Reviewerly/exHarmony huggingface dataset:
Two-Stage OTA Sizing Optimization Using Bio-Inspired Algorithms

The analog part of a mixed-signal integrated circuit represents a great amount of the circuit sizing effort. It is necessary to size each device separately and, in cases with several variables, the design space becomes quite large. The analog integrated circuit sizing can be modeled as an optimization problem and solved by optimization heuristics. In this work, we compare three bio-inspired heuristics to size a two-stage CMOS Miller operational transconductance amplifier: Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Firefly Algorithm (FA). The goal is to evaluate the applicability of these heuristics for the analog sizing problem and to determine the best configuration of the algorithms parameters for optimizing performance of the generated circuit, mainly power consumption and silicon area. Results show that PSO and CS are more suitable to find optimized solutions, while FA presents less efficient exploration of the design space. Although PSO is faster and generates good solutions, the best overall solution was achieved with CS algorithm.
Global collaboration through local interaction in competitive learning

Feature maps, that preserve the global topology of arbitrary datasets, can be formed by self-organizing competing agents. So far, it has been presumed that global interaction of agents is necessary for this process. We establish that this is not the case, and that global topology can be uncovered through strictly local interactions. Enforcing uniformity of map quality across all agents results in an algorithm that is able to consistently uncover the global topology of diversely challenging datasets. The applicability and scalability of this approach is further tested on a large point cloud dataset, revealing a linear relation between map training time and size. The presented work not only reduces algorithmic complexity but also constitutes first step towards a distributed self organizing map.
Tamper Detection in MongoDB and CouchDB Database

Tamper detection is the ability of a software or a device to sense that an active attempt to fail the device integrity or data associated with the system, the detection of any attack or threat may enable the device to initiate the defensive actions accordingly. In NoSQL database, detecting the tamper is not simple as in relational database. This paper mainly focuses on the database security. Whether the data is secured or not? or the data is tampered? To detect any tamper in NoSQL database, the model is been built which uses the logs, port connections as an input and set the authorization and authentication level which helps to retrieve logs accordingly. The paper generates hash as well as checksum of NoSQL database files and log files for checking data integrity, and we have created a simple task manager which stores data in real time, which shows tamper in database.
MapReduce Scheduler to Minimize the Size of Intermediate Data in Shuffle Phase

Hadoop MapReduce is one of the cost-effective ways for processing huge data in this decade. Despite it is opensource, setting up Hadoop on-premise is not affordable for small-scale businesses and research entities. Therefore, consuming Hadoop MapReduce as a service from cloud is on increasing pace as it is scalable on-demand and based on pay-per-use model. In such multi-tenant environment, virtual bandwidth is an expensive commodity and co-located virtual machines race each other to make use of the bandwidth. A study shows that 26%-70% of MapReduce job latency is due to shuffle phase in MapReduce execution sequence. Primary expectation of a typical cloud user is to minimize the service usage cost. Allocating less bandwidth to the service costs less but increases job latency, consequently increases makespan. This trade-off is compromised by minimizing the amount of intermediate data generated in shuffle phase at application level. To achieve this, we proposed Time Sharing MapReduce Job Scheduler to minimize the amount of intermediate data; thus, service cost is cut down. As a by-product, MapReduce job latency and makespan also are improved. Result shows that our proposed model minimized the size of intermediate data upto 62.1%, when compared to the classical schedulers with combiners.
SCR-QRNG: Side-Channel Resistant Design using Quantum Random Number Generator

Random number generators play a pivotal role in generating security primitives, e.g., encryption keys, nonces, initial vectors, and random masking for side-channel countermeasures. A quantum entropy source based on radioactive isotope decay can be exploited to generate random numbers with sufficient entropy. If a deterministic random bit generator (DRBG) is combined for post-processing, throughput of the quantum random number generator (QRNG) can be improved. However, general DRBGs are susceptible to side-channel attacks. In this paper, we propose a framework called SCR-QRNG framework, which offers Side-Channel Resistant primitives using QRNG. The QRNG provides sources of randomness for modulating the clock frequency of a DRBG to obfuscate side-channel leakages, and to generate unbiased random numbers for security primitives. The QRNG has robustness against power side-channel attacks and is in compliance with NIST SP 800-22/90B and BSI AIS 31. We fabricate a quantum entropy chip, and implement a PCB module for a random frequency clock generator and a side-channel resistant QRNG on an FPGA.
Intermediated Reality

This thesis explores technical solutions to address the gap between the virtual and physical worlds towards photo-realistic interactive Augmented Reality (AR). As mobile network bandwidth increases, latencies reduce and graphics processing power becomes more efficient, this work tackles the challenge of convincingly re-animating physical objects remotely through digital displays. A framework for distributed Intermediated Reality (IR) communication is introduced, and forms the structure of the constituent methods developed for seamless collaboration through the remote possession of entities that come to life in mobile AR.
Geostatistics and Spatial Structure in Archaeology

Geostatistics offer a framework for characterising the spatial structure of archaeological variables and for predicting their values at locations where no samples are available. They can also guide sampling strategies by relating spatial variation to sample spacing. In this chapter we review the principles of geostatistics and we consider, via case studies, how recent methodological developments can benefit archaeological applications. The case studies focus on (a) the use of geostatistics for exploring and mapping elevation data collected over an early Christian rath in Northern Ireland and (b) the coins of the Romano-British usurper Allectus. The studies demonstrate how spatial variation can be characterised and used to derive maps. The chapter concludes by arguing that the potential of geostatistics in archaeology is considerable, and recent methodological developments will open up new possibilities.
WHEN NATIONAL EXAMINATION NO LONGER DETERMINING GRADUATION, WILL STUDENTS ACCOMPLISH IT SERIOUSLY?

The National Examination in Indonesia, which began in 2015, was no longer a determinant of student graduation at the end of the education level. Utilization of the examination results will affect the seriousness of the students in working on it. This study aims to describe the level of seriousness of 12th grade students in working on National Examinations, when it no longer a determinant of graduation by examining teachers and school principals’ point of view. This research is a qualitative study by using the phenomenology approach. Data was collected using focus group discussions (FGD) conducted 3 times in 3 provinces in Indonesia, consisted of Yogyakarta, South Kalimantan and East Nusa Tenggara. FGD participants were 9 high school principals, 9 vice principal of curriculum, and 54 teachers who teach subjects that are tested on national exam. In each province, three schools were selected, consisted of two public schools, and one private school. Data were analyzed using data reduction stages, to find relationships between themes, and verification. The results showed that the motivation of 12th grade students in preparing National Examination were reduced. They no longer focused on preparing the exams, otherwise they preparing themselves for higher education. Furthermore, students tended to select subjects that were easier and the teacher should motivate students harder to prepare for the National Examination.
Construct of Sarcasm on Social Media Platform

The basic idea behind machine learning-based systems, or artificial intelligence in general, is mimicking how humans operate. This idea is particularly true for our problem, sarcasm detection on social networking sites (SNSs). Therefore, before proceeding to build a system that can detect sarcasm on SNSs, we attempt to understand how humans do the same. Many studies propose approaches based on personal experience and word-level definition of sarcasm. However, in this paper, we aim to find more general themes that are typical with users while detecting and expressing sarcasm on SNSs through a qualitative study to build a more effective sarcasm detection model.
A Fast Hybrid Feature Selection Method

To confront with high dimensional datasets, several feature selection schemes have been suggested in three types of wrapper, filter, and hybrid. Hybrid feature selection methods adopt both filter and wrapper approaches by compromising between the computational complexity and efficiency. In this paper, we proposed a new hybrid feature selection method, in which in the filter stage the features are ranked according to their relevance. Instead of running the wrapper on all the features, we use a split-to-blocks technique and show that block size has a considerable impact on performance. A sequential forward selection (SFS) method was applied to the ranked blocks of features in order to find the most relevant features. The proposed method rapidly eliminates a large number of irrelevant features in its ranking stage, and then different block sizes were evaluated in the wrapper phase by choosing a proper block size using SFS. It causes this method to have a low time complexity, despite the good results. Hybrid methods consist of components that have different criteria for them. we compare and analyze different criteria. To show the effectiveness of the proposed method, state-of-the-art hybrid feature selection methods like re-Ranking, IGIS, and IGIS+ were implemented and their classification accuracies, over the known benchmarks, were computed using the K-nearest neighbor (KNN) and decision tree classifiers. Applying statistical tests to the compared results supports the superiority of the proposed method to the counterparts.
23 из 217 возможных тетрахотомий, которые делят множество из 16 типов на четыре «однородных» подмножества по четыре типа в каждом

This article is the twenty-first in a series of publications devoted to the pedagogical application of the mathematical methods of socionics. There are 217 possible tetrachotomies that divide the set of 16 types of information metabolisms into four “homogeneous” subsets of four types. We consider the 23 tetrachotomies that can be generated with the help of left and right cosets of fourth order intertype relationship subgroups. Only these 23 tetrachotomies of the possible 217 can be organized by using Augustinavichiute?Reynin traits and Jung?Minaiev traits.
Unmanned Aircraft Systems Conflict Resolution Using Return-to-Course Maneuver

This Paper considers the problem of conflict resolution involving two constant-speed aircraft moving in a horizontal plane. A planar deconflicting maneuver that resolves the conflict with the desir...
Stream Reasoning to Improve Decision-Making in Cognitive Systems

Cognitive Vision Systems have gained a lot of interest from industry and academia recently, due to their potential to revolutionize human life as they are designed to work under complex scenes, adapting to a range of unforeseen situations, changing accordingly to new scenarios and exhibiting prospective behavior. The combination of these properties aims to mimic the human capabilities and create more intelligent and efficient environments. Contextual information plays an important role when the objective is to reason such as humans do, as it can make the difference between achieving a weak, generalized set of outputs and a clear, target and confident understanding of a given situation. Nevertheless, dealing with contextual information still remains a challenge in cognitive systems applications due to the complexity of reasoning about it in real time in a flexible but yet efficient way. In this paper, we enrich a cognitive system with contextual information coming from different sensors and propose the use of stream reasoning to integrate/process all these data in real time, and provide a better understanding of the situation in analysis, therefore improving decision-making. The proposed approach has been applied to a Cognitive Vision System for Hazard Control (CVP-HC) which is based on Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) and has been designed to ensure that workers remain safe and compliant with Health and Safety policy for use of Personal Protective Equipment (PPE).
Identifikasi Wajah Dengan Segmentasi Warna Kulit Menggunakan Metode Viola Jones

Abstract - Face detection (face detection) is one of the initial steps that is very important before the face recognition process (face recognition). Face detection is the detection of objects in the form of faces in which there are special features that represent the shape of faces in general. One method of face detection is the Viola Jones method. Viola Jones method is used to detect faces and skin color segmentation, test data processing using Matlab and capture on a Smartphone. The test is carried out at normal light intensity with a predetermined distance and face position. The results of this study indicate the level of accuracy of detection of face image variations in the position of face images facing forward (frontal), sideways left and right 45̊. But it has a weakness of this face detection system that is unable to determine faces in images that have faces that are not upright (tilted) or not frontal (facing sideways) at a 90̊ angle. Face position that is upright / not upright will determine the success of this face detection. The level of identification of the Viola Jones simulation was 100% with 4 images consisting of 3 boys and 1 girl.
A Rapid Prototyping Approach for High Performance Density-Based Clustering

Big Data has significantly increased the dependence of data analytics community on High Performance Computing (HPC) systems. However, efficiently programming an HPC system is still a tedious task requiring specialized skills in parallelization and the use of platform-specific languages as well as mechanisms. We present a framework for quickly prototyping new/existing density-based clustering algorithms while obtaining low running times and high speedups via automatic parallelization. The user is required only to specify the sequential algorithm in a Domain Specific Language (DSL) for clustering at a very high level of abstraction. The parallelizing compiler for the DSL does the rest to leverage distributed systems - in particular, typical scale-out clusters made of commodity hardware. Our approach is based on recurring, parallelizable programming patterns known as Kernels, which are identified and parallelized by the compiler. We demonstrate the ease of programming and scalable performance for DBSCAN, SNN, and RECOME algorithms. We also establish that the proposed approach can achieve performance comparable to state-of-the-art manually parallelized implementations while requiring minimal programming effort that is several orders of magnitude smaller than those required on other parallel platforms like MPI/Spark.
An Image Visual Cryptography Using Double Encryption and Hiding Technology

In this paper, an image visual cryptography based on double encryption and hiding technology is proposed. The secret plain-image is scrambled, added under modulo operation and cyclically left-shifted to obtain the pre-encrypted image. Carrier image is also encrypted by scrambling and diffusion, and then the pre-encrypted image is embedded into the encrypted carrier image by lifting wavelet transform (LWT) and QR decomposition. As a result, the final encrypted carrier image with encrypted secret image is generated. After realizing double visual encryption and hiding technology, attacker cannot know that the meaningless of encrypted carrier image containing an encrypted secret image. Chaotic sequences used in the scrambling and diffusion stages are generated by a 2D logistic-sine-coupling map (2D-LSCM), which is related to secret plain-image and can resist effectively the known-plaintext and chosen-plaintext attacks. Experimental results show that the proposed algorithm has high security and good application prospects.
Thesaurus Approach to the Study of Socio-Cultural Threats in Historical Dynamics

The article considers the application of the thesaurus approach to the analysis of socio-cultural threats that existed in previous historical periods (since the war with Napoleon of 1812) and exist nowadays. The authors propose the idea of a multidisciplinary, polythematic, multidiscursive thesaurus, based on the advanced expertise in thesauri building of the last decades in Russia and abroad. The methodology of the research is based on the principles of the cognitive paradigm: interdisciplinarity, conceptual analysis of the material, verifiability of the data received for various types of discourse and different historical periods. The main results of the research. It is demonstrated that within a cognitive approach thesaurus organization of knowledge about socio-cultural threats forms a cognitive macro-category, representing mental models of socio-cultural threats in different historical periods. The principles, that formed the basis for the creation of sociocultural threats thesaurus, are described. The conclusion is made about the predictive potential of the thesaurus with the appearance of new sociocultural threats in the 21st century. That defines the field of the thesaurus application.
Towards Nature-Inspired Intelligence Search for Optimization of Multi-Dimensional Feature Selection

Since news document includes complex and high-dimensional feature, the conventional feature selection scheme becomes inefficient and ineffective for feature engineering in text mining field. Meanwhile, the nature-inspired intelligence (NII) is advanced significantly for solving the complex problem such as multi-dimensional feature selection. NII is the category of non-deterministic that is used the meta-heuristic search capability which consists of a group of search agents for exploring the feasible region based on both randomization and some rules. In this paper, Wolf intelligence-based optimization of multi-dimensional feature selection approach (WI-OMFS) is proposed for news document classification and compared the results of performance to conventional search-based feature selection approach. The performance (accuracy) is used as fitness function. Moreover, various measurements of performance and computation complexity are also used to evaluate the proposed system. According to the experimental results, WI-OMFS can provide robustness for performance according to the objective function.
Lower Bound for the Class Number of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>ℚ</mml:mi><mml:mfenced open="(" close=")" separators="|"><mml:mrow><mml:msqrt><mml:mrow><mml:msup><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mo>+</mml:mo><mml:mn>4</mml:mn></mml:mrow></mml:msqrt></mml:mrow></mml:mfenced></mml:mrow></mml:math>

In this paper, we give an explicit lower bound for the class number of real quadratic field <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>ℚ</mml:mi><mml:mfenced open="(" close=")" separators="|"><mml:mrow><mml:msqrt><mml:mi>d</mml:mi></mml:msqrt></mml:mrow></mml:mfenced></mml:mrow></mml:math>, where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:mi>d</mml:mi><mml:mo>=</mml:mo><mml:msup><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mo>+</mml:mo><mml:mn>4</mml:mn></mml:mrow></mml:math> is a square-free integer, using <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mrow><mml:mtext> </mml:mtext><mml:mi>ω</mml:mi><mml:mfenced open="(" close=")" separators="|"><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:mfenced></mml:mrow></mml:math> which is the number of odd prime divisors of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mi>n</mml:mi></mml:math>.
An Optimized Method of Target Tracking Based on Correlation Matching and Kalman Filter

Correlation matching is an easy method for target image tracking. But global searching is large amounts of calculation, and target will be lost when shaded. The combining use of correlation matching and Kalman filter will predict a possible position in the next frame which reduces the calculation time and prevents from losing target. In this paper, this combined algorithm is improved by using a self-updated matching template and a self-adapted detecting window. This method can well overcome the disturbing by spinning, scaling, or transforming of the target and indeterminacy of movement. Because of these advantages, this algorithm can be used in target tracking system on spacecraft.
A Privacy Threat Model in XR Applications

A new visualization user experience is expected to be empowered with XR technologies. XR accommodates a wide range of computerized reality technology, such as AR (Augmented Reality), MR (Mixed Reality), and VR (Virtual Reality). XR is providing more immerse and entertaining user experience. The number of devices increases and it is leveraging a wide range of applications. As consumer XR applications start to grow, it is important to understand the privacy threats. The author describes privacy threats in XR. First, the author discusses privacy threat elements. Second, the author describes a 3-dimensional model of privacy threats in XR. It clarifies the XR-specific privacy threats in addition to other sensor API-enabled applications.
MuSe: Multi-Sensor Integration Strategies Applied to Sequential Monte Carlo Methods

Recursive state estimation is often used to estimate a probability density function of a specific state, e.g. a robot's pose, over time. Compared to Kalman filters, Sequential Monte Carlo (SMC) methods are less constrained in regard to state propagation and update model definition, which makes it easier to implement any suitable problem. In this work, we present a generic Sequential Monte Carlo framework, which uses abstract formulations for importance weighting, propagation and resampling and provides an independent core algorithm that is usable for any problem instantiation, such that diverse SMC problems can be implemented easily and quickly, since the basic algorithms are already provided. Current applications include 2D localization, 2D tracking in a SLAM system and contact point localization on a manipulator surface. Further, we introduce concepts to deal with data input synchronization and fair execution of different weighting models, which makes it possible to incorporate data from as many update sources, e.g. sensors, as desired. As a typical application scenario, we provide a plugin-based and hence easily extensible instantiation for 2D localization and demonstrate the capabilities of our framework and methods based on a well-known dataset.