Reviewerly/exHarmony huggingface dataset:
An Identity Authentication Method Combining Liveness Detection and Face Recognition

In this study, an advanced Kinect sensor was adopted to acquire infrared radiation (IR) images for liveness detection. The proposed liveness detection method based on infrared radiation (IR) images can deal with face spoofs. Face pictures were acquired by a Kinect camera and converted into IR images. Feature extraction and classification were carried out by a deep neural network to distinguish between real individuals and face spoofs. IR images collected by the Kinect camera have depth information. Therefore, the IR pixels from live images have an evident hierarchical structure, while those from photos or videos have no evident hierarchical feature. Accordingly, two types of IR images were learned through the deep network to realize the identification of whether images were from live individuals. In comparison with other liveness detection cross-databases, our recognition accuracy was 99.8% and better than other algorithms. FaceNet is a face recognition model, and it is robust to occlusion, blur, illumination, and steering. We combined the liveness detection and FaceNet model for identity authentication. For improving the application of the authentication approach, we proposed two improved ways to run the FaceNet model. Experimental results showed that the combination of the proposed liveness detection and improved face recognition had a good recognition effect and can be used for identity authentication.
An Abstract Syntax Tree Encoding Method for Cross-Project Defect Prediction

In the last few years, with the development of deep learning theory, researchers have tried to introduce the method of artificial intelligence into the field of software defect prediction (SDP) to improve its prediction effect.To be fed into the neural network, the sample codes are represented as an abstract syntax tree (AST), and the AST is encoded as real numbers.However, in most cross-project defect prediction (CPDP) task, the method for converting the AST into a real number cannot effectively estimate the semantic distance between the ASTs, resulting in a significant reduction in training effects.To solve that problem, we present a new encoding framework, tree-based-embedding (TBE), to convert AST into real vectors and make the semantic gap between the ASTs measurable.To estimate the effect of this encoding method, we promise a tree-based-embedding convolutional neural network with transferable hybrid feature learning (TBCNN-THFL) to perform the CPDP tasks.TBCNN-THFL is fed data encoded with TBE method for learning the transferable joint features between different projects; meanwhile, TBCNN-THFL introduces a transfer component analysis algorithm.Furthermore, the model combines the handcrafted and deep-learninggenerated features and then feeds them into the classifier to train a defect prediction model.A sufficient number of experiments demonstrate that TBCNN-THFL is superior to referential models on 72 pairs of CPDP tasks formed by 9 open-source projects.
Kube-Knots: Resource Harvesting through Dynamic Container Orchestration in GPU-based Datacenters

Compute heterogeneity is increasingly gaining prominence in modern datacenters due to the addition of accelerators like GPUs and FPGAs. We observe that datacenter schedulers are agnostic of these emerging accelerators, especially their resource utilization footprints, and thus, not well equipped to dynamically provision them based on the application needs. We observe that the state-of-the-art datacenter schedulers fail to provide fine-grained resource guarantees for latency-sensitive tasks that are GPU-bound. Specifically for GPUs, this results in resource fragmentation and interference leading to poor utilization of allocated GPU resources. Furthermore, GPUs exhibit highly linear energy efficiency with respect to utilization and hence proactive management of these resources is essential to keep the operational costs low while ensuring the end-to-end Quality of Service (QoS) in case of user-facing queries.Towards addressing the GPU orchestration problem, we build Knots, a GPU-aware resource orchestration layer and integrate it with the Kubernetes container orchestrator to build Kube- Knots. Kube-Knots can dynamically harvest spare compute cycles through dynamic container orchestration enabling co-location of latency-critical and batch workloads together while improving the overall resource utilization. We design and evaluate two GPU-based scheduling techniques to schedule datacenter-scale workloads through Kube-Knots on a ten node GPU cluster. Our proposed Correlation Based Prediction (CBP) and Peak Prediction (PP) schemes together improves both average and 99 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile cluster-wide GPU utilization by up to 80% in case of HPC workloads. In addition, CBP+PP improves the average job completion times (JCT) of deep learning workloads by up to 36% when compared to state-of-the-art schedulers. This leads to 33% cluster-wide energy savings on an average for three different workloads compared to state-of-the-art GPU-agnostic schedulers. Further, the proposed PP scheduler guarantees the end-to-end QoS for latency-critical queries by reducing QoS violations by up to 53% when compared to state-of-the-art GPU schedulers.
Dynamic hair modeling from monocular videos using deep neural networks

We introduce a deep learning based framework for modeling dynamic hairs from monocular videos, which could be captured by a commodity video camera or downloaded from Internet. The framework mainly consists of two neural networks, i.e., HairSpatNet for inferring 3D spatial features of hair geometry from 2D image features, and HairTempNet for extracting temporal features of hair motions from video frames. The spatial features are represented as 3D occupancy fields depicting the hair volume shapes and 3D orientation fields indicating the hair growing directions. The temporal features are represented as bidirectional 3D warping fields, describing the forward and backward motions of hair strands cross adjacent frames. Both HairSpatNet and HairTempNet are trained with synthetic hair data. The spatial and temporal features predicted by the networks are subsequently used for growing hair strands with both spatial and temporal consistency. Experiments demonstrate that our method is capable of constructing plausible dynamic hair models that closely resemble the input video, and compares favorably to previous single-view techniques.
Handwritten Tamil Character Recognition UsingDeep Learning

Character recognition is developed for various patterns of handwritten or optical characters to be recognized digitally. There are many Tamil literatures in undigitized form. Using deep learning the undigitized Tamil literatures can be converted into readable format. Many researches were carried on character recognition using deep learning for languages like Arabic, Devanagari, Telugu, etc… Due to the larger category set and confusion in similarities between handwritten characters Tamil character recognition is a challenge. In this paper, we propose a character recognition system for handwritten Tamil characters using deep learning. Here, VGG 16 approaches is carried out. The proposed work gives efficiency of 94.52% on our datasets.
A Learning Automata-Based Scheduling for Deadline Sensitive Task in The Cloud

The evolution of cloud computing facilitates applications with varying demands to operate in a virtualized environment. For instance, applications like the healthcare system, video streaming, Internet of Things (IoT) that are moving to the cloud, demand responses within a particular time limit, i.e., deadline. However, the cloud computing system consumes a considerable amount of electric energy while providing services to these type of applications, which in turn contribute to the high operational cost. Specifically, it becomes cumbersome to offer services to deadline sensitive task while minimizing energy consumption. In this regard, efficient task scheduling is an attractive way to cut down energy usage while ensuring satisfactory services for cloud users. In this paper, the task scheduling problem is considered as a bi-objective minimization problem which includes minimization of energy consumption and makespan. First, we proposed a novel learning automata-based scheduling framework for deadline sensitive tasks in the cloud. Learning automata (LA) is an adaptive decision-making unit that helps the scheduler to select the best responses. Later, the LA-based Scheduling (LAS) algorithm is introduced which exploits the heterogeneity of tasks and virtual machines (VMs) while ensuring the timing requirements of the tasks. Extensive simulation is carried out to designate the effectiveness and applicability of LAS for deadline sensitive task scheduling in the heterogeneous cloud environment.
Leveraging Implicit Relative Labeling-Importance Information for Effective Multi-Label Learning

Multi-label learning deals with training examples each represented by a single instance while associated with multiple class labels, and the task is to train a predictive model which can assign a set of proper labels for the unseen instance. Existing approaches employ the common assumption of equal labeling-importance, i.e., all associated labels are regarded to be relevant to the training instance while their relative importance in characterizing its semantics are not differentiated. Nonetheless, this common assumption does not reflect the fact that the importance degree of each relevant label is generally different, though the importance information is not directly accessible from the training examples. In this article, we show that it is beneficial to leverage the implicit relative labeling-importance (RLI) information to help induce multi-label predictive model with strong generalization performance. Specifically, RLI degrees are formalized as multinomial distribution over the label space, which can be estimated by either global label propagation procedure or local k-nearest neighbor reconstruction. Correspondingly, the multi-label predictive model is induced by fitting modeling outputs with estimated RLI degrees along with multi-label empirical loss regularization. Extensive experiments clearly validate that leveraging implicit RLI information serves as a favorable strategy to achieve effective multi-label learning.
MELT - Matching EvaLuation Toolkit

In this paper, we present the Ontology Matching EvaLuation Toolkit (MELT), a software toolkit to facilitate ontology matcher development, configuration, evaluation, and packaging. Compared to existing tools in the ontology matching domain, our framework offers detailed evaluation capabilities on the correspondence level of alignments as well as extensive group evaluation possibilities. A particular focus is put on a streamlined development and evaluation process along with ease of use for matcher developers and evaluators. Our contributions are twofold: We present an open source matching toolkit that integrates well into existing platforms, as well as an exemplary analysis of two OAEI 2018 tracks demonstrating advantages and analytical capabilities of MELT.
On the Costs and Profit of Software Defect Prediction

Defect prediction can be a powerful tool to guide the use of quality assurance resources. However, while lots of research covered methods for defect prediction as well as methodological aspects of defect prediction research, the actual cost saving potential of defect prediction is still unclear. Within this article, we close this research gap and formulate a cost model for software defect prediction. We derive mathematically provable boundary conditions that must be fulfilled by defect prediction models such that there is a positive profit when the defect prediction model is used. Our cost model includes aspects like the costs for quality assurance, the costs of post-release defects, the possibility that quality assurance fails to reveal predicted defects, and the relationship between software artifacts and defects. We initialize the cost model using different assumptions, perform experiments to show trends of the behavior of costs on real projects. Our results show that the unrealistic assumption that defects only affect a single software artifact, which is a standard practice in the defect prediction literature, leads to inaccurate cost estimations. Moreover, the results indicate that thresholds for machine learning metrics are also not suited to define success criteria for software defect prediction.
Coordinated Optimization Algorithm Combining GA with Cluster for Multi-UAVs to Multi-tasks Task Assignment and Path Planning

With the rapid development of UAVs, the application of UAVs in civil and military fields has greatly expanded. UAVs are often used to perform tasks in hazardous areas. In order to ensure the rapid execution of critical tasks, it is critical to determine the number of UAVs to be dispatched and to find the path for each UAV to perform tasks. This paper proposes a coordinated optimization algorithm combing the GA and cluster algorithm to resolve the problem of multi-UAVs to multi-tasks task assignment and path planning, which can effectively determine the amount of UAVs satisfies with the mission time constraint and find the best task flight path for each UAV simultaneously. The simulation results demonstrate that the coordination optimization algorithm is effective to resolve this kind of task assignment problem. The comparison between the coordinated optimization algorithm and GA is performed, and the result shows that the coordinated optimization algorithm is more effective than GA.
Two-Thirds Honest-Majority MPC for Malicious Adversaries at Almost the Cost of Semi-Honest

Secure multiparty computation (MPC) enables a set of parties to securely carry out a joint computation of their private inputs without revealing anything but the output. Protocols for semi-honest adversaries guarantee security as long as the corrupted parties run the specified protocol and ensure that nothing is leaked in the transcript. In contrast, protocols for malicious adversaries guarantee security in the presence of arbitrary adversaries who can run any attack strategy. Security for malicious adversaries is typically what is needed in practice (and is always preferred), but comes at a significant cost. In this paper, we present the first protocol for a two-thirds honest majority that achieves security in the presence of malicious adversariesat essentially the exact same cost as the best known protocols for semi-honest adversaries. Our construction is not a general transformation and thus it is possible that better semi-honest protocols will be constructed which do not support our transformation. Nevertheless, for the current state-of-the-art for many parties (based on Shamir sharing), our protocol invokes the best semi-honest multiplication protocol exactly once per multiplication gate (plus some additional local computation that is negligible to the overall cost). Concretely, the best version of our protocol requires each party to send on average of just 2 2/3 elements per multiplication gate (when the number of multiplication gates is at least the number of parties). This is four times faster than the previous-best protocol of Barak et al. (ACM CCS 2018) for small fields, and twice as fast as the previous-best protocol of Chida et al. (CRYPTO 2018) for large fields.
Fringe-Projection-Based Normal Direction Measurement and Adjustment for Robotic Drilling

In a large-scale structure assembly, a highly accurate normal direction measurement for robotic drilling is required. However, the robustness and accuracy of the existing normal direction measurement method with a range sensor would dramatically decrease for a high-curvature surface, such as the surface edge, due to the low resolution of the range sensor. To this end, a high-resolution fringe projection sensor is proposed to improve the measurement robustness and accuracy. In this method, different from the control scheme design with a reconstructed three-dimensional point cloud in fringe projection profilometry, which involves a high computational complexity, a novel two-dimensional phase-map-based two-stage control scheme, including coarse and refined normal direction adjustments, is designed to improve the robustness and accuracy. The coarse normal direction adjustment uses a phase map to estimate the normal direction, which is used to synthesize a position-based control scheme, leading to high robustness; the refined normal direction adjustment uses a phase map to synthesize an image-based control scheme, resulting in high accuracy. The proposed method is validated by simulations and experiments, which indicate that the proposed method is effective.
BurScale

Cloud providers have recently introduced burstable instances - virtual machines whose CPU capacity is rate limited by token-bucket mechanisms. A user of a burstable instance is able to burst to a much higher resource capacity ("peak rate") than the instance's long-term average capacity ("sustained rate"), provided the bursts are short and infrequent. A burstable instance tends to be much cheaper than a conventional instance that is always provisioned for the peak rate. Consequently, cloud providers advertise burstable instances as cost-effective options for customers with intermittent needs and small (e.g., single VM) clusters. By contrast, this paper presents two novel usage scenarios for burstable instances in larger clusters with sustained usage. We demonstrate (i) how burstable instances can be utilized alongside conventional instances to handle the transient queueing arising from variability in traffic, and (ii) how burstable instances can mask the VM startup/warmup time when autoscaling to handle flash crowds. We implement our ideas in a system called BurScale and use it to demonstrate cost-effective autoscaling for two important workloads: (i) a stateless web server cluster, and (ii) a stateful Memcached caching cluster. Results from our prototype system show that via its careful combination of burstable and regular instances, BurScale can ensure similar application performance as traditional autoscaling systems that use all regular instances while reducing cost by up to 50%.
Liver segmentation based on region growing and level set active contour model with new signed pressure force function

Segmentation of the whole liver in computed tomography (CT) images is important in liver disease diagnosis. Although the commonly used level set methods (LSMs) and active contour extraction method, are used to segment liver, the segmentation results are not very good. A semiautomatic method consisting of three stages to overcome the leakage and over-segmentation problems is proposed in this paper. Firstly, the original input images are preprocessed by a series of methods to obtain binary images. Then some seed points are set on the binary image for the region-growing to get the initial contour of the liver. The new level set active contour model is built finally to refine the initial liver segmentation. In the first step, the gradient magnitude of original images is smoothed by Gaussian function to suppress noise, and enhance the edges. In addition, a binarization method is put forth on the basis of gradient information of images to adaptively select the optimal threshold without artificially setting parameters. In the third step, a new signed pressure function (SPF) is presented. It can integrate both local information and global information, automatically allocate the ratio of local information and global information based on gradient information of images. The two public datasets SLIVER07, 3Dircadb were used to verify the validity of the method by five measurements. Experimental results show that the proposed liver segmentation is better than other semiautomatic methods based on the SLIVER07 and 3Dircadb dataset and required less interaction.
Software Fault Prediction Using Deep Learning Algorithms

Software faults prediction (SFP) processes can be used for detecting faulty constructs at early stages of the development lifecycle, in addition to its being used in several phases of the development process. Machine learning (ML) is widely used in this area. One of the most promising subsets from ML is deep learning that achieves remarkable performance in various areas. Two deep learning algorithms are used in this paper, the Multi-layer perceptrons (MLPs) and Convolutional Neural Network (CNN). In order to evaluate the studied algorithms, four commonly used datasets from NASA are used i.e. (PC1, KC1, KC2 and CM1). The experiment results show how the CNN algorithm achieves prediction superiority of the MLP algorithm. The accuracy and detection rate measurements when using CNN has reached the standard ratio respectively as follows: PC1 97.7% - 73.9%, KC1 100% - 100%, KC2 99.3% - 99.2% and CM1 97.3% - 82.3%. This study provides promising results in using the deep learning for software fault prediction research.
The Struggle for Recognition in Advanced Dementia

Focusing on the person with advanced dementia as a social being presents a new opportunity for Experience-Centered Design (ECD), opening design to appreciate the agency and intentional actions of the person with advanced dementia. If Human-Computer Interaction is to shift from the predominantly assistive approach to a focus on experience, a theoretical framing that emphasizes the relational nature of selfhood is needed. In this article, we present Recognition Theory—a social theory based on an inter-subjectivist account of the struggle for recognition—to extend ECD approaches for advanced dementia. Focusing on people with advanced dementia, we examine recognition as a social and ethical perspective for establishing and maintaining self. We present a framework for design based on research with people with advanced dementia, experience-centered engagement and social identity, that will support designers to craft opportunities for mutual recognition in the design process and the practice of making.
Passenger flow counting in buses based on deep learning using surveillance video

An efficient traffic management system is crucial for public transportation. If the passenger flow can be detected accurately and instantaneously, the routes and schedules for public transportation can be effectively improved. However, previous research identified many challenges in passenger counting, such as messy image background, variations in lighting, and occlusions. In this paper, we propose a passenger flow counting model for buses, based on deep learning. First, we design a straightforward way to understand the opening state of the door. Next, a single shot multibox detector is used to learn the features of passengers and detect them. Finally, a particle filter with a three-step cascaded data association scheme is used for passenger tracking. To demonstrate the performance of the proposed algorithm, surveillance videos of three different situations, i.e., day, night, and a rainy day, are selected. Additionally, to make the system applicable to real cases, a few special scenes such as different objects worn by the passengers, passenger occlusions, and dense crowds, are considered. According to the experimental results, our method exhibits better performance than some existing methods.