Reviewerly/exHarmony huggingface dataset:
Deep Learning for Assistive Computer Vision

This paper revises the main advances in assistive computer vision recently fostered by deep learning. To this aim, we first discuss how the application of deep learning in computer vision has contributed to the development of assistive techinologies, then analyze the recent advances in assistive technologies achieved in five main areas, namely, object classification and localization, scene understanding, human pose estimation and tracking, action/event recognition and anticipation. The paper is concluded with a discussion and insights for future directions.
A Two-Dimensional Feature Space-Based Approach for Human Locomotion Recognition

The current state-of-the-art methods utilize multiple sensors and derive numerous features to perform human locomotion recognition. The use of multiple sensors causes discomfort to the users. Also, the derivation of numerous features for multiple sensors poses real-time processing issues. This paper presents a real-time human locomotion recognition system using a single inertial measurement unit placed on the human thigh to capture thigh angular data. Two features, amplitude (A) and omega (ω), are derived from the adaptive time window of thigh angular data. An adaptive time window is built using two consecutive peaks in the thigh angle signal. The machine learning algorithms, such as linear support vector machine and k-nearest neighbors, are employed on the A-ω feature space to perform the classification. A total number of six periodic activities are considered in this paper that includes level walking, stair ascent, stair descent, uphill, downhill, jogging, and running. The experiments are performed on six healthy subjects in controlled (motion capture laboratory) and uncontrolled (outdoor) environmental conditions to evaluate the efficacy of the algorithm. The A-ω feature-based method achieved a significant classification accuracy of 98.1% and 93.3% for controlled and uncontrolled experiments, respectively. Also, it reported a fast mean activity recognition time of 80 ms.
Bitcoin at High Frequency

This paper studies the behaviour of Bitcoin returns at different sample frequencies. We consider high frequency returns starting from tick-by-tick price changes traded at the Bitstamp and Coinbase exchanges. We find evidence of a smooth intra-daily seasonality pattern, and an abnormal trade- and volatility intensity at Thursdays and Fridays. We find no predictability for Bitcoin returns at or above one day, though, we find predictability for sample frequencies up to 6 h. Predictability of Bitcoin returns is also found to be time–varying. We also study the behaviour of the realized volatility of Bitcoin. We document a remarkable high percentage of jumps above 80 % . We also find that realized volatility exhibits: (i) long memory; (ii) leverage effect; and (iii) no impact from lagged jumps. A forecast study shows that: (i) Bitcoin volatility has become more easy to predict after 2017; (ii) including a leverage component helps in volatility prediction; and (iii) prediction accuracy depends on the length of the forecast horizon.
MAM: Transfer Learning for Fully Automatic Video Annotation and Specialized Detector Creation

Accurate point detection on image data is an important task for many applications, such as in robot perception, scene understanding, gaze point regression in eye tracking, head pose estimation, or object outline estimation. In addition, it can be beneficial for various object detection tasks where minimal bounding boxes are searched and the method can be applied to each corner. We propose a novel self training method, Multiple Annotation Maturation (MAM) that enables fully automatic labeling of large amounts of image data. Moreover, MAM produces detectors, which can be used online afterward. We evaluated our algorithm on data from different detection tasks for eye, pupil center (head mounted and remote), and eyelid outline point and compared the performance to the state-of-the-art. The evaluation was done on over 300,000 images, and our method shows outstanding adaptability and robustness. In addition, we contribute a new dataset with more than 16,200 accurate manually-labeled images from the remote eyelid, pupil center, and pupil outline detection. This dataset was recorded in a prototype car interior equipped with all standard tools, posing various challenges to object detection such as reflections, occlusion from steering wheel movement, or large head movements. The data set and library are available for download at http://ti.uni-tuebingen.de/Projekte.1801.0.html .
Towards Reliable and Secure Post-Quantum Co-Processors based on RISC-V

Increasingly complex and powerful Systems-on-Chips (SoCs), connected through a 5G network, form the basis of the Internet-of-Things (IoT). These technologies will drive the digitalization in all domains, e.g. industry automation, automotive, avionics, and healthcare. A major requirement for all above domains is the long-term (10 to 30 years) secure communication between the SoCs and the cloud over public 5G networks. The foreseeable breakthrough of quantum computers represents a risk for all communication. In order to prepare for such an event, SoCs must integrate secure quantum-computer-resistant cryptography which is reliable and protected against SW and HW attacks. Empowering SoCs with such strong security poses a challenging problem due to limited resources, tight performance requirements and long-term life-cycles. While current works are focused on efficient implementations of post-quantum cryptography, implementation-security and reliability aspects for SoCs are still largely unexplored. To this end, we present three contributions. First, we present a RISC-V co-processor for post-quantum security, able to support lattice-based cryptography. Second, we use HW/SW co-design techniques to accelerate the NTT transformation and hash generation. Third, we perform the fault analysis of the implementation. We show that our coprocessor achieves high reliability and security capabilities while preserving good performance.
Design methodology for Radial Basis Function Neural Networks classifier based on locally linear reconstruction and Conditional Fuzzy C-Means clustering

In this study, a new design method for Fuzzy Radial Basis Function Neural Networks classifier is proposed. The proposed approach is based on conditional Fuzzy C-Means clustering algorithm realized with the aid of auxiliary information, which is extracted by the locally linear reconstruction algorithm. Conditional Fuzzy C-Means can analyze the distribution of data (patterns) over the input space when being supervised by the auxiliary information. The conditional fuzzy C-Means clustering can substitute the conventional fuzzy C-Means clustering which has been usually used to define the radial basis functions over the input space. It is advocated that the auxiliary information extracted by using the locally linear reconstruction can determine which patterns among the entire data set are more important than the others. This assumption is based on the observation that the data, which cannot be fully reconstructed by the linear combination of their neighbors, may convey much more information than the other data to be reconstructed. It is well known that in the case of radial basis function neural networks classifier, the classification performance of this classifier is predominantly based on the distribution of the radial basis function over the input space. Several experiments are provided to verify the proposed design method for classification problems.
Semi-supervised target-oriented sentiment classification

Target-oriented aspect-based sentiment analysis (TABSA) is a sentiment classification task that requires performing fine-grained semantical reasoning about a given aspect. The amount of labeled data is usually insufficient for supervised learning because the manual annotation w.r.t. the aspects is both time-consuming and laborious. In this paper, we propose a novel semi-supervised method to derive and utilize the underlying sentiment of unlabeled samples via a deep generative model. This method assumes that when given the aspect, the sentence is generated by two stochastic variables, i.e., the context variable and the sentiment variable. By explicitly disentangling the representation into the context and sentiment, the meaning of sentiment variable can be kept clean during the training phase. An additional advantage is that the proposed method uses a standalone classifier, and as such, our system is able to integrate with various supervised models. In terms of the implementation, since capturing the conditional input is non-trivial for a sequential model, special structures are put forward and investigated. We conducted experiments on SemEval 2014 task 4 and the results indicate that our method effectively handles five kinds of advanced classifiers. The proposed method outperforms two general semi-supervised methods and achieves state-of-the-art performance on this benchmark.
A weighted fuzzy classification approach to identify and manipulate coincidental correct test cases for fault localization

Identifying the location of faults effectively and accurately is highly important in the debugging process of software engineering. Coverage-based Fault Localization (CBFL) has been widely studied that can alleviate the effort of developers to find the faults position using the execution information of test cases. Coincidental Correct (CC) test cases are the specific test cases that execute the faulty statements but with a correct output, which have been illustrated with a negative effect on the accuracy of CBFL. In this paper, we propose a weighted fuzzy classification approach to identify CC test cases and three fuzzy strategies are suggested to manipulate CC test cases for CBFL. Firstly, we present a simple but efficient approach to identify some CC test cases for single fault programs, which provide labeled samples that enable the application of supervised classification algorithms for CC identification. Then, a Fuzzy Weighted K-Nearest Neighbor (FW-KNN) algorithm is proposed to classify potential CC from the passed test cases, in which a ‘weighted’ similarity measure and a “weighted” CC probability computation are presented. Finally, three fuzzy CC test cases manipulation strategies are presented to mitigate the impact of CC test cases in CBFL. Various empirical studies are conducted on 190 faulty versions of 12 programs to investigate the impact of “weighted” and “fuzzy” methods for CC identification by the comparison of the effectiveness and efficiency between FW-KNN and three popular cluster and classification techniques. The results indicate that the proposed FW-KNN has higher accuracy and lower time cost. The Precision, Recall and False Positive Rate of FW-KNN is 96.47%, 83.40% and 2.85%, respectively. Besides, by utilizing code block coverage, the time cost can be reduced by 72.97% in average compared to statement coverage. The experimental results also indicate that the fault localization accuracy of CBFL can be improved by the proposed CC test cases manipulation strategies.
Mining Behavioral Sequence Constraints for Classification

Sequence classification deals with the task of finding discriminative and concise sequential patterns. To this purpose, many techniques have been proposed, which mainly resort to the use of partial orders to capture the underlying sequences in a database according to the labels. Partial orders, however, pose many limitations, especially on expressiveness, i.e., the aptitude towards capturing certain behavior, and on conciseness, i.e., doing so in a compact and informative way. These limitations can be addressed by using a better representation. In this paper, we present the interesting Behavioral Constraint Miner (iBCM), a sequence classification technique that discovers patterns using behavioral constraint templates. The templates comprise a variety of constraints and can express patterns ranging from simple occurrence, to looping and position-based behavior over a sequence. Furthermore, iBCM also captures negative constraints, i.e., absence of particular behavior. The constraints can be discovered by using simple string operations in an efficient way. Finally, deriving the constraints with a window-based approach allows to pinpoint where the constraints hold in a string, and to detect whether patterns are subject to concept drift. Through empirical evaluation, it is shown that iBCM is better capable of classifying sequences more accurately and concisely in a scalable manner.
Feature fusion and non-negative matrix factorization based active contours for texture segmentation

This paper presents a robust and convex active contour model for texture segmentation. Firstly, to achieve more comprehensive feature description, we compute a set of feature maps by combining local variation degree (LVD) of intensity and Gabor features. This feature fusion improves the separability between sub-regions and the robustness against complex textures. Upon these feature maps, we compute local histograms over fixed-size windows to describe the local structures formed by feature values. For each pixel, its feature vector is defined as the concatenation of all computed histograms. Secondly, to localize region boundaries more accurately, we formulate the proposed energy functional via Non-negative Matrix Factorization (NMF), which encourages each pixel to fall into the sub-region that has the largest coverage area in its neighborhood. Finally, the functional is explored further using convex optimization theory. Our segmentation results are therefore insensitive to different initial contours. The experiments performed on synthetic images, histology images and natural images demonstrate that our approach can obtain high-quality object boundaries in the presence of image noise and cluttered scenes.
Developing Sustainable Healthcare Systems in Developing Countries: Examining the Role of Barriers, Enablers and Drivers on Knowledge Management Adoption

Knowledge management (KM) adoption is crucial to integrating sustainable development within the healthcare sector. Different barriers, enablers, and drivers affect KM adoption. Identifying these barriers, enablers, and drivers and their role in KM adoption is the core of successful KM adoption. However, there is scarcity of studies applying quantitative models and combing barriers, enablers and drivers to check their effect on KM adoption, especially form a developing country’s perspective such as Pakistan. Therefore, this study explores the role of barriers, enablers and drivers on KM adoption in Pakistan. Healthcare professionals participated in the data collection process, and results were analyzed using structural equation modeling. The findings described that: (1) organizational and strategic barriers have significant negative association with KM adoption; (2) government related enablers have significant positive association with KM adoption; (3) healthcare related drivers, and performance-based drivers have significant positive association with KM adoption. This study concludes that government intervention to promote KM adoption is necessary especially in developing countries. These findings will be helpful for the healthcare professionals and policy makers to promote KM adoption in healthcare sector. Current study contributes to the healthcare literature and body of knowledge by providing the empirical evidence of checking the quantitative effect of barriers, enablers and drivers on KM adoption.
Miscommunication Detection and Recovery in Situated Human–Robot Dialogue

Even without speech recognition errors, robots may face difficulties interpreting natural-language instructions. We present a method for robustly handling miscommunication between people and robots in task-oriented spoken dialogue. This capability is implemented in TeamTalk, a conversational interface to robots that supports detection and recovery from the situated grounding problems of referential ambiguity and impossible actions. We introduce a representation that detects these problems and a nearest-neighbor learning algorithm that selects recovery strategies for a virtual robot. When the robot encounters a grounding problem, it looks back on its interaction history to consider how it resolved similar situations. The learning method is trained initially on crowdsourced data but is then supplemented by interactions from a longitudinal user study in which six participants performed navigation tasks with the robot. We compare results collected using a general model to user-specific models and find that user-specific models perform best on measures of dialogue efficiency, while the general model yields the highest agreement with human judges. Our overall contribution is a novel approach to detecting and recovering from miscommunication in dialogue by including situated context, namely, information from a robot’s path planner and surroundings.
A distributed swarm control for an agricultural multiple unmanned aerial vehicle system

In this study, we propose a distributed swarm control algorithm for an agricultural multiple unmanned aerial vehicle system that enables a single operator to remotely control a multi-unmanned aerial vehicle system. The system has two control layers that consist of a teleoperation layer through which the operator inputs teleoperation commands via a haptic device and an unmanned aerial vehicle control layer through which the motion of unmanned aerial vehicles is controlled by a distributed swarm control algorithm. In the teleoperation layer, the operator controls the desired velocity of the unmanned aerial vehicle by manipulating the haptic device and simultaneously receives the haptic feedback. In the unmanned aerial vehicle control layer, the distributed swarm control consists of the following three control inputs: (1) velocity control of the unmanned aerial vehicle by a teleoperation command, (2) formation control to obtain the desired formation, and (3) collision avoidance control to avoid obstacles. The three controls are input to each unmanned aerial vehicle for the distributed system. The proposed algorithm is implemented in the dynamic simulator using robot operating system and Gazebo, and experimental results using four quadrotor-type unmanned aerial vehicles are presented to evaluate and verify the algorithm.
Substream-Centric Maximum Matchings on FPGA

Developing high-performance and energy-efficient algorithms for maximum matchings is becoming increasingly important in social network analysis, computational sciences, scheduling, and others. In this work, we propose the first maximum matching algorithm designed for FPGAs; it is energy-efficient and has provable guarantees on accuracy, performance, and storage utilization. To achieve this, we forego popular graph processing paradigms, such as vertex-centric programming, that often entail large communication costs. Instead, we propose a substream-centric approach, in which the input stream of data is divided into substreams processed independently to enable more parallelism while lowering communication costs. We base our work on the theory of streaming graph algorithms and analyze 14 models and 28 algorithms. We use this analysis to provide theoretical underpinning that matches the physical constraints of FPGA platforms. Our algorithm delivers high performance (more than 4x speedup over tuned parallel CPU variants), low memory, high accuracy, and effective usage of FPGA resources. The substream-centric approach could easily be extended to other algorithms to offer low-power and high-performance graph processing on FPGAs.
Efficient, dynamic and identity-based Remote Data Integrity Checking for multiple replicas

Nowadays, cloud storage plays an increasingly important role in our daily life. However, the cloud users do not have the physical possession of their own data anymore. To confirm whether the outsourced files are maintained intact without downloading them entirely, a mechanism namely Remote Data Integrity Checking (RDIC) is invented. Currently, some RDIC schemes allow the data owners with limited computation or communication power to delegate the checking task to a third-party verifier. However, most of these schemes rely on the complicated and resource consuming public key infrastructure (PKI). In this paper, we propose a novel identity-based RDIC scheme, namely Efficient, Dynamic and Identity-based Multiple Replication Provable Data Possession (EDID-MRPDP) without the burden of PKI. We introduce a new construction of Homomorphic Verifiable Tag (HVT) and a novel data structure namely Compressed Authentication Array (CAA), which allow EDID-MRPDP to perform batch verification for multiple data owners and cloud servers simultaneously and efficiently, both from computation and communication aspects. To the best of our knowledge, EDID-MRPDP is the first ID-based RDIC scheme with full dynamic updates and multi-replica batch checking. We provide comprehensive correctness and soundness proofs of EDID-MRPDP. Meanwhile, the detailed performance analyses and simulations show that EDID-MRPDP is practical for large-scale cloud applications.
Online updating belief-rule-base using Bayesian estimation

The common point of traditional BRB parameters training methods is taking the training process as an optimization problem, which may result in overfitting problem when training data is insufficient or contains strong noises. To solve the problems, we propose a novel method based on Bayesian estimation to update parameters of BRB online. While the optimization methods consider BRB parameters as unknown but determinate values, the Bayesian estimation regards BRB parameters as random variables. Instead of finding single optimal values of parameters, the proposed method is to estimate the posterior distribution of BRB parameters and produce prediction outputs by considering all possible parameters. Since the posterior distribution of BRB parameters cannot be calculated by analytical methods due to the nonlinearity of BRB models, the Sequential Monte Carlo (SMC) sampling technique is adopted to on-line approximate the posterior distribution of BRB parameters. A numerical function and a practical case on pipeline leak detection are studied to verify the performance of proposed algorithm.
Page-sharing-based virtual machine packing with multi-resource constraints to reduce network traffic in migration for clouds

Virtual machine (VM) packing plays an important role in improving resource utilization in cloud data centers. Recently, memory content similarity among VM instances has been used to speed up multiple VM migration in large clouds. Based on this, many VM packing algorithms have been proposed, which only considered the memory capacity of physical machines (PMs) as the resource constraint. However, in practice the results of such algorithms are not feasible, because thy may not satisfy the constraints of multiple resources (e.g., CPU of the PMs). Besides, the granularities of memory sharing in existing studies are very coarse, and they cannot fully leverage the benefits of memory content similarity which mainly appears at memory page level. In this paper, we study the page-sharing-based VM packing that considers constraints in multiple resources. Given a set of VM instances that share a large number of common memory pages, we pack them into the minimum number of PMs, subject to the constraints in the multiple resources on the PMs. This problem is solved in two steps. First, we pack the maximum number of VMs into a given PM, and then propose an approximation algorithm. The approximation ratio is better than that of the existing algorithm. Then, based on this approximation algorithm, we propose a heuristic algorithm to solve the general problem. Experimental results show that our heuristic algorithm outperforms existing approaches with at most 25% less required PMs and at most 40% less memory page transferring.