Reviewerly/exHarmony huggingface dataset:
AI-based object detection latest trends in remote sensing, multimedia and agriculture applications

Object detection is a vital research direction in machine vision and deep learning. The object detection technique based on deep understanding has achieved tremendous progress in feature extraction, image representation, classification, and recognition in recent years, due to this rapid growth of deep learning theory and technology. Scholars have proposed a series of methods for the object detection algorithm as well as improvements in data processing, network structure, loss function, and so on. In this paper, we introduce the characteristics of standard datasets and critical parameters of performance index evaluation, as well as the network structure and implementation methods of two-stage, single-stage, and other improved algorithms that are compared and analyzed. The latest improvement ideas of typical object detection algorithms based on deep learning are discussed and reached, from data enhancement, a priori box selection, network model construction, prediction box selection, and loss calculation. Finally, combined with the existing challenges, the future research direction of typical object detection algorithms is surveyed.
Differential and TriPhase Adaptive Learning-Based Privacy-Preserving Model for Medical Data in Cloud Environment

This letter proposes a novel secure data protection model for privacy preservation in the cloud environment by partitioning, sanitizing, and analyzing the data effectively to improve the model’s privacy. Several experiments and comparisons of the proposed model with existing works indicate that it protects data with high accuracy, precision, recall, and F1-score up to 87.03%, 84.87%, 87.03%, and 85.00%, for diverse datasets with a relative improvement up to 15.89%, 27.73%, 15.89%, and 21.44%, respectively.
Blockchain technology in supply chain management: an organizational theoretic overview and research agenda

Blockchain technology is touted as a game-changer. Many experts consider blockchain technology as one of the disruptive innovations. Following significant success in the banking and finance sector, blockchain technology has found significant success in all fields, including health, manufacturing, transportation, disaster relief operations, and many others. Recently, the academician has contributed significantly towards understanding blockchain technology and its application in the management field. To understand how the literature on blockchain technology in the supply chain has progressed, we undertook an extensive review of the literature published in peer-reviewed journals using databases such as SCOPUS. We have further classified our literature into four stages (pre-adoption, adoption, implementation, and application). Finally, we synthesized the findings of the study and proposed a research framework to explain how an organization can build supply chain resilience and enhance supply chain performance with the help of blockchain technology. Finally, we have noted the limitations of the study and future research directions.
Survey on Federated Learning enabling indoor navigation for industry 4.0 in B5G

With the expansion of intelligent services and applications powered by Artificial Intelligence (AI), the Internet of Things (IoT) permeates many aspects of our everyday lives. In order to enable several intelligent IoT applications (i.e., indoor and outdoor), Federated Learning (FL) allows AI training at remote smart devices without the requirement for data sharing. First, with a discussion of the recent developments in FL and indoor smart device networks and their integration, we present a thorough assessment of the growing applications of FL in indoor networks. Next, we investigate and evaluate FL’s potential for allowing various indoor applications and services, such as data sharing, localization, navigation, human activity recognition, and security and privacy. FL in numerous critical indoor services and applications, including smart manufacturing, smart homes, and smart healthcare, is then thoroughly discussed. Finally, we conclude this survey by outlining the challenges and potential paths for further study in this rapidly expanding field.
CrossFormer++: A Versatile Vision Transformer Hinging on Cross-Scale Attention

While features of different scales are perceptually important to visual inputs, existing vision transformers do not yet take advantage of them explicitly. To this end, we first propose a cross-scale vision transformer, CrossFormer. It introduces a cross-scale embedding layer (CEL) and a long-short distance attention (LSDA). On the one hand, CEL blends each token with multiple patches of different scales, providing the self-attention module itself with cross-scale features. On the other hand, LSDA splits the self-attention module into a short-distance one and a long-distance counterpart, which not only reduces the computational burden but also keeps both small-scale and large-scale features in the tokens. Moreover, through experiments on CrossFormer, we observe another two issues that affect vision transformers' performance, i.e., the enlarging self-attention maps and amplitude explosion. Thus, we further propose a progressive group size (PGS) paradigm and an amplitude cooling layer (ACL) to alleviate the two issues, respectively. The CrossFormer incorporating with PGS and ACL is called CrossFormer++. Extensive experiments show that CrossFormer++ outperforms the other vision transformers on image classification, object detection, instance segmentation, and semantic segmentation tasks.
An MPEG-7 Compatible Video Retrieval System with Integrated Support for Complex Multimodal Queries

Early prototype multimedia database management systems used the query-by-example paradigm to respond to user queries. Users needed to formulate their queries by providing examples or sketches. The query-by-keyword paradigm, on the other hand, has emerged due to the desire to search multimedia content in terms of semantic concepts using keywords or sentences rather than low-level multimedia descriptors. After all, it's much easier to formulate some queries by using keywords. However, some queries are still easier to formulate by examples or sketches-for example, the trajectory of a moving object.
Efficient Constant-Round Multi-party Computation Combining BMR and SPDZ

Recently, there has been huge progress in the field of concretely efficient secure computation, even while providing security in the presence of malicious adversaries. This is especially the case in the two-party setting, where constant-round protocols exist that remain fast even over slow networks. However, in the multi-party setting, all concretely efficient fully secure protocols, such as SPDZ, require many rounds of communication. In this paper, we present a constant-round multi-party secure computation protocol that is fully secure in the presence of malicious adversaries and for any number of corrupted parties. Our construction is based on the constant-round protocol of Beaver et al. (the BMR protocol) and is the first version of that protocol that is concretely efficient for the dishonest majority case. Our protocol includes an online phase that is extremely fast and mainly consists of each party locally evaluating a garbled circuit. For the offline phase, we present both a generic construction (using any underlying MPC protocol) and a highly efficient instantiation based on the SPDZ protocol. Our estimates show the protocol to be considerably more efficient than previous fully secure multi-party protocols.
HOW DOES FINANCIAL DEVELOPMENT INTERACT WITH ECONOMIC GROWTH IN FIVE ASEAN COUNTRIES?

This study examines the causal relationship between financial development, liberalization and economic growth through technological innovation channel in five South East Asia countries during the period 1980–2012, using a fully modified ordinary least square estimation technique. We find that technological deepening is driven by deepening in the financial system and financial liberalization rather than changes in a country’s market capitalization. We also find a negative effect from the financial openness, and a positive effect from financial deregulation.
Stochastic Workload Scheduling for Uncoordinated Datacenter Clouds with Multiple QoS Constraints

Cloud computing is now a well-adopted computing paradigm. With unprecedented scalability and flexibility, the computational cloud is able to carry out large scale computing tasks in parallel. The datacenter cloud is a new cloud computing model that uses multi-datacenter architectures for large scale massive data processing or computing. In datacenter cloud computing, the overall efficiency of the cloud depends largely on the workload scheduler, which allocates clients' tasks to different Cloud datacenters. Developing high performance workload scheduling techniques in Cloud computing imposes a great challenge which has been extensively studied. Most previous works aim only at minimizing the completion time of all tasks. However, timeliness is not the only concern, reliability and security are also very important. In this work, a comprehensive Quality of Service (QoS) model is proposed to measure the overall performance of datacenter clouds. An advanced Cross-Entropy based stochastic scheduling (CESS) algorithm is developed to optimize the accumulative QoS and sojourn time of all tasks. Experimental results show that our algorithm improves accumulative QoS and sojourn time by up to 56.1 and 25.4 percent respectively compared to the baseline algorithm. The runtime of our algorithm grows only linearly with the number of Cloud datacenters and tasks. Given the same arrival rate and service rate ratio, our algorithm steadily generates scheduling solutions with satisfactory QoS without sacrificing sojourn time.
A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data

Subspace clustering is the problem of partitioning unlabeled data points into a number of clusters so that data points within one cluster lie approximately on a low-dimensional linear subspace. In many practical scenarios, the dimensionality of data points to be clustered is compressed due to the constraints of measurement, computation, or privacy. In this paper, we study the theoretical properties of a popular subspace clustering algorithm named sparse subspace clustering (SSC) and establish formal success conditions of SSC on dimensionality-reduced data. Our analysis applies to the most general fully deterministic model, where both underlying subspaces and data points within each subspace are deterministically positioned, and also a wide range of dimensionality reduction techniques (e.g., Gaussian random projection, uniform subsampling, and sketching) that fall into a subspace embedding framework. Finally, we apply our analysis to a differentially private SSC algorithm and established both privacy and utility guarantees of the proposed method.
Agile Scalability for Large Scale Projects: Lessons Learned

In modern well known approaches, has emerged as the leading approach in software industry for the development of the software projects. With different innovative shapes agile is applicable for handling the issues regarding cost, time, continuously change environment and requirements. Agile has proved to be successful in the small and medium size project, however, it have several limitations when applied on large size projects. The aim of this study is to analyze agile approaches in detail, finding its success stories in small and medium size projects and highlighting its limitations for large size projects .This study will identify the current research problem of the agile scalability for large size projects by giving a detail literature review of the identified problem, and will synthesize the existing work for covering the identified problem in the agile scalability. Based on it, we can judge the limitations of agile scalability for large size projects and can think of some remedial approach for overcoming these limitations in future.
Typing your way to technical identity

Informal, online environments facilitate creative self-expression through typographic and orthographic stylistics. Yet, ideologies of writing may be invoked to discourage written forms that are purportedly difficult to read. This paper analyzes how members of an online, text-based, gaming community negotiate appropriate, written communications as expressions of technical identity. These encounters may reify communities of technologists who are associated with using or avoiding forms such as abbreviations, capital letters, and “leet speak.” Amid the technologizing of the word, the paper argues that those who do not conform to assumed norms may be indexed as less technical than those who do. By examining troubled encounters, the paper explores how metapragmatic negotiations affect creativity and technical identity performance online. The paper argues that contrary to discourses that online interactants pay little attention to written stylistics, the present participants closely attended to subtle and small forms. Further, it discusses how ideologies may be idiosyncratically applied to assist in forming asymmetrical, technical identities. Finally, it argues that technical affiliations are just as important to study as other variables such as gender, ethnicity, age, and class that have traditionally received attention in analyses of ideologies of writing and New Literacy Studies.
Review of Pienemann (1998): Language processing and second language development: processability theory

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A Classical Sequent Calculus with Dependent Types

Dependent types are a key feature of the proof assistants based on the Curry-Howard isomorphism. It is well known that this correspondence can be extended to classical logic by enriching the language of proofs with control operators. However, they are known to misbehave in the presence of dependent types, unless dependencies are restricted to values. Moreover, while sequent calculi naturally support continuation-passing-style interpretations, there is no such presentation of a language with dependent types. The main achievement of this article is to give a sequent calculus presentation of a call-by-value language with a control operator and dependent types, and to justify its soundness through a continuation-passing-style translation. We start from the call-by-value version of the λμ˜μ -calculus. We design a minimal language with a value restriction and a type system that includes a list of explicit dependencies to maintain type safety. We then show how to relax the value restriction and introduce delimited continuations to directly prove the consistency by means of a continuation-passing-style translation. Finally, we relate our calculus to a similar system by Lepigre and present a methodology to transfer properties from this system to our own.
Tight upper and lower bounds for leakage-resilient, locally decodable and updatable non-malleable codes

Dachman-Soled et al. (TCC '15) proposed a notion called locally decodable and updatable non-malleable codes, which provide the security guarantees of a non-malleable code while allowing for efficient random access. They also considered such codes that are leakage-resilient, allowing for adversaries who continually leak information in addition to tampering. The locality of their construction was Ω(log⁡n). We prove that super-constant locality is inherent by showing tight upper and lower bounds. We show that a locally decodable and updatable non-malleable code with block size χ∈poly(λ) requires locality δ(n)∈ω(1), where n=poly(λ) is the message length and λ is security parameter. Furthermore, we present a construction of a locally decodable and updatable non-malleable code with block size χ∈Ω(λ1/μ) (for constant 0<μ<1) with locality δ(n), for any δ(n)∈ω(1), and n=poly(λ).
Using Agent Solutions and Visualization Techniques to Manage Cloud-based Education System

Over the past few years, there are many requests from academic institutions, eLearning developers, education businesses owners, and global enterprises concerning cloud-based education systems. Nowadays, a range of software and applications have been created for managing teaching and learning resources via internet. Many of them have been even trying to integrate all the educational resources into a single cloud system. This paper proposes using agent technologies and visualization solutions as a tool to manage cloud-based education systems to match streamline of day to day business and operations. It focuses on adopting agents for University of Westminster's Cloud computing education system and mobile learning project. It aims to reduce complexity in the management of cloud computing stacks, service allocation and provision, data storage and system maintenance e.g. the exploration of Infrastructure as a Service (IaaS)level control, Platform as Service(PaaS) level deployment, Software as Service(SaaS) level apps adoption, as well as improving security for Cloud system protection. The paper also shows that using integrated agents and visualization can increase the effectiveness for Cloud-base education services provision and potentially improve business efficiency by providing flexible services management, operation and organization. It also greatly simplifies the system configuration and organization of services and the endowment of social and communication capability.
Cost-Efficient Tasks and Data Co-Scheduling with AffordHadoop

With today's massive jobs spanning thousands of tasks each, cost-optimality has become more important than ever. Modern distributed data processing paradigms can be significantly more sensitive to cost than makespan, especially for long jobs deployed in commercial clouds. This paper posits that minimized dollar costs can not be achieved unless data and tasks are scheduled simultaneously. In this paper, we introduce the problem of cost-efficient co-scheduling for highly data-intensive jobs in cloud, such as MapReduce. We show that while the problem is polynomial in some cases, its general problem is NP-Hard. We propose to tackle the problem by using integer programming techniques coupled with heuristic reduction and optimization to enable a near-realtime solution. AffordHadoop, a pluggable co-scheduler for Hadoop, is implemented as an example of such a co-scheduler. AffordHadoop can save up to 48 percent of the overall dollar costs when compared to existing schedulers and provides significant flexibility in fine-tuning the cost-performance tradeoff.
Suppressed antibunching via spectral filtering: An analytical study in the two-photon Mollow regime

Mollow physics in the two-photon regime shows interesting features such as path-controlled time-reordering of photon pairs without the need to delay them. Here, we calculate analytically the two-photon correlations $ g^{(2)}(\tau)$, essential to discuss and study such phenomena in the resonant-driven dressed-state regime. It is shown that there exists upper and lower bounds of the $ g^{(2)}(\tau)-$ function for certain spectrally-selected photon pairs. Recent reported experiments agree with the presented theory and thereby it is shown that the resonant-driven four-level system is an interesting source for steerable quantum light in quantum cascade setups. We furthermore discuss the unlikeliness to observe antibunching for the delay time $ \tau=0 $ in the exciton-biexciton correlation functions in such experiments, since antibunching stems from a coherent and in-phase superposition of different photon emission events. Due to the occuring laser photon scattering, this coherent superposition state is easily disturbed and leads to correlation functions of $ g^{(2)}(0)=1$.
Accelerated Directional Search with Non-Euclidean Prox-Structure

We consider smooth convex optimization problems whose full gradient is not available for their numerical solution. In 2011, Yu.E. Nesterov proposed accelerated gradient-free methods for solving such problems. Since only unconditional optimization problems were considered, Euclidean prox-structures were used. However, if one knows in advance, say, that the solution to the problem is sparse, or rather that the distance from the starting point to the solution in 1-norm and in 2-norm are close, then it is more advantageous to choose a non- Euclidean prox-structure associated with the 1-norm rather than a prox-structure associated with the 1-norm. In this work we present a complete justification of this statement. We propose an accelerated descent method along a random direction with a non-Euclidean prox-structure for solving unconditional optimization problems (in further work, we propose to extend this approach to an accelerated gradient-free method). We obtain estimates of the rate of convergence for the method and show the difficulties of transferring the above-mentioned approach to conditional optimization problems.
The effects of change decomposition on code review—a controlled experiment

Background Code review is a cognitively demanding and time-consuming process. Previous qualitative studies hinted at how decomposing change sets into multiple yet internally coherent ones would improve the reviewing process. So far, literature provided no quantitative analysis of this hypothesis. Aims (1) Quantitatively measure the effects of change decomposition on the outcome of code review (in terms of number of found defects, wrongly reported issues, suggested improvements, time, and understanding); (2) Qualitatively analyze how subjects approach the review and navigate the code, building knowledge and addressing existing issues, in large vs. decomposed changes. Method Controlled experiment using the pull-based development model involving 28 software developers among professionals and graduate students. Results Change decomposition leads to fewer wrongly reported issues, influences how subjects approach and conduct the review activity (by increasing context-seeking), yet impacts neither understanding the change rationale nor the number of found defects. Conclusions Change decomposition reduces the noise for subsequent data analyses but also significantly supports the tasks of the developers in charge of reviewing the changes. As such, commits belonging to different concepts should be separated, adopting this as a best practice in software engineering.