Reviewerly/exHarmony huggingface dataset:
Temperature-aware Adaptive VM Allocation in Heterogeneous Data Centers

Virtualized data centers usually consist of heterogeneous servers which have different specifications (performance). Though there are usually a number of unused servers with different performance in such heterogeneous data centers, conventional DVFS (Dynamic Voltage and Frequency Scaling)-based DTM (Dynamic Thermal Management) techniques do not exploit the unused servers to cool down hot servers. In this paper, we propose a novel DTM technique which adaptively exploits external computing resources (unused servers with different performance) as well as internal computing resources (unused CPU cores in the server) available in heterogeneous data centers. When the temperature of a CPU core in a server exceeds a pre-defined thermal threshold, our proposed technique first identifies memory intensiveness and usage of VMs (Virtual Machines). Depending on the memory intensiveness and usage of VMs, our technique adaptively employs the following three methods: 1) a method that migrates a VM to another server with different performance, 2) a method that migrates VMs among CPU cores in the server, and 3) a DVFS-based method. In our experiments, our proposed technique improves performance by 9.6% and saves system-wide EDP by 12.9%, on average (by up to 17.1% and 24.5%, respectively), compared to a conventional DVFS-based DTM technique, satisfying thermal constraints.
A wrist-worn motion evaluation system for fast and powerful down picking of heavy metal guitar

A fast and powerful down picking skill of rhythm guitar is crucial for heavy metal songs. At more than 400 beats per minute, which is a typical starting point for heavy metal songs, most players feel hard to keep the picking rhythm. In this paper, we propose a wrist-worn inertial motion tracking device for analyzing the guitar picking motion. A population test showed that a wrist twist angular velocity signal would give sufficient information on the picking timing to evaluate the accuracy of rhythm. It was also found that the power of sound had a strong correlation with the wrist twist angle and the upward linear acceleration signals. We developed a picking evaluation system by using characteristic motion parameters that informs the player of the speed, power, and smoothness of the picking motion. As a result, five out of eight beginners to play heavy metal guitar reached the speed of 400 beats per minute in a short training period.
A Model for Integrating Personality Traits into an Educational Recommender System

The Recommender Systems in the area of Education have been investigated in order to minimize the information overload for whom the students are subjected, and to improve the learning processes. Personality traits are also researched as a characteristic in order to personalize the students' teaching-learning process. The purpose of this article is to present a doctoral research which proposes the development model of an Educational Recommender System based on the context, by using characteristics of the students' personality traits to customize the recommendations of learning objects.
Visual Interpretation of Regression Error

Numerous sophisticated machine learning tools (e.g. ensembles or deep networks) have shown outstanding performance in terms of accuracy on different numeric forecasting tasks. In many real world application domains the numeric predictions of the models drive important and costly decisions. Frequently, decision makers require more than a black box model to be able to “trust” the predictions up to the point that they base their decisions on them. In this context, understanding these black boxes has become one of the hot topics in Machine Learning and Data Mining research. This paper proposes a series of visualisation tools that help in understanding the predictive performance of non-interpretable regression models. More specifically, these tools allow the user to relate the expected error of any model to the values of the predictor variables. This type of information allows end-users to correctly assess the risks associated with the use of the models, by showing how concrete values of the predictors may affect the performance of the models. Our illustrations with different real world data sets and learning algorithms provide insights on the type of usage and information these tools bring to both the data analyst and the end-user.
Single Image Super-Resolution for Optical Satellite Scenes Using Deep Deconvolutional Network

In this paper, we deal with the problem of super-resolution (SR) imaging and propose a deep deconvolutional network based model for the same. In principle, the SR problem considers the construction of the high-resolution (HR) version of a scene given a number of so-called low-level image instances of the respective scene. Moreover, if there is a single low-resolution (LR) image available, the problem becomes even difficult and ill-posed. We deal with such a scenario and show how the popular deconvolutional network can effectively reconstruct the HR image by learning the functional mapping at the patch level. We evaluate the proposed model on a number of optical remote sensing (RS) images obtained from the UC-Merced dataset. Experimental results suggest that the proposed model consistently outperforms the existing deep and shallow models for single image SR for the RS images.
Nonlinear Attitude Control Design and Verification for a Safe Flight of a Small-Scale Unmanned Helicopter

Autonomous small unmanned helicopter systems have been widely studied in the last decades. These systems are extremely agile due to their energy efficiency, overall costs and high levels of maneuverability compared to manned helicopters. This allows them to be used in urban environments for different applications such as search and rescue, aerial stunts for movie industry, fire fighting, surveillance, etc. Such applications require the control system to be robust and safe since a fault may lead to environmental damage and endangering human life. For reasons of the very high safety requirements, in this paper we propose a robust control design and also introduce formal verification of control for small-scale unmanned helicopters. The controller proposed is based on dynamic inversion control for a 3-DOF (degree-of-freedom) attitude dynamics while taking into account the system modelling uncertainty with variable payloads and external disturbances of wind. An invariant set called control-enabled-set is defined for flight envelope, which represents the dynamical state vectors comprised of the attitude and rotation rates, for which the stable control of the craft is feasible with our control scheme. Then the controller is verified using formal methods represented by MetiTarski automated theorem prover to ensure controller stability and robustness. Our approach also paves the way to the possibility that the autopilot system monitors whether it is getting near the boundary of its flight envelope, in which case it can propose or plan and execute an emergency landing to a safe location.
Decision Tree Optimization in Data Mining with Support and Confidence

Abstract Decision Tree is a classification technique in data mining that aims to predict behaviour from database. This goal is supported by several algorithms, one of which is Iterative Dichotomiser 3 (ID3) that displays predictions in a tree structure. With the application of decision trees, warehouses or heaps of data can be processed so as to produce rules or decision trees as decision support in solving problems faced by agencies. In fact, the information or rules produced by decision trees are limited to rules using the logic of propositions. The challenge in making decisions on decision trees is how to determine algorithms with a high degree of accuracy from various algorithms in the decision tree and how to find support and confidence for each rule produced by the decision tree to add support value and confidence level of each rule produced. The resulting rule has weaknesses, namely the unavailability of support and confidence, all rules are considered equal in strength based on data before being processed, found records that vary or different amounts of data. By making support and confidence, it will be easier to make decisions based on the results obtained.
Towards Scalable Video Analytics at the Edge

Breakthroughs in deep learning, GPUs, and edge computing have paved the way for always-on, live video analytics. However, to achieve real-time performance, a GPU needs to be dedicated amongst a few video feeds. But, GPUs are expensive resources and a large-scale deployment requires supporting hundreds of video cameras - exorbitant cost prohibits widespread adoption. To ease this burden, we propose Tetris, a system comprising of several optimization techniques from computer vision and deep-learning literature blended in a synergistic manner. Tetris is designed to maximize the parallel processing of video feeds on a single GPU, with a marginal drop in inference accuracy. Tetris performs CPU-based tiling of active regions to combine activities across video feeds. resulting in a condensed input volume. It then runs the deep learning model on this condensed volume instead of individual feeds, which significantly improves the GPU utilization. Our evaluation on Duke MTMC dataset reveals that Tetris can process 4x video feeds in parallel compared to any of the existing methods used in isolation.
Deep Garment Image Matting for a Virtual Try-on System

To improve online shopping experience, many fashion retailers try to provide high quality garment images, capturing fine details as well as various opacities. A skilled operator can deliver a satisfactory result using manual segmentation tools, but it is challenging to scale up this process to address seasonal demands. To balance the quality and the processing cost, we investigate the use of a deep learning based matting technique that can produce a high quality alpha map from an approximate garment segmentation. The proposed model adopts the deep image matting model, but we replace the refinement network with a sequence of recursive convolutional network (RCN) units. Our main motivation for this modification is that the fine garment details created by different materials are represented better with the mixture of the image features from different scales. Therefore, we need to construct deeper convolutional layers for better scale analysis but we also need to maintain the number of unknowns low as producing training data is expensive. The proposed RCN based refinement network can address these conflicting restrictions well and our experiments demonstrate that it can achieve a lower training loss and produce better prediction results than the baseline refinement model under the same training condition.
An approach for constructing the S-box using the CML system

Abstract We propose an approach for constructing S-box by using the CML chaotic system. The chaotic sequences produced by the CML chaotic system are used to permute and diffuse the elements of the S-box randomly. The obtained S-box has a strong randomness property due to the high dimensional feature of the CML chaotic system. We analyze bijective property, nonlinearity, strict avalanche criterion, outputs bit independence criterion, input/output XOR distribution and linear approximation probability of the constructed S-box. The experimental results show that the proposed approach for constructing S-box is effective.
Web Performance Optimization Techniques for Biodiversity Resource Portal

Abstract Monitoring and evaluation is a part of management that must be done if want to an increase, and improvement in performance. Therefore, these activities are needed as an effort to develop, manage and optimize the Biodiversity Resource Portal. This study aims to analyze the efficiency of the Biodiversity resource portal of the key performance indicators on Image and script (CSS / JScript) content. Pre-Test and Post-Test analysis methods using Web Performance Optimization Techniques. Evaluation the test results showed an increase in end-to-end web performance after optimization, from the efficiency score of pretest Grade F (13%) to Grade B (82%) post-test.
On Image Enhancement for Unsupervised Image Description and Matching

An image enhancer improves the visibility and readability of the content of any input image by modifying one or more features related to vision perception. Its performance is usually assessed by quantifying and comparing the level of these features in the input and output images and/or with respect to a gold standard, often regardless of the application in which the enhancer is invoked. Here we provide an empirical evaluation of six image enhancers in the specific context of unsupervised image description and matching. To this purpose, we use each enhancer as pre-processing step of the well known algorithms SIFT and ORB, and we analyze on a public image dataset how the enhancement influence image retrieval. Our analysis shows that improving perceptual features like image brightness, contrast and regularity increases the accuracy of SIFT and ORB. More generally, our study provides a scheme to evaluate image enhancement from an application viewpoint, promoting an aware usage of the evaluated enhancers in a specific computer vision framework.
CSMDSE-Cuckoo Search Based Multi Document Summary Extractor

In the current scenario, managing of a useful web of information has become a challenging issue due to a large amount of information related to many fields is online. The summarization of text is considered as one of the solutions to extract pertinent text from vast documents. Hence, a novel Cuckoo Search-based multi document summary extractor (CSMDSE) is presented to handle the multi-document summarization (MDS) problem. The proposed CSMDSE is assimilating with few other swarm-based summary extractors, such as Cat Swarm Optimization based Extractor (CSOE), Particle Swarm Optimization based Extractor (PSOE), Improved Particle Swarm Optimization based Extractor (IPSOE) and Ant Colony Optimization based Extractor (ACOE). Finally, a simulation of CSMDSE is compared with other techniques with respect to the traditional benchmark datasets for summarization problem. The experimental analysis clearly indicates CSMDSE has good performance than the other summary extractors discussed in this study.
Feature extraction from app reviews in google play store by considering infrequent feature and app description

Abstract Google Play Store is one of the platforms used for distributing various kinds of mobile app from the developer to the users. Through this platform, users are allowed to give their comments about the app. These user reviews could be used to extract potential app’s requirements. They are important information for developer to further develop the app. There have been some previous researches about extracting mobile app features which are frequently mentioned in user reviews. There are less researches that focus on extracting infrequent features. Nevertheless, extracting infrequent features is also important. It is because there is a possibility that important needs contained in the review which are not extracted as frequent features. One of the challenges in infrequent feature extraction was the irrelevant features contained in extracted features. To overcome the problem, this study aims to extract app frequent feature in reviews by finding collocation and infrequent feature in reviews based on dependency as extraction rules. Afterward, it compares the similarity of all the extracted features from review with features from app description. The implied technique of similarity measure includes similarity of 1) single-term by matching each term of feature, 2) synonym referring to WordNet synsets, and 3) sentence based on calculation of lexical semantic vector and cosine similarity. The implementation result is evaluated using precision and recall calculations. The result shows that features extracted by proposed method are more relevant than previous method.
Consensus Mechanism in Enterprise Blockchain

From a decentralized to centralized architecture, from a permissionless to a permissioned platform, Blockchain has been adopted and used in business processes referred to as enterprise blockchain. The destiny of blockchain is yet to be entertained. Specifically, the consensus mechanism is transformed PBFT algorithm to Smart Contract, then, chain-code. Together, these participating business partners make contributions to the enterprise blockchain platform.
Vehicle Trajectories from Unlabeled Data Through Iterative Plane Registration

One of the most complex aspects of autonomous driving concerns understanding the surrounding environment. In particular, the interest falls on detecting which agents are populating it and how they are moving. The capacity to predict how these may act in the near future would allow an autonomous vehicle to safely plan its trajectory, minimizing the risks for itself and others. In this work we propose an automatic trajectory annotation method exploiting an Iterative Plane Registration algorithm based on homographies and semantic segmentations. The output of our technique is a set of holistic trajectories (past-present-future) paired with a single image context, useful to train a predictive model.
Sibilant Consonants Classification with Deep Neural Networks

Many children suffering from speech sound disorders cannot pronounce the sibilant consonants correctly. We have developed a serious game that is controlled by the children’s voices in real time and that allows children to practice the European Portuguese sibilant consonants. For this, the game uses a sibilant consonant classifier. Since the game does not require any type of adult supervision, children can practice the production of these sounds more often, which may lead to faster improvements of their speech. Recently, the use of deep neural networks has given considerable improvements in classification for a variety of use cases, from image classification to speech and language processing. Here we propose to use deep convolutional neural networks to classify sibilant phonemes of European Portuguese in our serious game for speech and language therapy. We compared the performance of several different artificial neural networks that used Mel frequency cepstral coefficients or log Mel filterbanks. Our best deep learning model achieves classification scores of $$95.48\%$$ using a 2D convolutional model with log Mel filterbanks as input features.
Wait a second

Hanabi is a cooperative card game in which communication plays a key role. The game provides an interesting challenge for AI agents, because the game state is only partially observable, and the game limits what players can tell each other. This limit on communication channels is similar to a common scenario in system security research, and has been researched extensively in that context, for example by bypassing a system's isolation by establishing a covert communication channel. Such channels can be established through anything that the sending party can influence and the receiving party can observe, such as photonic emission, resource contention, or latency. In this paper, we present Hanabi agents that utilize timing as a covert channel so effectively that they can eschew the communicative actions provided by the game entirely. In addition to a thorough evaluation of the effectiveness of our approach, and a comparison to other Hanabi agents, we provide its context in the area of security, and an outlook on how it could be related to human behavior in future work.
A Business-Context-Based Approach for Message Standards Use - A Validation Study

While necessary for a successful integration of enterprise services and business-to-business (B2B) applications, message standards can be difficult to use. This paper proposes an innovative, business-context approach and a software tool that we believe can overcome those difficulties. To accomplish both, the paper shows how the business-context approach and the new tool address difficult issues common to traditional approaches of using message standards. The paper also identifies research questions that need to be addressed to increase scalability of the new approach and tool.
Navigated learning: An approach for differentiated classroom instruction built on learning science and data science foundations

Classroom teachers are often provided with instructional resources and assessment systems that dictate one pathway for every student's learning and evaluation. These practices remain common despite new affordances available through data-rich, emerging digital technologies that draw on data science and learning science foundations to complement and enhance traditional instruction. This paper presents a conceptual framework for Navigated Learning, a pedagogical approach that operationalizes learning principles using emerging ideas in artificial intelligence and data science, resulting in the continuous, real-time generation of students' cognitive and noncognitive data to support a teacher's ability to utilize the system to customize instruction. The paper articulates the learning principles underlying the pedagogical approach and the features afforded by the Learning Navigator system. The paper concludes with two cases of very different implementation of Navigated Learning focused on fifth grade and ninth grade students' learning of mathematics.
Histogram based Human Computer Interaction for Gesture Recognition

Computer remains as the inevitable part of our daily life, or we can say that, the computer is used by every person in their work. In this project, our main aim is to make computers easily understand the human language. This paper introduces a technique for human computer interaction using open CV and python. We have first detect, pre-processing and recognize the hand fingers and the count. Then with the help of recognized fingers count, it is act as a mouse to perform the different operations and this hand mouse interface known as a monitor. The hand mouse is controlled by the virtual monitor provides a virtual space. The accuracy of the proposed algorithm is 80%. This envisioned concept controlling a system by hand has been implemented successfully with effective efforts.
SETH-Hardness of Coding Problems

We show that assuming the strong exponential-time hypothesis (SETH), there are no non-trivial algorithms for the nearest codeword problem (NCP), the minimum distance problem (MDP), or the nearest codeword problem with preprocessing (NCPP) on linear codes over any finite field. More precisely, we show that there are no NCP, MDP, or NCPP algorithms running in time q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(1-ε)n</sup> for any constant ε > 0 for codes with q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> codewords. (In the case of NCPP, we assume non-uniform SETH.) We also show that there are no sub-exponential time algorithms for y-approximate versions of these problems for some constant -y > 1, under different versions of the exponential-time hypothesis.