Reviewerly/exHarmony huggingface dataset:
Study on the visual tracking control technology of six-crawler machine

In this paper, the autonomous navigation of six-crawler machine is studied, and a visual tracking control method based on machine vision for fuzzy proportional–integral–derivative control of six-crawler machine is proposed. The steering principle of the six-crawler machine and the matching relationship between the steering angle and the speed of each crawler are introduced, and the control system is described in detail. Besides, the mathematical model for the unsteady steering is introduced to analyze the influence of deflection angle on the steering trajectory of the six-crawler machine. The image processing algorithm is programmed by LabVIEW software. After the image is fitted by graying, binary, filtering, edge detection, and least square method, the navigation line-fitting curve is obtained. The fuzzy proportional–integral–derivative control algorithm is programmed in the control system to control the six-crawler machine to drive along the navigation line. In order to obtain reasonable control parameters, a virtual prototype model of a six-crawler machine is established. In the CoLink module, the control algorithm of a six-crawler machine is established, and the co-simulation is carried out. By analyzing the simulation results, the control parameters of the fuzzy proportional–integral–derivative controller of the six-crawler machine are established. In order to verify the control effect of the visual tracking control system of the six-crawler machine, a physical prototype of the six-crawler machine is constructed and tested. The results show that the visual tracking control system of the six-crawler machine can complete the preset functions.
TOWARDS AUTOMATED ERROR LOCALIZATION IN C PROGRAMS WITH LOOPS

The most recent trends in the C-light verification system are MetaVCG, semantic labels appropriate for verification condition (VC) explanation and symbolic method of definite iterations.MetaVCG takes a C-light program together with some Hoare's logic and produces on-the-fly a VC generator (VCG), which in turn processes the input program.Hoare's logic for definite iterations is a good choice if we try to get rid of loop invariants.Finally, if a theorem prover was unable to validate some VCs we could follow two ways.Obviously, we could revise/enrich specifications or/and underlying proof theory to prove the truth of VCs.Or, perhaps, we could concentrate upon establishment of falsity, which meant there were errors in annotated program.This is where semantic labels play crucial role providing some natural language comments about wrong VC as well as a back-trace to the error location.The newly developed ACL2 heuristics to prove VC falsity is the main theme of this paper.
A System of Associated Intelligent Integration for Human State Estimation

We propose a learning architecture for integrating multi-modal information e.g., vision, audio information.In recent years, artificial intelligence (AI) is making major progress in key tasks like a language, vision, voice recognition tasks.Most studies focus on how AI could achieve human-like abilities.Especially, in human-robot interaction research field, some researchers attempt to make robots talk with human in daily life.The key challenges for making robots talk naturally in conversation are to need to consider multi-modal non-verbal information same as human, and to learn with small amount of labeled multi-modal data.Previous multi-modal learning needs a large amount of labeled data while labeled multi-modal data are shortage and difficult to be collected.In this research, we address these challenges by integrating single-modal classifiers which trained each modal information respectively.Our architecture utilized knowledge by using bi-directional associative memory.Furthermore, we conducted the conversation experiment for collecting multi-modal non-verbal information.We verify our approach by comparing accuracies between our system and conventional system which trained multi-modal information.
Deep Segmentation Domain Adaptation Network With Weighted Boundary Constraint

Semantic segmentation domain adaptation is used to deal with segmentation problems in a new domain even without pixel-level labels. Highly precise boundaries are the major indicator of segmentation performance, but the previous methods mainly have focused on global representation rather than on local representation, leading to an inferior performance of object boundaries in domain adaptation. In this paper, we propose weighted boundary constraint to refine those segmentation predictions and incorporate it into a generative adversarial network (GAN)-based network for domain adaption to achieve further significant improvement. The boundary constraint loss is designed as the cross-entropy between the intermediate result and the refined result. In addition, confidence from discriminator of GAN is used to constrain the boundary constraint loss to reduce the negative impact from inaccurate object boundaries. The entire network can be learned in an end-to-end manner. Both quantitative and qualitative experiments demonstrate the benefits of our approach which shows the competitive performance with the state-of-the-art methods.
Forensics and Digital Criminal Investigation Challenges in Cloud Computing and Virtualization

I'll address the fundamentals of Cloud Computing and Virtualization.The types of cloud computing deployment models and their relationship with the responsibility of the users are developed.The fundamentals of digital criminal investigation applied to Cloud Computing are discussed, and the most significant challenges are presented to criminal investigation and forensic sciences in this type and digital environment.The implications of Virtualization used in Cloud Computing in Criminal Investigation and Forensic Science are discussed.The paradigm case of Nested Virtualization technology is presented as an obstacle to Criminal investigation and forensic investigation.In cases of criminal investigations in traditional environments, it is common practice for computer expertise to turn off the equipment and make a copy of the disks that will be analysed later in the laboratory.This is unfeasible in a cloud computing environment, due to the large storage capacity, legal issues, geographic distribution and data control, which may vary depending on the model of service contracted.In addition, lack of physical access to data collection and lack of control over the system make information acquisition a challenging task for cloud expertise.Therefore, forensic computing has been restructured, bringing new techniques, solutions and research methods, giving rise to cloud forensics or expertise in the cloud.Thus, the so-called Forensic as a Service (FaaS) is dedicated to solving the security challenges inherent in the cloud environment.In this paper we will analyse some of these challenges.
STORY CIRCLE TECHNIQUE TO HELP NON-ENGLISH MAJOR STUDENTS IN WRITING NARRATIVE TEXT

The objective of this study was to find out the significant difference of the students in writing achievement before and after using Story Circle technique. The second objective was to find out the improvement of the students in writing achievement before and after Story Circle technique. The pre-experimental method through one group pre test-post test design was used in this study. The population and sample of the study was all 3rd semester students of S1 Nursing Science program at STIK Siti Khadijah Palembang in academic year 2018/2019. The total number of the population and sample was 35 students. The sample was taken by using total sampling technique. The tests were administrated twice as pre-test and post-test. After the pre-test was administered, the treatment was implemented. The post test was given after the treatment. The results of the tests were analyzed by using t-test through SPSS (Statistical Package for Social Science) program. The result of the study showed that there was a significant difference of the students in writing achievement before and after implementing Story Circle technique. It can be concluded that Story Circle technique could improve 3rd semester students’ writing achievement at of S1 Nursing Science program at STIK Siti Khadijah Palembang in academic year 2018/2019.
Kernelized Cross-view Quadratic Discriminant Analysis for Person Re-Identification

In person re-identification, Keep It Simple and Straightforward MEtric (KISSME) is known as a practical distance metric learning method. Typically, kernelization improves the performance of metric learning methods. Nevertheless, deriving KISSME on a reproducing kernel Hilbert space is a non-trivial problem. Nyström method approximates the Hilbert space in low-dimensional Euclidean space, and the application of KISSME is straightforward, yet it fails to preserve discriminative information. To utilize KISSME in a discriminative subspace of the Hilbert space, we propose a kernel extension of Cross-view Discriminant Analysis (XQDA) which learns a discriminative low-dimensional subspace, and simultaneously KISSME in the learned subspace. We show with the standard kernel trick, the kernelized XQDA results in the case when the empirical kernel vector is used as the input of XQDA. Experimental results on benchmark datasets show the kernelized XQDA outperforms XQDA and Nyström-KISSME.
A Novel Attention Mechanism Considering Decoder Input for Abstractive Text Summarization

Recently, the automatic text summarization has been widely used in text compression tasks. The Attention mechanism is one of the most popular methods used in the seq2seq (Sequence to Sequence) text summarization models. The current attention mechanisms usually use the hidden states of the encoder and the decoder to generate attention distributions. However, they ignore the information of the word waiting to be input into the decoder, leading to possible failures to obtain accurate attention distributions. In this work, we propose a novel attention mechanism further adding the decoder inputs into the operation of generating attention distributions. To our best knowledge, this is the first time that the decoder input has been added to the process of calculating the attention vector. The attention mechanism we proposed to generate the attention distributions considers context similarities as well as semantic similarities, which is closer to the behavior of the human summarizer. We also applied our attention mechanism to the seq2seq based summarization model and trained it on a large corpus containing hundreds of thousands of article-summary pairs. The experimental results on two summarization datasets demonstrate that our attention mechanism outperforms the existing well-known ones. For the popular evaluation metric of the text summarization, our method obtains a 2.93 ROUGE-2 score relative gain compared with the popular attention mechanism Bahdanau Attention, and a 2.21 ROUGE-2 score improvement compared with the best baseline method Luong Attention.
Attention-Based Deep Learning Model for Image Captioning: A Comparative Study

Image captioning is the description generated from images.Generating the caption of an image is one part of computer vision or image processing from artificial intelligence (AI).Image captioning is also the bridge between the vision process and natural language process.In image captioning, there are two parts: sentence based generation and single word generation.Deep Learning has become the main driver of many new applications and is also much more accessible in terms of the learning curve.Image captioning by applying deep learning model can enhance the description accuracy.Attention mechanisms are the upward trend in the model of deep learning for image caption generation.This paper proposes the comparative study for attention-based deep learning model for image captioning.This presents the basic analyzing techniques for performance, advantages, and weakness.This also discusses the datasets for image captioning and the evaluation metrics to test the accuracy.
STIMULASI DALAM MEMAKSIMALKAN KEMAMPUAN BERBAHASA ANAK USIA DINI

The language activities of early childhood are interesting things to study. A child develops language competencies from the use of the language of the people in the surrounding environment. The environment is one of the factors that determine children in acquiring language. Outer stimulation of the child can influence children in developing language skills. The discussion of this article is going to focus on stimuli that can be given to early childhood in supporting language activities.
Enhancement of Micro-Images Using Feature Linking Model for Cerebellum of <i>Alligator sinensis</i>

In order to realize the three-dimensional reconstruction of the cerebellum structure of alligator sinensis, it is necessary to observe the structure by using a microscope. In the process of slice making and micro-image shooting, individual operation differences lead to good or bad quality of micro-images. To solve this problem, the feature linking model (FLM) is used to enhance the micro-image of alligator cerebellum and improve the quality of micro-image. The transverse section and longitudinal section were prepared by using the tissue of cerebellum of alligator sinensis as a raw material, and the micro-images were obtained by the light microscopy. The different enhancement methods were used to process the transverse section micro-images, and the enhancement was evaluated subjectively by direct observation. It was found that histogram equalization (HEQ) and fuzzy set method (FSM) did not enhance the darker parts well, while enhanced the brighter parts excessively. And the enhancement with pulse coupled neural network (PCNN), spiking cortical model (SCM), and FLM are better. On the basis of subjective evaluation, three objective evaluation indexes (contrast, spatial frequency, and gradient) were used to compare and analyze the image quality improvement of PCNN, SCM, and FLM, and we found that FLM had the best enhancement on transverse section micro-images. Then, FLM was used to process longitudinal section micro-images, and the objective evaluation indexes before and after processing were compared. We found FLM also had a good enhancement on longitudinal section micro-images. The results show that FLM was effective in enhancing micro-images of the cerebellum of alligator sinensis, and FLM improved the quality of the longitudinal section micro-images more obviously. The proposed method can improve the quality of the microscopic image of the cerebellum of alligator sinensis so that higher quality images can be obtained, which is beneficial to improving the utilization rate of the material.
Mineralogical and crystallographic characteristics of bauxites from some Grebnik’s (Metohija, Serbia) ore deposits

Five typical bauxite samples from three ore deposits from the wider area of Grebnik Mt. (Metohija, Serbia) were examined with the optical microscopic, X-ray powder diffraction (XRPD) and chemical methods. The occurrences in bauxites were studied and described and the presence of major minerals was determined: diaspore, boehmite and hematite; minor minerals: quartz, goethite, rutile, kaolinite and hydro-hematite; as well as anatase, brookite, magnetite and chromite, which occur only sporadically. According to the quantity of main Al-bearers, three types of bauxite ore were recognized: diaspore, boehmite and boehmite-diaspore. Unit cell dimensions of major minerals were determined, mostly with values within reference data. However, all of the studied hematites have smaller a0 and V0 values, most probably due to the substitution of Fe3+ by Al3+. All samples are further classified as iron-rich and ferritic bauxites. It was found that there were changes in origin conditions between major Al-hydroxides minerals and hematite, as well as between the diaspore and boehmite. Determined chemical compositions put this raw material into high-quality raw materials for obtaining the electro corundum and alumina. The synthesis of the obtained data indicate that Grebnik?s bauxites have some unique characteristics, more or less different from most of the World?s known bauxite ore deposits.
VULNERABILITY ASSESSMENT AND PENETRATION TESTING AS CYBER DEFENCE

In last ten years, use of internet applications,web hacking activities have exaggerated speedily.Organizations facing very significant challenges in securing their web applications from rising cyber threats, as compromise with the protection issues don't seem to be reasonable.Vulnerability Assessment and Penetration Testing (VAPT) techniques help them to go looking out security loopholes.These security loopholes could also be utilized by attackers to launch attacks on technical assets.Thus it is necessary ascertain these vulnerabilities and install security patches.VAPT helps organization to determine whether their security arrangements are working properly.This paper aims to explain overview and various techniques used in vulnerability assessment and penetration testing (VAPT) in detail.Also focuses on making cyber security awareness and its importance at various level of an organization for adoption of required up to date security measures by the organization to stay protected from various cyber-attacks
Improving Artificial Bee Colony Algorithm Using a Dynamic Reduction Strategy for Dimension Perturbation

To accelerate the convergence speed of Artificial Bee Colony (ABC) algorithm, this paper proposes a Dynamic Reduction (DR) strategy for dimension perturbation. In the standard ABC, a new solution (food source) is obtained by modifying one dimension of its parent solution. Based on one-dimensional perturbation, both new solutions and their parent solutions have high similarities. This will easily cause slow convergence speed. In our DR strategy, the number of dimension perturbations is assigned a large value at the initial search stage. More dimension perturbations can result in larger differences between offspring and their parent solutions. With the growth of iterations, the number of dimension perturbations dynamically decreases. Less dimension perturbations can reduce the dissimilarities between offspring and their parent solutions. Based on the DR, it can achieve a balance between exploration and exploitation by dynamically changing the number of dimension perturbations. To validate the proposed DR strategy, we embed it into the standard ABC and three well-known ABC variants. Experimental study shows that the proposed DR strategy can efficiently accelerate the convergence and improve the accuracy of solutions.
Solution of class imbalance of k-nearest neighbor for data of new student admission selection

The objective of this research is to correct the inconsistencies associated with the response differences by each examiner with respect to the assessment of each hafiz candidate. To carry out this research, 259 students were selected within a week using 4testers. However, the examiners are also tasked with another essential mandate which must be immediately fulfilled asides testing candidates for hafiz. In order to overcome this problem, the Educational Data Mining (EDM) system is applied during classification. The problems associated with the use of this technique however, is the limited number of attributes and the imbalance data class. This study was proposed to apply the kNN (k-Nearest Neighbor) technique. The results obtained indicates that kNN can provide recommendations to testers who are students and it is suitable for the solving the problem associated with class imbalance as indicated by the application of Shuffled and Stratified sampling techniques which has values of accuracy, precision, recall and AUC &gt; 0.8%.
Augmented Reality and Sensory Technology for Treatment of Anxiety Disorders

This solution aims at promoting well-being through Augmented Reality (AR) Sensing Based Technology, by developing a predictive system for treatment of specific phobias. Treatment of such mental disorders involves exposure therapy, which is usually conducted on a gradual and systematic basis, allowing modifications in the therapeutic setting with different exposure sessions. To meet these conditions, the therapist needs to evaluate the patient's responses in the previous sessions to define the content of the subsequent sessions. Therefore, we intend to use the psycho-physiological responses from the participants during the sessions through peripheral (i.e. ECG, EDA, EMG) and central nervous system responses (i.e. EEG) associated with emotional expression, as well as the participatory input to a visual analog scale (i.e. tactile feedback input system, avoiding huge hands movements and interruptions during carrying session) as an indicator of the subjective experience of emotions during the session (i.e. figures with different moods). This data will be also used to define the contents of each session, through a decision algorithm based on the history of the previous sessions for the same individual. All data resulting from this application will be recorded in a database to create individual reports on the session outcomes. The hardware to be used for psycho-physiological recording will be the Bitalino system (Plux) and the software for the AR development the NyARToolkit and Unity 3D software. We plan to carry out a pilot study with data collection in the laboratory setting to test the module related to the integration of psycho-physiological and participatory data in the AR system.
MACHINE LEARNING ALGORITHMS FOR HETEROGENEOUS DATA: A COMPARATIVE STUDY

In the present digital era massive amount of data being continuously generated is at exceptional and increasing scales.This data has become an important and indispensable part of every economy, industry, organization, business and individual.Further handling of these large datasets due to the heterogeneity in their formats is one of the major challenge.There is a need for efficient data processing techniques to handle the heterogeneous data and also to meet the computational requirements to process this huge volume of data.The objective of this paper is to review, describe and reflect on heterogeneous data with its complexity in processing, and also the use of machine learning algorithms which plays a major role in data analytics.
Deep Model Compression via Filter Auto-sampling.

The recent WSNet [1] is a new model compression method through sampling filterweights from a compact set and has demonstrated to be effective for 1D convolutionneural networks (CNNs). However, the weights sampling strategy of WSNet ishandcrafted and fixed which may severely limit the expression ability of the resultedCNNs and weaken its compression ability. In this work, we present a novel auto-sampling method that is applicable to both 1D and 2D CNNs with significantperformance improvement over WSNet. Specifically, our proposed auto-samplingmethod learns the sampling rules end-to-end instead of being independent of thenetwork architecture design. With such differentiable weight sampling rule learning,the sampling stride and channel selection from the compact set are optimized toachieve better trade-off between model compression rate and performance. Wedemonstrate that at the same compression ratio, our method outperforms WSNetby6.5% on 1D convolution. Moreover, on ImageNet, our method outperformsMobileNetV2 full model by1.47%in classification accuracy with25%FLOPsreduction. With the same backbone architecture as baseline models, our methodeven outperforms some neural architecture search (NAS) based methods such asAMC [2] and MNasNet [3].