Reviewerly/exHarmony huggingface dataset:
Smoking recognition with smartwatch sensors in different postures and impact of user’s height

Currently, smartwatches are mainly used as an extension of smartphones. However, equipped with various motion sensors, they are also effective devices for human activity recognition, particularly for those involving hand and arm movements. In this paper, we investigate the smoking recognition probl em with motion sensors on smartwatches using supervised learning algorithms. For this purpose, we collected a dataset from 11 participants including ten different activities. The dataset includes different smoking variations in four different postures, such as smoking while standing, as well as similar activities, such as eating, and other activities, such as walking. Instead of approaching the problem as a binary classification problem, such as smoking and other, we are interested in differentiating smoking in different postures. Our aim is to explore the parameter space that may affect the recognition process on a large and complex dataset, considering 4 different window sizes and overlaps, 63 different features extracted from each sensor, 4 different sensors, 2 different sensor combinations, 3 classifiers and 10 different activities. Additionally, we analyze the impact of participants’ height on the recognition performance. The results show that, simple time-domain features and the combination of accelerometer and gyroscope sensors perform the best. When we consider the impact of height on the recognition performance, the results show that it does not have a significant effect when all activities are considered, however, it does have an effect on smoking while standing, particularly for participants with a significant height difference than others.
Real-time Face Video Swapping From A Single Portrait

We present a novel high-fidelity real-time method to replace the face in a target video clip by the face from a single source portrait image. Specifically, we first reconstruct the illumination, albedo, camera parameters, and wrinkle-level geometric details from both the source image and the target video. Then, the albedo of the source face is modified by a novel harmonization method to match the target face. Finally, the source face is re-rendered and blended into the target video using the lighting and camera parameters from the target video. Our method runs fully automatically and at real-time rate on any target face captured by cameras or from legacy video. More importantly, unlike existing deep learning based methods, our method does not need to pre-train any models, i.e., pre-collecting a large image/video dataset of the source or target face for model training is not needed. We demonstrate that a high level of video-realism can be achieved by our method on a variety of human faces with different identities, ethnicities, skin colors, and expressions.
Learning to Generate Correct Numeric Values in News Headlines

Motivated by the significant role of numeric values to convey concise and accurate information in news headlines, we focus the headline generation task on displaying correct numbers. We propose various ways to present the numeric values to the generative model. In the end, we come up with a simple but effective pre-train task to guide the generator to correctly process the values, which outperforms other base models even if the numbers in the headline are newly generated from the article.
Relational rule discovery in complex discrimination learning.

Failure to learn and generalize abstract relational rules has critical implications for education. In this study, we aimed to determine which training conditions facilitate relational transfer in a relatively simple (patterning) discrimination versus a relatively complex (biconditional) discrimination. The amount of training participants received had little influence on rates of relational transfer. Instead, trial-sequencing of the training contingencies influenced relational transfer in different ways depending on the complexity of the discrimination. Clustering instances of relational rules together during training improved transfer of both simpler patterning and more difficult biconditional rules, regardless of individual differences in cognitive reflection. However, blocking all trials of the same type together improved rule transfer only for biconditional discriminations. Individual differences in cognitive reflection were also more predictive of relational rule use under suboptimal training conditions. The results highlight the need for comprehensive accounts of relational learning to consider how learning conditions and individual differences affect the likelihood of engaging in learning relational structures. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Low-Dimensional Dynamics of Encoding and Learning in Recurrent Neural Networks

In this paper, we use dimensionality reduction techniques to study how a recurrent neural network (RNN) processes and encodes information in the context of a classification task, and we explain our findings using tools from dynamical systems theory. We observe that internal representations develop a task-relevant structure as soon as significant information is provided as input and this structure remains for some time even if we let the dynamics drift. However, the structure is only interpretable by the final classifying layer at the fixed time step for which the network was trained. We measure that throughout the training, the recurrent weights matrix is modified so that the resulting dynamical system associated with the network's neural activations evolves into a non-trivial attractor, reminiscent of neural oscillations in the brain. Our findings suggest that RNNs change their internal dynamics throughout training so that information is stored in low-dimensional cycles, rather than in high-dimensional clusters.
Perception of Head Motion Effect on Emotional Facial Expression in Virtual Reality

In this paper we present a study investigating the impact of head motion on realism, its effect on perceived emotional intensity, and how it affects the affinity for facial expressions. The purpose of this work is to enhance the realism of interactive virtual characters. We designed an experiment to measure the impact through a combination of methods. This included subject behavioural data rating designed facial animations in Virtual Reality (VR) and questionnaire ratings. The results showed that head motions had a positive impact on facial expressions, they enhance realism, perceived emotional intensity, and affinity for virtual characters.
Using Augmented Reality to Assist Seated Office Workers’ Data Entry Tasks

The data entry task is one of the common office tasks performed in the seated position that can be assisted by augmented reality technology. The paper introduces an ongoing project to develop an AR-based data presentation interface to assist office workers’ data entry tasks. The interface developed in this project was designed to facilitate the transition between the two steps including reading data and typing the data into a computer system using a keyboard. Usability evaluation is conducted in a controlled experiment with human subjects to examine whether the AR-based data presentation interface improves data entry performance and mitigates office workers’ physical and mental workload.
eThor: Practical and Provably Sound Static Analysis of Ethereum Smart Contracts

Ethereum has emerged as the most popular smart contract development platform, with hundreds of thousands of contracts stored on the blockchain and covering a variety of application scenarios, such as auctions, trading platforms, and so on. Given their financial nature, security vulnerabilities may lead to catastrophic consequences and, even worse, they can be hardly fixed as data stored on the blockchain, including the smart contract code itself, are immutable. An automated security analysis of these contracts is thus of utmost interest, but at the same time technically challenging for a variety of reasons, such as the specific transaction-oriented programming mechanisms, which feature a subtle semantics, and the fact that the blockchain data which the contract under analysis interacts with, including the code of callers and callees, are not statically known. In this work, we present eThor, the first sound and automated static analyzer for EVM bytecode, which is based on an abstraction of the EVM bytecode semantics based on Horn clauses. In particular, our static analysis supports reachability properties, which we show to be sufficient for capturing interesting security properties for smart contracts (e.g., single-entrancy) as well as contract-specific functional properties. Our analysis is proven sound against a complete semantics of EVM bytecode and an experimental large-scale evaluation on real-world contracts demonstrates that eThor is practical and outperforms the state-of-the-art static analyzers: specifically, eThor is the only one to provide soundness guarantees, terminates on 95% of a representative set of real-world contracts, and achieves an F-measure (which combines sensitivity and specificity) of 89%.
Blockchain Security and Privacy in Education: A Systematic Mapping Study

Blockchain is a technology that promotes security, transparency and authenticity. It is defined by as distributed ledger technology and consensus algorithm, it’s first application were in the financial sector with Bitcoin, Ethereum and other cryptocurrency, but now the concept has spread to other sectors like Healthcare, Governance, Education, etc. In the education sector all stakeholders need a reliable technology that will improves the education system and takes it to the next step. The Blockchain has all the key characteristics to do just that (distributed ledger, no third parties, chronological timestamp, cryptographically sealed, consensus based), and to be used in bigger scale it has to gives assurance such as data privacy and data security.This Systematic Mapping Study (SMS) tries to study existing papers in blockchain security and privacy and its application in Education to provide a classification and to explore the artifacts and the domains.
Latency Detection and Illusion in a Head-Worn Virtual Environment

Through the history of virtual environments research there has been significant interest in understanding how latency in a system affects a user’s experience. Though latency cannot be avoided, previous work has observed that there may be ranges within which small latencies are not discernible. However, the majority of the work examining latency detection thresholds was conducted using hardware and software that are no longer commonly used in contemporary research. In the current study, we examine whether similar latency tolerances exist for modern, off-the-shelf systems. We also look at the effect of increasing and decreasing latency on such tolerances. This revealed evidence of a “latency illusion” that presents in cases of decreasing latency resulting in subjects perceiving less latency than is actually present in the environment.
ReTiCo: An open-source framework for modeling real-time conversations in spoken dialogue systems

In this paper we present ReTiCo, a python-based programming frame-work that utilizes the concepts of incremental processing to create interactive spo-ken dialogue systems and simulations of conversational behavior. In contrast toalready existing toolkits like InproTK, our framework allows for quick visual cre-ation of complex networks and is able to save and load incremental networks forsimulations, automated testing as well as analysis. It is focused on simplifying theuse of incremental modules to make research on specific tasks of an incrementaldialogue system (like real-time speech signal processing) and on the interactionbetween different incremental modules easier.We make this framework accessible as open source so that it can be used in researchon spoken dialogue systems and conversation simulation.
A real-time 3D shape measurement with color texture using a monochromatic camera

High-speed real-time three-dimensional (3D) shape measurement with color texture has a large potential in various fields. The majority of the existing methods retrieve the 3D shape with color texture by employing an RGB camera or additional infrared camera; however, the RGB camera is more expensive and has lower sensitivity to light, and employing additional infrared camera requires complex alignment. Given this, a novel method is proposed to simultaneously retrieve the 3D shape and color texture. Specifically, compared with the existing methods, the proposed method only uses one monochromatic camera; as a result, the cameras alignment is not required, simplifying the 3D measurement system and reducing its cost. Three fringe patterns with different frequencies and three corresponding uniform gray-level patterns are set to different colors of red, green, and blue. Then Fourier transform profilometry (FTP) is employed to retrieve the 3D shape from the captured six images. While three uniform gray-level patterns can be used to eliminate the effects of the zero frequency term, they can be also used to generate the color texture of objects meanwhile. The proposed method only requires the capture of six monochromes, and the experimental results demonstrate the success of the proposed method.
Using Deep Learning and Steam User Data for Better Video Game Recommendations

Recommendation systems have been used widely in many industries, including online retail, movies, and news media. Indeed, video game recommendation systems are one of the most important tools available to users and game distribution platforms today. A good recommender can help customers find games they might like faster, not only making it easier for them but also helping game distributors and developers to increase their sales and improve customer satisfaction ratings. One such platform that can greatly benefit from a recommendation system is Steam, the largest digital PC game distribution platform. Steam sees over a dozen million users login every day. It collects a considerable amount of data on each user, and this data may be used to help make better game recommendations. This paper proposes STEAMer, a solution for a new video game recommendation system for the Steam platform. STEAMer utilizes the Steam user data in conjunction with a deep autoencoder learning model to generate potential recommendations; we also apply the additional user data to an existing deep neural network-based recommendation system. Performance evaluation shows that the additional user data does indeed improve recommendation performance. Furthermore, when both systems use the additional user data, the deep autoencoder-based STEAMer still proves superior to the baseline deep neural network-based system in both mean average precision @ 10 ([email protected]) and normalized discounted cumulative gain @ 10 ([email protected]) scores and in diversity.
Effect of Visual Awareness of the Real Hand on User Performance in Partially Immersive Virtual Environments: Presence of Virtual Kinesthetic Conflict

Considering 3D interactions in Virtual-Reality (VR), it is critical to study how visual awareness of real hands influences users scaled interaction performance in different VR environments. We used Fitts’s law to analyze user performance with five different Control-Display (CD) ratios (1:1 to 1:5). Fifteen participants performed a 3D selection task in three different setups: Head-Mounted Display (HMD), and two variations of the Active One-walled 3D Projection (AOP), with and without visual awareness of the real hand (AOP-A and AOP-B, respectively). The results show that the throughput of AOP-B is significantly higher than that of the AOP-A and HMD (p = .00001 and 0.0002, respectively) which suggests the existence of a conflict between the kinesthetic and visual real-hand movements, which we term as Virtual Kinesthetic Conflict (VKC). To reduce VKC during scaled movements, tasks should be designed such that the visual awareness of the real hand is avoided.
Graph-Structured Visual Imitation

We cast visual imitation as a visual correspondence problem. Our robotic agent is rewarded when its actions result in better matching of relative spatial configurations for corresponding visual entities detected in its workspace and teacher's demonstration. We build upon recent advances in Computer Vision,such as human finger keypoint detectors, object detectors trained on-the-fly with synthetic augmentations, and point detectors supervised by viewpoint changes and learn multiple visual entity detectors for each demonstration without human annotations or robot interactions. We empirically show the proposed factorized visual representations of entities and their spatial arrangements drive successful imitation of a variety of manipulation skills within minutes, using a single demonstration and without any environment instrumentation. It is robust to background clutter and can effectively generalize across environment variations between demonstrator and imitator, greatly outperforming unstructured non-factorized full-frame CNN encodings of previous works.
A Novel Fuzzy System With Adaptive Neurons for Earthquake Modeling

Data driven fuzzy neural networks have some disadvantages, such as high dimensions and complex learning process. Also, the obtained models are difficult to interpret. In this paper, we propose a novel simple fuzzy system, which uses fuzzy adaptive neurons. This novel model takes the advantages of the interpretability of the fuzzy system and good approximation ability of the neural networks. We propose a simple learning algorithm for the novel fuzzy system. The stability analysis is given. We successfully apply this fuzzy model for the earthquake modeling. Comparisons with the popular fuzzy neural model are proposed.
The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks

We study the effects of mild over-parameterization on the optimization landscape of a simple ReLU neural network of the form $\mathbf{x}\mapsto\sum_{i=1}^k\max\{0,\mathbf{w}_i^{\top}\mathbf{x}\}$, in a well-studied teacher-student setting where the target values are generated by the same architecture, and when directly optimizing over the population squared loss with respect to Gaussian inputs. We prove that while the objective is strongly convex around the global minima when the teacher and student networks possess the same number of neurons, it is not even \emph{locally convex} after any amount of over-parameterization. Moreover, related desirable properties (e.g., one-point strong convexity and the Polyak-Łojasiewicz condition) also do not hold even locally. On the other hand, we establish that the objective remains one-point strongly convex in \emph{most} directions (suitably defined), and show an optimization guarantee under this property. For the non-global minima, we prove that adding even just a single neuron will turn a non-global minimum into a saddle point. This holds under some technical conditions which we validate empirically. These results provide a possible explanation for why recovering a global minimum becomes significantly easier when we over-parameterize, even if the amount of over-parameterization is very moderate.
A study of the correctness of the execution of a class file with an embedded digital watermark in different environments

Abstract In this work, the authors set a goal of carrying out a complex study. Preparation of different types of environments and operating systems of different bit version is made. After that, preparation, creation, and embedding of a digital watermark in a class file of the program module written in Java take place. Creation and embedding of a digital watermark are carried out according to the method, previously developed by the authors and associated with the use of undocumented features of Java virtual machine. After all the steps, we execute the file on various platforms with different presets, and then we check if a digital watermark in a class file is nor overwritten nor destroyed and if the class file itself functions in the same way on all platforms. Among other things, the checks are performed to ensure that the digital watermark meets the previously defined criteria.
Face Recognition-Based Attendance System Using Real-Time Computer Vision Algorithms

The face identification system is one of the most emerging methods for authentication of user; it is drawing wide attraction to the surveillance system which reflects innovation in a video surveillance system. This system here represents the automated attendance system using real-time computer vision algorithms and adaptive techniques to track the faces during a specific period of time. Our system works on eigenface recognizers and Intel’s Haar cascades which make the attendance-taking process easier and less time-consuming rather than the traditional process. Our system provides the cheapest solution rather than a previous biometric system like fingerprint authentications. The recorded data is being compared with the training dataset, and the attendance is recorded if the match is found with the help of Python libraries. The camera is being installed at the entry location, so that attendance is recorded as soon as the match of the person entering the particular area is found. So, our main aim is to provide an alternative which is very much convenient to process the attendance and also is very much safe and authentic to have faced as a security option.
Research on Image Classification Method Based on DCNN

Image classification is a kind of image processing technology, which can recognize different things by the feature information given by pictures. With the rapid development of science and technology and people's higher and higher demand for quality of life, image automatic classification technology has been applied to various fields of development. When we classify the image, the traditional image classification method can not accurately grasp the internal relationship between the recognition objects, and the traditional method also has the limitation of the recognition object's feature expression because of the too high characteristic dimension of the data, so the experimental results are not ideal. In view of the above content, this paper proposes an image detection method based on convolutional neural network. The experimental algorithm mainly refers to deep learning and convolutional neural network. Different from the traditional image classification methods, the deep convolution neural network model can be used for feature learning and image classification at the same time. By improving the structure of each part of the experiment and optimizing the convolution neural network model, the over fitting phenomenon can be prevented, and then the accuracy of image detection can be improved. The experiment on cifar-10 database shows that the improved deep learning model of this method has achieved effective results in image detection.