Reviewerly/exHarmony huggingface dataset:
Optimization of Server Scheduling Based on Cloud Platform

Aiming at the problem of low efficiency of services deployment of cloud computing platform by using exhaustive search algorithm, and in order to obtain a better services deployment scheme, a novel cloud computing services deployment optimization method, based on improved particle swarm optimization algorithm, is proposed. Firstly, it analyzes the deployment problem of the cloud computing service, converts the cloud computing service deployment problem into a multi-objective combination optimization problem and establishes a corresponding mathematical model. Then it uses the particle swarm algorithm with a strong global search ability to solve the mathematical model and improves the problem of slow convergence and premature convergence of the standard particle swarm algorithm. Finally, it verifies the feasibility of the simulation test. The experimental results show that the proposed method can find the optimal cloud computing service deployment scheme quickly and provide a valuable reference for cloud computing service providers.
What Are the Differences Between Good and Poor User Experience?

In the 2010s, concepts of both the human-centered design (HCD) and user experience (UX) have proved to be significant in providing efficient systems and services. Additionally, many methodologies to improve UX and usability are being proposed. However, UX is basically a complicated concept, and its design process requires not only engineering skills but also factors such as psychological knowledge, social understanding, and human-behavior consideration among others. To acquire the necessary abilities to realize a practical UX design, the differences between good UX and poor UX must be understood. In this study, a survey was conducted by collecting the opinions of university students. The questionnaire asked them to mention several examples of systems/services that provided good UX or poor UX. The results imply what types of services are liked and disliked by students. Therefore, this study would be informative for developers and designers who plan to provide their services for the young generation.
Adaptive Directional Walks For Pose Estimation From Single Body Depths

In this paper, we introduce a novel body pose estimation method based on single depth images with our proposed random forest classifier, whereby it is possible to estimate the positions of joints directly with significant accuracy. We train randomized classification trees based on ajoint entropy objective function combined with the geodesic distances and directional vectors simultaneously, to estimate the probability distribution for the label of the directional vector towards a particular bodyjoint by considering the geodesic structural information. At a test step, an arbitrary point moves as much as the magnitude of the probability predicted adaptively by following the predefined kinematic graph, which is referred to as adaptive directional walks. Numerical and visual experiments with real datasets confirm the usefulness of the proposed method.
Investigating the Application of Human Motion Recognition for Athletics Talent Identification using the Head-Mounted Camera

The purpose of this experiment is to introduce a method of talent identification, based on creating and using human motion data set. This procedure has been done by capturing three different movement types from seven professional athletes with the head-mounted camera from first person egocentric viewpoint, and ten middle-range athletes were volunteers to test the reliability of the model. To recognize each action, the long short term memory architecture has been used for classifying the training dataset. During this step, the efficient-perspective-n-point algorithm has been utilized to acquire the head and the correspondence camera position and this information can be used to recognize the motion pattern. Simultaneously, the motion capture system has been used for creating the three-dimensional pose model to distinguish and compare between train and test results visually. The more similarities between the test and our train dataset imply that the person is more talented and by means of this comparison, we can identify how much is a person talented.
The analysis of accelerator concentration effect on setting time and morphology surface impression materials made of brown algae Sargassum sp

To analyze the accelerator concentration on alginate impression material setting time made of brown algae Sargassum sp. This study was an experimental laboratory using samples of impression material made of brown algae Sargassum sp. Measurement of setting time using stopwatch and morphological surface analysis using Scanning Electron Magnetic (SEM). The average setting time for the fastest calcium sulfate accelerator is 3.11 min (17 g) and the longest is 6.13 min (15 g). As for the fastest potassium titanium fluoride accelerator material the timeline setting is 3.81 min (4 g) and the longest is 5.73 min (2 g). SEM analysis showed that alginate printing impression materials with accelerator materials of calcium sulfate 17 g and potassium titanium fluoride 4 g showed the smaller, finer denser and more homogenous surface morphology. Impression material made of Sargassum sp. with accelerator calcium sulfate 17 g and potassium titanium fluoride 4 g has a better setting time and surface morphology.
IT Project Showstopper Framework: The View of Practitioners

The study intended to unravel critical IT project showstoppers which tend to halt IT projects temporarily or permanently, and ultimately cause them to fail, by positioning them in the systems development life cycle (SDLC) framework. Interviewing 8 IT project and program managers of the banking and telecommunications industries in Ghana individually and in a group, 19 critical showstoppers were identified spanning the whole SDLC. Generally, it was observed that for the successful completion of IT projects, the expertise and availability of project managers and team members are critical. Again, the project manager must be able to prove that the project is in line with the objectives and strategic direction of the business, is being mounted to gain competitive advantage, and has a solid business case. Thirdly, funding is key at all stages of the cycle, as well as approval for continuation at various stages.
REVIEW OF DESIGN RESEARCH FOR SUSTAINABLE DEVELOPMENT IN AFRICA: A DESIGN SCIENCE PERSPECTIVE

Abstract The design community can contribute significantly to the success of the United Nations Sustainable Development Goals in Africa. Currently, alignment of the design research community on sustainable development goals in Africa is not well understood. In this paper, we review relevant literature and identify trends in research topics studied and in patterns of collaboration between researchers. We find differences in topic representation and collaboration trends between African-based and non-African based researchers. Understanding these differences better will be important for future research.
A Privacy-Preserving Scheme For Convolutional Neural Network-Based Applications In Mobile Cloud

In recent years, more and more mobile applications adopt deep learning technologies, especially CNN-based image recognition. To protect service providers' interests, the CNN models are usually deployed on the cloud, and the users are required to upload raw images, which cause serious privacy concerns since images may contain sensitive information unrelated to the desired recognition tasks. The previous solution off-loads the shallow portions of the CNN to the clients, and thus the uploaded data becomes the extracted lower-level features rather than the raw images. Nevertheless, although service providers are prevented from obtaining the original images, it is still probably for them to perform some sensitive recognition tasks other than the desired one on the lower-lever features (even after being perturbed to satisfy Differential Privacy). Different from such solution, in this paper, we propose an independent local CNN, which is dedicated for the image perturbation on the clients. It is co-trained with the cloud CNN to learn to intelligently allocate diverse noises among pixels depending on their significance to the desired recognition service. Extensive experiments demonstrate that our mechanism can well prevent curious service providers from performing undesired recognition tasks while maintaining the high accuracy of the desired one.
A Robust Tamil Text to Speech Synthesizer Using Support Vector Machine (SVM)

Speech synthesis systems aim at generating high-quality, natural-sounding speech. Synthesizers become more static due to the inaccessibility of large databases for Indian languages. The prominence of the artificial speech made by these synthesizers is poor. Though statistical-based technique on hidden Markov models (HMMs) and Gaussian mixture model (GMM) is a powerful technique in Text to Speech (TTs) synthesis, recent work in TTS has concentrated on support vector machine (SVM). In this paper, a TTS system is developed for Tamil language using SVM. By using SVM, better sensitivity measures for Tamil Text to Speech are obtained. Output portrays that SVM can produce effectively natural speech compared to HMM and GMM.
Demonstration and Validation of an Advanced Symptom Checker

Symptom checkers are becoming very popular. Yet a recent review on them concluded that the performance of most of these are suboptimal despite much hype. Taking into account lessons learned from the development and deployment of some of the most current popular symptom checkers available in the Internet, an advanced symptom checker was designed and developed with the following features. They were - to diagnose the common problems encountered in the medical self-care in the community, to provide evidence based treatment mostly with over the counter drugs and to provide timely and appropriate referral to other specialty physicians. The proposed solution will be referred to as Computer Assisted Evaluation of Symptoms based symptom checker (CAMEOS - CHECKER).
Weaponizing Favorite Test Functions for Testing Global Optimization Algorithms: An illustration with the Branin-Hoo Function

Some popular functions used to test global optimization algorithms, such as the Branin-Hoo and Himmelblau functions, have multiple local optima, all with the same value of the objective function. That is all local optima are also global optima. This renders them easy to optimize, because it is impossible for the algorithm to get stuck in a local optimum that is not the global one. Such functions actually present an opportunity to create challenging problems for optimization algorithms, because, as illustrated here, it is easy to convert them to functions with competitive local optima by adding a localized bump at the location of one of the optima. This process is illustrated here for the Branin-Hoo function, which has three global optima. We use the popular Python SciPy differential evolution (DE) optimizer for the illustration, because its wide use is likely to imply a well written code. DE also allows the use of the gradient-based BFGS local optimizer for final convergence. By making a large number of replicate runs we establish the probability of reaching a global optimum with the original and weaponized Branin-Hoo. With the original function we find 100% probability of success with a moderate number of function evaluations. With the weaponized version, we found that the probability of getting trapped in a non-global optimum can be made small only with a much larger number of function evaluations.
Iterated conditional expectation algorithm on DAGs and regression graphs

Constructions for regression graphs and verification of the statistical model via linear, linearized, and logistic regressions along them have recently been intensively studied. Given the graph, an iterative regression method, using local averaging estimators, is introduced for prediction, based on a complete training sample. The method makes it possible to perform nonparametric regressions recursively, irrespective of the type of the context and response variables. As a consequence, predictions for the multiple response variables of a test sample are performed in the possession of their context variables only. Consistency is proved if the joint distribution is Markov compatible with a DAG or with a regression graph. In the latter case, the prediction goes on from chain component to chain component, with vector valued smoothers, mainly of product kernel types in the implementation. Practical considerations and application to randomly generated and real-world data are also presented.
Education of Labor Protection and Environmental Engineering Specialists Based on Neural Network Data Processing Technology

The application of the express-analysis model of the breathing cycle in the educational process using a computer complex developed at the Department Engineering ecology and labor safety of the National Research University "MPEI" is considered. The model helps to work out the skills for detecting symptoms of impairment of the functional state of the respiratory system. The proposed model is based on artificial neural networks and can be used for other subject areas and applications, including engineering education.
Comparison of multinomial logistic discriminant analysis (mlgda) and classification and regression tree (cart) performance in classifying the impact of working children

Abstract Working children have some impacts that befall them. They are hampered by access to education, exploited in working hours and even experience the two impacts at the same time. This research focus on applying and comparing the classification performance of the impact of working children by multinomial logistic discriminant analysis (MLgDA) and classification and regression tree (CART). MLgDA is useful for dealing with cases that do not meet the linearity assumption and suitable for data containing numerical and categorical predictors. CART is not affected by outliers, collinearity and heteroscedasticity among predictors. The data sourced from National Labor Force Survey 2018. All predictors are categorical and characteristics between regions are different. More than half of working children in Indonesia face some impacts, reaching 54.31%. The best classification performance is produced by MLgDA, with an accuracy of 78.73%. MLgDA is superior to CART in almost all measures.
Xr360: A Toolkit for Mixed 360 and 3d Productions

Due to the affordability and high quality of the emerging XR devices and platforms, immersive media content has become more accessible to a wide variety of consumers while their technology gains increased interest from content creators. However, the previously mentioned technological advancements are focused more on sensor or rendering-display technologies, while the development of software tools that assist professionals, indies or hobbyists to create immersive content and narratives is left behind. In this work we introduce XR360, a unified toolkit for producing 360° immersive experiences by embedding 3D captured human performances in 360° captured scenes. The presented toolkit comprises a complete solution for content acquisition, 3D asset creation and 3D-360° fusion. It combines a volumetric capturing studio, a performance capture production tool and a 360° fusion application, designed to operate in a cascaded, but disjoint, manner while remaining user-friendly by hiding complex technicalities from its end-users.
Adaptive Framework for Evolutionary Algorithms to Evaluation-iteration Constrained Optimization Problems

This paper deals with the optimization problem subject to an objective function evaluation-iteration constraint. This problem is practical when the objective function is expensive on money and/or time because practitioners usually face money and/or time constraints in any real-world project. For the problem, this paper suggests a solver framework which adapt evolutionary algorithms to the evaluation-iteration constraint. Specifically, the proposed framework updates setting parameters of a target evolutionary algorithm every generation by solving a small size optimization problem related to the evaluation-iteration constraint. Effectiveness of the proposed framework was confirmed for the standard particle swarm optimization algorithm through the numerical experiment.
Kernel Clustering On Symmetric Positive Definite Manifolds Via Double Approximated Low Rank Representation

As an effective descriptor, Symmetric Positive Definite (SPD) matrix is widely used in several areas such as image clustering. Recently, researchers proposed some effective methods based on low rank theory for SPD data clustering with nonlinear metric. However, single nuclear norm is always adopted to formulate the low rank model in these methods, which would lead to suboptimal solution. In this paper, we proposed a novel double low rank representation method for SPD clustering problem, in which matrix factorization and nonconvex rank constraint are combined to reveal the intrinsic property of the data instead of employing the nuclear norm. Meanwhile, kernel method and Log-Euclidean metric are combined to better explore the intrinsic geometry within SPD data. The proposed method has been evaluated on several public datasets and the experimental results demonstrate that the proposed method outperforms the state-of-the-art ones.
Graph-based topic models for trajectory clustering in crowd videos

Probabilistic topic modelings, such as latent Dirichlet allocation (LDA) and correlated topic models (CTM), have recently emerged as powerful statistical tools for processing video content. They share an important property, i.e., using a common set of topics to model all data. However, such property can be too restrictive for modeling complex visual data such as crowd scenes where multiple fields of heterogeneous data jointly provide rich information about objects and events. This paper proposes graph-based extensions of LDA and CTM, referred to as GLDA and GCTM, to learn and analyze motion patterns by trajectory clustering in a highly cluttered and crowded environment. Unlike previous works that relied on a scene prior, we apply a spatio-temporal graph to uncover the spatial and temporal coherence between the trajectories of crowd motion during the learning process. The presented models advance the conventional approaches by integrating a manifold-based clustering as initialization and iterative statistical inference as optimization. The output of GLDA and GCTM are mid-level features that represent the motion patterns used later to generate trajectory clusters. Experiments on three different datasets show the effectiveness of the approaches in trajectory clustering and crowd motion modeling.
Transformative and cognitive approaches to information retrieval and security procedures

Summary Transformative computing is a new area of computer technologies. It is also a new paradigm, which allows to join wireless communication with sensor networks and artificial intelligence technologies. It considerably enhances computing possibilities, increases efficiency of data exploration and analysis, as well as information retrieval using sensors or augmented cognition. In this article, we describe possible applications of cognitive security protocols for such computing application, especially focused on security, and data exploration in distributed systems. Also will be presented the impact of cognitive protocols for securing wireless systems and sensors infrastructures.
AIRV: Enabling Deep Learning Inference on RISC-V

Recently the emerging RISC-V instruction set architecture (ISA) has been widely adopted by both academia and industry. Meanwhile, various artificial intelligence (AI) applications have been extensively deployed in cloud, edge, mobile and IoT devices due to latest breakthroughs in deep learning algorithms and techniques. Therefore, there is an increasing need for enabling deep learning inference on RISC-V. However, at present mainstream machine learning frameworks have not been ported to RISC-V, which poses challenges to deep learning application developers. In this paper, we explore approaches to enabling deep learning inference on RISC-V. Experimental results show that in our work, there is a great gap between the performance of deep learning inference on RISC-V and that on x86; thus compared with direct compilation on RISC-V, cross-compilation on x86 is a better option to significantly improve development efficiency.
Intelligent Navigation in Urban Environments Based on an H-infinity Filter and Reinforcement Learning Algorithms

In urban areas, robustness of a positioning solution suffers from relatively unpredictable reception of attenuated, non-line-of-sight and multipath-contaminated signals. To reflect a GNSS signal propagation environment, parameters of a state estimation filter need to be adjusted on-the-fly. A mixed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> / H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter has been considered here to address the vulnerability of a minimum error variance estimator to measurement outliers. An emphasis between the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> filter and the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter (minimizing the worst-case error) is continuously adjusted by a reinforcement learning (RL) model. Specifically, a continuous action actor-critic RL model with eligibility traces is implemented. The Cramér-Rao lower bound is considered for the filter performance evaluation allowing for the RL reward computation. The algorithm has been tested on a real-world dataset collected with mass-market hardware applying tightly-coupled IMU/GPS sensor integration. A positive RL model learning trend has been identified in two segments of the trajectory with the highest obstruction environment, suggesting the applicability potential of the technique.
DSM: A Case for Hardware-Assisted Merging of DRAM Rows with Same Content

The number of cores and the capacities of main memory in modern systems have been growing significantly. Specifically, memory scaling, although at a slower pace than computation scaling, provided opportunities for very large DRAMs with Terabytes (TBs) capacity. Consequently, addressing the performance and energy consumption bottlenecks of DRAMs is more important than ever.
RIT-18: A Novel Dataset for Compositional Group Activity Understanding

Group activity understanding is a challenging task as multiple people are involved, and their relations may vary over time. Currently, the literature of group activity is limited to group activity recognition, because videos are trimmed in very short duration and focus on a single activity. This slows down the progress in the group activity domain. In this paper, we propose a new largescale untrimmed compositional group activity dataset RIT-18 based on the volleyball games captured from YouTube. Each clip in our dataset depicts an entire rally which spans the duration from serve to a point being scored. Comprehensive annotations including group activity labels, temporal boundaries of activities, key persons, and winning teams are provided. We describe group activity recognition, future activity anticipation, and rally-level winner prediction challenges, and evaluate several baseline methods over these challenges. We report their performance on our dataset and demonstrate further efforts need to be made. The dataset is available at https://pht180.rit.edu/actionlab/rit-18.