Reviewerly/exHarmony huggingface dataset:
Improving Student Learning Satisfaction by Using an Innovative DASH-Based Multiple Sensorial Media Delivery Solution

Recently, innovative technologies such as Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and Multi-Sensorial Media (mulsemedia) have introduced new sensorial effects including vibration, smell, airflow, etc. to human life. These effects which have been largely deployed for entertainment, and gaming have positively impacted user satisfaction. This paper explores the potential of mulsemedia in the education context. It describes a novel Dynamic Adaptive Streaming over HTTP (DASH)-based Multi-sensory Media Delivery Solution (DASHMS) which supports adaptive mulsemedia content distribution based on the operational environment which includes network, device, and user settings.DASHMS was evaluated in a real-life educational experiment involving 44 students in an Irish university. The evaluation focused on both learner satisfaction, and the impact on learning. The results demonstrate the potential of adaptive multi-sensorial media delivery to result in a statistically significant increase in user experience. In terms of benefit to learning outcomes however, it was only memory recall which was statistically improved in the experiment.
Transitioning360: Content-aware NFoV Virtual Camera Paths for 360° Video Playback

Moreover, our interface shows other candidate views, providing a sense of concurrent events.At any time, the user can transition to other candidate views for fast navigation and exploration.Experimental results including a user study demonstrate that the viewing experience using our method is more enjoyable and convenient than previous methods.
Demonstrating Principled Uncertainty Modeling for Recommender Ecosystems with RecSim NG

We develop RecSim NG, a probabilistic platform that supports natural, concise specification and learning of models for multi-agent recommender systems simulation. RecSim NG is a scalable, modular, differentiable simulator implemented in Edward2 and TensorFlow.
Automatic Detection and Location of Seismic Events From Time‐Delay Projection Mapping and Neural Network Classification

Abstract The past several decades have seen an exponential increase in the volume of available seismic data, and with it has come the need to develop fast, automatic earthquake detection, and location algorithms. Some of the most recent and promising tools come from the field of machine learning. In this study, we combine a recent seismic detection and location method with neural network classification and analyze 4 months of continuous data recorded by a network of 76 stations in northern California. While these approaches have been used separately, our implementation is unique in that it is not constrained by source templates and avoids user‐defined detection thresholds. In particular, we partition our data set into 234,240, 3‐min long time windows with 75% overlap. For each time window, we create a 3D image that captures information about the coherence of the seismic wavefield. We then devise four features as input and train a neural network classifier to predict which time windows in the data set are likely to contain regional seismic events. These features include the second and fourth Hu image moments computed from 2D cross sections of our 3D images and statistical p values that quantify the probability of observing network‐wide power‐spectral density values at 0.2 and 0.5 s. Our neural network model predicts that 2,522 time windows contain seismic events, from which we locate 1,192 unique events.
Biometric Based Secured Remote Electronic Voting System

India is world's largest democracy and the essence of any democracy lies in the fact that people choose their own representatives. But in present era, the fair election process is facing a lot of problems like booth capturing, rigging, fake voting, tampering with the Electronic Voting Machines (EVMs) etc. Being responsible engineers, it's our duty to do something to curb this menace. In the commonly used EVMs, the voting process takes place electronically and this eliminates the use of ballot paper to cast votes in elections as it is very time consuming and errors might crawl in intentionally or unintentionally. Today authenticity of the voter is a big concern and it also should be made sure that a same voter is not able to vote two times. This issue can be dealt with by introducing biometric based voting system, where the authenticity of a voter is established based on fingerprints. Hence, the principle shall be one person, one authentic vote. In the present work, a prototype fingerprint based biometric voting machine has been developed. It is proposed that a feature that will link the Aadhaar database of Unique Identification Authority of India (UIDAI), Govt. of India, New Delhi; can be embedded. This shall facilitate all the voters to get registered on the portal automatically, which can be classified on the basis of regions and constituencies based on their unique identification i.e. their finger prints. This shall enable the device developed in the present research work, at the national level of application by using it in elections conducted around the country. This shall lead to a significant contribution for the betterment of the Indian election system.
Effect of Instrument Design and Technique on the Precision and Accuracy of Objective Refraction Measurement

To evaluate the precision and accuracy of objective refraction measurement obtained with combinations of instrument design and technique. We also compared the performance of the instruments with subjective refraction measurements. Method and analysis: The objective refraction was measured in 71 subjects with three autorefractometers that have different designs and measurement principles (binocular with fogging, binocular without fogging, and monocular with fogging). Repeatability and reproducibility metrics were calculated for the objective refraction measurements. The agreement of the objective refraction measurements between the three instruments and the agreement with the subjective refraction measurements were evaluated.All three autorefractometers had repeatability and reproducibility limits smaller than 0.70D. The smallest difference (0.10D) in the spherical equivalent was seen between the two binocular instruments. Compared with the subjective refraction, the binocular without fogging technique had the smallest mean difference in spherical equivalent (<0.20D) whereas the binocular fogging technique had the smallest limit of agreement interval (1.00D). For all comparisons, the mean difference and limit of agreement interval for the cylindrical components were lower than 0.10D and 0.75D, respectively.All three instruments evaluated had good repeatability and reproducibility. The binocular fogging technique provided the best agreement with subjective refraction.
Transparency as Threat at the Intersection of Artificial Intelligence and Cyberbiosecurity

Have separate actions designed to meet ethical norms of transparency inadvertently led to a situation of greater possible harm from biological warfare attacks? Efforts to make artificial intelligence and data from biological sciences more publicly available have raised novel concerns about national security.
A Blockchain-Based Framework for IoT Data Monetization Services

Abstract Within internet of things (IoT) research, there is a growing interest in leveraging the decentralization properties of blockchains, towards developing IoT authentication and authorization mechanisms that do not inherently require centralized third-party intermediaries. This paper presents a framework for sharing IoT data in a decentralized and private-by-design manner in exchange for monetary services. The framework is built on a tiered blockchain architecture, along with InterPlanetary File System for IoT data storage and transfer. The goal is to enable IoT data users to exercise fine-grained control on how much data they share with entities authenticated through blockchains. To highlight how the framework would be used in real-life scenarios, this paper presents two use cases, namely an IoT data marketplace and a decentralized connected vehicle insurance. These examples showcase how the proposed framework can be used for varying smart contract-based applications involving exchanges of IoT data and cryptocurrency. Following the discussion about the use cases, the paper outlines a detailed security analysis performed on the proposed framework, based on multiple attack scenarios. Finally, it presents and discusses extensive evaluations, in terms of various performance metrics obtained from a real-world implementation.
Smart home R&amp;D system based on virtual reality

Smart home products and equipment are relatively expensive while using specific physical objects to prove functional characteristics, the cost is high, and it is difficult to meet the personal needs of customers. Based on the above background, the purpose of this research is the application and design of a smart home R&amp;D system based on virtual reality. This study proposes the concept of introducing virtual reality methods into the control scene given the shortcomings of the existing smart home control interface interaction methods. From the perspective of being more suitable for the user’s needs, the virtual reality method is used to optimize the smart home interaction methods. Through the analysis of the user’s lifestyle and needs, the functional module model of applying virtual reality to the smart home control scheme is established. Then, by collecting data, use Sketchup software to build and optimize the model of the simulation system to build a realistic family scene model. Finally, through the integrated use of the Unity 3D rendering engine and the virtual simulation system technology, the intelligent simulation of the interior functions of the house is realized. Experimental results show that using virtual reality to optimize the interaction of smart homes, the control method is relatively simple, and the cost can be reduced by about 20%.
Thresholding Strategies for Deep Learning with Highly Imbalanced Big Data

A variety of data-level, algorithm-level, and hybrid methods have been used to address the challenges associated with training predictive models with class-imbalanced data. While many of these techniques have been extended to deep neural network (DNN) models, there are relatively fewer studies that emphasize the significance of output thresholding. In this chapter, we relate DNN outputs to Bayesian a posteriori probabilities and suggest that the Default threshold of 0.5 is almost never optimal when training data is imbalanced. We simulate a wide range of class imbalance levels using three real-world data sets, i.e. positive class sizes of 0.03–90%, and we compare Default threshold results to two alternative thresholding strategies. The Optimal threshold strategy uses validation data or training data to search for the classification threshold that maximizes the geometric mean. The Prior threshold strategy requires no optimization, and instead sets the classification threshold to be the prior probability of the positive class. Multiple deep architectures are explored and all experiments are repeated 30 times to account for random error. Linear models and visualizations show that the Optimal threshold is strongly correlated with the positive class prior. Confidence intervals show that the Default threshold only performs well when training data is balanced and Optimal thresholds perform significantly better when training data is skewed. Surprisingly, statistical results show that the Prior threshold performs consistently as well as the Optimal threshold across all distributions. The contributions of this chapter are twofold: (1) illustrating the side effects of training deep models with highly imbalanced big data and (2) comparing multiple thresholding strategies for maximizing class-wise performance with imbalanced training data.
Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient Approach

Rehabilitative mobility aids are being used extensively for physically impaired people. Efforts are being made to develop human machine interfaces (HMIs), manipulating the biosignals to better control the electromechanical mobility aids, especially the wheelchairs. Creating precise control commands such as move forward, left, right, backward and stop, via biosignals, in an appropriate HMI is the actual challenge, as the people with a high level of disability (quadriplegia and paralysis, etc.) are unable to drive conventional wheelchairs. Therefore, a novel system driven by optical signals addressing the needs of such a physically impaired population is introduced in this paper. The present system is divided into two parts: the first part comprises of detection of eyeball movements together with the processing of the optical signal, and the second part encompasses the mechanical assembly module, i.e., control of the wheelchair through motor driving circuitry. A web camera is used to capture real-time images. The processor used is Raspberry-Pi with Linux operating system. In order to make the system more congenial and reliable, the voice-controlled mode is incorporated in the wheelchair. To appraise the system’s performance, a basic wheelchair skill test (WST) is carried out. Basic skills like movement on plain and rough surfaces in forward, reverse direction and turning capability were analyzed for easier comparison with other existing wheelchair setups on the bases of controlling mechanisms, compatibility, design models, and usability in diverse conditions. System successfully operates with average response time of 3 s for eye and 3.4 s for voice control mode.
Curriculum Enhanced Supervised Attention Network for Person Re-Identification

Though deep learning methods in Person Re-ID have increased its performance, the field is still confronted with great challenges such as pedestrian misalignment due to the inferior pedestrian detector. To solve such problems, we propose Curriculum Enhanced Supervised Attention Network (CE-SAN): Firstly, an attention module is trained under supervision, which helps the network further emphasize the key information and associate the local and global branch together, leading to a better exploitation of the discriminative features. Secondly, a curriculum design is adopted to divide the dataset into subsets according to the distribution density of the training samples, enabling the network to learn gradually to increase the capability. Moreover, The CE-SAN is easy to be plugged in most of the backbones with high generalization ability. To prove the CE-SAN's superiority, experiments are conducted on three datasets, CE-SAN achieves competitive performance with the state-of-the-arts on Market-1501 and DukeMTMC-reID. Particularly, on the most challenging MSMT17, it outperforms the state-of-the-art methods by 5.2% in Rank-1 and 6.5% in mAP.
The Neural Network Technologies Effectiveness for Face Detection

The paper is devoted to estimation of the effectiveness of the use of modern convolutional neural networks for face detection. On standard open datasets, learning of neural networks and comparison of the effectiveness of their functioning are carried out. Conclusions are drawn regarding the practical application of the neural networks for detecting faces on digital photographs.
Bridging the Semantic Gap Between Customer Needs and Design Specifications Using User-Generated Content

Although product design has been considered as a collaborative activity, progress in developing methods to facilitate the design process has been hampered by difficulties in building common bases among various stakeholders. Customers may only have general needs of the product in layman's terms instead of the sufficient domain knowledge to identify the product specifications. Thus, there is a great need in design research to translate the expressed needs in natural language to design specifications and bridge this semantic gap. By leveraging the massive amount of online user-generated content, we develop a deep learning-based method to automatically identify product design parameters or product specifications. Specifically, we crawl product review data and the corresponding product metadata from e-commerce websites. A convolutional neural network based solution is provided to map product reviews to product specifications. Experimental results show that the method can be well adapted to the mapping from customer needs in the natural language to product specifications. The method facilitates product design by addressing the semantic gap between general customer needs and detailed product specifications. In addition, the automated mapping greatly improves the efficiency and reduces labor in the design process.
Computer Vision in Control and Optimization of Road Traffic

This paper presents the approaches and methods used in computer vision for the detection of moving objects and their tracking. Ways of using computer vision methods to optimize and control traffic are investigated. Every day, the number of vehicles on the roads is increasing and congestion problems are getting worse. The main goal in the development of modern traffic management systems is to create effective traffic management mechanisms in accordance with dynamic traffic conditions. Nowadays, the systems that regulate traffic have many drawbacks. The main ones, that such systems are working according to a predefined program and not being aware of the proper real-time data. This paper focuses on a novel approach to road traffic management by incorporating an intelligent traffic light controlling system using an algorithm that consumes real data from closed-circuit television (CCTV) cameras. As part of the solution, have been developed a program using a popular programming platform that would calculate sets of drive orders for traffic signal lights. The main goal of the proposed system is to provide better results in terms of reduced waiting delay for pedestrians and vehicles, shorter travel time, and increased average velocity of vehicles.
Sorry to Bother You Again

Software robots, or bots, are useful for automating a wide variety of programming and software development tasks. Despite the advantages of using bots throughout the software engineering process, research shows that developers often face challenges interacting with these systems. To improve automated developer recommendations from bots, this work introduces developer recommendation choice architectures. Choice architecture is a behavioral science concept that suggests the presentation of options impacts the decisions humans make. To evaluate the impact of framing recommendations for software engineers, we examine the impact of one choice architecture, actionability, for improving the design of bot recommendations. We present the results of a preliminary study evaluating this choice architecture in a bot and provide implications for integrating choice architecture into the design of future software engineering bots.
Spam Detection On Social Media Platforms

With the increased quality of online social platforms, spammers have come up with various techniques to lure users into accessing malicious links. This is done by generating spam on the comment section of various social media networks. In this paper, we've taken YouTube comments as the dataset and performed spam YouTube comment detection. The current methods to stop spammers include using tools such as Google Safe Browsing which help to detect and also block irrelevant spam on YouTube. Although these tools help in blocking harmful links, it fails to secure the users in real-time scenarios. Thus, many different approaches have been applied to form an environment that is spam free. A few of them are solely supported user-based options whereas others are based on YouTube content. We have assessed our answer with four completely different algorithms based on machine learning, namely - Logistic Regression, Decision Trees Classifier, Random Forest, Ada Boost Classifier and Support Vector Machine. With Logistic Regression, an accuracy of 95.40% is possible, surpassing the current solution by roughly 18%.
Augmenting Cybersecurity in Autonomous Vehicles: Innovative Recommendations for Aspiring Entrepreneurs

Autonomous and connected vehicles are a collection of smart components such as sensors, Radar/Lidar, Electronic Control Units (ECUs), and cameras programmed through voluminous lines of codes, which enable a seamless communication system between the user, vehicle, and infrastructure. The concept of autonomous vehicles is gaining significant traction in the global markets, generating higher revenues, and witnessing high market penetration, thus attracting entrepreneurs to make new and disruptive innovations. However, the underlying technology and communication system of autonomous and connected vehicles are prone to the cybersecurity threats, which circumvent any product that uses sensors, transmits data over the network, and can be controlled through a remote computer or a mobile application. In this article, we attempt to outline the concerns that an aspiring entrepreneur should be aware of before entering into the autonomous vehicle industry, followed by innovative recommendations that can guide addressing the article's issues. The presented recommendations and guidelines are based on the cybersecurity principles of confidentiality, integrity, and availability.
High-accuracy simultaneous measurement of surface profile and film thickness using line-field white-light dispersive interferometer

White-light dispersive interferometry (WLDI) is an instantaneous, high-resolution optical metrological technique for measuring precise and complicated surfaces. This method enables nondestructive inspection of transparent-film-structure devices that are widely used in semiconductor packaging. We propose in this paper to use a home-built WLDI system with line-by-line spectral calibration, phase-shifting algorithm, and single-wave-number method for high-accuracy, simultaneous measurements of surface profiles and film thickness. By calibrating the relationship between wave-number and pixel position on each line of a two-dimensional detector, the line-by-line calibration method improves the measurement accuracy by correcting the spectral distortion caused by the optical system. Moreover, the spectral signal phase is obtained by the phase-shifting algorithm instead of by the Fourier transform method. The single-wave-number method is applied to the computation process by introducing the fringe order, and the measurement result indicates that it enhances the system immunity to environmental noise. A 1.806-µm-high standard step and a 1052.2-nm-thick standard film are measured to verify the system performance and show that the system is highly accurate and reliable.
A BFS-Tree of ranking references for unsupervised manifold learning

Contextual information, defined in terms of the proximity of feature vectors in a feature space, has been successfully used in the construction of search services. These search systems aim to exploit such information to effectively improve ranking results, by taking into account the manifold distribution of features usually encoded. In this paper, a novel unsupervised manifold learning is proposed through a similarity representation based on ranking references. A breadth-first tree is used to represent similarity information given by ranking references and is exploited to discovery underlying similarity relationships. As a result, a more effective similarity measure is computed, which leads to more relevant objects in the returned ranked lists of search sessions. Several experiments conducted on eight public datasets, commonly used for image retrieval benchmarking, demonstrated that the proposed method achieves very high effectiveness results, which are comparable or superior to the ones produced by state-of-the-art approaches.