Reviewerly/exHarmony huggingface dataset:
Voice Antispoofing System Vulnerabilities Research

Recently, the problem of protecting information systems from various types of spoofing is gaining relevance. The article presents a study of the voice anti-spoofing system for the search for vulnerabilities to text-to-speech attack. As part of the study, a new test dataset was created for the voice anti-spoofing system, which includes about 150,000 audio from more than 15,000 phrases in 25 languages by 8 TTS engines. The study showed uneven recognition quality depending on the voice of the text-to-speech converter and vulnerability to signal noise, which indicates the features of the detector. The results will allow improving the quality of detection of text-to-speech converters.
Research on Power Network Regulation Mechanism Based on Knowledge Mapping

Abstract Knowledge map plays an increasingly important role in industrial processes, but traditional knowledge map representation mechanism can not completely represent the elements required in grid regulation and multi-level coordination for the entity map with entity-relationship as the core or the event graph with the extended time and space concept. Therefore, based on the current representation mechanism, a new knowledge map representation mechanism is intended to be designed in this paper where a knowledge flow representation language for grid regulation will be designed according to the work flow representation language. In the paper, core elements of multi-level regulation in power grid are first analyzed, and then representation language and its query expansion mechanism is designed to realize the power grid regulation mechanism based on knowledge map.
Maritime Target Detection and Tracking

Method of object detection and tracking is one of hot topics in image processing and computer vision field, which is significant not only in military such as imaging guidance and military target tracking, but also for civil use such as security and monitoring and the intelligent man-machine interaction. For marine targets, how to eliminate interference effectively and achieve accurate detection of moving targets is a key problem to be solved. In this paper, we propose a method of image segmentation and line detection to detect the horizon, which can help to determine the area to be detected. We use prior knowledge to detect the ship target in this area. To identify the suspected ship, we detect it in multiple frames. After identifying the ship, we use the mean shift algorithm to lock tracked target. The experiment shows that the algorithm we proposed has good effect of detection and tracking for the ship target in the infrared image, and this algorithm has low computational complexity and small computation. It has been realized in the embedded hardware system and has high application value.
Shaping new ideas: A case study on a library developed 3D model service for university instruction

3D printers represent a significant financial investment for academic libraries. The development of makerspaces and other library provided services centered around this technology is a testament to the role these technologies can play in offering new resources to students. However, given the investment it behooves the academic library to work to develop new models of applying 3D printing in other areas in order to maximize these resources. One way in which the academic library can extend the use of 3D printers is by offering them as a mechanism for creating custom designed 3D models for use in the classroom. By closing the skills gap in providing both the printer and the ability to design objects for faculty, academic libraries can open a new avenue for providing instructional services on campus.
Application of ANN for solar radiation forecasting - case study of Oryahovo

The forecasting of photovoltaic output power is one of the major problems for integration of large-scale solar systems for energy producing into the grid. The most challenging aspect of the solar forecasting is to predict the very short term of meteorological variables, such as solar radiance, ambient temperature, and cloud movement variations. Several forecast techniques for prediction of the solar irradiance are known, but generally the forecast techniques could be classified in three main categories physical, statistical and hybrid method. This paper presents an application of ANN for forecasting of the solar radiance. For this purpose are used data from Oryahovo, Bulgaria. After the training of the ANN it is verified by comparison of the forecasted values with measured data. The precision is satisfying with relative root mean square error of 5%.
Accelerating GPU-based Evolutionary Induction of Decision Trees - Fitness Evaluation Reuse

The rapid development of new technologies and parallel frameworks is a chance to overcome barriers of slow evolutionary induction of decision trees (DTs). This global approach, that searches for the tree structure and tests simultaneously, is an emerging alternative to greedy top-down solutions. However, in order to be efficiently applied to big data mining, both technological and algorithmic possibilities need to be fully exploited. This paper shows how by reusing information from previously evaluated individuals, we can accelerate GPU-based evolutionary induction of DTs on large-scale datasets even further. Noting that some of the trees or their parts may reappear during the evolutionary search, we have created a so-called repository of trees (split between GPU and CPU). Experimental evaluation is carried out on the existing Global Decision Tree system where the fitness calculations are delegated to the GPU, while the core evolution is run sequentially on the CPU. Results demonstrate that reusing information about trees from the repository (classification errors, objects’ locations, etc.) can accelerate the original GPU-based solution. It is especially visible on large-scale data where the cost of the trees evaluation exceeds the cost of storing and exploring the repository.
Essboard: a collaborative tool for using Essence in software development

Knowing the current situation and goals of software development is an activity that takes into account the participation of the whole team in the evaluation of many aspects of it. The Essence standard provides a set of basic aspects that are presented in any software development, which helps achieve this goal. Currently, a set of tools has been developed to support Essence for that activity, however, they leave aside the participation and collaboration of the team, which is necessary to generate a common vision of the state and goals of a project. In this paper, we propose Essboard, a tool that takes a collaborative approach by putting in the forefront, the perspective of each member and the holistic vision of the team about the situation and progress of a project. We confirm that Essboard fulfills this goal through a Formal Technical Review (FTR) based on an Awareness Checklist, showing a noticeable improvement to the support of the collaboration in contrast to the existing Essence tools.
Task Activity Recognition and Workspace Extraction for Nursing Care Assistance in Intelligent Space

With the rapid increase in the elderly population, there is a shortage of caregivers in Japan. Consequently, the workload per caregiver has increased, which is now a major social issue. In response to this, information and communication technology (ICT) systems have been introduced to assist caregivers. However, types of tasks that can be supported are limited. In addition, caregivers need to form a plan of nursing tasks according to various states of care receivers. This requires caregivers to be highly skilled, which in turn increases their cognitive burden. To resolve this, in this study, we aim to realize an assistive system to reduce the cognitive burden of caregivers and improve the efficiency of their performance. We mainly apply the iSpace (intelligent Space) concept to realize the provision of assistance based on the observations of the caregivers and care receivers. Firstly, task activity recognition is required. This paper focuses on task activity recognition. We assume that task activities of caregivers are well related with locations in the nursing environments. Thus, we propose a method to determine a caregiver's task activity based on his or her position and orientation in the potential workspaces defined in advance. In addition, to enhance this approach to wider situations, the potential workspaces should be extracted automatically. For this, we also propose a method to extract potential workspaces of caregivers based on hierarchical clustering of movement trajectories of caregivers. Through experiments, the caregiver's task activity was shown to be accurately recognized and the potential workspace of the caregiver was also successfully extracted.
Human action recognition based on multi-mode spatial-temporal feature fusion

Motion representation plays a vital role in human action recognition. In recent few years, the application of deep learning in action recognition has become popular. However, there are great challenges in extracting accurate motion features. In this study, a novel feature representation that combines multi-scale spatial-temporal feature is proposed. This descriptor contains spatial-temporal information for three mode, which are extracted from three input channels of RGB images, RGB difference images and binary XOR images. Specifically, a network that consist of convolutional neural network (CNN) and long short-term memory (LSTM) extract spatial-temporal feature from RGB images and RGB difference images respectively. On the other hand, global motion information is extracted from binary XOR images using another separate CNN network. Then, we combine this features from the three channels as a new video feature representation. Finally, an extreme learning machine (ELM) is adopted as classifier. Experimental results on UCF-50 dataset show the superiority of the proposed method.
Machine Translation Using Deep Learning: A Comparison

In recent days, machine translation is rapidly evolving. Today one can find several machine translation systems that provide reasonable translations, although they are not perfect. The main objective of machine translation is to provide interaction among the people speaking two different languages. Machine translation, being an important task of natural language processing, leads to the development of different approaches, namely, rule-based machine translation, statistical machine translation, and neural machine translation for the translation process. The recently proposed method is the neural machine translation which improves the quality of translation between natural languages through neural networks. Neural machine translation led to remarkable improvements in the translation process by retaining the contextual information. End-to-end neural machine translation uses RNN Encoder-Decoder mechanism to train the neural translation model with bilingual corpora which is bilingual parallel sentence pairs, an important resource of machine translation. NMT has a reasonable BLEU score which is the evaluation metrics for machine translation. In this paper, we present a survey on the different kinds of machine translation approaches with their strengths and limitations and the various evaluation metrics to measure the accuracy of the translation.
Article Detection on the Web using DOM and text based features

Web pages are constantly getting flooded with distractive content like ads, popups, etc. which take away the focus from the main article of the page. Compared to desktops, smartphones-whose usage for browsing online has been on a steep upward trend-have a smaller screen size thereby compounding the problem. To give the user a good browsing experience on phone and hence increase their engagement with the browser, it is imperative to provide a clutter-free experience to him. Existing techniques do this in two phases-phase one detects if there is an article on a given Web page and phase two proceeds to extract the article in order to display it, removing unwanted elements of the Web page. In this paper, we propose to use a random forest model for article detection in Web pages using DOM and text-based features. With this model, we were able to achieve a precision of 0.91 and a recall of 0.92 which beats the state of the art techniques which are being used by major Android browsers in the market today.
Evaluation and Analysis of Service Broker Algorithms in Cloud-Analyst

Cloud computing is a reasonably distributed computing with heterogeneous computing resources. Resource sharing, convenience of resources that correspond to the needs and a lot of box-management issues facing the cloud. Here, during this paper, analysis and comparison of varied existing algorithms of service brokers and load equalization algorithms in cloud computing . To check the implementation of existing algorithms, varied modeling tools were developed, for instance, a Cloud Analyst. The performance of the present policies of the service broker is compared by considering total response time and execution time in data center.
Comparison of Emoji Use in Names, Profiles, and Tweets

Online social networking applications are popular venues for self-expression, communication, and building connections between users. One method of expression is that of emojis, which is becoming more prevalent in online social networking platforms. As emoji use has grown over the last decade, differences in emoji usage by individuals and the way they are used in communication is still relatively unknown. This paper fills this gap by comparing emoji use across users and collectively in user names, profiles, and in original and re-shared content. We present a methodology that enables comparison of semantically similar emojis based on Unicode emoji categories and subcategories. We apply this methodology to a corpus of over 44 million tweets and associated user names and profiles to establish a baseline which reveals differences in emoji use in user names, profile descriptions, non-retweets, and retweets. In addition, our analysis reveals emoji super users who have a significantly higher proportion and diversity of emoji use. Our methodology offers a novel approach for summarizing emoji use and enables systematic comparison of emojis across individual user profiles and communication patterns, thus expanding methods for semantic analysis of social media data beyond just text.
Robotic Coverage for Continuous Mapping Ahead of a Moving Vehicle

In this paper we investigate the problem of using a UAV to provide current map information of the environment in front of a moving ground vehicle. We propose a simple coverage plan called a conformal lawn mower plan that enables a UAV to scan the route ahead of the ground vehicle. The plan requires only limited knowledge of the ground vehicle's future path. For a class of curvature-constrained ground vehicle paths, we show that the proposed plan requires a UAV velocity that is no more than twice the velocity required to cover the optimal plan. We also establish necessary and sufficient UAV velocities, relative to the ground vehicle velocity, required to successfully cover any path in the curvature restricted set. In simulation, we validate the proposed plan, showing that the required velocity to provide coverage is strongly related to the curvature of the ground vehicle's path. Our results also illustrate the relationship between mapping requirements and the relative velocities of the UAV and ground vehicle.
Data Mined Models for Predicting In-hospital Mortality of Emergency Admissions at Time of Hospital Admission Robert Steele

Emergency admissions involve unplanned admissions for which admission must occur at the earliest possible point of time. Being able to predict in-hospital mortality at the point of admission has significant clinical importance. In this work we have drawn from a large state-wide dataset to train predictive models for in-hospital mortality of emergency admissions, that can be applicable at time of admission. A number of models demonstrate high predictive performance, with AUC scores of up to 0.861. The approach taken represents an alternative to models based upon rich clinical data and may offer a number of comparative advantages in terms of very early point-in-time applicability and broad generalizability.
Hand Gesture Recognition for Indian Sign Language using Skin Color Detection and Correlation-Coefficient algorithm with Neuro-Fuzzy Approach

Sign language recognition (SLR) system is helpful for individuals who can't talk or hear. One can easily communicate with this kind of people using SLR system. In this paper we are focusing on SLR approach where we have apply our proposed algorithm to recognize hand gestures of routine words like Morning, Night, Monday, Tuesday and so forth. Total 10 routine words we have recognize using our proposed technique. For recognize hand pose we have utilized skin color detection algorithm. Further we have applied correlation-coefficient algorithm to distinguish closeness features which are further continue in Neuro-fuzzy(NF) classification algorithm to recognize words. Presented algorithm tried on Matlab. With taken of 10 routine words our proposed framework of techniques got 92% correction rate.
On the equational graphs over finite fields

In this paper, we generalize the notion of functional graph. Specifically, given an equation E(X,Y)=0 with variables X and Y over a finite field Fq of odd characteristic, we define a digraph by choosing the elements in Fq as vertices and drawing an edge from x to y if and only if E(x,y)=0. We call this graph as equational graph. In this paper, we study the equational graph when choosing E(X,Y)=(Y2−f(X))(λY2−f(X)) with f(X) a polynomial over Fq and λ a non-square element in Fq. We show that if f is a permutation polynomial over Fq, then every connected component of the graph has a Hamiltonian cycle. Moreover, these Hamiltonian cycles can be used to construct balancing binary sequences. By making computations for permutation polynomials f of low degree, it appears that almost all these graphs are strongly connected, and there are many Hamiltonian cycles in such a graph if it is connected.
Radio signals modulation mode recognition based on semisupervised deep learning

With the rapid development of radio communication technology, the application requirement for recognizing modulation mode of radio communication has also increased, therefore it has become a research focus. This paper proposes a method by combining semi-supervised ideas with the CNN network to realize radio modulation recognition. The basic idea is to design a deep learning model based on convolutional neural networks, which will make full use of a large number of unlabeled radio signals in air as well as the labeled radio signals to train convolutional neural networks. Our experimental results illustrate that the performance of our proposed method in recognizing BPSK, QPSK, 8PSK, 4QAM, 16QAM, and 64QAM modulation modes in various environments is better than MLP and CNN.
Implementation of Dubido Based on Contextual in Improving Students Achievement on Rate Reaction

Abstract In recent year, efforts to develop learning media for various learning models continue to be the center of attention of education stakeholders. This is caused not only because it is facilitated by information technology but there is a change in the lifestyle of students due to the presence of information and communication technology. In this research, the development of android-based mobile education (Dubido) is based on contextual learning for reaction rate material. Research which is an R&amp;D research with the aim of improving student learning outcomes and interests. The sample of this research were undergraduates’ students from chemistry education study program. The sample was divided into two classes where the experimental class 1 was taught using dubido based on contextual while the second experimental class was taught by direct instruction. The results showed that the media of dubido that had been developed increased the interest of students studying the material reaction rates. And the implementation of dubido based on contextual was given positive effect to increase student’s achievement.
ProveDoc: A Blockchain Based Proof of Existence with Proof of Storage

In the digital world, document management is transitioning from piling up physical documents to electronic documents for efficient way of managing the information towards increasing the productivity. A number of digital artefacts such as educational certificates, Memorandum of Understanding (MoU), driver's licenses, health records, employee service records, audit reports, sale deeds, movable & immovable property records, birth & death certificates, tax returns and so on are generated by ICT systems. Usually these documents are associated with important aspects such as temporal existence, origin / owner & specific content. These all play an important role in identifying ownership of particular asset, parties involved in transactions, MoU content & date of signing etc. Present day document management systems lack transparency, security, central storage and efficiency. This is resulting in fraudsters fabricating fake documents beyond recognition. This problem can be solved only when the documents are stored in tamperproof manner and there is a mechanism for verifying the temporal existence of the document, origin & integrity of the document. C-DAC proposes Blockchain based ProveDoc solution to address this problem.
Accuracy-Aware Adaptive Traffic Monitoring for Software Dataplanes

Network operators have recently been developing multi-Gbps traffic monitoring tools on commodity hardware, as part of the packet-processing pipelines realizing software dataplanes. These solutions allow the execution of sophisticated per-packet monitoring using the processing power available on servers. Although advances in packet capture have enabled the interception of packets at high rates, bottlenecks can still arise in the monitoring process as a result of concurrent access to shared processor resources, variations of the traffic skew, and unbalanced packet-rate spikes. In this paper we present an adaptive monitoring framework, MONA, which is resilient to bottlenecks while maintaining the accuracy of monitoring reports above a user-specified threshold. MONA dynamically reduces the measurement task sets under adverse conditions, and reconfigures them to recover potential accuracy degradations. To quantify the monitoring accuracy at run time, MONA adopts a novel task-independent technique that generates accuracy estimates according to recently observed traffic characteristics. With a prototype implementation based on a generic packet-processing pipeline, and using well-known measurements tasks, we show that MONA achieves lossless traffic monitoring for a wide range of conditions, significantly enhances the level of monitoring accuracy, and performs adaptations at the time scale of milliseconds with limited overhead.