Knowledge to Sentence Paraphrase Generation

Paraphrase generation aims to rewrite a text with different words while keeping the same meaning. Previous work performs the task based solely on the given dataset while ignoring the availability of external linguistic knowledge. However, it is intuitive that a model can generate more expressive and diverse paraphrase with the help of such knowledge. To fill this gap, we propose Knowledge-Enhanced Paraphrase Network (KEPN), a transformer-based framework that can leverage external linguistic knowledge to facilitate paraphrase generation. (1) The model integrates synonym information from the external linguistic knowledge into the paraphrase generator, which is used to guide the decision on whether to generate a new word or replace it with a synonym. (2) To locate the synonym pairs more accurately, we adopt an incremental encoding scheme to incorporate position information of each synonym. Besides, a multi-task architecture is designed to help the framework jointly learn the selection of synonym pairs and the generation of expressive paraphrase. Experimental results on both English and Chinese datasets show that our method significantly outperforms the state-of-the-art approaches in terms of both automatic and human evaluation.
Selecting an Optimal Feature Set for Stance Detection

Stance detection is an automatic recognition of author’s view point in relation to a given object. An important stage of the solution process is determining the most appropriate way to represent texts. The paper proposes a new method of selecting an optimal feature set. The method is based on a homogenous ensemble of feature selection methods and a procedure of determining the optimal number of features. In this procedure the dependence of task performance on the number of features is approximated and the optimal number of features is determined by analyzing the growth rate of the function. There have been conducted experiments with text corpora consisting of “for” and “against” stances towards vaccinations of children, the Unified State Examination at school, and human cloning. The results demonstrate that the proposed method allows to achieve better performance in comparison with individual methods and even an overall feature set with a considerably fewer number of features.
Modern Technologies in Foreign Language Teaching: The Case of L.N. Gumilyov Eurasian National University

Abstract Research purpose. The teacher of higher education should motivate students to use modern information technology training to study the discipline and develop professional competencies in foreign language teaching. The purpose of this research is to highlight the problem of finding the optimal didactic capabilities of modern information technologies used for improving the system of training specialists in the field of foreign languages teaching and to discuss the results of current studies in this direction. Design/Methodology/Approach. The authors summarized the relevant literature and results of the research and teaching experience. The main theoretical methods of research are modelling and designing the process of incorporating modern information technologies into foreign language teaching at the university. Theoretical methods are supplemented by empirical methods, such as observation, survey, testing, experimental work and methodological analysis. Findings. The article reveals the main components of the system of using modern technologies of foreign languages teaching at Theory and Practice of Foreign Languages Department of L.N. Gumilyov Eurasian National University. The article presents a description of training and monitoring online programs, their approbation in real conditions of pedagogical activity, the results of a pedagogical experiment, which proves the effectiveness of using modern technologies in the training of foreign and second language students. Originality/Value/Practical implications. The electronic educational materials, recommendations developed by the authors, can be used in the teaching of foreign language and can serve as a basis for the development of information, communication and instrumental provision in other subjects. The need for further research is as follows: to create online platforms, multimedia and testing programs and to develop variants of using modern technologies in foreign language teaching.
Analyzing and Processing of Supplier Database Based on the Cross-Industry Standard Process for Data Mining (CRISP-DM) Algorithm

In recent years, Data Mining has grown significantly in almost every field. Sectors such as banking, insurance, pharmaceuticals and retailing utilize data mining techniques widely to reduce costs, improve research and increase sales. However, large projects are being carried out on this issue and standards on data mining technique are required. In order to respond to this request, it can be said that CRoss-Industry Standard Process for Data Mining (CRISP-DM) is the most important effort. CRISP-DM is used in many studies, grew as an industry standard, and is defined as a series of sequential steps that guide the application of data mining technique. The CRISP-DM reference model for data mining provides an overview of the life cycle of a data mining project and includes the phases, related tasks and outputs of a project. CRISP-DM is an effort to provide industrial standards for DM applications, including business understanding, data understanding, data preparation, modeling, evaluation and deployment steps. The quality and accuracy of each of the CRISP-DM steps related to DM applications used in different fields is very important for the success of the whole project. In this study, firstly the CRISP-DM algorithm and steps are investigated, later, the data related stages of the CRISP-DM usage in a project (data monitoring and evaluation) are examined, and explained by using an example application. In this process, the supplier database based on the CRISP-DM algorithm for Data Mining has been analyzed and processed. The aim of this study is to explain the role of the steps of CRISP-DM, and specially to process, understand and prepare information in the process of data discovery. In this way, it is expected that create awareness about CRISP-DM, and the impact of this process on projects is expected to be clearly understood.
COMPETITIVENESS OF REGIONAL UNIVERSITIES: CONSIDERATION OF THE VIEWS OF CONSUMERS

It has been indicated to the differences in the meanings of the concept of “competitiveness of the University” in the context of state educational policy, macroeconomics, international and national education markets and in the context of regional consumer markets. The necessity of taking into account the consumer rating of universities in the regional market for the forecast and formation of demand for educational products of universities has been substantiated. The results of the analysis of the market of higher education in the Ulyanovsk region in the 2018-2019 academic year have been adduced. The assessment of consumer preferences of universities of the Ulyanovsk region has been made, the professional interests of school leavers have been determined. The author’s method of evaluation of competitive positions of universities, based on the coefficients of acceptance, rejection, indifference, has been explained. A discrepancy in consumer assessments of educational institutions among school leavers and their parents has been noticed, which confirms the phenomenon of inertial competitiveness of universities. The regularities of the formation of demand for educational programs of regional universities, the factors and risks of their development in the current socio-economic situation have been specified.
Partial Differential Equation-Based Trajectory Planning for Multiple Unmanned Air Vehicles in Dynamic and Uncertain Environments

Abstract This paper proposes a physics-inspired method for unmanned aerial vehicle (UAV) trajectory planning in three dimensions using partial differential equations (PDEs) for application in dynamic hostile environments. The proposed method exploits the dynamical property of fluid flowing through a porous medium. This method evaluates risk to generate porosity values throughout the computational domain. The trajectory that encounters the highest porosity values determines the trajectory from the point of origin to the goal position. The best trajectory is found using the reaction of the fluid in porous media by the way of streamlines obtained by numerically solving the PDEs representing the fluid flow. Constraints due to UAV dynamics, obstacles, and predefined way points are applied to the problem after solving for the best trajectory to find the optimal and feasible trajectory. This method shows near-optimality and much reduced computational effort when compared to the other typical numerical optimization methods.