Reviewerly/exHarmony huggingface dataset:
Polysemy Deciphering Network for Human-Object Interaction Detection

Human-Object Interaction (HOI) detection is important in human-centric scene understanding. Existing works typically assume that the same verb in different HOI categories has similar visual characteristics, while ignoring the diverse semantic meanings of the verb. To address this issue, in this paper, we propose a novel Polysemy Deciphering Network (PD-Net), which decodes the visual polysemy of verbs for HOI detection in three ways. First, PD-Net augments human pose and spatial features for HOI detection using language priors, enabling the verb classifiers to receive language hints that reduce the intra-class variation of the same verb. Second, we introduce a novel Polysemy Attention Module (PAM) that guides PD-Net to make decisions based on more important feature types according to the language priors. Finally, the above two strategies are applied to two types of classifiers for verb recognition, i.e., object-shared and object-specific verb classifiers, whose combination further relieves the verb polysemy problem. By deciphering the visual polysemy of verbs, we achieve the best performance on both HICO-DET and V-COCO datasets. In particular, PD-Net outperforms state-of-the-art approaches by 3.81% mAP in the Known-Object evaluation mode of HICO-DET. Code of PD-Net is available at https://github.com/MuchHair/PD-Net .
Dual branch convolutional neural network for copy move forgery detection

The advent of digital era has seen a rise in the cases of illegal copying, distribution and forging of images. Even the most secure data channels sometimes suffer to validate the integrity of images. Forgery of multimedia data is devastating in various important applications like defence and satellite. Increased illegal tampering of images has paved way for research in the area of digital forensics. Copy move forgery is one of the various tampering techniques which is used for manipulating an image's content. A deep learning–based passive Copy Move Forgery Detection algorithm is proposed that uses a novel dual branch convolutional neural network to classify images as original and forged. The dual branch convolutional neural network extracts multi-scale features by employing different kernel sizes in each branch. Fusion of extracted multi-scale features is then performed to achieve a good accuracy, precision and recall scores. Experiment analysis on MICC F-2000 dataset has been performed under two different kernel size combinations. Extensive result analysis and comparative analysis proves the efficacy of proposed architecture over existing architecture in terms of performance scores, computation time, and complexity.
Blockchain based Service: A Case Study on IBM Blockchain Services &amp; Hyperledger Fabric

IBM is one of the world’s leading American multinational technology company, alwaysinnovative and experimenting for new things with constant new trials and hands on differenttechnologies. The company is recently focused its research and innovation on one of ICCTunderlying technology applications called Hyperledger fabric and blockchain. A businessblockchain presented by Linux foundation is hyper ledger fabric becoming the de factostandard for enterprise it provides blockchain-as-a-service (BaaS) and lets the usage ofblockchain components any possible business environment with ease. Blockchain has severaledges, together with decentralization, durability, transparency, and audit ability. The IBM goalis to develop company-ready solutions that address existing technology limitations relating toprivacy, confidentiality, audit ability, performance, and measurability. Proven, scalable, andbuilt for running on any cloud Simplifies complexness quickly, unlocks new price, andexpands competitive edges. Hyperledger cloth system for companies might be thought to be apublic platform. Within which all transactions committed are held on in an exceedingblockchain. The chain grows endlessly as new blocks are added. To figure towards a frictionfree network by facilitating easy functionality this article illustrates Blockchain, IBMblockchain, Hyperledger fabric its framework, tools, and blockchain-as-a service. Thesecondary data was obtained from various scholarly journals and websites. We have analysedthe Hyperledger framework, tools and examined their blockchain services to differentindustries using the ABCD analysis framework as a research case study.
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art

Abstract Graphs have been widely used to represent complex data in many applications, such as e-commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data is important for graph-based applications. However, most graph analysis tasks are combinatorial optimization (CO) problems, which are NP-hard. Recent studies have focused a lot on the potential of using machine learning (ML) to solve graph-based CO problems. Most recent methods follow the two-stage framework. The first stage is graph representation learning, which embeds the graphs into low-dimension vectors. The second stage uses machine learning to solve the CO problems using the embeddings of the graphs learned in the first stage. The works for the first stage can be classified into two categories, graph embedding methods and end-to-end learning methods. For graph embedding methods, the learning of the the embeddings of the graphs has its own objective, which may not rely on the CO problems to be solved. The CO problems are solved by independent downstream tasks. For end-to-end learning methods, the learning of the embeddings of the graphs does not have its own objective and is an intermediate step of the learning procedure of solving the CO problems. The works for the second stage can also be classified into two categories, non-autoregressive methods and autoregressive methods. Non-autoregressive methods predict a solution for a CO problem in one shot. A non-autoregressive method predicts a matrix that denotes the probability of each node/edge being a part of a solution of the CO problem. The solution can be computed from the matrix using search heuristics such as beam search. Autoregressive methods iteratively extend a partial solution step by step. At each step, an autoregressive method predicts a node/edge conditioned to current partial solution, which is used to its extension. In this survey, we provide a thorough overview of recent studies of the graph learning-based CO methods. The survey ends with several remarks on future research directions.
Going deep into schizophrenia with artificial intelligence

Despite years of research, the mechanisms governing the onset, relapse, symptomatology, and treatment of schizophrenia (SZ) remain elusive. The lack of appropriate analytic tools to deal with the heterogeneity and complexity of SZ may be one of the reasons behind this situation. Deep learning, a subfield of artificial intelligence (AI) inspired by the nervous system, has recently provided an accessible way of modeling and analyzing complex, high-dimensional, nonlinear systems. The unprecedented accuracy of deep learning algorithms in classification and prediction tasks has revolutionized a wide range of scientific fields and is rapidly permeating SZ research. Deep learning has the potential of becoming a valuable aid for clinicians in the prediction, diagnosis, and treatment of SZ, especially in combination with principles from Bayesian statistics. Furthermore, deep learning could become a powerful tool for uncovering the mechanisms underlying SZ thanks to a growing number of techniques designed for improving model interpretability and causal reasoning. The purpose of this article is to introduce SZ researchers to the field of deep learning and review its latest applications in SZ research. In general, existing studies have yielded impressive results in classification and outcome prediction tasks. However, methodological concerns related to the assessment of model performance in several studies, the widespread use of small training datasets, and the little clinical value of some models suggest that some of these results should be taken with caution.
Sample-Based Neural Approximation Approach for Probabilistic Constrained Programs

This article introduces a neural approximation-based method for solving continuous optimization problems with probabilistic constraints. After reformulating the probabilistic constraints as the quantile function, a sample-based neural network model is used to approximate the quantile function. The statistical guarantees of the neural approximation are discussed by showing the convergence and feasibility analysis. Then, by introducing the neural approximation, a simulated annealing-based algorithm is revised to solve the probabilistic constrained programs. An interval predictor model (IPM) of wind power is investigated to validate the proposed method.
Unsupervised deep clustering via contractive feature representation and focal loss

Deep clustering aims to promote clustering tasks by combining deep learning and clustering together to learn the clustering-oriented representation, and many approaches have shown their validity. However, the feature learning modules in existing methods hardly learn a discriminative representation. In addition, the label assignment mechanism becomes inefficient when dealing with some hard samples. To address these issues, a new joint optimization clustering framework is proposed through introducing the contractive representation in feature learning and utilizing focal loss in the clustering layer. The contractive penalty term added in feature learning would cause the local feature space contraction, resulting in learning more discriminative features. To our certain knowledge, this is also the first work to utilize the focal loss to improve the label assignment in deep clustering method. Moreover, the construction of the joint optimization framework enables the proposed method to learn feature representation and label assignment simultaneously in an end-to-end way. Finally, we comprehensively compare with some state-of-the-art clustering approaches on several clustering tasks to demonstrate the effectiveness of the proposed method.
Emerging Technologies: Leveraging Digital Literacy for Self-Sufficiency Among Library Professionals

In today's rapidly evolving information landscape, library professionals are pivotal in helping individuals and communities access, evaluate, and utilize digital resources effectively. As technology continues to reshape how information is accessed and disseminated, library professionals must embrace digital literacy as a critical competency. This essay explores the importance of digital literacy in the context of emerging technologies and how library professionals can leverage it to achieve self-sufficiency in their roles. The paper concludes that digital literacy empowers library professionals to navigate the digital landscape, harness AI and data analytics, curate digital resources effectively, teach online information evaluation, and promote digital inclusion. Given the ever-evolving digital information landscape, librarians are entrusted with the critical task of providing access to knowledge and guiding patrons through a complex web of digital resources. From the paper, it is believed that leveraging digital literacy is the key to achieving self-sufficiency in these roles. As information navigators in the digital age, librarians must continue to cultivate their digital literacy skills and adapt to emerging technologies. The paper recommends that librarians should invest in continuous training and professional development programs focused on digital marketing, e-commerce, and content creation, libraries should allocate resources strategically to invest in the necessary digital tools and platforms for income generation, and libraries can implement change management strategies to address resistance to digital transformation and foster a culture of innovation.
Energy efficient strategy for placement of virtual machines selected from underloaded servers in compute Cloud

Workload consolidation is a phase in Cloud datacenter where tasks are allocated among the available hosts in such a way that a minimal number of hosts is used and users’ need in terms of service level agreement (SLA) is fulfilled. To achieve workload consolidation, hosts are divided among three groups based on their utilization namely overloaded hosts, underloaded host and normal hosts. Detection of over or underloaded host is a challenging issue. Most of the existing researchers propose to use threshold values for such detection. We believe that there is a scope of improvement in existing methods of deciding underloaded hosts and subsequently taking off virtual machines (VMs) from them and placing them on other hosts. In this research, we propose Host Utilization Aware (HUA) Algorithm for underloaded host detection and placing its VMs on other hosts in a dynamic Cloud environment. We compare our proposed mechanism with existing one and with empirical analysis; it is shown that our proposal results into shutting off more number of hosts without compromising user’s workload requirement which leads to an energy-efficient workload consolidation with minimal migration costs and efficient utilization of active hosts.
Facial Attribute Recognition by Recurrent Learning With Visual Fixation

This paper presents a recurrent learning-based facial attribute recognition method that mimics human observers' visual fixation. The concentrated views of a human observer while focusing and exploring parts of a facial image over time are generated and fed into a recurrent network. The network makes a decision concerning facial attributes based on the features gleaned from the observer's visual fixations. Experiments on facial expression, gender, and age datasets show that applying visual fixation to recurrent networks improves recognition rates significantly. The proposed method not only outperforms state-of-the-art recognition methods based on static facial features, but also those based on dynamic facial features.
Ignorance, Debt, and Cryptocurrencies: The Old and the New in the Law and Economics of Concurrent Currencies*

Abstract Cryptocurrencies are expected to have a significant impact on banking, finance, and monetary systems. Due to the uncertainty as to the possible future trajectories of the evolving cryptocurrency ecosystem, governments have taken a relatively hands-off approach to regulating such currencies. This approach may be justified within the theoretical information-economics framework of this paper, which draws parallels between the information economics of money and quasi-money creation within the current central banking, commercial banking, and shadow banking systems with that of the cryptocurrency ecosystem. In particular, drawing lessons from the literature on the role of information in creating ‘safe assets’, in this paper the authors find that by building on symmetric (common) knowledge as to the inner workings of the Bitcoin Blockchain—though in a different way—bitcoin possesses a degree of endogenous information insensitivity typical of safe assets. This endogenous information insensitivity could support bitcoin’s promise of maturing into a viable store of value and a niche medium of exchange. This finding should not be overlooked in the policy discussions for potential future regulatory interventions in the cryptocurrency ecosystem.
Putting the Next 500 VM Placement Algorithms to the Acid Test: The Infrastructure Provider Viewpoint

Most current infrastructures for cloud computing leverage static and greedy policies for the placement of virtual machines. Such policies impede the optimal allocation of resources from the infrastructure provider viewpoint. Over the last decade, more dynamic and often more efficient policies based, e.g., on consolidation and load balancing techniques, have been developed. Due to the underlying complexity of cloud infrastructures, these policies are evaluated either using limited scale testbeds/in-vivo experiments or ad-hoc simulators. These validation methodologies are unsatisfactory for two important reasons: they (i) do not model precisely enough real production platforms (size, workload variations, failure, etc.) and (ii) do not enable the fair comparison of different approaches. More generally, new placement algorithms are thus continuously being proposed without actually identifying their benefits with respect to the state of the art. In this article, we show how VMPlaceS, a dedicated simulation framework enables researchers (i) to study and compare VM placement algorithms from the infrastructure perspective, (ii) to detect possible limitations at large scale and (iii) to easily investigate different design choices. Built on top of the SimGrid simulation platform, VMPlaceS provides programming support to ease the implementation of placement algorithms and runtime support dedicated to load injection and execution trace analysis. To illustrate the relevance of VMPlaceS, we first discuss a few experiments that enabled us to study in details three well known VM placement strategies. Diving into details, we also identify several modifications that can significantly increase their performance in terms of reactivity. Second, we complete this overall presentation of VMPlaceS by focusing on the energy efficiency of the well-know FFD strategy. We believe that VMPlaceS will allow researchers to validate the benefits of new placement algorithms, thus accelerating placement research and favouring the transfer of results to IaaS production platforms.
Automated Prototype Generation From Formal Requirements Model

Prototyping is an effective and efficient way of requirements validation to avoid introducing errors in the early stage of software development. However, manually developing a prototype of a software system requires additional efforts, which would increase the overall cost of software development. In this article, we present an approach with a developed tool RM2PT to automated prototype generation from formal requirements models for requirements validation. A requirements model consists of a use case diagram, a conceptual class diagram, use case definitions specified by system sequence diagrams, and the contracts of their system operations. A system operation contract is formally specified by a pair of pre and postconditions in object constraint language. We propose a method with a set of transformation rules to decompose a contract into executable parts and nonexecutable parts. An executable part can be automatically transformed into a sequence of primitive operations by applying their corresponding rules, and a nonexecutable part is not transformable with the rules. The tool RM2PT provides a mechanism for developers to develop a piece of program for each nonexecutable part manually, which can be plugged into the generated prototype source code automatically. We have conducted four case studies with over 50 use cases. The experimental result shows that the 93.65% system operations are executable, and only 6.35% are nonexecutable, which can be implemented by developers manually or invoking the third-party application programming interface (APIs). Overall, the result is satisfactory. Each 1 s generated prototype of four case studies requires approximate one day's manual implementation by a skilled programmer. The proposed approach with the developed computer-aided software engineering tool can be applied to the software industry for requirements engineering.
Methods of decreasing the unambiguous state discrimination probability for subcarrier wave quantum key distribution systems

In this paper, we compare two solutions (well known and newly proposed) for subcarrier wave quantum key distribution systems that decrease the crucial impact of the errorless unambiguous state discrimination attack in order to ensure security in channels with higher losses. One of the well-known methods is based on adding more signal states and allows a decrease in the probability of successful discrimination. However, sifting fractions will decrease too, leading to lower key rates. Thus, we propose a technique that utilizes additional vacuum state (unmodulated carrier and vacuum states at the sidebands) with low a priori sending probability and that provides the same decrease (in the first order) of errorless unambiguous discrimination as for four signal states while maintaining the same sifting fraction as for two states.
Resultant Based Incremental Recovery of Camera Pose From Pairwise Matches

Incremental (online) structure from motion pipelines seek to recover the camera matrix associated with an image I_n given n-1 images, I_1,...,I_n-1, whose camera matrices have already been recovered. In this paper, we introduce a novel solution to the six-point online algorithm to recover the exterior parameters associated with I_n. Our algorithm uses just six corresponding pairs of 2D points, extracted each from I_n and from any of the preceding n-1 images, allowing the recovery of the full six degrees of freedom of the n'th camera, and unlike common methods, does not require tracking feature points in three or more images. Our novel solution is based on constructing a Dixon resultant, yielding a solution method that is both efficient and accurate compared to existing solutions. We further use Bernstein's theorem to prove a tight bound on the number of complex solutions. Our experiments demonstrate the utility of our approach.
Graphical-model based estimation and inference for differential privacy

Many privacy mechanisms reveal high-level information about a data distribution through noisy measurements. It is common to use this information to estimate the answers to new queries. In this work, we provide an approach to solve this estimation problem efficiently using graphical models, which is particularly effective when the distribution is high-dimensional but the measurements are over low-dimensional marginals. We show that our approach is far more efficient than existing estimation techniques from the privacy literature and that it can improve the accuracy and scalability of many state-of-the-art mechanisms.
Generating Photographic Faces From the Sketch Guided by Attribute Using GAN

From a sketch image or text description, generating a semantic and photographic face image has always been an extremely important issue in computer vision.Sketch images generally contain only simple profile information but not the detail of the face.Therefore, it is difficult to generate facial attributes accurately.In this paper, we treat the sketch to face the problem as a face hallucination reconstruction problem.In order to solve this problem, we propose an image translation network by exploiting attributes with the generated adversarial network.And it can significantly contribute to the authenticity of the generated face by supplementing sketch image with the additional facial attribute feature.The generator network is composed of a feature extracting network and downsampling-upsampling network, both networks use skip-connection to reduce the number of layers without affecting network performance.The discriminator network is designed to examine whether the generated faces contain the desired attributes or not.In the underlying feature extraction phase, our network is different from most attribute-embedded networks, we fuse the sketch images and attributes perceptually.We set the network sub-Branch A and B, which receive a sketch image and attribute vector in order to extract low-level profile information and high-level semantic features.Compared with the state-of-the-art methods of image translation, the performance of the proposed network is excellent.