Reviewerly/exHarmony huggingface dataset:
A New Methodology for Language Identification in Social Media Code-Mixed Text

Nowadays, Transliteration is one of the hot research areas in the field of Natural Language Processing. Transliteration means that transferring a word from one language to another language and it is mostly used in cross-language platforms. Generally, people use code-mixed language for sharing their views on social media like Twitter, WhatsApp, etc. Code-mixed language means one language is written using another language script and it is very important to identify the languages used in each word to process such type of text. Therefore, a deep learning model is implemented using Bidirectional Long Short-Term Memory (BLSTM) for Indian social media texts in this paper. This model identifies the origin of the word from language perspective in the sequence based on the specific words that have come before it in the sequence. The proposed model gives better accuracy for word-embedding model as compared to character embedding.
Thinking Outside the Book: Research on Non-traditional Cataloging &amp; Classification. A Report of the ALCTS CaMMS Cataloging &amp; Classification Research Interest Group Meeting. American Library Association Annual Conference, Washington DC, June 2019

The ALCTS CaMMS Cataloging & Classification Research Interest Group held its program at the ALA Annual Conference in Washington, D.C, Marriott Marquis Hotel on Sunday, June 23, 2019. It was very we...
Developing Competency Statements for Computer Science Curricula

This Working Group aims to take the current approved Computer Science curricula document, CS2013, and redevelop it into competency statements. The CC2020 project has designed and built a prototype of a visualization tool to compare and contrast current computing curricula. Three basic approaches were taken to portray the base data that will be used for the tool: the first being expert-defined competencies, the second based on mining, and the third based on expert-defined knowledge areas. The visualization tool takes competency statements from each of the current approved computing curricula and visually represents them. Using competency to frame curricula and describe educational outcomes in computing is not new. Since the CC2005 report was published several additional curricula have appeared and the information technology, information systems, and software engineering communities have developed three approaches to defining computing competency in the context of developing their curricula reports. In future the CC2020 report advocates that all new curricula will be written as competency statements. Currently the CS2013 curricula is expressed in learning outcomes rather than the competency statements, so it is essential to be able to demonstrate Computer Science curricula in these new terms to accommodate the new direction and demonstrate Computer Science in the new visualization tool.
An Earth Coordinate System for Earthquake Forecasting Using SLHGN

An attempt for an earthquake forecasting has been challenged by the current earth coordinate system. The latest architecture of mHGN which is called SLHGN requires that the observed locations must be spread out regularly, that is within regular grid-like distances. Such a requirement would not be fulfilled, as within the current earth coordinate system a longitude-difference would produce different distances. The extreme examples are locations in equator compared to those on the earth poles. Therefore, an earth coordinate system has been developed to support the ongoing earthquake forecasting technology using SLHGN. Additionally, two important positive issues related to this earth coordinate system have been developed, they are: 1) each location is not represented through two-value (longitude and latitude), but only a single value. This value does not represent a point but an area; 2) the conversion of this earth coordinate system to the x-y Cartesian System requires no angular formulas, which is therefore fast. These issues have given positive support to the SLHGN in forecasting earthquakes. Although the accuracy and the performance are not yet ready to be analyzed properly, because local weather data at the time of an earthquake occurrence is not always available, the characteristics of the SLHGN experiments show very promising results
Reinforcement Learning for Altitude Hold and Path Planning in a Quadcopter

The control and stability of drones is a challenging problem. There is need for a more dynamic and robust control that the drone can use to adjust itself to an unknown environment directly. This paper presents a framework for using reinforcement learning to control altitude of a drone. We use PID to stabilize x and y axis of the drone. The drone is trained using Q-learning of Reinforcement Learning in a simulated environment. The trained model is then tested in the real world. Furthermore, a comparative analysis of reinforcement learning and PID algorithm is presented. Finally, an application of way-point navigation from one given point to other in an environment filled with obstacles at different points formulated as a 3-dimensional grid is presented using Q-learning of Reinforcement Learning.
Mobile Human-Robot Interaction for personalized Health Training

North-Western Germany and the Northern Netherlands Provinces have experienced increasing rural youth-out migration over the last decades. This has had stark economic, social and health implications for elderly populations that continue to reside in these areas. Many are at risk of spending their final years in isolation and increasing immobility, which has a severe impact on people's quality of life in older age and can lead to illness and loss of independence. In order to combat this problem a mobile training assistant application for movement exercises that utilizes a humanoid robot, NAO by Softbank Robotics, was prototypically implemented. The training assistant is designed to be used at home or in senior groups in isolated communities. Primarily, it is aimed to mobilize elderly people to empower them to live independent lives in their own homes and communities for as long as possible. The training assistant has been developed with elderly care workers and physiotherapists.
Comparing Input Modalities for Shape Drawing Tasks

With the growing interest in Immersive Analytics, there is also a need for novel and suitable input modalities for such applications. We explore eye tracking, head tracking, hand motion tracking, and data gloves as input methods for a 2D tracing task and compare them to touch input as a baseline in an exploratory user study (N=20). We compare these methods in terms of user experience, workload, accuracy, and time required for input. The results show that the input method has a significant influence on these measured variables. While touch input surpasses all other input methods in terms of user experience, workload, and accuracy, eye tracking shows promise in respect of the input time. The results form a starting point for future research investigating input methods.
Presentation Attacks in Mobile and Continuous Behavioral Biometric Systems

Active authentication allows an individual’s identity to be continuously verified in a transparent fashion. For devices centered on user convenience, active authentication using behavioral biometrics is an appealing solution to user authentication since behavioral data can be captured as consumers naturally interact with their devices. However, while such implementations are user-friendly and help to counter some of the challenges associated with knowledge-based authentication methods (e.g., easily guessed passcodes), an adversarial attack must be carefully considered. In this regard, to gain unauthorized access to a secured device, an adversary may falsify biometric information through impersonating the legitimate user. This attack is often referred to as a presentation attack or biometric spoofing. Throughout this chapter, various attack scenarios on mobile devices are discussed for gait, keystroke and touch dynamics, and user-device interaction modalities. Presentation attacks are categorized according to the biometric modality, which may differ given the context of the sensor component involved. This chapter exposes multiple research gaps and challenges which could significantly strengthen adversary detection once addressed, while discussing novel research in which no sensor information is required.
Verification of Keyboard Acoustics Authentication on Laptops and Smartphones Using WebRTC

This paper evaluates authentication based on sounds emitted while typing on a laptop's keyboard only with the help of the integrated microphones and a smartphone placed nearby. The sound samples of 26 individuals were recorded in a novel real-world scenario using only web technologies. The data of laptop and the smartphone was compared based on existing approaches. They can reach Equal-Error-Rates (EERs) of 11.2% on the laptop data and 9.3% on the smartphone data.
Public-Key Encryption In The Standard Model Against Strong Leakage Adversary

Abstract Over the years, security against adaptively chosen-ciphertext attacks (CCA2) is considered as the strongest security definition for public-key encryption schemes. With the uprise of side-channel attacks, new security definitions are proposed, addressing leakage of secret keys together with the standard CCA2 definition. Among the new security definitions, security against continuous and after-the-fact leakage-resilient CCA2 can be considered as the strongest security definition, which is called as security against (continuous) adaptively chosen-ciphertext leakage attacks (continuous CCLA2). In this paper, we present a construction of a public-key encryption scheme, namely LR-PKE, which satisfies the aforementioned security definition. The security of our public-key encryption scheme is proven in the standard model, under decision BDH assumption. Thus, we emphasize that our public-key encryption scheme LR-PKE is (continuous) CCLA2-secure in the standard model. For our construction of LR-PKE, we have used a strong one-time signature scheme and a leakage-resilient refreshing protocol as underlying building blocks. The leakage bound is $0.15n\log p -1$ bits per leakage query, for a security parameter $k$ and a statistical security parameter $n$, such that $\log p \geq k$ and $n$ is a function of $k$. It is possible to see that LR-PKE is efficient enough to be used for real-world usage.
Graph-based Data for Accessible Indoor Navigation

In the paper the optimal representation of data based on iLOC and iACC ontologies in labeled property graph data model is explored. We describe and analyse the process where RDF-type data is transformed into Neo4j. The focus is on a specific subtask of the transformation, namely how instance-of a subclass type relationships can be expressed in labeled property graphs. Different solutions of the problem are described and analysed from the perspective of path traversal effectiveness.
Automatic Image Pixel Clustering based on Mussels Wandering Optimization

Image segmentation as a clustering problem is to identify pixel groups on an image without any preliminary labels available. It remains a challenge in machine vision because of the variations in size and shape of image segments. Furthermore, determining the segment number in an image is NP-hard without prior knowledge of the image content. This paper presents an automatic color image pixel clustering scheme based on mussels wandering optimization. By applying an activation variable to determine the number of clusters along with the cluster centers optimization, an image is segmented with minimal prior knowledge and human intervention. By revising the within- and between-class sum of squares ratio for random natural image contents, we provide a novel fitness function for image pixel clustering tasks. Comprehensive empirical studies of the proposed scheme against other state-of-the-art competitors on synthetic data and the ASD dataset have demonstrated the promising performance of the proposed scheme.
Handwritten Tibetan Character recognition based on ELM using modified HOG features

Among the recent variants of neural networks, Extreme learning machine (ELM) is widely popular in the domain of computer vision due to having short training time. Handwritten character recognition is one of the typical problem which has shown promising results using several neural networks. Classification of handwritten characters is primarily a two-step problem where we need to obtain the features from the data set and then applying a classifier to recognize the characters. We have proposed a Center of Gravity (CG) based quad-partitioning of images to extract Histogram of Oriented Gradients(HOG) features which are normally calculated over equally partitioned images. These modified HOG features are used to extract more local shape of the handwritten Tibetan characters, a newly introduced dataset. ELM is then applied for classification the characters. We have also compared our result with traditional HOG features across five popular classifiers for handwritten Tibetan characters. In every cases modified HOG features performed better than the traditional HOG features. Even if we combine modified HOG features along with traditional HOG features the performance show improvement for some classifiers.
On the challenges in programming mixed-precision deep neural networks

Deep Neural Networks (DNNs) are resilient to reduced data precision, which motivates exploiting low-precision data formats for more efficient computation, especially on custom hardware accelerators. Multiple low-precision types can be mixed to fit the dynamic range of different DNN layers. However, these formats are not often supported on popular microprocessors and Deep Learning (DL) frameworks, hence we have to manually implement and optimize such novel data types and integrate them with multiple DL framework components, which is tedious and error-prone. This paper first reviews three major challenges in programming mixed-precision DNNs, including generating high-performance arithmetic and typecast functions, reducing the recompilation time and bloated binary size caused by excessive template specialization, and optimizing mixed-precision DNN computational graphs. We present our approach, Lowgen, a framework that addresses these challenges. For each challenge, we present our solution implemented and tested on our in-house, TensorFlow-like DL framework. Empirical evaluation shows that Lowgen can automatically generate efficient data type implementations that enable significant speed-up, which greatly lowers the development effort and enhances research productivity on mixed-precision DNN.
Elite (E-Book Literacy) for Junior High School Student’s Scientific Literacy in Solar System Material

Abstract Many researches on e-book development have been done, but they are different from the development of e-book literacy (ELITE). The developed ELITE has several advantages, including features that can be utilized to train students’ scientific literacy, the integration as a source of student learning, and teacher’s teaching material in learning. This study aims to produce ELITE which is reliable in terms of validity, practicality, and effectiveness review. The e-book developed is said to be worthy if at least it has good enough criteria. Type of research that is appropriate with the design of ELITE media development to practice scientific literacy is ADDIE (Analysis, Design, Develop, Implement, and Evaluate). The data collection employed included validation, observation and questionnaires. The obtained results show that the validity was in very valid category (learning 91.67%, material 92.71%, media 94.27% and language 90.74%). The practicality results are categorized very good with a percentage of 87.50%. The results of effectiveness based on the pre-test and post-test results are as follows: the scientific literacy competence to explain the phenomenon scientifically increased by 52% and the scientific literacy competence to interpret data and scientific evidence increased by 59%. The results of this effectiveness are included in the quite good category. In addition, the effectiveness results based on students’ positive responses are categorized very well (85.71%). Based on the analysis results, it can be concluded that the ELITE media developed is feasible to be used to train junior high school students’ scientific literacy particularly on solar system material.
Metaheuristic approach to optimal pathfinding in wireless charging of sensor nodes using autonomous bots

Wireless Sensor Nodes play a prominent role in many military and civilian applications. They are used to collect data for various disciplines from diverse environments. The main drawback of this technology is the decrease in energy of the wireless sensor nodes as time progresses. A significant amount of works has been done and justified in using an autonomous bot to charge the wireless sensor nodes. There are a lot of difficulties to overcome to implement the concept of using an autonomous bot to charge the independent wireless sensor nodes. One such cumbersome issue is the scheming of an optimal path for the autonomous bot to charge these wireless sensor nodes efficiently. In this paper, we present a comparative study of various methods Ant Colony Optimization algorithm, Simulated Annealing and Tabu search to compute the tour of the Autonomous bot to recharge the nodes, considering the tour length and the time taken to compute the tour length. The result of this study gives which algorithm is best suited for a given network with a given number of nodes with respect to tour length and time taken to compute the tour.
Engineering education in the conditions of the digital economy

It is shown that one of the consequences of the digital technologies development and associated transformations was the formation of new approaches in the system of specialists training. in higher education The main trends in the transformation of the educational process are analyzed on the example of engineering education. In particular, the necessity of forming interdisciplinary knowledge in engineering competences of interdisciplinary nature in the conditions of digitalization is substantiated, the dynamics of changes in the content of professional competencies of engineering education from Federal State Education Standards3 + to Federal State Education Standards3 ++ and CDIO is shown in conjunction with professional standards.
Visualization of principal strain direction by a mechanical device

Generally, the strains at a point must be measured at least in three directions simultaneously in order to find the principal strain. In this paper, a simple device is proposed to visualize the principal strain direction in structure in real-time. Different from the conventional methods, it is able to indicate the principal strain by detecting the largest deformation in the elliptical zone which is deformed from a circular zone. The mechanism and key structure of the device are introduced in detail. The main factors which influence the sensitivity and accuracy of the visualization are analyzed. The fundamental experiments including a uniaxial compression test and the simply supported beam experiment were conducted to verify the validity of the method. It is found that the device can work well to visualize the direction of principal strain.
Privacy Preserving in Digital Health

Recently, digital health solutions are taking advantage of recent advances in information and communication technologies. In this context, patients' health data are shared with other stakeholders. Moreover, it's now easier to collect massive health data due to the rising use of connected sensors in the health sector. However, the sensitivity of this shared healthcare data related to patients may increase the risks of privacy violation. Therefore, healthcare-related data need robust security measurements to prevent its disclosure and preserve patients' privacy. However, in order to make well-informed decisions, it is often necessary to allow more permissive security policies for healthcare organizations even without the consent of patients or against their preferences. The authors of this chapter concentrate on highlighting these challenging issues related to patient privacy and presenting some of the most significant privacy preserving approaches in the context of digital health.
Improving second grade elementary students’ writing and storytelling skills through digital literacy

Abstract The aims of this study was to improve writing and storytelling skills of second grade elementary school students. Through digital literacy, students can have a meaningful experience when they learn to make and tell a story they have made. Students will be able to explore their competences while using a lot of audiovisual media for finishing writing and storytelling assignment. This research conducted with a qualitative research design by using observation and students’ assessment as research instruments. Based on the findings, a conclusion can be drawn that digital literacy is able to improving second grade students’ writing and storytelling skills
Performance Evaluation and Analysis of Encryption Schemes for Wireless Sensor Networks

Wireless sensor networks (WSNs) technology is one of the most important technologies of the Internet of things. It is effectively applied within several applications in the actual world, including health care, environmental monitoring, tracking, etc. WSNs consist of small sensor nodes having limited resources. The communication between the components of WSNs is unsecured though. Therefore, in order to secure the exchanged data, it is crucial to implement efficient and lightweight cryptographic schemes. In this paper, we evaluate the performance of different cryptosystems for WSNs. Our paper includes a comparison study between the studied encryption algorithms in terms of power consumption, required space memory, and computational time.
A Critical Analysis for Microgrid Formation based on Hourly Load Growth and Available Renewable Energy

In this 21st century, Microgrid is becoming prevalent in distribution System because of a number of benefits that it has. A lot of work is going on to make the integration of microgrid in the present system easier. Our conventional power generation concept is the generation following the load profile and the distribution system is passive in nature. But on integration of the microgrid concept in the distribution system, it becomes an active system. Due to the incorporation of Distributed Generations (DGs), the idea becomes load following the source. It is therefore essential to have the information of availability of the renewable resources and installed generation capacities in advance. The advantage of microgrid lies in the fact that it can operate both in utility grid connected mode and also in autonomous mode. However, the intermittent nature of the renewable resources demands advanced knowledge of their availability for power management within and outside the microgrid. This requires proper prediction of resources a day ahead of actual operation. Wind and solar energy have the highest eligibility as renewable energy resources. Many forecasting techniques are developed and utilized for the prediction of the availability of these energies. In this field of forecasting, Artificial Neural Network (ANN) has proved itself to be very competitive. Long Short Term Memory (LSTM) network is the recent improvement of ANN which has been applied in this work for forecasting both solar and wind energy. This is compared with the forecasting using multiple feed forward neural networks. It is seen that LSTM performs better for day ahead forecasting.