Reviewerly/exHarmony huggingface dataset:
A multiple-UAV architecture for autonomous media production

Cinematography with Unmanned Aerial Vehicles (UAVs) is an emerging technology promising to revolutionize media production. On the one hand, manually controlled drones already provide advantages, such as flexible shot setup, opportunities for novel shot types and access to difficult-to-reach spaces and/or viewpoints. Moreover, little additional ground infrastructure is required. On the other hand, enhanced UAV cognitive autonomy would allow both easier cinematography planning (from the Director's perspective) and safer execution of that plan during actual filming; while integrating multiple UAVs can additionally augment the cinematic potential. In this paper, a novel multiple-UAV software/hardware architecture for media production in outdoor settings is proposed. The architecture encompasses mission planning and control under safety constraints, enhanced cognitive autonomy through visual analysis, human-computer interfaces and communication infrastructure for platform scalability with Quality-of-Service provisions. Finally, the architecture is demonstrated via a relevant subjective study on the adequacy of UAV and camera parameters for different cinematography shot types, as well as with field experiments where multiple UAVs film outdoor sports events.
Container placement and migration strategies for cloud, fog, and edge data centers: A survey

Summary The last decade has witnessed important development of network softwarization that has revolutionized the practice of networks. Virtualized networks bring novel and specific requirements for the control and orchestration of containerized network functions that are scattered across the network. In this regard, the migration of virtualized network functions plays a pivotal role to best meet the requirements of optimal resource utilization, load balancing and fault tolerance. The purpose of this survey is to offer a detailed overview of the progress on container migration so as to provide a better understanding of the trade‐off between the benefits associated with the migration and the practical challenges. The paper includes a classification of the placement algorithms that map the containerized network functions on the virtualized infrastructure. Following, a taxonomy of the migration techniques that perform the transfer of the containerized microservices is proposed.
Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection

Breast cancer is the most common cancer in women, and the breast mass recognition model can effectively assist doctors in clinical diagnosis. However, the scarcity of medical image samples makes the recognition model prone to overfitting. A breast mass recognition model integrated with deep pathological information mining is proposed: constructing a sample selection strategy, screening high-quality samples across different mammography image datasets, and dealing with the scarcity of medical image samples from the perspective of data enhancement; mining the pathology contained in limited labeled models from shallow to deep information; and dealing with the shortage of medical image samples from the perspective of feature optimization. The multiview effective region gene optimization (MvERGS) algorithm is designed to refine the original image features, improve the feature discriminate and compress the feature dimension, better match the number of samples, and perform discriminate correlation analysis (DCA) on the advanced new features; in-depth cross-modal correlation between heterogeneous elements, that is, the deep pathological information, can be mined to describe the breast mass lesion area accurately. Based on deep pathological information and traditional classifiers, an efficient breast mass recognition model is trained to complete the classification of mammography images. Experiments show that the key technical indicators of the recognition model, including accuracy and AUC, are better than the mainstream baselines, and the overfitting problem caused by the scarcity of samples is alleviated.
Polygon-to-Polygon Distance Loss for Rotated Object Detection

There are two key issues that limit further improvements in the performance of existing rotational detectors: 1) Periodic sudden change of the parameters in the rotating bounding box (RBBox) definition causes a numerical discontinuity in the loss (such as smoothL1 loss). 2) There is a gap of optimization asynchrony between the loss in the RBBox regression and evaluation metrics. In this paper, we define a new distance formulation between two convex polygons describing the overlapping degree and non-overlapping degree. Based on this smooth distance, we propose a loss called Polygon-to-Polygon distance loss (P2P Loss). The distance is derived from the area sum of triangles specified by the vertexes of one polygon and the edges of the other. Therefore, the P2P Loss is continuous, differentiable, and inherently free from any RBBox definition. Our P2P Loss is not only consistent with the detection metrics but also able to measure how far, as well as how similar, a RBBox is from another one even when they are completely non-overlapping. These features allow the RetinaNet using the P2P Loss to achieve 79.15% mAP on the DOTA dataset, which is quite competitive compared with many state-of-the-art rotated object detectors.
Uncertainty Modeling with Second-Order Transformer for Group Re-identification

Group re-identification (G-ReID) focuses on associating the group images containing the same persons under different cameras. The key challenge of G-ReID is that all the cases of the intra-group member and layout variations are hard to exhaust. To this end, we propose a novel uncertainty modeling, which treats each image as a distribution depending on the current member and layout, then digs out potential group features by random samplings. Based on potential and original group features, uncertainty modeling can learn better decision boundaries, which is implemented by two modules, member variation module (MVM) and layout variation module (LVM). Furthermore, we propose a novel second-order transformer framework (SOT), which is inspired by the fact that the position modeling in the transformer is coped with the G-ReID task. SOT is composed of the intra-member module and inter-member module. Specifically, the intra-member module extracts the first-order token for each member, and then the inter-member module learns a second-order token as a group feature by the above first-order tokens, which can be regarded as the token of tokens. A large number of experiments have been conducted on three available datasets, including CSG, DukeGroup and RoadGroup. The experimental results show that the proposed SOT outperforms all previous state-of-the-art methods.
VM Scheduling for Efficient Dynamically Migrated Virtual Machines (VMS-EDMVM) in Cloud Computing Environment

With the massive demand and growth of cloud computing, virtualization plays an important role in providing services to end-users efficiently.However, with the increase in services over Cloud Computing, it is becoming more challenging to manage and run multiple Virtual Machines (VMs) in Cloud Computing because of excessive power consumption.It is thus important to overcome these challenges by adopting an efficient technique to manage and monitor the status of VMs in a cloud environment.Reduction of power/energy consumption can be done by managing VMs more effectively in the datacenters of the cloud environment by switching between the active and inactive states of a VM.As a result, energy consumption reduces carbon emissions, leading to green cloud computing.The proposed Efficient Dynamic VM Scheduling approach minimizes Service Level Agreement (SLA) violations and manages VM migration by lowering the energy consumption effectively along with the balanced load.In the proposed work, VM Scheduling for Efficient Dynamically Migrated VM (VMS-EDMVM) approach first detects the over-utilized host using the Modified Weighted Linear Regression (MWLR) algorithm and along with the dynamic utilization model for an underutilized host.Maximum Power Reduction and Reduced Time (MPRRT) approach has been developed for the VM selection followed by a two-phase Best-Fit CPU, BW (BFCB) VM Scheduling mechanism which is simulated in CloudSim based on the adaptive utilization threshold base.The proposed work achieved a Power consumption of 108.45 kWh, and the total SLA violation was 0.1%.The VM migration count was reduced to 2,202 times, revealing better performance as compared to other methods mentioned in this paper.
Boundary Preserved Salient Object Detection Using Guided Filter Based Hybridization Approach of Transformation and Spatial Domain Analysis

Techniques for detecting salient objects mimic human behavior by recognizing the most noticeable parts of images as objects. Salient object detection has attracted many researchers&#x2019; attention nowadays for various computer vision and pattern recognition applications. In this paper, a unique approach is proposed based on the global and local saliency detection using wavelet transform and hybridizing it with learning-based saliency detection using a guided filter. First, the input image is subjected to superpixel segmentation to achieve visually uniform regions and to reduce the computational cost. The global and local saliency maps are then generated using global and local features extracted by the wavelet transform of the segmented image, as the wavelet transform gives a multiscale analysis of images in frequency as well as in spatial domain. The learning-based saliency maps are generated using random forest regression which considers the location, color, and textural features of the segmented image. The global and local saliency maps are fused to generate the wavelet-based saliency map which is further hybridized with the saliency map generated using random forest regression. The paper discusses the novel technique for hybridizing wavelet-based and learning-based saliency maps using a guided filter-based attention map generation. Several experiments are conducted on five different saliency datasets containing images with complex backgrounds, multiple objects, and low contrast. To evaluate the efficacy of the proposed method, extensive qualitative and quantitative performance analysis is carried out. Experimental results validate the significant improvement in the detection of salient regions as compared to the state-of-the-art methods.
Blockchain in global finance make-over: Exploring the mediating role of supply chain flexibility

This study examined the relationship among blockchain technology utilization, information sharing, supply chain flexibility, and competitive advantage in UAE financial institutions. For this purpose, a theoretical framework was examined regarding the relationship among blockchain technology utilization, information sharing, supply chain flexibility, and competitive advantage. Additionally, mediating effect of supply chain flexibility was also examined. This research is cross-sectional by design. The respondents collected the data in the form of questionnaires. The response rate of this study was 63.03%. The data collected was examined by Smart PLS. The study's findings confirm that supply chain flexibility is affected by blockchain technology and information technology, which affects the financial institutes' competitive advantage. The implications of these findings cannot be neglected in the financial industry.
Multimodal Sarcasm Target Identification in Tweets

Sarcasm is important to sentiment analysis on social media.Sarcasm Target Identification (STI) deserves further study to understand sarcasm in depth.However, text lacking context or missing sarcasm target makes target identification very difficult.In this paper, we introduce multimodality to STI and present Multimodal Sarcasm Target Identification (MSTI) task.We propose a novel multi-scale crossmodality model that can simultaneously perform textual target labeling and visual target detection.In the model, we extract multi-scale visual features to enrich spatial information for different sized visual sarcasm targets.We design a set of convolution networks to unify multi-scale visual features with textual features for cross-modal attention learning, and correspondingly a set of transposed convolution networks to restore multi-scale visual information.The results show that visual clues can improve the performance of TSTI by a large margin, and VSTI achieves good accuracy.
Gaussian Multi-head Attention for Simultaneous Machine Translation

Simultaneous machine translation (SiMT) outputs translation while receiving the streaming source inputs, and hence needs a policy to determine where to start translating.The alignment between target and source words often implies the most informative source word for each target word, and hence provides the unified control over translation quality and latency, but unfortunately the existing SiMT methods do not explicitly model the alignment to perform the control.In this paper, we propose Gaussian Multi-head Attention (GMA) to develop a new SiMT policy by modeling alignment and translation in a unified manner.For SiMT policy, GMA models the aligned source position of each target word, and accordingly waits until its aligned position to start translating.To integrate the learning of alignment into the translation model, a Gaussian distribution centered on predicted aligned position is introduced as an alignment-related prior, which cooperates with translation-related soft attention to determine the final attention.Experiments on En→Vi and De→En tasks show that our method outperforms strong baselines on the trade-off between translation and latency.
NOLEAKS: Differentially Private Causal Discovery Under Functional Causal Model

Causal inference is widely used in clinical research, economic analysis, and other fields. As is the case with many statistical data, the findings of causal discovery (i.e., causal graph) might leak demographic information of participants. For example, a causal link between one genome and a rare disease can reveal the participation of a minority patient in genome-ide association studies. To date, differential privacy has served as the de facto foundation for guaranteeing the privacy of causal discovery algorithms. However, existing approaches to protecting causal discovery from privacy leakage rely heavily on private conditional independence tests, which generate a considerable amount of noise and are thus prone to inaccuracy. As a result of their limited accuracy and scalability, they are insufficient for non-trivial datasets (e.g., those with more than ten variables). In this paper, we advocate a novel focus on enforcing privacy for causal discovery algorithms based on functional causal models. First, we propose NOLEAKS, a differentially private causal discovery algorithm, which manifests both high accuracy and efficiency compared with prior works. Second, we design a quasi-Newton numerical optimization algorithm for solving NOLEAKS in a highly efficient way. Third, we evaluate NOLEAKS using both public benchmarks and synthetic data. We observe that NOLEAKS achieves comparable performance or even surpasses the state-of-the-art (non-private) approaches. We also find encouraging results that NOLEAKS can smoothly scale to large datasets, on which existing works would fail. Through a case study and a downstream application, we observe encouraging results on the versatile usages of NOLEAKS.
Overcoming Catastrophic Forgetting beyond Continual Learning: Balanced Training for Neural Machine Translation

Neural networks tend to gradually forget the previously learned knowledge when learning multiple tasks sequentially from dynamic data distributions.This problem is called catastrophic forgetting, which is a fundamental challenge in the continual learning of neural networks.In this work, we observe that catastrophic forgetting not only occurs in continual learning but also affects the traditional static training.Neural networks, especially neural machine translation models, suffer from catastrophic forgetting even if they learn from a static training set.To be specific, the final model pays imbalanced attention to training samples, where recently exposed samples attract more attention than earlier samples.The underlying cause is that training samples do not get balanced training in each model update, so we name this problem imbalanced training.To alleviate this problem, we propose Complementary Online Knowledge Distillation (COKD), which uses dynamically updated teacher models trained on specific data orders to iteratively provide complementary knowledge to the student model.Experimental results on multiple machine translation tasks show that our method successfully alleviates the problem of imbalanced training and achieves substantial improvements over strong baseline systems.
Efficient Hyper-parameter Search for Knowledge Graph Embedding

While hyper-parameters (HPs) are important for knowledge graph (KG) learning, existing methods fail to search them efficiently.To solve this problem, we first analyze the properties of different HPs and measure the transfer ability from small subgraph to the full graph.Based on the analysis, we propose an efficient two-stage search algorithm KG-Tuner, which efficiently explores HP configurations on small subgraph at the first stage and transfers the top-performed configurations for fine-tuning on the large full graph at the second stage.Experiments show that our method can consistently find better HPs than the baseline algorithms within the same time budget, which achieves 9.1% average relative improvement for four embedding models on the large-scale KGs in open graph benchmark.Our code is released in https://github.com/AutoML-Research/KGTuner. 1evaluate on subgraph ) " !search algorithm update $ !select stage one: efficient evaluation on subgraph → (a) Conventional methods !→ !!evaluate on subgraph ) ℳ(# $ * , * & , ) -'. )" !
Time-Optimal Trajectory Planning With Interaction With the Environment

Optimal motion planning along prescribed paths can be solved with several techniques, but most of them do not take into account the wrenches exerted by the end-effector when in contact with the environment. When a dynamic model of the environment is not available, no consolidated methodology exists to consider the effect of the interaction. Regardless of the specific performance index to optimize, this article proposes a strategy to include external wrenches in the optimal planning algorithm, considering the task specifications. This procedure is instantiated for minimum-time trajectories and validated on a real robot performing an interaction task under admittance control. The results prove that the inclusion of end-effector wrenches affects the planned trajectory, in fact modifying the manipulator's dynamic capability.
StATIK: Structure and Text for Inductive Knowledge Graph Completion

Knowledge graphs (KGs) often represent knowledge bases that are incomplete.Machine learning models can alleviate this by helping automate graph completion.Recently, there has been growing interest in completing knowledge bases that are dynamic, where previously unseen entities may be added to the KG with many missing links.In this paper, we present StATIK-Structure And Text for Inductive Knowledge Completion.StATIK uses Language Models to extract the semantic information from text descriptions, while using Message Passing Neural Networks to capture the structural information.StATIK achieves state of the art results on three challenging inductive baselines.We further analyze our hybrid model through detailed ablation studies.
Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment classification task.Most recent efforts adopt pre-trained model to classify the sentences with aspects.However, the aspect sentiment bias from pre-trained model brings some noise to the ABSA task.Besides, traditional methods using cross-entropy loss are hard to find the potential associations between sentiment polarities.In this work, we analyze the ABSA task from a novel cognition perspective: humans can often judge the sentiment of an aspect even if they do not know what the aspect is.Moreover, it is easier to distinguish positive and negative sentiments than others for human beings because positive and negative are two opposite sentiments.To this end, we propose a no-aspect differential sentiment (NADS) framework for the ABSA task.We first design a no-aspect template by replacing the aspect with a special unbiased character to eliminate the sentiment bias and obtain a stronger representation.To better get the benefits from the template, we adopt contrastive learning between the no-aspect template and the original sentence.Then we propose a differential sentiment loss instead of the cross-entropy loss to better classify the sentiments by distinguishing the different distances between sentiments.Our proposed model is a general framework and can be combined with almost all traditional ABSA methods.Experiments on SemEval 2014 show that our framework is still able to predict the sentiment of the aspect even we don't konw what the aspect is.Moreover, our NADS framework boosts three typical ABSA methods and achieves state-of-the-art performance.
Dynamic Virtual Machine Consolidation Algorithm Based on Balancing Energy Consumption and Quality of Service

Virtual machine consolidation (VMC) is an effective way to solve the problems of high power consumption and low utilization in cloud data centers. However, large-scale virtual machine migrations (VMMs) can result in additional workloads, service-level agreement violations (SLAVs), and considerable energy consumption (EC). Existing studies have made great progress in this respect, but the following problems remain: first, the potential overload of the physical host is not considered in the load detection of the physical host; second, the resource-demand scaling of physical hosts is not considered during virtual machine (VM) placement, which results in the lack of accuracy in selecting suitable hosts. In view of the above problems, this study firstly constructs a virtual resource consolidation model based on green energy conservation (GEC-VRCM), which defines the specific process and related attributes of VMC, which is beneficial to improve the consolidation efficiency of virtual resources. Second, based on this model, we propose a dynamic virtual machine consolidation algorithm based on balancing energy consumption and quality of service (EQ-DVMCA) to achieve efficient consolidation of virtual resources. Finally, experiments show that, compared with the selected 12 benchmark algorithms and two advanced VMC algorithms, EQ-DVMCA not only reduces the number of VMMs and EC, but also maintains a high level of Quality of Service (QoS) and achieves a balance between EC and QoS.
The impact of training and development programs on employees performance: the case of Lebanese SMEs

It is the department's initiative to promote training among its workers and establish an initiative that relies more on improving an employee's future accountability skills using training and development programs. Technical development initiatives may inspire and engage workers to increase results. One of the principal goals of leadership in human resources is to improve the circumstances under which employees' latent talents are observed. Their contribution to achieving the organisation is safeguarded. The study applied quantitative methodologies in the survey technique distribution and interpretation of the data gathered using the methodological instrument of the SPSS. The findings revealed a clear correlation between training and growth, e-learning, information management and success because of a ratio below 5% in the regression study.