Reviewerly/exHarmony huggingface dataset:
Periodicity for the 3-state quantum walk on cycles

Dukes (2014) and Konno, Shimizu, and Takei (2017) studied the periodicity for 2-state quantum walks whose coin operator is the Hadamard matrix on cycle graph C_N with N vertices. The present paper treats the periodicity for 3-state quantum walks on C_N. Our results follow from a new method based on the cyclotomic field. This method gives a necessary condition for the coin operator for quantum walks to be periodic. Moreover, we reveal the period T_N of typical two kinds of quantum walks, the Grover and Fourier walks. We prove that both walks do not have any finite period except for N=3, in which case T_3=6 (Grover), =12 (Fourier).
Behaviour Based Authentication: A New Login Strategy for Smartphones

In today's world, we are having enough user name and passwords to remember; often causing accounts to be locked due to forgetting them. Here, we propose a different perspective of login, where you only need to remember your recent activities on the smartphones, which will be posed in such a way that a genuine user could easily answer. The proposed system will collect the events happening on the phone and put intelligent algorithms to generate challenges specific to user. The challenge-response will give a score to the user, where a threshold is set to allow or disallow user from entering into the system. The proposed model uses machine learning based techniques to learn user's behavior and creates a continuously improving user profile. The system found to be effective with 85% accuracy, where out of 30 real world test users 26 could easily able to authenticate themselves.
Explaining with Impact: A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms

There has been a significant surge of interest recently around the concept of explainable artificial intelligence (XAI), where the goal is to produce an interpretation for a decision made by a machine learning algorithm. Of particular interest is the interpretation of how deep neural networks make decisions, given the complexity and `black box' nature of such networks. Given the infancy of the field, there has been very limited exploration into the assessment of the performance of explainability methods, with most evaluations centered around subjective visual interpretation of the produced interpretations. In this study, we explore a more machine-centric strategy for quantifying the performance of explainability methods on deep neural networks via the notion of decision-making impact analysis. We introduce two quantitative performance metrics: i) Impact Score, which assesses the percentage of critical factors with either strong confidence reduction impact or decision changing impact, and ii) Impact Coverage, which assesses the percentage coverage of adversarially impacted factors in the input. A comprehensive analysis using this approach was conducted on several state-of-the-art explainability methods (LIME, SHAP, Expected Gradients, GSInquire) on a ResNet-50 deep convolutional neural network using a subset of ImageNet for the task of image classification. Experimental results show that the critical regions identified by LIME within the tested images had the lowest impact on the decision-making process of the network (~38%), with progressive increase in decision-making impact for SHAP (~44%), Expected Gradients (~51%), and GSInquire (~76%). While by no means perfect, the hope is that the proposed machine-centric strategy helps push the conversation forward towards better metrics for evaluating explainability methods and improve trust in deep neural networks.
Exploring Trade-offs in Batch Bounded Distance Decoding

Algorithms for solving the Bounded Distance Decoding problem (BDD) are used for estimating the security of lattice-based cryptographic primitives, since these algorithms can be employed to solve variants of the Learning with Errors problem (LWE). In certain parameter regimes where the target vector is small and/or sparse, batches of BDD instances emerge from a combinatorial approach where several components of the target vector are guessed before decoding. In this work we explore trade-offs in solving “Batch-BDD”, and apply our techniques to the small-secret Learning with Errors problem. We compare our techniques to previous works which solve batches of BDD instances, such as the hybrid lattice-reduction and meet-in-the-middle attack. Our results are a mixed bag. We show that, in the “enumeration setting” and with BKZ reduction, our techniques outperform a variant of the hybrid attack which does not consider time-memory trade-offs in the guessing phase for certain Round5 (17-bits out of 466), Round5-IoT (19-bits out of 240), and NTRU LPrime (23-bits out of 385) parameter sets. On the other hand, our techniques do not outperform the Hybrid Attack under standard, albeit unrealistic, assumptions. Finally, as expected, our techniques do not improve on previous works in the “sieving setting” (under standard assumptions) where combinatorial attacks in general do not perform well.
An Improved Affine-Scaling Interior Point Algorithm

In this paper, an Improved Affine-Scaling Interior Point Algorithm for Linear Programming has been proposed.Computational results of selected practical problems affirming the proposed algorithm have been provided.The proposed algorithm is accurate, faster and therefore reduces the number of iterations required to obtain an optimal solution of a given Linear Programming problem as compared to the already existing Affine-Scaling Interior Point Algorithm.The algorithm can be very useful for development of faster software packages for solving linear programming problems using the interior-point methods.
SHACL Shapes Generation from Textual Documents

Shapes Constraint Language (SHACL) is the new recommendation by W3C consortium to uniform both describing and constraining the content of an RDF graph. Based on the inspiration of model generation from textual requirements specifications, we investigate the possibility of mapping parts of a textual document to shapes described by SHACL. In this contribution, we present our approach of the patterns (based on a grammatical inspection) that indicates candidates of domain description in SHACL language. We argue that the standard methods of linguistics can be supported by ontology resources as Schema.org.
Inovasi Digital di Industri Smart Farming: Konsep dan Implementasi

Budiharto W.  2019. Digital innovation in the smart farming industry: concept and implementation. In : Herlinda S et al. ( Eds .), Prosiding Seminar Nasional Lahan Suboptimal 2019, Palembang 4-5 September 2019. pp. 31-37. Palembang: Unsri Press. Agriculture is one of the oldest industrial sectors ever created by humans. The FAO predicts that by 2050 the world population will be 9.6 billion. That means agricultural production must increase by 70% in that year in order to meet the needs of the population with this amount. Yet as we know that there is a classic problem of food namely the population increases but the amount of agricultural land is getting narrower. Therefore, increasing agricultural technology-based productivity is very urgent, one of which is based on the Internet of Things (IoT). IoT is the latest technology where devices around us can communicate with each other through the internet network. Wireless Sensor Network (WSN) technology was applied more broadly to IoT-based agriculture which came to be known as Smart Farming. The sensors used in agricultural land are used to determine the chemical condition of the soil, the quality of plant health and other useful information. Smart farming, which was originally called Precision Agriculture, is predicted to become a compulsory concept of agriculture in the future because of limited land. Smart farming utilizes technologies such as big data, machine learning, robotics and IoT to improve the quality and quantity of production in the agricultural industry. This paper gradually explains the concept of smart farming and its implementation in Indonesia. Keywords: concept, implementation, smart farming
IRIS: Inter-Reality Interactive Surface

While many metaphors were developed for interactions from a specific point at the reality-virtuality continuum, much less attention has been paid to designing metaphors that allow the users to cross the boundaries between the virtual, the augmented, and the real. We propose a use of an Inter-Reality Interactive Surface (IRIS) that enables users to collaborate across the reality-virtuality continuum within the same application. While we examine IRIS in the context of an immersive educational platform, UniVResity, the metaphor can be generalized to many other application domains.
Hybrid Fuzzy CNN Network in the Problem of Medical Images Classification and Diagnostics

The problem of classification of breast tumors on medical images is considered. For its solution new class of convolutional neural networks-hybrid CNN-FNN network is developed in which convolutional neural network VGG-16 is used as feature extractor while fuzzy neural network NEFClass is used as classifier. Training algorithms of FNN were implemented. The experimental investigations of the suggested hybrid network on the standard data set were carried out and comparison with known results was performed. The problem of data dimensionality reduction is considered and application of PCM method is investigated.
Multivariate Medians for Image and Shape Analysis

Having been studied since long by statisticians, multivariate medianconcepts found their way into the image processing literature in thecourse of the last decades, being used to construct robust and efficientdenoising filters for multivariate images such as colour images but alsomatrix-valued images.Based on the similarities between image and geometric data as results ofthe sampling of continuous physical quantities, it can be expected that theunderstanding of multivariate median filters for images provides a startingpoint for the development of shape processing techniques.This paper presents an overview of multivariate median concepts relevantfor image and shape processing. It focusses on their mathematical principlesand discusses important properties especially in the context of imageprocessing.
Research on Campus Network Security Management Technology Based on Big Data

This paper improves the security management and control ability of campus network management, studies the security management control model of campus network management, and puts forward a security evaluation and evading model of campus network management based on big data. The management security data mining is carried out by using the statistical analysis method of campus network transmission traffic, and the constraint distribution model of campus network management security control is constructed. Big data fusion and association rule mining methods are used to evaluate the security of campus network management quantitatively, and the data of campus network management security evaluation are tested by grouping regression, and the correlation dimension characteristic quantity of traffic transmission sequence of campus network management is extracted. This paper analyzes the cross-correlation characteristic quantity of the output traffic of campus network management and evaluates the network security according to the anomaly of the characteristic to realize the optimization control of campus network security management. The simulation results show that the traffic anomaly prediction ability is higher and the network intrusion detection ability is stronger by using this method in campus network security management.
Data work by frontline health workers in pregnancy care

The role of frontline health worker (FHW) is of utmost importance especially in lower socio-economic settings of India. An in-depth understanding of the system is crucial for designing tools to support their work practices. This paper is an early and exploratory design research study that focuses on the data work done by three kinds of FHW, namely ASHA workers (AW), Anganwadi workers (AWW) and Junior health assistant (JHA), who collaboratively form the foundation of community-based pregnancy care. Our findings focus on some of the challenges faced by FHW as well as how they go beyond their structured role to serve good health service in pregnancy care. Researchers & designers can use these findings to open up the future possibilities of designing advanced data-driven tools to support frontline health worker's work.
Comparison of Preprocessing Techniques for Dental Image Analysis

Various dental disorders, such as lesions, masses, carries, etc. may affect the human dental structure. Dental radiography is a technique, which passes X-rays through dental structures and records the radiographic images. These radiographic images are used to analyze the disorders present in the human teeth. Preprocessing is a primary step to enhance the radiographic images for further segmentation and classification of images. In this work, the preprocessing techniques such as unsharp masking using high pass filter, bi-level histogram equalization and hybrid metaheuristic have been utilized for dental radiographs. The performance measures of the preprocessing techniques were analyzed. Results demonstrate that a hybrid metaheuristic algorithm for dental radiographs achieves higher performance measures when compared to other enhancement methods. An average Peak Signal-to-Noise Ratio (PSNR) value of 21.6 was observed in the case of a hybrid metaheuristic technique for dental image enhancement.
Acción tutorial y tecnología: propuesta formativa en educación primaria

The vast majority of under-age people use technology daily. We believe that it is essential to work on these topics and mastering digital competences in tutorial actions through the guiding function of all teachers. The current investigation describes the initial knowledge of teachers on topics related to the tutorial action plan and the digital competences in tutorial action. The study sample was 26 participants of pre-school and primary education teachers. The tools used to collect information were two questionnaires and a Satisfaction Survey. The results of the analysis show that there is a favourable attitude towards digital competences and their inclusion in the tutorial action, this training course led to a slight improvement in the competences in technology and tutorial action.
Rehabilitation system for children with cerebral palsy based on body vector analysis and GMFM-66 standard

Abstract This paper mainly proposes a rehabilitation training system that uses motion recognition technology to help children with cerebral palsy complete the evaluation of gross motor functions. This article makes a comprehensive introduction to this product with regard to the system functional architecture, application technology principles and its application scope.
Research on An Ensemble Anomaly Detection Algorithm

Abstract Aiming at the problem that the applicability of single anomaly detection algorithm is not strong in aerospace experiment, an ensemble anomaly detection algorithm is proposed. This algorithm combines multiple machine algorithms and can obtain better detection performance than any other algorithm. Through comparison, k-NN, PCA and HBOS are selected. These three algorithms have fast calculation speed and different algorithm mechanisms, which can effectively process various data sets. This paper first introduces the basic concept of anomaly detection, then introduces and explains the three algorithms, then integrates the three algorithms, and introduces the voting mechanism to vote on whether the sample points are normal. Finally, the performance of the algorithm is tested through simulation experiments. Compared with a single algorithm, the ensemble algorithm has better performance in precision and accuracy.
Recycling a Landmark Dataset for Real-time Facial Capture and Animation with Low Cost HMD Integrated Cameras

Preparing datasets for use in the training of real-time face tracking algorithms for HMDs is costly. Manually annotated facial landmarks are accessible for regular photography datasets, but introspectively mounted cameras for VR face tracking have incompatible requirements with these existing datasets. Such requirements include operating ergonomically at close range with wide angle lenses, low-latency short exposures, and near infrared sensors. In order to train a suitable face solver without the costs of producing new training data, we automatically repurpose an existing landmark dataset to these specialist HMD camera intrinsics with a radial warp reprojection. Our method separates training into local regions of the source photos, i.e., mouth and eyes for more accurate local correspondence to the mounted camera locations underneath and inside the fully functioning HMD. We combine per-camera solved landmarks to yield a live animated avatar driven from the user’s face expressions. Critical robustness is achieved with measures for mouth region segmentation, blink detection and pupil tracking. We quantify results against the unprocessed training dataset and provide empirical comparisons with commercial face trackers.
Iterative Domain Adaptation Methods

Self-iterative strategies consist of labeling (unlabeled) target data using a hypothesis built from the available source examples in a step-by-step fashion such that at each step the classifier is learned on a modified sample that includes the pseudo-labeled (or semilabeled) examples from the previous step. In this scenario, a new classifier is trained in way that allows us to take into account the information provided by both domains with a stopping point corresponding to a state where all target instances are labeled. This iterative process, taking its origins from self-training or cotraining strategies, has led to several strategies for unsupervised domain adaptation with the most notorious examples being given by the DASVM algorithm and the approach of Perez and Sánchez-Montañés based on the expectation–maximization algorithm. More recently, several approaches have also extended these ideas based on different algorithmic principles with regularization information coming from pseudo-labeled data.
Pedwarn-Enhancement of Pedestrian Safety Using Mobile Application

Mobile phones usages are emerging as fast as the evolution of a man. Starting from the sunrise to sunset people are constantly using the mobile phone. It has more features like gaming, music, camera, alarm, etc. and people are using it to perform their day-to-day tasks. Even though the mobile phone has a lot of benefits, it also leads to life threatening problems due to pedestrian collision. The probability of such incidents may happen, when the pedestrians are not taking their eyes form the mobile phone while walking or crossing the road, so they met with an accident which causes serious injuries. To avoid such happenings and to notify the pedestrians about the obstacles, a mobile application called Pedwarn is developed with the help of built in phone sensors like accelerometer sensor, gyroscope sensor. It provides solutions to the problem without prior knowledge of the surroundings by calculating the distance to the nearby objects with phone speakers and microphones. The obstacles are detected within 2–4 m. Its accuracy is also strengthened by using the visual detector which will capture the images of the surrounding with the rear camera. Pedwarn is evaluated using variety of environmental settings and in different devices which are common in our day to day surroundings. The power consumed by each and every component is measured periodically of about one hour. Averages of Pedwarn measurements is noted that, its detection accuracy is 95% higher and 2%.
Keyword Analysis Visualization for Chinese Historical Texts

Historical texts form the basis of the study of antiquities. In the case of Chinese historical texts different genres exist, e.g. chronological and biographical works etc. The contents of these texts normally consist of complex and interrelated information which covers long time period. Traditional history research relies heavily on information extraction and analysis by human researchers. With the recent development of the internet, data science and visualization technologies, digital history gradually attracts more and more attentions and in turn significantly impacts the field of historical study through altering the accessibility of the source materials, the narrative strategy and the analytical methodologies. This paper provides a system that enhances the Chinese historical research using word segmentation, texts analysis and visualization technologies. We can improve the workflow of traditional historical research via automatically detecting important keywords in Chinese historical texts and extracting, analyzing and visualizing the relations between a keyword and other words. This does not only accelerate the text based historical study but also to a great extent increase the scope of the search and analysis of the keywords in Chinese historical texts which used to be limited by the capacity of human researchers.
Performance Analysis of Collaborative Recommender System: A Heuristic Approach

Security of Recommender systems from various type of attacks from non-genuine users are main point of research these days. The performance of Recommender systems get degraded due to these attacks from the non-genuine users. So exploration of the performance of Recommender Systems by considering possible attacks is the main issue of discussion in this paper. Here, a collaborative filtering based Recommender system is designed and implemented. Out of the generated recommendations, genuine and non-genuine recommendations are investigated. Push and Nuke attacks are considered, while testing matrix model. Various metrics are used to evaluate the performance of proposed Collaborative Recommender System.
A Weighted Frequent Itemset Mining Algorithm for Intelligent Decision in Smart System

Identifying the frequent item set is the challenging task in data mining as data is increased day by day in all fields. To analyze the accurate item set in that data like market basket is the key factor of improving the economical strategy of the marketing management. Frequent itemset mining, as an imperative of association rule examination, one of the mainly essential study fields in data mining. Weighted frequent itemset mining in vague databases equally the current prospect and significance of items into version in order to discover frequent itemsets of great importance to users. But many data are inconsistency because of the incomplete field in the collected data. This brings less stability in predicting the accurate information in the data which has the many fields. Many existing research have developed many technique or algorithm to bring the stable procedure to predict the data. But achieving the 100% accurate data from the collected dataset is still not completed. In this thesis, the proposed system will bring various parameters that will analyze dataset with Apriori and weighted Downwards Frequency Itemset Mining (WDFIM). In this analysis the minimum support, confidence level and time consumption are the parameters that analyzed where WDFIM is analyzing more accurate result when compared to Apiori algorithm.