Reviewerly/exHarmony huggingface dataset:
Zoom motion estimation for color and depth videos using depth information

Abstract In this paper, two methods of zoom motion estimation for color and depth videos by using depth information are proposed. Color and depth videos are independently estimated for zoom motion. Zoom for color video is scaled by spatial domain, and depth video is scaled by both spatial and depth domains. For color video, instead of existing methods of zoom motion estimation that apply all of possible zoom ratios for a current block, the zoom ratio of the proposed method is determined as the ratio of the average depth values of the current and reference blocks. Then, the reference block is resized by multiplying the zoom ratio and the reference block is mapped to the current block. For depth video, the reference block is first scaled in the spatial direction by the same methodology used for color video and then scaled by a distance ratio from a camera to the objects. Compared to the conventional motion estimation method, the proposed method reduces MSE by up to about 30% for the color video and up to about 85% for the depth video.
Balancing care with technology

FigureQ How do we get back to the caring environment amidst the proliferation of technology within and outside of our healthcare delivery system? The Future of Nursing report suggested that nurses will be called upon to fill expanding roles and master technologic tools and information systems. Nursing is an information-based profession, and technology helps bring information to the point of care to support nurses' decision-making processes.1 A goal of informatics is to use technology to increase efficiency, make healthcare safer and more effective, and improve quality and outcomes.2 As part of nursing practice, clinical decision support tools have been designed to improve regulatory compliance, such as incorporation of automated measurement tools, point-of-care alerts, links to evidence-based standards of care and clinical guidelines available within the electronic health record, and graphs depicting trending critical parameters. Amidst these technologies, nurses can use the “head, hand, and heart” approach, which incorporates practical know-how with empathic understanding and technical knowledge to provide humane and sensitive care.3 Practicing respect, actively listening, committing to taking the time to sit with patients, and establishing trust and transparency will balance the ubiquitous presence of technology while promoting safe, quality care. The theory of technologic competency as caring in nursing illuminates the coexistence of technology and caring with three key nursing processes: technology knowing (the competent use of technology in treating and caring for the patient as a coparticipant), mutual designing (the nurse and patient codesign a care plan), and participative engagement (shared activities in implementing the care plan and evaluating the patient's response and outcomes).4 Nurse leaders have a dual role in promoting a vision that ensures the clinical perspective is front and center. As experts on the care environment, we can advocate on behalf of our nurses in guiding technology decisions that affect their workflow and patients. This entails being at the table and driving conversations with key stakeholders to understand how technologic tools impact communication, patient interactions, and workflow efficiencies. By engaging staff members through council meetings and listening to how nurses are adopting and using the right tools to reduce patient frustration and improve the care experience, we can provide important feedback for our technology partners. And by defining compassionate and caring behaviors in our education and training, we can mentor staff members in delivering high-tech, evidence-based care and foster a reflective practice that prepares nurses to competently cope with the inevitable change that technology brings. This year we celebrate the Year of the Nurse and Midwife along with the 200th anniversary of Florence Nightingale's birth, whose use of patient data through observations and analysis to guide care paved the way for nursing informatics. Remember the reason why you became a nurse and share your caring stories on social media using #yearofthenurse.
A quantum algorithm for automata encoding

Encoding of finite automata or state machines is critical to modern digital logic design methods for sequential circuits. Encoding is the process of assigning to every state, input value, and output value of a state machine a binary string, which is used to represent that state, input value, or output value in digital logic. Usually, one wishes to choose an encoding that, when the state machine is implemented as a digital logic circuit, will optimize some aspect of that circuit. For instance, one might wish to encode in such a way as to minimize power dissipation or silicon area. For most such optimization objectives, no method to find the exact solution, other than a straightforward exhaustive search, is known. Recent progress towards producing a quantum computer of large enough scale to surpass modern supercomputers has made it increasingly relevant to consider how quantum computers may be used to solve problems of practical interest. A quantum computer using Grover?s well-known search algorithm can perform exhaustive searches that would be impractical on a classical computer, due to the speedup provided by Grover?s algorithm. Therefore, we propose to use Grover?s algorithm to find optimal encodings for finite state machines via exhaustive search. We demonstrate the design of quantum circuits that allow Grover?s algorithm to be used for this purpose. The quantum circuit design methods that we introduce are potentially applicable to other problems as well.
Mobile App for Learning Economics Terminologies

The use of the educational mobile app in teaching and learning is getting popular nowadays due to improvement in technology, better accessibility, users' acceptance and affordances of mobile devices.This paper discusses the conceptual design of Economics mobile app for learning economics terminologies.The app is designed for ubiquitous self-learning based on cognitive theories, which are information processing theory and cognitive theory in multimedia learning.The users are secondary school students in Malaysia.A qualitative study is adopted for data collection where interviews will be conducted with Economics teachers to identify the list of economic terminologies to be included in the app and also to explore the effective methods of delivering the meaning of those terminologies.The app is aimed to aid students to build understanding and knowledge of the economics terminologies, process the information for long term retention and increase their interest in Economics subject to improve economic literacy.The details of the app are explained in this paper.
Single document keywords extraction in Bahasa Indonesia using phrase chunking

Keywords help readers to understand the idea of a document quickly. Unfortunately, considerable time and effort are often needed to come up with a good set of keywords manually. This research focused on generating keywords from a document automatically using phrase chunking. Firstly, we collected part of speech patterns from a collection of documents. Secondly, we used those patterns to extract candidate keywords from the abstract and the content of a document. Finally, keywords are selected from the candidates based on the number of words in the keyword phrases and some scenarios involving candidate reduction and sorting. We evaluated the result of each scenario using precision, recall, and F-measure. The experiment results show: i) shorter-phrase keywords with string reduction extracted from the abstract and sorted by frequency provides the highest score, ii) in every proposed scenario, extracting keywords using the abstract always presents a better result, iii) using shorter-phrase patterns in keywords extraction gives better score in comparison to using all phrase patterns, iv) sorting scenarios based on the multiplication of candidate frequencies and the weight of the phrase patterns offer better results.
Efficient Computation of Boomerang Connection Probability for ARX-Based Block Ciphers with Application to SPECK and LEA

The boomerang connectivity table (BCT) was introduced by C. Cid et al. Using the BCT, for SPN block cipher, the dependency between sub-ciphers in boomerang structure can be computed more precisely. However, the existing method to generate BCT is difficult to be applied to the ARX-based cipher, because of the huge domain size. In this paper, we show a method to compute the dependency between sub-ciphers in boomerang structure for modular addition. Using bit relation in modular addition, we compute the dependency sequentially in bitwise. And using this method, we find boomerang characteristics and amplified boomerang characteristics for the ARX-based ciphers LEA and SPECK. For LEA-128, we find a reduced 15-round boomerang characteristic and reduced 16-round amplified boomerang characteristic which is two rounds longer than previous boomerang characteristic. Also for SPECK64/128, we find a reduced 13-round amplified boomerang characteristic which is one round longer than previous rectangle characteristic.
Analyzing problems of distribution transformer service areas based on customer-side data mining

Abstract With the development of economy and the continuous improvement of living standard, the contradiction between the rapid growth of electric load and the untimely renewal of power grid equipment is increasingly prominent. Power supply reliability issues or frequent power outages and customer complaints exist in many areas. Due to the limited human and material resources of power supply companies, the problems existing in the distribution transformer service areas cannot be resolved timely and thoroughly. As the direct service objective of electric power, customer’s experience of using electricity will reflect the power quality of the distribution transformer service areas to a great extent. Therefore, this paper will start with work orders from customer sides, find out the existing problems in distribution transformer service areas based on the data association rules, and help make informed decisions. The proposed method not only reduces the investment of capital and manpower, but also reduces the number of power outages, and improves the customer satisfaction.
Design and Implementation of a Fuzzy Logic Controller for Inverted Pendulum System Based on Evolutionary Optimization Algorithms

The inverted pendulum is a standard classical problem in the branch ofcontrol and systems. If a cart is bushed by force then its position and angleof the pendulum will be changed. Several controllers may employed,keeping the pendulum arm upright by controlling at the cart location. Inthis search paper, the fuzzy-like PID (FPID) controller has been used tocontrol the inverted pendulum, and the parameters of the controller aretuned with several evolutionary optimization algorithms like a geneticalgorithm (GA), ant colony optimization (ACO), and social spideroptimization (SSO.) The result of tuned FPID with evolutionaryoptimization is compared with conventional PID, and it shows that FPIDwith SSO has been given the best result.
Personal Data and Power Asymmetries in U.S. Collegiate Sports Teams

Collaborations increasingly draw on personal data. We examine personal-data-supported collaborations in a high stakes, high-performance environment: collegiate sports. We conducted 22 interviews with people from four common roles within collegiate sports teams: athletes, sport coaches, athletic trainers, and strength and conditioning coaches. Using boundary negotiating artifacts as a lens for analysis, we describe an ecology of personal data in collaborations among these four roles. We use this ecology to highlight tensions and foreground issues of power asymmetry in these collaborations. To characterize these power asymmetries in the collaborative use of personal data, we propose an extension of boundary negotiating artifacts: extraction artifacts.
COMPARATIVE ANALYSIS OF 5 ALGORITHM BASED PARTICLE SWARM OPTIMIZATION (PSO) FOR PREDICTION OF GRADUATE TIME GRADUATION

&lt;strong&gt;Graduation information is very important for Higher Education involved in education. The data received by students each year is an important part as a source of information for making decisions on the Higher Education side in admitting new students. The results show the PSO-based K-NN Algorithm at k-optimum = 19 has the best performance of the 5 existing algorithms, with an Accuracy value = 74.08% and an Under Curve Area (AUC) value = 0.788. Attributes of Gender, Semester Achievment Index 1, 2, 4, 6 and 7 as well as Employment Status make a real contribution to the right graduation of students. The addition of the Particle Swarm Optimization (PSO) feature always increases the accuracy value, while the highest increase in accuracy value in the Decision Tree (C4.5) Algorithm is 5.21%, the lowest in the Vector Support Engine Algorithm of 1.79%. The K-Nearest Neighbor (k-NN) algorithm corresponds to the third order, it remains the algorithm that has the best value, the highest accuracy value, this is due to the questionable value before discussing the PSO features. For the evaluation phase, the results of the accuracy are much better if using the overall data training (Angaktan 2007-2011), both with the 2011 Force test data or the 2010-2011 Force. Timely graduation of students begins in the class of 2011, with a value of 98.18% (100% resolution), meaning students start graduating on time&lt;/strong&gt;
Smart instruments : Towards an ecosystem of interoperable devices connecting performers and audiences

This paper proposes a new class of augmented musical instruments, “Smart Instruments”, which are characterized by embedded computational intelligence, bidirectional wireless connectivity, an embedd ...
Establishing Trust in the Cloud Using Machine Learning Methods

In the modern era of digitization, cloud computing is playing an important key role. Tons of user’s data is on the cloud and to manage it with all the odds is a mesmerizing task. To provide user’s data integrity and security are the major task for any cloud service provider. In the last few decades, researchers have been motivated to provide security on a cloud with reference to the categorization and classification of security concerns. In the cloud, trust is a major issue with respect to cloud security. The facilities provided by the cloud are too attractive for customers. Cloud has distributed and non-transparent services due to that the users may lose their control over the data and they are not sure whether the cloud provider is trusted one or not. This paper mainly focuses on establishing trust in the cloud using machine learning methods. The trust parameters like availability, confidentiality, accuracy, integrity, resource management, non-repudiation, risk management, protecting communication, hardware security, reliability, and secure architecture were analyzed, and the result shows the value of trust for system can be predicted based on the proposed algorithm.
Robustifying Visual Place Recognition with Semantic Scene Categorization

This research proposes an accurate loop closure detection method for the real-time robot navigation in changing light, viewpoint and weather conditions. It focuses on enhancing the accuracy performance of visual place recognition by integrating the semantics of image scenes with handcrafted features. This method reduces the computational cost of the feature matching process by segmentation of the dataset. The results presented depicts that scene recognition can improve the place recognition even in the large viewpoint, weather and light changes.
Countermeasure against backdoor attacks using epistemic classifiers

In machine learning, backdoor or trojan attacks during model training can cause the targeted model to deceptively learn to misclassify in the presence of specific triggers. This mechanism of deception enables the attacker to exercise full control on when the model behavior becomes malicious through use of a trigger. In this paper, we introduce Epistemic Classifiers as a new category of defense mechanism and show their effectiveness in detecting backdoor attacks, which can be used to trigger default mechanisms, or solicit human intervention, on occasions where an untrustworthy model prediction can adversely impact the system within which it operates. We show experimental results with multiple public datasets and explain the reasons with visualization for effectiveness of the proposed approach. This empowers the war fighter to trust the AI on the tactical edge to be reliable and to become sensitive to scenarios with deception and noise where reliability cannot be provided.
Education 4.0 in Teaching/Learning Strategies

This paper considers training of IT-specialists in a context of the concept of Education 4.0. Based on our experience paper reviews approaches to educational content development and to organization of teaching/learning process of IT-students. The paper discusses organization of interdisciplinary educational content based on infocommunication infrastructure of an enterprise. Formation of a students' personal electronic information-educational environment (EIEE) as well as introduction of elements of informal education into traditional educational process are also considered.
Modification of Q-learning to Adapt to the Randomness of Environment

Q-learning is a typical model-free algorithm in reinforcement learning to achieve a goal by interacting with an uncertain environment. However, conventional Q-learning cannot reach convergence and even learns bad policies when the state transition and the immediate reward of the environment are randomly distributed. This paper gives a modification of the Q-learning algorithm by exploring a Monte Carlo method to settle the above problems. Furthermore, simulation experiments are performed to validate the modified Q-learning algorithm.
The stability of sample storage for complete blood count (CBC) toward the blood cell morphology

Background: Peripheral blood smear examination had a pivotal role in determining diagnosis and as confirmation of the automatic hematology analyzer results. The storage process was highly influential on the morphological stability of the cell. This study aimed to assess the morphological changes in blood cells stored at certain period and temperature. Method: Sample of 30 blood specimens of healthy people with dipotassium ethylenediaminetetraacetic acid (K2EDTA) anticoagulants. The specimens were stored at room temperature (18-25ÂºC) and refrigerator temperature (2-8ÂºC) and were analyzed at the preliminary examination i.e., 8 hours, 16 hours, 24 hours, 48 hours, 72 hours, and 96 hours. Kappa test was used in validating the reading of PBS (Peripheral Blood Smear). The discrimination testing used were paired t-test and Kolmogorov Smirnov test with p value &lt;0.005, which stated to be significant.Â Result: The changes in erythrocytes morphology stored at room temperature (18-25ÂºC) was that the erythrocytes crenation started to happen at 8 hours storage with the grading scale of +2 that were found in 24 (80%) samples, whereas at refrigerator temperature (2-8ÂºC) the grading scale was +1 and found in 13 (43.3%) samples. Spherocytes on erythrocytes began to form at room temperature (18-25ÂºC) at 8 hours storage with grading scale of +1 and was found in 2 (6.7%) samples, whereas at refrigerator temperatures (2-8ÂºC) spherocytes on erythrocytes began to form at 24 hours storage with grading scale of + 2 and was found in 3 (10%) samples.Conclusion: Peripheral blood smear (PBS) examination shall be done immediately to obtain significant results.
Emoji Understanding and Applications in Social Media

introduction Free AccessEmoji Understanding and Applications in Social Media: Lay of the Land and Special Issue Introduction Share on Authors: Sanjaya Wijeratne Holler Technologies, Inc., CA, USA Holler Technologies, Inc., CA, USAView Profile , Horacio Saggion Universitat Pompeu Fabra, Barcelona, Spain Universitat Pompeu Fabra, Barcelona, SpainView Profile , Emre Kiciman Microsoft Research AI, WA, USA Microsoft Research AI, WA, USAView Profile , Amit P. Sheth Artificial Intelligence Institute, University of South Carolina, SC, USA Artificial Intelligence Institute, University of South Carolina, SC, USAView Profile Authors Info & Affiliations ACM Transactions on Social ComputingVolume 3Issue 2May 2020 Article No.: 6pp 1–5https://doi.org/10.1145/3386120Published:01 May 2020 2citation839DownloadsMetricsTotal Citations2Total Downloads839Last 12 Months594Last 6 weeks36 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
THE USE OF THE MOBILE APPLICATION "DROPS" IN THE PROCESS OF LEARNING FOREIGN LANGUAGES

The article deals with the issues of modernization of the educational process and outlines one of the most effective ways of implementing modern methods and techniques of language learning that are crucial for a successful person today. It is determined that despite the fact that in Ukraine there is an educational program for primary, secondary and higher education institutions, it is considered rather outdated and, unfortunately, negatively affects not only the knowledge base, but also the attitude of people to the educational process. The article analyzes the works of domestic and foreign scientists who have studied the process of introduction of digital technologies and mobile applications in the educational process. The research is a confirmation of previous scientific research and is aimed directly at the question of learning foreign languages, especially English, using personal computer (PCs) programs, tablets, smartphones and other gadgets. The study focuses on exploring the benefits and weaknesses of the Drops mobile app and analyzing its impact on users' level of knowledge and prospects for future interactions with users and mobile apps to successfully learn foreign languages. The article demonstrates the functionalities of the Drops mobile application and identifies the ability to apply it among three different age groups of users with different levels of English, as well as make predictions about the further progress of users in learning the language if the Drops mobile application is used regularly. The experiment analyzes the conditions, which determined: download the Drops application; to study a specific block of vocabulary of the British English language "City &amp; Shops" within one week, the recipients of the experiment were "users" of different age and level of language proficiency. The main problems faced by language learners are highlighted, which confirms the benefits and highlights more of the program's disadvantages that the creators of the Drops mobile application should work on. It was noted that the success in learning a foreign language by using the Drops mobile application was improved, and the effectiveness of using modern technologies in the process of acquiring new knowledge of English was demonstrated. The widespread introduction of mobile applications in English language education programs is recommended.