Reviewerly/exHarmony huggingface dataset:
End-to-End Convolutional Neural Network for Speaker Recognition Based on Joint Supervision

Speech recognition accuracy has been greatly improved due to the application of Deep Neural Network (DNN) in it. A variety of DNN-based speaker recognition methods have recently been proposed, which have been greatly improved over traditional recognition methods. However, the speaker recognition model based on DNN is usually used for the extraction of bottleneck features or as the extraction part of the sufficient statistics in the traditional i-vector model. The training speed of the model is not ideal, and the recognition accuracy needs to be improved. In this paper, Convolutional Neural Network (CNN) is used for speaker recognition, which reduces the complexity of model training. The method of hopping connection is used to solve the problem of network degradation to some extent, and a method based on softmax loss and center loss joint supervision is also introduced. The performance of the recognition model is further improved. The experimental results show that the Equal Error Rate (EER) of End-to-End CNN for speaker recognition is improved by 13.4% compared with the baseline method, and the performance is better.
Automated Probe Repositioning for On-Die EM Measurements

In side-channel analysis attacks, on-die localized EM monitoring enable high bandwidth measurements of only a relevant part of the Integrated Circuit (IC). This can lead to improved attacks compared to cases where only power consumption is measured. Combined with profiled attacks which utilize a training phase to create precise models of the information leakage, the attacks can become even more powerful. In contrast, localized EM measurements can cause difficulties in applying the learned models as the probe should be identically positioned for both the training and the attack even when the setup was used otherwise in between. Even small differences in the probe position can lead to significant differences in the recorded signals. In this paper we present an automated system to precisely and efficiently reposition the probe when performing repeated measurements. Based on the training IC, we train a machine learning system to return the position of the probe for a given measurement. By taking a small number of measurements on the IC under attack, we can then obtain the coordinates of the measurements and map it to correct the coordinate system. As the target for our practical analyses, we use an STM32L0 ARM-M0+ microcontroller with integrated hardware AES.
Development of a debutanizer column soft sensor by means of evolutionary machine learning approaches

Soft sensors have been widely employed to predict variables from a process that cannot be measured.In literature, there are many techniques that can be used to develop a soft sensor.The robustness of the model is directly related to the complexity of the techniques employed in the model.However, it does not mean that simple models are not able to achieve good results.Support Vector Machines (SVM) and Decision Trees (DT) has been shown as good candidates to build soft sensors.Two experiments are proposed in this work.First, three benchmark regression data sets are studied.Finally, a debutanizer column is provided as study case in order to build a soft sensor.Using Genetic Algorithm (GA) as a tool to improve the developed models, it is possible to perceive that there are no significant differences between the results achieved with SVM and DT in terms of RMSE.However, analyzing the aspects of the predicted curves, the results obtained with SVM is the closest to the actual data.
Implementation of Kampoeng Hijrah Residence Housing Catalog Based on Virtual Reality

This research is an Android application development by applying Virtual Reality technology to the housing catalog. With the existence of virtual reality, technology raises a motivation to represent a real-world object into a virtual world. Previously, the visualization commonly used by Kampoeng Hijrah Residence was using brochure media, but the lack of brochure media was that visualization presented was static and less interactive. However, humans tend to be lazy to read. So, the possibility of delivering information is not all absorbed by users; therefore, this virtualization needs to be applied to the housing catalog, which makes all information adequately conveyed to humans, and recorded by the memory of their imagination. The application of VR aims to facilitate prospective buyers in receiving information related to housing. Utilizing the ability of human imagination can create a factor of attraction to prospective buyers. VR Housing Catalog Application using the Luther Method approach to inform its customers about their housing projects. The results of this research are virtual reality application product that uses smartphone and Gear VR as the VR Headset. Based on the testing that has been done, the alpha test results are functionally appropriate, from beta testing obtained a value of 96% is declared feasible to use with the interpretation of “Approaching Worthy” which means this application is feasible to use and can be developed.
The Use of Adobe Flash as a New Technology in Teaching Essay Writing

The development of information and technology has affected many aspects of life including education.Essay writing has an important role in the language academic writing process. Technology also gives apositive effect toward the students’ motivation in learning English, especially in essay writing. Nowadays, acomputer technology application is developing writing materials by using adobe flash professional. Thepurpose of this research is to show that the role of technology today is very important in learning process specifically in teaching essay writing, so that it can help teacher and students to make lots variations inteaching. In the past, teaching writing only used conventional ways like books but nowadays teachers andstudents can use multimedia technology like education interactive software, smartphone, and computerwhich can make studying more effective and interactive. In this research the writers try to analyze anddemonstrate the new technology as a solution to the old problem of teaching essay writing.
Facial Localization Based on Skin Color Modeling

In recent years, due to the application value of facial localization in the fields of secure access control, visual monitoring and content-based retrieval, it has gradually received the attention of researchers. In this thesis, facial region localization is mainly carried out by modeling the skin color. This method obtains the missed detection rate (20.28%) and the false detection rate (14.80%) by performing repeated experiments on 100 facial color images in the FERET face database. Compared with the other two methods, the accuracy and real-time of the integrated facial localization, the facial localization based on skin color modeling has a better detection effect.
Umple-TL: A Model-Oriented, Dependency-Free Text Emission Tool

We describe the text-emission templating capabilities of Umple (Umple-TL) and compare this Umple feature to other technologies for text generation. Umple, which is written in itself, combines modeling synergistically with programming in languages like Java and C++. Umple-TL further builds on these synergies. With the use of Umple-TL, we eliminated dependencies on third-party libraries for text emission. We demonstrate how Umple-TL attains benefits such as smaller and faster executables, target-language independence and IDE independence. We compare Umple-TL and other text emission tools in order to show how Umple-TL can overcome many of the challenges a tool can face. The word ‘template’ in this paper refers to patterns for the generation of output, and not to generic types, another common use of the term.
Blockchain-Based Secure Recognized Air Picture System Proposal for NATO Air C2 Capabilities

The command and control (C2) systems are of prime importance for leveraging the superiority of the air forces in combat operations. The key element for these systems is a single, coherent recognized air picture (RAP) which provides the real time situational awareness for the theatre commander and the subordinate operational elements. The manipulation of the air picture can trigger a chain of events that may cause the total failure of the air campaign. Blockchain technology can be used to prevent this exploitation. This technology has the potential to eliminate the security problems by its decentralized architecture and its irrevocable registry. It can be used as the key element for enabling the security of friendly air C2 (Command & Control) capabilities in the cyber domain. In this study, the function of the blockchain system is questioned for the benefit of air operations. In this context, a blockchain-based system, which benefits and can enhance the security of an Allied Joint Operational Air C2 system, is proposed. The prototype is developed in Hyperledger Fabric platform. The findings and future work is given.
Copy-Rotate-Move Forgery Detection using Complex Wavelet Transform and Local Binary Pattern

Image region duplication is a type of modification attack on digital images, where a region of the digital image is copied and pasted onto another location within itself. In the recent decade, various techniques have been presented for image region duplication detection based on investigation of matching pixel blocks. However, such forgery detection becomes a challenge when accompanied with geometrical operation such as rescaling, rotation on the forged portions of the image. In this work, we provide a solution of image region duplication detection in a rotation-invariant way. The proposed method is based on Complex Wavelet Transform, utilizes Local Binary Pattern features, and is capable of detecting arbitrary degrees of rotation, unlike existing techniques. Mean-Variance method is used in order to optimize false matches resulting from naturally similar blocks in an image. According to our experimental analysis forgery detection efficiency of the presented methodology is higher, and its false positive rate is lower as compared to the existing methods.
Data Privacy Protection in Medical Alliance Chain Based on K-Anonymity

At present, there are many threats to medical data security. Because of the different standards of data storage and system, it is very difficult to share medical data and protect data privacy. This paper proposes a data privacy protection method based on K-anonymity for medical alliance chain. The data privacy protection method of Medical Alliance chain in this paper consists of four steps: (1) constructing equivalent classes; (2) medical data slicing; (3) data iteration; (4) medical data reorganization. The scheme of data privacy protection in Medical Alliance chain proposed in this paper has high security, no trusted third party and low energy consumption. It is a privacy protection method suitable for application and medical alliance chain data.
Controlling Complexity and Accuracy of Classification Decision Tree Extracted from Trained Artificial Neural Network

There is growing number of publications devoted to knowledge extraction from fully connected feed-forward artificial neural networks. Although there are not many publications covering ways allowing to control extracted knowledge complexity and precision. The higher complexity is, the higher accuracy can be gained. But in case ANN should be validated by domain expert or just be explainable it should be simple enough - this will lower accuracy of extracted knowledge. The current paper explores influence of parameters used for ANN pruning and neurons outputs discretization and clustering onto accuracy of extracted classification decision tree. Hence reader is presented with experimental validation of effects produced by variation in parameters combination.
A Heterogeneous Battlefield Situation Information Sharing Method Based on Content

Various information systems adopt different information category standards and description methods even for the same battlefield space. This leads to the heterogeneity of information distributed in different systems, which hinders the information sharing among different systems. In this paper, we adopt the idea of schema mapping and design a framework of realizing heterogeneous information sharing based on content. We design a concept-logic tree model to organize the battlefield situation information and realize the mapping from local concept models in different systems to a global unified concept model. By constructing a unified information space, we realize the centralized organization, storage, and management of entity description information. Then, the information broadcasting mechanism and content based information query mechanism are designed to realize information sharing. Theoretical analysis and experiment results verify the effectiveness of the proposed framework.
KARAKTERISASI PANEL SURYA HYBRID BERBASIS SENSOR INA219

Solar energy can be used as alternative energy both radiation and its thermal to meet daily energy needs.One tool that can be used is a hybrid solar cell that converts solar radiation into electricity using solar cells and combined with a thermoelectric module to convert solar heat into additional electrical power to increase the efficiency of solar energy utilization.In this study hybrid solar panels were equipped with thermoelectric generators.Current and voltage measuring devices on this hybrid solar panel use the INA219 sensor, while the control system uses Arduino Uno.The result of INA219 sensor characterization in the measurement of current strength has a relative error of 2.48% and a voltage measurement of 2.52%.The result of DS18B20 temperature sensor characterization has a relative error of 2.87%.While the results of thermoelectric generator characterization obtained that the power produced is 0.64 mW / ° C. Using thermoelectric generators can increase solar panel power by 8.22% on the first day and 6.45% on the second day.
Compressing DNN Parameters for Model Loading Time Reduction

Deep neural network (DNN) has been applied to a variety of computer vision tasks these days. However, DNN often suffers from its enormous execution time even with the aid of GPU. In this paper, we argue that the bandwidth bottleneck between GPU and GDRAM has to be addressed. To reduce loading time, we propose a DNN acceleration approach which compresses DNN parameters before loading model information to GPU and performs decompressing on GPU. Using JPEG compression as an example, the loss of the test accuracy can be kept within 4%, while an 8 × parameter-size reduction is achieved for VGG16.
Pravas Sarthi - A Convenience: MultiLingual Virtual Assistant

Travelling is a necessity nowadays since it breaks the monotony of life and work. Life, for many, has become a mad rush to such an extent that they have forgotten the essence of life. Travelling gives this very essence back to the people. Meeting new people, getting to know different cultures, visiting new places brings a level of peace to one's mind. But, people are more comfortable communicating in their own language when they feel perplexed in an alien area. This paper thus proposes a system that would ease travellers journey by helping them communicate in their native language. The proposes a novel rule-based algorithm which will extract the feature words from the sentence spoken by the user, performs preprocessing using Natural Language Processing(NLP) and based on some predefined rules, it will display the response to the query asked by the user. This paper thereby focuses on the societal benefits of all travelers in an airport with a language friendly system to make their travel comfortable across different cultures and backgrounds.
Imbalanced-Free Memory Selection Scheme Based Continual Learning by Using K-means Clustering

Recently, continual learning is a new training strategy based on replaying memory and allowing beneficial effects to previous tasks. To preserve old information, current continual learning scheme accumulates observed examples into limited buffer or repeatedly trains generative model. This idea of learning scheme is effective to reduce catastrophic forgetting which is deterioration in overall performance when training sequentially. However, there still exists the problem of imbalanced data distribution in limited buffer and it is hard to apply on real-time system due to too long time to train both generative model and classification model. In this paper, we propose sample selection algorithm based on iterative k-means algorithm to improve the memory based continual learning. This approach selects examples to store into a buffer in a unsupervised manner using k-means cluster information. Our experiments on a variant of MNIST and CIFAR-100 datasets show the effects on classification accuracy performance when compared to the state-of-the-art.
A Needs Analysis of Teacher’s Job Competency in Career Decisions in Fisheries and Maritime High School

The purpose of this study was to analyze job competencies which are required when fisheries and maritime high school teachers conduct their job and investigate what job competency is the top priority. The purpose of this study was to help teachers with job competency, and provide students with helpful educational materials through job competency. This study was conducted by all fisheries and maritime high school in Korea were surveyed. From the collected data, paired t-test, Borich needs analysis, and the Locus or Focus model were performed.BR According to the survey on the demand for teachers at fisheries and shipping high schools, the highest priority was Job Guidance Ability(ranking 1st). Based on the Borich requirement analysis results, the retention is low, but is higher than other competencies in the recognition of importance, and was in the first quadrant, which is also the highest priority area in The Locus for Focus model.
Novice Programmer to New-Age Application Developer: What Makes Python their First Choice?

Novice programmers as well as analysts and domain experts associated with development of systems and applications in the new technology era have dilemmas in choosing the right programming language or tool. The issue assumes significance, especially, considering the block of time required to learn a new language or tool, understand its implementation techniques, and provide timely solutions to meet the client requirements. This paper examines why Python is most preferred for all such users.
Automatic Speech Recognition for Ukrainian Broadcast Media Transcribing

A set of speech recognition techniques that allow for Ukrainian broadcast monitoring are covered: speech-to-text conversion; speaker diarization and recognition; text perception enhancement; multilingual aspects.The experimental results are presented and discussed.
Media Competences and Academic Performance of Education Sciences’ Teachers of UCE

Research’s objective was to identify relationship between media competences and academic performance teachers at Education Sciences Faculty of Central University of Ecuador (UCE) during 2018 year; for which, into each career was characterized development’s level of media competences of teachers from faculty of Philosophy, Letters and Education Sciences, identifying areas of media competences application in the teaching, research and connection with society. Research was carried out with a quantitative, non-experimental, field and cross-sectional focus, which includes a phase of descriptive analysis about characteristics and development’s level of variables research, with the purpose of describing phenomenology and analyzing its behavior in educational process of superior level. It is complemented with a second phase of contingency analysis and hypothesis test of correlation with chi-square test to check independence between variables and describe some associated factors. After this process, there is no evidence of relationship between variables, however, there is significant statistically relationship with 95% confidence of media competencies with the subject according to area of each career, in teaching dimension of academic performance.
Speaker Identification Based Proxy Attendance Detection System

Recently, voice biometrics has gained popularity due to its large number of applications. The paper presents Siamese Network and CIFAR network-based architecture for speaker identification. It uses the voice metrics and voice features independent of the uttered words. The model is trained on a dataset containing hundreds of audio clips of 10 speakers. The model achieved a test accuracy of 88% and training accuracy of 98.5%. The proposed model is used for proxy attendance detection in the classroom.
Optical Flow based Crowd Counting in Video Frames

The aim of this paper is to count crowd in video frames. We propose to detect the crowd by focusing on the stationary and moving people in the crowd separately. A deep learning based model is trained on the crowd videos for the stationary and moving part. Features are extracted in two different pipelines (i) from each pair of consecutive frames in a video and (ii) from the optical flow between each pair of frames using CNN. The feature maps obtained from the two pipelines are combined at feature level by mapping them to a single density map. The density map has been used for detecting crowd in video frames. Use of the features from both the pipelines have shown to perform well in crowd detection and counting.
Dependency of Optimum Value of Regularization Parameter in Total Variation Filter on Noise Statistics - A MR Phantom Study

Total Variation - L1 (TV-L1) filter is a widely used smoothing filter, equipped with edge preserving feature. It has the capability of restoring noise-free images from heavily corrupted images. However, the performance of the TV filter heavily depends on the choice of the adequate value of the regularization parameter (λ). In medical image processing, there is a usual practice of adaptively determining ideal values of parameters of smoothing filters from the noise statistics. In this paper, the dependency of the optimum value of λ on noise statistics is studied. Shepp-Logan phantom images corrupted with different levels of noise are denoised with TV filter by varying the value of λ. For each noise level, Peak Signal to Noise Ratio (PSNR) between ground-truth and denoised images is calculated for all λ values. Value of λ at which PSNR exhibits maximum value is considered as the optimum at a particular noise level. The Pearson correlation between optimum values of the regularization parameter and noise variance is observed to be 0.7743, which is not appealing. Variation of λ with respect to the monotonic increase in noise variance is observed to be random. Hence, the adaptive method of computing the ideal value of regularization parameter from the noise statistics is less feasible.
Hashtag Recommender System Based on LSTM Neural Reccurent Network

The successfulness reached by numerous neural network models for encoding word embedding has conducted approaches for encoding vector representation for sentences, paragraphs or even micro-blogs.. Meanwhile, the hashtag is a keyword that denotes the topic of a tweet. Hashtags supply significant information for several text mining tasks, for instance sentiment classification, news analysis, etc. Hence, an appropriate hashtag recommender system is needed to assist users in choosing relevant hashtags for their tweets. Therefore, we present a hashtags recommender method to encode the tweet vector-based representation by using a long short-term memory recurrent neural network. Specifically, we first learn words vector-based representation by training the Skip gram model to generate embeddings of tweets. Then we apply the density-based spatial clustering to gather similar tweets into homogeneous clusters. Subsequently, we recommend the k top highest results of tweets to the user, via calculating their co-occurrence with the nearest clusters centers to a given tweet. Our experiments on a real data set show appropriate results.