Reviewerly/exHarmony huggingface dataset:
An Evaluation Model for Measuring the Usability of Mobile Office Applications through User Interface Design Metrics

Usage of mobile devices and particularly smart phones has seen an enormous hike due to the advancement of mobile phone technology in recent times. People of different age groups, one way or the other, are now connected to different mobile phone applications, such as, Social Networking, Chatting, VoIP (Voice Over Internet Protocol) applications, Gaming etc. This rapid advancement has made it necessary for user interface designers of mobile applications to design user friendly interfaces for their applications, so that users can interact and use those applications with ease irrespective of their location. Usability plays a vital role for measuring the usefulness of such applications. After examining different experimental studies on usability assessment techniques imparted by various research workers, it has been determined that there is still a great deal of requirement where application designers have to guarantee more adept and improved usability of offline mobile applications, such as, Conversion Apps, offline encyclopedia, Translation apps, business use applications (office applications) etc. The primary objective of this study is to render a model for usability metrics of measuring the usability of office applications for smart phones. The effectiveness, usefulness and reliability of the proposed model is measured through two office applications named Office Suite Pro7 for Android and Office 365 for Windows 8 touch screen Smartphone.The results of usability testing and t-test show the significance of the proposed approach. The model in this study will enable the application designers to guarantee a more adept and enhanced usability of office applications for smart phones during the designing stage.
A Gradient-Interleaved Scheduler for Energy-Efficient Backpropagation for Training Neural Networks

This paper addresses design of accelerators using systolic architectures for training of neural networks using a novel gradient interleaving approach. Training the neural network involves backpropagation of error and computation of gradients with respect to the activation functions and weights. It is shown that the gradient with respect to the activation function can be computed using a weight-stationary systolic array while the gradient with respect to the weights can be computed using an output-stationary systolic array. The novelty of the proposed approach lies in interleaving the computations of these two gradients to the same configurable systolic array. This results in reuse of the variables from one computation to the other and eliminates unnecessary memory accesses. The proposed approach leads to 1.4-2.2× savings in terms of number of cycles and 1.9× savings in terms of memory accesses. Thus, the proposed accelerator reduces latency and energy consumption.
Importance of Blockchain Use in Cross-Border Payments and Evaluation of the Progress in this Area

In the banking industry, blockchains are currently being investigated as an alternate interbank money transfer platform. However, the blockchain network structure has not matured yet, and there could be many issues and opportunities on fully operating such a system. The research about the use of blockchain for cross-border payment systems is speedily progressing, which is why there is need to frequently emphasize the progress and make assessments. In this paper we analyze and discuss the significance and progress towards using blockchain technology for cross-border payments through recent literature consisting of research articles and cases.
Waiter Bots for Casual Restaurants

Waiter robots have been introduced in the food and beverage industry in the early 2000s in a number of countries, especially in Asia but with mixed receptions.Problem in designs appropriate for the environment, high costs and operation constraints are some reasons for the robots not being widely implemented on a larger scale.In this paper, it will discuss solutions that address these problems.To keep cost manageable, mechanical design is highly simplified, open source software algorithms for mobile robotics and integration with mobile Wi-Fi technologies currently in use in F&B settings are used.Wi-Fi apps for QR code allow for the tracking of customers' location and a secured local area Wi-Fi provides for the integration to business system.Using digital map and LiDAR and an array of short range IR sensors, the waiter robot is able to navigate autonomously from a serving point to a target point about 5 m away (repeated 10 times) to within 0.3 m (radius).It was next tested in an office environment and was ascertained to fulfil delivery tasks.
Geometry of generalized Pauli channels

We analyze the geometry of the generalized Pauli channels constructed from the mutually unbiased bases. The Choi-Jamio\l{}kowski isomorphism allows us to express the Hilbert-Schmidt line and volume elements in terms of the eigenvalues of the generalized Pauli maps. After determining appropriate regions of integration, we analytically compute the volume of generalized Pauli channels and their important subclasses. In particular, we obtain the volumes of the generalized Pauli channels that can be generated by a legitimate generator and are entanglement breaking. We also provide the upper bound for the volume of positive, trace-preserving generalized Pauli maps. This helps us to better understand the structure of high-dimensional sets of quantum channels.
A Novel Approach for Handwritten Character Recognition Using K-NN Classifier

Saha, Suprativ Bhattacharya, TanmayIn this digital era, it is crucial to identify the authenticity of the words where the writer identification becomes a big challenge. This paper highlights an efficient approach to recognize the character from hand written document using k-nearest neighbor algorithm. Then, a supervised-learning algorithm is employed to recognize the character. From the experimental results, it is observed with our proposed model, we achieved about 92% accuracy for the digits and about 94.15% accuracy for English alphabets. To see the merits of the proposed model, comparison is made against the state-of-the-art models.
Optimized Rate Control Algorithm of High-Efficiency Video Coding Based on Region of Interest

Aiming at the problems that the strategy of target bit allocation at the CTU layer has deviations from the human subjective observation mechanism, and the update phase of parametric model has a higher complexity in the JCTVC-K0103 rate control algorithm of ITU-T H.265/high efficiency video coding (HEVC) standard. Optimized rate control (ORC) algorithm of ITU-T H.265/HEVC based on region of interest (ROI) is proposed. Firstly, the algorithm extracts the region of interest of video frames based on time and space domains by using the improved Itti model. Then, the weight of target bits <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>w</mml:mi></mml:math> is recalculated based on space-time domains to improve the rate control accuracy, and the target bits are distributed based on ROI by the adaptive weight algorithm once again to make the output videos more attuned with the human visual attention mechanism. Finally, the quasi-Newton algorithm is used to update the rate distortion model, which reduces the computational complexity in the update phase of the parametric model. The experimental results show that the ORC algorithm can obtain a better subjective quality in the compressed results with less bit error compared with the other two algorithms. Meanwhile, the rate distortion performance of the ORC algorithm is better on the premise of guaranteeing rate control performance.
Auto-CNNp: a component-based framework for automating CNN parallelism

Effectively training of Convolutional Neural Networks (CNNs) is a computationally intensive and time-consuming task. Therefore, scaling up the training of CNNs has become a key approach to decrease the training duration and train CNN models in a reasonable time. Nevertheless, introducing parallelism to CNNs is a laborious task in practice. It is a manual, repetitive and error-prone process. In this paper, we present Auto-CNNp, a novel framework that aims to address this challenge by automating CNNs training parallelization task. To achieve this goal, the Auto-CNNp introduces a key component which is called CNN-Parallelism-Generator. The latter component aims to streamline routine tasks throughout (1) capturing cumbersome CNNs parallelization tasks within a backbone structure while (2) keeping the framework flexible enough and extensible for user-specific personalization. Our proposed reference implementation provides a high level of abstraction over MPI-based CNNs parallelization process, despite the CNN-based imaging task and its related architecture and training dataset. We introduce the design and the core building blocks of Auto-CNNp. We further conduct an extensive assessment of our proposal that not only shows its effectiveness in accelerating the process of scaling up CNNs training, but also its generalization for a wider variety of use cases.
Dissipative quantum phase transition in a biased Tavis–Cummings model*

We study the dissipative quantum phase transition (QPT) in a biased Tavis–Cummings model consisting of an ensemble of two-level systems (TLSs) interacting with a cavity mode, where the TLSs are pumped by a drive field. In our proposal, we use a dissipative TLS ensemble and an active cavity with effective gain. In the weak drive-field limit, the QPT can occur under the combined actions of the loss and gain of the system. Owing to the active cavity, the QPT behavior can be much differentiated even for a finite strength of the drive field on the TLS ensemble. Also, we propose to implement our scheme based on the dissipative nitrogen-vacancy (NV) centers coupled to an active optical cavity made from the gain-medium-doped silica. Furthermore, we show that the QPT can be measured by probing the transmission spectrum of the cavity embedding the ensemble of the NV centers.
Elastic Executor Provisioning for Iterative Workloads on Apache Spark

In memory data analytic frameworks like Apache Spark are employed by an increasing number of diverse applications-such as machine learning, graph computation, and scientific computing, which benefit from the long-running process (e.g. executor) programming model to avoid system I/O overhead. However, existing resource allocation strategies mainly rely on the peak demand normally specified by users. Since the resource usages of long-running applications like iterative computation vary significantly over time, we find that peak-demand-based resource allocation policies lead to low cloud utilization in production environments. In this paper, we present an elastic utilization aware executor provisioning approach for iterative workloads on Apache Spark (i.e., iSpark). It can identify the causes of resource underutilization due to an inflexible resource policy, and elastically adjusts the allocated executors over time according to the real-time resource usage. In general, iterative applications require more computation resources at the beginning stage and their demands for resources diminish as more iterations are completed. iSpark aims to timely scale up or scale down the number of executors in order to fully utilize the allocated resources while taking the dominant factor into consideration. It further preempts the underutilized executors and preserves the cached intermediate data to ensure the data consistency. Testbed evaluations show that iSpark averagely improves the resource utilization of individual executors by 35.2 % compared to vanilla Spark. At the same time, it increases the cluster utilization from 32.1% to 51.3% and effectively reduces the overall job completion time by 20.8% for a set of representative iterative applications.
Monitoring scheme against local oscillator attacks for practical continuous-variable quantum-key-distribution systems in complex communication environments

In a practical continuous-variable quantum-key-distribution (CVQKD) system, a strong local oscillator (LO) signal in a quantum channel may be manipulated by eavesdropper Eve to steal information about secret keys through general attacks (e.g., entangling cloner attack or intercept-resend attack) without being detected, which is an effective quantum hacking strategy, i.e., LO attacks. To guarantee the practical security of the system, a general monitoring scheme has been previously demonstrated to resist this quantum hacking attack, where the channel transmittance was regarded as a fixed value related to transmission distance. However, practical communication environments are complex, which may result in the time-varying transmittance. This deviation may affect the effectiveness of this monitoring scheme. In this paper, we investigate the monitoring of CVQKD systems running in complex communication environments. We first model the LO attacks on practical CVQKD systems in complex environments, where the channel transmittance is assumed to obey a fixed distribution. Then, the low bound of intensity disturbance of the LO signal for Eve successfully concealing herself is obtained based on this model, where we consider all noise that can be used by Eve in complex communication environments. Simultaneously, we obtain an optimal monitoring condition to resist the LO attacks. Our numerical analysis confirms that the proposed monitoring scheme can effectively resist the LO attacks on practical CVQKD systems in complex environments. Subsequently, the feasibility of this way is again demonstrated by using a quantitative example. It is important that this monitoring scheme can also be extended to resist other quantum hacking attacks.
VQA-LOL: Visual Question Answering under the Lens of Logic

Logical connectives and their implications on the meaning of a natural language sentence are a fundamental aspect of understanding. In this paper, we investigate whether visual question answering (VQA) systems trained to answer a question about an image, are able to answer the logical composition of multiple such questions. When put under this \textit{Lens of Logic}, state-of-the-art VQA models have difficulty in correctly answering these logically composed questions. We construct an augmentation of the VQA dataset as a benchmark, with questions containing logical compositions and linguistic transformations (negation, disjunction, conjunction, and antonyms). We propose our {Lens of Logic (LOL)} model which uses question-attention and logic-attention to understand logical connectives in the question, and a novel Fr\'echet-Compatibility Loss, which ensures that the answers of the component questions and the composed question are consistent with the inferred logical operation. Our model shows substantial improvement in learning logical compositions while retaining performance on VQA. We suggest this work as a move towards robustness by embedding logical connectives in visual understanding.
Visual analysis of customer switching behavior pattern mining for takeout service

Visualization of customer behavior is urgently needed for an increasing number of customer orders on takeout service. Although many works have been done on visualizing customer opinion or customer click events of one store, visualizing customer switching behavior among stores is still challenging. The challenge is to show customer order records over time and structure the inter-connection among different stores when customer switching behavior happens. In this work, we focus on takeout service to present a novel visual analysis system for retailers focusing on customer switching behavior patterns. Firstly we define five customer segments based on switching behavior. Then this system enables temporal-spatial driver exploration for different segments through several interactive views. Moreover, in order to visualize inter-connection sequences, augmented streamgraph with the bundled parallel coordinates is proposed as one alternative technique to visualize temporal event sequences. Evaluation including user and case studies also demonstrates the usefulness and effectiveness of this system in helping customer relationship management.
Forming of competences for intercultural communication in educational process

The modern age,imbued by globalism, more than before imposes the need for intercultural communication. It implies interactions between participants of different nations, which has as the basis understanding and accepting of cultural differences. Informational development and representation of internet allow to people from different parts of the Earth to communicate and share their experiences, events, attitudes, different features. Such communication, also, implies developed competences for understanding of our own cultural features and their relating to features of participants of other cultures, in the aim of creating an new integrated value system which is based on mutual respect. Educational process takes the special part in developing of these competences.
A Dataset-Independent Model for Estimating Software Development Effort Using Soft Computing Techniques

Abstract During the recent years, numerous endeavours have been made in the area of software development effort estimation for calculating the software costs in the preliminary development stages. These studies have resulted in the offering of a great many of the models. Despite the large deal of efforts, the substantial problems of the offered methods are their dependency on the used data collection and, sometimes, their lack of appropriate efficiency. The current article attempts to present a model for software development effort estimation through making use of evolutionary algorithms and neural networks. The distinctive characteristic of this model is its lack of dependency on the collection of data used as well as its high efficiency. To evaluate the proposed model, six different data collections have been used in the area of software effort estimation. The reason for the application of several data collections is related to the investigation of the model performance independence of the data collection used. The evaluation scales have been MMRE, MdMRE and PRED (0.25). The results have indicated that the proposed model, besides delivering high efficiency in contrast to its counterparts, produces the best responses for all of the used data collections.
Proof of usage: user-centric consensus for data provision and exchange

This paper presents a new consensus algorithm, Proof of Usage (PoU), for the blockchain technology. This consensus is introduced for permissioned (or private) blockchains and is designed for a user-centric personal data market. This market is subject to specific regulations with which conventional blockchains fail to comply. Proof of Usage aims to promote a new paradigm dedicated to usage incentivization, valuation, and control of user data in various sectors, such as banking and insurance. Other consensuses such as Proof of Stake or historical Proof of Work do not encourage coin spending and usage (in fact, Proof of Stake promotes the opposite). However, the value of the currency mainly depends on its use. This paper first introduces a contextualization of blockchain technology and decentralized consensus models. The motivation is then discussed for a new model of personal data exchange in a decentralized but supervised environment. The PoU protocol and its process flow are defined in detail. Furthermore, the paper explores two different approaches regarding the reward mechanism and the incentive model. Finally, the paper focuses on security requirements and how PoU meets such requirements in a permissioned-based blockchain system.
Profesores noveles de educación física: percepciones sobre su formación docente en base al desempeño en el sistema escolar (Novel physical education teachers: perceptions on their teaching training based on performance in the school system)

La formación docente como actividad académica universitaria debe asegurar que cada futuro profesor sea capaz de responder a los diferentes requerimientos que tiene en la actualidad el sistema escolar. Aquí se devela una dificultad ya que el ingreso del docente novel a sus primeros pasos laborales está configurado como una etapa llena de incertidumbre y tensiones que no le permiten tener un desempeño óptimo. En este estudio se buscó comprender los significados otorgados por profesores noveles a su formación profesional desde la experiencia del ejercicio profesional. Para poder abordar el problema de estudio se orientó una investigación bajo el Paradigma Interpretativo con el método de la Fenomenología. Se utilizó la técnica Entrevista Semiestructurada para recoger datos de 15 profesores noveles (6 damas y 9 varones) utilizando el Análisis de Contenido y el Análisis de Discurso para el proceso de análisis de los datos. Como evidencia se resaltan las dificultades que presentan en el ejercicio profesional docente referido en habilidades para resolver problemas emergentes y el escaso manejo de aspectos administrativos y técnicos que implica el trabajo docente en la escuela.Abstract. Teacher training as a university academic activity must ensure that each trained teacher is able to respond to the different requirements that the school system currently establishes. At this phase, issues occur since novice teachers’ first steps in the work environment are described as a stage full of uncertainty and tensions, not allowing them to perform at optimal levels. In this study, we sought to understand the meanings given by novice teachers to their professional training from the experience of professional practice. In order to address the study problem, an investigation was carried out under the Interpretative Paradigm, employing the Phenomenology Method. Semi-structured Interview technique was used to collect data from 15 new teachers (6 women and 9 men) using Content Analysis and Speech Analysis for the data analysis process. As highlighted, the difficulties encountered while exercising the profession referred to skills to solve emerging problems and the poor management of administrative and technical aspects involved in the school teaching work.
A Case-Based Reasoning System for Aiding Physicians in Decision Making

Physicians gather a vast amount of information about patients' medical procedures, treatments, insurance coverage, and other clinical data.Such information is crucial in formulating diagnosis or treatment plans for patients with similar traits.A Case-Based Reasoning (CBR) system has been developed to address the effective organization and retrieval of vital patient information to aid physicians in making decisions.Integers are used to uniquely represent various medical procedures, treatments, etc.In this research, a new algorithm is presented to retrieve suitable cases to recommend to physicians.The system is tested in a simulated environment and the results prove that the system can adapt to changes such as new medical procedures or treatments that take place in the medical field.