Reviewerly/exHarmony huggingface dataset:
Secure reputation and morphism‐based offloading scheme: A veritable tool for multi‐party computation in Industrial Internet of Things

Summary Computations offload for multi‐party computation (MPC) in the Industrial Internet of Things (IIoT) involves the transfer of resource‐intensive industrial computations of resource‐constraint industrial nodes (sourcers) to idle and powerful nodes (workers) such as hardware accelerator grids, IIoT gateways, and cloud servers. However, verifying the results of the offloaded computations and solving the ensuing security and privacy problems have been the drawbacks of MPC in IIoT. Although the morphism approach is currently being used for ensuring the correctness of the results of the outsource computations, it has been proved that its overhead increases as the number of the computations increases. In this article, we formulate a secure offloading scheme capable of achieving perfect verification of the results using reputation and morphism and providing security requirements for effective MPC. Performance and security analyses show that the scheme is not only secure, but also ensures privacy preservation, fairness, and perfect verification of the result at a low cost.
How Good Governance (GG) Affects Diaspora Motivations for Remittances to Iran

Abstract In this study, we examined different models for the motivation of the Iranian diaspora to send remittances to Iran, with and without governance indicators, to analyse the impact of economic factors and governance indicators on remittances. The results based on the generalised method of moments ( GMM ) method show that the income level of the origin and host country had positive and significant effects on the diasporas’ motivation for remittances to Iran. Also, Iran’s real exchange rate has a negative impact on remittances, whereas the interest rate differential is positive. The results confirm that the quality of governance and its indicators are essential factors for remittances. Overall, the macroeconomic conditions, along with good governance in Iran, have influenced remittances. Between investment and altruistic motives, altruism has been the primary motivation of remittances due to the lack of good economic and governance conditions in Iran during the years under review.
IoT Analytics and ERP Interoperability in Automotive SCM

Abstract Objective – The objective of this paper is to understand the potential of Interoperability between ERP and IOT Analytics in enabling the agile performance in Automotive supply chain by exploring the influence between Interoperability, SC Visibility and SCM agile performance and propose design for decision making system using Artificial neural network integration with fuzzy logic technique. Design/methodology/approach – TOE view was used to develop theoretical framework in addition to the elaborate literature review. Empirical analysis on the collected data from professionals in the automotive industry used to conclude on the findings. Findings – The IOT-Analytics and ERP interoperability identified as an enabler of SCM agile performance. Originality/value – The research article provides theoretical and empirical evidence over the IOT analytics and ERP interoperability potential impact in the Automotive SCM with novel approach towards designing effective decision support system using artificial neural network-fuzzy logic integration technique.
A 2D-graph model-based heuristic approach to visual backtracking security vulnerabilities in physical protection systems

The paper presents a heuristic backward path tracing algorithm in combination with optimization EASI model for effective estimation of time delay remaining after detection TTR. The heuristic backward search algorithm is implemented based on a graph-based model representation of a hypothetical facility for visual path tracing and planning. A comparative analysis between the implementation of non-heuristic and heuristic search for the most vulnerable adversary paths identification in terms of the shortest distance (d), probability of detection (PD), and probability of interruption (PI) on different intrusion detection fields is carried out and in detail discussed. The comparison results show that the global optimal solutions can be obtained using the heuristic backward path tracing algorithm with high search efficiency and accuracy. The knotty problem encountered in unknown path generation under uncertainties for calculating TTR and PI is effectively solved for admissible and consistent heuristic function design.
Spatial mapping of hydrothermal alterations and structural features for gold and cassiterite exploration

Mineral deposits show an intrinsic relationship with hydrothermal alteration and structures features which may be invaluable for regional scale exploration survey using spaceborne data. Within the Kaiama sheet, spatial data such as Landsat 8 OLI and ASTER digital elevation model were processed by enhancing hydrothermal alteration and geological lineaments. Image processing techniques such as band ratio and principal component analysis were used for alteration mapping, while Fry analysis and distance correlation analysis were applied for evaluating the structural attributes of gold and cassiterite mineralization. Evidence from image processing suggests the presence of clay and iron alterations within zones of known mineral deposits, while structural studies by application of the Fry and distance correlation analysis suggests gold and cassiterite occurrence is effectively controlled mainly by a NE-SW tectonic trend. The proximal association of gold and cassiterite mineralization with geological structures and hydrothermal alterations may be invaluable for defining a digital exploration criterion for the Kaiama sheet.
Extricating web pages from deep web using deaima architecture

Due to remarkable development of advanced data, it has gotten drawn-out to recover applicable query items from the web crawler. The customary web search tool furnishes pertinent site pages however with the blend of unessential pages with less coverage rate and high slithering time. To overcome the issues, this paper proposes an Intelligent Multi-Agent design that utilizes Differential Evolution Algorithm (DEA) to extricate more pertinent site pages with high coverage rate and less slithering time. Three intelligent agents, specifically, Trajectory pattern mining agent, Query clustering agent and Web page classification agent are employed to track the relevancy of the pages, hidden web links and classify the searchable and non-searchable forms respectively. The extrication of relevant pages is optimized by the DEA algorithm. DEA calculation like selection, crossover and mutation are utilized for optimizing the extraction process. In any case, in Genetic Algorithm (GA) determination of chromosome depends on the wellness esteem, where the low quality chromosomes don't get by in the future. The Differential Evolutionary activity help the helpless chromosome to get by in the opposition and it will create the upgraded results. The learnt by experience of the intelligent agents help to work on the presentation of the framework. The exploration result shows that the proposed architecture gives the high precision and recall ratesover the current web crawlers. The creeping time for recovering the website pages is likewise diminished utilizing the proposed DEAIMA architecture.
Transparent assessment of information quality of online reviews using formal argumentation theory

Review scores collect users’ opinions in a simple and intuitive manner. However, review scores are also easily manipulable, hence they are often accompanied by explanations. A substantial amount of research has been devoted to ascertaining the quality of reviews, to identify the most useful and authentic scores through explanation analysis. In this paper, we advance the state of the art in review quality analysis. We introduce a rating system to identify review arguments and to define an appropriate weighted semantics through formal argumentation theory. We introduce an algorithm to construct a corresponding graph, based on a selection of weighted arguments, their semantic distance, and the supported ratings. We also provide an algorithm to identify the model of such an argumentation graph, maximizing the overall weight of the admitted nodes and edges. We evaluate these contributions on the Amazon review dataset by McAuley et al. (2015), by comparing the results of our argumentation assessment with the upvotes received by the reviews. Also, we deepen the evaluation by crowdsourcing a multidimensional assessment of reviews and comparing it to the argumentation assessment. Lastly, we perform a user study to evaluate the explainability of our method, i.e., to test whether the automated method we use to assess reviews is understandable by humans. Our method achieves two goals: (1) it identifies reviews that are considered useful, comprehensible, and complete by online users, and does so in an unsupervised manner, and (2) it provides an explanation of quality assessments.
A geometric alignment for human temporal bone CT images via lateral semicircular canals segmentation

Due to the different posture of the subject and settings of CT scanners, the CT images of the human temporal bone should be geometrically aligned with multiplanar reconstruction to ensure the symmetry of the bilateral anatomical structure. Manual alignment is a time-consuming task for radiologists and an important preprocessing step for further computer-aided CT analysis. We propose a fully automatic alignment algorithm for temporal bone CT images via lateral semicircular canals (LSCs) segmentation.The LSCs are segmented with our proposed multifeature fusion network as anchors at first. Then, we define a standard 3D coordinate system and propose an alignment procedure.The experimental results show that our LSC segmentation network achieved a higher segmentation accuracy. The acceptable rate is achieved 85% over 910 raw temporal bone CT sequences. The alignment speed is reduced from 10 min by manual to 60s.Aiming at the problem of bilateral asymmetry in the raw temporal bone CT images, we propose an automatic geometric alignment method. Our proposed method can help to perform alignment of temporal bone CT images efficiently.
Graph Neural Network based Recommender System

With the rapid rise of online content, Recommender Systems are becoming increasingly important in reducing information overload. Because of the important technology worth of recommendation system, new studies on the subject have been published on a regular basis. The major difficulty in recommender systems is to create suitable user/item models using user activities as well as other data. Graph neural network techniques have lately become widely employed in recommender systems because most of the data is fundamentally graph architecture, and Graph Neural Network has advantage in graph supervised learning. This Paper aims to provide recent work done in the Graph Neural Network based recommender system. The paper also discusses the open issues and challenges pertaining to Graph Neural Network.
E-commerce Merchant Fraud Detection using Machine Learning Approach

At present, e-commerce has become a global phenomenon. With the great achievement of ecommerce, many are cruel Promotional services are also increasing: with the aim of growing sales, spiteful marketers try to improve their target spectators by improving the outcomes of an illegal search using false travel, shopping, etc. In this report, we read about the problem of deception in major commerce platforms. First, we want to list the merchant fraud, the names of those who have previously committed fraud in the business will be marked on the list. And will train machines using machine learning approach. So that, if a merchant id is given in the system, it can detect whether the id is fraud or not. Our lesson here paper is predictable to hut light on the defense in contradiction of e-commerce fraud of active commerce platforms. In this research report, we proposed a machine learning model to analyze and identify merchant fraud. As a machine learning model, we choose the Random forests, decision tree and logistic regression algorithm for our model.
Comparative Study of TRANS - GAN Architecture for Bio-Medical Image Semantic Segmentation

In the area of biomedical image processing, medical image segmentation plays a crucial role. Today due to the deep sculptures of deep neural networks and innovative by-passes like the Transformers this field has rejuvenated. This paper analyzes various algorithmic advancements regarding Transformers and Generative Adversarial Networks (GAN) which have paved the anatomy of image segmentation. The GAN network is not only able to regenerate feature spaces but also can transpose the inputs from the transformer. These algorithms are not only at par with the bifurcation of networks like U-net and ResNet but showcase a strong and more intuitive idea of human neural intelligence. Finally, all methods are compared based on accuracy, dice score, architectural evaluation, etc. The ability of these networks are deduced to regenerate and recreate the feature spaces for the given input image and thus can segment high features for prediction.
Progressive feature generation with mask awareness for image inpainting

Most existing image inpainting methods have achieved remarkable progress in repairing small image defects. However, when the holes are large, their filling contents suffer from structural distortion and center blur due to the weak correlation between known and unknown pixels. In this paper, we propose a Progressive Feature Generation (PFG) network which is mask awareable during the process of filling irregular holes. Specifically, in order to strengthen the constraint for the hole center, we propose dynamic partial convolution which can adaptively adjust the inpainting proportion according to mask ratio in each recurrence. To synthesize high-quality features in the feature generation phase, two parallel encoders are designed which could effectively improve the capability of model to restore large holes. We argue that during feature merging, the signals generated earlier at the same location are more credible. To this end, we develop the sub-regional weighted merging(SWM) method for PFG-Net to accurately merge the feature maps generated during the whole reconstruction process. Experiments on CelebA, Places2, and Paris StreetView datasets show that our model has excellent performance especially for large holes.
Automatically repairing tensor shape faults in deep learning programs

Software developers frequently invoke deep learning (DL) APIs to incorporate artificial intelligence solutions into software systems. However, misuses of these APIs can cause various DL faults, such as tensor shape faults. Tensor shape faults occur when restriction conditions of operations are not met; they are prevalent in practice, leading to many system crashes. Meanwhile, researchers and engineers still face a strong challenge in detecting tensor shape faults — static techniques incur heavy overheads in defining detection rules, and the only dynamic technique requires human engineers to rewrite APIs for tracking shape changes. This paper introduces a novel technique that leverages machine learning to detect tensor shape faults, and as well uses patterns to repair faults detected. We first construct SFData, a set of 146 buggy programs with crashing tensor shape faults (i.e., those causing programs to crash). We also conduct an empirical study on crashing tensor shape faults, categorizing them into four types and revealing twelve repair patterns. Then we propose Tensfa2, an automated approach to detecting and repairing crashing tensor shape faults. Tensfa2 employs a machine learning method to learn from crash messages and decision trees to detect tensor shape faults. Next, Tensfa2 tracks shape properties by a customized Python debugger, analyzes their data dependences, and uses the twelve patterns to generate patches. Tensfa2 is an extended version of Tensfa—our previous approach presented at ISSRE’21. Its performance is enhanced by two techniques: a search-based method for repairing shape value faults, and a bundle of three ranking strategies for prioritizing the repair patterns. Tensfa2 is evaluated on SFData and IslamData (another dataset of tensor shape faults). The results show the effectiveness of Tensfa2. In particular, Tensfa2 achieves an F1-score of 96.88% in detecting the faults and repairs 82 out of 146 buggy programs in SFData. We believe that repair patches generated by our approach will help engineers fix their deep learning programs much more efficiently, saving their time and efforts.
A Case Study on an Online Korean Discussion Class with Foreign Undergraduate Students

The aim of this study is to suggest a flexible and differentiated way of managing online Korean discussion classes. For this study, an actual online Korean discussion class was selected. The class consisted of four phases: preparation, check, discussion, and reflection. Each of the phases was gone through online, either live or recorded, based on their nature. In preparation phase, video lectures were provided to have students ready for the discussion topic and familiar with related expressions. In check phase, knowledge and expressions relevant to the topic were checked as a quiz on LMS. In discussion phase, the students discussed the topic in a Zoom meeting room. The discussion was subdivided into groundwork, rehearsal, discussion, and presentation. After every three or four students had a discussion in a small Zoom meeting room, the moderator in each group gave a presentation about their discussion for five minutes. In reflection phase, the students by themselves wrote a reflection journal on LMS about their discussion.&lt;br/&gt; The implication of this case study is that online Korean discussion can enhance learning about the topic. With the small-grouping function in Zoom, the students were able to participate in the discussion actively and the teacher observed their discussions in detail. Also, due to the characteristics of online setting, the participants had less pressure on instant speech. On top of that, various functions built in LMS helped the teacher manage the class more dynamically than the offline one.
Where did my Lines go? Visualizing Missing Data in Parallel Coordinates

Abstract We evaluate visualization concepts to represent missing values in parallel coordinates. We focus on the trade‐off between the ability to perceive missing values and the concept's impact on common tasks. For this purpose, we identified three missing value representation concepts: removing line segments where values are missing, adding a separate, horizontal axis onto which missing values are projected, and using imputed values as a replacement for missing values. For the missing values axis and imputed values concepts, we additionally add downplay and highlight variations. We performed a crowd‐sourced, quantitative user study with 732 participants comparing the concepts and their variations using five real‐world datasets. Based on our findings, we provide suggestions regarding which visual encoding to employ depending on the task at focus.
Missing Data Filling Algorithm for Big Data-Based Map-Reduce Technology

In big data, the large number of missing values has a serious problem to compute the correct decision. This problem seriously affects the quality of information query, distorts data mining and analysis, and misleads the decisions. Therefore, in order to solve the missing values in the real database, we have pre populated the missing data, and filled in the classification attributes based on the probabilistic reasoning. The reasoning process is completed in Bayesian network to realize the parallelization of big data processing. The proposed algorithm has been presented in the Map-Reduce framework. The experimental results show that the Bayesian network construction method and probabilistic inference are effective for the classification data processing, and the parallelism of algorithm in Hadoop.
Towards Real-Time and Energy Efficient Siamese Tracking – A Hardware-Software Approach

Siamese trackers have been among the state-of-the-art solutions in each Visual Object Tracking (VOT) challenge over the past few years. However, with great accuracy comes great computational complexity: to achieve real-time processing, these trackers have to be massively parallelised and are usually run on high-end GPUs. Easy to implement, this approach is energy consuming, and thus cannot be used in many low-power applications. To overcome this, one can use energy-efficient embedded devices, such as heterogeneous platforms joining the ARM processor system with programmable logic (FPGA). In this work, we propose a hardware-software implementation of the well-known fully connected Siamese tracker (SiamFC). We have developed a quantised Siamese network for the FINN accelerator, using algorithm-accelerator co-design, and performed design space exploration to achieve the best efficiency-to-energy ratio (determined by FPS and used resources). For our network, running in the programmable logic part of the Zynq UltraScale+ MPSoC ZCU104, we achieved the processing of almost 50 frames-per-second with tracker accuracy on par with its floating point counterpart, as well as the original SiamFC network. The complete tracking system, implemented in ARM with the network accelerated on FPGA, achieves up to 17 fps. These results bring us towards bridging the gap between the highly accurate but energy-demanding algorithms and energy-efficient solutions ready to be used in low-power, edge systems.
On Ground Convergence and Completeness of Conditional Equational Program Hierarchies

Both complete definition of functions by equations and determinism (i.e., evaluation to a unique result), are fundamental correctness properties of equational programs. But for expressive functional languages supporting conditional equations, types and subtypes and rewriting modulo axioms, proof methods for verifying such properties under general conditions are currently quite limited. This work proposes a hierarchical proof methodology where both properties are simultaneously verified in a hierarchical manner under termination assumptions.
Camg: Context-Aware Moment Graph Network for Multimodal Temporal Activity Localization Via Language

Temporal Activity Localization via Language (TALL) is a challenging task for language based video understanding, especially when a video contains multiple moments of interest and the language query has words describing complex context dependencies between the moments. Latest studies have proposed various ways to exploit the temporal context of adjacent moments, but two apparent limitations remained. First, only limited context information was encoded based on RNNs or 2-D convolutions, which highly depended on the pre-sorting of proposals and lacked flexibility. Second, semantically correlated content in different moments was ignored, i.e., semantic context. To address these limitations, we propose a novel GCN-based framework, i.e., Context-Aware Moment Graph (CAMG) network, to jointly model the temporal context and semantic context. GCNs enable the CAMG to capture long-range dependencies with high flexibility. Also, we design a multi-step fusion scheme to aggregate object, motion and textual