Reviewerly/exHarmony huggingface dataset:
EMG Sensor based Wheel Chair Control and Safety System

This work presents the development of a smart wheelchair, controlled and guided by EMG signals for those facing the problems of physical disabilities. Conventional wheelchairs were not effective for people with disabilities, as it requires a great pedalling power and gets affected by users’ limitations such as defects of the fingers. This project proposes a solution in the form of a 4-channel EMG controlled Electric wheelchair. The EMG signals are acquired using EMG sensors attached to the muscles of arms and are read in Arduino Uno board working with an AVR microcontroller. The battery powered wheelchair has 3 different modules- Signal acquisition Module, Signal Analysis Module and finally the Wheelchair controlled module. Unlike its previous versions, this wheelchair will move auto-forward whenever powered ON, moves right and left with the required muscle movements, and given further more delay on either of the directions it will turn backwards. Apart from this, an additional feature of designated stop switch is also included for ceasing wheelchair’s motion. Hence this project ensures the wheelchair's motion to be smoother.
MT/MV investigation of faulty tectonic zones of the western part of the Ukrainian shield

Summary Experimental synchronous magnetotelluric and magnetovariational investigations were carried out by modern long-period digital stations with ferromagnetic magnetometers LEMI-417. Measurements of the low-frequency natural electromagnetic field of the Earth of ionospheric-magnetospheric origin were carried out along four profiles: Radomyshl-Fastov, Ruzhin-Skvira, Belilivka-Antonov and Shirmivka-Logvin. By qualitative interpretation of complex tiper and curves of MTZ along the profile of Radomyshl-Fastiv, one can distinguish anomalies of conductivity, shallow occurrence. The part of the Zvizdal-Zaliskaya fault zone separating the Podolsky and Rosinsky megablock of the Ukrainian Shield was manifested in superficial electrical conductivity anomalies as revealed by the data of the MT of studies conducted along three profiles: Ruzhin-Skvira, Belilivka-Antonov and Shirmivka-Logvin. From the interpretation of magnetotelluric and magnetovariational studies on periods larger than 700–1000 s, one can assume a deep anomaly of electrical conductivity of a complex isometric shape with , which coincides geographically with the Brusilivsky interblocked suture zone. Thus, the presence of anomalies of high electrical conductivity 1) both regional and local, 2) along extended fault zones, 3) confined to zones of metallogenic ore and non-metallic fields – are geoelectric criteria for the search for ore deposits of minerals.
Decision Support System for Business Intelligence Using Data Mining Techniques: A Case Study

Business intelligence is an arrangement of strategies, designs, and innovations that change crude information into significant and helpful data used to empower more compelling vital and operational experiences and basic leadership. Decisions support-systems (DSSs) assist in translating raw information into further understandable forms to be used by the advanced stage executives. Business intelligence apparatuses are utilized to make DSS which separate required information from an extensive database to produce easy to use outlines for basic leadership, to create such client graphs; we utilize an open-source business intelligence apparatus fusion charts—which have the capacity to use the accessible data, to pick up a superior comprehension of the past, and to foresee or impact the future through better basic leadership. Extensively characterized, information mining depends on marketable insights, counterfeit awareness and machine learning or information disclosure in databases. DSS uses accessible data and data mining techniques (DMT) to give a basic leadership instrument more often than not depending on human–PC cooperation. Together, DMT and DSS tell us about the range of investigative data advancements and gives us information-directed human-driven goals. Here, we are presenting a case study of DSS for BI for relating data mining procedures for the calculation of energy produced by wind power plant; remarkable outcomes were accomplished in by placing the cutoff. Hence, the data mining procedures were capable to be trained and to ascertain enhanced reliance among variables and are a lot nearer to in fact calculated values.
Transforming User Experience of Nutrition Facts Label - An Exploratory Service Innovation Study

Nutrition facts label is an important tool for consumers to get information regarding servings, calories and nutrients in a packaged food product. Previous research shows that nutrition labels are generally confusing and difficult to use. Nutrition information in the label can be transformed into dynamic feedback to make nutrition facts labels easy to use and helpful in making healthy decisions. In this research, we created a decision support system using a smartphone application that scans the label using OCR, then apply the FDA’s 5-20 rule to determine if a particular nutrient is in healthy amount and visualizes this feedback in either an augmented reality or a static popup format using color-coded thumbs up and thumbs down signs. Our results show that the app significantly helped consumers in making healthy decisions and improved the overall experience of using nutrition facts labels. While our results did not show a significant difference between the impact of augmented reality and static popup feedback on user behavior, they indicated a slightly more favorable reaction toward feedback that used augmented reality.
How well does ANT equip designers for socio-material speculations?

Taking advantage of Deleuze’s image of the wasp and orchid, this chapter considers how ANT and design can productively become-with one another, rather than merely bolster each other’s practices. The chapter draws on the case of an interdisciplinary project, involving designers and STS practitioners, to discuss how a workshop for exploring the use of smart energy monitors involved retroscriptive and procompositional practices. With these notions, the chapter confronts the conundrum of how to unite the legacy of ANT, as an ostensibly descriptive and agnostic commitment towards the collectives it traces, with the prospective attitude of designing as that of adding to and modifying the collectives it renders. This, I argue, involves a shift towards engaging the speculative compossibility of a collective where the question of what an issue, concern or care is capable of elaborating on the question of what it is composed of.
MSE-Net: Pedestrian Attribute Recognition Using MLSC and SE-Blocks

Pedestrian attributes recognition draw significant interest in the field of intelligent video surveillance. Despite that the convolutional neural networks are remarkable in learning discriminative features from images, the learning of comprehensive features of pedestrians for fine-grained tasks remains an challenging problem. In this paper, we proposed a novel multi-level skip connections and squeeze-and-excitation convolutional neural network (MSE-Net), which is composed of multi-level skip connections (MLSC) and Squeeze-and-Excitation blocks (SE-Blocks). Additionally, the proposed MSE-Net brings unique advantages: (1) Multi-level skip connections (MLSC) obtain more meaningful fine-grained information from both the low-level and high-level features and can maintain gradient flow in the network. For fine-grained attributes, such as glasses and accessories, MLSC retains fine-grained information and local information from shallow layers; (2) Squeeze-and-Excitation blocks (SE-blocks) strengthen the sensitivity of the network to information, compress the features, and perceive global receptive field. It can select important feature channels, and then weights the previous features by multiplication and then recalibrates the original features in the channel dimension. Intensive experimental results have been provided to prove that the proposed network outperforms the state-of-the-art methods on RAP dataset, and the robustness against predicting positive and negative samples in each attribute.
Software and Hardware Implementation of Geometric Correction Technology and Fusion of Images on a Mobile Platform

The article discusses the solution to the task of mutual geometric correction implementation on specialized computing machine algorithms, as also integration and subsequent visualization of television and thermal images obtained from mobile sensors. Description of a modified geometric image transformation algorithm using projective transformations is given.
Low-Dimensional euclidean embedding for visualization of search spaces in combinatorial optimization

This abstract summarizes the results reported in the paper [3]. In this paper a method named Low-Dimensional Euclidean Embedding (LDEE) is proposed, which can be used for visualizing high-dimensional combinatorial spaces, for example search spaces of metaheuristic algorithms solving combinatorial optimization problems. The LDEE method transforms solutions of the optimization problem from the search space Ω to Rk (where in practice k = 2 or 3). Points embedded in Rk can be used, for example, to visualize populations in an evolutionary algorithm.
Investigating the Relation Between Voice Corpus Design and Hybrid Synthesis Under Reduction Constraint

Hybrid TTS systems generally try to optimise their cost function with the voice provided to generate the best signal. The voice is based on a speech corpus usually designed for a specific purpose. In this paper, we consider that the voice creation is realized through a corpus design step under reduction constraints. During this stage, a recording script is crafted to be optimal for the target TTS engine and its purpose. In this paper, we investigate the impact of sharing information between the corpus design step and the hybrid TTS optimisation step. We start from a reduced voice optimized for a unit selection system using a CNN-based model. This baseline is compared to a hybrid TTS system that uses, as its target cost, a linguistic embedding built for the recording script design step. This approach is also compared to a standard hybrid TTS system trained only on the voice and so that does not have information about the corpus design process. Objective measures and perceptual evaluations show how the integration of the corpus design embedding as target cost outperforms a classical hard-coded target cost. However, the feed-forward DNN acoustic model from the standard hybrid TTS system remains the best. This emphasizes the importance of acoustic information in the TTS target cost, which is not directly available before the voice recording.
Privacy-Preserving Big Data Analytics: From Theory to Practice

In the last decade, with the advent of Internet of Things (IoT) and Big Data phenomenons, data security and privacy have become very crucial issues. A significant portion of the problem is due to not utilizing appropriate security and privacy measures in data and computational infrastructures. Secure multiparty computation (secure MPC) is a cryptographic tool that can be used to deal with the mentioned problems. This computational approach has attracted increasing attention, and there has been significant amount of advancement in this domain. In this paper, we review the important theoretical bases and practical advancements of secure multiparty computation. In particular, we briefly review three common cryptographic primitives used in secure MPC and highlight the main arithmetic operations that are performed at the core of secure MPC protocols. We also highlight the strengths and weaknesses of different secure MPC approaches as well as the fundamental challenges in this domain. Moreover, we review and compare the state-of-the-art secure MPC tools that can be used for addressing security and privacy challenges in the IoT and big data analytics. Using secure MPC in the IoT and big data domains is a challenging task and requires significant expert knowledge. This technical review aims at instilling in the reader an enhanced understanding of different approaches in applying secure MPC techniques to the IoT and big data analytics.
Animal Trail: An Augmented Reality Experience in the Amazon Rainforest

Sidia, a Research and Development Institute, with the natural science park “Bosque da Ciência” (Forest of Science) have teamed up to develop a solution for the community of Manaus learn interactively about the Amazon Forest with support of scientific knowledge. This partnership was motivated because the park has an important structure but can't properly communicate about the diversity for its visitors. In other hand, Augmented Reality (AR) enables richer and immersive experience. Along with the context where it would be applied, AR brought many issues that needed to be directed, in this case: how can AR contribute to enriching the experience of users visiting the park? Considering that AR is a technology that allows the imposition of a layer of information on reality; first, we mapped the main opportunities of interaction within the journey of the visitors to improve their experience. To do so, Double Diamond British Design Council was chosen as a methodology to assist in discovery until delivery. The application started with the most interesting attraction for the visitants: the animals. They live loose in the forest and sometimes can’t appear in visitors’ journey but AR collaborates in these interactions by unveiling 3D virtual animal. The visitants can interact and consult all content whenever they want since it is stored in the application after unlocked inside the park. We concluded that AR had been prove to be a relevant solution for encouragement and support visitor’s interaction in the park. This research was funded by Samsung by resources originated by Informatic Law.
Work-in-Progress: Development of Augmented Reality Application for Learning Pneumatic Control

This paper describes the development of an augmented reality application for learning pneumatic control. The goal is to improve the workbook of pneumatic control with additional digital information. Through the implementation of the developed application, the assumption is that it will be easier for students to understand the functionality of complex pneumatic schemes. The augmented reality application was developed for the Android platform. Students are able to download it from the Internet and use it at home, at the university or anywhere else. The application recognizes the solution of the exercise and the pneumatic components in the workbook and covers it with additional information (for example, simulation in form of a video clip). The developed augmented reality application can be easily introduced into teaching activities in two university study courses of pneumatic control. Augmented reality applications, as the one described in this paper, can be a very useful tool for students during their studies, as well as for others who want to learn more about this topic.
Identifying a set of line manager personas to guide new product introduction strategy

Purpose The purpose of this paper is to introduce a new strategic management tool – the reverse persona. In doing so, the methods, use and benefits documented from a case study with a global franchisee organization are presented. Design/methodology/approach This tool was derived from working with a global franchisee organization sought to design and launch a new product into the market. The reverse persona was deployed through n=14 qualitative interviews with franchisee owners were conducted to understand their perceptions of customers, awareness and concern of competition and their willingness to take risks. These insights were collated to develop reverse personas for the senior leadership team within the organization. Findings Changing the scope of personas from external customers to internal employee development, can further strengthen the method’s effectiveness in decision-making and strategic management, particularly for the implementation and roll out of new products. Practical implications In the case study, the senior leadership team saw the manager persona as a strategic aid to, “Help target the implementation of new products in stores, select franchise owners for potential new roles and to deeply understand the motivations, challenges and attributes of their middle management contributing to the competitive advantage of the organisation.” Originality/value This article is the first to explore the use of personas for internal strategic planning use within a company.
Direct CCA-Secure KEM and Deterministic PKE from Plain LWE

We present a particularly simple and efficient CCA-secure public-key encapsulation scheme without random oracles or costly sampling. The construction is direct in the sense that it eschews generic transformations via one-time signatures or MACs typically found in standard-model constructions. This gives us a compact, conceptually simpler, and computationally efficient operation, that in particular does not require any Gaussian sampling. Nevertheless, security is based on the hardness of the plain learning-with-errors (LWE) problem with polynomial modulus-to-noise ratio. Of further interest, we also show how to obtain CCA-secure deterministic public-key encryption (for high-entropy messages), that is more compact and efficient than existing constructions.
Research on Evaluation of Product Image Design Elements Based on Eye Movement Signal

A product design elements evaluation model was proposed, which was constructed by eye tracking experiments, using eye movement indicators such as first gaze time, gaze order, and number of times of return, to accurately and effectively analyze product model and quantify users’ emotion. The purpose of sorting the product model contribution was achieved through the weight calculation of the design elements. The soymilk machine shape was used as a case to verify the rationality of the evaluation model. The results showed that the “cup” part of the soymilk machine was the most concerned by the subjects, while the “handle” part was the least concerned. At the same time, the further research direction of design elements evaluation in eye movement experiment and the coupling between design elements, and product modeling image were discussed at the end of the article. The further research directions and experimental design methods were clarified.
Artificial Bee Colony Algorithm Combined with Uniform Design

As artificial bee colony algorithm is sensitive to the initial solutions, and is easy to fall into local optimum and premature convergence, this study presents a novel artificial bee colony algorithm based on uniform design to acquire the better initial solutions. It introduces an initialization method with uniform design to replace random initialization, and selects the better ones of those initial bees generated by the initialization method as the initial bee colony. This study also introduces a crossover operator based on uniform design, which can search evenly the solutions in the small vector space formed by two parents. This can increase searching efficiency and accuracy. The best two of the offsprings generated by the crossover operator based on uniform design are taken as new offsprings, and they are compared with their parents to determine whether to update their patents or not. The crossover operator can ensure that the proposed algorithm searches uniformly the solution space. Experimental results performed on several frequently used test functions demonstrate that the proposed algorithm has more outstanding performance and better global searching ability than standard artificial bee colony algorithm.
Labelled GM‐CBMEMBER filter with adaptive track initiation

The initial Gaussian mixture cardinality balanced multi-target multi-Bernoulli (GM-CBMeMBer) filter does not provide track information and assumes that the target birth intensity is known as a priori, but in reality the target may appear anywhere in the detection area. Therefore, the study proposes a labelled GM-CBMeMBer filter with adaptive track initiation. First, the filter introduces track label information and selects the measurements of newborn targets in the prediction process. In addition, it also makes full use of the Doppler information of the airborne Doppler radar. Then, the position estimate and velocity estimate of newborn targets are calculated by using position measurements converted and Doppler measurements, respectively. Further the label information in the prediction process is inherited during the update process, and the target states are updated sequentially with the Doppler measurements after having been updated by using position measurements. Monte–Carlo experiments show that the proposed filter can perform adaptive track initiation effectively, with good tracking performance, and provide target track information well.
Emojis in Textual-Based Communication Among College Students: A Study in Perception and Frequency

Emojis have revolutionized text-based communication as we know it as users employ this personal utility to add nuance and avoid miscommunication in inflectionless text. Emojis express a more diverse and deeper span of emotions than traditional text-based communication. This study will focus on frequency and perception of emojis in traditional-aged college students (ages 16–28). As a young and technologically heavy-user generation, who see themselves as drivers of personal technology trends, college students make a good population study in this area. This interdisciplinary study applies Linguistics to HCI to further technological advances in emoji development and maintenance.
Semi-automated Augmentation of Pandas DataFrames

Creative feature engineering is an important aspect within machine learning prediction tasks which can be facilitated by augmenting datasets with additional data to improve predictions. This paper presents an approach towards augmenting existing datasets represented as pandas dataframes with data from open data sources, semi-automatically, with the aims of (1) automatically suggesting data augmentation options given an existing set of features, and (2) automatically augmenting the data when a suggestion is selected by the user. This paper demonstrates the performance of the approach in terms of aligning typical machine learning datasets with open data sources, suggesting useful augmentation options, and the design and implementation of a software tool implementing the approach, available as open-source software.
Interactive cinematic scientific visualization in unity

Cinematic scientific visualizations turn complex scientific phenomena and concepts into stunning graphics and make them easier for the general public to comprehend. Adding interactivity to cinematic scientific visualizations is highly beneficial especially for educational purposes, as it keeps the viewers engaged and promotes active learning [Cano et al. 2017]. Although there are existing software tools such as VisIt that are capable of handling large data sets and allow for interactive exploration, they are usually designed for scientists and not meant for producing cinematic visualizations for the general public. Creating aesthetically pleasing visualizations of scientific data helps to better communicate the scientific concepts, increase impact, and reach a broader audience [Borkiewicz et al. 2018]. As existing examples of visualizations that are both interactive and cinematic have mainly been produced with custom software, there is a lack of easily accessible tools for developing this type of scientific visualization.