Reviewerly/exHarmony huggingface dataset:
A Distributed Occupancy Distribution Estimation Method For Smart Buildings

Occupancy distribution is of great importance for the reason that it helps improve the energy efficiency, safety and comfort of the buildings. Many existing occupancy distribution estimation methods require large scale deployment of sensors and a centralized data processing system. It makes the estimation system hard to deploy and maintain. In this paper, we propose a distributed occupancy distribution estimation method based on the video data from surveillance cameras in buildings. The proposed method mainly comprise three parts. The first part is a distributed architecture for occupancy estimation. By processing data in a distributed measure, it makes the estimation system more flexible and easy to implement. The second part is an estimation algorithm detecting the passing occupants from one region to another using camera video data. The third part is a correction algorithm reducing the detection error and accumulated error of the estimation. We experimented our method in the FIT Building, Tsinghua University and the results show the validity of our method.
Patterns of Forming the Urban Surface Deposited Sediments

Abstract The contemporary sedimentation processes participate in the formation of fluxes of the solid substances in an urban area, forming together a source of pollution, transit and depositing media. The patterns of forming the urban surface deposited sediments were determined on the example of Russian metropolis Nizhny Novgorod. Two schemes of the research were implemented in residential districts of the city in the summer field season 2018. The first research design scheme represented a detailed survey of residential quarters of the city. It included a collection of 25 samples of environmental compartments from five residential quarters with multi-storey buildings. The sampling was conducted in various functional landscape parts at the courtyard and facade areas. Each sample was separated into particle size fractions. Chemical and mineral analysis was performed in each granulometric subsample of the collected samples. The second scheme represented the urban geochemical study based on sampling the sediments from local surface depressed zones of microrelief in the city. The 40 samples were collected at the territory of the city on an irregular grid. The sampling was carried out in residential areas in blocks with multi-storey buildings. One sample represented a combined sample of sediments from local surface zones of microrelief by 3-5 localizations from the area of the block. Chemical analysis of the collected samples was conducted. The maximum content of dust (0.002-0.01 mm) 39% was found in soil, while the lower mean dust content was found in the samples of sediment from local surface depressed zones of microrelief (30 %) and in the road deposited sediments (30 %). The mineral composition of soil and sediment in Nizhny Novgorod is represented by quartz sand (approx. 55 %). In the samples of sediments, the minerals’ content is basically characteristic for acid and metasomatic rocks. Building materials’ content in the sediments reaches up to 20%. The maximum organic matter content was found in fine dust fraction 2-10 microns 21% in soils and 5% on the roads. Differentiation of the mineral content in the samples is observed for the various particle size fractions and different functional parts of the quarters. The highest content of metals of anthropogenic association (such as Pb, Zn and Cu) was found in the dust fraction. The Pb and Zn concentrations in samples of sediment of depressed areas of microrelief and road dust were significantly lower than in the soil.
Heterogeneous Data Ensemble Learning in End-to-End Diagnosis for IPTV

In this paper, we propose a deep learning method to identify which region has failures when clients make complaints. The format of the data we collect includes structured data, unstructured data, and time-series data. Experimental results show that our model can accurately distinguish which site is the root cause for a malfunction or a breakage in the process of the service. The results of this research can speed up maintenance personnel to repair faults.
Mechanical energy conservation investigation using photodiode sensor based on Arduino Uno

Mechanical energy (ME) is the sum of potential energy (U) and kinetic energy (K) and associated with the motion and position of an object. The values of ME will always perpetually if the force is constant, while the value of K and U depend on the object position. This study aims to design an apparatus for investigation of the mechanical energy conservation experiment through free fall events using three point photodiode-sensor measurements based on Arduino Uno platform. A prototype is made from PVC cylinder of 100 mm diameter and a height of 100 cm, holed with a height of 100 cm, holed with surface pattern to minimize air friction inside the tube and the acceleration of falling objects is equivalent to the acceleration of the earth’s gravity, which is 9.8067 ms−2. Three photodiode sensors are installed straight on the prototype belt to enable the receiver collects light directly from the transmitter while the second sensor is movable up and down to latch the ball travel time, time between interrupts is then measured as well as U and K on an interface. The experiment shows that the decrease in U is proportional to the increase in ME, where the U of objects decreases by 0.0099 Joule every 100 mm distance while K of objects increases by 0.0098 Joule. In the middle of the cylinder with the height of falling objects is 50 cm, then U = K, so the ME value has remained constant.
Extended Reality for Chronic Pain Relief

Chronic pain is ongoing pain lasting for long periods of time after the initial injury or disease has healed. Chronic pain is difficult to treat and can affect the daily lives of patients. Distraction therapy is a proven way of relieving pain by redirecting the focus of patients' attention. Virtual reality is an effective platform for distraction therapy as it immerses the user visually, aurally, and even somewhat physically in a virtual world detached from reality. There is little research done on the effects that physical interactions have on pain management. This project aims to evaluate different types of extended reality (XR) interactions, including full body movement, for chronic pain patients to determine which is the best for pain relief. We are building a prototype for participants to interact both mentally and physically and measuring the reduction in subjective pain ratings at various points of the XR experience.
Discontinuity-induced measurement artifact reduction of structured light system based on a deconvolution method

Structured light three-dimensional (3D) measurement techniques have obtained increasing popularity in the field of industrial automation, inverse engineering, and graphics. Recent literatures show that the imperfect of the imaging system in the structured light 3D metrology will cause the discontinuous-induced measurement artifact (DMA) in the area around the discontinuous edge. Existing DMA reduction methods need to detect the all edges accurately first. This procedure is hard to accomplish when the edges are defocused. Meanwhile, the corrected date in the error area relies heavily on the data in its nearest unaffected area, which makes the corrected data unreliable in some situations. In this work, a flexible deconvolution-based method is proposed to solve the above two problems in this paper. Simulation and experiment show that our proposed method can reduce the Root Mean Square phase/height error of DMA by up to 4 times.
Learning Regular Expressions Using XCS-Based Classifier System

Evolutionary machine learning research aims to develop classifier systems that can solve complex and hard tasks. This paper addresses the problem of inferring a regular expression from a given set of strings for automating the task of information extraction. To the best of our knowledge, this paper is the first to propose the extension of accuracy-based classifier system XCS to learn the regular expressions for text extraction. This new system named as XCSREA includes tree-like code fragments to learn regular expressions. The genetic algorithm in action sets uses two-point crossover with uniform mutation and Roulette wheel parent selection method. Seven different datasets, each with three different lengths, are used to compare the performance of the proposed model with standard genetic programming (GP) approach. The experimental results demonstrate that XCSREA outperforms standard GP approach when sufficiently large numbers of classifiers are used.
Geosr: A Computer Vision Package for Deep Learning Based Single-Frame Remote Sensing Imagery Super-Resolution

Recently, owing to the outstanding capability of deep learning in solving ill-posed problems, the single-frame super-resolution (SR) researches tend to focus on deep learning methods largely. However, related researches are implemented and evaluated through various datasets and different deep learning frameworks, which hinders the comparison of performance among different methods and heavily hampers the progress of SR techniques. In this study, we present GeoSR, an open source computer vision package for deep learning based single-frame remote sensing imagery super-resolution to facilitate the development of the SR community. As a unified, simple, and flexible package, GeoSR contains pipeline-like integrated tools from data retrieval to final result evaluation, which enables users to develop self-defined models conveniently; several state-of-the-art models trained through the same high-quality dataset are provided as the baseline in the package as well. Moreover, the proposed package could potentially serve as a viable backend for other related packages such as image segmentation with high efficiency.
Enhancing Piecewise Planar Scene Modeling from a Single Image via Multi-View Regularization

Recent studies on planar scene modeling from a single image employ multi-branch neural networks to simultaneously segment pla-nes and recover 3D plane parameters. However, the generalizability and accuracy of these supervised methods heavily rely on the scale of available annotated data. In this paper, we propose multi-view regularization for network training to further enhance single-view reconstruction networks, without demanding extra annotated data. Our multi-view regularization emphasizes multi-view consistency in the training phase, making the feature embedding more robust against view change and lighting variation. Thus, the neural network trained with our regularization can be better generalized to a wide range of views and lightings. Our method achieves state-of-the-art reconstruction performance compared to previous piecewise planar reconstruction methods on the public ScanNet dataset.
PMSGM: PatchMatch Semi-Global Matching for Efficient Stereo Correspondence

Semi-Global Matching (SGM) is a robust and widely used method for stereo correspondence because of its good trade-off between disparity image quality and computation efficiency. The computational complexity of the previous works are proportional to the disparity range, thus an efficient SGM-based solution is still very challenging especially for the increasing demand of large disparity stereo matching. This paper proposes a PatchMatch Semi-Global Matching (PMSGM) algorithm which significantly reduces the number of candidate disparities by means of the PathchMatch spatial propagation scheme. The evaluation results on KITTI2015 training dataset demonstrate that, the proposed method can achieve competitive disparity accuracy with approximately 5x and orders of magnitude efficiency improvement compared with the original Semi-Global Matching algorithm and the other PatchMatch-based stereo matching algorithms respectively.
An Efficient Perpetual Learning Algorithm

One of the emerging research opportunities in machine learning is to develop computing systems that learn many tasks continuously and improve the performance of learned tasks incrementally over time. In real world, learners have to adapt to labeled and unlabeled samples from various tasks which arrive randomly. In this paper, we propose an efficient algorithm called Efficient Perpetual Learning Algorithm (EPLA) which is suitable for learning multiple tasks in both offline and online settings. The algorithm, which is an extension of ELLA,4 is part of what we call perpetual learning that can learn new tasks or refine knowledge of learned tasks for improved performance with newly arrived labeled samples in an incremental fashion. Several salient features exist for EPLA. The learning episodes are triggered via either extrinsic or intrinsic stimuli. Agent systems based on the proposed algorithm can be engaged in an open-ended and alternating sequence of learning episodes and working episodes. Unlabeled samples can be used to self-train the learner in small data setting. Compared with ELLA, EPLA shows almost equivalent performance without memorizing any labeled samples learned previously.
Deep Learning Based Approaches for Recommendation Systems

Recommendation systems are one of the most widely used Machine Learning algorithms in the industry. Deep learning, a branch of machine learning, is popularly used in fields like Computer Vision, Natural Language Processing etc. Recommender systems have started widely using Deep Learning for generation of recommendations. This paper studies different deep learning methods for the recommendation system highlighting the important aspects of design and implementation.
Comparison Of Cellular Video Quality For Object Detection Using Neural Network Convolution

Abstract— The purpose of this study is to compare the video quality between the Samsung HP camera and the Xiaomi HP camera. The object of study was UNPRI students who walked through the front yard of the UNPRI SEKIP campus. Here we test how accurate the camera's HP capture capacity is used to take the video. The method used to test this research is the Convolution Neural Network method. Object detection and recognition aim to detect and classify objects that can be applied to various fields such as face, human, pedestrian, vehicle detection (Pedoeem &amp; Huang, 2018), besides the ability to find, identify, track and stabilize objects in various poses and important backgrounds in many real-time video applications. Object detection, tracking, alignment and stabilization have become very interesting fields of research in the vision and recognition of computer patterns due to the challenging nature of several slightly different objects such as object detection, where the algorithm must be precise enough to identify, track and center an object from the others
Rotating Disk Cryptosystem: RDC

Cryptography is a hot and active topic, it plays a crucial role in many aspects nowadays, from internet banking and e-commerce to email and web-based business processes. An important area of research today is to test the security of cryptosystems. A cryptographic system is safe as long as no one could not break it. Modern cryptographic algorithms are based on mathematical problems, known to be difficult, so breaking the security of a cryptosystem requires the solution of a such problem, as in the case of RSA, which is based on the factorization problem in number theory. This paper proposes a symmetric-key cryptosystem named Rotating Disk Cryptosystem and is denoted as RDC used for encryption and decryption of text as well as images which is based on chaotic function and the factorization problem.
Of Ulti, 'hajano', and "Matachetar otanetak datam"

Strategies of hiding information over communication media have long been an interest of CSCW and related communities. Most of the studies in this area have focused on various computational means of data protection and their vulnerabilities, and occasionally on social practices situated in the West. However, communities in the Global South often have a rich trove of vernacular arts and crafts of hiding information that might be leveraged to design novel kinds of privacy-preserving technologies. In this paper, we present findings from our three-month long original ethnographic work with various communities in Dhaka, Bangladesh that reveal a wide range of culturally embedded techniques of hiding confidential and sensitive information from their 'others'. Our analysis demonstrates the dynamic nature of these techniques, the learning process associated with them, and their deep relationship with contextual politics that these communities are embedded in. We further connect our findings to the broader interests of CSCW around otherness, ethics, and democracy, and also discuss their implications for design.
A class of functions with low-valued Walsh spectrum

Let l≡3(mod4), l≠3, be a prime, N=l2, f=l(l−1)2 the multiplicative order of a prime p modulo N, and q=pf. In this paper, we investigate the Walsh spectrum of the monomial functions f(x)=Trq∕p(xq−1l2) in index two case. In special, we explicitly present the value distribution of the Walsh transform of f(x) if 1+l=4ph, where h is the class number of Q(−l).
Instructional Designs and Educational Technologies Within Augmented Reality Transmedia Storytelling: IDET ARTS

A mixed methodology study measuring the usage rates of Augmented Reality (AR) information overlay mapping time-on-task in informal learning environments at a local art museum. This study investigates whether AR systems provide a uniquely beneficial learning context due to AR’s native function to overlay information onto physical spaces at an art museum and the impact on participant perceived self-efficacy and overall engagement within the AR-enhanced environment. Participants also took part in an open-ended survey within the application. The quantitative data collected suggest that participants were highly engaged and utilized the application at the art museum extensively (N = 143); the qualitative results indicated that AR participants were exceedingly motivated and perceived an enhanced sense of engagement with the exhibit sites.
Application of Geocognitive Technologies to Basin &amp; Petroleum System Analyses

Abstract Objectives/Scope When dealing with new exploration areas, basin geologists face the challenge of collecting relevant information from all available sources. This include a number of structured commercial databases, but also large corpora of technical documents in which an invaluable amount of information is scattered across. Even if assisted by search tools to filter the documents of interest, extracting information requires a human effort in reading and understanding the documents. Methods, Procedures, Process Eni and IBM developed a cognitive engine exploiting a deep learning approach to scan documents searching for basin geology concepts, extracting information about petroleum system elements (e.g. formation name, geological age and lithology of source rocks, reservoirs and seals) and enabling basin geologists to perform automated queries to collect all the information related to a basin of interest. The collected information is fully referenced to the original paragraphs, tables or pictures of the document in which it was discovered, therefore enabling to validate the robustness of the results. Results, Observations, Conclusions The cognitive engine has been integrated within an application which enables to build a graphical representation of the Petroleum System Event Charts of the basin, integrating the information extracted from commercial databases, the results from the cognitive engine and the manual input from the geologist. The quality of the results from the cognitive engine has been evaluated using a commercial database which provides both tabular data about basins and detailed pdf reports. The cognitive engine has been trained on the pdf reports alone, and the results have been compared with the tabular content of the database, representing the ground truth. The cognitive engine succeeded in identifying the right formations, lithologies and geological ages of the petroleum systems with an accuracy in the range 75% – 90%. Novel/Additive Information The cognitive engine is built with highly innovative technologies, combining the data driven capabilities of deep neural networks with more traditional natural language processing methods based on ontologies. Documents are processed with a three-step approach. In the first step, convolutional neural networks (CNN) are used to recognize the structural elements within a technical paper (e.g. title, authors, paragraphs, figures, tables, references) and to convert a complex pdf structure into a clean sequence of text, which can be analyzed. In the second step, concepts are extracted from these processed documents using extractors, NLP annotators (based on recurrent neural networks) and aggregators. Finally, the joint use of the results from the deep learning tools and the provided ontologies are used to build a knowledge graph, which links together all the discovered entities and their relationships. A fit-for-purpose high efficient graph database has been developed so that the graph can be traversed with full flexibility, collecting all the concepts needed for basin geology studies.
Crime and Victimization in Cyberspace

The development of information technologies in recent years has transformed our society into a “hyper-connected space” in which the pitfalls, the risks, as well as the damages to the victims have grown exponentially. Identity theft, hacking, information piracy, threats to data integrity, on-line scams, or CEO fraud are the commonplace keywords that are part of the internet of things. Cybercrime can cause serious harm and long-term effects, whether the victims are individuals or companies. It is important to address the definition of “cybercrime,” since the term itself refers to a harmful behavior that is in some way related to a single computer or to a computer network and examine the main types of computer crimes in order to understand which countermeasures can be implemented to counteract these phenomena where the human factor is the fundamental component to promote the concept of “conscious attention” as a necessary resource to limit the risks of “cyber victimization.”
Wound image segmentation using clustering based algorithms

Abstract Classic methods of measurement and analysis of the wounds on the images are very time consuming and inaccurate. Automation of this process will improve measurement accuracy and speed up the process. Research is aimed to create an algorithm based on machine learning for automated segmentation based on clustering algorithms Methods. Algorithms used: SLIC (Simple Linear Iterative Clustering), Deep Embedded Clustering (that is based on artificial neural networks and k-means). Because of insufficient amount of labeled data, classification with artificial neural networks can't reach good results. Clustering, on the other hand is an unsupervised learning technique and doesn't need human interaction. Combination of traditional clustering methods for image segmentation with artificial neural networks leads to combination of advantages of both of them. Preliminary step to adapt Deep Embedded Clustering to work with bio-medical images is introduced and is based on SLIC algorithm for image segmentation. Segmentation with this method, after model training, leads to better results than with traditional SLIC.