Reviewerly/exHarmony huggingface dataset:
A Robust Fused Descriptor Under Unconstrained Conditions

This work presents local difference binary pattern (LDBP) and local neighborhood difference binary pattern (LNDBP) descriptors for face analysis. In LDBP, the difference is computed between A and B. A computes the difference between current neighbor pixel and adjacent neighbor pixel in clockwise direction, and B computes the difference between current neighbor pixel and center pixel. Same concept is performed for all the neighborhood positions. In LNDBP, the difference is determined between neighbor pixels at a distance of +2 in clockwise direction. Those possesses (differences) the value > or = to 0 are allocated a label 1 else 0. All patterns encoding gives the emergence of respective transformed image. From respective image, the subregional (3 × 3) information is extracted. The fused features from the corresponding subregions are the feature dimension of the respective descriptor. Further the LDBP and LNDBP dimensions are integrated to make the discriminative feature LDBP + LNDBP. PCA is applied next for compaction, and classification matching is done by SVMs and NN.
Hybrid Annealing Method Based on subQUBO Model Extraction With Multiple Solution Instances

Ising machines are expected to solve combinatorial optimization problems efficiently by representing them as Ising models or equivalent quadratic unconstrained binary optimization (QUBO) models .However, upper bound exists on the computable problem size due to the hardware limitations of Ising machines.This paper propose a new hybrid annealing method based on partial QUBO extraction, called subQUBO model extraction, with multiple solution instances.For a given QUBO model, the proposed method obtains N I quasi-optimal solutions (quasi-ground-state solutions) in some way using a classical computer.The solutions giving these quasi-optimal solutions are called solution instances.We extract a size-limited subQUBO model as follows based on a strong theoretical background: we randomly select N S ðN S < N I Þ solution instances among them and focus on a particular binary variable x i in the N S solution instances.If x i value is much varied over N S solution instances, it is included in the subQUBO model; otherwise, it is not.We find a (quasi-)ground-state solution of the extracted subQUBO model using an Ising machine and add it as a new solution instance.By repeating this process, we can finally obtain a (quasi-) ground-state solution of the original QUBO model.Experimental evaluations confirm that the proposed method can obtain better quasi-ground-state solution than existing methods for large-sized QUBO models.
A Hybrid CNN-LSTM Model for Virtual Machine Workload Forecasting in Cloud Data Center

It is vital to precisely forecast the workload of Virtual Machines (VMs) to achieve efficient cloud resources management and reduce power consumption. In this research study, a deep learning-based hybrid strategy for VM workload prediction is proposed. To create an accurate prediction, the suggested prediction model integrated a convolutional neural network (CNN) architecture and a long-short-term memory (LSTM) neural network. The CNN component is used to elicit complex distinctive attributes of the VM workload data, while the LSTM component models temporal information and predict the future VM workload. Experimental results on real-world dataset have shown that the proposed CNN-LSTM model is effective on VM workload prediction, and when compared to frequently used workload prediction models, the proposed approach enhances workload forecasting performance.
Semantic guide for semi-supervised few-shot multi-label node classification

We study a new research problem named semi-supervised few-shot multi-label node classification which has the following characteristics: 1) the extreme imbalance between the number of labeled and unlabeled nodes that are connected on graphs (handled by semi-supervised node learning); 2) the few labeled nodes per label (few-shot learning); and 3) the semantical correlations among labels for they share the same subsets of nodes (multi-label classification). In this paper, we propose a Label-Aware Representation Network (LARN) model to tackle this problem, by taking advantage of the semantic knowledge of labels to characterize nodes and their neighbors. Such a label-aware feature learning process allows a node to prepare its representation by knowing how it will be classified. The learned rich representations so can combat the scarcity of labeled training nodes. A label correlation scanner is then proposed to adaptively capture the label correlation and extract the useful information to generate the final node representation. Experimental results demonstrate that LARN consistently outperforms the state-of-the-art methods with significant margins, especially when only a few-shot labeled nodes are available.
SmartPatch: A patch prioritization framework

This paper is about modeling vulnerability patch prioritization in complex and interdependent systems such as the operational technology or Industrial Control Systems (ICSs). In these environments, often patching is neither automated nor cost-effective, demanding large manual administrative efforts in a timely manner with as much less system downtime as possible. The impact or risk of a vulnerability could depend on the network characteristics, context that defines the vulnerability and circumstances that led to it. Moreover, not all vulnerabilities are always exploited by the attackers; and not all vulnerabilities can be patched due to the resource constraints such as people, infrastructure, tools and time available to patch every vulnerability. Also, ICSs such as SCADA have strict requirements of system uptime and availability. These constraints place significant importance on the patch prioritization of networks and devices, which needs to be strategic and efficient. Addressing this challenge in the prioritization of patches in ICSs, we present SmartPatch a three-step, systematic patch prioritization method to address patch sequencing in an interdependent and complex network. SmartPatch is a seamless integration of system modeling, risk management and game theory. SmartPatch utilizes prior knowledge, learnings and experiences about the system dynamics and identifies an efficient and effective defensive strategy. The framework’s output is a patch prioritization strategy that is cost-constrained and reduces the impact of the possible attacks to a large extent. We propose a security metric called the “Residual Impact Score” (RIS) to analyze the impact of all discovered vulnerabilities on the system. We validate the applicability of SmartPatch by considering the case study of an interdependent, complex SCADA chain in the smart grid system using the IEEE 5-Bus system. Our comparative analysis of the proposed approach with state-of-the-art approaches demonstrates that SmartPatch reduces RIS by a faster rate i.e. after each iteration, the RIS value for SmartPatch is the least.
Classification of human's activities from gesture recognition in live videos using deep learning

Abstract The automated behavior analysis is a significant problem in the examination. In the presence of invigilators and examiners, examinees use various unfair means to cheat the staff. Examinees may use different gestures like moving their head, eyes and using different suspicious objects (mobile phone, calculator) and so forth. This research is intended to automatically identify and distinguish examinees in live videos who cheat by their activities in examination hall during exams. Hence, we took the privilege of ensemble learning using deep learning‐based algorithms. The suspicious head movements and prohibited objects have been monitored using fine‐tuned Faster RCNN algorithm. At the same time, interactive use‐age of prohibited objects find out using Open‐Pose architecture. For this purpose, intersection over union (IOU) has been calculated between the region of interest (ROI) of detected objects and pose points of hands and face. To get the identity of a specific examinee, we stored the features of facial ROI after the last convolution layer and matched them at the time of testing for statistical analysis. We achieved state‐of‐the‐art results using standard evaluating measures. Moreover, for experiments, dataset of automatic examinee invigilation is available currently. So, this research also contributes toward the generation and annotation of the dataset.
Dynamic projection mapping on deformable stretchable materials using boundary tracking

We present the first method for dynamic projection mapping on rectangular, deformable and stretchable elastic materials using consumer-grade time of flight (ToF) depth cameras. We use a B-Spline patch to model the projection surface without explicitly modeling the deformation. Leveraging the nature of deformable and stretchable materials, we propose an efficient tracking method that can track the boundary of the surface material. This achieves realistic mapping even in the interior of the surface such that the projection appears to be printed on the material. The surface representation is updated in real-time using GPU based computations. Further, we also show that the speed of these updates is limited by the speed of the camera and therefore can be adopted for higher speed cameras as well.
Real-time embedded eye detection system

The detection of a person’s eyes is a basic task in applications as important as iris recognition in biometric identification or fatigue detection in driving assistance systems. Current commercial and research systems use software frameworks that require a dedicated computer, whose power consumption, size and price are significantly large. This paper presents a hardware-based embedded solution for eye detection in real-time. From an algorithmic point-of-view, the popular Viola–Jones approach has been redesigned to enable highly parallel, single-pass image-processing implementation. Synthesized and implemented in an All-Programmable System-on-Chip (AP SoC), this proposal allows us to process more than 88 frames per second (fps), taking the classifier less than 2 ms per image. Experimental validation has been successfully addressed in an iris recognition system that works with walking subjects. In this case, the prototype module includes a CMOS digital imaging sensor providing 16 Mpixels images, and it outputs a stream of detected eyes as 640 × 480 images. Experiments for determining the accuracy of the proposed system in terms of eye detection are performed in the CASIA-Iris-distance V4 database. Significantly, they show that the accuracy in terms of eye detection is 100%.
Pedestrian detection network with multi-modal cross-guided learning

Most of the current feature generation modules based on infrared and visible modal are independent of each other, lacking long-term dependence between the modalities. It results in large differences between different modal features, which affects fusion effects, and leads to false and miss detection of targets. To tackle the problem, a pedestrian detection network with multi-modal cross-guided learning was proposed. First, the paired multi-modal images were sent to the feature generation module to generate deep and shallow features. Starting from the middle stage, the paired multi-modal features were sent to the designed weight-aware module, which output the weighted features of each modal, together with the fusion features. Then the weighted features of each modal were returned to the feature generation module of another modal, which enabled the weighted information gradually transmitted to the next stage in a joint cross-guidance manner to establish long-term dependence between modalities. At the same time, the fusion features were also input to the weight-aware module of the next stage to strengthen the connection between the fusion features at different stages and obtain more discriminative features. Finally, both the modal-specific features and the fusion features were sent to the detection module to generate the position and classification score of pedestrian targets. The experimental results indicated that the average precision on Kaist multispectral pedestrian detection dataset reached 77.16%, and the log-average miss rate dropped to 25.03% which reduced by 29.77% compared with the baseline.
SEE Your Clones With Your Teammates

We discuss how collaborative clone analysis in distributed teams can be supported by modern software-visualization technology. We present our multi-user multi-purpose software visualization platform SEE that is based on the popular code-as-a-city metaphor. We discuss how clone data can be visualized in SEE without compromising the other use cases of SEE. Our design decisions are based on principles of cognitive psychology, namely, the laws of Gestalt.
AutoMoDe-Cedrata: Automatic Design of Behavior Trees for Controlling a Swarm of Robots with Communication Capabilities

Behavior trees are a control architecture that has gained recent attention in AI and robotics. Previous research on the use of behavior trees in swarm robotics has shown the necessity for the behaviors to have proper return values, instead of running indefinitely. This work extends our previous work in which we defined AutoMoDe-Cedrata, an automatic modular design that makes use of modules that have been explicitly defined for behavior trees. While the search space is sufficiently large to include well-performing solutions, Cedrata had problems discovering communication-based strategies. In this work, we extend Cedrata by introducing Cedrata-GP and Cedrata-GE which are based on genetic programming and grammatical evolution, respectively. We test these design methods on two missions and compare the performance of the automatic design methods against the performance of solutions created by human designers. The results show that the structure of Cedrata allows for well-performing solutions that are reliably found by human designers. However, the automatic design methods fail to discover the same communication strategies as the human designers.
A use case of anomaly detection for identifying unusual water consumption in Jordan

Abstract We present a use case of anomaly detection for identifying the unusual water consumption of consumers. Unusual water consumption may be due to a faulty water meter, fraudulent tampering with a water meter, or a leak in the water pipes within the consumer's property. We apply several anomaly detection methods to a real dataset of 22,877 mechanical water meters located in Amman, the capital city of Jordan. The dataset is unlabeled such that no discrimination is given for any meter whether it records a normal water consumption or not. The objective of this study is to test the hypothesis that abnormal water consumption (registered by a given water meter) can be identified based on previous records of water consumption measured by the same meter. We tested our hypothesis using well-known anomaly detection methods, namely: z-score (zs), local outlier factor (lof), density-based spatial clustering of applications with noise (dbscan), minimum covariance determinant (mcd), one-class support vector machine (ocsvm), and isolation forest (forest). In the settings of our experiments, we observed that zs, lof, ocsvm and forest support our hypothesis, contrasting with dbscan and mcd.
Cost Efficient GPU Cluster Management for Training and Inference of Deep Learning

Expanding the scale of GPU-based deep learning (DL) clusters would bring not only accelerated AI services but also significant energy consumption costs. In this paper, we propose a cost efficient deep learning job allocation (CE-DLA) approach minimizing the energy consumption cost for the DL cluster operation while guaranteeing the performance requirements of user requests. To do this, we first categorize the DL jobs into two classes: training jobs and inference jobs. Through the architecture-agnostic modeling, our CE-DLA approach is able to conduct the delicate mapping of heterogeneous DL jobs to GPU computing nodes. Second, we design the electricity price-aware DL job allocation so as to minimize the energy consumption cost of the cluster. We show that our approach efficiently avoids the peak-rate time slots of the GPU computing nodes by using the sophisticated mixed-integer nonlinear problem (MINLP) formulation. We additionally integrate the dynamic right-sizing (DRS) method with our CE-DLA approach, so as to minimize the energy consumption of idle nodes having no running job. In order to investigate the realistic behavior of our approach, we measure the actual output from the NVIDIA-based GPU devices with well-known deep neural network (DNN) models. Given the real trace data of the electricity price, we show that the CE-DLA approach outperforms the competitors in views of both the energy consumption cost and the performance for DL job processing.
Planning a Minimum Sequence of Positions for Picking Parts From Multiple Trays Using a Mobile Manipulator

Mobile manipulators are able to operate in a large workspace, and have the potential to replace human workers to perform a sequence of pick-and-place tasks at separate locations. Many existing works optimize the base position or manipulator configuration for a single manipulation task, however, very few of them consider a sequence of tasks. In this paper, we present a planner that plans a minimum sequence of base positions for a mobile manipulator to robustly collect objects stored in multiple trays. We use inverse kinematics to determine the base region where a mobile manipulator can grasp the target objects stored in a tray, and move the mobile manipulator to the intersections of base regions to reduce the operation time for moving the base. We ensure robustness by only considering the intersection whose radius of the inscribed circle is larger than the base positioning error. Then the minimization of the number of base positions is formulated as a 0-1 knapsack problem. Besides, considering different object placements in the tray, we analyze feasible policies for dynamically updating the base sequence based on either the remaining objects or the target objects to be picked. In the experiment, we examine our planner on various scenarios, including different object placements: (1) Regularly placed toy objects; (2) Randomly placed industrial parts; and different implementation policies: (1) Apply globally static base positions; (2) Dynamically update the base positions. The experiment results show that the time for moving the base decreases by 11.22 seconds (29.37%) to 17.26 seconds (36.77%) by reducing one base movement, and demonstrate the feasibility and potential of the proposed method.
Lattice Computing: A Mathematical Modelling Paradigm for Cyber-Physical System Applications

By “model”, we mean a mathematical description of a world aspect [...]
BlastFunction: A Full-stack Framework Bringing FPGA Hardware Acceleration to Cloud-native Applications

“Cloud-native” is the umbrella adjective describing the standard approach for developing applications that exploit cloud infrastructures’ scalability and elasticity at their best. As the application complexity and user-bases grow, designing for performance becomes a first-class engineering concern. As an answer to these needs, heterogeneous computing platforms gained widespread attention as powerful tools to continue meeting SLAs for compute-intensive cloud-native workloads. We propose BlastFunction, an FPGA-as-a-Service full-stack framework to ease FPGAs’ adoption for cloud-native workloads, integrating with the vast spectrum of fundamental cloud models. At the IaaS level, BlastFunction time-shares FPGA-based accelerators to provide multi-tenant access to accelerated resources without any code rewriting. At the PaaS level, BlastFunction accelerates functionalities leveraging the serverless model and scales functions proactively, depending on the workload’s performance. Further lowering the FPGAs’ adoption barrier, an accelerators’ registry hosts accelerated functions ready to be used within cloud-native applications, bringing the simplicity of a SaaS-like approach to the developers. After an extensive experimental campaign against state-of-the-art cloud scenarios, we show how BlastFunction leads to higher performance metrics (utilization and throughput) against native execution, with minimal latency and overhead differences. Moreover, the scaling scheme we propose outperforms the main serverless autoscaling algorithms in workload performance and scaling operation amount.
Quantifying Location Privacy for Navigation Services in Sustainable Vehicular Networks

Current connected and autonomous vehicles will contribute to various and green vehicular services. However, sharing personal data with untrustworthy Navigation Service Providers (NSPs) raises serious location concerns. To address this issue, many Location Privacy-Preserving Mechanisms (LPPMs) have been proposed. In addition, several quantification methods have been designed to help understand location privacy and illustrate how location privacy is leaked. However, their assessment is insufficient due to the incomplete assumptions about the adversary’s model. In particular, users tend to request the same navigation routes from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">home</i> to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">workplace</i> and acquire traffic information along the route. An adversary can collect the coordinates of adjacent locations and infer the two true locations. In this paper, we provide a formal framework for the analysis of LPPMs in navigation services. Our framework captures extra information that is available to an adversary performing localization attacks. By formalizing the adversary’s performance, we also propose and justify two new metrics to quantify location privacy in navigation services, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">accuracy</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visibility</i> . We assess the efficacy of two popular LPPMs for location privacy, i.e., differential privacy and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -anonymity. Experimental results demonstrate that the adversary can recover users’ locations with a high probability.
FAIR: Fair adversarial instance re-weighting

With growing awareness of societal impact of artificial intelligence, fairness has become an important aspect of machine learning algorithms. The issue is that human biases towards certain groups of population, defined by sensitive features like race and gender, are introduced to the training data through data collection and labeling. Two important directions of fairness ensuring research have focused on (i) instance weighting in order to decrease the impact of more biased instances and (ii) adversarial training in order to construct data representations informative of the target variable, but uninformative of the sensitive attributes. In this paper we propose a Fair Adversarial Instance Re-weighting (FAIR) method, which uses adversarial training to learn instance weighting function that ensures fair predictions. Merging the two paradigms, it inherits desirable properties from both interpretability of reweighting and end-to-end trainability of adversarial training. We propose four different variants of the method and, among other things, demonstrate how the method can be cast in a fully probabilistic framework. Additionally, theoretical analysis of FAIR models' properties is provided. We compare FAIR models to ten other related and state-of-the-art models and demonstrate that FAIR is able to achieve a better trade-off between accuracy and unfairness. To the best of our knowledge, this is the first model that merges reweighting and adversarial approaches by means of a weighting function that can provide interpretable information about fairness of individual instances.