Reviewerly/exHarmony huggingface dataset:
Deep Hybrid Networks Based Response Selection for Multi-turn Dialogue Systems

Proper response selection is an important challenge for a meaningful multi-turn dialogue. To this end, not only the coherence among the whole dialogue but also the interaction between utterance in adjacent turns need to be properly employed as the context for response selection. In this paper, we propose a deep hybrid network (DHN) to distill such contextual information. First, we match the response with each utterance and filter internal noises with recurrent neural networks. Second, several deep convolutional blocks perform as a feature extractor and output a matching vector to be fused into a final matching score. During this period, complex contextual information across the whole conversation can be thoroughly blended and captured. The empirical study on two commonly used public datasets has shown the proposed model's potential.
A Conceptual Framework for an Enhancing Running Motivation by Matching Music and Real-time Training Load

Running with music, it can not only capture attention, trigger emotions, adjust or control mood, encourage rhythmic movement, but also achieve higher values of maximal heart rate with music than without music. Moreover, when a runner run with the difference music tempo such as slowtempo, medium-tempo, and fast-tempo, the runner will achieve differences maximal heart rate and running performance. In general, the training benefit will be separate to 5 target zones by the percent of maximum heart rate range. However, running control without notification and trigger is hard to achieve the benefit in the specific target zones. In this paper, a conceptual framework that facilitates the development for enhancing the running motivation by automatic matching difference music tempo and specific training load in real-time is proposed. Specifically, it provides an essential mechanism that enables the running motivation service to achieve the training benefit for developers and then builds into the corresponding services.
Automation of Business Processes in Education as a Tool of Effective Management

: The development of the digital economy and the modernization of education in Russia necessitate the automation of business processes in the education. The research features the management of educational activities in university. The paper describes the process of creating a single automated information system for university management. The research objective was to identify the main elements of a single automated information system related to the management of educational activities in university and their compliance with legislation in the field of education of the Russian Federation. To provide a systematic approach to the solution of the problem, the authors used the following research methods: analysis, synthesis, formalization, and modeling. The article introduces aninformation systems based on different approaches, their advantages and disadvantages. It describes the subsystems that automate the main business processes related to the management of educational activities in the Altai State Technical University. The case can help to solve similar problems in other educational organizations.
Detection and localization of image tamper with scalable granularity

Image authentication techniques have recently gained great attention due to its importance for a large number of applications. To protect the authenticity of images, some approaches have been proposed in the past years. These approaches include cryptography, semi-fragile watermarking and digital signatures. In this paper, we propose an authentication technique based on what we call characteristic data digest. The characteristic data is derived from the relative difference between the DC value in a central block and its counterpart estimated by adjacent blocks. The size of the block is scalable, from 1x1 to 8x8, depending on the requirements on granularity of tamper localization. Experimental results show that the technique is significantly superior to semi-fragile watermarking based authentication techniques under the same image fidelity, and feature-based authentication techniques, in tolerance range of legitimate processing, and granularity of detection and localization on the tampered area. Because of the simplicity of the algorithm, our technique can be used in real-time image or video frame authentication.
Contributory Broadcast Encryption on Sharingdata Over Dynamic Group Key Agreement

The Multicasting with Key Administration enables every part to steadfastly keep up a solitary open key pair. To execute the encryption along with decoding ye transmitter and recipient ought to own comparing encryption along with unscrambling keys. For transportation safety measure data to aggregate required communicate encryption (BE). BE sanctions a sender to safely communicate to any subset of people and demand a believed gathering to disperse decoding keys. A sender seeing the open gathering encryption key can confine the decoding to a subset of people from his decision. Following this model, we propose a CBE conspire with short ciphertexts. The program is proved to be completely conspiracy safe under the option n-Bilinear Diffie-Hellman Exponentiation (BDHE) suspicion in the conventional model. As our exhibited frameworks expressed that communicate encryption (BE) is required for secure information redistributing over a gathering and Gathering key understanding (GKA) convention let'screate a classified channel among gathering individuals however because of lack of keymanagement and gathering part denial is an up to now testing issues. To beat the difficulties over prented framework we proposed a Symmetric key communicate encryption (SKBE) which drives the aforementioned issues adequately than our exhibited framework.
Miscellaneous Cost Estimation Techniques : A Study

As the software are increasing in a rapid rate. Due to rise in technology the complex software are increasing vastly there is special need to manage them all based on their quality. Cost estimation is the basic technique that should be used by the software companies for the further cope up with the rise in complex software. It is the major concern and the biggest challenge that should be used by all the companies. So the various techniques used here will tell us about all the basic features of cost estimation. If the cost estimation challenge is resolved than the bigger software's can be easily managed with good quality and at a reasonable cost.
Comparison of phase shifting and spatial carrier methods for 2D to 3D image conversion

Profile measurement system based on optical method is becoming widely applied. Among many methods that has been developed, Digital Fringe Projection Profilometry (DFPP) technique provides high resolution results. In processing the 2D image to 3D image, DFPP goes through some steps, which are phase extraction, phase unwrapping, and baseline offset removal. The most important step is phase extraction because this part will extract the deformed fringe information that can determine the accuracy of the 3D reconstruction results. In this paper, there are two methods of phase extraction that is observed, which are phase shifting interferometry and spatial carrier interferometry. Experiments are done with a specific made object that has 3 depths and 2 types of indentations. The results of each phase extraction methods are compared on not only how they reconstruct the flat part and the indentation type, but also how they restore the depth information.
Innovative Trends and Technologies in LIS

The paper gives an introduction to the present study. This paper provides the need of the study, and its conceptual background. It gives a graphic overview of the use of RFID in libraries. Stating the problem, delineates the aims and objectives to the study. Further, the paper highlights the hypotheses, scope and limitations, the purpose of study. It ends with the Research Design which reflects the research methodology. It deals with review of available literature related to Barcode and RFID in relation to library operations. The paper introduces the models of decision making, which forms the backbone of this study. It presents the analysis and interpretation of the collected data. The treatment of the data includes a number of tables and some illustrations. It culls out and consolidates all the general observations, functional/technical findings and managerial criteria to be considered for choosing RFID technology. This is followed by testing of hypothesis, compliance of objectives and development of the two required models. It gives the suggestions and conclusions. Key words: Innovative Trends, Barcode and RFID technologies, LIS Cite this Article Lad Ashok Tukaram. Innovative Trends & Technologies in LIS. Journal of Advancements in Library Sciences. 2019; 6(Special Issue 1): 99s–105s.
How mental imagery helps robot learning

This work presents a computational model underlying learning in robots. It demonstrates the way in which during exploration, the robots not just gaining knowledge about movement primitives but also mental imagery capability. The target experiment involves permitting a humanoid robot to learn how to retrieve an out-of-reach object using a tool (action sequencing). Exhibiting exploratory behavior in the robot is possible through the help of a reinforcement learning technique and the dynamic movement primitive framework. The results show that the robot can complete a given action sequencing task in a reasonable period of times. In addition, by mean of planning, the robots able to examine actions’ outcome using mental images alone. This could help reducing times and power consumption. Furthermore, two experiments which might be identical to the infant’s age have been conducted and might be used to explain the characteristic of tool use development found in humans.
Maximally Separated Averages Prediction for High Fidelity Reversible Data Hiding

Recently pixel pairing and pixel sorting/selection have been used in prediction-error expansion based reversible data hiding schemes to generate low entropy prediction-error histograms (PEH) necessary for achieving high fidelity. Such schemes generally use the four-neighbor average rhombus predictor as it allows pixel sorting and flexible pixel pairing. In this paper, we propose the maximally separated averages (MSA) predictor that uses the four-neighborhood context. It can replace the rhombus predictor in pixel pairing and sorting based schemes for lowering PEH entropy further to achieve higher performance. At each pixel location, we choose the two maximally separated average values and decide either on using one of them as the predicted value or on avoiding prediction at the pixel location. This is based on the observation that the prediction-error sequence entropy decreases with the increase in the separation between the two average values. Experimental results demonstrate that the state-of-the-art schemes achieve considerable performance improvement by using the proposed MSA predictor.
Implementation of Quantum Decision-Making Based Recommendation Method for Adaptive Bitrate Streaming

Various services including video streaming are provided via the Internet. In the future, as network virtualization technology advances, building and controlling a network for each service is expected to make the network suitable for the service. In this case, it is necessary to design the network construction and operation from the viewpoint of the quality of experience (QoE) of users. Although research on modeling QoE of users has been advanced, the QoE changes according to the user’s psychological effect, so it can be hard to model the complete QoE in the conventional model. On the other hand, quantum decision-making has begun to be drawing attention in recent years as a model for expressing human cognitive state and decision-making. In quantum decision-making, it is possible to represent human behavior, which is hard to express in the conventional cognitive model, and it is expected as a more general model. In this paper, we implement the recommendation algorithm in MPEG-DASH, which recommends the user to select the appropriate bitrate by avoiding the cognitive bias captured by the quantum decision-making model.
Encrypted control system with quantizer

Introduction: As communication networks and cloud services become to play essential roles for many control systems, it becomes desirable to develop security and privacy-preserving algorithms for these control systems. Motivated by this trend, this study considers encrypted control systems.
A Contextual Analysis and Challenges Between Agile and Non-Agile Teams in Working Environment

There are thinks about demonstrating what occurs if coordinated groups are brought into a non-light-footed association, for example higher overhead expenses and the need of a comprehension of deft techniques even outside the groups. This contextual analysis demonstrates a case of work inspirational perspectives that may surface when a dexterous group exists amidst a progressively conventional structure. This contextual investigation was directed at a vehicle producer in Sweden, comprising of an unstructured meeting with the Scrum Master and a semi-organized center gathering. The outcomes demonstrate that the groups felt that the criticism from the encompassing association was unsynchronized bringing about them not feeling acknowledged while conveying their work. Besides, they felt baffled when dealing with non-dexterous groups after have been taking a shot at coordinated ones. This investigation reasons that there were work inspirational effects of fitting a nimble group into a non-spry encompassing association, and in this way this may likewise be valid for different associations.
The Act of Volition as an Ultimate Principle of Classical Rationality

The author, considering an ideal of classical rationality, reveals three its fundamental premises: 1) everything, occurring in the world, is carried out according to rigid laws strictly and precisely described by mathematical language; 2) these laws have universal character and operate in the same manner without an exception everywhere in the Universe; 3) an universality of these laws has not only spatial but also time character as these laws always operated, operate and will operate. The first of these premises was introduced by R. Descartes, which into the Discourse On Method asserts that “a thing is clearly and distinctly perceived that it is of itself true.” The second premise is rooted in I. Newton’s statement that the laws of mechanical interaction act absolutely in the same manner in all the pars of the Universe. The third premise is introduced by C. Lyell, arguing an invariance and eternity of laws of “natural history.” Thus, the ideal of scientific rationality is based on a number of volitional statements that express strong belief in a certain model of the world order. Though founders of this scientific tradition realized the conditional character of these statements, their followers not always take into consideration their presumable nature and accept the paradigmatic statements as the “reality itself.”
Product Prediction and Recommendation in Sustainable E-Commerce Using Association Rule Mining and K-Means Clustering

The tremendous growth of customers and products in recent years poses some key challenges for recommender systems. These are producing high quality recommendations and performing many recommendations per second for millions of customers and products. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. The authors address the performance issues by scaling up the neighborhood formation process through the use of clustering techniques. By using association rule learning, it has been observed that customers who purchase the items t-shirt and jeans have an increasing trend to buy shoes, etc. These systems, especially the k-means clustering-based ones, are achieving widespread success in e-commerce nowadays, and the results are encouraging (i.e., the category silver is preferable as purchasing amount is concerned). Enterprises can use the model to predict the stock and customer for their business sustainability.
Exploring Different Approaches for Parsing Telugu

In this paper we explore different approaches for parsing Telugu. We consider three popular dependency parsers namely, MaltParser, MSTParser and TurboParser. We first experiment with different parser and feature settings and show the impact of different settings. We then explore different ways of ensembling these parsers. We also provide a detailed analysis of the performance of all the approaches on major dependency labels and different distance ranges. We report our results on test data of Telugu dependency treebank provided in the ICON 2010 tools contest on Indian languages dependency parsing. We obtain state-of-the art performance of 91.8% in unlabelled attachment score and 70.0% in labelled attachment score.
A Cross-Distribution User Intention Identification Based on Topic Transfer

At present, in the field of financial science, so as to make intelligent recommendation, the potential financial intention is usually analyzed by the behavior patterns of users. However, due to the diversity of user' behavior and distribution on different platforms, the feature of users' financial behavior are also composed of weak features. In this paper, a cross-distributed user' behavior recognition method based on topic transfer model is proposed. To training a transfer model with APP data in source domain, the model can weaken the influence of different distributions and recognize the intention of the target user. Finally, based on the user's APP online data, we prove that the model based on topic transfer has better performance on cross-distributed data.
Random Variables and Probability Distributions

This chapter discusses the concepts related to discrete and continuous random variables. Initially, we calculate the expected value, the variance, and the cumulative distribution function of discrete and continuous random variables. After that, the main types of probability distribution for discrete random variables are described: discrete uniform, Bernoulli, binomial, geometric, negative binomial, hypergeometric, and Poisson. For continuous random variables, the distributions studied are: uniform, normal, exponential, gamma, chi-square (χ2), Student’s t, and Snedecor’s F. Thus, it will be possible to determine the most suitable distribution for a certain set of data.
Teaching mathematics using computers: Algorithms for problem solving and the role of visualization

We present some examples of problem solving both from secondary school mathematical curricula and from applications and research. Most examples were constructed with the help of a computer and a direct use of numeric or symbolic calculus and interactive geometric software providing graphical solutions. The role of visualization is always pointed out and used to describe solutions and their algorithmic process.
FOCUS ON SECURITY EDUCATION TO INCREASE SECURITY AWARENESS

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 3086-3091ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.0815Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Image compression in resource-constrained eye tracking devices

Resource-constrained embedded devices, operating with images, are becoming increasingly common. Examples include remote low-power smart sensors, wireless sensor networks, autonomous cameras, eye tracking devices, etc. The principal requirements of such devices are the operation in real time, low power, low heat as well as low MIPS. These requirements can be fulfilled with the use of approximated version of original image processing algorithms. The EyeDee™ embedded eye tracking solution (developed by SuriCog) is the world's first innovative solution using the eye as a real-time mobile digital cursor, while maintaining full mobility. Being an example of resource-constrained embedded device, the system consists of a wearable device (Weetsy™ frame) capturing images on the human's eye and an embedded pre-processing device (Weetsy™ pre-processing board) sending these eye images over a transmission medium (wire/wireless transmission) to a remote processing unit for the further gaze reconstruction. This paper is aimed at introducing image compression approaches in the resource-constrained devices in general and some of their implementation in the Weetsy™ pre-processing board in particular.
Eye Tracking ,Control Gestures, Raspberry Pi, Image Processing.

Smart eye tracking system is designed for controlling any devices which has digital screen, with the eye ball movements and gestures without the help of required hardware. This paper proposes the design and implementation of cursor control system based on the movement of the eye ball. Then the movement of the eye ball is tracked and the cursor movement is regulated accordingly and gestures like blinking enables enter and blinking twice enables right click and left click. These gestures and tracking system enables the users to use the entire device. The image processing module consists of webcam and python customized image processing, the eye movement image is captured and transmitted to Raspberry pi 3 model B version 2 microcontroller for processing with open CV to derive the coordinate of eyeball. The coordinate of eyeball is utilized for cursor control on the Raspberry pi screen to control the system.
Mixed reality using binocular smart glasses for surgical navigation

In surgical navigation, to accurately know the position of a surgical instrument in a patient’s body is very important. Using transparent smart glasses is very useful for surgical navigation because a surgeon does not need to move his/her line of sight from the operative field. We propose a new application software development method that is able to show a stereoscopic vision of highly precise 3D-CG medical models and surgical instruments using transparent smart glasses for surgical navigation. We used Mixed Reality (MR) which is a concept exceeding Augmented Reality (AR) by using Microsoft® HoloLensTM. In the operation room, the 3D-CG medical model made from patient’s CT/MRI image data is superimposed on the actual patient position. Since the positional relationship between the 3D-CG medical model and surgical instruments is clear, so it is very useful for surgical navigation. Multiple persons can see one 3D-CG medical model at the same time using multiple HoloLensTM. A user can make the application software within only 10 minutes by preparing 3D-CG medical model file. Therefore, surgeon dentists and clinical staff can make the content easily by themselves. As a result, the method can be utilized daily for routine medical treatment and education.