Reviewerly/exHarmony huggingface dataset:
Learning Novel Policies For Tasks

In this work, we present a reinforcement learning algorithm that can find a variety of policies (novel policies) for a task that is given by a task reward function. Our method does this by creating a second reward function that recognizes previously seen state sequences and rewards those by novelty, which is measured using autoencoders that have been trained on state sequences from previously discovered policies. We present a two-objective update technique for policy gradient algorithms in which each update of the policy is a compromise between improving the task reward and improving the novelty reward. Using this method, we end up with a collection of policies that solves a given task as well as carrying out action sequences that are distinct from one another. We demonstrate this method on maze navigation tasks, a reaching task for a simulated robot arm, and a locomotion task for a hopper. We also demonstrate the effectiveness of our approach on deceptive tasks in which policy gradient methods often get stuck.
Comparative Analysis for Task Scheduling Algorithms on Cloud Computing

Recently, there has been a significant growth in the popularity of cloud computing systems as an on-demand computing service with a utility computing model with which the users pay as per the usage. One of the main goals of cloud computing is to provide efficient access to resources remotely while obtaining a maximum profit. Therefore, the principal issue in building cloud computing systems is the scheduling where it concentrates on assigning tasks to the available resources at a particular time. In this paper, we provided a comparative simulation study considering the most popular and common task scheduling algorithms in cloud computing, namely FCFS, STF, LTF, and RR algorithms. The evaluation was conducted by CloudSim considering both scheduling allocation policies which are time-shared and space-shared. The Simulation results showed that with space-shared allocation policy the STF algorithm outperformed the others in terms of the total completion time. On the other hand, time-shared allocation policy has a better performance in reducing the completion time of tasks compared to the space-shared policy.
Recognizing Human Actions Using 3D Skeletal Information and CNNs

In this paper we present an approach for the recognition of human actions targeting at activities of daily living (ADLs). Skeletal information is used to create images capturing the motion of joints in the 3D space. These images are then transformed to the spectral domain using 4 well-known image transforms. A deep Convolutional Neural Network is trained on those images. Our approach is thoroughly evaluated using a well-known, publicly available challenging dataset and for a set of actions that resembles to common ADLs, covering both cross-view and cross-subject cases.
A Survey on Authentication Techniques for the Internet of Things

Internet of Things (IoT) is a new computing paradigm that refers to interconnected physical devices or IoT devices that contain sensors, actuators and connectivity to enable these IoT devices to connect, interact and exchange data. With the growing number of IoT devices, many security issues have been exposed especially when sensitive data is exchanged. One of these security issues is authentication. In IoT environments, authentication is a big challenge because of the heterogeneity in protocols, devices, and topologies. IoT devices that communicate with each other need to be authentic, in order to avoid different types of attacks that are related to the negligence of authentication. Also, authentication is the basis of access control and accountability. Existing authentication schemes are not suitable anymore for IoT devices. They need to be modified to consider the limitations of the capabilities of these devices, for example, limited memory and energy, and low processing abilities. This paper mainly focuses on reviewing and comparing several recent authentication techniques that have been proposed for IoT environments.
A Study of Facial Features of American and Japanese Cartoon Characters

Many researchers think that the characters in animated cartoons and comics are designed according to the exaggeration or reduction of some features based on the human face. However, the feature distribution of the human face is relatively symmetrical and uniform. Thus, to ensure the characters look exaggerated, but without breaking the principle of symmetry, some questions remain: Which facial features should be exaggerated during the design process? How exaggerated are the faces of cartoon characters compared to real faces? To answer these questions, we selected 100 cartoon characters from American and Japanese animation, collected data from their facial features and the facial features of real people, and then described the features using angles, lengths, and areas. Finally, we compared cartoon characters’ facial features values with real facial features and determined the key parts and degree of facial exaggeration of animated characters. The research results show that American and Japanese cartoon characters both exaggerate the eyes, nose, ears, forehead, and chin. Compared with human faces, taking the eye area as an example, American animation characters are twice as large compared with human faces, whereas Japanese animation characters are 3.4 times larger than human faces. The study results can be used for reference by animation character designers and researchers.
Quantitative Analysis of Volcanic Lithic Fragments

Research Article| June 03, 2019 Quantitative Analysis of Volcanic Lithic Fragments Mathew D. Affolter; Mathew D. Affolter 1Geoscience Department, Salt Lake Community College, Salt Lake City, Utah 84123, U.S.A. Search for other works by this author on: GSW Google Scholar Raymond V. Ingersoll Raymond V. Ingersoll 2Department of Earth, Planetary, and Space Sciences, University of California, Los Angeles, California 90095-1567, U.S.A. Search for other works by this author on: GSW Google Scholar Journal of Sedimentary Research (2019) 89 (6): 479–486. https://doi.org/10.2110/jsr.2019.30 Article history first online: 03 Jun 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Mathew D. Affolter, Raymond V. Ingersoll; Quantitative Analysis of Volcanic Lithic Fragments. Journal of Sedimentary Research 2019;; 89 (6): 479–486. doi: https://doi.org/10.2110/jsr.2019.30 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyJournal of Sedimentary Research Search Advanced Search Abstract Textural subcategories of volcanic lithic fragments in sand and sandstone have been correlated qualitatively with volcanic compositional types, but these relationships have not been tested quantitatively. Lathwork grains were assumed to be derived mainly from mafic lavas, microlitic from intermediate lavas, and felsitic (sometimes divided into granular and seriate) from silicic lavas. Vitric volcanic lithic fragments were not initially assigned a composition, but they commonly have been associated with silicic compositions. More precise definitions of volcanic lithic types facilitate the quantification of modal proportions of these types, which can be used to test correlations between lithic type and SiO2 content of volcanic rocks. In order to establish which types of volcanic lithic fragments are produced by volcanic rocks of specific compositions, 94 thin sections from diverse volcanic rocks with 45.57 to 76.93 weight % SiO2 were counted using the Gazzi-Dickinson point-counting method. Relationships between volcanic lithic-fragment type and SiO2 content are complex. The only definitive conclusion is that samples containing dominantly vitric and granular volcanic lithic fragments can be confidently assigned to high-SiO2 sources. More tentative conclusions are that mafic volcanic rocks have lithic types anywhere on the seriate–microlitic–lathwork spectrum, and that intermediate volcanic rocks dominantly consist of microlitic and lathwork textures. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
A Simple Review of Audio Steganography

Stegnography is very important because it conceals the existence of communication itself. Nowadays, people use a variety of multimedia files as stegnography carriers. Recently, audio as a type of stegnography carrier has attracted more and more attention because of its unique characteristics. The growing popularity of VOIP has also pushed audio into more applications. Audio information hiding technology has its strengths and weaknesses. This paper begins with a brief introduction of basic stegnography methods. Then, the related evaluation indexes of audio stegnography are introduced. Next, the papar summarizes the performance of main audio stegnograhy methods. Finally, the paper concludes with recommendations and puts forward suggestions for the coming research focus.
ESLMT: a new clustering method for biomedical document retrieval

Abstract MEDLINE is a rapidly growing database; to utilize this resource, practitioners and biomedical researchers have dealt with tedious and time-consuming tasks such as discovering, searching, reading and evaluating of biomedical documents. However, making a label for a group of biomedical documents is expensive and needs a complicated operation. Otherwise, compound words, polysemous and synonymous problems can influence the search in MEDLINE. Therefore, designing an efficient way of sharing knowledge and information organization is essential so that information retrieval systems can provide ideal outcomes. For this purpose, different strategies are used in the retrieval of biomedical documents (RBD). However, still a number of unrelated results for the users’ query are obtained in the RBD process. Studies have shown that well-defined clusters in the retrieval system exhibit a more efficient performance in contrast to the document-based retrieval. Accordingly, the present study proposes the Expanding Statistical Language Modeling and Thesaurus (ESLMT) for clustering and retrieving biomedical documents. The results showed that Clustering with ESLM Similarity and Thesaurus (CESLMST) in all those criteria in this study have a higher value than the other compared methods. The results indicated that the mean average precision (MAP) has improved in the Clusters’ Retrieval Derived from ESLM Similarity-Query (CRDESLMS-QET) method in comparison to the previous methods with the Text REtrieval Conference (TREC) data set.
Log2Intent

Modeling user behavior from unstructured software log-trace data is critical in providing personalized service (\emphe.g., cross-platform recommendation). Existing user modeling approaches cannot well handle the long-term temporal information in log data, or produce semantically meaningful results for interpreting user logs. To address these challenges, we propose a Log2Intent framework for interpretable user modeling in this paper. Log2Intent adopts a deep sequential modeling framework that contains a temporal encoder, a semantic encoder and a log action decoder, and it fully captures the long-term temporal information in user sessions. Moreover, to bridge the semantic gap between log-trace data and human language, a recurrent semantics memory unit (RSMU) is proposed to encode the annotation sentences from an auxiliary software tutorial dataset, and the output of RSMU is fed into the semantic encoder of Log2Intent. Comprehensive experiments on a real-world Photoshop log-trace dataset with an auxiliary Photoshop tutorial dataset demonstrate the effectiveness of the proposed Log2Intent framework over the state-of-the-art log-trace user modeling method in three different tasks, including log annotation retrieval, user interest detection and user next action prediction.
Fusing Behavioral Projection Models for Identity Theft Detection in Online Social Networks

We aim at exploiting users' coarse behavioral records for identity theft detection in online services. We concentrate on this issue in online social networks (OSNs) that users' behavioral records usually consist of multiple dimensional behavior data. The behavioral records in each dimension are possibly coarse and insufficient for effectively modeling users' behavioral patterns. In this paper, we investigate whether there is a complementary effect among different dimensions of records for modeling users' behavioral patterns. We focus on three typical dimensions of behaviors in OSNs, i.e., offline check-ins, online tip-postings, and social contacts. We devise the dedicated behavior models based on each dimension of data, i.e., users' behavioral projection models. Then, by examining all feasible logical combinations of them, we find the optimal ones for two real-world data sets: Foursquare and Yelp. Notably, we analyze the potential correlation between customized demand and optimal logical fusion scheme. As an insightful result, we find that the correlation is independent of the specific data. This study would give the cybersecurity community new insights into the possibility and methodology to achieve a customized identity theft detection in OSNs by integrating multiple behavioral projection models.
EUHSA: Extending Usability Heuristics for Smartphone Application

Emerging development trends in smartphone computing assist the user to enhance skills, health improvement, earning opportunities, sharing ideas, providing entertainment, and so on.Appropriate transfer of these assistances demands a vibrant appearance to a smartphone user.End users emphasize easy-to-use, ergonomics, efficiency, and visual aesthetic, among others.Measuring usability issues allows for improving these features.An inspection-based heuristics set identifies the usability issues for improving the quality of smartphone interfaces.Using suitable heuristics is highly relevant.In this paper, we present an extended and relevant set of 14 Usability Heuristics for Smartphone (EUHSA) application, where 13 heuristics were selected after cross-linking the identified usability flaws with the previously proposed heuristics through a user study with 800 students from a human-computer interaction course.An additional heuristic is proposed against uncaptured usability flaws.Furthermore, the EUHSA was validated using an expert evaluation study.Findings were compared with the previously proposed Joyce and SMASH sets of heuristics.The results explicitly justify the EUHSA and prove its effectiveness in evaluating the usability issues.
Lessons learned from integrating concept mapping and gaming approaches into learning scenarios using mobile devices: analysis of an activity for a geology course

In this study, an integrated concept mapping and gaming-based learning approach was proposed to support mobile learning activities. To investigate the effects of the interaction between concept mapping and gaming on the participants' mobile learning outcomes, a quasi-experimental design was adopted and conducted for a learning activity of the elementary school course unit 'knowing the representative rocks in different geographical locations in Taiwan'. The participants were divided into three experimental groups and one control group. The experimental groups used the integrated concept mapping and gaming-based mobile learning approach, the gaming-based mobile learning approach and the integrated concept mapping and guidance-based mobile learning approach, respectively. On the other hand, the control group used the conventional guidance-based mobile learning approach. The experimental results showed that no interaction was observed between the gaming approach and the concept mapping approach. Furthermore, it was found that the concept mapping approach was not beneficial for students' learning achievement, while the gaming approach significantly improved their learning motivation, learning satisfaction and flow experience. Finally, discussion is provided to explain the findings, in particular, to infer why concept mapping did not achieve the expected positive impacts, as a reference for future studies.
Investigating the role of eye movements and physiological signals in search satisfaction prediction using geometric analysis

Two general challenges faced by data analysis are the existence of noise and the extraction of meaningful information from collected data. In this study, we used a multiscale framework to reduce the effects caused by noise and to extract explainable geometric properties to characterize finite metric spaces. We conducted lab experiments that integrated the use of eye‐tracking, electrodermal activity (EDA), and user logs to explore users' information‐seeking behaviors on search engine result pages (SERPs). Experimental results of 1,590 search queries showed that the proposed strategies effectively predicted query‐level user satisfaction using EDA and eye‐tracking data. The bootstrap analysis showed that combining EDA and eye‐tracking data with user behavior data extracted from user logs led to a significantly better linear model fit than using user behavior data alone. Furthermore, cross‐user and cross‐task validations showed that our methods can be generalized to different search engine users performing different preassigned tasks.
RcDT: Privacy Preservation Based on R-Constrained Dummy Trajectory in Mobile Social Networks

The boom of mobile devices and location-based services (LBSs) greatly enriches the mobile social network (MSN) applications, which bring convenience to our daily life and, meanwhile, raise serious privacy concerns due to the potential disclosure risk of location privacy. Besides the single-location privacy, trajectory privacy is another important type for location privacy leakage. In this paper, focusing on the trajectory privacy preservation in MSNs, we propose a privacy preservation scheme based on the radius-constrained dummy trajectory (RcDT) in MSNs. Particularly, by constraining the generated circular range with radius <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> for the location where a user sends LBS requests, we present the radius-constrained dummy location (RcDL) algorithm to generate the dummy location set of the user’s real location. Furthermore, based on the generated dummy locations, we put forward the RcDT algorithm to generate the dummy trajectory set that has higher similarity to the real trajectory comprehensively considering the constraints of both the single-location exposure risk and trajectory exposure risk. Thus, the user’s trajectory privacy preservation in MSNs is enhanced since the possibility of identifying users’ real trajectories and malicious attacks are reduced. The simulation results demonstrate that our RcDT scheme can have better performance and privacy degree than the existing methods.
SVRG-MKL: A Fast and Scalable Multiple Kernel Learning Solution for Features Combination in Multi-Class Classification Problems

In this paper, we present a novel strategy to combine a set of compact descriptors to leverage an associated recognition task. We formulate the problem from a multiple kernel learning (MKL) perspective and solve it following a stochastic variance reduced gradient (SVRG) approach to address its scalability, currently an open issue. MKL models are ideal candidates to jointly learn the optimal combination of features along with its associated predictor. However, they are unable to scale beyond a dozen thousand of samples due to high computational and memory requirements, which severely limits their applicability. We propose SVRG-MKL, an MKL solution with inherent scalability properties that can optimally combine multiple descriptors involving millions of samples. Our solution takes place directly in the primal to avoid Gram matrices computation and memory allocation, whereas the optimization is performed with a proposed algorithm of linear complexity and hence computationally efficient. Our proposition builds upon recent progress in SVRG with the distinction that each kernel is treated differently during optimization, which results in a faster convergence than applying off-the-shelf SVRG into MKL. Extensive experimental validation conducted on several benchmarking data sets confirms a higher accuracy and a significant speedup of our solution. Our technique can be extended to other MKL problems, including visual search and transfer learning, as well as other formulations, such as group-sensitive (GMKL) and localized MKL (LMKL) in convex settings.
Spin-Wave Multiplexed Atom-Cavity Electrodynamics

We introduce multiplexed atom-cavity quantum electrodynamics with an atomic ensemble coupled to a single optical cavity mode. Multiple Raman dressing beams establish cavity-coupled spin-wave excitations with distinctive spatial profiles. Experimentally, we demonstrate the concept by observing spin-wave vacuum Rabi splittings, selective superradiance, and interference in the cavity-mediated interactions of two spin waves. We highlight that the current experimental configuration allows rapid, interchangeable cavity coupling to 4 profiles with an overlap parameter of less than 10%, enough to demonstrate, for example, a quantum repeater network simulation in the cavity. With further improvements to the optical multiplexing setup, we infer the ability to access more than 103 independent spin-wave profiles.Received 14 August 2019DOI:https://doi.org/10.1103/PhysRevLett.123.263601© 2019 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasCavity quantum electrodynamicsQuantum memoriesQuantum networksQuantum repeatersSuperradiance & subradianceAtomic, Molecular & OpticalQuantum Information
Performance Analysis of DAG-Based Cryptocurrency

In this paper, we address the performance analysis of Direct Acyclic Graph (DAG)-based cryptocurrency on the basis of its native structure. Several attempts on existing blockchain-based cryptocurrencies to resolve the limitation of the transaction processing rate has resulted in numerous form of solutions, such as side-chain, off-chain or data sharding. While those efforts are dedicated to overcome the limitation that the one-dimensional data structure has, a two-dimensional form of distributed ledger as known as DAG-based cryptocurrency has been emerged. Comparing with blockchain-based cryptocurrencies, DAG-based cryptocurrencies feature relatively faster issuance and confirmation of the transactions due to the individual processing method of the transactions. However, existing DAG-based cryptocurrencies also suffer from the instability of the performance, since the confirmation or validation delay can be varied by network condition such as transaction incoming rate. We derive the generic performance model of DAG-based cryptocurrency while varying the parameters. From the derived models, we propose some novel strategies to improve the performance of the DAG-based cryptocurrency system which encourages the participants of the cryptocurrency (both traders and nodes) to boost the transaction processing speed while keeping the ledger healthy. We show the effectiveness of our proposal by numerical statistics obtained by simulation, which results in maximum 46 percent of increase in our case.