Reviewerly/exHarmony huggingface dataset:
On Lempel-Ziv Decompression in Small Space

Lempel-Ziv (LZ77) parsing is a powerful tool for data compression that has been the subject of intense research in the past 40 years and is now used in popular and widely-used compression software and as part of larger software systems. In this paper we study algorithms to efficiently decompress strings from the LZ parsing that use working memory proportional to the size, z, of the parsing itself, not that of the output string, n, as is the usual case. The only work we are aware of on this problem is recent and due to Bille~et~al. who describe an algorithm using O(n log^δσ time and O(z log^1 - δσ) space for any 0 ≤ δ ≤ 1. We provide the first implementation and experimental analysis of Bille~et~al.'s approach. Our results show that this approach, when implemented as described, is extremely slow in practice compared to the naive decompression algorithm, and uses lots of space. To remedy this we introduce several novel optimizations that drastically improve performance and lead to relevant space-time tradeoffs in practice on all datasets we tested.
Benchmarking Tool for Modern Distributed Stream Processing Engines

There is an upsurge in the usage and adaptation of streaming applications in the recent years by both industry and academia. At the core of these applications is streaming data processing engines that perform resource management and allocation in order to support continuous track of queries over distributed data streams. Several stream processing engines exists to handle these distributed streaming applications. In this paper, we present different challenges of the stream processing systems, in particular to stateful operators and implement Linear Road benchmark to examine the characteristic and performance metrics of the streaming system, in particular Apache Flink. Furthermore, we examine that Apache Flink can be used as a core for an efficient Linear Road application implementation for distributed environments without breaching the SLA requirements of the application.
Detection on Abnormal Usage of Spectrum by Electromagnetic Data Mining

With the rapid development of radio services and monitoring facilities, the application of spectrum monitoring steps into the big data era. As a limited resource, the electromagnetic spectrum needs to be authorized for use. According to the spatiotemporal periodic characteristic of radio frequency usage, the new method is put forward for quickly detecting abnormal spectrum usage and abnormal electromagnetic targets (appearing in abnormal time or space). To deal with frequency spectrum data, the piecewise modeling algorithm based on Mahalanobis distance is proposed to find the abnormities in them, which can effectively detect the differences between two spectrums in real time. To deal with the electromagnetic target positioning data, the outlier detection and non-outlier classification algorithm based on Euclidean distance is proposed. Compared with the spatiotemporal data of historical target points, the unknown target points which appear in abnormal time or space can be found in real time. Their validity and applicability are verified by the real monitoring data collected by CS-S05F. It is of great significance for the detection of targets which use spectrum resources illegally.
Modeling the Game of Go by Ising Hamiltonian, Deep Belief Networks and Common Fate Graphs

Three different models of the game of Go are developed by establishing an analogy between this game and physical systems susceptible to analysis under the well-known Ising model in two dimensions. The Ising Hamiltonian is adapted to measure the energy of the Go boards generated by the interaction of the game pieces (stones) as players make their moves in an attempt to control the board or to capture rival stones. The proposed models are increasingly complex. The first or Atomic-Go model consists of the straightforward measurement of local energy employing the adapted Ising Hamiltonian. The second or Generative Atomic-Go model employs a Deep Belief Network (a generative graphical model popular in machine learning) to generate board configurations and compensate for the lack of information in mostly-empty boards. The third or Molecular-Go model incorporates Common Fate Graphs, which are an alternative representation of the Go board that offers advantages in pattern analysis. The simulated games between different Go playing systems were used to test whether the models are able to capture the energy changes produced by moves between players of different skills. The results indicate that the latter two models reflect said energy differences correctly. These positive results encourage further development of analysis tools based on the techniques discussed.
An Improved Apriori Algorithm Research in Massive Data Environment

Smart grid computing environment is an information platform that has lots of production data, data management, and the real-time and non real-time data. Under such massive data environment, the classic Apriori algorithm of mining association rules has a significant performance bottleneck. After analyzing the Apriori algorithm, the MapReduce programming model is used to realize the parallel Apriori algorithm. In order to improve the mining efficiency further, auxiliary tables and attribute columns are added and parallel strategy is improved in the process of candidate itemsets generation. Simulation experiments show that the improved Apriori algorithm can effectively reduce the algorithm execution time and improve the efficiency of data mining under the massive data environment.
Agile System Architecture in Large Organizations: An Experience Report from Volvo Cars

System architecture is considered a fundamental element in the development of large and complex electrical systems. This is because a good architecture assures that the system fulfils and will fulfil after future upgrades all required quality demands. In addition to the well-designed architecture, the development of such systems today requires agile methods, especially when it comes to the development of software. This is mostly to assure quality and fast innovations cycles reaching customers early and directly incorporating their feedback into the development process. In order to coordinate the work of many agile teams in large organizations, it is usually necessary to employ frameworks for scaling agile development. One such frameworks is SAFe (Scaled Agile Framework) proven to work in many industries. Despite dedicating roles to the development of the system architecture at all organizational levels (e.g., System and Solution Architects), SAFe does not recognize the need for holistic architectural teams responsible for assuring that all architectural decisions are made in the best interest of the entire system. In this paper, we discuss the organization, responsibility and collaboration between such architecture teams - the Complete System Architects - and System/Solution Architects in SAFe. The results are based on the ongoing agile transformation at Volvo Cars based on SAFe.
FamilyLog: Monitoring Family Mealtime Activities by Mobile Devices

By learning from the existing family mealtime activities, family members can be motivated to make the positive changes towards better relationships, which are important for the physical and mental health of children. Moreover, the details of family mealtime activities provide rich information for study in sociology and culture. This paper presents FamilyLog - a practical system to log family mealtime activities using smartphones and smartwatches. FamilyLog automatically detects and logs details of activities during the mealtime, including occurrence and duration of meal, conversations, participants, TV viewing, etc., in an unobtrusive manner. Based on the sensor data collected from real families, we carefully design robust yet lightweight signal features from a set of complex activities during the meal, including clattering sound, arm gestures of eating, human voice, TV sound, etc. Moreover, FamilyLog opportunistically fuses data from built-in sensors of multiple mobile devices available in a family with a CRFs-based classifier. To evaluate the real-world performance of FamilyLog, we perform extensive experiments that consist of 77 days of sensor data from 37 subjects in 8 families with children. FamilyLog can detect those events with high accuracy across different families and home environments.
SURF Based Copy Move Forgery Detection Using kNN Mapping

Digital images can be edited with the help of photo editing tools to improve or enhance the image quality. On the other hand, digital images can also be subject to manipulations which can alter the visual information being conveyed by the image. The forged images can also be used to spread false information through various media platforms and in some cases may be surreptitiously used as false evidence in a court of law. Therefore, it is crucial to test the authenticity of such images and ensure that it does not spread falsified information. One of the most common types of forgery being used today is copy-move forgery in which one part of the image is copied and placed over another part of the same image in order to either conceal certain details or multiply certain features seen in the original image. This work introduces a method of detecting copy-move forgery in digital images using speeded-up robust features (SURF) to extract keypoints from the image and then uses k-nearest neighbor (kNN) training and mapping to yield accurate matches. The SURF algorithm is capable of performing equally or even exceed the more widely accepted SIFT-based counterparts in terms of ensuring distinctive features, reproducibility, and robustness. As a result, this technique ensures a robust detection of copy move forgery while ensuring lower computational costs compared to the SIFT-based techniques used for the same purpose.
A Wearable Intraoral System for Speech Therapy using Real-Time Closed-Loop Artificial Sensory Feedback to the Tongue

This paper describes a wearable intraoral system for speech therapy using artificial sensory feedback timed with the undesired tongue movement. The system has been implemented as a custom-made palatal retainer, which includes two optical distance sensors and two stimulators to provide error feedback to the tongue. Subjects wore the palatal retainer to test the system performance. By testing the system with phonetic targets /t/ and /d/, we showed that the system was able to detect the tongue movement during pronunciation. The system was also tested to see if it can help a non-native English speaker with Hindi mother tongue to correct the pronunciation of alveolar consonant /t/. The subject was asked to read multiple words containing /t/ consonant repeatedly, with distracting words in between to minimize the involvement of cognition or intentional correction. Test results indicated that the error feedback via stimulation helped the subject to move his tongue forward towards the alveolar ridge in pronouncing /t/. The result justified our hypothesis that intrinsic sensory feedback can be an effective way to train non-native speakers to correct their pronunciation.
Feasibility analysis of sensor modalities to control a robot with eye and head movements for assistive tasks

Assistive robotics has offered a way for people with severe motor disabilities (i. e. tetraplegics) to perform every day tasks without help. New sensor modalities to control a robot system are investigated within this work to enable tetraplegics to gain more autonomy in everyday life. In this work several modalities to capture information related to the user are tested and compared. The five sensor modalities, electrooculography, video-based eye tracking, MARG sensors, video-based head tracking and electromyography of the posterior auricular muscle, can be used to control a robot hands-free. It is proposed to use movements of the head as continuous control and eye movements as discrete event control. The tests show that the MARG sensors are most reliable to track head movements and eye tracking glasses to capture movements of the eyes.
VOLTimebank

With the process of the population aging, the problem of the aged in the urban is becoming increasingly prominent in China, especially the shortage of pension resources. In order to solve this problem, we proposed Volunteer time bank (VOLTimebank) based on blockchain, a novel distributed ledger that allows transactions without any third party organization. VOLTimebank is a record management system to handle volunteer service records. The system provides a channel for volunteers to serve the elderly and gives volunteers a way to exchange the services they can offer today with the services that they hope to get in the future. And a Delegated Proof of Stake (DPOS) consensus mechanism is used to select auditors, whose main task is to audit volunteers' services. Each holder of the (volunteer medals (VOLMedals) has the right to vote for auditors and be voted to be an auditor. As a proof of concept, we present a prototype implementation of VOLTimebank via smart contracts based on Ethereum. VOLTimebank is a true realization of "Serve today, enjoy tomorrow", encouraging more people to take part in the volunteer services, for the elderly and for themselves. This paper is intended to illustrate the ideas and methods of VOLTimebank.
Vehicular Obstruction Detection In The Zebra Lane Using Computer Vision

Computer Vision and Image Analysis are used in researches with an objective of extracting information from a set of scenarios. Multiple researches with varying objectives like vehicle speed detection, traffic density estimation, vehicle counting, or in general, observation of behaviors of multiple objects, have been applying Computer Vision. This research paper is about utilizing Computer Vision for obstruction detection by observing temporal state of vehicles situated in a pedestrian crossing lane. The researchers gathered data by taking videos of real traffic in a road containing a pedestrian crossing lane (PCL). The method starts with a pre-processing phase wherein the image was de-noised, converted to grayscale and derived the Image Binarization, and establishment of the PCL for region of interest (ROI). Connected components are extracted then assigned its own structure with corresponding properties called `track'. Tracks are monitored by using Kalman Filter and Hungarian Algorithm. Then, Ray-Casting algorithm is applied to determine if an object violates the traffic rule. For violators, a snapshot will be taken and determine the license plate. Based on the result, True Positive Rate of 65.28% and True Negative Rate of 98.26% were obtained.
Deep Supervised Hashing with Spherical Embedding

Deep hashing approaches are widely applied to approximate nearest neighbor search for large-scale image retrieval. We propose Spherical Deep Supervised Hashing (SDSH), a new supervised deep hashing approach to learn compact binary codes. The goal of SDSH is to go beyond learning similarity preserving codes, by encouraging them to also be balanced and to maximize the mean average precision. This is enabled by advocating the use of a different relaxation method, allowing the learning of a spherical embedding, which overcomes the challenge of maintaining the learning problem well-posed without the need to add extra binarizing priors. This allows the formulation of a general triplet loss framework, with the introduction of the spring loss for learning balanced codes, and of the ability to learn an embedding quantization that maximizes the mean average precision. Extensive experiments demonstrate that the approach compares favorably with the state-of-the-art while providing significant performance increase at more compact code sizes.
BitFlow: Enabling real‐time cash‐flow evaluations through blockchain

Summary Disbursement registration has always been a cumbersome, opaque, and inefficient process, up to the point that most businesses perform cash‐flow evaluations only on a quarterly basis. We believe that automatic cash‐flow evaluations can actively mitigate these issues. In this paper, we present BitFlow, a blockchain‐based architecture that provides complete cash‐flow transparency and diminishes the probability of undetected frauds through the BitKrone, a non‐volatile cryptocurrency that maps to the Danish Krone ( DKK ). We show that confidentiality can be effectively achieved on a permissionless blockchain using Zero‐Knowledge proofs, ensuring verifiable transfers and automatic evaluations. Furthermore, we discuss several experiments to evaluate our proposal, in particular, the impact that confidential transactions have on the whole system, in terms of responsiveness and from an economical expenditure perspective.
Approaches to the Development of the ICT Competence Standard in the System of Research-Based Training for the Future Specialist of Social Sphere in Ukraine

The article is devoted to the study of the peculiarities of applying the recommendations of the UNESCO project “The Structure of ICT Competence of Teachers” in the system of preparation of future specialists of the social sphere. The authors formulate the key approaches to building of the standard of ICT competence of future specialists of the social sphere on the basis of elaboration of the basic concepts and approaches to the definition of the principles of a competent approach in education. The theoretical component of the construction educational process of the future specialists of the social sphere on the basis of research-based training principles is substantiated by developing principles, methods, characteristics, key approaches, and the main types of such training. The parallels and interrelations between the training system for future specialists in the social sphere on the research-based training principles and the peculiarities of the development of ICT competencies are based on the inclusion into the professional training of the basic principles of the developed standard.
Carving Time and Space: A Mutual Stimulation of IT and Archaeology to Craft Multidimensional VR Data-Inspection

Interactive inspection of semantically-enriched Immersive Virtual Environments (IVEs) is designed on top of complex hierarchies combining both semantic and rendering aspects. Within Cultural Heritage, multi-dimensional IVEs represent a common solution in order to understand, query and inspect virtual reconstructions across different time-spans. The contribution presents innovative experiments about how the digital heritage record is organized and represented. Such approaches fit several scientific requirements within the Cultural Heritage domain as the annotation of the sources employed and the reasoning that are behind a reconstructive hypothesis. The methodological implications on the use of IT approaches can improve both the quality of the user fruition and the scientific content, offering, at the same time, formalisms and tools to boost the scientific research with real-time immersive representation of complex CH record. Graph-databases are already employed in such contexts since they represent one of the best solutions to address complex and dynamic relationships in highly connected datasets, also in terms of performance and scalability. A set of formalisms and replicable models for immersive inspection will be presented and discussed, addressing their interplay with a graph-based formalism specifically designed for 3D hypothesis creation and visualization in Cultural Heritage (CH) domain, targeting multitemporal scenarios - namely the Extended Matrix (EM).
Design and Application of Fog Computing Model Based on Big Data

Fog computing based on big data is a hot topic in the research of computing technology at home and abroad. With the wide application and popularity of IoT (Internet of Things), the big data generated by edge devices is exploding, and cloud computing models are becoming increasingly inadequate to meet the needs of big data processing and communication, which is mainly manifested as follows. Slow data processing, insufficient storage space, prolonged communication and many other issues. Fog computing, of which the advantage is distributed computing, namely the "de-centralized" mode calculation, is the suitable solution to solve these problems. In the IoT system, fog computing model based on big data is constructed to distribute the big data computing, storage and communication in the system to the edge device. The purpose is to make the system structure simpler, more modular and intelligent, duce network congestion, exploit advantages of edge devices and improve high quality intelligence of IoT applications, and moreover, to reduce the deployment of IoT hardware and operating costs. Taking the cloud robotics as an example, it is proposed to embed the fog computing technology in the cloud robotics system, which greatly improves the computing function of the cloud robotics system. In short, it provides theoretical support and scientific experimental basis for the informationization and intelligence of all walks of life, and its research has certain value and significance.
John Cage's <i>Silent Piece</i> and the Japanese gardening technique of <i>shakkei</i>: Formalizing Whittington's conjecture through conceptual graphs

This article examines John Cage's Silent Piece (commonly known as 4′33′′). I analyze the work through formalizing the frame conjecture of Stephen Whittington [2013. “Digging In John Cage's Garden: Cage and Ryōanji.” Malaysian Music Journal 2 (2): 12–21], which suggests that the work's spatial conditions are akin to those found in the Japanese gardening technique of shakkei, or borrowed scenery. By firstly expressing core axioms in natural language that describe the spatial conditions of Cage's Silent Piece, its' 1952 premiere as 4′33′′, the technique of shakkei, and an exemplar manifestation of shakkei at the garden Adachi Teien (Japan), I construct an ontology that enables the fundamental assumptions of Whittington's conjecture to be explicated through conceptual graphs John Sowa [1984. Conceptual Structures: Information Processing in Mind and Machine. Reading, MA: Addison-Wesley]. From the ontology, I create a number of graphs and derive first-order logic formulas that are then used for inference and further reasoning. I conclude by providing a formalism of Whittington's conjecture via a conceptual graph rule that accounts for both auditory and visual conditions that generate shakkei in the cited examples of the premiere of the Silent Piece as 4′33′′ and the garden of Adachi Teien.
Automating Mockup-Based Usability Testing on the Mobile Device

User interface usability is a very relevant aspect, especially for mobile applications. In this paper we propose a methodology for automating user interface usability testing in the early phases of the development process directly on the mobile device. Mockup interfaces are designed, then the way the designer interacts with the interface is compared with the way the end-user interacts with the same interface and the discrepancies between the two usage models are highlighted. The methodology is supported by a mobile tool named PlatoS, which captures a case of use of a mockup in terms of a sequence of actions. On the end-user side, PlatoS captures the user interaction with the mockups and performs the automatic identification of usability problems on the base of the log data. An example of evaluation of an eCommerce mobile prototypes is also presented.