Reviewerly/exHarmony huggingface dataset:
Weak Values and Two-State Vector Formalism in Elementary Scattering and Reflectivity—A New Effect

The notions of Weak Value (WV) and Two-State Vector Formalism (TSVF), firstly introduced by Aharonov and collaborators, provide a quantum-theoretical formalism of extracting new information from a system in the limit of small disturbances to its state. Here, we explore two applications to the case of non-relativistic two-body scattering with one body weakly interacting with its environment. We present a physically compelling analysis of a new quantum effect: momentum transfer deficit and an accompanying enhanced energy transfer; or, equivalently, an apparent mass-deficit of the struck body. First, incoherent inelastic neutron scattering (INS) from protons of H 2 molecules in C-nanotubes is investigated. The data of the H 2 translational motion along the nanotube shows that the neutron apparently exchanges energy and momentum with a fictitious particle with mass of 0.64 atomic mass units (a.m.u.), which is in blatant contradiction with the expected value of 2 a.m.u. Second, the same theory is applied to neutron reflectivity—which is elastic and coherent—from the interface of (single crystal) Si with H 2 O-D 2 O liquid mixtures. The data shows a striking enhanced reflectivity in a wide range of momentum transfers, which is tantamount to a momentum-transfer deficit with respect to conventional expectations. However, these effects find a natural interpretation within the WV-TSVF theoretical analysis under consideration. In summary, both scattering effects contradict conventional theoretical expectations, thus also supporting the novelty of the theoretical framework of WV and TVSF. Additionally, it should be pointed out that the two dynamical variables in the interaction Hamiltonian of the theoretical model belong to two different physical bodies.
A Design Workbench for Interactive Music Systems

This chapter discusses possible links between the fields of computer music and human-computer interaction (HCI), particularly in the context of the MIDWAY project between Inria, France and McGill University, Canada. The goal of MIDWAY is to construct a “musical interaction design workbench” to facilitate the exploration and development of new interactive technologies for musical creation and performance by bringing together useful models, tools, and recent developments from computer music and HCI. Such models and tools can expand the means available for musical expression, as well as provide HCI researchers with a better foundation for the design of tools for a class of “extreme” users who are accustomed to devoting decades of practice towards the development of expertise with their instruments. We conclude with a discussion of design guidelines for Interactive Music Systems.
Accurate and Cost-Effective Micro Sun Sensor based on CMOS Black Sun Effect

An accurate and cost-effective micro sun sensor based on the extraction of the sun vector using a phenomenon called the “black sun” is presented. Unlike conventional image-based sun sensors where there is difficulty in accurately detecting the sun center, the black sun effect allows the sun center to be accurately extracted even with the sun image appearing irregular and noisy due to glare. This allows the proposed micro sun sensor to achieve high accuracy even when a 1 mm × 1 mm CMOS image sensor with a resolution of 250 × 250 pixels is used. The proposed micro sun sensor is implemented in two application modes: (1) a stationary mode targeted at tracking the sun for heliostats or solar panels with a fixed pose of single image sensor of 1 mm × 1 mm × 1.74 mm in size and (2) a non-stationary mode targeted at determining the orientation of moving platforms with six sensors on the platform, which is configured in an icosahedron geometry of 23 mm × 23 mm × 12 mm in size. For the stationary mode, we obtained an accuracy of 0.013° by applying Kalman filter to the sun sensor measurement for a particular sensor orientation. For the non-stationary mode, we obtained an improved accuracy of 0.05° by fusing the measurements from three sun sensors available at any instant of time. Furthermore, experiments indicate that the black sun effect makes the precision of sun vector extraction independent of the sun location captured on the image plane.
PRANATACARA LEARNING: MODELING, MIND MAPPING, E-LEARNING, OR HYBRID LEARNING?

This research was aimed to examine the most effective learning model for Pranatacara (Master of Ceremony) course. By having effective learning, the students can master the skills at being a professional Pranatacara. The study used the experimental method with the pretest-posttest nonequivalent group design. Four parallel classes were given learning treatments each with modeling, mind mapping, e-learning, and hybrid learning. The data of the practice test were analyzed by the one-way Anova. The result indicates that there is a significant difference among the learning models. The hybrid learning can be considered as the most effective model to increase the total score, with the effectiveness of 11.58%; followed by modeling (5.58%); e-learning (4.10%); and mind mapping (3.54%) respectively. The hybrid learning was found as the most effective to improve the fluency score, i.e. 14.23%; followed by mind mapping (8.31%); modeling (4.74%) and e-learning (4.00%) respectively. Moreover, there was a significant difference in the effectiveness of vocal exercise. The highest increase in vocal score was through hybrid learning, with 10.85%; followed by modeling (10.09%); e-learning (4.39); and mind mappling (2.88%). Therefore, hybrid learning was declared as the most effective for learning Pranatacara courses and on the contraty, the e-learning was found as the most ineffective.
Markov decision processes with sequential sensor measurements

Markov decision processes (MDPs) have been used to formulate many decision-making problems in science and engineering. The objective is to synthesize the best decision (action selection) policies to maximize expected rewards (or minimize costs) for a stochastic dynamical system. In this paper, we introduce a new type of sensor measurement to the MDP model that provides additional information about the stochastic process, and hence that information can be incorporated in the decision policy to increase the performance. The new model is tailored for environments with high uncertainty. With the additional measurements, more refined information on the possible state transition is provided in real-time before taking an action. This new MDP model with sequential measurements is referred to as sequentially-observed MDP (SO-MDP). We show that the SO-MDP shares some similar properties with a standard MDP; among randomized history dependent policies, deterministic Markovian policies are still optimal. Optimal SO-MDP policies have the advantage of producing better total rewards than standard MDP policies due to the additional measurements, however computing these policies is more complex. We present two algorithms for solving the finite-horizon SO-MDP problem: the first algorithm is based on linear-programming, and the second algorithm is based on dynamic programming. We show that the complexity of computing optimal policies of the SO-MDP model with perfect sensors is the same as standard MDP. Simulations demonstrate that the SO-MDP model outperforms the standard MDP model in the presence of high environmental uncertainty.
Stochastic Resonance with Unital Quantum Noise

The fundamental quantum information processing task of estimating the phase of a qubit is considered. Following quantum measurement, the estimation efficiency is evaluated by the classical Fisher information which determines the best performance limiting any estimator and achievable by the maximum likelihood estimator. The estimation process is analyzed in the presence of decoherence represented by essential quantum noises that can affect the qubit and belonging to the broad class of unital quantum noises. Such a class especially contains the bit-flip, the phase-flip, the depolarizing noises, or the whole family of Pauli noises. As the level of noise is increased, we report the possibility of non-standard behaviors where the estimation efficiency does not necessarily deteriorate uniformly, but can experience non-monotonic variations. Regimes are found where higher noise levels prove more favorable to estimation. Such behaviors are related to stochastic resonance effects in signal estimation, shown here feasible for the first time with unital quantum noises. The results provide enhanced appreciation of quantum noise or decoherence, manifesting that it is not always detrimental for quantum information processing.
Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks

We investigate the internal representations that a recurrent neural network (RNN) uses while learning to recognize a regular formal language. Specifically, we train a RNN on positive and negative examples from a regular language, and ask if there is a simple decoding function that maps states of this RNN to states of the minimal deterministic finite automaton (MDFA) for the language. Our experiments show that such a decoding function indeed exists, and that it maps states of the RNN not to MDFA states, but to states of an {\em abstraction} obtained by clustering small sets of MDFA states into superstates. A qualitative analysis reveals that the abstraction often has a simple interpretation. Overall, the results suggest a strong structural relationship between internal representations used by RNNs and finite automata, and explain the well-known ability of RNNs to recognize formal grammatical structure.
VR Road and Construction Site Safety Conceptual Modeling Based on Hand Gestures

Engineering road design and construction site safety design can be lengthy processes, incompatible with situations where only a conceptual design is required, and/or when required by users unfamiliar with such complex pieces of software. In this study, an intuitive road design VR application based on hand gesture was proposed and developed as a proof of concept. Results show the resulting application is easy, natural and fun to use, and could represent a good alternative to more complex design applications.
Quality Work Ethics and Job Satisfaction: An Empirical Analysis

Quality is very crucial to understand how well an employee is doing. Quality of work shows how much an employee value them. Thus, this study examines the impact of strong quality work ethics on job satisfaction among banking employees in Jordan. There has been growing evidence supporting the association between quality work ethics and job satisfaction of employees. However, it is not clear why such association exists. This paper argues that in order to provide a clear understanding and convincing explanation of this association there is a need to improve the theoretical and analytical framework. Quantitative method with survey questionnaire was administered to collect information on quality work ethics and job satisfaction from 89 employees working in Jordanian banks. The finding of the study revealed that quality work ethics are positively associated with job satisfaction. These findings imply that quality work ethics is one of the key attributes to increase employee's job satisfaction together with other organizational outcomes. Most importantly, it is crucial for the managers to be aware that increasing employee's job satisfaction can lead to increased organizational commitment, lower labor turnover and improve productivity.
Emerging Visual Literacy through Enactments by Visual Analytics and Students

This paper investigates the potential aspects of visual literacy that might appear when visual analytics and students interact in social science secondary classrooms. Interacting with visual technology likely demands new forms of literacy as various dimensions of complexity emerge in such learning activities where reading imposes order and relevance on what is displayed. However, only a few studies have evaluated how these visual processes emerge. Applying a socio-material semiotic approach, this paper examines the interactions between teachers, students and a visual analytics application, clarifying what strengthens or weakens the socio-material relations at work in emerging visual literacy. Methodologically, a design-based research approach is chosen. Notably, it is the early stages of the designed-based research cycle that are applied. Interventions were designed and conducted in five classes in three secondary schools in Sweden (97 students). The visual analytics application introduced was Statistics eXplorer. For each class, two to three lessons were video recorded to capture how the students interacted with the application. The socio-material analyses show that the interactions between the visual analytics and the students were both strengthened and weakened by different social as well as material forces. The actions were directed by visual properties such as movement, highlighting, and color, properties that often resulted in quick vision or locked vision. This paper argues that there needs to be a close didactic alignment and deeper knowledge of how visual interfaces attract students' attention and how students' visual literacy emerges in that relationship.
Unknowable Unknowns

Addresses the concept of "knowable unknowns" and discusses this concept in the context of security models driven by artificial intelligence (AI). Kurt Gödel proved that there are problems for which it is impossible to construct an algorithm that always leads to a correct yes-or-no answer; those problems are undecidable. Alan Turing proved that the halting problem is undecidable in Turing machines. Alfred Tarski proved that truth in the standard model of a system cannot be defined within that system. Olav Lysne proved that it is not possible to verify electronic equipment procured from untrusted vendors, and that a vendor cannot build a system that supports verification by untrusting customers. Ben-David et al. proved that scenarios exist where learnability can neither be proved nor refuted. Finally, Donald Rumsfeld made commonplace the phrase unknown unknowns. And so we come to artificial intelligence
An Anomaly Diagnosis Method for Redundant Inertial Measurement Unit and Its Application with Micro-Electro-Mechanical System Sensors

The application of the micro-electro-mechanical system inertial measurement unit has become a new research hotspot in the field of inertial navigation. In order to solve the problems of the poor accuracy and stability of micro-electro-mechanical system sensors, redundant design is an effective method under the restriction of current technology. Redundant data processing is the most important part in the micro-electro-mechanical system redundant inertial navigation system, which includes the processing of anomaly data and the fusion estimation of redundant data. To further improve the reliability of the micro-electro-mechanical system redundant inertial measurement unit, an anomaly detection, isolation, and recognition method for data anomalies is proposed. The relationship between the parity space method detection function and the deterioration degree of anomaly data is mathematically deduced. The parity space method detection functions of different anomalies are analyzed, and five indicators are designed to quantitatively analyze the detection function values. According to these indicators, the detection and recognition method are proposed. The new method is tested by a series of simulation experiments.
Web and Android Application for Comparison of E-Commerce Products

E-Commerce websites are currently the most popular sources for shopping all kinds of products online.Currently, users have many different websites to access and search for the desired products available in the market.Nowadays, different kinds of strategies are being implemented to analyze and understand the customer's behavior to increase growth of the business.As, variety of websites are currently available it becomes difficult for the users to purchase the desired product for an affordable price.The paper presents, Comparison of E-Commerce products available on these different websites in order to help users to grab their desired products for the best affordable price.Techniques like Web Crawling and Web Scraping are adopted to collect detailed product information from the websites and MongoDB (NoSql Database) is used to store the scraped details of the products.Libraries like Requests and BeautifulSoup4 were implemented for crawling and scraping techniques using Python and Indexing method is used in MongoDB to acquire best possible results thereby savings customers time, efforts and money.
Gaze-Guided Control of an Autonomous Mobile Robot Using Type-2 Fuzzy Logic

Motion control of mobile robots in a cluttered environment with obstacles is an important problem. It is unsatisfactory to control a robot’s motion using traditional control algorithms in a complex environment in real time. Gaze tracking technology has brought an important perspective to this issue. Gaze guided driving a vehicle based on eye movements supply significant features of nature task to realization. This paper presents an intelligent vision-based gaze guided robot control (GGC) platform that uses a user-computer interface based on gaze tracking enables a user to control the motion of a mobile robot using eyes gaze coordinate as inputs to the system. In this paper, an overhead camera, eyes tracking device, a differential drive mobile robot, vision and interval type-2 fuzzy inference (IT2FIS) tools are utilized. The methodology incorporates two basic behaviors; map generation and go-to-goal behavior. Go-to-goal behavior based on an IT2FIS is more soft and steady progress in data processing with uncertainties to generate better performance. The algorithms are implemented in the indoor environment with the presence of obstacles. Experiments and simulation results indicated that intelligent vision-based gaze guided robot control (GGC) system can be successfully applied and the IT2FIS can successfully make operator intention, modulate speed and direction accordingly.
A New Ultrasound Speckle Reduction Algorithm Based on Superpixel Segmentation and Detail Compensation

Speckle is a kind of noise commonly found in ultrasound images (UIs). Although traditional local operation-based methods, such as bilateral filtering, perform well in de-noising normal natural images with suitable parameters, these methods may break local correlations and, hence, their performance will be highly degraded when applied to UIs with high levels of speckle noise. In this work, we propose a new method, based on superpixel segmentation and detail compensation, to reduce UI speckle noise. In particular, considering that superpixel segmentation has the advantage of adhering accurately to the boundaries of objects or local structures, we propose a superpixel version of bilateral filtering to better protect the local structure during de-noising. Additionally, a human visual system (HVS)-inspired strategy for spatial compensation is introduced, in order to recover sophisticated edges as much as possible while weakening the high-frequency noise. Experiments on synthetic images and real UIs of different organs show that, compared to other methods, the proposed strategy can reduce ultrasound speckle noise more effectively.
The Life-Cycle and Character of Crypto-Assets: A Framework for Regulation and Investor Protection

Crypto-assets have the potential for advancing financial exchange, storage of value, venture capital, and contracting, but lack clarity of form, governance, and disclosure for investor decision making. This article reviews the nature of crypto-assets from an investment perspective, clarifying their evolving ecosystem, and making recommendations for classification in order to provide policy makers a framework for elevating investor protection. The review includes advantages and disadvantages of these assets, an evaluation of crypto-investment indicators, governance and regulatory environment. Cryptoassets may change classification over time, and many crypto-assets appear to be more centralized than common belief among market-participants.
CHInclusion

HCI has a growing body of work regarding important social and community issues, as well as various grassroots communities working to make CHI more international and inclusive. In this workshop, we will build on this work: first reflecting on the contemporary CHI climate, and then developing an actionable plan towards making CHI2019 and subsequent SIGCHI events and sister conferences more inclusive for all.
Machine Learning for Handoffs Classification Based on Effective Communication History

An important step in data effective communication in handoffs process analysis is data exploration and representation. Communication in handoff treatment is crucial to protect the patients and it can lead to patient’s safety, discontinue care of a patient or the cause loss of important information related to the continuum of care. In this case, we use the machine learning technique by using Support Vector Machine for classification the handoffs for twenty weeks to analysis and represented based on the effective communication history. We used handoffs dataset which employed from Arifin Achmad Hospital in Pekanbaru, Indonesia. The result indicated the performance of the designed system was successful and could be used in handoffs analysis based on the effective communication histories in Arifin Achmad Hospital in Pekanbaru, Indonesia.
Analysis of the use of multimedia components in modern mobile learning technologies

The article analyzes the pedagogical conditions of the implementation of mobile learning, which allows to optimize the learning process in open distance education. The algorithm of creation of high-quality infographics for mobile learning is developed, which provides an opportunity for the formation of sensitive content for users. Methodical recommendations for ensuring the quality of the use of web-based multimedia resources of mobile learning systems are developed, on the basis of which the opportunity to increase the productivity of the learning process is provided