Reviewerly/exHarmony huggingface dataset:
Clustering Based on Kolmogorov–Smirnov Statistic with Application to Bank Card Transaction Data

Abstract Rapid developments in third-party online payment platforms now make it possible to record massive bank card transaction data. Clustering on such transaction data is of great importance for the analysis of merchant behaviours. However, traditional methods based on generated features inevitably lead to much loss of information. To make better use of bank card transaction data, this study investigates the possibility of using the empirical cumulative distribution of transaction amounts. As the distance between two merchants can be measured using the two-sample Kolmogorov–Smirnov test statistic, we propose the Kolmogorov–Smirnov K-means clustering approach based on this distance measure. An approximation step is conducted to ensure the feasibility of the proposed method even for large-scale transaction data, and the associated theoretical properties are investigated. Both simulations and an empirical study demonstrate that our method outperforms feature-based methods and is computationally efficient for large-scale data sets.
Hot Topics Detection by Using 2-Layers Keywords Extraction

Hot topics analysis is one of the important task for users to organize information from WWW and especially for social networking websites. Therefore, how to design an efficient approach for users to extract those hot topics is very essential. Thus, we proposed a so-called 2-layers keywords extraction approach and three empirical analyses are then be applied to validate the usability and performance of the proposed approach.
Application of FMCW Radar for the Recongnition of Hand Gesture Using Time Series Convolutional Neural Networks

Gesture recognition methods based on traditional optical camera and sensor technologies are suscetible to the effects of lighting environment and have poor body sensations and robustness. This paper proposes an end-to-end Time Series convolutional neural network(TS-CNN) gesture recognition method based on FMCW radar signals. When the FMCW radar captures hand movements, the spectrum of the intermediate frequency signal is first extracted using a two-dimensional fast Fourier transform to obtain a Range Doppler map. Second, the Range Doppler map is used to map the gestures into a set of multi-frame Range Doppler maps over time. Finally, the TS-CNN is used for feature extraction, learning, and classification. Convolutional neural networks are powerful classifiers. To determine the optimal structure, we trained multiple convolutional neural networks by changing the hyperparameters. Experimental results show that compared with traditional machine learning methods, the classification accuracy obtained from the best convolutional neural network structure is improved by about 5%.
InterCriteria Analysis of the Human Factor Assessment in a Mobile Company

In this paper is presented an application of the recently proposed approach for multicriteria decision making – InterCriteria Analysis (ICrA) – to the assessment of the human factor in a mobile company. The approach is based on the concepts of index matrices (IMs) and intuitionistic fuzzy sets (IFSs). Here, the proposed method is applied to establish the relations and dependencies between pairs of criteria referred to the assessment in a mobile company of Bulgaria. The open dependencies between the evaluation criteria will lead to optimization of the company’s staff rating system by removing part of the evaluation criteria and will support the decision-making process related to motivation of human resources. In the paper presented here the authors have applied ICrA method to the real datasets with the evaluations of the human factor in the surveyed company. The main contribution of the paper is that it shows the effectiveness of the proposed hybrid method combining fuzzy logic with classic correlation analysis to optimize the staff rating system of a mobile company. The outlined approach for ICrA, can be applied to both the crisp parameters and the fuzzy ones and can be expanded to retrieve information to other types of multi-dimensional data. The comparative analysis of the correlation dependencies between the evaluation criteria in a mobile company has been performed in the paper after application of ICrA, Pearson’s (PCA), Spearman’s (SCA) and Kendall’s (KCA) rank correlation analyzes. The comparison of the results obtained shows that there is no comparative difference between those, obtained from the ICrA and the other three classic correlation analyzes. It is observed that considerable divergence of the ICrA results from those obtained by the PCA, SCA and KCA is only found when the input data contain mistakes. That is, ICrA is less sensitive, so the use of them together can be used as a way of detecting errors in the input data.
Detection of Concave Points in Closed Object Boundaries Aiming at Separation of Overlapped Objects

Separation of overlapping objects is one of the critical pre-processing tasks in biomedical and industrial applications. Separation of the attached objects is necessary, aiming at some qualitative research analysis or diagnosis of some existing behavior. It has been observed that a partial overlap between two or more convex shape objects introduces concave regions over the boundary. Here, in this paper, we present a novel approach for detecting the concavities along the overlapped object boundaries. The proposed algorithm works by computing the visibility matrix concerning the boundary pixels, which works as a variant of the chord method. Furthermore, our proposed method selects strong boundary candidates using the visibility matrix to ignore smaller concave-zones. Some distance thresholds introduced by us guide the selection. These detected concave points might be used subsequently for the separation and counting of objects. Experimental results show the degree of correctness and usefulness of the proposed method.
On the empirical indistinguishability of knowledge structures

In recent years a number of articles have focused on the identifiability of the basic local independence model. The identifiability issue usually concerns two model parameter sets predicting an identical probability distribution on the response patterns. Both parameter sets are applied to the same knowledge structure. However, nothing is known about cases where different knowledge structures predict the same probability distribution. This situation is referred to as 'empirical indistinguishability' between two structures and is the main subject of the present paper. Empirical indistinguishability is a stronger form of unidentifiability, which involves not only the parameters, but also the structural and combinatorial properties of the model. In particular, as far as knowledge structures are concerned, a consequence of empirical indistinguishability is that the existence of certain knowledge states cannot be empirically established. Most importantly, it is shown that model identifiability cannot guarantee that a certain knowledge structure is empirically distinguishable from others. The theoretical findings are exemplified in a number of different empirical scenarios.
The double hypergeometric series for the partition function of the 2D anisotropic Ising model

In 1944 Lars Onsager published the exact partition function of the ferromagnetic Ising model on the infinite square lattice in terms of a definite integral. Only in the literature of the last decade, however, has it been recast in terms of special functions. Until now all known formulas for the partition function in terms of special functions have been restricted to the important special case of the isotropic Ising model with symmetric couplings. Indeed, the anisotropic model is more challenging because there are two couplings and hence two reduced temperatures, one for each of the two axes of the square lattice. Hence, standard special functions of one variable are inadequate to the task. Here, we reformulate the partition function of the anisotropic Ising model in terms of the Kampé de Fériet function, which is a double hypergeometric function in two variables that is more general than the Appell hypergeometric functions. Finally, we present hypergeometric formulas for the generating function of multipolygons of given length on the infinite square lattice, for isotropic as well as anisotropic edge weights. For the isotropic case, the results allow easy calculation, to arbitrary order, of the celebrated series found by Cyril Domb.
Average of complete joint weight enumerators and self-dual codes

In this paper, we give a representation of the average of complete joint weight enumerators of two linear codes of length n over $$\mathbb {F}_q$$ and $$\mathbb {Z}_k$$ in terms of the compositions of n and their distributions in the codes. We also obtain a generalization of the representation for the average of g-fold complete joint weight enumerators of codes over $$\mathbb {F}_q$$ and $$\mathbb {Z}_{k}$$ . Finally, the average of intersection numbers of a pair of Type $$\mathrm{{III}}$$ (resp. Type $$\mathrm{{IV}}$$ ) codes, and its second moment are found.
Comparing Oracle and PostgreSQL, Performance and Optimization

Relational databases are getting bigger and more complex. Also, current Database Management Systems (DBMSs) need to respond efficiently to operations on their data. In this context, database optimization is evident as a process of refining database systems, aiming to improve their throughput and performance. This paper evaluates and compares the performance of Oracle and PostgreSQL database systems with the TPC-H benchmark, following a strategy of adding column-based indexes to optimize query execution. Ten TPC-H queries are performed on tables without any restrictions, with primary and foreign keys and with index constraints. The performance in each set of executions is analyzed. The results allow inferring a positive impact when using constraints with a significant speedup as well as better throughput. Oracle has shown stability and robustness for queries, with best results in scenarios with poor performance conditions. However, PostgreSQL showed shorter execution times after the optimizations made and proved to be more sensitive. Global performance results show that Oracle can improve 7% performance with indexes and PostgreSQL 91%. When comparing the results of Oracle with PostgreSQL, no indexes, Oracle/PostgreSQL is 64% faster, and with indexes, PostgreSQL/Oracle is 75% faster.
Waste object classification with AI on the edge accelerators

The classification of waste with neural networks is already a topic in some scientific papers. An application in the embedded systems area with current AI processors to accelerate the inference has not yet been discussed. Therefore a prototype is created which classifies waste objects and automatically opens the appropriate container for the object. The area of application is in the public space. For the classification a dataset with 25.681 images and 11 classes was created to retrain the CNNs EfficentNet-B0, MobileNet-v2 and NASNet-Mobile. These CNNs run on the current Edge AI -accelerator processors from Google, Intel and Nvidia and are compared for performance, consumption and accuracy. The result of these comparisons and shows the advantages and disadvantages of the respective processors and the CNNs. For the prototype, the most suitable combination of hardware and AI architecture is used and exhibited at the university fair KasetFair2020.
An Analysis of Energy Consumption of Blockchain Mining and Techniques to Overcome It

Blockchain is found to be an evolving technology that is playing a major role in storing information securely. The architecture of blockchain is such that it provides security at the cost of an enormous amount of energy. The proof of work mechanism that it uses to mine block consumes a lot of energy. This protocol as a whole is responsible for causing pollution. It also increases the energy cost associated with it which overshadows the benefits that one can get from blockchain. The hash rate and the difficulty level, while mining of blocks, are the factors responsible for the consumption of the maximum amount of energy. This paper here presents a relationship between the hash rate and difficulty level and also a predictive model of the difficulty level for mining blocks. It also explores the solution for reducing energy consumption by looking for efficient mining hardware and a mining hardware recommender system which can help in finding more similar hardware.
Identity Linkage Across Diverse Social Networks

User identity linkage across online social networks has gained a significant interest in the last few years in diverse applications such as data fusion, de-duplication, personalized advertisement, user profiling, and expert recommendation. Existing techniques investigated the use of personal discrete attributes such as user name, gender, location, and email which are not always available. Other techniques explored the use of network relations. In our proposal, we attempt to design a generic framework for user identity linkage across diverse social networks based exclusively on the widely available textual user generated content. We intentionally selected two social networks, Twitter and Quora, which have different contribution models and serve different purposes, and explore different supervised and unsupervised techniques for matching profiles as well as different language models ranging from simple tf*idf vectorization to more sophisticated BERT embeddings. We discuss the limits of different choices and present some encouraging preliminary results. For example, we find that prolific users can be identified with 84% accuracy. We also present a framework we designed to create the largest publicly available annotated dataset for profile linkage in social networks.
Ranking Associative Entities in Knowledge Graph by Graphical Modeling of Frequent Patterns

Ranking associative entities in Knowledge Graph (KG) is critical for entity-oriented tasks like entity recommendation and associative inference. Existing methods benefit from explicit linkages in KG w.r.t. exactly two query entities via the closely appearing co-occurrences. Given a query including one or more entities in KG, it is necessary to obtain the implicit associative entities and uncover the strength of associations from data. To this end, we leverage KG with Web resources and propose an approach to ranking associative entities based on frequent pattern mining and graph embedding. First, we construct an entity dependency graph from the frequent patterns of entities generated from both KG and Web resources. Thus, the existence and strength of associations between entities could be depicted effectively in a holistic way. Second, we embed the dependency graph into a lower-dimensional space and consequently fulfill entity ranking on the embedding. Finally, we conduct an extensive experimental study on real-life datasets, and verify the effectiveness of our proposed approach compared to competitive baselines.
Representing Autonomous Probabilistic Automata by Minimum Characteristic Polynomials over a Finite Field

This paper proposes a method to represent a finite autonomous probabilistic automaton with a transition function defined by an ergodic stochastic matrix with rational elements, using the minimum characteristic polynomial over a finite field. The minimum polynomial constructed develops a Markovian sequence with a law that coincides with the pre-defined stochastic matrix of the automaton.
Commercial hypervisor-based task sandboxing mechanisms are unsecured? But we can fix it!

Cyber–Physical–Social Systems are frequently prescribed for providing valuable information on personalized services. The foundation of these services is big data which must be trustily collected and efficiently processed. Though High Performance Computing and Communication technique makes great contributions to addressing the issue of data processing, its effectiveness still relies on the veracity of data generated from Internet of Things (IoT) devices. Nevertheless, IoT devices, as basic production facilities to ensure data’s security, are unable to deploy expensive security extensions. Consequently, it causes the implementation of the task sandboxing, the fundamental security mechanism in Real-Time Operating Systems (RTOSs), much simpler and more vulnerable. In this paper, we take ARM Mbed uVisor as an example system, utilizing hypervisor-based task sandboxing mechanisms, and presents three new findings: First, we discover vulnerabilities against Mbed task sandboxing, which can be exploited to compromise system-maintained data structure to manipulate any tasks’ data. Second, we present LIPS (Lightweight Intra-Mode Privilege Separation), building a special protection domain to isolate particular system-maintained data structures. Finally, thorough evaluation and experimental tests show the efficiency of LIPS to defeat these attacks, with small runtime overheads and good portability.
In-Car Safety Judgment Using Machine Learning

In the case of a mobility service using an automated vehicle, it is necessary to determine the safety of the passengers inside and outside the vehicle before starting the vehicle, and automation is required. Therefore, we have developed a system that uses camera image processing to determine the safety of passengers in and out of a vehicle. This paper describes a system for determining the passenger's status using a machine learning based image analysis method.
Analysis of water pollution using the STORET method in the Upper Citarum Watershed

Abstract River water pollution in the Upper Citarum Watershed is increasingly being reported, research shows that the quality of river water has decreased drastically where along the 127 km or 47.1% of the Citarum river has been heavily polluted, it is estimated that every day the Citarum River accommodates 280 tons of waste. There are 31 water quality monitoring stations in the Upper Citarum Watershed: Cisanti Springs, Situ Cisanti, Bd. Wangisagara, Majalaya, Cengkrong, Solokan Jeruk, Sapan, Peundeuy, Komp. Radio, Cisirung, Pataruman, Kamasan, Dayeuh Kolot, Jb. Cilampeni, Cukang Genteng, Sadu, Nanjung, Citarum Kp. Pariang Pojok, Kertajaya, Cb. Bungin Leuwigajah, Cimahi Cihujung, Teras Cikapundung, Sukapada, Cikapundung Asia-Africa, Cinambo, Ciwastra, Jatiroke, Cikuda, Cikijing, Cimanggung, and Sindang Pakuon. Based on data analysis obtained from BBWS Citarum, data from 2011-2019 are obtained. This research aims to determine water quality status using STORET method in the Upper Citarum Watershed as indicator of environment pollution. This research method is quantitative, using the STORET method. The results show that the water quality conditions are heavily polluted, so it is necessary to make various efforts to tackle the pollution that occurs regularly, one of the ways is revitalization.
A Retrospective of Information Management in the Long Term Ecological Research Program

This chapter describes the evolution of information management protocols for Long Term Ecological Research, starting with work conducted at the Andrews Forest Long Term Ecological Research site in Oregon in the 1980s. This early work involved the design, testing, and implementation of a data and information management system that helped establish standards and protocols across the Long Term Ecological Research Network. Following this initial work, a growth period ensued. The chapter discusses the creation of Ecological Metadata Language, explores the impact of the internet on ecological data management and shows how other countries adopted the same model for their long-term research networks. The Network Information System that was developed had broad applications to other projects, such as EcoTrends, the Environmental Data Initiative, and DataONE. Innovations in information management represent major and far-reaching accomplishments of the Long Term Ecological Research Program, and have influenced the entire field of interdisciplinary ecological research. They have helped to change the culture of scientific collaboration by making it both feasible and fair for scientists to share their data, and have in general promoted greater data literacy within the long-term ecological network.
Cyber-security Enhancement of Smart Grid's Substation Using Object's Distance Estimation in Surveillance Cameras

Cyber-attacks toward cyber-physical systems are one of the main concerns of smart grid's operators. However, many of these cyber-attacks, are toward unmanned substations where the cyber-attackers needs to be close enough to substation to malfunction protection and control systems in substations, using Electromagnetic signals. Therefore, in this paper, a new threat detection algorithm is proposed to prevent possible cyber-attacks toward unmanned substations. Using surveillance camera's streams and based on You Only Look Once (YOLO) V3, suspicious objects in the image are detected. Then, using Intersection over Union (IOU) and Generalized Intersection Over Union (GIOU), threat distance is estimated. Finally, the estimated threats are categorized into three categories using color codes red, orange and green. The deep network used for detection consists of 106 convolutional layers and three output prediction with different resolutions for different distances. The pre-trained network is transferred from Darknet-53 weights trained on 80 classes.
Efficient Replication for Fast and Predictable Performance in Distributed Computing

Master-worker distributed computing systems use task replication to mitigate the effect of slow workers on job compute time. The master node groups tasks into batches and assigns each batch to one or more workers. We first assume that the batches do not overlap. Using majorization theory, we show that a balanced replication of batches minimizes the average job compute time for a general class of service time distributions. We then show that the balanced assignment of non-overlapping batches achieves a lower average job compute time than the overlapping schemes proposed in the literature. Next, we derive the optimum redundancy level as a function of the task service time distribution. We show that the redundancy level that minimizes the average job compute time may not coincide with the redundancy level that maximizes job compute time predictability. Therefore, there is a trade-off in optimizing the two metrics. By running experiments on Google cluster traces, we observe that redundancy can reduce the job compute time by one order of magnitude. The optimum level of redundancy depends on the distribution of task service time.
Improve conditional adversarial domain adaptation using self‐training

Domain adaptation for image classification is one of the most fundamental transfer learning tasks and a promising solution to overcome the annotation burden. Existing deep adversarial domain adaptation approaches imply minimax optimization algorithms, matching the global features across domains. However, the information conveyed in unlabelled target samples is not fully exploited. Here, adversarial learning and self-training are unified in an objective function, where the neural network parameters and the pseudo-labels of target samples are jointly optimized. The model's predictions on unlabelled samples are leveraged to pseudo-label target samples. The training procedure consists of two alternating steps. The first one is to train the network, while the second is to generate pseudo-labels, and the loop continues. The proposed method achieves mean accuracy improvements of 2% on Office-31, 0.7% on ImageCLEF-DA, 1.8% on Office-Home, and 1.2% on Digits than the baseline, which is superior to most state-of-the-art approaches.