Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges and Practices Identification via a Systematic Literature Review in the Adoption of Green Cloud Computing: Client’s Side Approach

Green cloud computing gains popularity over the last decade due to the extensive use of cloud computing. The trend towards cloud computing is spreading at a fast rate. Cloud computing providers extend their data centers at regular intervals. However, cloud providers face severe issues about sustaining the environment because big data centers are responsible for consuming a vast amount of energy and producing a carbon footprint. The solution to address such an issue is green cloud computing. In the literature, a considerable amount of research has been done to determine the challenges and practices in adopting green cloud computing. Still, the field is immature and needs further research to identify the challenges and practices faced and addressed by various stakeholders. This research focuses on identifying the challenges faced by client organizations in adopting the green cloud and the associated practices for addressing the challenges. The challenges and patterns were identified via a systematic literature review from the relevant literature. After thorough conduction of the systematic literature review (SLR), we have identified 9 challenges and 63 practices from 68 papers in the cloud domain. In the future, we plan to develop a comprehensive framework by using some robust soft computing techniques like Fuzzy logic or AHP, etc., to assist the clients in adopting the green cloud environment.
A Deep Domain Adaptative Network for Remaining Useful Life Prediction of Machines Under Different Working Conditions and Fault Modes

As one of the key techniques in prognostic health management (PHM), remaining useful life (RUL) prediction of machine relies on sufficient prior observed degradation data. Most previous RUL prediction methods assume that feature distributions of both datasets from source and target domains are similar. However, the data distributions are different across different domains because of different operating conditions and fault modes, which raises the performance deterioration of these methods in machine RUL prediction. In this article, a novel transfer learning method, deep domain adaptative network (DDAN) is proposed for machine RUL prediction that can handle feature distribution shift across domains under different working conditions and fault modes. First, a novel feature extractor, selective convolutional recurrent neural network (SCRNN), is developed for feature extraction from sensor signals. The selective convolutional recurrent blocks in SCRNN are able to effectively extract temporal and spatial features from vibration signals simultaneously. It incorporates heterogeneous temporal data of multiple sensors in the RUL prediction task. A collaborative domain alignment method is proposed to guide SCRNN to learn domain-invariant features of source and target domains. The DDAN-based RUL prediction method is evaluated on the systematic dataset, i.e., NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPPS), and component dataset, i.e., PHM Challenging 2012 bearing dataset. The testing results illustrate the effectiveness of DDAN in machine RUL prediction under different operation conditions.
Algorithm of Random Search in the Optimal-Terminal Control of a Multi-Mode Moving Object

The article deals with the problem of development and research of a computational method for multi-mode moving objects' control optimization. This class of objects is characterized by an optimal-terminal control, with restrictions on control and phase coordinates under conditions of parametric uncertainty, variations of external environment and the controlled object's parameters. The executive subsystems of the controlled object are usually equipped with non-terminal regulators. Their tasks are formed by a human operator (a pilot, a driver, a locomotive driver, a helmsman) or a top-level system (for example, a system of terminal control, trajectory planning, or a travel time regulating). During operation, there are often complicated driving situations when the operator has to interfere with the control process. In this regard, the development of multi-mode moving objects' optimal-terminal control methods is vital issue, because they allow to improve the control quality and to eliminate the human factor. In particular, this problem is characteristic for the railway electric rolling stock, and that is confirmed by an operation analysis of existing locomotive fleet. One of the promising directions for solving this problem is the development of optimal-terminal control computational methods. The article represents an algorithm based on random search, and proposes a software complex for calculation the program-reference trajectory of a train moving on a given section in accordance with the schedule. At the same time, optimal power consumption for traction is achieved. The method reduces original optimal control task to the one of parametric synthesis, and its further solution based on random search on a set of basic solutions. The proposed method of moving object's control optimization differs from existing ones: it allows to identify a set of local extremums of the objective function, and - as a result - to find the global extremum. Computational experiments confirm high quality of control and good convergence of the algorithm in terms of accuracy. Speaking of limitations, the method requires significant computational resources for its implementation.
Two-Stage Cascaded Decoder for Semantic Segmentation of RGB-D Images

Exploiting RGB and depth information can boost the performance of semantic segmentation. However, owing to the differences between RGB images and the corresponding depth maps, such multimodal information should be effectively used and combined. Most existing methods use the same fusion strategy to explore multilevel complementary information at various levels, likely ignoring different feature contributions at various levels for segmentation. To address this problem, we propose a network using a two-stage cascaded decoder (TCD), embedding a detail polishing module, to effectively integrate high- and low-level features and suppress noise from low-level details. Additionally, we introduce a depth filter and fusion module to extract informative regions from depth cues with the guidance of RGB images. The proposed TCD network achieves comparable performance to state-of-the-art RGB-D semantic segmentation methods on the benchmark NYUDv2 and SUN RGB-D datasets.
Evolutionary Algorithms for Real Time Engineering Problems: A Comprehensive Review

This paper presents a variety of contemporary optimization techniques inspired by the real life in nature. Optimization reveals substantial developments in computing systems as well as has come to be the most encouraging strategy for several design applications. The study is conducted on single-objective, multi-objective, and hybrid optimization strategies. These optimization schemes will be of excellent help to organizations to identify optimum criteria and to improve process as well as product high quality. For selected optimization strategies, the process of formulating the objective function/stiffness function for a minimal issue exists. Over the last few years, the most combinatoric problems of all traditional optimization approaches were solved by using metaheuristic algorithms to have optimal solutions for real-time applications. This paper discussed some of the important and feasible optimization scheme and the related algorithms and approaches.
DIRECT STRUCTURAL DETERMINATION IN ULTRATHIN FERROELECTRIC FILMS BY ANALYSIS OF SYNCHROTORN X-RAY SCATTERING MEASUREMENTS

In order to better understand ferroelectricity in thin films, it is important to explore the atomic-scale structure and the spatial distribution of polarization near the interfaces. We present sub-Angstrom-resolution electron density maps of three ultrathin PbTiO{sub 3} films grown epitaxially on SrTiO{sub 3} (001) substrates. The maps were obtained by analysis of synchrotron x-ray scattering measurements of Bragg rod intensities using the recently developed coherent Bragg rod analysis method. A four- and a nine-unit-cell-thick film were studied at room temperature, and a nine-unit-cell-thick film was studied at 181 deg. C. The results show that at room temperature, the PbTiO{sub 3} films are polar, monodomain, and have their polarization oriented away from the substrate. The four-unit-cell film may be the thinnest monodomain perovskite film found to be in the polar phase. At 181 deg. C, the electron density map of the nine-unit-cell film is consistent with the presence of 180 deg. stripe domains. In the monodomain samples, details of the atomic-scale structure of the PbTiO{sub 3}/SrTiO{sub 3} interface are observed, which may provide evidence for the nature of the positive charge layer required to stabilize polarization in monodomain films.
Synthesis of nanoparticles, nanorods, and mesoporous SnO_2 as anode materials for lithium-ion batteries

The mesoporous and nanorods SnO_2 are synthesized by controlling the state of SnCl_2·2H_2O precursor with SBA-15 as hard template, and the possible formation mechanisms at different assembling modes inside the ordered mesoporous silica templates are proposed. In addition, SnO_2 nanoparticles are synthesized by hydrolysis depositing method. The electrochemical tests of as-prepared samples indicate that the reticular stacking structure of the nanorods would limit the Li^+ ions to intercalate, but the effect of volume expansion in this case upon cycling is insignificant. The mesostructure SnO_2 tends to be stable after partial structural collapse at first few cycles. And the Li^+ ions can readily intercalate and de-intercalate into/from its ordered channels structure, which provides a high capacity and an improved cycle property. Although SnO_2 nanoparticles deliver high capacity at an early stage, the agglomeration may induce the capacity to drop rapidly after a certain number of cycles.
Discovering conﬂicting groups in signed networks Supplementary material

Proof: We have ‖v‖2 = 1 and, without loss of generality, we can assume that the coordinates of v are sorted in non-increasing order. Let T = {ti} i=0 be all possible thresholds for v and T ′ = {ti} n+1 i=0 be all possible thresholds for −v. Recall the definition of θ(·, ·) from Section 3 that θ(a,b) = arccos(〈a,b〉/‖a‖2‖b‖2) ∈ [0, π] for any two nonnegative vectors a,b ∈ R, so sin θ(a,b) is always non-negative. Let u∗ be the minimizer of sin θ(v,u) over all u ∈ Γ(v)∪Γ(−v). For simplicity, we assume u∗ ∈ Γ(v) and 〈v,u∗〉 ≥ 0. This is because if the dot product is negative, we can make it positive by reversing the sign of v. Let k∗ 1 , k ∗ 2 be the two thresholds such that u∗ = σtk∗ 1 ,tk∗ 2 (v). We will show that sin θ(v,u) ≥ sin θ(v,u∗) for any u ∈ {0,−1, q}.
An Unusual Case of Acute Hemorrhagic Necrotizing Encephalomyelitis in a COVID-19 Patient

Acute disseminated encephalomyelitis, also known as ADEM, is a rare autoimmune demyelinating disease of the central nervous system that has been correlated with viral infections and vaccinations and has a range of presentations, where it can present as a mild neurological dysfunction or more severe manifestations ending in chronic neurological sequelae or even death; therefore, it is considered to be a diagnostic challenge. We present a case of ADEM diagnosed in a previously healthy male patient with a recent infection of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). Early diagnosis and management with intravenous immunoglobulins held the key to a good outcome.
INFORMATION EXCHANGE BETWEEN VEHICLES USING ABETTED UNIVERSAL LOCATING STRUCTURE IN MOBILE VEHICULAR NETWORK

: Vehicular Ad Hoc Network (VANET) is a type of Mobile Ad-Hoc Network or MANET which gives correspondence among vehicles and among vehicles and street side base stations. A vehicle in VANET is viewed as a savvy portable hub fit for speaking with its neighbors and different vehicles in the system. VANET is unique in relation to MANET because of high portability of hubs and the huge size of systems. Secure transmission and protection of data are the important constraint for structuring a VANET. Since there are many proposed work for improving protections in VANET, however secure transmission despite everything stays a sensitive research field. The primary destination of our work is to enhance the security concerns in vehicular networks. The fundamental endeavors were centered on the potential applications, potential assaults, secure prerequisites and the writing perusal. The long haul objective of this venture is to concoct an altogether new arrangement that can be executed in structuring a vehicular network.
Analysis of Global Word Representations for Depression Detection

Social media such as Twitter, Facebook, Google plus, Reddit, Tumblr have been a widely used platform for people to communicate, share views and feelings with others freely. The information obtained from this short text messages helps in predicting their emotions, views, sentiment, opinion and it is applied in different fields like marketing, election, product review, sentiment analysis, emotion detection etc. Behavioral analysis from text data is another widely popular field. This paper gives an analysis of global word representations and overview of the work done on depression detection related tasks. Major steps such as pre-processing of data, feature extraction, representation and classification methods are summarized.
Photovoltaic Energy Production Forecasting using LightGBM

 Precise predictions of solar photovoltaic (PV) energy production have an important role in day-ahead planning of power grid and power plant operators since they help to improve stability and power quality of the electricity distributed. In this work, the machine learning (ML) algorithm, called as the LightGBM, is applied to the challenge of forecasting energy yield of PV power plants. We compare the performance of the LightGBM with different ML and empirical models. The advantages of the LightGBM are highlighted and this model is introduced as a new approach to be used in the forecast of PV energy. We trained and tested the models using 2 years of operational data in 30 min re-sampling resolution. The prediction results on the test set showed nRMSE of 1.56 % for the LightGBM model, and processing time of 0.181 s, presenting much better accuracy than empirical models, comparable accuracy than other ML models, and outperforming in terms of processing time being significantly faster than the rest of models studied.
A Generative-Symbolic Model for Logical Reasoning in NLU

Despite the recent success of language models (LMs) in natural language understanding (NLU), there are growing concerns about LMs’ lack of logical reasoning abilities resulting in poor generalization and robustness. Facing these concerns, neuro-symbolic integration may be a solution, which guides neural networks to understand the whole reasoning process by symbolic reasoners. In this paper, we propose a generative-symbolic model to beneﬁt the logical reasoning in natural language understanding. The experiment on the CLUTRR dataset shows that this neural-symbolic model performs better than the corresponding neural model.
Graphene-based electrodes for neural stimulation and signal recording

This work is focused on the integration of graphene-based materials with electrodes for neural stimulation and recording of signals. The need for better materials in the field on neuroscience and the growing interest in graphene inspired this study, in which material science and neurotechnology join to develop innovative types of electrodes. In this thesis the fabrication and characterization of different types of graphene are presented, followed by the results of electrochemical measurements, performed in order to evaluate the properties and performances of graphene-based neuroelectrodes. We demonstrate that different types of graphene electrodes show different electrochemical properties: in particular single layer graphene and few layer graphene coatings on platinum substrate are not suited for this kind of applications, while porous laser induced graphene networks on polymeric substrate have extremely promising performances for electrical stimulation of neural tissue.
A Study Of Breast Cancer Analysis Using K-Nearest Neighbor With Different Distance Measures And Classification Rules Using Machine Learning.

Breast Cancer is one of the life threatening disease among females all over the world. This killer disease however when it can be detected in its early stages can be a life saver for many. Radiologists uses the mammography images to detect the presence and absence of Breast Cancer. The field of Bio-informatics leverages the Machine learning techniques for diagnosis of Breast cancer in particular. This research work experiments with the two most popularly used Supervised Machine Learning Algorithms, K-Nearest Neighbour and Naive Bayes. This work predicts Breast Cancer on the The Breast Cancer Data Set (BCD) taken from the UCI Machine Learning Repository. A comparative analysis between the two approaches are made in terms of its performance metrics using CV techniques. The proposed work has achieved a best accuracy of 97.15% by employing the KNN algorithm and a lowest error rate of 96.19% using NB classifier.
VISIOCITY: A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization

Automatic video summarization has attracted a lot of interest, but is still an un-1 solved problem due to several challenges. The currently available datasets either 2 have very short videos or have a few long videos of only a particular type. We 3 introduce a new benchmarking video dataset called V ISIOCITY (VIdeo SummarIza-4 tiOn based on Continuity, Intent and DiversiTY) which consists of longer videos 5 across six different domains with dense concept annotations capable of supporting
Partially Unsupervised Deep Meta-Reinforcement Learning

Reinforcement Learning (RL) recently has been applied successfully to many domains. One particular example is the continuous control domain, where RL has been shown to solve complex tasks in simulation. However, it turns out that agents trained in simulation can usually not directly be transferred to real systems, and training directly using a real system is considered infeasible due to the large number of samples required. This work focuses on the simulation-to-reality (sim-to-real) transfer technique called domain randomization and shows that it closely relates to Meta-Reinforcement Learning (meta-RL). Based on this assessment, we propose a novel algorithm that is based on recent advancements in deep meta-RL to expand on the capabilities of domain randomization. In particular, we show that domain randomization faces issues with wide domain parameter distributions and demonstrate that our Partially Unsupervised Deep Meta-Reinforcement Learning (PUDM-RL) approach can handle these scenarios well. Furthermore, we improve the training stability by splitting the training into two separate parts. In one case, we train a dynamics encoder using unsupervised learning, while in the other case, we use the dynamics embedding from the learned dynamics encoder to aid the training of the agent. Experimentally we show that the learned dynamics encoder exhibits an informative latent space by compressing an interaction history from a particular environment into an embedding characterizing its dynamics. We find that agents aided by the dynamics embeddings perform much better than alternative approaches in the case of domain randomization with broad distributions over the dynamics parameters.
A Computational Model of Control Allocation based on the Expected Value of Control

While cognitive control has long been known to adjust ﬂexibly in response to signals like errors or conﬂict, when and how the decision is made to adjust control remains an open question. Recently, Shenhav and colleagues ( 1 ) described a theoretical framework whereby control allocation follows from a reward optimization process, according to which the identities and intensities of potential cognitive control signals are selected so as to maximize expected reward, while at the same time discounting this reward by an intrinsic cost that attaches to increases in control allocation. This discounted expected reward quantity is referred to as the Expected Value of Control (EVC). While the form of the reward optimization policy was described, Shenhav et al. left open the question of how this optimization process might be implemented in explicit computational mechanisms, and used to make predictions concerning performance in experimental tasks. Here we describe such an implementation. To simulate the inﬂuence of cognitive control on behavior in relevant task settings we parameterize such tasks as processes of accumulation to bound and allow control to inﬂuence the parameters of that accumulation process, resulting in attendant changes in reward rate. Control signals are speciﬁed based on an internal model of the task environment, as well as the intrinsic cost of control allocation. The latter scales both with the amount of overall control exerted and with the change in control allocation from the previous time step. After control is applied, feedback from the actual task environment is used to update the internal model, and control speciﬁcation is re-optimized. We show that the behavior of a simulated agent using these mechanisms replicates classic ﬁndings in the cognitive control literature related to sequential adaptation and task switching, and is able to generate testable predictions for future studies of voluntary rather than instructed allocation of control
I Believe I Can Fly—Conceptual Foundations for Behavioral Rebound Effects Related to Voluntary Carbon Offsetting of Air Travel

Voluntary carbon offsets (VCO) have been introduced as a means of compensating personal carbon emissions related to travelling. Purchases of VCO have remained low in the past, but might increase in the future due to rising awareness about climate change. VCO have been assumed to increase the acceptability of flying among eco-minded people. Therefore, VCO might not only be a tool to offset emissions but also to compensate for “flight shame”. Much research has been carried out to detect VCO purchasers’ motives, but none has explored the potential behavioral rebound effects of VCO with regard to flying. This article contributes to the debate by presenting a conceptual framework that was developed to investigate these rebound effects. First, we present the motives that travelers have for offsetting their flight emissions. These motives already indicate the possibility of a rebound effect. Second, we discuss several conceptual ideas which should be considered for the design of empirical studies. Overall, we argue that the use of VCO might lead to unintended carbon emissions; however, isolating the specific role of VCO remains a difficult task. Nevertheless, research on behavioral rebound effects is needed to clarify whether VCO counteract sustainability in the transport sector.