Reviewerly/exHarmony huggingface dataset:
A binary Bird Swarm Optimization technique for cloud computing task scheduling and load balancing

Cloud computing is a new paradigm for highperformance computation that combines a wide collection of autonomous mixed devices with a flexible computational construction to manage and deliver services. High-performance cloud computation is a subset of cloud computation. When it derives to improving the overall performance of cloud computation, task preparation is one of the most difficult things to improve. This can include response time, make span, and the degree of imbalance. When it comes to reducing power usage, processing time and increasing profit for service providers, task scheduling is vital. Heuristic methods like Binary bird swarm optimization (BBSO) were created to address the problem of inefficient procedures. However, if these algorithms are not paired with additional experiential or meta-heuristic procedures, the optimal solution will not be produced. Because of their high temporal complexity, these algorithms are less useful in real-world scenarios. BSO’s binary form is being proposed for cloud computing workload scheduling and balancing in the NP-problem. Our goal function determines if heterogeneous VMs have the greatest completion time difference by taking into account the updating and optimization limits discussed in this research. We developed a technique for updating particle positions in conjunction with load balancing. Metaheuristics and heuristics fail to outperform the proposed technique when it comes to job scheduling and load balancing. Achieved this level of success thanks to the deployment of an artificial neural network. In terms of resource allocation, ANN has shown encouraging outcomes. CNNs are more accurate and faster in predicting targets than multilayer perceptron networks.
Point-to-Box Network for Accurate Object Detection via Single Point Supervision

Object detection using single point supervision has received increasing attention over the years. However, the performance gap between point supervised object detection (PSOD) and bounding box supervised detection remains large. In this paper, we attribute such a large performance gap to the failure of generating high-quality proposal bags which are crucial for multiple instance learning (MIL). To address this problem, we introduce a lightweight alternative to the off-the-shelf proposal (OTSP) method and thereby create the Point-to-Box Network (P2BNet), which can construct an inter-objects balanced proposal bag by generating proposals in an anchor-like way. By fully investigating the accurate position information, P2BNet further constructs an instance-level bag, avoiding the mixture of multiple objects. Finally, a coarse-to-fine policy in a cascade fashion is utilized to improve the IoU between proposals and ground-truth (GT). Benefiting from these strategies, P2BNet is able to produce high-quality instance-level bags for object detection. P2BNet improves the mean average precision (AP) by more than 50% relative to the previous best PSOD method on the MS COCO dataset. It also demonstrates the great potential to bridge the performance gap between point supervised and bounding-box supervised detectors. The code will be released at www.github.com/ucas-vg/P2BNet .
Effect of Denoising on Performance of Deep Convolutional Neural Network For Mammogram Images Classification

Digital mammograms are an important imaging modality for breast cancer screening and diagnosis. Several types of noise appear in mammograms and make the job of detecting breast cancer even more challenging due to missing details in the information of the image. In this study, we analyze the effect of mammogram images quality on the performance of the Deep Convolutional Neural Network on a mammogram images classification task. Thus, our objective is to show how the classification accuracy varies with the application of a denoising step. Indeed, we investigated two different approaches to breast cancer detection. The first is the classification of the original mammo-gram images without being denoised, and the second is the classification of mammogram images that are denoised using a Deep Convolutional Neural Network, Wiener filter and Median filter. Therefore, the mammogram images are first denoised using each of the three denoising methods and then, classified into two classes: cancer and normal, using AlexNet, a pre-trained Deep Convo-lutional Neural Network in order to show whether the denoising method used is effective when grafted onto a Deep Convolutional Neural Network by measuring accuracy, sensitivity, and specificity. Interesting results are achieved where the DCNN denoising step improved the Deep Convolutional Neural Network classification task with an increase of 3.47% for overall accuracy, 5.34% for overall specificity, and 0.56% for overall sensitivity.
Hybrid Quantum-Classical Computing for Future Network Optimization

Future communication networks will require increased flexibility, scalability, and data computation capabilities to adequately respond to the growing number of service demands. Advanced mixed-integer network resource optimization models and algorithms are required to meet these requirements. The purpose of this article is to introduce a hybrid quantum-classical computing framework for addressing future network resource optimization issues. We begin by discussing the fundamentals of quantum computing and its parallelism. Following that, we discuss in detail the hybrid quantum-classical computing paradigm. Then, we discuss the potential applications of the proposed paradigm for network resource optimization, including network function virtualization (NFV), multi-access edge computing/fog/cloud computing, and cloud radio access networks (C-RANs). Finally, we discuss the difficulties associated with the design and implementation of hybrid quantum-classical algorithms.
Learning based Multi-modality Image and Video Compression

Multi-modality (i.e., multi-sensor) data is widely used in various vision tasks for more accurate or robust perception. However, the increased data modalities bring new challenges for data storage and transmission. The existing data compression approaches usually adopt individual codecs for each modality without considering the correlation between different modalities. This work proposes a multi-modality compression framework for infrared and visible image pairs by exploiting the cross-modality redun-dancy. Specifically, given the image in the reference modality (e.g., the infrared image), we use the channel-wise alignment module to produce the aligned features based on the affine transform. Then the aligned feature is used as the context information for compressing the image in the current modality (e.g., the visible image), and the corresponding affine coefficients are losslessly compressed at negligible cost. Furthermore, we introduce the Transformer-based spatial alignment module to exploit the correlation between the intermediate features in the decoding procedures for different modalities. Our framework is very flexible and easily extended for multi-modality video compression. Experimental results show our proposed framework outperforms the traditional and learning-based single modality compression methods on the FLIR and KAIST datasets.
Investigating Top-k White-Box and Transferable Black-box Attack

Existing works have identified the limitation of top-1 attack success rate (ASR) as a metric to evaluate the attack strength but exclusively investigated it in the white-box setting, while our work extends it to a more practical black-box setting: transferable attack. It is widely reported that stronger I-FGSM transfers worse than simple FGSM, leading to a popular belief that transferability is at odds with the white-box attack strength. Our work challenges this belief with empirical finding that stronger attack actually transfers better for the general top-k ASR indicated by the interest class rank (ICR) after attack. For increasing the attack strength, with an intuitive analysis on the logit gradient from the geometric perspective, we identify that the weakness of the commonly used losses lie in prioritizing the speed to fool the network instead of maximizing its strength. To this end, we propose a new normalized CE loss that guides the logit to be updated in the direction of implicitly maximizing its rank distance from the ground-truth class. Extensive results in various settings have verified that our proposed new loss is simple yet effective for top-k attack. Code is available at: https://bit.ly/3uCiomP
Understanding 3D Object Articulation in Internet Videos

We propose to investigate detecting and characterizing the 3D planar articulation of objects from ordinary RGB videos. While seemingly easy for humans, this problem poses many challenges for computers. Our approach is based on a top-down detection system that finds planes that can be articulated. This approach is followed by optimizing for a 3D plane that explains a sequence of detected articulations. We show that this system can be trained on a combination of videos and 3D scan datasets. When tested on a dataset of challenging Internet videos and the Charades dataset, our approach obtains strong performance.
On the accuracy of bot detection techniques

Development bots are often used to automate a wide variety of repetitive tasks in collaborative software development. Such bots are commonly among the most active project contributors in terms of commit activity. As such, tools that analyse contributor activity (e.g., for recognizing and giving credit to project members for their contributions) need to take into account the bots and exclude their activity. While there are a few techniques to detect bots in software repositories, these techniques are not perfect and may miss some bots or may wrongly identify some human accounts as bots. In this paper, we present an exploratory study on the accuracy of bot detection techniques on a set of 540 accounts from 27 GitHub projects. We show that none of the bot detection techniques are accurate enough to detect bots among the 20 most active contributors of each project. We show that combining these techniques drastically increases the accuracy and recall of bot detection. We also highlight the importance of considering bots when attributing contributions to humans, since bots are prevalent among the top contributors and responsible for large proportions of commits.
Language Model Agnostic Gray-Box Adversarial Attack on Image Captioning

Adversarial susceptibility of neural image captioning is still under-explored due to the complex multi-model nature of the task. We introduce a GAN-based adversarial attack to effectively fool encoder-decoder based image captioning frameworks. Unique to our attack is the systematic disruption of the internal representation of an image at the encoder stage which allows control over the captions generated at the decoder stage. We cause the desired disruption with an input perturbation that promotes similarity between the features of the input image with a target image of our choice. The target image provides a convenient handle to control the incorrect captions in our method. We do not assume any knowledge of the decoder module, which makes our attack ‘gray-box’. Moreover, our attack remains agnostic to the type of decoder module, thereby proving effective for RNNs as well as Transformers as the language models. This makes our attack highly pragmatic.
Modeling and Enforcing Access Control Policies for Smart Contracts

Ethereum smart contracts expose their functions to an untrusted network. Therefore, access control is of utmost importance. Nevertheless, many smart contracts have suffered exploits due to improper design or implementation of access control policies. In this work, we propose an approach for modeling role-based access control policies for Ethereum smart contracts on the architecture level, and we describe a process for ensuring that the implementation is correct w.r.t. that model. We achieve this through a combination of code generation, formal verification and static code analysis. Additionally, we provide an argument for the correctness of our approach and demonstrate its feasibility by detecting manually introduced violations in a case study.
A Deep Neural Approach to KGQA via SPARQL Silhouette Generation

Knowledge Graph Question Answering (KGQA) has become a prominent area in natural language processing due to the emergence of large scale Knowledge Graphs (KGs). Semantic parsing based approach is the predominant direction to solve the KGQA task where natural language question is translated into a logic form such as SPARQL query. Recently Neural Machine Translation (NMT) based approaches are gaining momentum in order to translate natural language query to structured query languages thereby solving the KGQA task. However, most of these methods struggle with out-of-vocabulary words where test entities and relations are not seen during training time. In this work, we propose a modular two stage neural architecture to solve the KGQA task. Stage-I of our approach comprises a NMT-based seq2seq module that translates a question into a sketch of the desired SPARQL query called a SPARQL silhouette. Stage-II of our approach comprises a Neural Graph Search (NGS) module which aims to improve the quality of the SPARQL silhouette by detecting the right relations in the underlying knowledge graph. Experimental results show that we achieve substantial improvements and obtain state-of-the-art performance or comparable results to the best performing systems on two benchmark datasets. We believe, our proposed approach is novel and will lead to dynamic KGQA solutions that are well-suited for practical applications.
ELSR: Efficient Line Segment Reconstruction with Planes and Points Guidance

Three-dimensional (3D) line segments are helpful for scene reconstruction. Most of the existing 3D-line-segment reconstruction algorithms deal with two views or dozens of small-size images; while in practice there are usually hundreds or thousands of large-size images. In this paper, we propose an efficient line segment reconstruction method called ELSR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Available at https://skyearth.org/publication/project/ELSR. ELSR exploits scene planes that are commonly seen in city scenes and sparse 3D points that can be acquired easily from the structure-from-motion (SfM) approach. For two views, ELSR efficiently finds the local scene plane to guide the line matching and exploits sparse 3D points to accelerate and constrain the matching. To reconstruct a 3D line segment with multiple views, ELSR utilizes an efficient abstraction approach that selects representative 3D lines based on their spatial consistence. Our experiments demonstrated that ELSR had a higher accuracy and efficiency than the existing methods. Moreover, our results showed that ELSR could reconstruct 3D lines efficiently for large and complex scenes that contain thousands of large-size images.
$\phi$-SfT: Shape-from-Template with a Physics-Based Deformation Model

Shape-from-Template (SfT) methods estimate 3D surface deformations from a single monocular RGB camera while assuming a 3D state known in advance (a template). This is an important yet challenging problem due to the under-constrained nature of the monocular setting. Existing SfT techniques predominantly use geometric and simplified deformation models, which often limits their reconstruction abilities. In contrast to previous works, this paper proposes a new SfT approach explaining 2D observations through physical simulations accounting for forces and material properties. Our differentiable physics simulator regularises the surface evolution and optimises the material elastic properties such as bending coefficients, stretching stiffness and density. We use a differentiable renderer to minimise the dense reprojection error between the estimated 3D states and the input images and recover the deformation parameters using an adaptive gradient-based optimisation. For the evaluation, we record with an RGB-D camera challenging real surfaces exposed to physical forces with various material properties and textures. Our approach significantly reduces the 3D reconstruction error compared to multiple competing methods. For the source code and data, see https://4dqv.mpi-inf.mpg.de/phi-SfT/.
Adaptive Cross-domain Learning for Generalizable Person Re-identification

Domain Generalizable Person Re-Identification (DG-ReID) is a more practical ReID task that is trained from multiple source domains and tested on the unseen target domains. Most existing methods are challenged for dealing with the shared and specific characteristics among different domains, which is called the domain conflict problem. To address this problem, we present an Adaptive Cross-domain Learning (ACL) framework equipped with a CrOss-Domain Embedding Block (CODE-Block) to maintain a common feature space for capturing both the domain-invariant and the domain-specific features, while dynamically mining the relations across different domains. Moreover, our model adaptively adjusts the architecture to focus on learning the corresponding features of a single domain at a time without interference from the biased features of other domains. Specifically, the CODE-Block is composed of two complementary branches, a dynamic branch for extracting domain-adaptive features and a static branch for extracting the domain-invariant features. Extensive experiments demonstrate that the proposed approach achieves state-of-the-art performances on the popular benchmarks. Under Protocol-2, our method outperforms previous SOTA by 7.8% and 7.6% in terms of mAP and rank-1 accuracy.
Learning to Learn by Jointly Optimizing Neural Architecture and Weights

Meta-learning enables models to adapt to new environments rapidly with a few training examples. Current gradient-based meta-learning methods concentrate on finding good model-agnostic initialization (meta-weights) for learners. In this paper, we aim to obtain better meta-learners by co-optimizing the architecture and meta-weights simultaneously. Existing NAS-based meta-learning methods apply a two-stage strategy, i.e., first searching architectures and then re-training meta-weights on the searched architecture. However, this two-stage strategy would break the mutual impact of the architecture and meta-weights since they are optimized separately. Differently, we propose progressive connection consolidation, fixing the architecture layer by layer, in which the layer with the largest weight value would be fixed first. In this way, we can jointly search architectures and train the meta-weights on fixed layers. Besides, to improve the generalization performance of the searched meta-learner on all tasks, we propose a more effective rule for co-optimization, namely Connection-Adaptive Meta-learning (CAML). By searching only once, we can obtain both adaptive architecture and meta-weights for meta-learning. Extensive experiments show that our method achieves state-of-the-art performance with 3x less computational cost, revealing our method's effectiveness and efficiency.
Trident

The recent advancements in storage technologies have popularized the use of tiered storage systems in data-intensive compute clusters. The Hadoop Distributed File System (HDFS), for example, now supports storing data in memory, SSDs, and HDDs, while OctopusFS and hatS offer fine-grained storage tiering solutions. However, the task schedulers of big data platforms (such as Hadoop and Spark) will assign tasks to available resources only based on data locality information, and completely ignore the fact that local data is now stored on a variety of storage media with different performance characteristics. This paper presents Trident, a principled task scheduling approach that is designed to make optimal task assignment decisions based on both locality and storage tier information. Trident formulates task scheduling as a minimum cost maximum matching problem in a bipartite graph and uses a standard solver for finding the optimal solution. In addition, Trident utilizes two novel pruning algorithms for bounding the size of the graph, while still guaranteeing optimality. Trident is implemented in both Spark and Hadoop, and evaluated extensively using a realistic workload derived from Facebook traces as well as an industry-validated benchmark, demonstrating significant benefits in terms of application performance and cluster efficiency.
Neural Color Operators for Sequential Image Retouching

We propose a novel image retouching method by modeling the retouching process as performing a sequence of newly introduced trainable neural color operators. The neural color operator mimics the behavior of traditional color operators and learns pixelwise color transformation while its strength is controlled by a scalar. To reflect the homomorphism property of color operators, we employ equivariant mapping and adopt an encoder-decoder structure which maps the non-linear color transformation to a much simpler transformation (i.e., translation) in a high dimensional space. The scalar strength of each neural color operator is predicted using CNN based strength predictors by analyzing global image statistics. Overall, our method is rather lightweight and offers flexible controls. Experiments and user studies on public datasets show that our method consistently achieves the best results compared with SOTA methods in both quantitative measures and visual qualities. The code and pretrained models are provided at https://github.com/amberwangyili/neurop
A ROBUST ENSEMBLE MODEL FOR SPOKEN LANGUAGE RECOGNITION

Effective decision-making in industry conditions requires access and proper presentation of manufacturing data on the realised manufacturing process. Although the frequently applied ERP systems allow for recording economic events, their potential for decision support is limited. The article presents an original system for reporting manufacturing data based on Business Intelligence technology as a support for junior and middle management. As an example a possibility of utilising data from ERP systems to support decision-making in the field of purchases and logistics in small and medium enterprises.
Enhancing QoS with Resource Optimization Technique Based on Harmony Search in Cloud Environment

In cloud environment, enhancing QoS is a challenging task to solve the multi objective problems. Effective load balancing and scheduling is a solution to enhance QoS. Job scheduling is a NP complete problem and is difficult to get the best scheduling especially for large number of jobs to enhance QoS. Multiple activities may need to be scheduled efficiently on various resources (CPU, virtual machines) by reducing completion time and maximizing resource utilization. A meta-heuristic method has been proposed for optimizing multi-objective scheduling problems in cloud systems using harmony search algorithm. According to cloud user's expectations, throughput should be high such that most of the user tasks are completed before the deadline expires thereby resulting in a low task rejection rate. Major purpose of the HS-based, NEW HS scheduling algorithm is to arrange upcoming cloud applications at virtual machines in such a way that tasks are completed in least amount of time (execution and makespan time) with low execution cost and take deadline of cloud application as a QoS constraint.