Reviewerly/exHarmony huggingface dataset:
ResLPA: Label Propagation With Residual Learning

Label Propagation Algorithm (LPA) predicts what label a node in a graph has by its accumulated label information after iteratively propagating label information among adjacent nodes. Subsequent studies of LPA try to assign weights on connections to adjust the propagation between different node pairs. However, such approaches still rely on the assumption that adjacent nodes should have similar labels. To predict when the label distribution may differ from this assumption, we propose Residual Label Propagation (ResLPA). It approximates residuals between labels with node features, then adds the approximated residuals to the label information as corrections before propagating. In experiments on five real-world graphs, ResLPA matches or exceeds state-of-the-art LPA-based methods, GCN-based methods and their integrations.
Automated creation of parallel Bible corpora with cross-lingual semantic concordance

Here we present a novel approach for automated creation of parallel New Testament corpora with cross-lingual semantic concordance based on Strong's numbers.As scientific editions and translations of Bible texts are often not free to use for scientific purposes and are rarely free to use, and due to the fact that the annotation, curation and quality control of alignments between these texts are quite expensive, there is a lack of available Biblical resources for scholars.We present two approaches to tackle the problem, a dictionary-based approach and a Conditional Random Field (CRF) model and a detailed evaluation on annotated and non-annotated translations.We discuss a proof-of-concept based on English and German New Testament translations.The results presented in this paper are novel and according to our knowledge unique.They present promising performance, although further research is necessary.
Determination of available heavy metal concentrations in River Nile sediments, Damietta, Egypt

The present study investigates the distribution and available concentrations of As, Co, Cu, Fe, Sb, Zn, Cd, Se, Ni, Pb in the bottom sediment of the Damietta Nile branch. Available concentrations were determined using the DTPA extraction method. The mean available concentrations were arranged as follows As > Fe > Cu > Zn > Co > Se > Ni > Pb > Sb > Cd. Clustering analysis represents a clear difference between two groups of heavy metal concentrations before and after Damietta Dam. Textural classifications and Total organic matter have been studies. The matrix correlation coefficient, comparison between other studies has been evaluated.
Variational Marginal Particle Filters

Variational inference for state space models (SSMs) is known to be hard in general. Recent works focus on deriving variational objectives for SSMs from unbiased sequential Monte Carlo estimators. We reveal that the marginal particle filter is obtained from sequential Monte Carlo by applying Rao-Blackwellization operations, which sacrifices the trajectory information for reduced variance and differentiability. We propose the variational marginal particle filter (VMPF), which is a differentiable and reparameterizable variational filtering objective for SSMs based on an unbiased estimator. We find that VMPF with biased gradients gives tighter bounds than previous objectives, and the unbiased reparameterization gradients are sometimes beneficial.
The Difference of Machine Learning and Deep Learning Algorithms

In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studiedand provided in order to handle and extract value and knowledge from these datasets. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Such minimal human intervention can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper aims to analyse some of the different machine learning algorithms and methods which can be applied to big data analysis, as well as the opportunities provided by the application of big data analytics in various decision making domains.
A Bibliometric Review of Soft Computing for Recommender Systems and Sentiment Analysis

Soft computing, which focuses on approximate models and provides solutions to complicated real-life issues, has gained increasing momentum in application-specific domains, such as sentiment analysis and recommender systems, to emulate cognitive processes behind decision-making. In this work, bibliometrics and structural topic modeling (STM) were adopted to analyze the text contents of research articles concerning soft computing for sentiment analysis and recommender systems. Results indicated that this research field had experienced a dramatic increase in both quantity and quality as measured by scientific output and their received citations. Using STM, we identified 17 research topics frequently discussed within the analyzed articles. The analysis of annual topic prevalence indicated a shift in research foci from recommender applications to sentiment analysis and a growing interest in soft computing. This study served as a guideline for those seeking to contribute to research on soft computing for sentiment analysis and recommender systems. We also made methodological contributions by combining the leading-edge text mining algorithms to make the time-honored bibliometrics adaptive to the analysis of large quantities of unstructured texts beyond structured publication data statistics.
Why should Business schools teach blockchain technology? The case of Botswana Accountancy College

In this digital era, tertiary education institutions students need to be acquainted with the latest technology such as blockchain, to equip them for industry readiness. There is a paradigm shift in blockchain technology. Thus, business schools would do well to teach tertiary students about blockchain technology which in turn can help them to solve real-life and industry challenges efficiently and effectively. This paper aimed to investigate tertiary students’ awareness and competency of blockchain technology and cryptocurrency. The paper’s objectives were to assess students’ level of knowledge on blockchain, to explore students’ depth of awareness and competency on bitcoin and altcoins, and to recommend steps that business schools can take to teach students on blockchain. In July 2018, a closed-ended questionnaire was distributed to 266 students studying Business and Leisure related programs at Botswana Accountancy College, School of Business &amp; Leisure. Quota-randomized system was used to allow participation of respondents that are more entrepreneurially aligned according to course content and aims. The data was analyzed using Statistical Package for Social Sciences (SPSS). The study findings revealed that most students are challenged or know little about blockchain, let alone Bitcoin and Altcoins. Thus, business schools should take it upon themselves to educate not only students, but the community at large about blockchain to bring awareness. Furthermore, business schools such as BAC should make policies that allow them to embed blockchain technology into their teaching/learning strategies, record-keeping, and information management.
With False Friends Like These, Who Can Notice Mistakes?

Adversarial examples crafted by an explicit adversary have attracted significant attention in machine learning. However, the security risk posed by a potential false friend has been largely overlooked. In this paper, we unveil the threat of hypocritical examples---inputs that are originally misclassified yet perturbed by a false friend to force correct predictions. While such perturbed examples seem harmless, we point out for the first time that they could be maliciously used to conceal the mistakes of a substandard (i.e., not as good as required) model during an evaluation. Once a deployer trusts the hypocritical performance and applies the "well-performed" model in real-world applications, unexpected failures may happen even in benign environments. More seriously, this security risk seems to be pervasive: we find that many types of substandard models are vulnerable to hypocritical examples across multiple datasets. Furthermore, we provide the first attempt to characterize the threat with a metric called hypocritical risk and try to circumvent it via several countermeasures. Results demonstrate the effectiveness of the countermeasures, while the risk remains non-negligible even after adaptive robust training.
A U-net and KMeans based method for brain tumor segmentation and measurement

Medical image segmentation is an essential task in the field of image recognition, and has a high requirement for accuracy. As a popular model, although U-net has accomplished many achievements, such as locating localizing the lesion area and object segmentation, it has shortcomings on segmenting quite small details. In this paper, we improve the U-net algorithm by adding ResBlock and Batch Normalization modules, as well as increasing the network depth, so as to improve its accuracy for tumor site segmentation in medical images of the brain. The K-Means algorithm is also used to segment the brain region and the background region to find out the relative size of the brain occupied by the tumor, which achieves both image segmentation and tumor size measurement.
Medical Information Extraction Using Natural Language Interpretation

NLP Based Retrieval of Medical Information is the extraction of medical data from narrative clinical documents. In this paper, we review Natural Language Processing (NLP) applications designed to extract medical problems from narrative text clinical documents. This paper also covers the methods used in this field and it also describes the architecture of the proposed system. However extraction of medical information is the difficult task due to complex symptom names and complex disease names. Proposed system is an expert system which will try to understand the input that can be the question about disease or list of symptoms and the system will try to give out the proper answer. Proposed system will consist of modules such as data processing, query processing, data extraction, answer matching, user interface etc. Lots of data are available in the medical field in free text form which is not used by normal rule based systems, so with the help of NLP we can use this free text data such as it will try to give out the answers that we search on our own.
DEVELOPMENT OF THE INFLOW PREDICTION MODEL ON TROPICAL RESERVOIR USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

Inflow prediction is a key in component planning, design, operation, development, and maintenance of availiable water resources.The inflow prediction model have many benefits in the application of Water Resources such as flood control, preventing drought, and optimizing reservoir operation for hydropower system sustainability.The main purpose of the research is to develop of model to predict the inflow of hydro electric power plant reservoir considering the importance of information on sufficient in this reservoir everyday so that the hydro electric power plant can continue to operate.The method of research approach used is using Adaptive Neuro Fuzzy Inferences System developed a hybrid system between fuzzy logic and artificial neural network as branch of softcomputing.The location of research on the Kotopanjang hydro electric power plant reservoir in Merangin Village, Kampar Residence, Riau Province.The data used in this research is secondary data in the form of inflow data Development of the Inflow Prediction Model on Tropical Reservoir Using Adaptive Neuro Fuzzy Inference System https://iaeme.com/
Controlled-Phase Gate by Dynamic Coupling of Photons to a Two-Level Emitter.

We propose an architecture for achieving high-fidelity deterministic quantum logic gates on dual-rail encoded photonic qubits by letting photons interact with a two-level emitter (TLE) inside an optical cavity. The photon wave packets that define the qubit are preserved after the interaction due to a quantum control process that actively loads and unloads the photons from the cavity and dynamically alters their effective coupling to the TLE. The controls rely on nonlinear wave mixing between cavity modes enhanced by strong externally modulated electromagnetic fields or on AC Stark shifts of the TLE transition energy. We numerically investigate the effect of imperfections in terms of loss and dephasing of the TLE as well as control field miscalibration. Our results suggest that III-V quantum dots in GaAs membranes is a promising platform for photonic quantum information processing.
A Critique of Strictly Batch Imitation Learning

Recent work by Jarrett et al. attempts to frame the problem of offline imitation learning (IL) as one of learning a joint energy-based model, with the hope of out-performing standard behavioral cloning. We suggest that notational issues obscure how the psuedo-state visitation distribution the authors propose to optimize might be disconnected from the policy's $\textit{true}$ state visitation distribution. We further construct natural examples where the parameter coupling advocated by Jarrett et al. leads to inconsistent estimates of the expert's policy, unlike behavioral cloning.
Cloud Computing into Respiratory Rehabilitation Training-Assisted Treatment of Patients with Pneumonia

In order to study the therapeutic effect of respiratory rehabilitation training on patients with pneumonia, this paper proposes an integrated adjuvant therapy program based on the cloud computing model. A total of 60 pneumonia patients admitted to Zhujiang Hospital of Southern Medical University from January to July 2020 were selected as the research objects and Southern Medical University pneumonia patients as the research object, to be evenly divided into two groups, each group of 30 people. The control group was treated with conventional anti-infection treatment, and the observation group was treated with supplementary respiratory rehabilitation training on the basis of conventional treatment. The therapeutic effects of the two groups were compared. The results showed that the absorption time of lung lesions was (9.17 ± 3.46) days in the observation group and (13.97 ± 3.07) days in the control group, and the difference between the two groups was statistically significant (t = 5.683, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> <mo>&lt;</mo> <mn>0.001</mn> </math> ). Respiratory therapy based on the cloud computing model has the characteristics of integration and extensibility, which can be effectively applied to the treatment effect analysis of patients with pneumonia and is of great significance for the effective analysis of patients’ blood gas indexes and lung function indexes.
RobFR: Benchmarking Adversarial Robustness on Face Recognition

Face recognition (FR) has recently made substantial progress and achieved high accuracy on standard benchmarks. However, it has raised security concerns in enormous FR applications because deep CNNs are unusually vulnerable to adversarial examples, and it is still lack of a comprehensive robustness evaluation before a FR model is deployed in safety-critical scenarios. To facilitate a better understanding of the adversarial vulnerability on FR, we develop an adversarial robustness evaluation library on FR named \textbf{RobFR}, which serves as a reference for evaluating the robustness of downstream tasks. Specifically, RobFR involves 15 popular naturally trained FR models, 9 models with representative defense mechanisms and 2 commercial FR API services, to perform the robustness evaluation by using various adversarial attacks as an important surrogate. The evaluations are conducted under diverse adversarial settings in terms of dodging and impersonation, $\ell_2$ and $\ell_\infty$, as well as white-box and black-box attacks. We further propose a landmark-guided cutout (LGC) attack method to improve the transferability of adversarial examples for black-box attacks by considering the special characteristics of FR. Based on large-scale evaluations, the commercial FR API services fail to exhibit acceptable performance on robustness evaluation, and we also draw several important conclusions for understanding the adversarial robustness of FR models and providing insights for the design of robust FR models. RobFR is open-source and maintains all extendable modules, i.e., \emph{Datasets}, \emph{FR Models}, \emph{Attacks\&Defenses}, and \emph{Evaluations} at \url{https://github.com/ShawnXYang/Face-Robustness-Benchmark}, which will be continuously updated to promote future research on robust FR.
An Efficient Motion Based Group Level Activity Recognition for Intelligent Video Surveillance

Human crowd analysis is a critical topic in video surveillance and a demanding process because of the non-rigid shapes of the crowds. Due to occlusions, tracking and analysing every single person in a crowd is a difficult operation. As a result, rather than following every individual in the crowd, the crowd can be treated as a single entity. Due to notable spatio-temporal properties, the crowd's behaviour can be differentiated by motion patterns. Optical flows are initially estimated and then used as a clue to group human crowds into clusters using the suggested adjacency matrix-based clustering algorithm (AMC). First, video is converted into frames and noise is removed in the stage of pre-processing. Second, foreground blob is extracted in the stage of background subtraction. Third, in the stage of motion tracking, velocity, position and direction of the motion are obtained by estimating the optical flows. Fourth, clustering is done using AMC algorithm in the stage of clustering. Fifth, features are extracted by using Harris algorithm along with centroid, orientation etc. and finally, the human crowd is detected and labelled using multi-class Support Vector Machine (SVM) algorithm. Obtained experimental results on PETS and UMN video datasets with a recognition rate of 95.6% and 95.1% respectively show the effectiveness of the system.
Historical Awareness Through the Use of the WhatsApp Group Application as a Distance Learning Media

Since the establishment of the emergency period of corona virus desease in Indonesia, the policy was implemented based on government circular number 4 of 2020 which contains about the change of learning system from face-to-face system to distance learning system, namely learning from home (online).The use of communication and information technology-based learning media is a major requirement in achieving these efforts.WhatsApp as one of the most popular social media today, has full features that can be used as interactive learning media between teachers and students for distance learning.The purpose of the discussion in this paper seeks to explain the use of the WhatsApp Group application in history learning in order to foster an understanding of students' historical awareness.This study uses a naturalistic study whose presentation is based on qualitative descriptive analysis.The results show that the use of WhatsApp Group as a learning medium can create interactive and effective communication between teachers and students.Conversations through WhatsApp Group are able to stimulate active involvement both physically, mentally and emotionally of students, of course this will make it easier to provide an understanding of a historical event study, which in turn will lead to an understanding of students' historical awareness about the journey of their nation in the past and can also foster ideas.ideas for present and future life.
Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation

Adversarial training is promising for improving robustness of deep neural networks towards adversarial perturbations, especially on the classification task. The effect of this type of training on semantic segmentation, contrarily, just commences. We make the initial attempt to explore the defense strategy on semantic segmentation by formulating a general adversarial training procedure that can perform decently on both adversarial and clean samples. We propose a dynamic divide-and-conquer adversarial training (DDC-AT) strategy to enhance the defense effect, by setting additional branches in the target model during training, and dealing with pixels with diverse properties towards adversarial perturbation. Our dynamical division mechanism divides pixels into multiple branches automatically. Note all these additional branches can be abandoned during inference and thus leave no extra parameter and computation cost. Extensive experiments with various segmentation models are conducted on PASCAL VOC 2012 and Cityscapes datasets, in which DDC-AT yields satisfying performance under both white- and black-box attack.
GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worlds

We present GANcraft, an unsupervised neural rendering framework for generating photorealistic images of large 3D block worlds such as those created in Minecraft. Our method takes a semantic block world as input, where each block is assigned a semantic label such as dirt, grass, or water. We represent the world as a continuous volumetric function and train our model to render view-consistent photorealistic images for a user-controlled camera. In the absence of paired ground truth real images for the block world, we devise a training technique based on pseudo-ground truth and adversarial training. This stands in contrast to prior work on neural rendering for view synthesis, which requires ground truth images to estimate scene geometry and view-dependent appearance. In addition to camera trajectory, GANcraft allows user control over both scene semantics and output style. Experimental results with comparison to strong baselines show the effectiveness of GANcraft on this novel task of photorealistic 3D block world synthesis. The project website is available at https://nvlabs.github.io/GANcraft/ .