Reviewerly/exHarmony huggingface dataset:
gVMP: A multi-objective joint VM and vGPU placement heuristic for API remoting-based GPU virtualization and disaggregation in cloud data centers

The diverse needs of customers drive cloud providers to incorporate more GPU-enabled services. It is known that users barely utilize GPUs. Hence, GPU virtualization techniques are employed to enable GPU sharing and increase resource utilization. Among GPU virtualization techniques, API remoting leverages disaggregation for GPU provisioning, allowing GPUs to be accessed remotely by non-resident VMs. It enables a VM to access any available GPU in the data center which, in turn, provides more flexibility to reach a better placement. It also enables users to choose VM and vGPU instances separately based on computational needs as GPU-enabled VM instances with fixed configurations may not proportionally utilize allocated resources. However, an inefficient VM or vGPU placement may result in poor performance and increase in energy consumption and rejection ratio. There is also a network communication overhead for remotely allocated vGPUs. The main challenge is how to place VMs and vGPUs such that rejection ratio is decreased and the negative performance impacts caused by GPU sharing and remote access are kept at a minimum. In this work, we formally define the joint problem of VM and vGPU placement based on API remoting as a multi-objective ILP model and introduce gVMP as a heuristic to solve it. The proposed method is compared against state-of-the-art heuristics and commercial solutions that are based on API remoting and GRID technologies for the placement of GPU-enabled VMs. We study the effectiveness of the proposed gVMP algorithm for low and high arrival rates in slow and fast networks. Our results show that under different scenarios and in comparison to state-of-the-art policies, our heuristic improves average request duration, efficiency, and SLA by up to 40%, 25% and 29%, respectively.
Energy storage capacity competition-based demand response method in blockchain ancillary service market

With the development of new energy, the ancillary service demand of power grid is increasing. The published literature focuses on building a blockchain ancillary service market to ensure the safety and efficiency of ancillary service transactions. However, due to the high threshold and poor scalability, the existing blockchain market is hard to meet the demand of the response rate of ancillary services. Therefore, this paper proposes an energy storage capacity competition-based demand response method in blockchain ancillary service market. Firstly, a new market structure is proposed, which uses the Proof of Capacity (POC) as the consensus mechanism. The structure uses POC to realize the competition among users’ energy storage capacity. Secondly, the demand response process of ancillary service market is designed. During the transaction processes, the probability of users participating in the transaction is changed according to the users’ storage capacity. Dealing with different demands, the market chooses the user with the largest capacity or the combination of users to respond. The simulation shows that, compared with the traditional ancillary service market, the capacity competition-based market demand response rate is increased by 20%, the time to fulling all users’ capacity in the market is reduced by 2 h, and the call rate of user’s capacity is increased by 10%.
Software Compliance Requirements, Factors, and Policies: A Systematic Literature Review

Recent statistics reveal that 56% of software attacks are caused by insider negligence and 26% are caused by malicious insiders. They also show that 67% of organizations experience at least 21 incidents per year. Most of these incidents require significant time and effort to contain them. In this regard, ensuring compliance with corporate policies, regulations, and industry best practices is paramount. This study investigates software compliance requirements, factors, and policies together with the challenges they address. By taking a wider perspective, this study aims at bringing an understanding of existing research foci, evolving issues, and research directions. The study uses a systematic literature review and keyword analysis, to identify relevant studies that address the derived research questions. Considering scholarly articles published in the last decade, 4,772 results were retrieved and checked through an initial screening. A thorough screening is then conducted to further reduce the results to 77 primary articles. The requirement on security of end users is gaining more attention. There is an emphasis on the gap between domain and compliance experts on the one side and software engineers on the other side. The review also identified 55 factors (and their underlying theories) that impact behavioral compliance with a majority of them focusing on individuals. Our results also list nineteen policies and compliance challenges they address. No distinction is found between open-source and proprietary software among the reviewed studies. The most mentioned policies are security education, training, and awareness (SETA), compliance automation, and organizational climate. The evolving topics in the field are: theory of workarounds, compliance and privacy by design, policy as code, security stress, and home-office users. The review provides 9 recommendations, comprising practical implications for decision makers, theoretical implications for future research, and potential enhancement of the underlying theories.
SRRS: A blockchain fast propagation protocol based on non-Markovian process

The blockchain technology that underlies many cryptocurrencies, such as Bitcoin and Ethereum, is demanded to raise for more efficient transactions. Thus, in the last decade, many solutions have been proposed to improve the propagation of blocks. Motivated by SRRS (social reinforcement rumor spreading), we propose a blockchain fast propagation protocol named SRRS protocol based on a non-Markovian process, where the verification and transmission of a block depend on its propagation history. The simulated experimental results show that our protocol can significantly accelerate the propagation of blocks. Compared with existing major solutions, our protocol does not change the main procedure of Bitcoin and the block data structure, which is a huge advantage for practical applications. Moreover, employing the theories of complex networks and percolation, we develop a method of theoretical analysis, which can find the optimal setting of our protocol.
Gated information bottleneck for generalization in sequential environments

Deep neural networks suffer from poor generalization to unseen environments when the underlying data distribution is different from that in the training set. By learning minimum sufficient representations from training data, the information bottleneck (IB) approach has demonstrated its effectiveness to improve generalization in different AI applications. In this work, we propose a new neural network-based IB approach, termed gated information bottleneck (GIB), that dynamically drops spurious correlations and progressively selects the most task-relevant features across different environments by a trainable soft mask (on raw features). Using the recently proposed matrix-based Rényi’s $$\alpha $$ -order mutual information estimator, GIB enjoys a simple and tractable objective, without any variational approximation or distributional assumption. We empirically demonstrate the superiority of GIB over other popular neural network-based IB approaches in adversarial robustness and out-of-distribution detection. Meanwhile, we also establish the connection between IB theory and invariant causal representation learning and observed that GIB demonstrates appealing performance when different environments arrive sequentially, a more practical scenario where invariant risk minimization fails.
Intelligent Chatbot for Student Monitoring and Mentoring

Education is a sector where innovation moves in slowly. The traditional method of teaching, which is summarized by a teacher and students in a classroom, has not changed in over a century. Even with all the recent innovation in AI and machine learning, little effort has been spared in thinking about applying it in the education sector (Vazhayil et al. in 2019 IEEE Tenth International Conference on Technology for Education (T4E), pp. 71–77, 2019 [1]). Our project involves the use of a hybrid chatbot which can take up some of the responsibilities of a teacher. As the first product of its type, the chatbot will be mainly used to act as the first point of contact for students. It will also take care of handling and correcting the assignments given to the students. As a result of this, more time can be allocated to future lectures. In other words, the amount of time spent on assignment evaluation could be spent better on continuing the lectures and educating the students. This would definitely yield a better and more stable workflow for the student as well as the educator (Banerji and Prasad in PURUSHARTHA-J Manag Ethics Spirituality 5:98–109, 2012 [2]).
A Comparison of Transaction Fees for Various Data Types and Data Sizes of Blockchain Smart Contracts on a Selection of Blockchain Platforms

Blockchain smart contracts promote collaboration and heighten the proliferation of automated production processes in the modern manufacturing supply chain. This is mostly done to enhance traceability of products whilst maintaining data security, integrity, and provenance. Blockchain technology with its obvious advantages is perceived as a possible solution to ensuring data provenance in the modern manufacturing supply chain. Data provenance pertaining to the supply chain process flow from obtaining raw materials, manufacturing, and shipping of the order is incorporated into the blockchain smart contracts. However, there are contributing factors for the selection of a blockchain platforms for incorporating data provenance into smart contracts. These factors include maturity, centralised or decentralised operation, throughput, and transaction fees to name a few. This paper is written based on research done in comparing transaction fees for various data types and data sizes of blockchain smart contracts on a selection of blockchain platforms. With respect to storing data type string of data size 256 bits into Ethereum, blockchain platforms transaction fees are high. With respect to storing data type string of data size 768 bits into Ethereum 2.0, Tron and Polygon blockchain platforms transaction fees are low. With respect to storing data type string of data size 2304 bits Ethereum 2.0, Tron and Polygon blockchain platforms transaction fees are low.
Sentiment Analysis of Malayalam Tweets—A Comparative Study of AutoML and Conventional ML Approaches

AutoML or Automatic Machine Learning selects the suitable model and optimizes the value of hyperparameters without human intervention. It reduces the skill of data scientists in building both Machine Learning (ML) and Deep Learning (DL) models. In this paper, sentiment analysis of Malayalam tweets has been done using AutoML and conventional ML approaches. Auto-sklearn and Fast and Lightweight AutoML (FLAML) have been used for creating the AutoML models. The AutoKeras package has been used for building the automatic deep learning models. The ML algorithms like Random Forest (RF), Decision Tree (DT), Logistic Regression, XGBoost and LGBM Classifiers are created using sklearn packages and fine-tunings are done manually. Two different datasets have been categorized as positive, negative, or neutral using both AutoML and conventional human performance approaches. The analysis shows that the conventional approaches performed well in Dataset I and AutoML shows the highest accuracy for Dataset II.
Wiki Loves Monuments: Crowdsourcing the Collective Image of the Worldwide Built Heritage

The wide adoption of digital technologies in the cultural heritage sector has promoted the emergence of new, distributed ways of working, communicating, and investigating cultural products and services. In particular, collaborative online platforms and crowdsourcing mechanisms have been widely adopted in the effort to solicit input from the community and promote engagement. In this work, we provide an extensive analysis of the Wiki Loves Monuments initiative, an annual, international photography contest in which volunteers are invited to take pictures of the built cultural heritage and upload them to Wikimedia Commons. We explore the geographical, temporal, and topical dimensions across the 2010–2021 editions. We first adopt a set of CNN-based artificial systems that allow the learning of deep scene features for various scene recognition tasks, exploring cross-country (dis)similarities. To overcome the rigidity of the framework based on scene descriptors, we train a deep convolutional neural network model to label a photo with its country of origin. The resulting model captures the best representation of a heritage site uploaded in a country, and it allows the domain experts to explore the complexity of cross-national architectural styles. Finally, as a validation step, we explore the link between architectural heritage and intangible cultural values, operationalized using the framework developed within the World Value Survey research program. We observe that cross-country cultural similarities match to a fair extent the interrelations emerging in the architectural domain. We think this study contributes to highlighting the richness and the potential of the Wikimedia data and tools ecosystem to act as a scientific object for art historians, iconologists, and archaeologists.
Investigation of Optimum Reserve Capacity Requirement in Ancillary Services with Extreme Learning Machine

In advanced power systems, reserves are used to balance load mismatches in the face of unexpected events and to meet unforeseen contingencies. This reserve used in the units is defined as the spinning reserve (SR), which is realized with the loading and unloading instructions within the scope of the Ancillary Services (AS). As the contribution of solar and wind turbines increases in installed power, it requires larger amounts of SR and creates a significant increase in production costs. This article uses the Extreme Learning Machine (ELM) method to estimate SR capacity in the day-ahead and intraday market. In this study, experiments were conducted to determine the effectiveness of ELM-SR in estimating the SR parameter, by randomly dividing the data set into 60–40% training-test sets and using 1–100 neurons in the latent layer. The results obtained with the ELM were also compared with the results observed with the ANN, LR, and SVM methods, which determine the optimum reserve amount of the method used. Thus, even in the face of increased production in renewable energy in the power system with a properly planned reserve with ELM, a significant economic gain is achieved with the allocation of the optimum reserve.
Utilizing student-led contact tracing initiative to alleviate COVID-19 disease burden in central Pennsylvania

Contact tracing elicits probable contacts from COVID-19 cases. Our student-led contact tracing initiative promoted isolation of both confirmed and probable cases and quarantine of contacts to reduce disease in Central Pennsylvania.Close contacts of COVID-19 cases were contacted by tracers, advised to quarantine, and monitored for 14 days for symptoms. Symptomatic contacts were classified as probable cases and advised to isolate. Data was collected from March 24, 2020 to May 26, 2020. Poisson regression and linear regression were utilized to examine the relationships between case and number of contacts and proportion of symptomatic contacts.Study sample comprised of 346 confirmed and 157 probable cases. Our results indicate a significant difference in percent of household contacts who became symptomatic between confirmed and probable cases (22% vs. 3%; adjusted P<.01). Similarly, probable cases had significantly fewer non-household contacts compared to confirmed cases (0.87 vs. 0.55; adjusted P<.01).Timely notification of exposure to a COVID-19 positive individual by student contact tracers allowed for probable cases to quarantine early in the disease process. Our data suggests that early quarantine and/or isolation may have directly contributed to probable cases having fewer non-household contacts and a smaller proportion of symptomatic household-contacts compared to confirmed cases.
Determination of the cloud coverage using ground-based camera images in the visible and infrared spectral range

In this study, ground based image sequences of the sky will be evaluated to analyse the cloud coverage. These images are taken in the visual and infrared spectrum. The main ambition is to determine the cloud coverage without the knowledge of additional measurements (like temperature or precipitable water vapor). The determination of the cloud coverage is deduced from camera images only. In the visual spectrum, methods from literature are extended according to this application. For example, the ratio of the color channels red and blue is formed. In the infrared spectral range a method is developed that can distinguish the cloud-covered from the cloudless image areas by using the maximum and minimum occurring values in the image. The grey values are parameterised using statistical boundary values in such a way that a temperature relationship is unambiguously possible and consequently a statement about the degree of coverage can be made by the algorithm. The determination of the cloud coverage reaches a higher accuracy and reliability in the infrared spectral range.
Infrared saliency enhancement techniques for extended naval target detection in open sea scenario

In the maritime environment, Situational Awareness (SA) is a crucial task for many applications, including the defense of the naval tactical space. In this context, Electro-Optical (EO) sensors and, particularly InfraRed (IR) sensors, contribute to building the Local Area Picture (LAP). The purpose of this study is to face the challenging task of highlighting extended targets with respect to the open sea background without any prior knowledge about the size and position within the images. In this work, only single-frame object detection algorithms have been considered. As this task has been extensively explored in the three-channel color image domain, we adapted some color native state-of-the-art strategies on the IR monochromatic dimension. The algorithms have been tested on a dataset collected through a cooled Medium Wavelength (MW) sensor and an uncooled Long Wavelength (LW) sensor. The ground truth (GT) has been built through direct observation. Each technique has been then evaluated on the two sub-bands images according to broadly used performance indices.
Investigating sanitized and controlled image dataset to train deep convolutional neural networks for remote object detection on the field

Performing specific object detection and recognition at the imaging sensor level, raises many technical and scientific challenges. Today state-of-the-art detection performances are obtained with Deep Convolutional Neural Network (CNN) models. However reaching the expected CNN behavior in terms of sensitivity and specificity require to master the training dataset. We explore in this paper, a new way of acquiring images of military vehicles in sanitized and controlled conditions of the laboratory in order to train a CNN to recognize the same visual signature with real vehicles in realistic outdoor situations. By combining sanitized images, counter-examples and different data augmentation techniques, our investigations aim at reducing the needs of complex outdoor image acquisition. First results demonstrate the feasibility to detect and classify, in real situations, military vehicles by exploiting only signatures from miniature models.
Exploration of Semantic Label Decomposition and Dataset Size in Semantic Indoor Scenes Synthesis via Optimized Residual Generative Adversarial Networks

In this paper, we revisit the paired image-to-image translation using the conditional generative adversarial network, the so-called "Pix2Pix", and propose efficient optimization techniques for the architecture and the training method to maximize the architecture's performance to boost the realism of the generated images. We propose a generative adversarial network-based technique to create new artificial indoor scenes using a user-defined semantic segmentation map as an input to define the location, shape, and category of each object in the scene, exactly similar to Pix2Pix. We train different residual connections-based architectures of the generator and discriminator on the NYU depth-v2 dataset and a selected indoor subset from the ADE20K dataset, showing that the proposed models have fewer parameters, less computational complexity, and can generate better quality images than the state of the art methods following the same technique to generate realistic indoor images. We also prove that using extra specific labels and more training samples increases the quality of the generated images; however, the proposed residual connections-based models can learn better from small datasets (i.e., NYU depth-v2) and can improve the realism of the generated images in training on bigger datasets (i.e., ADE20K indoor subset) in comparison to Pix2Pix. The proposed method achieves an LPIPS value of 0.505 and an FID value of 81.067, generating better quality images than that produced by Pix2Pix and other recent paired Image-to-image translation methods and outperforming them in terms of LPIPS and FID.
Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis

Smart factories are complex; with the increased complexity of employed cyber-physical systems, the complexity evolves further. Cyber-physical systems produce high amounts of data that are hard to capture and challenging to analyze. Real-time recording of all data is not possible due to limited network capabilities. Limited network capabilities are the reason for a chain of faults introduced via active surveillance during fault diagnosis. These introduced faults may slow down production or lead to an outage of the production line. Here, we present a novel approach to automatically select production-relevant shop floor parameters to decrease the number of surveyed variables and, at the same time, maintain quality in fault diagnosis without overloading the network. We were able to achieve higher throughput, mitigate communication losses and prevent the disruption of factory instructions. Our approach uses an autoencoder ensemble via minority voting to differentiate between normal-always on-variables and production variables that may yield a higher entropy. Our approach has been tested in a production-equal smart factory and was cross-validated by a domain expert.