Reviewerly/exHarmony huggingface dataset:
3D reconstruction of multi-target moving objects based on phase-shifting method

Phase shifting profilometry (PSP) can reach high accuracy for the 3D shape measurement of static object. However, errors will be introduced when moving object was reconstructed. The fundamental reason was PSP required multiple fringe patterns to reconstruct the object and the traditional PSP did not contain movement information. Aiming at the above, a new method for the 3D reconstruction multiple 2D moving objects was proposed. Different objects can have different movement. Firstly, the multiple objects were identified and the areas of interests are defined. Then, the KCF algorithm was used to track the moving object and SIFT algorithm was used to retrieve the feature points of the object before movement and after movement. The rotation matrix and translation vector describing the movement was then obtained. With the help of the reconstruction model with movement information, the least-square algorithm was employed to retrieve the correct phase value. The results show that the proposed method can reduce the errors introduced by the movement and has the potential to be applied in industrial field.
Better Safe than Sorry - Implementing Reliable Health Data Anonymization.

Modern biomedical research is increasingly data-driven. To create the required big datasets, health data needs to be shared or reused, which often leads to privacy challenges. Data anonymization is an important protection method where data is transformed such that privacy guarantees can be provided according to formal models. For applications in practice, anonymization methods need to be integrated into scalable and reliable tools. In this work, we tackle the problem of achieving reliability. Privacy models often involve mathematical definitions using real numbers which are typically approximated using floating-point numbers when implemented as software. We study the effect on the privacy guarantees provided and present a reliable computing framework based on fractional and interval arithmetic for improving the reliability of implementations. Extensive evaluations demonstrate that reliable data anonymization is practical and that it can be achieved with minor impacts on executions times and data utility.
A 3D reconstruction method of image sequence based on deep learning

Abstract Camera calibration, image feature detection, matching and other aspects have become barriers that traditional 3D reconstruction methods are difficult to break through. The important role of deep learning in data detection and classification has a deep impact on the real world, and has become a research hotspot at home and abroad to deal with this problem. In this paper, a 3D reconstruction method of image sequence based on depth learning is proposed. Firstly, the principle and method of 3D reconstruction are introduced. Then, the new method of 3D reconstruction based on image is studied and discussed in combination with deep learning theory. Finally, the conclusion and prospect are given.
Towards Constructive Hybrid Semantics

With hybrid systems becoming ever more pervasive, the underlying semantic challenges emerge in their entirety. The need for principled semantic foundations has been recognized previously in the case of discrete computation and discrete data, with subsequent implementations in programming languages and proof assistants. Hybrid systems, contrastingly, do not directly fit into the classical semantic paradigms due to the presence of quite specific non-programmable features, such as Zeno behaviour and the inherent indispensable reliance on a notion of continuous time. Here, we analyze the phenomenon of hybrid semantics from a constructive viewpoint. In doing so, we propose a monad-based semantics, generic over a given ordered monoid representing the time domain, hence abstracting from the monoid of constructive reals. We implement our construction as a higher inductive-inductive type in the recent cubical extension of the Agda proof assistant, significantly using state-of-the-art advances of homotopy type theory. We show that classically, i.e. under the axiom of choice, our construction admits a charaterization in terms of directed sequence completion.
Off-Chain Trusted Computing

This column delves into privacy risks of the IoT using risk concepts that are more native to the security domain in order to conceptually bridge our collective understanding, articulation, and management of privacy concerns in the IoT which otherwise might not be sufficiently considered or foreseen by existing legal and technical controls. Trustworthiness and privacy are of primary concern as companies connect their manufacturing and logistic infrastructures to the Internet of Things (IoT). They want to reap the benefit of automated asset management, process control and predictive maintenance. However, to do so effectively, companies need to facilitate information sharing among trustworthy partners while complying with data protection and privacy preserving regulations. In this respect, Distributed Ledgers (a.k.a. Blockchains) offer a viable solution by enabling their participants to discover one another and establish peer-to-peer trust relations without a centralized intermediary.
An Automatic Shoplifting Detection from Surveillance Videos (Student Abstract)

The use of closed circuit television (CCTV) surveillance devices is increasing every year to prevent abnormal behaviors, including shoplifting. However, damage from shoplifting is also increasing every year. Thus, there is a need for intelligent CCTV surveillance systems that ensure the integrity of shops, despite workforce shortages. In this study, we propose an automatic detection system of shoplifting behaviors from surveillance videos. Instead of extracting features from the whole frame, we use the Region of Interest (ROI) optical-flow fusion network to highlight the necessary features more accurately.
GENO – Optimization for Classical Machine Learning Made Fast and Easy

Most problems from classical machine learning can be cast as an optimization problem. We introduce GENO (GENeric Optimization), a framework that lets the user specify a constrained or unconstrained optimization problem in an easy-to-read modeling language. GENO then generates a solver, i.e., Python code, that can solve this class of optimization problems. The generated solver is usually as fast as hand-written, problem-specific, and well-engineered solvers. Often the solvers generated by GENO are faster by a large margin compared to recently developed solvers that are tailored to a specific problem class.An online interface to our framework can be found at http://www.geno-project.org.
Histogram-Based Image Color Features

A color histogram (CH) in photography and image processing is a depiction of color variation within an image. A color histogram for digital images portrays the pixel number of that have colors in every one of defined color range list, and that include the color model of the image and the collection of all probable colors [, ].
Introduction to Image Color Feature

Two main factors motivate the need for color in image processing. First, color is a strong descriptor frequently simplifying the recognition and extraction of objects from a picture. Second, people can distinguish thousands of tones of color and intensity comparable to just about two dozen tones of gray [, , , ].
HERMENEUTIC ANALYSIS OF A MUSIC PIECE AS A FACTOR FOR DEVELOPMENT OF METHODOLOGICAL CULTURE OF A FUTURE MUSIC TEACHER

The collective monograph describes the theoretical and practical aspects of development of the European research area. The general issues of the history of pedagogy, theory and methods of teaching, correctional pedagogy, theory and methods of vocational education and educational management, the current state of philological research, etc. are considered. The publication is intended for scholars, teachers, postgraduate students, and students of pedagogical specialties, as well as a wide readership interested in pedagogy and philological. Theoretical and practical aspects of the development of the European Research Area: collective monograph / edited by authors. – 2nd ed. – Riga, Latvia : “Baltija Publishing”, 2020. – 334 p.
CPU-Memory Aware VM Consolidation for Cloud Data Centers

The unbalanced usage of resources in cloud data centers cause an enormous amount of power consumption. The Virtual Machine (VM) consolidation shuts the underutilized hosts and makes the overloaded hosts as normally loaded hosts by selecting appropriate VMs from the hosts and migrates them to other hosts in such a way to reduce the energy consumption and to improve physical resource utilization. Efficient method is needed for VM selection and destination hosts selection (VM placement). In this paper, a CPU-Memory aware VM placement algorithm is proposed for selecting suitable destination host for migration. The VMs are selected using Fuzzy Soft Set (FSS) method VM selection algorithm. The proposed placement algorithm considers both CPU, Memory, and combination of CPU-Memory utilization of VMs on the source host. The proposed method is experimentally compared with several existing selection and placement algorithms and the results show that the proposed consolidation method performs better than existing algorithms in terms of energy efficiency, energy consumption, SLA violation rate, and number of VM migrations.
Unital Anti-Unification: Type and Algorithms

Unital equational theories are defined by axioms that assert the existence of the unit element for some function symbols. We study anti-unification (AU) in unital theories and address the problems of establishing generalization type and designing anti-unification algorithms. First, we prove that when the term signature contains at least two unital functions, anti-unification is of the nullary type by showing that there exists an AU problem, which does not have a minimal complete set of generalizations. Next, we consider two special cases: the linear variant and the fragment with only one unital symbol, and design AU algorithms for them. The algorithms are terminating, sound, complete, and return tree grammars from which the set of generalizations can be constructed. Anti-unification for both special cases is finitary. Further, the algorithm for the one-unital fragment is extended to the unrestricted case. It terminates and returns a tree grammar which produces an infinite set of generalizations. At the end, we discuss how the nullary type of unital anti-unification might affect the anti-unification problem in some combined theories, and list some open questions.
Hot Topic Extraction from News Websites

Considering the frequency of hot terms itself is not sufficient for detection of terms of burning topics. A term is a word which act as a element of news, changes to the news result in variation of terms used. Because a subject consists of many associated events, adjustments in a topics recognition are consequently accompanied by variation utilization of key terms or hot phrases. Focusing on hot term it self is not sufficient since the news in the websites is changed frequently and hence the need for extraction of topic is relevant to scan the news websites. Information in the websites varies on the basis of categories like sports, political, regional etc. Thus this paper attempts to find Hot Topic based on multi Dimensional Sentence Modelling from News websites.
Identification and Visualization of the Conceptual Structure and Main Research Themes of Studies in Informatics and Control Journal from 2008 to 2019

Studies in Informatics and Control journal is a quarterly publication for all those involved in the field of Information Technology (IT).Studies in Informatics and Control journal provides important perspectives on topics relevant to IT, with an emphasis on useful applications in the most important areas of IT, and it is aimed at advanced practitioners and researchers in the field of IT.The core subjects covered by Studies in Informatics and Control journal are relating to innovative research and practice in Information Technology: IT use in control and management systems; integration of IT with control; application of IT in socio-economic systems and manufacturing processes, mainly.The current research conducts a bibliometric performance and conceptual structure analysis of Studies in Informatics and Control journal from 2008 to 2019.Firstly, the journal performance is analysed according to the data retrieved from the Web of Science Core Collection, putting the focus on the productivity of the authors, citations, countries, organizations and most relevant publications.Finally, the conceptual structure of the journal is analysed with bibliometric software tool SciMAT, identifying the main thematic areas that have been the object of research, their composition, relationship and evolution during the period analysed.
Crypto communities as legal orders

Several crypto communities have claimed to have revolutionary potential and to present a possible alternative to the state legal order.In this paper, we first mention three different generations of crypto communities, revealing their shared narrative, before focusing on the Silk Road dark web marketplace.Based on the qualitative content analysis of the Silk Road forum, we look into whether this platform should be understood as a legal order, a revolutionary movement, or simply a capitalist marketplace.Lastly, the article includes a short discussion of the question whether or not an online platform such as the Silk Road poses a serious challenge for state sovereignty.
Cloud-based Framework for Efficient Storage of Unstructured Patient Health Records

Recently, in healthcare sector, the data is steadily growing and becomes more vital.Most of this data is embedded in the medical record of the patient.In fact, Patient Health Records (PHRs) refer to those records that the patient can maintain, access and share among different specialists.Storing these PHRs to the cloud allow the patient to maintain and share them with different practitioners anywhere and anytime.However, he still suffers from some security and privacy issues.Hence, it is necessary to guarantee the security and privacy of this immense volume of patient's confidential data on the cloud.Anonymization and encryption are the two methods that can be adopted to ensure the security and privacy of PHRs on cloud.In this paper, a cloudbased framework for securing the storage and the retrieval of unstructured PHRs is proposed.This framework combines different encryption techniques to encrypt the different contents of the PHR, to compress medical images and to control the access to these records.In addition, the encrypted files are partitioned into a random number of files before being sent to the cloud storage server.These files are of variable number and variable size.When a user requests to access a PHR from the cloud, the proposed framework first controls access of this user before merging the partitioned files.The decryption of these files is performed on the client side not on the cloud using the secret key, which is owned by authorized user only.Finally, extensive analytical and experimental results are presented.It shows the security, scalability, and efficiency of the proposed framework.
Using Big Data to Predict Outcomes of Opioid Treatment Programs.

Potential of big data analytics in analyzing outcomes of opioid treatment programs (OTP) has not been fully explored. The goal of this study was to assess potential of big data in predicting OTP outcomes based on the initial intake forms which includes demographics, social and health history. The analytical sample comprised over 30,000 people admitted in OTP. Around 66% of patients reported improvements after completing OTP. We compared the results of Logistics Regression, Random Forest, and XGBoost for predictive modeling. XGBoost with sampling and threshold tuning performed the best (44% F1 score) with over 60% accuracy. Further big data exploration of OTP is warranted.
On experimenting large dataset for visualization using distributed learning and tree plotting techniques

Visualization as one major field making up data science has played significant roles in data exploration. With visualization at the center of every data analysis and application, exploratory analysis has proved the basis for which data analyst comparatively implement what-if scenario before and after processing. Interesting patterns generated from models visualized is very helpful in fast decision-making, model tuning and optimization. Although, conventional methods such as histogram, pie chart, box plot and bar graph are in most occasions not adequate enough to effectively convey the interesting pattern to be mined in large dataset. This paper therefore, presents a tree-plot approach which make use of an In-memory node mechanism from the h2o package to place the large dataset in memory. A Gradient Boosted Model (GBM) from same was implemented as the underlying learning algorithm to build the tree model, while the modeled trees were plotted using plotting techniques in data.tree. Execution process time, AUC, MSE and RMSE results obtained provide basis for evaluating how well the data was trained and for visualizing the modeled tree. It further substantial how a learner algorithm could work with a plotting method with less computational cost.
Simultaneous and Spatiotemporal Detection of Different Levels of Activity in Multidimensional Data

In this work, we present a novel and promising approach to autonomously detect different levels of simultaneous and spatiotemporal activity in multidimensional data. We introduce a new multilabeling technique, which assigns different labels to different regions of interest in the data, and thus, incorporates the spatial aspect. Each label is built to describe the level of activity/motion to be monitored in the spatial location that it represents, in contrast to existing approaches providing only a binary result as the presence or absence of activity. This novel Spatially and Motion-Level Descriptive (SMLD) labeling schema is combined with a Convolutional Long Short Term Memory-based network for classification to capture different levels of activity both spatially and temporally without the use of any foreground or object detection. The proposed approach can be applied to various types of spatiotemporal data captured for completely different application domains. In this paper, it was evaluated on video data as well as respiratory sound data. Metrics commonly associated with multilabeling, namely Hamming Loss and Subset Accuracy, as well as confusion matrix-based measurements are used to evaluate performance. Promising testing results are achieved with an overall Hamming Loss for video datasets close to 0.05, Subset Accuracy close to 80% and confusion matrix-based metrics above 0.9. In addition, our proposed approach's ability in detecting frequent motion patterns based on predicted spatiotemporal activity levels is discussed. Encouraging results have been obtained on the respiratory sound dataset as well, while detecting abnormalities in different parts of the lungs. The experimental results demonstrate that the proposed approach can be applied to various types of spatiotemporal data captured for different application domains.
Cut-n-Reveal

Recent hurricane events have caused unprecedented amounts of damage on critical infrastructure systems and have severely threatened our public safety and economic health. The most observable (and severe) impact of these hurricanes is the loss of electric power in many regions, which causes breakdowns in essential public services. Understanding power outages and how they evolve during a hurricane provides insights on how to reduce outages in the future, and how to improve the robustness of the underlying critical infrastructure systems. In this article, we propose a novel scalable segmentation with explanations framework to help experts understand such datasets. Our method, CnR (Cut-n-Reveal), first finds a segmentation of the outage sequences based on the temporal variations of the power outage failure process so as to capture major pattern changes. This temporal segmentation procedure is capable of accounting for both the spatial and temporal correlations of the underlying power outage process. We then propose a novel explanation optimization formulation to find an intuitive explanation of the segmentation such that the explanation highlights the culprit time series of the change in each segment. Through extensive experiments, we show that our method consistently outperforms competitors in multiple real datasets with ground truth. We further study real county-level power outage data from several recent hurricanes (Matthew, Harvey, Irma) and show that CnR recovers important, non-trivial, and actionable patterns for domain experts, whereas baselines typically do not give meaningful results.
Merging Artificial Intelligence &amp; Blockchain Technologies to Solve Academic Qualification Forgery Issues

This paper emphasizes on the possibility of merging Artificial Intelligence and Blockchain technologies to solve academic qualifications forgery issues in the educational sectors. Empirical data is collected through interviews with many specialist and technical people who is interested in the emerging technologies of the Fourth Industrial Revolution and focused group discussion in the field, as well as from reports in the reviewed literary articles. Scientific journals have also been accessed to analyze the paper goals and objectives. The findings are based on the conclusion suggest that emerging technologies can be integrated to become more efficient and effective in detecting fraud and forgery before it occurs. Considerable attention should be given to reducing and combating these issues because they have significant negative impacts on the economy and education. Accordingly, the study makes recommendations based on the results and areas of future research, considering the establishment of a unified and integrated system. Initially will be applied as a pilot in Sultanate of Oman, then gradually will be extended to the Gulf Cooperation Council States (GCC) and internationally particularly the affiliated and the recognized educational institutions to avoid the phenomena that affect the reputation and quality of education institutions and academic qualifications. In the conclusion considering the impacts of the proposed system in the education and economy as well in general. This research aims to investigate academic forgery cases in the world and then focuses in Oman. Furthermore, it explores the possibilities of merging technologies (Artificial Intelligence and Blockchain) to contribute in eliminating fraud before it occurs. In addition, to propose a framework that will help to find solutions based on the suggested integrated technologies. Finally, it assess the probabilities of the proposed solutions performance and its impacts in the academic sector.
Model for Elimination of Mixed Noise from MRI Heart Images

A method for the preliminary processing of MRI images of the heart that allows for the elimination of fluctuation and impulse noise from useful signals is proposed. These types of noise are due to the regular geometric structure of the photoelectric elements of the MRI scanner matrix and the structure of the signal transmission channel. The aim of this work is to develop a comprehensive mathematical model for eliminating noise in the signal of an MRI scanner. In this work, mathematical models of linear and median filtering of impulse noise, fluctuation, and geometric noise are implemented. The mathematical models consist of the combined use of linear and median filters for recording MRI images of the heart. In the experiments, real MRI images of the heart from six patients with different diseases were used after noise was added to them. We were able to eliminate the impulse noise, geometric noise, and fluctuation noise in the MRI images by applying our filtering techniques. The filtering technique not only removed the noise, but also increased the contrast of the cancerous volumetric heterogeneous formations in the heart region.