Reviewerly/exHarmony huggingface dataset:
Ray-Casting Based Interaction Using an Extended Pull-Out Gesture for Interactive Tabletops

In this paper, we present a method that allows the users to manipulate a remote object on tabletops using an extended pull-out gesture. The user performs the gesture by moving a ray-cast point projected from the dominant hand between two ray-cast points projected from the non-dominant hand. This allows the user to manipulate a remote object on a tabletop in a similar manner to manipulating a close object. We implemented a test system to the feasibility of our method and we showed applications to manipulate a remote object.
Web Based Design for E-Learning Contents on Cloud Network

The purpose of this research is to study and develop e-learning lessons based on learning theories and the designed principles of e-learning lessons for effective e-learning lessons that the students are satisfied with the learning through the lessons and learning using e-learning lessons can achieve the academic achievement. Therefore, this research has developed e-learning lessons for each course providing the knowledge and techniques of learning contents in all interested subjects and we will deliver them to all faculty members at King Mongkut's University of Technology North Bangkok as a teaching medium for future generations of students to develop knowledge sustainably. The related research can be divided into 3 areas: (1) code writing (2) data storage (3) server simulation. This will demonstrate that communication lessons by the developed e-learning system effectively.
Point of Interest Recommendation by Exploiting Geographical Weighted Center and Categorical Preference

Point of interest (POI) recommendation is one of the indispensable services in location-based social networks (LBSNs). POI recommendation helps users find new locations and better understand the city. In LBSNs, the aspects, such as geographical information and categorical information, improve the accuracy of POI recommendation. In this paper, we propose two new techniques to improve the recommendation accuracy; 1) weighted center of a target user's each active area and 2) category-dependent threshold for categorical preference. The weighted center represents density-based center of a target user's active area. The geographical aspect usually adopts the target user's active areas that he frequently visited. Although previous researches define the active area by its active center and its radius, they choose the location of the most frequently visited POI as the active center even if there exist several POIs that have similar number of check-ins, which results in miss-definition of active center. Our weighted center is able to handle the target user's check-in probability, which follows a power-law distribution. Besides, previous researches predict users' preference for categories; however, they neglect the fact that different categories have different users' preference distributions. For example, a specific category has wide-range of subcategories to be preferred by user, but another category has a few subcategories to be preferred, even if there are many subcategories in the category. Thus, we set different thresholds to select candidate subcategories in each category. Experimental result with Weeplaces dataset shows that our method outperforms other baselines by at least 16.93% in F1-score@5.
Enabling Design of Middleware for Massive Scale IOT-based Systems

Recently, the Internet of Things (IoT) technology has rapidly advanced to the stage where it is feasible to discover, locate and identify various smart sensors and devices based on the context, situation, their characteristics and relevancy to query for their data or control actions. Taking things, a step further when developing Large Scale Applications requires to overcome two serious issues. The first issue is find a solution for data sensing and collection from a massive number of various ubiquitous devices when converging these into the next generation networks. The second important issue is to deal with the "Big Data" that arrive from a very large number of sources. This research emphasizes the need for finding a solution for a large scale data aggregation and delivery. The paper introduces biomimetic design methods for data aggregation in the context of large scale IoT-based systems.
Language Identification of Bengali-English Code-Mixed Data using Character &amp; Phonetic based LSTM Models

Language identification of social media text still remains a challenging task due to properties like code-mixing and inconsistent phonetic transliterations. In this paper, we present a supervised learning approach for language identification at the word level of low resource Bengali-English code-mixed data taken from social media. We employ two methods of word encoding, namely character based and root phone based to train our deep LSTM models. Utilizing these two models we created two ensemble models using stacking and threshold technique which gave 91.78% and 92.35% accuracies respectively on our testing data.
An Efficient Encryption Algorithm for Perfect Forward Secrecy in Satellite Communication

Satellite communication is among those applications where data confidentiality is required, but at the same time, computational resources are limited as well as the delay is critical. For these types of applications, symmetric key encryption algorithms are preferred over asymmetric key algorithms, due to lower computational cost and faster speed of operation. Furthermore, due to various critical aspects of information security, using Perfect Forward Secrecy (PFS) in many communication applications is advantageous. There are two ways to achieve PFS; either a mechanism for new key generation is required, or some protocols are used for sharing new session keys before communication. Both of these ways, at minimum, append additional computational load and delay in communication, which are critical in satellite communication. This is probably the most significant barrier that satellites do not use PFS for every communication. This paper presents a novel encryption algorithm where a message key is generated such that later only Exclusive OR (XOR) operation can be used to produce the ciphertext. Hence, the proposed algorithm eliminates the need for two separate algorithms for message key generation and encryption for PFS. Security analysis and experimental results show that proposed encryption algorithm is not only secure but also faster and cheaper than the widely used AES-CTR encryption algorithm. Therefore it is suitable to be used in satellite communication for PFS.
A Latitudinal Study on the Use of Sequential and Concurrency Patterns in Deviance Mining

Deviance miningGenga, Laura is an emerging area in the field of Process Mining, with the aim of explaining the differences between normal andPotena, Domenico deviant process executions. Deviance mining approaches typically extract representative subprocesses characterizing normal/deviant behaviors from an event log and useChiorrini, Andrea these subprocesses as features for classification. Existing approaches mainly differ for the employedDiamantini, Claudia feature extractionZannone, Nicola technique and, in particular, for the representation of the patterns extracted, ranging from patterns consisting of sequence of activities to patterns explicitly representing concurrency. In this work, we perform a latitudinal study on the use of sequential and concurrency patterns in deviance mining. Comparisons between sequential and concurrency patterns is performed through experiments on two real-world event logs, by varying both classification and feature extraction algorithms. Our results show that the pattern representation has limited impact on classification performance, while the use of concurrency patterns provides more meaningful insights on deviant behavior.
Aspects of Deep Learning: Hyper-Parameter Tuning, Regularization, and Normalization

Deep learning today is applied to many different application areas and that intuitions about hyper-parameter settings from one application will differ to another application. There is a lot of cross-fertilization among different application domains. To implement more complex models, such as convolutional neural networks or recurring neural networks, is not practical to implement everything yourself from scratch. Fortunately, there are now many good deep learning software frameworks that can help you implement these models. This chapter, focus on learning how to implement a Neural Network. Ranging from aspects like: Hyper-parameter tuning helps the network to train quickly. Setting up user data helps in quickly finding a good high-performance neural network. Analyzing the impact of bias and variance on overall performance. Applying different forms of regularization toward reducing variance of Neural network. Making Optimization algorithm runs quickly with reasonable learning rate using Batch normalization. Additionally, A sense of the typical structure of a TensorFlow program is developed using Python in Jupyter notebook and to start up TensorFlow. The Recommendations made by the research work 172is as follows: 99.5% training and 0.25% development, 0.25% testing dataset ratio, is preferred in setting up the data. L2 Regularization is used to reduce variance, Adam optimization algorithm to be used update weights of the gradient. Batch normalization makes hyper-parameter search problem much easier, makes the neural network much more robust. So all the modern deep learning programming framework makes it really easy to code up even pretty complex neural networks.
Implementation Of Medical Pendant Interactive System Based On Virtual Reality Technology

Abstract Nowadays, virtual reality is in the stage of rapid development. Rational application will improve the immersive feeling of human-computer interaction. This paper discusses the construction of interactive experience learning system from two perspectives of platform interaction experience and user demand. This system can provide the operation and practice platform of medical tower related equipment, and build a realistic medical environment based on Unity3D and HTC Vive. The base station of the equipment realizes the real-time positioning of users through the signals transmitted by the handle, in order to ensure the effectiveness and immersion of user interaction, and providing information such as equipment introduction, operating instructions, precautions and emergency operation tips of the medical tower. In order to improve the learning efficiency of users, the learning progress of users is recorded in real time, the tactile feedback device on the handle is used to prompt users when they make mistakes. This system provides an interactive platform for medical staff and professionals to learn and train medical hanging towers. It is also expected to serve as the basis for further research on virtual reality technology in the medical industry and improve people’s interactive experience in the virtual environment.
Multilevel Data Encryption Using AES and RSA For Image and Textual information Data

In our day by day life day's style we moving starting with one spot then onto the next spot with writings, records over the web are the undertakings in like manner identified with puzzle PC key arrangement of PC guidelines assumes an a major job in a cryptosystem, which secures those touchy and private information. With the extended cryptanalysis, verifying intuitive media data sight and sound information against variations of assaults against different sorts of ambushes might be a testing work. This paper presents a nascent picture mapping method for doing thing for encoding the message into relative concentration by scramble plain content believer into HEXADECIMAL. The changed over Hex qualities are assembled together to type of framework. Pseudo Random Number Engenderer (PRNG) circuits are basic central of cryptographic structure. These days, the aim of information security has wound up more prevalent than the back, due to the particularly that especially in open system frameworks, there's a potential risk of making fragile information, for example, military requests, wellbeing records and individual data to interceptor. The enhancement of overwhelming cryptographic plans to arrangement of information security. In the correspondence medium, secured information utilizes identified with ways and working or delivering something of we tried our method for getting things done with various info content sizes. The substance Cover to use to stow away the fragile information was vigorously effectively and did not reveal the closeness of any puzzle code data. It tends to be combination these two cryptographic systems gives a viable answer for information concealing and data transmission through unbound channels. The outcome demonstrates that the proposed sizably voluminous arrangement/format/duplicitous arrangement is inducing a ton with next to no waste and equipped for accomplishing something great/great when contrasted with famous subsisting methods for getting things done.
A Riemannian approach for free-space extraction and path planning using catadioptric omnidirectional vision

This paper presents a Riemannian approach for free-space extraction and path planning using color catadioptric vision. The problem is formulated considering color catadioptric images as Riemannian manifolds and solved using the Riemannian Eikonal equation with an anisotropic fast marching numerical scheme. This formulation allows the integration of adapted color and spatial metrics in an incremental process. First, the traversable ground (namely free-space) is delimited using a color structure tensor built on the multi-dimensional components of the catadioptric image. Then, the Eikonal equation is solved in the image plane incorporating a generic metric tensor for central catadioptric systems. This built Riemannian metric copes with the geometric distortions in the catadioptric image plane introduced by the curved mirror in order to compute the geodesic distance map and the shortest path between image points. We present comparative results using Euclidean and Riemannian distance transforms and show the effectiveness of the Riemannian approach to produce safest path planning.
Advanced Logic Level Design Methodology for a Secure DPA Resistant FPGA

The detection of both software and introduced faults by differential fault analysis attacks is used to protect the circuit that can be added in to the cryptographic systems. The trustworthy of the specified devices can be improved by using protection to the circuit, it is also responsible to suffer more power reduction and the other methods. The attacks of power analysis attacks and differential fault analysis attacks are a section of side channel attacks; it is used to give power consumption measurements, responsible for giving preservation of secret information stored in crypto-system. In this paper, to increase the security of crypto-system against PAAs, we introduce the secure double rate register (SDRRs) as a register transfer level (RTL) countermeasures standard (AES)-128 designs is exploited in the SDRR. The generation of random and real information safeguarded to protect the both combinational and sequential logics by calculating combinational path. The previous technique of RTL countermeasure requires the fake combinational path to generate the random information; our proposed technique does not need this duplication process. To execute a protected DPA resistant crypto-system, a new design methodology is described in this paper. An experimental result indicates a maximum time reduction in the power consumption fluctuations.
Finger Motion Measurement System for Telexistence Hand Manipulation

This research focuses on finger motion measurement system of telexistence robot to realize precise hand manipulation. Wearable sensor glove which measures operator's finger joint angles is used in most cases for reproducing his/her finger motion to robot hand. However, angle-based finger motion measurement is difficult to estimate correct position of fingertip because of hand shape difference between individuals. On the other hands, optical position measurement such as motion capture can obtain exact position, but has problem of measuring stability because of occlusion. To overcome this issue, we propose a finger motion measurement system which consists of sensor glove with motion capture. To calibrate individual differences, the kinematic parameters of operator's hand such as bone length are estimated at first, and then the hand model predicts fingertip position from sensor glove data. Once the parameters are obtained, both motion capture and hand model provide position of fingertip in parallel, and the measurement becomes more stably against occlusion. The performances of the proposed system regarding parameter estimation and precision accuracy were evaluated during finger movement in 3D space.
Non-numerical nearest neighbor classifiers with value-object hierarchical embedding

Non-numerical classification plays an essential role in many real-world applications such as DNA analysis, recommendation systems and expert systems. The nearest neighbor classifier is one of the most popular and flexible models for performing classification tasks in these applications. However, due to the complexity of non-numerical data, existing nearest neighbor classifiers that use the overlap measure and its variants cannot capture the inherent ordered relationship and statistic information of non-numerical data. This phenomenon leads to the classification limitation of nearest neighbor classifiers in non-numerical data environments. To overcome this challenge, we propose a novel object distance metric, i.e., value-object hierarchical metric (VOHM), which is able to capture inherent ordered relationships within non-numerical data. Then, we construct two nearest neighbor classifiers, i.e., the value-object hierarchical embedded nearest neighbor classifier (VO-kNN) and the two-stage value-object hierarchical embedded nearest neighbor classifier (TSVO-kNN), which take advantages of both VOHM and non-numerical feature selection. Experiments show that both VO-kNN and TSVO-kNN could mine more knowledge from data and achieve better performance than state-of-the-art classifiers in non-numerical data environments.
G2P and ASR techniques for low-resource phonetic transcription of Tagalog, Cebuano, and Hiligaynon

Philippine linguists are tasked with documenting over 170 indigenous languages. A key part of this documentation is the phonetic transcription of recorded speech, which is typically done by hand, and is often expensive and time-consuming. Automated phonetic transcription systems provide a faster and cheaper alternative to manual transcription, but no such system has yet been developed for most Philippine languages. In this paper, we present an implementation of three APT methods—grapheme-to-phoneme conversion, automatic speech recognition, and adaptive alignment—for transcription of small speech corpora in Tagalog, Cebuano, and Hiligaynon. We show that the G2P, adaptive, and select ASR models perform at par with human transcribers while greatly reducing total time and costs. These systems serve as a competent baseline for future developments in APT for Philippine languages, and are expected to facilitate further research and advancements in Philippine linguistics and speech technology.
Probing Yarns about Ageing and Making

Making often involves creating, repurposing, handiwork and shared knowledge. Making is beneficial to its participants, with craft groups for older adults becoming more popular. In order to engage with older makers and explore their approach to ageing through their making, we created yarn probes: a method of enquiry to gain rich insight into a person's lived experience by building on their passions, creativity, the reflective and tactile nature of crafting, and storytelling. The yarn probes method involves eliciting stories and insights from participants as they craft artifacts in yarn and reflect on their making practices. We reflect on the use of yarn probes with three participants from two making communities, over a period of 2 months. Yarn probes led to a nuanced understanding of the experience of ageing through making, the ways in which it supports health (meditative, healing, remaining active), participation (altruism, teaching, social interaction, learning) and security (family, sharing, home). We discuss three considerations that make yarn probes effective in generating design insight: the meaning that materials and artifacts hold, opportunities for active engagement and reflection through the making of an artifact, and dialogue between makers and HCI researchers.
Human Pose Detection: A Machine Learning Approach

With the recent advancements in computer science and information technology, computers can now leverage human-like abilities in their operations. Image processing and computer vision technology made many practical applications easy and thus making our life comfortable with machine aid. Medical diagnosis and automatic car driving are two prominent examples in this field. Different types of human poses will be observed in our day to day life. The poses are distinguished from each other at the joints of body parts. In this paper we will discuss the methods to identify four fundamental human poses viz. sitting, standing, handshaking and waving. We are proposing here a framework which can automatically recognize the aforementioned four human poses in any angle or direction from the digital image. For working in this frame work we have created a image database of our own, which is described in subsequent chapter. Our working methodology involves image processing and neural network techniques. Finally, the performance of the proposed system is illustrated.
Comparing the Efficacy of Static and Dynamic Graph Types in Communicating Complex Statistical Relationships

Graphs are useful tools to communicate meaningful patterns in data, but their efficacy varies considerably based on the figure's construction and presentation medium. Specifically, a digital format figure can be dynamic, allowing the reader to manipulate it and little is known about the efficacy of dynamic figures. This present study compared how effectively static and dynamic graphical formats convey relationship information, and in particular variable interactions. Undergraduates (N = 128, 56% female, Mage = 18.9) were given a brief tutorial on main effects and interactions in data and then answered 48 multiple-choice questions about specific graphs. Each question involved one of four figure types and one of four relationship types (main effect only, interaction only, main effect and interaction, or no relationship), with relationship types and graphical formats fully crossed. Multilevel logistic regression analysis revealed that participants were fairly accurate at detecting main effects and null relationships but struggled with interaction effects. Additionally, the static 3D graph lowered performance for detecting main effects, although this negative effect disappeared when participants were allowed to rotate the 3D graph. These results suggest that dynamic figures in digital publications are a potential tool to effectively communicate data, but they are not a panacea. Undergraduates continued to struggle with more complicated relationships (e.g., interactions) regardless of graph type. Future studies will need to examine more experienced populations and additional dynamic graph formats, especially ones tailored for demonstrating interactions (e.g., profiler plots).