Reviewerly/exHarmony huggingface dataset:
Solution and Fitness Evolution (SAFE): Coevolving Solutions and Their Objective Functions

We recently highlighted a fundamental problem recognized to confound algorithmic optimization, namely, conflating the objective with the objective function. Even when the former is well defined, the latter may not be obvious, e.g., in learning a strategy to navigate a maze to find a goal (objective), an effective objective function to evaluate strategies may not be a simple function of the distance to the objective. We proposed to automate the means by which a good objective function may be discovered—a proposal reified herein. We present Solution And Fitness Evolution (SAFE), a commensalistic coevolutionary algorithm that maintains two coevolving populations: a population of candidate solutions and a population of candidate objective functions. As proof of principle of this concept, we show that SAFE successfully evolves not only solutions within a robotic maze domain, but also the objective functions needed to measure solution quality during evolution.
The Cryptography of Elliptical Curves Application for Formation of the Electronic Digital Signature

Today, commercial structures can no longer run their business without using the Internet and cloud technologies. Electronic transactions conducted through the global Internet play an increasing role in the modern global economy, and their importance is rapidly increasing every year. However, the reality is the Internet is quite vulnerable in the terms of threats of most diverse types. Facing reality makes commercial organizations increasingly focus on Web security. In this article we are focuses on encryption of Web pages using encryption based on elliptical curves. We examined the algorithm of this approach and engagements are possible protecting information by transforming data from a distortion by an attacker. Even a simple transformation of information is a very effective means, making it possible to hide its meaning from the majority of unskilled offenders. We also considered an algorithm of constructing an electronic-digital message signature based on encryption using elliptic curves.
HiGRU: Hierarchical Gated Recurrent Units for Utterance-level Emotion Recognition

In this paper, we address three challenges in utterance-level emotion recognition in dialogue systems: (1) the same word can deliver different emotions in different contexts; (2) some emotions are rarely seen in general dialogues; (3) long-range contextual information is hard to be effectively captured. We therefore propose a hierarchical Gated Recurrent Unit (HiGRU) framework with a lower-level GRU to model the word-level inputs and an upper-level GRU to capture the contexts of utterance-level embeddings. Moreover, we promote the framework to two variants, HiGRU with individual features fusion (HiGRU-f) and HiGRU with self-attention and features fusion (HiGRU-sf), so that the word/utterance-level individual inputs and the long-range contextual information can be sufficiently utilized. Experiments on three dialogue emotion datasets, IEMOCAP, Friends, and EmotionPush demonstrate that our proposed HiGRU models attain at least 8.7%, 7.5%, 6.0% improvement over the state-of-the-art methods on each dataset, respectively. Particularly, by utilizing only the textual feature in IEMOCAP, our HiGRU models gain at least 3.8% improvement over the state-of-the-art conversational memory network (CMN) with the trimodal features of text, video, and audio.
IMPROVING THE WALL-FOLLOWING ROBOT PERFORMANCE USING PID-PSO CONTROLLER

A wall-following robot is one of the main issues in autonomous mobile robot behavior. However, a wall-following robot needs a robust controller to perform specific tasks accurately. This paper presents an optimization method termed Particle Swarm Optimization (PSO). It was used to automatically produce necessary parameters of the PID controller; henceforth, it was termed as PID-PSO Controller. A new technique of PSO was introduced to enhance the ability of a PID controller to maintain the linear velocity of a mobile robot. The PID-PSO controller was applied to a wheeled wall-following robot. A number of experiments were carried out, and the simulated results were adopted and performed in real applications. Based on several experimental results it can be obtained that the accumulative errors the robot use PID controllers tuned manually, tuned by GA and tuned by PSO are 0.7866, 0.78543 and 0.74619, respectively. Also, the convergence process of PID parameters using the proposed PSO is faster and more optimal than GA. Therefore, it can be said that the proposed system can improve the performance of wall-following robots by decreasing the accumulative error of up to 9%.
Should one use term proximity or multi-word terms for Arabic information retrieval?

Recently, several information retrieval (IR) models have been proposed in order to boost the retrieval performance using term dependencies. However, in the context of the Arabic language, most IR researchers have focused on the problem of stemming, which is highly challenging in this language. In this paper, we propose to explore whether term dependencies can help improve Arabic IR systems, and what are the best methods to use. To do so, we consider both explicit term dependencies based on multi-word terms (MWTs) that are extracted using syntactic patterns and statistical filters, as well as implicit ones based on the notion of cross-terms or term proximities. Our experiments, performed on standard TREC Arabic IR collections, show the importance of taking into account term dependencies for Arabic IR. To the best of our knowledge, this is the first study that provides complete extensions, and their comparison, of most standard IR models to deal with term dependencies in the Arabic language.
Epistasis-Based Basis Estimation Method for Simplifying the Problem Space of an Evolutionary Search in Binary Representation

An evolutionary search space can be smoothly transformed via a suitable change of basis; however, it can be difficult to determine an appropriate basis. In this paper, a method is proposed to select an optimum basis can be used to simplify an evolutionary search space in a binary encoding scheme. The basis search method is based on a genetic algorithm and the fitness evaluation is based on the epistasis, which is an indicator of the complexity of a genetic algorithm. Two tests were conducted to validate the proposed method when applied to two different evolutionary search problems. The first searched for an appropriate basis to apply, while the second searched for a solution to the test problem. The results obtained after the identified basis had been applied were compared to those with the original basis, and it was found that the proposed method provided superior results.
Deep Optics for Monocular Depth Estimation and 3D Object Detection

Depth estimation and 3D object detection are critical for scene understanding but remain challenging to perform with a single image due to the loss of 3D information during image capture. Recent models using deep neural networks have improved monocular depth estimation performance, but there is still difficulty in predicting absolute depth and generalizing outside a standard dataset. Here we introduce the paradigm of deep optics, i.e. end-to-end design of optics and image processing, to the monocular depth estimation problem, using coded defocus blur as an additional depth cue to be decoded by a neural network. We evaluate several optical coding strategies along with an end-to-end optimization scheme for depth estimation on three datasets, including NYU Depth v2 and KITTI. We find an optimized freeform lens design yields the best results, but chromatic aberration from a singlet lens offers significantly improved performance as well. We build a physical prototype and validate that chromatic aberrations improve depth estimation on real-world results. In addition, we train object detection networks on the KITTI dataset and show that the lens optimized for depth estimation also results in improved 3D object detection performance.
A Robot for Test Bed Aimed at Improving Telepresence System and Evasion from Discomfort Stimuli by Online Learning

This study contributes to improving the comfort of telepresence communication by adaptations to people. We developed a handheld telepresence robot comprised of a binaural microphone and a head mounted display as a test bed and conducted surveys on unpleasantness caused by special devices in use. We found that numerous people experienced an increase in unpleasant sound when the binaural microphone was being used. It was also found that types of unpleasant stimuli differed from person to person. Furthermore, we propose an automatic unpleasant stimuli avoidance system using online machine learning architecture constructed from echo state networks (ESNs) and Accumulator Based Arbitration Models (ABAMs) that can also flexibly adapt to remote users. Because the handheld telepresence robot can avoid unpleasant stimuli before remote users experience these stimuli, it provides them with a more comfortable communication environment while notifying people around the robot that uncomfortable stimulation is being avoided.
The complete form of the propagation constant in a noninertial reference frame for numerical analysis.

In this paper we present a derivation of the strict form of the propagation constant for a rotating disk, taking into account the inertial forces of the reference system.This work is a generalization of known facts in a complete methodology.We use formulations related to the use of deflection angletransition of the ray from a straight-line trajectory under the action of inertia forces.This study allows conducting high-precision computer simulations of electromagnetic effects arising in non-inertial frames of reference.Strict conclusions are achieved by the exception of rest frame theory.Two-dimensional electromagnetic propagation problem is solved in a non-inertial frame of reference.A rotating frame of reference is a good example of any noninertial frame of reference, therefore, this particular model was chosen to be considered.We use the classical approach for describing the conditions for the propagation of electromagnetic waves in noninertial reference frames: the refined refractive index of the medium.The possibility of modeling using the obtained formulations is demonstrated on a rotating circular infinite cylinder.Verification of the results was carried out.Distributions of the propagation constant in a rotating medium are presented.The results are obtained using CAD of electromagnetic modeling.The comparison was made for data from open sources.Validation of the method proved its effectiveness.
The Use of Charades Games in Teaching Vocabulary to the Junior High School Students

Vocabulary is a knowledge of words and word meanings. To increase the student’s vocabulary, the writer chosen game. A game is an alternative way to teach. One of the alternative games is a charades game. So, based on it the aim of this study is to investigate the effect of charades game toward student’s vocabulary. This study was conducted at the first-grade students of SMPN 18 Mataram in academic year 2017/2018. This study used a quasi-experimental design that is used pre-test and post-test control group design. The population of study involved 40 students from two classes SMPN 18 Mataram in academic year 2017/2018. The writer took all of the students as a sample, and they are 20 students belong to the experimental group and 20 students in the control group. In this study, the writer used the vocabulary test consists of 20 items multiple choice covered with the indicator of vocabulary consist of spelling, pronunciation, and meaning. The result of this study, the students could understand spelling, pronunciation, and meaning and they could answer the test vocabulary easily through charades game. In this data, pre-test, and post-test as the research technique of data collection, then followed by analyzing the data using the t-test formula. The level of significance is at 0, 05%, t-test = 2,99 and t-table= 2,02, Alternative Hypothesis (Ha) was accepted and Null Hypothesis (Ho) was rejected. It means that there was a significant difference between the student's pre-test and post-test scores. Thus, the writer concludes that the charades game on the student’s vocabulary affects.
Human activity recognition method based on molecular attributes

Acceleration sensor is extensively used in the field of human activity recognition, since it provides better recognition rate of human activity. Based on the principle of molecular attribute, a simple and adaptive activity recognition method is proposed using the acceleration data flow, which constitutes a serial activity, when the acceleration data are treated as the material flow with certain molecular structure. Then five molecular attributes including relative molecular mass, density, internal forces in a molecule, molecule stability, and attraction between molecules are introduced to recognize six human activities, since the closer molecular attribute means the more similar activity. Based on the calculated molecular attributes, a reliability-based voting method for human activity recognition is developed. Since each activity has respective motion cycle, a sliding window with variable sizes is put forward to enhance the recognition rate. Furthermore, adaptive incremental learning is designed to adapt to the different users. The long-time experimental results show that the proposed method is rather accurate and robust for different crowds. The average recognition rate achieves 97.2% for six human activities including walking, jogging, running, going upstairs, going downstairs, and sitting down.
Graph Convolutional Networks with EigenPooling

Graph neural networks, which generalize deep neural network models to graph structured data, have attracted increasing attention in recent years. They usually learn node representations by transforming, propagating and aggregating node features and have been proven to improve the performance of many graph related tasks such as node classification and link prediction. To apply graph neural networks for the graph classification task, approaches to generate the \textit{graph representation} from node representations are demanded. A common way is to globally combine the node representations. However, rich structural information is overlooked. Thus a hierarchical pooling procedure is desired to preserve the graph structure during the graph representation learning. There are some recent works on hierarchically learning graph representation analogous to the pooling step in conventional convolutional neural (CNN) networks. However, the local structural information is still largely neglected during the pooling process. In this paper, we introduce a pooling operator $\pooling$ based on graph Fourier transform, which can utilize the node features and local structures during the pooling process. We then design pooling layers based on the pooling operator, which are further combined with traditional GCN convolutional layers to form a graph neural network framework $\m$ for graph classification. Theoretical analysis is provided to understand $\pooling$ from both local and global perspectives. Experimental results of the graph classification task on $6$ commonly used benchmarks demonstrate the effectiveness of the proposed framework.
Development of Visual-Spatial Attention

This chapter reviews literature on development of visual-spatial attention. A brief overview of brain mechanisms of visual perception is provided, followed by discussion of neural maturation in the prenatal period, infancy, and childhood. This is followed by sections on gaze control, eye movement systems, and orienting. The chapter concludes with consideration of development of space, objects, and scenes. Visual-spatial attention reflects an intricate set of motor, perceptual, and cognitive systems that work jointly and all develop in tandem.
Region-Based Active Learning with Hierarchical and Adaptive Region Construction

Learning of classification models in practice often relies on human annotation effort in which humans assign class labels to data instances. As this process can be very time-consuming and costly, finding effective ways to reduce the annotation cost becomes critical for building such models. To solve this problem, instead of soliciting instance-based annotation we explore region-based annotation as the human feedback. A region is defined as a hyper-cubic subspace of the input space X and it covers a subpopulation of data instances that fall into this region. Each region is labeled with a number in [0,1] (in binary classification setting), representing a human estimate of the positive (or negative) class proportion in the subpopulation. To quickly discover pure regions (in terms of class proportion) in the data, we have developed a novel active learning framework that constructs regions in a hierarchical and adaptive way. Hierarchical means that regions are incrementally built into a hierarchical tree, which is done by repeatedly splitting the input space. Adaptive means that our framework can adaptively choose the best heuristic for each of the region splits. Through experiments on numerous datasets we demonstrate that our framework can identify pure regions in very few region queries. Thus our approach is shown to be effective in learning classification models from very limited human feedback.
Frame-wise Motion and Appearance for Real-time Multiple Object Tracking

The main challenge of Multiple Object Tracking (MOT) is the efficiency in associating indefinite number of objects between video frames. Standard motion estimators used in tracking, e.g., Long Short Term Memory (LSTM), only deal with single object, while Re-IDentification (Re-ID) based approaches exhaustively compare object appearances. Both approaches are computationally costly when they are scaled to a large number of objects, making it very difficult for real-time MOT. To address these problems, we propose a highly efficient Deep Neural Network (DNN) that simultaneously models association among indefinite number of objects. The inference computation of the DNN does not increase with the number of objects. Our approach, Frame-wise Motion and Appearance (FMA), computes the Frame-wise Motion Fields (FMF) between two frames, which leads to very fast and reliable matching among a large number of object bounding boxes. As auxiliary information is used to fix uncertain matches, Frame-wise Appearance Features (FAF) are learned in parallel with FMFs. Extensive experiments on the MOT17 benchmark show that our method achieved real-time MOT with competitive results as the state-of-the-art approaches.
Finding a Needle in a Haystack: The Traffic Analysis Version

Abstract Traffic analysis is the process of extracting useful/sensitive information from observed network traffic. Typical use cases include malware detection and website fingerprinting attacks. High accuracy traffic analysis techniques use machine learning algorithms (e.g. SVM, kNN) and require to split the traffic into correctly separated blocks. Inspired by digital forensics techniques, we propose a new network traffic analysis approach based on similarity digest. The approach features several advantages compared to existing techniques, namely, fast signature generation, compact signature representation using Bloom filters, efficient similarity detection between packet traces of arbitrary sizes, and in particular dropping the traffic splitting requirement altogether. Experimental results show very promising results on VPN and malware traffic, but low results on Tor traffic due mainly to the single-size cells feature.
PENERAPAN DESIGN THINKING PADA MEDIA KOMUNIKASI VISUAL PENGENALAN KEHIDUPAN KAMPUS BAGI MAHASISWA BARU STMIK PALCOMTECH DAN POLITEKNIK PALCOMTECH

The development of visual communication design has grown and developed and become familiar with human life. Design Thinking is a tool used in problem-solving, problem-design, to problem-forming. Not only resolves a problem, but also for forming and designing a problem. In the process design thinking is human centered or human centered. Every design thinking process originates and is aimed at humans. The application of many design functions is applied in various ways, such as for the education sector, tourism, business and others. The application of design functions in the education sector is like solving problems related to design in order to prepare new students in passing the transition process to become an adult and independent student, as well as to accelerate the process of student adaptation to the new environment and provide provision for success in higher education. Introduction of Campus Life for New Students (PKKMB), visual communication media are needed. The purpose of this study was to design visual communication media about the implementation of PKKMB at STMIK PalComTech and PalComTech Polytechnic in 2018. This study used the Design thinking method. Design Thinking is a user / user focused problem solving method. The stages in design thinking are Emphatize, Define, Ideate, Prototype, and Test. The results obtained from this study are in the form of designs that have been conceptualized well for the implementation of PKKMB at STMIK PalComTech and PalComTech Polytechnic in 2018 in the form of name tag design, back drop design, and banner.
CMMI-DEV Implementation Simplified

With the advance technology and increase in customer requirements, software organizations pursue to reduce cost and increase productivity by using standards and best practices.The Capability Maturity Model Integration (CMMI) is a software process improvement model that enhances productivity and reduces time and cost of running projects.As a reference model, CMMI does not specify systemic steps of how to implement the model practices, leaving a room for organization development approaches.Small organizations with low budgets and those who are not looking for CMMI appraisals cannot cope with the high price of CMMI implementation.However, they need to manage the risk of CMMI implementation under their administration.Therefore, this paper proposes a simplified plan using the spiral model to implement the CMMI to reach level 2. The objective is to make the implementation more straightforward to implement and fit CMMI specification without hiring external experts.Compared to related implementation frameworks, the proposed model is deemed competitive and applicable under organizations' conditions.
A Hardware-Oriented and Memory-Efficient Method for CTC Decoding

The Connectionist Temporal Classification (CTC) has achieved great success in sequence to sequence analysis tasks such as automatic speech recognition (ASR) and scene text recognition (STR). These applications can use the CTC objective function to train the recurrent neural networks (RNNs), and decode the outputs of RNNs during inference. While hardware architectures for RNNs have been studied, hardware-based CTC-decoders are desired for high-speed CTC-based inference systems. This paper, for the first time, provides a low-complexity and memory-efficient approach to build a CTC-decoder based on the beam search decoding. Firstly, we improve the beam search decoding algorithm to save the storage space. Secondly, we compress a dictionary (reduced from 26.02MB to 1.12MB) and use it as the language model. Meanwhile, we further enable searching this dictionary to be trivial. Finally, a fixed-point CTC-decoder for an English ASR and an STR task using the proposed method is implemented with C++ language. It is shown that the proposed method has little precision loss compared with its floating-point counterpart. Our experiments show that the compression ratio of the storage required by the proposed beam search decoding algorithm are 29.49 (ASR) and 17.95 (STR).