Reviewerly/exHarmony huggingface dataset:
Carrot &amp; Stick: Electrical Muscle Stimulation output generated through incentivized/de-incentivized exergames

Exertion based video games have sought to encourage physical activity. In this paper we explore whether haptic intervention by the exertion game can encourage physical performance. Our system asks users to perform a series of repetitive strenuous tasks comparable to everyday exercise in a gym. Using Electrical Muscle Stimulation (EMS), we introduce two further conditions to the exercise. ‘Carrot’ condition applies EMS to enhance muscle contraction in compliance with the exercise motion, in our case exaggerating bicep contraction during a bicep flex. ‘Stick’ condition applies EMS to counteract muscle contraction in opposition to the exercise motion, in our case activating triceps contraction during a bicep flex. Our aim was to explore whether physical exertion in a virtual environment could be increased through these techniques, either by explicitly supporting or further challenging the movement with EMS. This paper discusses the design, implementation and results of this project.
Time-Dependent Decision-Making and Decentralization in Proof-of-Work Cryptocurrencies

Pool mining is a common way to reduce income variance for miners in Proof of Work Cryptocurrencies. A vast majority of mining does happen in pools, where a popular scheme to distribute rewards is Pay per last N Shares (PPLNS). In PPLNS and related schemes, miners are frequently making decisions whose rewards are not immediate and will only manifest in the future. This implies that models of inter-temporal utility are relevant when considering the incentives of miners. We show that when including these features of human behaviour in models of rational pool miners, the conditions that lead to decentralisation are hampered because larger pools may be more attractive to miners. We present a new game theoretical model of PPLNS where rational miners have time preferences. In this setup, the incentives of miners to work for a pool depend on the initial distribution of power between mining pools, as well as the specific details of how time is discounted. Agents jumping to larger pools face a trade-off between reducing the expected payoff from their shares in their current pool, or getting faster rewards in the future by joining a larger pool. We consider a case where pools of different mining power have the same size of reward window N. According to our study, in equilibrium larger pools have a tendency to accumulate a disproportionate share of the network power at the expense of smaller pools. This outcome is prevalent over a large range of realistic model parameters. Our model shows that PPLNS may be harmful to the decentralised governance of cryptocurrencies. A way to ameliorate these negative effects, is to encourage pools to have diverse window sizes, or use different reward mechanisms. Doing this in a decentralised fashion is an open challenge.
iTracker: Towards Sustained Self-Tracking in Dynamic Feature Environment with Smartphones

Self-tracking at 6 degrees of freedom in real-time is essential in lots of emerging applications such as VR/AR/MR simulation, indoor navigation, and so on. With the development of built-in sensors in smartphones, many self-tracking solutions have appeared. Many researchers try to utilize vision-based approaches combined with an Inertial Measure Unit (IMU) to realize self-tracking with smartphones. After testing these approaches, however, we find that tracking would be lost in four such common scenarios: 1) When the IMU rotates fast or for a long period of time, it will cause serious delays in orientation tracking; 2) The scenes where background features are not distinct enough; 3) When the smartphone moves fast, image features become quite different in successive frames; 4) Unstructured scenes where background features are not static. To address these issues, we propose iTracker, which utilizes Real-time Step-Length Adaption Algorithm to solve the scenario (1) and a Parallel-Multi-State Local Recovery method to deal with scenarios (2)-(4). Extensive experiments show that iTracker realizes robust and accurate self-tracking in these four scenarios with an error of 0.7% throughout the whole trajectory.
Bayesian nonparametric estimation of first passage distributions in semi‐Markov processes

Abstract Bayesian nonparametric (BNP) models provide a flexible tool in modeling many processes. One area that has not yet utilized BNP estimation is semi‐Markov processes (SMPs). SMPs require a significant amount of computation; this, coupled with the computation requirements for BNP models, has hampered any applications of SMPs using BNP estimation. This paper presents a modeling and computational approach for BNP estimation in semi‐Markov models, which includes a simulation study and an application of asthma patients' first passage from one state of control to another.
An Improved Extreme Learning Machine Tuning by Flower Pollination Algorithm

The second generation of algorithms intended for neural networks is named extreme learning machines (ELMs). Since the computing of output weights of ELM encounters the outliers problems, we advise a recently introduced Flower Pollination Algorithm (FPA) for accurately tuning synaptic input weights of ELM. The hybridization between ELM and FPA provides robust FPA-ELM approach which can efficiently solve outlier problems as well as it can significantly reduce the size of latent nodes. Extensive simulation results based on 16 well-known benchmark problems were conducted to reveal the effectiveness of the stated hybridization. Furthermore, it has been proved that our FPA-ELM approach is superior to other state-of-the-art algorithms from literature and that it can learn much faster weight coefficients compared to the other traditional learning methods, as well.
Multi-Person Pose Estimation via Learning Feature Integration

Abstract Human pose estimation is a fundamental but challenging task in computer vision. Although batch normalization is widely used for deep learning, feature extraction in deep convolutional neural networks (DCNN) is still not well explored. In this work, we propose a pose encoding module (PEM) to enhance the learning ability and generalization ability of feature extraction. Given input images, PEM integration instance normalization and batch normalization, combining them in an appropriate way to learn to capture and eliminate appearance changes while maintaining the distinction between learning features can be seen as an integration and adjustment of global information. In addition, we use a simple and efficient up-sampling strategy to recover high-resolution representations for predicting more accurate human keypoint heatmaps, which has achieved better performance than the average network in recent missions. We studied our approach on a standard benchmark for human pose estimation.
Noise effect analysis on edge detection in detecting digits with bilateral filter

Recognizing characters such as digits in natural images is a challenge in image processing. Edge detection is the first approach in image recognition. Edge detection using Bilateral Filter can improve image quality. However, in image processing, noise often occurs in the image which causes objects in the image to be more difficult to detect. In this paper, we conducted experiments to detect edges by using Bilateral filters on each edge detection operator against digits on SVHN which added noise and analyzes the effect of noise on edge detection. In the proposed method, edge detection of SVHN with Speckle noise does not produce many edges compared to Salt & Pepper noise so digits in SVHN can still be seen. The performance of the edge detection operator with Bilateral Filter can also be seen from the MSE and PSNR values. The lowest MSE value and the highest PSNR value is on Canny when detecting SVHN edges with Speckle noise.
Likelihood learning in modified Dirichlet Process Mixture Model for video analysis

Rapid advancement in machine learning has expedited computer vision-based research applicable to traffic analysis. A 2-stage inference process has been proposed in this paper to learn data distributions applicable to object motion modeling and path learning. In the first stage, a posterior probability learning has been used to get the initial clusters. In the subsequent stage, we use an inference method for likelihood learning by introducing a velocity parameter. It decides the speed at which the model converges to obtain the final clusters. A new sampling method has been proposed that performs better as compared to the Gibbs sampling in terms of computation time. The results demonstrate that the technique has relevance in computer vision applications. The proposed method performs better than the state-of-the-art unsupervised learning methods.
Deep Motion Model for Pedestrian Tracking in 360 Degrees Videos

This paper proposes a deep convolutional neural network (CNN) for pedestrian tracking in 360 $$^{\circ }$$ videos based on the target’s motion. The tracking algorithm takes advantage of a virtual Pan-Tilt-Zoom (vPTZ) camera simulated by means of the 360 $$^{\circ }$$ video. The CNN takes in input a motion image, i.e. the difference of two images taken by using the vPTZ camera at different times by the same pan, tilt and zoom parameters. The CNN predicts the vPTZ camera parameter adjustments required to keep the target at the center of the vPTZ camera view. Experiments on a publicly available dataset performed in cross-validation demonstrate that the learned motion model generalizes, and that the proposed tracking algorithm achieves state-of-the-art performance.
Roguelike ancestry network visualisation

Idea networks are inherently illustrative of the domain knowledge they seek to capture. They provide pathways to retrace known paths, and to explore the unknown. In this study, we conceptualised an idea network of 639 roguelike games, using data collected from RogueBasin and other sources, and conducted a user study for designer insight. Study participants interacted with three idea network visualisations that support exploration of design influence, accompanied by a brief historical context of roguelike games, and our design process of visualisations. The study uncovered 6 classes of insights from game developers: thinking in networks, insights on data consistency, accessibility and usability, visualisation preferences, exploring related games, and data contributions from the community. The study is limited by the data sources available about design influences, acknowledging that idea networks are only a partial representation of the full picture, which cannot be known. However, this paper shows that even a partial idea network can be useful for exploring the ancestry of roguelikes and their design influences. It also showed how idea networks can facilitate knowledge sharing through online conversations and new lines of inquiry.
The Impact of Event Processing Flow on Asynchronous Server Efficiency

Asynchronous event-driven server architecture has been considered as a superior alternative to the thread-based counterpart due to reduced multithreading overhead. In this paper, we conduct empirical research on the efficiency of asynchronous Internet servers, showing that an asynchronous server may perform significantly worse than a thread-based one due to two design deficiencies. The first one is the widely adopted one-event-one-handler event processing model in current asynchronous Internet servers, which could generate frequent unnecessary context switches between event handlers, leading to significant CPU overhead of the server. The second one is a write-spin problem (i.e., repeatedly making unnecessary I/O system calls) in asynchronous servers due to some specific runtime workload and network conditions (e.g., large response size and non-trivial network latency). To address these two design deficiencies, we present a hybrid solution by exploiting the merits of different asynchronous architectures so that the server is able to adapt to dynamic runtime workload and network conditions in the cloud. Concretely, our hybrid solution applies a lightweight runtime request checking and seeks for the most efficient path to process each request from clients. Our results show that the hybrid solution can achieve from 10 to 90 percent higher throughput than all the other types of servers under the various realistic workload and network conditions in the cloud.
Teaching Feynman’s quantum physics at secondary schools using current digital technologies

We present our experience and preliminary results from teaching modern physics using smartphones at one of Slovak secondary schools. To be more specific, we developed teaching materials in the framework of flipped learning, applying Feynman’s many-path quantum model together with current digital technologies - GeoGebra simulations (www.geogebra.org) designed for smartphones. Using theoretical and mixed methods research, we report our current progress in answering the following research questions: What impact do smartphones have on the teaching and learning process? How simulations in the context of flipped learning contribute to the visualization, motivation and students’ mental models in the modern physics world?
Representing probabilistic models of knowledge space theory by multinomial processing tree models

Knowledge Space Theory (KST) aims at modeling the hierarchical relations between items or skills in a learning process. For example, when studying mathematics in school, students first need to master the rules of summation before being able to learn multiplication. In KST, the knowledge states of individuals are represented by means of partially ordered latent classes. In probabilistic KST models, conditional probability parameters are introduced to model transitions from latent knowledge states to observed response patterns. Since these models account for discrete data by assuming a finite number of latent states, they can be represented by Multinomial Processing Tree (MPT) models (i.e., binary decision trees with parameters referring to the conditional probabilities of entering different states). We prove that standard probabilistic models of KST such as the Basic Local Independence Model (BLIM) and the Simple Learning Model (SLM) can be represented as specific instances of MPT models. Given this close link, MPT methods may be applied to address theoretical and practical issues in KST. By highlighting the MPT–KST link and its implications for modeling violations of local stochastic independence in Item Response Theory (IRT), we hope to facilitate an exchange of theoretical results, statistical methods, and software across these different domains of mathematical psychology and psychometrics.
Quantification of textual comprehension difficulty with an information theory-based algorithm

Abstract Textual comprehension is often not adequately acquired despite intense didactic efforts. Textual comprehension quality is mostly evaluated using subjective criteria. Starting from the assumption that word usage statistics may be used to infer the probability of successful semantic representations, we hypothesized that textual comprehension depended on words with high occurrence probability (high degree of familiarity), which is typically inversely proportional to their information entropy. We tested this hypothesis by quantifying word occurrences in a bank of words from Portuguese language academic theses and using information theory tools to infer degrees of textual familiarity. We found that the lower and upper bounds of the database were delimited by low-entropy words with the highest probabilities of causing incomprehension (i.e., nouns and adjectives) or facilitating semantic decoding (i.e., prepositions and conjunctions). We developed an openly available software suite called CalcuLetra for implementing these algorithms and tested it on publicly available denotative text samples (e.g., articles, essays, and abstracts). We propose that the quantitative model presented here may apply to other languages and could be a tool for supporting automated textual comprehension evaluations, and potentially assisting the development of teaching materials or the diagnosis of learning disorders.
A new end-to-end image compression system based on convolutional neural networks

In this paper, two new end-to-end image compression architectures based on convolutional neural networks are presented. The proposed networks employ 2D wavelet decomposition as a preprocessing step before training and extract features for compression from wavelet coefficients. Training is performed end-to-end and multiple models operating at di↵erent rate points are generated by using a regularizer in the loss function. Results show that the proposed methods outperform JPEG compression, reduce blocking and blurring artifacts, and preserve more details in the images especially at low bitrates.
Research on heat dissipation of car seat optimizing based on ARIZ analysis

Abstract The heat dissipation of car seat back is poor if there is no extra ventilation system. This paper applied ARIZ (Algorithm of Inventive Problem Solving) to get 6 proposals to optimize the heat dissipation of car seat back. Various TRIZ (Theory of Inventive Problem Solving) methods were used, such as Function Model, Cause and Effect Chain, Contradiction Analysis, Substance Field and SLP (smart little people) and so on. All these methods were used by a logical structured process (ARIZ) which incrementally evolves the complex problem to a point where it was simple to solve.
Key Management Systems at the Cloud Scale

This paper describes a cloud-scale encryption system. It discusses the constraints that shaped the design of Amazon Web Services’ Key Management Service, and in particular, the challenges that arise from using a standard mode of operation such as AES-GCM while safely supporting huge amounts of encrypted data that is (simultaneously) generated and consumed by a huge number of users employing different keys. We describe a new derived-key mode that is designed for this multi-user-multi-key scenario typical at the cloud scale. Analyzing the resulting security bounds of this model illustrates its applicability for our setting. This mode is already deployed as the default mode of operation for the AWS key management service.
Analogy-Based Preference Learning with Kernels

Building on a specific formalization of analogical relationships of the form "A relates to B as C relates to D", we establish a connection between two important subfields of artificial intelligence, namely analogical reasoning and kernel-based learning. More specifically, we show that so-called analogical proportions are closely connected to kernel functions on pairs of objects. Based on this result, we introduce the analogy kernel, which can be seen as a measure of how strongly four objects are in analogical relationship. As an application, we consider the problem of object ranking in the realm of preference learning, for which we develop a new method based on support vector machines trained with the analogy kernel. Our first experimental results for data sets from different domains (sports, education, tourism, etc.) are promising and suggest that our approach is competitive to state-of-the-art algorithms in terms of predictive accuracy.
Comparative analysis of interest points detection techniques for the registration of infrared and visible aerial photographs in pasture crops

Registration is used in precision agriculture to adjust near-infrared aerial photographs and visible crop spectra to analyze crop growth and development. Vegetation indices can be calculated from the registered images. It is therefore necessary to study and analyze the most popular techniques for detecting points of interest. The points of interest found are used as reference in the images of both spectra. Thus, a comparative study of pasture crop images with four of the most popular point of interest detection techniques (Harris, SIFT, SURF and ORB) is presented here.
SprintCon: Controllable and Efficient Computational Sprinting for Data Center Servers

Computational sprinting is an effective mechanism to temporarily boost the performance of data center servers. However, given the great effect on performance improvement, how to make the sprinting process controllable and how to maximize the sprinting efficiency have not been well discussed yet. Those can be significant problems for a data center when computational sprinting is needed for more than a few minutes, since it requires the support of energy storage, whose capacity is limited. The control and efficiency of sprinting not only involve how fast to run servers and how to allocate resources to corunning workloads, but also the impact on power overload, and how to handle the overload with circuit breakers and energy storage to ensure power safety. Different workloads can impact sprinting in different ways, and hence efficient sprinting requires workload-specific strategies. In this paper, we propose SprintCon to realize controllable and efficient computational sprinting for data center servers. SprintCon mainly consists of a power load allocator and two different power controllers. The allocator analyzes how to divide the power load to different power sources. The server power controller adapts the CPU cores that process batch workloads, to improve the efficiency in terms of computing, energy and cost. The UPS power controller dynamically adjusts the discharge rate of UPS energy storage to satisfy the time-varying power demand of interactive workloads, and ensure power safety. The experiment results show that compared to state-of-theart solutions, SprintCon can achieve 6-56% better computing performance and up to 87% less demand of energy storage.
Structured Bayesian Compression for Deep Models in Mobile-Enabled Devices for Connected Healthcare

Deep models, typically deep neural networks, have millions of parameters, analyze medical data accurately, yet in a time-consuming method. However, energy cost effectiveness and computational efficiency are important for prerequisites developing and deploying mobile-enabled devices, the mainstream trend in connected healthcare. Therefore, deep models' compression has become a problem of great significance for real-time health services. In this article, we first emphasize the use of Bayesian learning for model sparsity, effectively reducing the number of parameters while maintaining model performance. Specifically, with sparsity inducing priors, large parts of the network can be pruned with a simple retraining of arbitrary datasets. Then, we propose a novel structured Bayesian compression architecture by adaptively learning both group sparse and block sparse while also designing sparse-oriented mixture priors to improve the expandability of the compression model. Experimental results from both simulated datasets (MNIST) as well as practical medical datasets (Histopathologic Cancer) demonstrate the effectiveness and good performance of our framework on deep model compression.
A New Generic Framework for Mediated Multilateral Argumentation-Based Negotiation Using Case-Based Reasoning

Multilateral negotiation and its importance in today's society, makes it an interesting application in the Artificial Intelligence domain. This paper proposes a new generic framework for multilateral multi-issue Argumentation-Based Negotiation. Agents are first clustered based on their offers to reduce the negotiation complexity. These agents negotiate via a mediator who has two roles: (1) organize the dialogue between them and (2) step in when no mutual agreement is found to get them out of the bottleneck, we formulate this problem as a linear problem where the mediator maximizes both parties' utilities fairly. To highlight the performance of our framework, we tested it on the tourism domain using real data. We study the cases where agents are: not clustered, clustered using hard clustering techniques and clustered using soft clustering techniques. We also study the impact of the mediator in each variant by detecting agreements reached with and without his help.