Reviewerly/exHarmony huggingface dataset:
A Robust Zero-Sum Game Framework for Pool-based Active Learning

In this paper, we present a novel robust zero- sum game framework for pool-based active learning grounded on advanced statistical learning theory. Pool-based active learning usually consists of two components, namely, learning of a classifier given labeled data and querying of unlabeled data for labeling. Most previous studies on active learning consider these as two separate tasks and propose various heuristics for selecting important unlabeled data for labeling, which may render the selection of unlabeled examples sub-optimal for minimizing the classification error. In contrast, the present work formulates active learning as a unified optimization framework for learning the classifier, i.e., the querying of labels and the learning of models are unified to minimize a common objective for statistical learning. In addition, the proposed method avoids the issues of many previous algorithms such as inefficiency, sampling bias and sensitivity to imbalanced data distribution. Besides theoretical analysis, we conduct extensive experiments on benchmark datasets and demonstrate the superior performance of the proposed active learning method compared with the state-of-the-art methods.
Large-scale evaluation of V-Net for organ segmentation in image guided radiation therapy

Accurate segmentation of organs at risk (OARs) is a key step in image guided radiation therapy. In recent years, deep learning based methods have been widely used in medical image segmentation. Among them, U-Net and V-Net are the most popular ones. In this paper, we evaluate a customized V-Net on 16 OARs throughout the body using a large CT dataset. Specifically, two customizations are used to reduce the GPU memory cost of V-Net: 1) multi-resolution V-Nets, where the coarse-resolution V-Net aims to localize the OAR in the entire image space, while the fine-resolution V-Net focuses on refining detailed boundaries of OAR; 2) a modified V-Net architecture, which is specifically designed for segmenting large organs, e.g., liver. Validated on 3483 CT scans of various imaging and disease conditions, we show that, compared with traditional methods, the customized V-Net wins in speed (0.7 second vs 20 seconds per organ), accuracy (average Dice score 96.6% vs 84.3%), and robustness (98.6% successful rate vs 83.3% successful rate). Moreover, the customized V-Net is very robust against various image artifacts, diseases and slice thicknesses, and has much better performance even on the organs with large shape variations (e.g., the bladder) than traditional methods.
The Impact of Corporate Social Responsibility on Organizational Performance in Telecommunication Sector in Jordan

The study has aimed to investigate the impact of corporate social responsibility on organizational performance. The data from three telecommunication companies were collected through questionnaires, based on the Likert scale. The data has been collected from 500 employees of telecommunication companies. Statistical tools were used to analyze the data. The results showed that internal CSR positively affects both non-financial and financial performance. In addition, external CSR proved to positively affect non-financial performance. The effect of external CSR on financial performance was negative, but not significant. The current study provides insights into the value of corporate social responsibility key on organizational performance in telecommunication companies. Additionally, most of the respondents considered CSR as a key factor influencing the Organizational Performance of companies. This approach is expected to support telecommunication company&amp;rsquo;s managers in the developing world to evaluate their current performance, estimate the desired state based on the results.
Monocular Vision-based Sense and Avoid of UAV Using Nonlinear Model Predictive Control

Summary The potential use of onboard vision sensors (e.g., cameras) has long been recognized for the Sense and Avoid (SAA) of unmanned aerial vehicles (UAVs), especially for micro UAVs with limited payload capacity. However, vision-based SAA for UAVs is extremely challenging because vision sensors usually have limitations on accurate distance information measuring. In this paper, we propose a monocular vision-based UAV SAA approach. Within the approach, the host UAV can accurately and efficiently avoid a noncooperative intruder only through angle measurements and perform maneuvers for optimal tradeoff among target motion estimation, intruder avoidance, and trajectory tracking. We realize this feature by explicitly integrating a target tracking filter into a nonlinear model predictive controller. The effectiveness of the proposed approach is verified through extensive simulations.
Optimization of Advanced Encryption Standard (AES) Using Vivado High Level Synthesis (HLS)

Advanced Encryption Standard (AES) represents a fundamental building module of many network security protocols to ensure data confidentiality in various applications ranging from data servers to low-power hardware embedded systems. In order to optimize such hardware implementations, High-Level Synthesis (HLS) provides flexibility in designing and rapid optimization of dedicated hardware to meet the design constraints. In this paper, we present the implementation of AES encryption processor on FPGA using Xilinx Vivado HLS. The AES architecture was analyzed and designed by loop unrolling, and inner-round and outer-round pipelining techniques to achieve a maximum throughput of the AES algorithm up to 1290 Mbps (Mega bit per second) with very significant low resources of 3.24% slices of the FPGA, achieving 3 Mbps per slice area.
Android-based Teaching Material for Statistics Integrated with Social Media Whatsapp

The purpose of this study was to develop teaching material in the form of Android-based application for junior high school mathematics subjects in statistical material that are integrated with WhatsApp social media platform.The procedure for developing teaching materials was carried out through the stages of defining, designing, developing, and disseminating.The platform used to develop teaching material was MIT App Inventor.The teaching materials developed have been validated by two media experts and material experts.The trial of teaching material was carried out to educators and prospective mathematics educators in the Special Region of Yogyakarta and students at Depok 3 State Junior High School.This study has succeeded in developing Android-based teaching materials for junior high school mathematics subjects in valid and effective statistical material.The menus contained in this application consist of material and competency test.On the competency test menu, students can do the test and after completion, the application will display the score of the test results.What interesting about this application, the test results can be sent to the teacher via WhatsApp.
Consensus-Based Track Association with Multistatic Sensors under a Nested Probabilistic-Numerical Linguistic Environment

Track association is an important technology in military and civilian fields. Due to the increasingly complex environment and the diversity of the sensors, it is a key factor to separate the corresponding track from multiple maneuvering targets by multisensors with a consensus. In this paper, we first transform the track association problem to multiattribute group decision making (MAGDM), and describe the MAGDM with nested probabilistic-numerical linguistic term sets (NPNLTSs). Then, a consensus model with NPNLTSs is constructed which has two key processes. One is a consensus checking process, and the other is a consensus modifying process. Based on which, a track association algorithm with automatic modification is put forward based on the consensus model. After that, the solution of a case study in practice is given to obtain the corresponding track by the proposed method, and it provides technical support for the track association problems. Finally, we make comparisons with other methods from three aspects, and the results show that the proposed method is effective, feasible, and applicable. Moreover, some discussions about the situation where there is only one echo point at a time are provided, and we give a discriminant analysis method.
Formation of the university electronic information-educational environment: interaction, development of professional thinking, management

Purpose of the research. The development of electronic technologies in modern socio-economic environment and the urgent need for their integration into higher education require a different type of teachers’ professional thinking based on their sustainable self-development. The paper provides a reflexive analysis of the implementation of electronic information-educational environment (EIEE) by the Yaroslavl branch of Plekhanov Russian University of Economics and offers an insight into interactive and management aspects of EIEE integration and use in the educational process. Materials and methods. To achieve the goal, the EIEE structural and functional components were identified, stages, system requirements and technical features of EIEE implementation were determined along with the prospects for EIEE further integration and development within the university. Analysis, synthesis, comparison and generalization approaches were used as general scientific methods of this research. Results. The paper gives an insight into the project of the Yaroslavl branch of Plekhanov Russian University of Economics on the use of EIEE as an imperative for high-level development of graduates’ common cultural, as well as general and specific professional competencies, to facilitate their competitiveness in the labour market. The urgency of innovations in the EIEE integration in universities is substantiated. The authors emphasize that the EIEE practical application requires achievement of a new level of communication skills, interaction of all participants in the educational process, based on a broader vision of the educational environment and a new level of teachers’ professional thinking, presupposing both creativity and management of their own mental processes associated with metacognition. Conclusion. As a result, the authors make conclusions on the implementation of various aspects of the university EIEE, such as administrative, managerial and interactive ones. According to the study, a wide application of EIEE can become a priority tool in solving problems of the effectiveness, which is proved by the evidence and examples from the educational experience given in the article.
Naïve Bayes Algorithm For Sentiment Analysis Windows Phone Store Application Reviews

Reading reviews helps consumers choose the applications, helping companies and developers monitor user satisfaction to improve quality of features and services, read overall and manually could spend the time and laborious, if read at a glance, information not conveyed perfectly. This study analyzes user sentiment Windows Phone Store applications by automatically classifying reviews into positive or negative opinion category. Naïve bayes has good potential because of its simplicity and performance as a model of classifying text on many domains. The model was evaluated using 10 Fold Cross Validation. Measurements were made with the Confusion Matrix and the ROC curve. The accuracy produced in this study is 84.50%, indicating that Naïve Bayes is a good model in classifying text especially in the case of sentiment analysis.
Multiobjective evolutionary feature selection and fuzzy classification of contact centre data

Abstract In this work, a data set describing phone interactions arising in a multichannel and multiskill contact centre is considered with the aim of classifying inbound sessions into those that will be eventually managed by an agent and those that, instead, will be abandoned before. More precisely, the goal of the work is to extract interpretable pieces of information that allow us to predict whether a user will or will not abandon a call, which may turn out to be very useful for the purpose of contact centre managing. To this end, the performance of two well‐known, state‐of‐the‐art evolutionary algorithms for feature selection (evolutionary nondominated radial slots based algorithm and nondominated sorted genetic algorithm) is compared for the task of feature selection, under the criteria of accuracy and cardinality of the selection, as well as for the task of fuzzy rule extraction, under the criteria of interpretability, accuracy, and hypervolume test. The best obtained fuzzy classifier, chosen after a decision making process, is validated and interpreted by domain experts.
Design of SINS/LDV/OD autonomous positioning system based on carrier constraints

Aiming at the problem of vehicle positioning orientation technology in actual combat background, this paper studies the autonomous positioning system of SINS/LDV/OD, and designs the LDV/SINS and OD/SINS subfilters of the union kalman filter and the main filter of the autonomous positioning system. Based on the characteristics of the fault of the subfilter, the fault tolerant scheme of the autonomous positioning system was designed, and the 50Km roadster test was finally carried out. The longitude error is not more than 20m, the latitude is less than 20m, and the height error is not over 10 meters. The experimental results show that the design scheme is effective and can meet the actual operational requirements.
Application of Apriori Algorithms to Determine Associations in Outdoor Sports Equipment Stores

A good sales strategy has an effect on increasing the number of sales of goods. Problems that often occur in outdoor sports equipment stores are the difficulty in determining sales strategies because there is not much interest in outdoor sports in the community. In addition, the amount of inventory in the store is excessive, which affects the sales cycle of goods. One way to help determine strategy is to use apriori algorithm. In this method can determine consumer shopping behavior patterns. Apriori algorithms are part of the data mining analysis association. This algorithm is used to determine association rules. In the study, a combination of sports equipment purchased by consumers will be determined. Determination of the combination starts from 1 itemset to 3 itemsets, the combination of rule association produces different sales transaction patterns. The results of the study in the form of a combination of consumer shopping behavior patterns that will be used as recommendations for shop owners in determining the sales strategy. The resulting Rule association will help sales promotions and add the amount of inventory that many customers buy.
Communal data work: Data sharing and re-use in clinical genetics

In this article, we examine work with communal data in the context of clinical genetic testing. Drawing from prior research on digital research infrastructures and from the analysis of our empirical data on genetic testing, we describe how data generated in laboratories distributed all over the world are shared and re-used. Our research findings point to six different human-driven activities related to expanding, disambiguating, sanitizing and assessing the relevance, validity and combinability of data. We contribute to research within Health Informatics with a framework that foregrounds human-driven activities for data interoperability.
A LSTM Approach to Detection of Autonomous Vehicle Hijacking

In the recent decades, automotive research has been focused on creating a driverless future. Autonomous vehicles are expected to take over tasks which are dull, dirty and dangerous for humans (3Ds of robotization). However, augmented autonomy increases reliance on the robustness of the system. Autonomous vehicle systems are heavily focused on data acquisition in order to perceive the driving environment accurately. In the future, a typical autonomous vehicle data ecosystem will include data from internal sensors, infrastructure, communication with nearby vehicles, and other sources. Physical faults, malicious attacks or a misbehaving vehicle can result in the incorrect perception of the environment, which can in turn lead to task failure or accidents. Anomaly detection is hence expected to play a critical role improving the security and efficiency of autonomous and connected vehicles. Anomaly detection can be defined as a way of identifying unusual or unexpected events and/or measurements. In this paper, we focus on the specific case of malicious attack/hijacking of the system which results in unpredictable evolution of the autonomous vehicle. We use a Long Short-Term Memory (LSTM) network for anomaly/fault detection. It is, first, trained on non-abnormal data to understand the system’s baseline performance and behaviour, monitored through three vehicle control parameters namely velocity, acceleration and jerk. Then, the model is used to predict over a number of future time steps and an alarm is raised as soon as the observed behaviour of the autonomous car significantly deviates from the prediction. The relevance of this approach is supported by numerical experiments based on data produced by an autonomous car simulator, capable of generating attacks on the system.
The Regression of MNIST Dataset Based on Convolutional Neural Network

The MNIST dataset of handwritten digits has been widely used for validating the effectiveness and efficiency of machine learning methods. Although this dataset was primarily used for classification and results of very high accuracy (99.3%+) on it have been obtained, its important application of regression is not directly applicable, thus substantially deteriorates its usefulness and the development of regression methods for such types of data. In this paper, to allow MNIST to be usable for regression, we firstly apply its class/label with normal distribution thereby convert the original discrete class numbers into float ones. Modified Convolutional Neural Networks (CNN) is then applied to generate a regression model. Multiple experiments have been conducted in order to select optimal parameters and layer settings for this application. Experimental results suggest that, optimal outcome of mean-absolute-error (MAE) value can be obtained when ReLu function is adopted for the first layer with other layers activated by the softplus functions. In the proposed approach, two indicators of MAE and Log-Cosh loss have been applied to optimize the parameters and score the predictions. Experiments on 10-fold cross-validation demonstrate that, desired low values of MAE and Log-Cosh error respectively at 0.202 and 0.079 can be achieved. Furthermore, multiple values of standard deviation of the normal distribution have been applied to verify the applicability when data of label number at varied distributions is used. The experimental results suggest that a positive correlation exists between the adopted standard deviation and the loss value, that is, the higher concentration degree of data will contribute to the lower MAE value.
Out-of-time-ordered correlators and quantum walks

Out-of time-ordered correlators (OTOC) have recently attracted significant attention from the physics of many-body systems, to quantum black-holes, with an exponential growth of the OTOC indicating quantum chaos. Here we consider OTOC in the context of coined discrete quantum walks, a very well studied model of quantization of classical random walks with applications to quantum algorithms. Three separate cases of operators, variously localized in the coin and walker spaces, are discussed in this context and it is found that the approximated behavior of the OTOCs is well described by simple algebraic functions in all these three cases with different time scale of growth. The quadratic increase of OTOC signals the absence of quantum chaos in these simplest forms of quantum walks.
vCPU as a container: towards accurate CPU allocation for VMs

With our increasing reliance on cloud computing, accurate resource allocation of virtual machines (or domains) in the cloud have become more and more important. However, the current design of hypervisors (or virtual machine monitors) fails to accurately allocate resources to the domains in the virtualized environment. In this paper, we claim the root cause is that the protection scope is erroneously used as the resource scope for a domain in the current virtualization design. Such design flaw prevents the hypervisor from accurately accounting resource consumption of each domain. In this paper, using virtual CPUs as a container we propose to redefine the resource scope of a domain, so that the new resource scope is aligned with all the CPU consumption incurred by this domain. As a demonstration, we implement a novel system, called VASE (vCPU as a container), on top of the Xen hypervisor. Evaluations on our testbed have shown our proposed approach is effective in accounting system-wide CPU consumption incurred by domains, while introducing negligible overhead to the system.
Reinforcement adaptation of an attention-based neural natural language generator for spoken dialogue systems

Following some recent propositions to handle natural language generation in spoken dialogue systems with long short-term memory recurrent neural network models~\citep{Wen2016a} we first investigate a variant thereof with the objective of a better integration of the attention subnetwork. Then our next objective is to propose and evaluate a framework to adapt the NLG module online through direct interactions with the users. When doing so the basic way is to ask the user to utter an alternative sentence to express a particular dialogue act. But then the system has to decide between using an automatic transcription or to ask for a manual transcription. To do so a reinforcement learning approach based on an adversarial bandit scheme is retained. We show that by defining appropriately the rewards as a linear combination of expected payoffs and costs of acquiring the new data provided by the user, a system design can balance between improving the system's performance towards a better match with the user's preferences and the burden associated with it. Then the actual benefits of this system is assessed with a human evaluation, showing that the addition of more diverse utterances allows to produce sentences more satisfying for the user.
Using Reverse Engineering for Building Ontologies with Deeper Taxonomies from Relational Databases

The relational model is characterized by its high quality and has been widely used by information systems.However, unlike the conceptual model, the relational model is semantically poor since it doesn't enable the representation of inheritance.In this paper, we present an algorithmic approach to extract generalization/ specialization inheritance hierarchies.We perform a reverse engineering by analyzing stored data.Finally, we evaluated our approach by conducting several experiments on relational databases.The results were satisfying in terms of recovering the tables lost during the transformation from the entity relationship model to the relational model.
The Role of Blockchain in Enterprise Procurement

Disruptive technologies, such as artificial intelligence, machine learning, or the blockchain, have the potential to transform entire industries as previous publications have outlined.They especially offer new opportunities to improve existing processes in the dimensions of performance, cost, and quality.Despite a continuously growing number of contributions, research and practice lack an adequate understanding about how blockchain can be used to achieve these benefits.Against this backdrop, this paper presents the current state of blockchain research in the field of enterprise procurement and outlines several avenues for future research.Therefore, we define relevant processes and functions to build a research framework for structuring and categorizing the current body of literature.Our results suggest that previous work mostly focuses on blockchain application scenarios with a focus on communication and transaction, while widely neglecting numerous fields, such as data integrity and data access.