Reviewerly/exHarmony huggingface dataset:
Theoretical proof of parameter optimization for sinusoidal fringe projection profilometry

In sinusoidal fringe projection profilometry (FPP), many factors are known to improve measurement accuracy. However, the parameters of the projected fringe pattern, such as modulation and pitch, are the most basic factors. Some empirical principles have been described by researchers, but there is still a lack of theoretical support. In this study, we attempt to address this problem by providing a theoretical proof. We first build an error model for the FPP system. The model reveals how the pitch, modulation, and noise of the fringe pattern affect the measurement error. Combined with optical projection analysis, an optimal projection pitch is delivered that yields the best performance. Both simulations and experiments verify the proposed theoretical proof and optimization method, which will facilitate the construction of FPP systems and help users avoid empiricism and time-consuming experiments.
Fusing Skeleton Recognition With Face-TLD for Human Following of Mobile Service Robots

Target recognition is a challenging task for human following of mobile service robots. In this paper, we combine the principal-component-analysis (PCA)-based face recognition with the tracking-learning-detection applied to the human face (Face-TLD) to obtain an improvement, named as IFace-TLD. The proposed IFace-TLD can significantly improve the discrimination ability of the Face-TLD for ambiguous facial appearances. To further deal with motion uncertainties of the human head, especially the sudden motion change, which makes face-based target recognition methods unstable or even loses the target, a skeleton-based model is introduced to improve the accuracy and robustness of the target recognition. Specifically, within a walk half-cycle, the skeleton features are extracted from the upper-body three-dimensional skeleton coordinates. Then, the extracted skeleton features are fed into the support vector data description (SVDD) to identify the target person when the IFace-TLD becomes invalid. The seamless fusion of the skeleton recognition and the IFace-TLD, named as the SIFace-TLD, significantly enhances the robustness in complex scenarios, especially for people tracking from both front and behind. To achieve a complete human following system, the particle filter (PF) is adopted for estimating the state of the human motion. And then, a controller is designed to maintain the relative position between the robot and the target. Experimental results demonstrate that the proposed IFace-TLD is more accurate and flexible than the original Face-TLD. And the SIFace-TLD shows a robust performance to human motion uncertainties. Moreover, the developed controller can achieve a satisfactory human following performance.
Framework for Fast Memory Authentication Using Dynamically Skewed Integrity Tree

Integrity trees are widely used in computer systems to prevent replay, splicing, and spoofing attacks on memories. Such mechanisms incur excessive performance and energy overhead. We propose a memory authentication framework that combines architecture-specific optimizations of the integrity tree with mechanisms that enable it to restructure at runtime based on memory access patterns. The integrity tree structure is customized based on the cache configuration in order to minimize the performance and energy overhead through speculative authentication. At runtime, the tree nodes that are accessed more frequently will be dynamically shifted closer to the root such that fewer levels of the tree are accessed during authentication. The framework is simulated with Multi2Sim and compared with other existing mechanisms [i.e., tamper-evident counter (TEC) tree and ASSURE] to demonstrate its performance and energy benefits. Experimental results using benchmarks from SPEC-CPU2006, SPLASH-2, and PARSEC show that the proposed dynamic integrity tree leads to an average reduction in instruction per cycle of 13% and 10% over TEC tree and ASSURE, respectively. The corresponding average reduction in authentication time is 30% and 20%, respectively. We show that the proposed framework facilitates the selection of a processor with a smaller cache size such that the energy consumption is reduced without sacrificing performance.
Apache Spark and Apache Ignite Performance Analysis

Big Data represents an actual research topic. More and more it becomes part of people life's through different applications that are used daily, such as stock exchange, news, social media, health-care. All these applications make use of Big Data technologies for storing and processing information. There have been developed numerous technologies for implementing Big Data requirements and it is interesting to follow their strengths and weaknesses, when to use one over another and how well they perform in different situations. In this paper, we compare two frameworks Apache Spark and Ignite that are used for data processing. We perform the comparison taking into consideration the following aspects: features, implementation, architecture, and performance metrics. In order to test the performance, we used two popular applications such as word count and k-means clustering. Results show that Spark achieved better performance than Ignite.
Beggars Can't Be Choosers

Recommender systems are an essential component in many e-commerce platforms to drive sales and guide customers when exploring new products. With the increasing adoption of RFID technology in traditional brick-and-mortar stores, for example, in the form of smart fitting rooms that allow to display recommendations in the integrated mirror, retailers have only recently started to tap into existing product recommendation algorithms. However, due to limited data availability as well as sparsity, for example due to assortments adapted for different demographics, traditional retailers largely struggle to leverage this technology. In this paper we extend the state-of-the-art embedding-based recommender approach prod2vec by processing information about co-purchased products (i.e., shopping baskets) in retail stores. By adding point-of-sale information to shopping baskets we are able to provide recommendations aimed at individual stores, without having to maintain separate models for each location. Furthermore, we experiment with data augmentation methods to overcome the imposed limitations of the available data, and are able to increase the quality of the computed recommendations by more than 6.9%.
Android-Assisted Physics Comic Learning to Train Students’ Conceptual Understanding of Newton’s Gravity

Abstract This aims of the research to determine students’ conceptual understanding of Newton’s gravitation concept by producing comic physics media. This research was conducted in May 2018 for high school students of grade 10 science, using one class of rumbel with 10 students. Researchers used the ADDIE development model. Data was obtained through class discussion and assessment tests. The results showed that students’ understanding of concepts when treated using comic-assisted android media can be categorized as good.
Elderly Fall Detection and Location Tracking System Using Heterogeneous Wireless Networks

Elderly may encounter problems if they are living alone with their poor health condition at their old age. In this paper, a prototype of elderly fall detection and location tracking system is developed by using the Global Positioning System (GPS), Global System for Mobile communications (GSM), Wi-Fi and Arduino technologies. The proposed system is a wearable hardware equipped with cellular network and Wi-Fi connectivity for sending an emergency alert in multiples ways such as Short Message Services (SMS) and email in case of fall detected. The location coordinate is extracted from a GPS module and sent together with emergency alerts in the form of Google Map link to caretaker for immediate action. The prototype is tested. It sends emergency SMS and Email via GSM and cloud server, respectively, if a fall is detected. Moreover, the proposed system stored the fall event details on cloud server and display on a webpage.
Analysis of Inter-Pausal Units in Indian Languages and Its Application to Text-to-Speech Synthesis

Lack of punctuation in Indian language text makes the analysis of phrases difficult. In this paper, inter-pausal units (IPUs) in read sentences are considered as phrases and are analyzed. A key observation from this analysis is that the length of the IPUs in read sentences follow uniformly (across all languages) a Gamma distribution. Additionally, an analysis of the scale and shape parameters suggest that these parameters are governed by the location of the IPU in an utterance. This information is used in text-to-speech (TTS) systems for four Indian languages leading to an improvement in naturalness. A novel IPU-based TTS system is proposed for better prosody modeling as well. A given text is parsed into IPUs, and an appropriate TTS system for different IPUs is used for synthesis. It is observed that there is a significant improvement in the naturalness of synthesized speech compared to that of a single TTS system being used for the entire sentence.
HQR-Scheme: A High Quality and resilient virtual primary key generation approach for watermarking relational data

Most of the watermarking techniques designed to protect relational data often use the Primary Key (PK) of relations to perform the watermark synchronization. Despite offering high confidence to the watermark detection, these approaches become useless if the PK can be erased or updated. A typical example is when an attacker wishes to use a stolen relation, unlinked to the rest of the database. In that case, the original values of the PK lose relevance, since they are not employed to check the referential integrity. Then, it is possible to erase or replace the PK, compromising the watermark detection with no need to perform the slightest modification on the rest of the data. To avoid the problems caused by the PK-dependency some schemes have been proposed to generate Virtual Primary Keys (VPK) used instead. Nevertheless, the quality of the watermark synchronized using VPKs is compromised due to the presence of duplicate values in the set of VPKs and the fragility of the VPK schemes against the elimination of attributes. In this paper, we introduce the metrics to allow precise measuring of the quality of the VPKs generated by any scheme without requiring to perform the watermark embedding. This way, time waste can be avoided in case of low-quality detection. We also analyze the main aspects to design the ideal VPK scheme, seeking the generation of high-quality VPK sets adding robustness to the process. Finally, a new scheme is presented along with the experiments carried out to validate and compare the results with the rest of the schemes proposed in the literature.
A Novel Active Learning Regression Framework for Balancing the Exploration-Exploitation Trade-Off

Recently, active learning is considered a promising approach for data acquisition due to the significant cost of the data labeling process in many real world applications, such as natural language processing and image processing. Most active learning methods are merely designed to enhance the learning model accuracy. However, the model accuracy may not be the primary goal and there could be other domain-specific objectives to be optimized. In this work, we develop a novel active learning framework that aims to solve a general class of optimization problems. The proposed framework mainly targets the optimization problems exposed to the exploration-exploitation trade-off. The active learning framework is comprehensive, it includes exploration-based, exploitation-based and balancing strategies that seek to achieve the balance between exploration and exploitation. The paper mainly considers regression tasks, as they are under-researched in the active learning field compared to classification tasks. Furthermore, in this work, we investigate the different active querying approaches-pool-based and the query synthesis-and compare them. We apply the proposed framework to the problem of learning the price-demand function, an application that is important in optimal product pricing and dynamic (or time-varying) pricing. In our experiments, we provide a comparative study including the proposed framework strategies and some other baselines. The accomplished results demonstrate a significant performance for the proposed methods.
Dynamic Mathematical Figures with Immersive Spatial Displays: The Case of Handwaver

We report on the design and development of HandWaver, a mathematical making environment for use with immersive, room-scale virtual reality. A beta version of HandWaver was developed at the IMRE Lab at the University of Maine and released in the spring of 2017. Our goal in developing HandWaver was to harness the modes of representation and interaction available in virtual environments and use them to create experiences where learners use their hands to make and modify mathematical objects. In what follows, we describe the sandbox construction environment, an experience within HandWaver where learners construct geometric figures using a series of gesture-based operators, such as stretching figures to bring them up into higher dimensions, or revolving figures around axes. We describe plans for research and future development.
PMT: Opposition-Based Learning Technique for Enhancing Meta-Heuristic Performance

Meta-heuristic algorithms have shown promising performance in solving sophisticated real-world optimization problems. Nevertheless, many meta-heuristic algorithms are still suffering from a low convergence rate because of the poor balance between exploration (i.e., roaming new potential search areas) and exploitation (i.e., exploiting the existing neighbors). In some complex problems, the convergence rate can still be poor owing to becoming trapped in local optima. Addressing these issues, this research proposes a new general opposition-based learning (OBL) technique inspired by a natural phenomenon of parallel mirrors systems called the parallel mirrors technique (PMT). Like existing OBL-based approaches, the PMT generates new potential solutions based on the currently selected candidate. Unlike existing OBL-based techniques, the PMT generates more than one candidate in multiple solution-space directions. To evaluate the PMT's performance and adaptability, the PMT has been applied to four contemporary meta-heuristic algorithms, differential evolution (DE), particle swarm optimization (PSO), simulated annealing (SA), and whale optimization algorithm (WOA), to solve 15 well-known benchmark functions. The experimentally, the PMT shows promising results by accelerating the convergence rate against the original algorithms with the same number of fitness evaluations.
User Review Classification and Star Rating Prediction by Sentimental Analysis and Machine Learning Classifiers

With the digital media explosion, in today's increasing Internet usage, the data generated is wide and varied. Huge volumes of new data are injected daily into the Web for various prospects. Procedures like text mining and analysis are required to make the best use of this potential. User review analysis benefits us with the exact understanding of the user's feedback toward the product. In this paper, we have proposed a unique approach by performing abstract-level sentimental analysis of user review by n-gram classification and POS tagging. This classification is then used as entropy for machine learning algorithm. This paper leverages upon the proposed methodology with promising outcomes and improved accuracy by evaluating the data with the help of two algorithms, MaxEnt model and Naïve Bayes classifier, after analyzing few algorithms including SVM and random forest.
Protection against reverse engineering in ARM

With the advent of the mobile industry, we face new security challenges. ARM architecture is deployed in most mobile phones, homeland security, IoT, autonomous cars and other industries, providing a hypervisor API (via virtualization extension technology). To research the applicability of this virtualization technology for security in this platform is an interesting endeavor. The hypervisor API is an addition available for some ARMv7-a and is available with any ARMv8-a processor. Some ARM platforms also offer TrustZone, which is a separate exception level designed for trusted computing. However, TrustZone may not be available to engineers as some vendors lock it. We present a method of applying a thin hypervisor technology as a generic security solution for the most common operating system on the ARM architecture. Furthermore, we discuss implementation alternatives and differences, especially in comparison with the Intel architecture and hypervisor with TrustZone approaches. We provide performance benchmarks for using hypervisors for reverse engineering protection.
MoSSOT: An Automated Blackbox Tester for Single Sign-On Vulnerabilities in Mobile Applications

Mobile applications today increasingly integrate Single Sign-On (SSO) into their account management mechanisms. Unfortunately, the involved multi-party protocol, i.e., OAuth 2.0, was originally designed to serve websites for authorization purpose. Due to the complexity of the adapted protocol, a large number of insecure SSO implementations still exist in the wild. Although the security testing for real-world SSO deployments has attracted considerable attention in recent years, existing work either focuses on websites or relies on the manual discovery of specific and previously-known vulnerabilities. In the paper, we design and implement MoSSOT (Mobile SSO Tester), an automated blackbox security testing tool for Android applications utilizing the SSO services from three mainstream service providers. The tool detects the vulnerabilities within the practical SSO implementations by fuzzing related network messages. We used MoSSOT to examine over 500 first-tier third-party Android applications from US and Chinese app markets. According to the test result, around 72% of the tested applications incorrectly implement SSO and are thus vulnerable. Besides, our test identifies an unknown vulnerability as well as a new variant, in addition to four known ones. The vulnerabilities enable the attacker to illegally log into the mobile applications as the victims or gain access to the protected resources. MoSSOT has been released as an open-source project.
Answering Top-$k$ Graph Similarity Queries in Graph Databases

Searching similar graphs in graph databases for a query graph has attracted extensive attention recently. Existing works on graph similarity queries are threshold based approaches which return graphs with distances to the query smaller than a given threshold. However, in many applications the number of answer graphs for the same threshold can vary significantly for different queries. In this paper, we study the problem of finding top-k most similar graphs for a query under the distance measure based on maximum common subgraph (MCS). Since computing MCS is NP-hard, we devise a novel framework to prune unqualified graphs based on the lower bounds of graph distance, and accordingly derive four lower bounds with different tightness and computational cost for pruning. To further reduce the number of MCS computations, we also propose an improved framework based on both lower and upper bounds, and derive three new upper bounds. To support efficient pruning, we design three indexes with different tradeoffs between pruning power and construction cost. To accelerate the index construction, we explore bound relaxation techniques, based on which approximate indexes can be efficiently built. We conducted extensive performance studies on real-life graph datasets to validate the effectiveness and efficiency of our approaches.
Capacity based deadline aware dynamic load balancing (CPDALB) model in cloud computing environment

Cloud computing is a promising infrastructure in which many activities are carried out in both the commercial and academic spheres. Non-uniform utilization of virtual machines (VMs) in cloud creates an imbalance of the load, resulting in the violation of Service Level Agreements (SLAs) and tends to decreases the rate of revenue. There is a huge concern in scaling the load balancing algorithm due to the heterogeneous nature of the cloud. In this paper, a Capacity based Deadline Aware Dynamic Load Balancing (CPDALB) algorithm is proposed to address the problem. CPDALB is a novel heuristic based load balancing approach which focuses on the selection of VM for allocation of tasks to ensure customer satisfaction in terms of meeting deadline constraints and cost of running applications. CPDALB uses the concept of deploying requests to VMs based on their processing capacities to minimize load imbalance with satisfying the deadline. Other QoS parameters viz. turnaround time, response time and utilization are also computed for performance evaluation. An extensive simulation study has been carried out for the comparison of CPDALB with other standard scheduling algorithms given so far in the literature. The experimental results reveal that the proposed algorithm outperforms its competitors on account of almost all parameters under study.
Application of SVM for Evaluation of Training Performance in Exergames for Motion Rehabilitation

Nowadays, the tools for remote monitoring and training analysis are a matter of deep interest in the field of telerehabilitation. In this study we present a method for the automated evaluation of performance in exergames for motor rehabilitation that can be performed by the patient, even autonomously in a domestic environment, with Microsoft Kinect and Leap Motion. The proposed method is based on a machine learning approach utilizing the Support Vector Machine (SVM). It uses a radial basis function kernel that deals with a two-class classification problem. The performance outcomes for one of the 10 exergames developed by our team are provided as a case study. After a crucial phase consisting of hyperparameter optimization, the SVM algorithm proved to be able to distinguish the "Good" class from the "Other" class with an accuracy of 0.80.
Modeling of occupant’s head movement behavior in motion sickness study via time delay neural network

Passengers are more susceptible to experiencing motion sickness (MS) than drivers. The difference in the severity of MS is due to their different head movement behavior during curve driving. When negotiating a curve, the passengers tilt their heads towards the lateral acceleration direction while the drivers tilt their heads against it. Thus, to reduce the passengers’ level of MS, they need to reduce their head’s tilting angle towards the lateral acceleration direction. Designing MS minimization strategies is easier if the correlation between the head movement and lateral acceleration is known mathematically. Therefore, this paper proposes the utilization of a time delay neural network (TDNN) to model the correlation of the occupant’s head movement and lateral acceleration. An experiment was conducted to gather real-time data for the modeling process. The results show that TDNN manages to model the correlation by producing a similar output response to the actual response. Thus, it is expected that the correlation model could be used as an occupant’s head movement predictor tool in future studies of MS.