Reviewerly/exHarmony huggingface dataset:
An Invisible Flow Watermarking for Traffic Tracking: A Hidden Markov Model Approach

Flow watermarking is a promising active traffic tracking technology. It helps to establish the correspondence between the sender and the receiver, by embedding watermarks into packets with certain features of active interference traffic. Existing watermarking technologies have several drawbacks, such as the vulnerability to multi-flow attacks, low robustness and invisibility. This paper proposes a new traffic tracking technique based on hidden Markov model, called Hidden Markov State-based Flow Watermarking (HMSFW). HMSFW divides the observation range, i.e., inter-packet time, as Markov states and uses the State Transition Probability Mean (STPM) as the watermark carrier. It then adjusts the STPM of a given time interval according to historical traffic characteristics. The proposed HMSFW not only improves the robustness of embedded watermarks, but also effectively enhances their invisibility. The feasibility and invisibility of HMSFW are validated via extensive experimental results.
A component-wise study of K-RVEA

Kriging assisted reference vector guided evolutionary algorithm (K-RVEA) is a recently proposed algorithm to deal with many-objective optimization problems involving computationally expensive objective functions. It employs Kriging as the a surrogate and identifies multiple infill locations based on angle penalized distance (APD) metric guided by a set of reference vectors originating from the ideal point. In this paper we investigate the performance implications of the underlying schemes, in particular (a) is APD based selection necessary since it involves an additional parameter, (b) can the full archive be used for surrogate training as opposed to fixed archive size in K-RVEA (c) can the infill solutions be further improved through angle constrained local search and finally (d) understand the limitations of single set of reference vectors and investigate the benefits of dual set of reference vectors (one originating from the ideal and the other from the Nadir). These investigations are based on the suite of standard and inverted DTLZ and WFG problems with up to 10 objectives.
Research on Information Retrieval Behavior Based on College Readers’ Cognition

Abstract This paper takes the information retrieval behaviors of college students and researchers as the research object, and discusses the reading cognition and reading behavior habits of college readers through empirical analysis. The study found that there are large differences in reading cognition and reading needs among people of different backgrounds and levels. In the reading and retrieval behavior pattern, they are different in their grasp and use of information retrieval. The main innovation of this paper is to take the central colleges and universities as an example, and analyze the information retrieval behaviors of different levels of students in colleges and universities, and summarize the regular characteristics, which has certain demonstration significance.
Sampling below the Nyquist density using spectral subtiles

An image is assumed to have a spectrum zero outside of a defined support. To avoid aliasing, the replicated support due to sampling cannot overlap. The minimum sampling density corresponding to nonoverlapping supports is the Nyquist density. Replication often necessitate gaps. Support shapes that fill the frequency plane without gaps are tiles. We offer a strategy for achieving minimum sampling density when the spectrum is confined to a subtile. Cookie cutter versions of the subtile shape, when rotated, translated, and/or flipped, result in a tile. The composite signal can have symmetric redundancies that allow reduction of the sampling density to the area of the subtile. We analyze the cases for tiles with twofold point symmetry and mirror symmetry. Two subtiles are required to construct a tile. Threefold, fourfold, and sixfold symmetry is also considered. In the cases considered, the overall sampling density in terms of the samples&#x2019; required storage is reduced to the area of the support of the subtile.
Spatio-temporal eye contact detection combining CNN and LSTM

Eye contact (mutual gaze) is fundamental for human communication and social interactions; therefore, it is studied in many fields. To support the study of eye contact, much effort has been made to develop automated eye-contact detection using image recognition techniques. In recent years, convolutional neural network (CNN) based eye-contact detection techniques are becoming popular due to their performance; however, they mainly use single frame for recognition. Eye contact is a human communication behavior, so temporal information, such as temporal eye images and facial poses, is important to increase the accuracy of eye-contact detection. We incorporate temporal information into eye-contact detection by using temporal neural network structures that combine CNNs and long short-term memory (LSTM). We tested several network combinations of CNNs and LSTM and found the best solution that uses the outputs of CNNs as well as the cell state vectors of LSTM in the fully connected layers. We prepared two types of eye contact video datasets. One dataset is based on online videos, and the other was taken by a first-person camera in assumed conversational scenarios. The results show that our method is better than the approaches that use single frames. Namely, our method performs 0.8781, while the existing method (DeepEC) performed 0.8319, in F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score.
Speech-Based Interface for Embedded Systems

In recent years, there is a rising interest in aiding people with computer technologies. One of the possible research ways is the use of speech interface for the low resource device such as mobile phones or PDAs. In this paper, we proposed a systematic way to construct a speech interface for the mobile device such as an Android phone. First, an automatic speech recognition system is built based on the Hidden Markov Model (HMM) framework. In order to understand the utterance from user in daily life, the spontaneous speech recognition is implemented. For speech generation, a personalized speech synthesis system is also included for the proposed system. Experimental results have shown that the performance of the speech recognition and synthesis achieve good performance. Therefore, the proposed speech interface could be applied to other system such as computer vision system in order to help people to perceive the environment and interact with other people in daily life.
Data dependent similarity-based machine learning approach to criminal intelligence problems

Data dependent similarity-based machine learning approach to criminal intelligence problems
Cloud Management Systems - Load Balancing Algorithms and VDI Implementation Techniques

Internet technologies have upsurge the shift from earlier organizational computing processes which were characterized by main frames, client-server models and personal computers (PCs) and have restructured the concept of computing into a phenomenon that uses infrastructure across the globe. This has given rise to IT’s reliance on heterogeneous network services and corresponding protocols. Internet technologies and virtualization are key features of cloud management systems. Cloud management system, considered as an evolutionary development, has made the provision of cloud services possible. This is premised on knowledge advancement in technological innovation regarding virtualization, automation of data-centers and network connectivity. A cloud-based VDI solution is a computing model where an end user’s system can access all the essential files and data virtually in spite of being alienated from the physical IT infrastructure. However, issues of load balancing and VDI implementation undermine the Quality of Experience (QoE) users obtain from cloud management systems. This study adopts qualitative metrical analysis and comparative metrics on load balancing algorithms to determine algorithms that ensure workload balance. This paper focuses on load balancing algorithms in cloud management systems and virtual desktop infrastructure. The paper further presents rules for implementing a load balancer in cloud management systems, techniques for VDI implementation and a corresponding matrix table.
Sistem Informasi Pengaduan Gangguan PDAM Tanah Laut Berbasis Web

Tanah Laut Regional Water Supply Company (PDAM) is engaged in the management and sales of drinking water services with about 7,848 customers which spread throughout Tanah Laut Regency. Complaints of interference carried out by the customer manually to the customer relations department by coming directly to the office of the Tanah Laut PDAM which will then be distributed to the technical and distribution transmission. Based on these problems, a web-based PDAM Tanah Laut PDAM complaint information system is built that can make it easier for customers to make complaints of disturbances and to make it easier for officers to distribute it. This can produce accurate and efficient complaints data. This web-based information system for complaints of disturbance at PDAM Tanah Laut is built with programming languages PHP, HTML, CSS and JavaScript. And designed using Entity Relationship Diagrams and Data Flow Diagrams. The results of blackbox testing show that the functionalities of the system are perform properly.
Latent Space Growing of Generative Adversarial Networks

This thesis presents a system, which builds on the Generative Adversarial Network (GAN) framework, with the focus of learning interpretable representations of data. The system is able to learn representations of data that are ordered in regards to the saliency of the attributes, in a completely unsupervised manner. The training strategy expands the latent space dimension while appropriately adding capacity to the model in a controlled way. This builds on the intuition that highly salient attributes are easiest to learn first. Empirical results on the Swiss roll dataset show that the representation is structured in regards to the saliency of the attributes when training the latent space progressively on a very simple GAN architecture. Experiments using a more complex system, trained on the CelebA dataset, scales the idea to a more interesting use case. Experiments using latent space interpolations show that our model successfully structures the latent space with respect to the saliency of the attributes, while also generating at least as real looking images and in less training time, than state-of-the-art methods. (Less)
penerapan model pembelajaran tipe teams games tournament untuk meningkatkan aktivitas dan hasil belajar peserta PAI materi Ekonomi Islam

The purpose of study is to improve student activity and learning outcomes.The subject of this research is the fourth grade students of SD Negeri Blok Sawah Kota Bandung.The research procedure consists of two research cycles.Each cycle is done in two meetings.Cycle stages include planning, execution, observation and reflection.The data collection instrument uses observation sheets, interviews, pre test and post test questions.The result that the analysis of learning activity level after the implementation of TGT method on the first cycle of the first meeting, the percentage of student activity is 66%, including enough category.In the first cycle of the second meeting of student learning activities increased to 71%, including high category but has not reached the criteria of success.Then on the second cycle of the first meeting, the percentage of student's learning activity reached 72%, it's high category.Then, in the second cycle of the second meeting, the percentage of student learning activities by 85% including very high category and has reached the criteria of success.The increase of value from pre cycle to cycle I is from 67,8 to 75,9 whereas in cycle II 78,4.Based on the above, the results can be concluded that cooperative learning model of TGT type can increase student activity and learning outcomes.By using varied and innovative learning models are expected to increase student activity and learning outcomes.
Space partition based gene expression programming for symbolic regression

For the problem of symbolic regression, we propose a novel space partition based gene expression programming (GEP) algorithm named SP-GEP, which helps GEP escape from local optimum and improves the search accuracy of GEP by letting individuals jump efficiently between segmented subspaces and preserving population diversity. It firstly partitions the space of mathematical expressions into k subspaces that are mutually exclusive. Then, in order for individuals to jump efficiently between these subspaces, it uses a subspace selection method, which combines multi-armed bandit and &#x03F5;-greedy strategy. Through experiments on a set of standard SR benchmarks, the results show that the proposed SP-GEP always keeps higher population diversity, and can find more accurate results than canonical GEPs.
Runtime analysis of abstract evolutionary search with standard crossover

The Convex Search Algorithm (CSA) is a generalization across representations of Evolutionary Algorithms (EAs) with crossover and no mutation. The Standard Evolutionary Search Algorithm (SESA) is a more accurate generalization of EAs with crossover and no mutation, using a standard two-parents crossover. This work extends the runtime analysis of the CSA on quasi-concave landscapes [4] to the SESA. We instantiate the analysis to binary strings and integer vectors endowed with the Hamming distance and the Manhattan distance. We find that the SESA requires a larger population size to converge to a global optimum; resulting in a larger runtime upper bound than the CSA. Empirical studies on LeadingOnes confirmed the existence of a smallest population size above which both algorithms are guaranteed to find the global optimum. Below this threshold, the SESA is less successful than the CSA.
UPAYA MENINGKATKAN KETERAMPILAN MENGAJAR GURU MATEMATIKA SD DALAM MERANCANG PEMBELAJARAN BERBANTUAN ALAT PERAGA MELALUI BIMBINGAN BERKELOMPOK

This research is motivated by the learning carried out by teachers is less attractive to elementary school students, resulting in student learning outcomes being low. The purpose of this study is to improve teacher teaching skills in designing teaching aids assisted by group guidance. This research is a school action research conducted at the Lubuk Batu Jaya sub-district target schools. The implementation of this study began February 18 to April 27, 2019. The subjects of this study were 4 groups of Mathematics teachers. Data analysis techniques are carried out through steps: data reduction, data presentation, verification. The results of the study showed an increase from cycle I to cycle II with the percentage and each category being 62% (sufficient) and 73% (good).
A Study on the Exercise Method for Improvement of Cranial Nerve and Cognitive Ability in Middle Aged Women

The purpose of this study was to investigate the effect of Taekwon Dance Exercise and Task Oriented Circuit Exercise on BDNF(Brain-Derived Neurotrophic Factor), NGF(Nerve Growth Factor) and cognitive ability in middle aged women. The grouping was classified into a Control Group(CG: n=10), a Taekwondo Dance Exercise Group(TDEG: n=10) and a Task Oriented Circuit Exercise Group(TOCEG: n=10).BR After the end of the experiment, statistical analysis was carried out as follows.BR In the analysis of BDNF pre and post difference significantly increased in TOCEG(p.01). After post-hoc, the NGF was higher in TOCEG than CG and TDEG(p.05). In the TDEG, there was a significant increase in the difference between the pre and post test(p.05). VLT(Verbal Learning Test) showed a significant increase in the difference between the pre and post TDEG(p.05) and TOCEG(p.01). VST[Visual Span Test (reverse direction)] showed the grouping and timing effect(p.01) and there was a significant difference between groups(p.01). After post-hoc, VST showed that the TOCEG was significantly higher than CG and TDEG(p.01). There was a significant difference between pre and post test in TDEG(p.05) and TOCEG(p.01)BR It is considered that the cognitive function is improved by the positive effect of the brain in the case of the new exercise than the repetition of the simple, easy and simple movement.
Tube Inner Circumference State Classification Optimization by Using Artificial Neural Networks, Random Forest and Support Vector Machines Algorithms

Using Artificial Neural Networks, Random Forest and Support Vector Machines algorithms to optimize Tube inner circumference state classification and accomplish the process of Incoming Quality Control (IQC) is proposed in this paper. However, the traditional classification system is usually set the threshold by the developer in the early stages. The method is time-consuming and tedious to develop the module. In modern, machine learning technology can overcome the shortcomings of tradition classification system. However, machine learning exists a lot of algorithms, such as Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and so on. And, the different algorithms may cause the different characteristics and efficiencies, so it’s necessary to compare the different algorithms at application. This paper will use a method, called grid search to find the best parameter, and compare these algorithms which has the best characteristic, efficiency and the parameter. Finally, it is found from the experimental results that the method of this paper is workable for actual dataset.
Deriving Features for Designing Ambient Media

In this study, we focus on an information presentation method to reduce cognitive load. For example, a typical method of presenting information in a useful manner is Augmented Reality, which overlays information on the real world; however, this approach causes high cognitive load because it is too loud compared to the real world. Thus, we focus on an ambient media approach proposed by Hiroshi Ishii that works on the background of human consciousness to reduce the cognitive load. We aim to find expressions that are midway between information superposition and ambient media. In this study, we focus on the visual and auditory senses, which are mainly used for information presentation, and learn the features necessary to design ambient media through our case studies. We develop two case studies: HoloList and Ambient Table. We implement and evaluate these systems to learn the features required to design the ambient media.
My Practice of Live Performance of Spatial Electronic Dance Music

In this commentary I will discuss the technical implementation of sound spatialisation in EDM (electronic dance music) performance practice and outline my compositional approaches involving these techniques. The use of space as a musical parameter in EDM is becoming more common as the accessibility of the technology increases. The technical means of performance and the sonic material combine to create a unique musical aesthetic and listening experience in EDM culture. An historical overview of compositions using spatial considerations as a main musical parameter will situate my work within this artistic practice. Different implementations and propositions of sound spatialisation, as well as the principal locations dedicated to this form of activity will be discussed to contextualise my work. A fundamental part of my research concerns the use of spatialisation tools and techniques to enhance EDM through an immersive sound experience. Concepts and notions of musical 'flow’ and live improvisation have shaped this research and the compositional and performance aesthetics that have come to underpin my creative practice. Furthermore, the idea of immersivity and the sublime have informed my compositional thinking, and this will be assessed in relation to my objective to create an enhanced listening experience in my live performances. A discussion of the blurred roles of composer/producer/performer will demonstrate how I consider my live performance practice to redefine what a composer of EDM can be. Thus, I consider this research to propose a viable model for modern EDM composers.
SEGMENTASI RUANG WARNA L*a*b

Image segmentation functions to separate one object from another object. Separation is carried out based on regional boundaries which have the same form or composition. The form of light energy is worth 0 to infinity. The function of light intensity f (x, y) indicates the position of the image coordinates. Color segmentation is obtained through preprocessing conversions and transformations that produce color value values. The color of digital images results in light intensity, namely RGB, CMYK, HSV, Color Space. The colors R (Red) G (Green) and B (Blue) have a close degree of color correlation making it difficult to segment. each sample is converted from RGB - XYZ - Lab color. The average ratio of Red, Green and Blue in RGB and XYZ 1:10 colors. Segmentation uses the color L * a * b, showing the color composition of the sample data into six groups: background, yellow, magenta, purple, red and green using the grouping method. This work proposed the L*a*b color space chosen which is a homogeneous space for visual perception.
Deep Residual Neural Network Design for Super-Resolution Imaging

Convolution neural network recently confirmed the high-quality reconstruction for single-image super-resolution (SR). In this paper we present a Deep Level Residual Network (DLNR), a low-memory effective neural network to reconstruct super-resolution images. This neural network also has the following characteristics. (1) Ability to perform different convolution size operations on the image which can achieve more comprehensive feature extraction effects. (2) Using residual learning to expand the depth of the network and increase the capacity of learning. (3) Taking the skill of parameter sharing between the network module to reduce the number of parameters. After the experiment, we find that DLNR can achieve 37.78 in PSNR and 0.975 in SSIM when using Manga109 as testing set for 2× SR.
Equipment Fault Detection Based on SENCForest

Fault detection is a key element to ensure continuous operation of equipment. It is important to detect new emerging fault types of equipment in time and classify it. Existing fault detection methods are only suitable for detecting and classifying known faults, and cannot detect and classify new emerging unknown faults efficiently. By using the anomaly detection principle of the iForest, this paper proposes an effective classification method for both known and unknown faults. By extracting features from raw data, constructing a completely random forest, we achieve the classification for known faults. Then by setting reasonable partition boundaries according to the distribution characteristics of known anomaly data, we achieve the classification for unknown faults. Real data based simulations show the feasibility and effectiveness of our proposed classification method.
Sensitivity Analysis of a Species Conserving Genetic Algorithm&amp;#39;s Parameters for Addressing the Niche Radius Problem

Niche Genetic Algorithms (NGA) are a special category of Genetic Algorithms (GA) that solve problems with multiple optima. These algorithms preserve genetic diversity and prevent the GA from converging on a single optima. Many NGAs suffer from the Niche Radius Problem (NRP), which is the problem of correctly setting a radius parameter for optimal results. While the selection of the radius value has been widely researched, the effects of other GA parameters on genetic diversity is not well known. This research is a parameter sensitivity analysis on the other parameters in a GA, namely mutation rate, number of individuals and number of generations.