Reviewerly/exHarmony huggingface dataset:
PSO-based strategy for the segregation of heterogeneous robotic swarms

A robotic swarm is a particular type of multi-robot system that employs a large number of simpler agents in order to cooperatively perform different types of tasks. A topic that has received much attention in recent years is the idea of segregation. This concept is important, for example, in tasks that require maintaining robots with similar features or objectives arranged in cohesive groups, while robots with different characteristics remain separated in their own groups. In this paper, we propose a decentralized methodology to segregate heterogeneous groups of robots that are randomly distributed on the environment. Our approach to segregate robots consists of extending the Optimal Reciprocal Collision Avoidance (ORCA) algorithm with a navigation strategy inspired on the Particle Swarm Optimization (PSO). A series of simulations in different scenarios shows that the groups were able to successfully converge to a segregated state in all the evaluated cases. Furthermore, the methodology allowed for a faster convergence of the groups when compared to the state-of-the-art technique.
Achieving correlated differential privacy of big data publication

For protecting privacy of correlated data in big data, k-CRDP and r-CBDP models are proposed. Firstly r-CBDP uses MIC and machine learning to determine dependencies between data, accurately calculates correlated sensitivity, then divides big data into independent blocks, and implements k-CRDP for blocks to achieve big data correlated differential privacy.
A three-phase process for SLA negotiation of composite cloud services

Negotiation is used when two or more parties with conflicting goals pursue their own interests. Service trading in cloud computing is a case in which automated negotiation system (ANS) can be used to achieve high satisfaction levels for both contract parties. An ANS allows the parties negotiate automatically on some parameters that are important for them. Multiple services are combined together to provide a composite service that delivers a value-added service to consumers. With the increasing tendency to use composite cloud services, there is a need for designing ANSs capable of fulfilling requirements of consumers of these services. Unlike existing ANSs which support negotiation of single service, our proposed ANS supports negotiation of composite service. This work also proposes a novel negotiation strategy to enhance satisfaction of both trading parties. Some simulations were carried out to evaluate the performance of proposed negotiation strategy in terms of negotiating agents’ utility and negotiation speed. Analysis of the results of simulations shows that our recommended strategy provides more performance than the others.
<i>AutoBoT</i>: Resilient and Cost-Effective Scheduling of a Bag of Tasks on Spot VMs

Many data and task parallel applications can be modeled as a Bag of Tasks (BoT), and scheduled on distributed systems such as Grids, Clusters, and Clouds. We propose AutoBoT, a collection of scheduling strategies for BoTs with hard deadlines on Cloud Virtual Machines (VMs), to lower the overall monetary cost - a distinctive factor for Clouds. Besides reliable fixed-price VMs, AutoBoT uniquely reduces costs by including preemptible spot-priced VMs that are much cheaper, but are unreliable and have time-variant pricing. It guarantees timely completion by making active runtime decisions on pricing, number of VMs to acquire/release, and on task placement, checkpointing and migration. Our rigorous simulations of 7 Million BoT runs sampled from the Google cluster workload uses a realistic Cloud model and 6 months of Amazon EC2 pricing data to compare AutoBoT against two baseline algorithms. We analyze the impact of BoT size, data centers, time periods, deadline duration, loss budget and checkpointing strategies. AutoBoT often gives X80% profit and rare but bounded losses, compared to using only fixed-price VMs. Further, its 100 percent completion guarantee is 23-42 percent better than using only spot-priced VMs which offer a similar profit.
Single Image Super-Resolution Using Dual-Branch Convolutional Neural Network

Recent advances in convolutional neural networks have demonstrated impressive reconstruction for single image super-resolution (SR). However, most of the existing methods are to achieve better performance by directly stacking more convolution layers in a chain way. As the depth increases, it not only lacks time efficiency but also consumes more computer memory. In this paper, we propose a low-complexity dual-branch network, termed DBCN, to deal with the SR problem. The feature extraction part of the proposed DBCN mainly consists of two parallel sub-networks, one extracts fine local information via standard convolution layers, and the other focuses on more contextual information in larger regions by using dilated convolution layers. In addition, deconvolution layers are integrated into the network after feature extraction stage to accelerate the upscaling process. Specifically, a novel skip connection is proposed to learn residual mapping and ease the training. Extensive evaluations on benchmark datasets show the effectiveness of our algorithm, achieving comparable results in terms of accuracy and visual quality compared with several state-of-the-art methods.
Local ant system for allocating robot swarms to time-constrained tasks

We propose a novel application of the Ant Colony Optimization algorithm to efficiently allocate a swarm of homogeneous robots to a set of tasks that need to be accomplished by specific deadlines. We exploit the local communication between robots to periodically evaluate the quality of the allocation solutions, and agents select independently among the high-quality alternatives. The evaluation is performed using pheromone trails to favor allocations which minimize the execution time of the tasks. Our approach is validated in both static and dynamic environments (i.e. the task availability changes over time) using different sets of physics-based simulations.
CyclePro: A Robust Framework for Domain-Agnostic Gait Cycle Detection

The utility of wearable sensors for continuous gait monitoring has grown substantially, enabling novel applications on mobility assessment in healthcare. Existing approaches for gait cycle detection rely on predefined or experimentally tuned platform parameters and are often platform specific, parameter sensitive, and unreliable in noisy environments with constrained generalizability. To address these challenges, we introduce CyclePro, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> novel framework for reliable and platform-independent gait cycle detection. CyclePro offers unique features: 1) it leverages physical properties of human gait to learn model parameters; 2) captured signals are transformed into signal magnitude and processed through a normalized cross-correlation module to compensate for noise and search for repetitive patterns without predefined parameters; and 3) an optimal peak detection algorithm is developed to accurately find strides within the motion sensor data. To demonstrate the efficiency of CyclePro, three experiments are conducted: a clinical study including a visually impaired group of patients with glaucoma and a control group of healthy participants; a clinical study involving children with Rett syndrome; and an experiment involving healthy participants. The performance of CyclePro is assessed under varying platform settings and demonstrates to maintain over 93% accuracy under noisy signal, varying bit resolutions, and changes in sampling frequency. This translates into a recall of 95.3% and a precision of 93.4%, on average. Moreover, CyclePro can detect strides and estimate cadence using data from different sensors, with accuracy higher than 95%, and it is robust to random sensor orientations with a recall of 91.5% and a precision of 99.2%, on average.
Geometric quantum speed limits and short-time accessibility to unitary operations

The notion of quantum speed limit (QSL) refers to the fundamental fact that two quantum states become completely distinguishable upon dynamical evolution only after a finite amount time, called the QSL time. A different, but related concept is that of minimum control time (MCT), which is the minimum evolution time needed for a state to be driven (by suitable, generally time-dependent, control fields) to a given target state. While the QSL can give information about the MCT, it usually imposes little restrictions to it, and is thus unpractical for control purposes. In this work we revisit this issue by first presenting a theory of geometrical QSL for unitary transformations, rather than for states, and discuss its implications and limitations. Then, we propose a framework for bounding the MCT for realizing unitary transformations that goes beyond the QSL results and gives much more meaningful information to understand the controlled dynamics of the system at short times.
Improving Reliability: User Authentication on Smartphones Using Keystroke Biometrics

Keystroke biometrics is a well-investigated dynamic behavioral methodology that utilizes the unique behavioral patterns of users to verify their identity when tapping keys.However, the performance of keystroke biometrics is unreliable due to its high error rate and low robustness.In this paper, we propose differential evolution and adversarial noise-based user authentication (DEANUA), which is a verification scheme for enhancing reliability by reducing the error rate and improving robustness.We investigate the current mainstream features and build a more comprehensive feature set that composed of 146 features.Then, we use a differential evolution method to select an optimized feature set.With the support vector regression method on this feature set, we achieve an equal error rate (EER) of 0.12660% and also a 31.25%energy consumption reduction rate.In this paper, the model is trained with the training samples collected from one situation, but the model is used in various situations.Thus, the robustness of the model is inadequate.We constructed the adversarial noise samples to simulate users' behavioral characteristics in different situational contexts.We use the adversarial noise samples to test the models in a strict experimental environment, which raises the EER by 83.59%, to 10.9299%.Then, we enhance the model with adversarial noise samples to obtain an EER of 8.70932%, which is a reduction of 20.32%.
Updating Model of Software Component Trustworthiness Based on Users Feedback

The software trustworthiness measurement is one of the hot topics. Software component technology is the mainstream technology of software development. How to get the trustworthy degree of software component efficiently and accurately is a challenging issue for the component-based software development. Getting the trustworthy degree of software component needs many users' success cases. In this paper, we propose an updating model of software component trustworthiness. First, the trustworthy degree of the software component is computed based on users' feedback. Then, the weight of updating is determined by the number of users. Finally, the method of cluster different companies is based on the Euler distance. A case study shows that the method is reasonable and effective.
Understanding Faceted Search from Data Science and Human Factor Perspectives

Faceted search has become a common feature on most search interfaces in e-commerce websites, digital libraries, government’s open information portals, and so on. Beyond the existing studies on developing algorithms for faceted search and empirical studies on facet usage, this study investigated user real-time interactions with facets over the course of a search from both data science and human factor perspectives. It adopted a Random Forest (RF) model to successfully predict facet use using search dynamic variables. In addition, the RF model provided a ranking of variables by their predictive power, which suggests that the search process follows rhythmic flow of a sequence within which facet addition is mostly influenced by its immediately preceding action. In the follow-up user study, we found that participants used facets at critical points from the beginning to end of search sessions. Participants used facets for distinctive reasons at different stages. They also used facets implicitly without applying the facets to their search. Most participants liked the faceted search, although a few participants were concerned about the choice overload introduced by facets. The results of this research can be used to understand information seekers and propose or refine a set of practical design guidelines for faceted search.
An Investigation of Imbalanced Ensemble Learning Methods for Cross-Project Defect Prediction

Machine-learning-based software defect prediction (SDP) methods are receiving great attention from the researchers of intelligent software engineering. Most existing SDP methods are performed under a within-project setting. However, there usually is little to no within-project training data to learn an available supervised prediction model for a new SDP task. Therefore, cross-project defect prediction (CPDP), which uses labeled data of source projects to learn a defect predictor for a target project, was proposed as a practical SDP solution. In real CPDP tasks, the class imbalance problem is ubiquitous and has a great impact on performance of the CPDP models. Unlike previous studies that focus on subsampling and individual methods, this study investigated 15 imbalanced learning methods for CPDP tasks, especially for assessing the effectiveness of imbalanced ensemble learning (IEL) methods. We evaluated the 15 methods by extensive experiments on 31 open-source projects derived from five datasets. Through analyzing a total of 37504 results, we found that in most cases, the IEL method that combined under-sampling and bagging approaches will be more effective than the other investigated methods.
Modelling the social dimension of sustainable development using fuzzy cognitive maps

Sustainable development is portrayed as the interface between environmental, economic, and social sustainability. Social sustainability is often the vaguest and least explicit dimension in practical attempts to shape sustainable development. This research adopts fuzzy cognitive maps (FCMs) to model various factors affecting the social sustainability of a project. The various factors affecting the social dimension of sustainability in construction projects are first identified. The interactions that exist among these factors are then defined and the FCM model of social sustainability is constructed. The influencing factors are finally analyzed and ranked using the proposed dynamic and static analysis. The influencing factors with the highest rank are determined based on the results of performed dynamic analysis. Finally, the factors that should receive the highest attention to improve the social sustainability performance of construction projects are determined using static analysis.
Using Latent Knowledge to Improve Real-Time Activity Recognition for Smart IoT

Real-time/online activity recognition (AR) is an important technology in smart Internet of Things (IoT) systems where users are assisted by smart devices in their daily activities. How to generate appropriate feature representation from sensor event streaming is a challenging issue for accurate and efficient real-time AR. Previous AR models that rely on explicit domain knowledge are not appropriate for online recognition of complex human activities. We propose to use unsupervised learning to learn about the latent knowledge and embed the activity probability distribution prediction as high-level features to boost real-time AR performance. The proposed approach first learns the latent knowledge from explicit-activity window sequences using unsupervised learning, and derives the probability distribution prediction of activity classes for a given sliding window. Our approach then feeds the prediction with other basic features of the sliding window into a classifier to produce the final class result on each event-count sliding window. Experiments on five smart home datasets show that the proposed method achieves a higher accuracy by at least 20 percent improvement on F1_score than previous traditional algorithms, while maintaining a lower time cost than deep learning based methods. An analysis on the feature importance shows that the addition of probability distribution prediction about activity classes leads to a promising direction for real-time AR.
Optimized hardware accelerators for data mining applications on embedded platforms: Case study principal component analysis

With the proliferation of mobile, handheld, and embedded devices, many applications such as data mining applications have found their way into these devices. However, mobile devices have stringent area and power limitations, high speed-performance, reduced cost, and time-to-market requirements. Furthermore, applications running on mobile devices are becoming more complex requiring high processing power. These design constraints pose serious challenges to the embedded system designers. In order to process the applications on mobile and embedded systems, effectively and efficiently, optimized hardware architectures are needed. We are investigating the utilization of FPGA-based customized hardware to accelerate embedded data mining applications including handwritten analysis and facial recognition. For these biometric applications, Principal Component Analysis (PCA) is applied initially, followed by similarity measure. In this research work, we introduce novel and efficient embedded hardware architectures to accelerate the PCA computation. PCA is a classic technique to reduce the dimensionality of data by transforming the original data set into a new set of variables called Principal Components (PCs) that represent the key features of the data. We propose two hardware versions for PCA computation, each with its unique optimization techniques to enhance the performance of our designs, and one specifically with additional techniques to reduce the memory access latency of embedded platforms. To the best of our knowledge, we could not find similar work for PCA, specifically catered to the embedded devices, in the published literature. We perform experiments to evaluate the feasibility and efficiency of our designs using a benchmark dataset for biometrics. Our embedded hardware designs are generic, parameterized, and scalable; and achieve 78 times speedup as compared to its software counterparts.
Aspirations-based design

We present a case for aspirations-based design by drawing on a qualitative inquiry into the lives of young girls in rural West Bengal (India). These girls form a particularly vulnerable population, coming from an area known to be susceptible to sex trafficking and crimes against women. We leverage our findings to engage with Kentaro Toyama's call for greater attention to aspirations in designing technology for development [51]. We highlight the aspirations of and for these girls and reflect on the embedded, temporal, and mutable qualities of these aspirations. Finally, we examine how an aspirations-based design approach might factor these qualities into technology design. Although our analysis draws on empirical findings from rural/suburban India, the insights derived from this research are relevant for the process of designing technologies towards fulfillment of aspirations, more generally.
Software Architecture Social Debt: Managing the Incommunicability Factor

Architectural technical debt is the additional project cost connected to technical issues nested in software architectures. Similarly, many practitioners have already experienced that there exists within software architectures a form of social debt, that is, the additional project cost connected to sociotechnical and organizational issues evident in or related to software architectures. This paper illustrates four recurrent antipatterns or community smells connected to such architectural social debt and outlines a means to measure the additional project cost connected to their underlying cause: decision incommunicability. Evaluating the results in multiple focus groups, this paper concludes that studying social debt and community smells at the architecture level may prove vital to rid software development communities of critical organizational flaws incurring considerable additional cost.
Elapsed time estimates in virtual reality and the physical world: The role of arousal and emotional valence

Virtual reality (VR) allows for a close approximation of the real world, but interacting with VR differs from experiencing the real world in some key elements, one of which may be the perception of time. The main goal of the current experiment was to determine whether a time compression effect exists for VR and if so, to examine whether this is the result of the medium of VR itself, or the content used in VR. Participants viewed movie clips in either a real-life cinema or a VR replica of this cinema and were asked to rate the arousal and emotional valence they experienced during each clip. They estimated the duration of each clip in seconds. Results indicate that both level of arousal and valence as experienced by the observer positively contribute to the observed time compression effect, regardless of the viewing condition. Our data suggest there is no difference in the perception of temporal duration between VR and real life, and that the time compression effect that takes place is most likely the result of the materials displayed. So, even though VR has been claimed to result in time compression, for instance in clinical contexts, this may be caused more by the emotional content of the materials used, rather than the medium of VR itself.
Digital pathology: Review of current opportunities and challenges for oral pathologists

Whole slide imaging (WSI) has impacted the practice of pathology in the arenas of education, clinical practice, and research. With digital slides, pathologists can circumvent the limitations of traditional glass. Presently, digital pathology is primarily utilized for second opinion consults, clinical conferences, and education at select academic medical centers, with its mainstream adoption on the rise. However, challenges of adoption for oral pathologists are unique given the highly specialized nature of their work. The hurdles include the high-cost instrumentation and regular maintenance, need for additional training, changes in traditional workflow, and integration with present software. Given these barriers, it remains unclear the extent to which slide scanning and virtual pathology should be adopted by oral pathologists at this conjuncture. This review seeks to shed light on the current state of WSI and analyzes the opportunities and challenges for oral pathology in the rapidly evolving field of digital pathology.