Reviewerly/exHarmony huggingface dataset:
Creating Lean and Agile Supply Chains with Blockchain

This paper aims to explore how blockchain technology can help us to achieve supply chains that are agile but also lean, which is also demanded by the modern business environment. A role playing simulation was used to evaluate the benefits provided by different levels of visibility in a typical supply chain. A distributed ledger enabling supply chain visibility was designed. A supply chain performance framework was developed in order to measure the supply chain performance from both lean and agile aspects. The results show a significant correlation in performance with the degree of visibility, but it is not a linear relationship. An effective LeAgile supply chain can be achieved with the enhancement of visibility. In order to implement visibility, an appropriate information sharing frequency (drumbeat) is needed. Blockchain allows users to more securely and transparently track all types of transactions, to improve Supply Chain Visibility. We also find the complex relationship between trust and visibility. This is one of the first papers, taking human factors into consideration, to investigate how blockchain technology can be used to enhance supply chain visibility, which is needed for leagile supply chain performance.
A CNN-Based Solution for Breast Cancer Detection With Blood Analysis Data: Numeric to Image

Breast cancer has a high mortality rate worldwide. Early diagnosis is of great importance to reduce this rate. Early diagnosis ensures that patients receive appropriate treatment as soon as possible. Early diagnosis can be achieved by automatic classification through computerized systems. This study aims to detect breast cancer using frequently used blood analysis data recently. Data from the UCI library contains anthropometric values that can be collected in routine blood analysis, i.e. the features are numerical data. For high classification success, these numerical data are converted into image data. Then, the number of these images is increased by different data augmentation techniques and classification is performed with popular Convolutional Neural Network (CNN) models (AlexNet, ResNet50, and DenseNet201). Classification accuracy of 95.33% provided by ResNet50 is superior to other studies using the same data. In addition, converting numerical blood analysis images into image data is a strategy applied for the first time.
Qubit masking in multipartite qubit system

The no-masking theories show that it is impossible to mask the set of all qubit states into the quantum correlation of bipartite qubit system or tripartite qubit system. In this paper, we give a new proof of the no-masking situation of the tripartite qubit system. Recent work has shown that there exists a universal masker which can mask an arbitrary set of qubit states in four-qubit systems perfectly by means of the maximum entangled states. Here we show that there exist more than one masking scheme even for the same multipartite qubit system. Basing on the maximum entangled states we give the deterministic masking scenario for N-qubit system. In practice, decoherence hinders us from obtaining the maximum entangled states. From this viewpoint, the masking scenario based on non-maximum entangled states becomes more universal. Furthermore, we provide an approximate quantum masking scenario and investigate the relation between approximate masking and quantum entanglement.
ST-GCN Based Human Action Recognition with Abstracted Three Features of Optical Flow and Image Gradient

ST-GCN (Spatial temporal graph convolutional network) [1], which uses skeleton data as an input, enables efficient motion recognition because it can express the structural motion of the human body with neural network. However, if the correct skeleton data is not accurate, the network operation becomes unstable, and the recognition rate eventually drops. In this paper, we describe a new ST-GCN algorithm that can precisely recognize action using optical flow and image gradient, which can be obtained simply and stably from input image. Therefore, it has high robustness since it does not use skeleton data that can only be obtained by special devices or complex calculations. In the algorithm, firstly human body region is extracted from the continuous input image. And then optical flow and image gradient, namely the motion and posture information of the body, is obtained from partial regions (this is called Region of Motion: RoM) of the body region. The both data are described with a 16-dimensional HoG (Histogram of gradient) descriptor and it is converted into three features for being used for learning of the network. At this time, to express the scale change of the input information and the structural characteristics of the human body, the three features are combined into a graph structure that mimics the connection structure of the human body. In order to verify the performance of the proposed algorithm, an experiment was performed on 20 actions selected from the NTU RGB + D data set. As the result of the experiment, it was possible to obtain a Top-1 accuracy of 84.19%, equivalent to the existing method.
Explaining HCV prediction using LIME model

Machine learning is a state-of-art technique to infer complex data into information with high accuracy. Machine learning algorithms are black boxes because of the high complexity and non-transparency present in each data processing layer, making it difficult to rationalize the output performed. The predictive models are surmising techniques that use overly simplified accuracy measures, which alone cannot determine the quality of learning, because there is no interpretability of the process of the outcome. The purpose of this paper is manifold. The first objective is to discuss the need for interpretability in Machine Learning. The second objective is to study different interpretations models. The final objective is to familiarize oneself with the novel model interpretation framework: LIME (Local Interpretable Model-Agnostic Explanations) to yield explanation on the HCV test predicted results.
Analytical Studies of Behavior of Users of the Moscow Electronic School Service

The article presents the results of the study, which analyzed the database of the Moscow Electronic School (MES) in the amount of about 50 thousand observations. The relevance of the study is that with the emergence of viral threats and the transition of the education system to remote learning processes, MES educational content is becoming increasingly popular. In this regard, it is important to plan work to improve the quality of user characteristics of the accumulated content and its impact on the indicators of the remote educational process. The key subject on which the motivation of the students depends and the results of their education is the teacher. Teachers in the MES system act simultaneously in different roles - both as creators of new educational content and as its users and evaluators. The aim of the study was to identify “hidden” groups of users with different typical behaviors in the MES database. The behavior of users in this work has been investigated by decomposing it into six parameters. The study used the MES database, as well as statistical, neural network methods and application software packages implementing them, which solved data clustering and visualization tasks. Based on the results of the study, the relationships between the behavioral parameters of users were determined and 5 behavioral types of MES users were identified. The analysis of used methods of statistical analysis and clustering of MES data made it possible to assess their applicability in the prediction of user groups and to identify factors that significantly influence the achievement of the goal of improving the efficiency of the MES platform and design in the perspective of the recommendation system. #CSOC1120
Algebraic Key-Recovery Attacks on Reduced-Round Xoofff

Farfalle, a permutation-based construction for building a pseudorandom function (PRF), is really versatile. It can be used for message authentication code, stream cipher, key derivation function, authenticated encryption and so on. Farfalle construction relies on a set of permutations and on so-called rolling functions: it can be split into a compression layer followed by a two-step expansion layer. As one instance of Farfalle, Xoofff is very efficient on a wide range of platforms from low-end devices to high-end processors by combining the narrow permutation Xoodoo and the inherent parallelism of Farfalle. In this paper, we present key-recovery attacks on reduced-round Xoofff. After identifying a weakness in the expanding rolling function, we first propose practical attacks on Xoofff instantiated with 1-/2-round Xoodoo in the expansion layer. We next extend such attack on Xoofff instantiated with 3-/4-round Xoodoo in the expansion layer by making use of Meet-in-the-Middle algebraic attacks and the linearization technique. All attacks proposed here – which are independent of the details of the compression and/or middle layer – have been practically verified (either on the "real" Xoofff or on a toy-version Xoofff with block-size of 96 bits). As a countermeasure, we discuss how to slightly modified the rolling function for free to reduce the number of attackable rounds.
Fatigue Detection Using Deep Long Short-Term Memory Autoencoders

Efficient time series data mining techniques are an essential part of real world measurement systems and can yield meaningful results from unlabeled data by taking advantage of feature extraction principles. In this paper, we perform kinematic analysis on time series data from IMU sensors for fatigue detection on runners, using several unsupervised machine learning techniques. We propose a robust feature extraction scheme composed of an LSTM Autoencoder, to exploit the advantages of recurrent neural networks and the data compression capabilities of an Autoencoder. The proposed model combines the advantages of several clustering algorithms for accurate fatigue detection in real time, making it suitable for implementation in an embedded device. Experimental evaluation of the feature extraction algorithms showcased their capabilities to produce meaningful features, overcoming the obstacle of extremely limited training data. The inference procedure yielded successful detection in 43% of our representative sample, indicating the efficiency of our model in extracting robust features from unseen kinematic data.
Kernel Path for ν-Support Vector Classification

It is well known that the performance of a kernel method is highly dependent on the choice of kernel parameter. However, existing kernel path algorithms are limited to plain support vector machines (SVMs), which has one equality constraint. It is still an open question to provide a kernel path algorithm to ν -support vector classification ( ν -SVC) with more than one equality constraint. Compared with plain SVM, ν -SVC has the advantage of using a regularization parameter ν for controlling the number of support vectors and margin errors. To address this problem, in this article, we propose a kernel path algorithm (KP ν SVC) to trace the solutions of ν -SVC exactly with respect to the kernel parameter. Specifically, we first provide an equivalent formulation of ν -SVC with two equality constraints, which can avoid possible conflicts during tracing the solutions of ν -SVC. Based on this equivalent formulation of ν -SVC, we propose the KP ν SVC algorithm to trace the solutions with respect to the kernel parameter. However, KP ν SVC traces nonlinear solutions of kernel method rather than the errors of loss function, and it is still a challenge to provide the algorithm that guarantees to find the global optimal model. To address this challenging problem, we extend the classical error path algorithm to the nonlinear kernel solution paths and propose a new kernel error path (KEP) algorithm that ensures to find the global optimal kernel parameter by minimizing the cross validation error. We also provide the finite convergence analysis and computational complexity analysis to KP ν SVC and KEP. Extensive experimental results on a variety of benchmark datasets not only verify the effectiveness of KP ν SVC but also show the advantage of applying KEP to select the optimal kernel parameter.
On gradual-impulse control of continuous-time Markov decision processes with exponential utility

Abstract We consider a gradual-impulse control problem of continuous-time Markov decision processes, where the system performance is measured by the expectation of the exponential utility of the total cost. We show, under natural conditions on the system primitives, the existence of a deterministic stationary optimal policy out of a more general class of policies that allow multiple simultaneous impulses, randomized selection of impulses with random effects, and accumulation of jumps. After characterizing the value function using the optimality equation, we reduce the gradual-impulse control problem to an equivalent simple discrete-time Markov decision process, whose action space is the union of the sets of gradual and impulsive actions.
Multimedia traffic classification with mixture of Markov components

We study multimedia traffic classification into popular applications to assist the quality of service (QoS) support of networking technologies, including but not limited to, WiFi. For this purpose, we propose to model the multimedia traffic flow as a stochastic discrete-time Markov chain in order to take into account the strong sequentiality (i.e. the dependencies across the data instances) in the traffic flow observations. This addresses the shortcoming of the prior techniques that are based on feature extraction which is prone to losing the information of sequentiality. Also, for investigating the best application of our Markov approach to traffic classification, we introduce and test three data driven classification schemes which are all derived from the proposed model and tightly related to each other. Our first classifier has a global perspective of the traffic data via the likelihood function as a mixture of Markov components (MMC). Our second and third classifiers have local perspective based on k-nearest Markov components (kNMC) with the negative loglikelihood as a distance as well as k-nearest Markov parameters (kNMP) with the Euclidean distance. We additionally introduce to the use of researchers a rich multimedia traffic dataset consisting of four application categories, e.g., video on demand, with seven applications, e.g., YouTube. In the presented comprehensive experiments with the introduced dataset, our local Markovian approach kNMC outperforms MMC and kNMP and provides excellent classification performance, 89% accuracy at the category level and 85% accuracy at the application level and particularly over 95% accuracy for live video streaming. Thus, in test time, the nearest Markov components with the largest likelihoods yield the most discrimination power. We also observe that kNMC significantly outperforms the state-of-the-art methods (such as SVM, random forest and autoencoder) on both the introduced dataset and benchmark dataset both at the category and application levels.
From Boolean functions to quantum circuits: A scalable quantum compilation flow in C++

We propose a flow for automated quantum compilation. Our flow takes a Boolean function implemented in Python as input and translates it into a format appropriate for reversible logic synthesis. We focus on two quantum compilation tasks: uniform state preparation and oracle synthesis. To illustrate the use of our flow, we solve IBM's virtual hackathon challenge of 2019, called the Zed city problem, an instance of vertex coloring, by using quantum search algorithms. The expressiveness of Python in combination with automated compilation algorithms allows us to express quantum algorithms at a high level of abstraction, which reduces the effort to implement them, and leads to better and more flexible implementations. We show that our proposed flow generates a lower-cost circuit implementation of the oracle needed to solve IBM's challenge when compared to the winning submission.
Identifying the Conditions of Maximum Electricity Demand Coverage in a Direct Mode, by Wind and Photovoltaic sources

To reach the objectives of energy security and updated NDC targets, 100% RES scenario is considered for the R. Moldova. The specific goal of this study is to identify the conditions under which a maximum coverage of the annual electricity demand can be obtained directly from WF+PV sources. Two approaches are used to reach the goal: WF and PV cover energy demand in a direct mode without considering the balancing power - Technical Weight, and second, WF and PV cover the demand in a direct mode together with a source of balancing power - Gas turbine (GT) - Economic Weight. The results obtained: When the capacities of WF and PV are determined in a way to produce electricity equal to annual power demand, Technical Weight is equal to a maximum of 71-72%. If WF and PV electricity produced is higher than demand by 50%, WF+PV tandem may cover the energy demand in a direct mode at maximum level of 80.5%. Five variants of WF, PV and GT economic parameters variation were considered when these sources are operating together. The calculations done and analysis performed showed that: The minimum price of electricity produced by WF+PV+GT in V1 (basic variant, current data) takes place at a PV integration level of 18% (Zopt). At WF specific investment of $750/kW (V2), compared to $1445/kW in V1, Zopt is equal to zero. That is, at such WF specific investment, PV implementation is not feasible. Price for electricity is lower than in V1 by 25.4%. Even at a specific investment of about $1000/kW for WF, the minimum energy price is obtained in the absence of PV. Decreasing PV specific investment by up to 50% (V3) compared to the level established in V1 has little influence on the maximum Technical Weight. The electricity price is lower than in V1 by circa 8%. A 25% decline in TG specific investments (V4), or a 17% drop in the price for natural gas (V5) compared to V1, have no material effect on the maximum Technical Weight.
Improving Health Security and Intelligence Capabilities to Mitigate Biological Threats

The COVID-19 pandemic has fostered a greater appreciation of the significance of health security and intelligence capabilities. This article examines health security and intelligence capabilities in the United States and other countries, with a view to raising awareness of biological threats across the Five Eyes community. New biotechnologies offer vaccines, therapies, and healthcare solutions, but also present biological threats that must be managed. This article begins by discussing the rise of biological threats and the relative lax regulatory environment in the United States and overseas. The second part then examines the growing importance of health security intelligence and the role it can play in response to this potential threat. The third part focuses on intelligence collection, capability, and threat mitigation, and how this could be mobilized to address harmful impacts of biological threats. The final part of the article provides recommendations for building an intelligence capacity within ICs as well as associated regulatory reforms.
Cyber security: what are we talking about?

The term ‘cyber security’ is increasingly commonplace in everyday life, and is something that users of all ages will encounter in some form. However, there are varying interpretations about what it actually means, and because of this, people have similarly varying assumptions about the extent to which it applies to them or might affect them.
Beyond Similarity: Relation-based Collaborative Filtering

Given the effectiveness and ease of use, Item-based Collaborative Filtering (ICF) methods have been broadly used in industry and are widely investigated in recent years. The key of ICF lies in the similarity measurement between items, which however is a coarse-grained numerical value that can hardly capture users' fine-grained preferences toward different attributed aspects of items. In this paper, we propose a model called REDA to address this challenge, based on which a new paradigm called Relation-based Collaborative Filtering is designed for high-performance recommendation. REDA is essentially a deep neural network model that employs an item relation embedding scheme for inter-item relations representation. It features in multi-decomposed item embedding with dual-attention refinement and employs a novel relation-wise optimization scheme for end-to-end learning. A relational user embedding is then proposed by aggregating item relation embeddings between all purchased items of a user, which not only profiles users' fine-grained preferences but also alleviates the data sparsity problem. Extensive experiments are conducted on five real-world datasets and the proposed REDA is shown to outperform ten state-of-the-art methods. In particular, REDA shows great robustness against data and relation sparsity, the ability to learn explainable item aspects, and the potential for large-scale recommendation.
Detailed black-box monitoring of distributed systems

Modern containerized distributed systems, such as big data storage and processing stacks or micro-service based applications, are inherently hard to monitor and optimize, as resource usage does not directly match hardware resources due to multiple virtualization layers. For instance, interapplication traffic is an important factor in as it directly indicates how components interact, it has not been possible to accurately monitor it in an application independent way and without severe overhead, thus putting it out of reach of cloud platforms. In this paper we present an efficient black-box monitoring approach for gathering detailed structural information of collaborating processes in a distributed system that can be queried for various purposes, as it includes both information about processes, containers, and hosts, as well as resource usage and amount of data exchanged. The key to achieving high detail and low overhead without custom application instrumentation is to use a kernel-aided event driven strategy. We validate a prototype implementation by applying it to multi-platform microservice deployments, evaluate its performance with micro-benchmarks, and demonstrate its usefulness for container placement in a distributed data storage and processing stack (i.e., Cassandra and Spark).
Study on Mobile AR Guide System to Enhance User Experience in Cultural Heritage Sites

An outdoor mobile Augmented Reality (AR) system that integrates location-based navigation and puzzle game as an auxiliary tool for user experience in cultural heritage sites is designed. The proposed system is developed to provide learning information about cultural heritage sites and allow participants to interact with both virtual and physical objects. To evaluate the performance of the proposed system, an experiment is conducted in an actual cultural heritage site. The experiment is to compare the performance between the AR group and the non-AR group of participants in terms of learning effectiveness, focusing time, direction awareness, and the performance of the system. The performance factors include navigation accuracy, easiness of system, interaction naturalness, attraction, immersion and participants' attitude towards the AR system. The evaluation results show that the AR system can enhance participants' learning effectiveness and extend focusing time comparing to the non-AR system. Besides, it can improve participants' direction awareness in visiting cultural heritage sites. The AR guide system is more interactive and participants in the AR guide group can comprehend more content about the physical environment. The AR guide system is proved more attractive and interesting. Most of the participants think that the AR guide system is a better approach to navigation.