Reviewerly/exHarmony huggingface dataset:
A Multi-Agent System for Industrial Simulators Design

Industrial systems are of great importance in our daily life. They are, generally characterized by a huge number of interacting elements. Some of them are critical and cannot be stopped of functioning otherwise they will cause very significant financial damage to the plant. To observe the process of functioning of such systems is based on their modeling. This task is generally based on a fine division of sub-assemblies of components of less complexity. Each component can be modeled individually and then simulated. In this article, we present a top-down modeling method using a multi-agent systems approach. So, the overall system is considered as a set of interacting agents. The behavior of the global system emerges from the behavioral of its parts. This approach was applied to the liquefied natural gas treatment process (decarbonation) at a gas plant in Algeria. The results of the simulation can be used to train new recruits in this factory.
Enhancement in Security for Intercloud Scenario with the Help of Role-Based Access Control Model

Cloud according to the latest buzz is the third wave of digitization. Cloud brought all things back at the center. Cloud redefines the way of computing—“where you computing is not important than how you compute”. Sharing of public resources like platform, software and infrastructure easily depending upon requirements. The connectivity of cloud eroded the division of resource sharing. The ability to access data from the cloud—anywhere, anytime—increases the security risk. Security is an essential ingredient to reduce security risk. Trust on technology is good, but as data owner is not with data all the time, so control is better. Cryptographic scheme, identity-based proxy re-encryption, is used to transform data before transferring into public environment. Data owner is not with data, so rule-based data-centric solution is proposed to keep data secure. For preventing unauthorized access of data, role-based authorization access control is developed. The solution is developed using OpenStack.
Optimizing Intelligent Edge-clouds with Partitioning, Compression and Speculative Inference

Internet of Battlefield Things (IoBTs) are well positioned to take advantage of recent technology trends that have led to the development of low-power neural accelerators and low-cost high-performance sensors. However, a key challenge that needs to be dealt with is that despite all the advancements, edge devices remain resource-constrained, thus prohibiting complex deep neural networks from deploying and deriving actionable insights from various sensors. Furthermore, deploying sophisticated sensors in a distributed manner to improve decision-making also poses an extra challenge of coordinating and exchanging data between the nodes and server. We propose an architecture that abstracts away these thorny deployment considerations from an end-user (such as a commander or warfighter). Our architecture can automatically compile and deploy the inference model into a set of distributed nodes and server while taking into consideration of the resource availability, variation, and uncertainties.
Combining Document Embedding Techniques for Clustering and Analysis of Extractive Summaries

In today’s fast-paced world, where we are flooded with massive amounts of unstructured and written data every second, a streamlined approach to make these documents readable is required. We need to cluster these documents in such a way that documents with comparable contexts and categories are automatically grouped. Therefore, it is of interest to understand and study how various algorithms in existence merge together to provide us with a suitable approach to group subjects of similar nature. This work aims at stacking different algorithms to propose an alignment to find out which stack works best for the given use case. Here documents of different categories have been utilized, which undergoes different embedding techniques along with dimensionality reduction, which are later clustered using k-means clustering, to compare which stack works best. The dataset contains news article from five different categories namely entertainment, sports, business, politics, and technology. We can observe how well the different embedding algorithms preserve semanticity and context of the given document. The performance of different stacks in each category of the documents has been analyzed using clustering and the best results have been observed from TF-IDF and word2vec embeddings.
Divide and Conquer: Targeted Adversary Detection using Proximity and Dependency

Deep neural networks have demonstrated impressive performance on image classification tasks, but research shows that they are susceptible to adversarial attacks. In this paper, we propose an adversary detection method, TAPD, which divides and conquers adversary detection through utilizing dependency-based and proximity-based anomaly detection techniques. We innovatively use logits to categorize adversarial examples so that the knowledge of adversary generation is captured in the generated categories. Based on the characteristics of categories, two kNN distance-based detectors are proposed, and one existing dependency-based detector is selected to effectively detect adversarial examples in their corresponding category. Extensive experiments have been conducted, and the results have shown that TAPD outperforms the state-of-the-art adversary detection methods.
A new model for trustworthy web service <scp>QoS</scp> prediction

Abstract The number of web services available on the internet has exploded, and as a result, the number of services with the same functionality has exploded as well. Therefore, selecting the best web service from functionally similar services is a critical task in the web service domain. The Quality of Service (QoS) is one of the most common criteria used to select the best web service. Collaborative filtering (CF) has been utilized in several studies to predict the values of QoS attributes of web services for each user in a personalized way. The QoS histories of other users are employed in these methods to predict the QoS values of the active user. Although these methods function well and produce acceptable prediction results, the accuracy of their predictions can be harmed by incorrect data provided by untrustworthy users. In this study, we propose a new model that reduces the impact of unreliable user data, resulting in a trustworthy prediction. This model can be applied to any existing prediction method. In experiments, the proposed model was applied to seven known prediction methods. The results indicate that this model is able to eliminate the impact of unreliable users.
An Online Solution for Secured Deep Learning Models Based on Crowd Sourced SGX

Data security has become the focus of public concern in widely used Deep Learning (DL) applications. Existing attacks can accurately recover any input entered the models. Therefore, it is of the same importance to protect DL models as well as data. Although service providers may offer Trusted Execution Environment (TEE) such as Trusted Software Guard eXtensions (SGX) for model security. The additional delay introduced by security computation cannot be neglected even compared with the delay introduced by DL inferences. In this paper, we propose an online SGX-based system to protect the DL inference process using crowd sourced SGXs. To motivate devices to contribute their SGXs, we apply an online auction mechanism. We decompose the long-term problem into multi-rounds and solve the decomposed problem in an online manner. The evaluation results show that the proposed algorithm of the online system outperforms the baseline algorithms by 160% in terms of social cost.
Analysis on Optimization of Financial Sharing Center Based on “Cloud + End” Mode

For large enterprises, Financial Sharing Center can be widely used in the field of accounting, which can improve the efficiency and accuracy of financial management. However, there are some shortcomings in the current application of Financial Sharing Center. Based on the understanding and analysis of the current status of the Financial Sharing Center, this paper puts forward the optimal measures of the Financial Sharing Center under the “Cloud + End” mode, so that the Financial Sharing Center can promote the development of enterprises better and improve the efficiency and effect of financial management.
An encryption - authentication algorithms developed from the elgamal cryptosystem

The paper proposes encryption - authentication algorithms developed from the Elgamal cryptosystem. There are algorithms included: system parameters, keys, encryption, and authenticated decryption. New proposed algorithms ensure a level of security against attacks: revealing secret keys - compared with RSA, GOST; security - compare with ElGamal; anti-forgery. Simultaneously verify the origin of e-doc and ensure the sender's authentication.
CVTEE: A Compatible Verified TEE Architecture With Enhanced Security

Sensitive resources in Trusted Execution Environment (TEE) have suffered serious security threats in recent years. Previous protection approaches either lack a strong assurance of TEE security properties or are limited to a single platform. We propose a compatible verified TEE architecture, called <monospace>CVTEE</monospace> , which delegates a security monitor to manage TEE resources securely. This architecture has two key advantages: i) its functional correctness and security are guaranteed by a machine-checkable proof of security objectives of Trusted Application (TA) isolation, runtime confidentiality, and runtime integrity, and ii) it is applicable to different TEE platforms and implementation-independent due to its high level of abstraction and non-determinism of data types. Note that access control policy and information flow control policy are the core for security management of resources. After formally specifying the security attributes of TEE resources, we develop these policies based on Common Criteria (CC) in the security monitor and provide atomic interfaces. <monospace>CVTEE</monospace> is formally verified with 386 lemmas/theorems and <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 10,000 LOC of Isabelle/HOL. In addition, we implement a proof of concept for the access control module of Teaclave, and prove that the constructed access control model meets the security requirements through 5 theorems.
A Modern Paradigm for Cloud Computing Adoption That Brings into Account the Deployment Organisation’s Main Concerns

Over the past few decades, organizations have increasingly showcased a tendency to switch to digital solutions to make their operations more effective. Among them, Cloud computing has been considered as one of the best and easiest choices, especially for manufacturing companies with multiple geo-locations. This article has explored all the available variables before moving on to applying the modern IS technology such as cloud computing, using a qualitative model of study. The findings of this study is an outcome of the semi-structured interviews with the officials of an individual organization. Through this study, we have discovered that the key factors playing a major role in cloud services adoption are: provider activities and external computing support, top management support, competitive benefit, financial commitments, usability, capacity, previous expertise, trialability and complexity. The proposed adoption framework classifies the adoption pathway into three sections. Firstly, it outlined the selection of the cloud delivery model. Secondly, the framework addressing the processes in the preparation stage along with its responsible authority. Finally, it categorizes the implementation stage processes and its authority.
P-QALSH: Parallelizing Query Aware Locality-Sensitive Hashing for Big Data

QALSH (Query-Aware Locality Sensitive Hashing) is the representative Locality Sensitive Hashing scheme for answering the c-approximate nearest neighbor (c-ANN) search problems in high dimensional Euclidean space. In this paper, we present the first study on parallizing QALSH (P-QALSH) on a single multi-core computer. We first introduce parallel indexing designs to accelerate index construction. Then, we improve query search of QALSH by parallel range query and parallel collision counting technology. Note that the main challenges during the parallel collision counting process are update conflict problems. Hence, we propose a novel K-Counter Parallel Counting technology to ensure the correctness and efficiency of the parallel collision counting. Additionally, while QALSH may search c-ANNs with lots of search radiuses, resulting in large multi-round query overhead, we introduce a novel Search Radius Estimation Strategy to overcome this drawback. Moreover, we modify the termination condition of QALSH to search more nearby objects. Using four publicly available datasets and synthetic datasets, extensive experiments on a 16-core machine well demonstrate the superiority of P-QALSH on parallel computing. Specifically, compared to QALSH, P-QALSH is 9.6-13.8X faster on index construction, achieves 3.5-7.6X speedup on query search, and shows obvious improvement in query accuracy.
Unsupervised Domain Transfer for Task Automation in Unmanned Underwater Vehicle Intervention Operations

During underwater intervention and monitoring operations, large amounts of imagery and sensory data are produced and stored. Moreover, these data might have the potential to help automatize future operations. In this article, we propose a method for producing segmented data that can be utilized in training of neural networks used for performing desired control tasks. By combining unlabeled images from previous operations with a paired 3-D drawing of the same monitored object, we can train a generative adversarial network to learn the domain adaption between two domains and produce synthetic images with a high resemblance to the original footage. The clear advantage is that the enhanced synthetic images contain the segmented information of the object without the expensive cost of manually segmenting the images. The enhanced segmented data are used to train an object detector that predicts bounding boxes locating the segmented object. This is used in two ways: 1) to analyze the quality of the segmentation and 2) to command a control task for a remotely operated vehicle relative to the object of interest. Controlling the yaw is the main control task to maintain the object centered in the camera frame. Additionally, we explore how overtraining of the domain adaptation network negatively impacts the accuracy of the object detector. This is done by comparing the accuracy of the object detector trained with the datasets produced by different epochs of the domain adaptation network.
The Narratives and Images of Ancient Women in the Middle and High School 〈Korean History〉 Textbooks Based on the 2015 Revised National Curriculum

This article seeks improvement strategies by analyzing the descriptions and images of women in middle and high school ‘Korean History’ textbook of 2015’s Revision Curriculum. As a result of analyzing secondary school textbooks, gender inequality was found to be severe in quantitative terms. In addition, the content is focused on mens’ experiences and viewpoints, and it appears to be in a gender-discriminatory perception.<BR> This phenomenon may be partly because women are marginalized from historical sources, but above all, middle and high school ‘Korean history’ textbooks are centered on narrating the political history of the nation. Moreover, descriptions and images of women were further reduced as the proportion of pre-modern history in high school was reduced according to the serialization plan.<BR> In order to improve the description and image of ancient women in ‘Korean history’ textbooks, it is necessary to break away from the nation-centered political history narrative system. It is also necessary to consciously consider women’s role in history. Finally, it is necessary to publicize the narrative direction and the content of ancient women’s history from the stage of establishing the curriculum policy, then establish a writing plan, so that it can be reflected in the educational process.
Software/Hardware Co-Design Optimization for Sparse Convolutional Neural Networks

Deep convolutional neural network (DNN) has been widely used in image recognition, target detection, and natural language processing. Unfortunately, the previous state-of-the-art convolutional neural network (CNN) increase model accuracy by increasing the number of layers of the network, which leads to the problem of large models and complex calculations. Previous work has demonstrated that network compression can be achieved by weight pruning approaches, and that specialized hardware can be used to speed up reasoning. However, the format of compressed sparse rows (CSR) or compressed sparse columns (CSC) is generally used in model pruning. The approach leads to coding operations before calculation and decoding operations during calculation by processing units on specialized hardware. And the approach degrades the performance of the model.In this paper, we propose a combination of hardware and software to address above bottlenecks. Firstly, we propose a novel software-based structured pruning, called vector-pruning. Secondly, we design a novel dataflow for our pruning method, called Vector-Sparse, our method eliminates complex coding and decoding Then, we design the corresponding hardware architecture for vector-prune. Finally, we evaluate the performance of our pruning approach for AlexNet on Xilinx xcvu19p-fsva3824-2-e. Experiments show that compared with a state-of-the-art convolution network accelerator, we proposed a software/hardware co-design approach that is 1.01x and 1.3x better in performance, effectively improving the inference speed of the model on Field Programmable Gate Array (FPGA).
Empirical Performance Analysis of Speech Based Age Classification

Age forecast calculations are generally being used in voice assistants and other human-PC collaboration devices. The work presented here is on speech based age classification and comparing the performances of various models. An aggregate of 170 features is extracted from each sample and 88 features are chosen for training our models. Prominent features are chosen by utilizing PCA (Principal Component Analysis) along with RFE (Redundant Feature Elimination) techniques. Experimental analysis is done on a database called Common Voice, available in the Speech Database of Mozilla which comprises of around 5,000 speech samples from various age categories. From the forecast results, it was observed that the Multi-Layer Perceptron model is reasonably good for demonstrating this difficult assertion as it can learn non-linear and complex mathematical relations through its different neural layers, executed with appropriate activation, loss functions and optimal drop out values in different layers. An increase of around 10% in the training accuracy and about 5% in the testing scores were accomplished utilizing our MLP (Multilayer Perceptron) architecture when compared with other Regression models, Decision trees, Random forests and Gradient boosting algorithms.
Total cost of ownership of infrastructure as a service for SME

Cloud Computing offers significant cost benefits for SMEs that often do not manage internal IT infrastructure and start-up companies that do not have their own IT infrastructure. This paper presents a total cost of ownership (TCO) approach for cloud computing services with an emphasis on the infrastructure as a service (IaaS) model. Also, the paper presents a methodology for estimating total cost of ownership (TCO) when running computer instances in the IaaS cloud using the GARCH model to predict transaction volatility. The research results show that it is possible to successfully use GARCH models when there is historical data on the number of transactions. In addition, simulation shows that, when there are large oscillations in the number of transactions, the best choice is to reserve instances according to the Partial-Upfront price model. In contrast, if the transaction number is relatively stable, the best choice is the All-Upfront model.
AMDnet: An Academic Misconduct Detection Method for Authors’ Behaviors

In recent years, academic misconduct has been frequently exposed by the media, with serious impacts on the academic community. Current research on academic misconduct focuses mainly on detecting plagiarism in article content through the application of character-based and non-text element detection techniques over the entirety of a manuscript. For the most part, these techniques can only detect cases of textual plagiarism, which means that potential culprits can easily avoid discovery through clever editing and alterations of text content. In this paper, we propose an academic misconduct detection method based on scholars’ submission behaviors. The model can effectively capture the atypical behavioral approach and operation of the author. As such, it is able to detect various types of misconduct, thereby improving the accuracy of detection when combined with a text content analysis. The model learns by forming a dual network group that processes text features and user behavior features to detect potential academic misconduct. First, the effect of scholars’ behavioral features on the model are considered and analyzed. Second, the Synthetic Minority Oversampling Technique (SMOTE) is applied to address the problem of imbalanced samples of positive and negative classes among contributing scholars. Finally, the text features of the papers are combined with the scholars’ behavioral data to improve recognition precision. Experimental results on the imbalanced dataset demonstrate that our model has a highly satisfactory performance in terms of accuracy and recall.
Negatively-Associated Maximal Frequent Geneset Mining on DNA Methylation Profile

Association rule mining has been an important approach for feature and biomarker discovery in various omics data. One main challenge is that it generates a large number of itemsets. The effect of this shortcoming increases substantially in the case of negative association rule mining that is useful for detecting significant relationships between genes (items/features) in the form of either presence or absence in disease characterization. In this article, we propose a new algorithm, NegaMax (negatively-associated maximal frequent itemsets) for negative association itemset mining. Our method follows depth-first search rather than breadth-first search used in the other methods. It identifies a much fewer number of non-redundant itemsets than that by the existing methods. Thus, it saves elapsing time for itemset generation which potentially remove false positive intermediate results. We demonstrated NegaMax in a real-world DNA methylation dataset. The proposed method is highly beneficial from a medical perspective.