Reviewerly/exHarmony huggingface dataset:
Heavy Metal Content Analysis and Toxicity Assessment of Oil-Based Drilling Mud using Zebrafish Embryos

Aim: Used oil-based drilling muds (OBDMs) are toxic to marine organisms due to the fluid’s complex chemical nature. The illegal dumping of used OBDMs is still rampant in many parts of the world despite of the stringent laws and regulations pertaining to its disposal. To date, study of the assessment of heavy metal content in used OBDMs from the Malaysian oil and gas industry has been limited. Furthermore, the study of the toxic effect of OBDMs using a zebrafish embryo model has never been reported before. Therefore, in this research, the used oil-based drilling muds (OBDMs) were analyzed for their heavy metal content and toxicity against Zebrafish embryos. Methods: Used OBDMs were collected from an Anchor Handling Tug Supply (AHTS) vessel mud tank from Kemaman, Terengganu. The heavy metal content in the mud was analyzed using ICP-AES. Zebrafish embryos were exposed to OBDMs of varying concentrations (i.e., 0.25 mg/ml, 0.125 mg/ml, 0.06 mg/ml, 0.03 mg/ml, 0.015 mg/ml, 0.0076 mg/ml, 0.0034 mg/ml and 0.0019 mg/ml). The toxicity and teratogenicity of the mud on zebrafish embryos were assessed every 24 hours, for 96 hours, using endpoints like embryo mortality, heart rate and hatching rate. Results: The heavy metal content of used OBDMs had the highest concentration of barium (Ba) 2360 ppm, followed by lead (Pb) 120 ppm, zinc (Zn) 104 ppm, arsenic (As) 9 ppm, and chromium (Cr) 16 ppm, with cadmium (Cd) concentration of less than 1 ppm being the lowest. The Ba (2360 mg/L) concentration in the OBDM sample exceeded the concentration of Ba in normal marine sediment reported to be around 2000 mg/L. The 96 h LC50 of the OBDM in zebrafish embryos was reported to be 0.005 mg/mL (5 ppm). Zebrafish embryos that were exposed to high concentrations of OBDMs exhibited a lower hatching rate and reduced heart rate than the control group. Conclusion: The findings of this study are indicative of the highly toxic nature of used OBDMs and its dosage-dependent teratogenicity effects on zebrafish embryo. Knowledge of the potential environmental impacts of OBDMs released into the marine environment can be the basis for prudent decision-making that will help minimize environmental damage.
Identification of Leukemia Using Convolution Neural Networks

Leukemia is a type of malignant blood cell cancer that can affect both children and adults. In order to get the most effective treatment, the patient needs early diagnosis. Therefore, a support system  is needed for  early diagnosis to guide treatment for patients with Leukemia as soon as possible. This paper is focused on with the purpose of fast and early detection of Leukemia cell to prevent spreading of the cancer in patient’s body and to the treatment for patient. The proposed system uses convolution network that inputs a blood cell image and output whether cell is infected with cancer or not. The common algorithm for this approach consists of several rigid steps. The basic idea to develop a system which detect the Leukemia from blood cell images with more precision.
Robust Image Watermarking Using Support Vector Machine and Multi-objective Particle Swarm Optimization

Multimedia data security is paramount due to the high traffic on the internet. The internet traffic compromised with security threats such as intentional and time differential attack of multimedia data. This paper proposed an image watermarking method based on support vector machine and particle swarm optimization. The proposed algorithm works in dual mode as a selection of the embedding location of the watermark and increases the randomness factor of the watermark image. The increase randomness factor increases the value of imperceptibility and robustness factor. The support vector machine classifies the pattern of the host image and the watermark image. The processing of feature components of an image extracted by stationary wavelet transform (SWT). The stationary wavelet transform is also called redundant wavelet transform; these transforms overcome the discrete wavelet transform's limitation. The extraction of watermark image applies various attacks and measures standard parameters such as SSIM (similarity index matrix) and NC (number of correlation). The art of analysis of the proposed algorithm compares with SVM and DWT based watermarking methods. The proposed algorithm simulated in MATLAB software and tested with a reputed image dataset such as cameraman, peppers, baboon and Lena. The support vector machine applied the non-linear kernel function. The proposed algorithm decreases the 5–8% risk of geometrical attacks.
LIMEADE: A General Framework for Explanation-Based Human Tuning of Opaque Machine Learners.

Research in human-centered AI has shown the benefits of systems that can explain their predictions. Methods that allow humans to tune a model in response to the explanations are similarly useful. While both capabilities are well-developed for transparent learning models (e.g., linear models and GA2Ms), and recent techniques (e.g., LIME and SHAP) can generate explanations for opaque models, no method for tuning opaque models in response to explanations has been user-tested to date. This paper introduces LIMEADE, a general framework for tuning an arbitrary machine learning model based on an explanation of the model's prediction. We demonstrate the generality of our approach with two case studies. First, we successfully utilize LIMEADE for the human tuning of opaque image classifiers. Second, we apply our framework to a neural recommender system for scientific papers on a public website and report on a user study showing that our framework leads to significantly higher perceived user control, trust, and satisfaction. Analyzing 300 user logs from our publicly-deployed website, we uncover a tradeoff between canonical greedy explanations and diverse explanations that better facilitate human tuning.
Research and analytical review of the impact of youth's use of - smartphones on social relationships

In a world of globalization Smartphone has made communication more easier and convenient. Nowadays Smartphone are used by all group ages especially by youth. since Smartphone provides the facilities of internet access high quality camera social media platforms and other advance features to its users. It can be used for different prospects as well. as the Smartphone consumption is increasing among youth it is effecting real life interactions and attitudes in society especially among youth. So the purpose of this study is to examine the consumption and usage pattern of Smartphone by youth and the effect of Smartphone usage on the social interactions in society. To investigate the study quantitative research method approach was used. Online Google survey form was sent to 150 respondents through which 78 responses were received findings suggests that 78.2 percent of the respondents agree that usage of Smartphone is effecting the real life social interactions while further findings indicate that excessive use of Smartphone is effecting the attitude among youth in society.
Extracting Clinician's Goals by What-if Interpretable Modeling.

Although reinforcement learning (RL) has tremendous success in many fields, applying RL to real-world settings such as healthcare is challenging when the reward is hard to specify and no exploration is allowed. In this work, we focus on recovering clinicians' rewards in treating patients. We incorporate the what-if reasoning to explain clinician's actions based on future outcomes. We use generalized additive models (GAMs) - a class of accurate, interpretable models - to recover the reward. In both simulation and a real-world hospital dataset, we show our model outperforms baselines. Finally, our model's explanations match several clinical guidelines when treating patients while we found the previously-used linear model often contradicts them.
GIS Implementation in MNERGA Schema using Bhuvan Portal

Government of India through MNREGA scheme is trying to fulfill the critical requirement of focused Panchayat asset mapping for the proper analysis of the required resources to be channelized for the overall wholesome development of the Panchayat in India. One such step taken by ISRO was to develop an Android Application for Panchayat Asset Mapping of the villages for generating database that will assist the government to study and channelize resources and funds for a proper controlled futuristic development of the Panchayat. The Panchayat Asset Mapping Application gives the citizens the privilege to contribute to this national project through a citizen login that can be easily registered through the Panchayat Asset Mapping Android Application.
Multi-attribute Pizza Generator: Cross-domain Attribute Control with Conditional StyleGAN

Multi-attribute conditional image generation is a challenging problem in computervision. We propose Multi-attribute Pizza Generator (MPG), a conditional Generative Neural Network (GAN) framework for synthesizing images from a trichotomy of attributes: content, view-geometry, and implicit visual style. We design MPG by extending the state-of-the-art StyleGAN2, using a new conditioning technique that guides the intermediate feature maps to learn multi-scale multi-attribute entangled representationsof controlling attributes. Because of the complex nature of the multi-attribute image generation problem, we regularize the image generation by predicting the explicit conditioning attributes (ingredients and view). To synthesize a pizza image with view attributesoutside the range of natural training images, we design a CGI pizza dataset PizzaView using 3D pizza models and employ it to train a view attribute regressor to regularize the generation process, bridging the real and CGI training datasets. To verify the efficacy of MPG, we test it on Pizza10, a carefully annotated multi-ingredient pizza image dataset. MPG can successfully generate photo-realistic pizza images with desired ingredients and view attributes, beyond the range of those observed in real-world training data.
Legal Analysis of the Nature of Cyber Currency in Iran: A Comparison to EU Law

Electronic money as the monetary value stored in an electronic instrument is the last step in the gradual evolution of money, described as the immaterialization and invisibility of money. It is an emerging phenomenon that can perform the functions and duties of money. This study aimed to investigate the legal concept of cyber currency in Iran-Islamic and EU law in a comparative view. This study mainly considered e-money as a payment method and discusses it from different perspectives. In analyzing the legal nature of this phenomenon, it used two different approaches by combining an empirical-analytical method and a comparative study. The first approach was to analyze the nature of electronic money as a type of money. The second was to analyze the nature of electronic money in the light of non-monetary theories and describe it as one of the legal institutions used in business. This study concluded that electronic money would have different legal effects in its legal analysis, depending on who the publisher and acceptor are and how its publication and circulation process is defined and explained. The description of electronic money in the form of non-monetary theories ignored its role as an efficient payment tool in today's advanced business environment.&#x0D; KEYWORDS: Electronic Money, Cyber Currency, Islamic Law.
Automated Climate Analyses Using Knowledge Graph

The FAIR (Findable, Accessible, Interoperable, Reusable) data principles are fundamental for climate researchers and all stakeholders in the current digital ecosystem. In this paper, we demonstrate how relational climate data can be "FAIR" and modeled using RDF, in line with Semantic Web technologies and our Climate Analysis ontology. Thus, heterogeneous climate data can be stored in graph databases and offered as Linked Data on the Web. As a result, climate researchers will be able to use the standard SPARQL query language to query these sources directly on the Web. In this paper, we demonstrate the usefulness of our SPARQL endpoint for automated climate analytics. We illustrate two sample use cases that establish the advantage of representing climate data as knowledge graphs.
HD-cos Networks: Efficient Neural Architectures for Secure Multi-Party Computation

Multi-party computation (MPC) is a branch of cryptography where multiple non-colluding parties execute a well designed protocol to securely compute a function. With the non-colluding party assumption, MPC has a cryptographic guarantee that the parties will not learn sensitive information from the computation process, making it an appealing framework for applications that involve privacy-sensitive user data. In this paper, we study training and inference of neural networks under the MPC setup. This is challenging because the elementary operations of neural networks such as the ReLU activation function and matrix-vector multiplications are very expensive to compute due to the added multi-party communication overhead. To address this, we propose the HD-cos network that uses 1) cosine as activation function, 2) the Hadamard-Diagonal transformation to replace the unstructured linear transformations. We show that both of the approaches enjoy strong theoretical motivations and efficient computation under the MPC setup. We demonstrate on multiple public datasets that HD-cos matches the quality of the more expensive baselines.
The Strategy of e-learning Crisis Management and Effectiveness of Intelligent e-education During Covid-19 for a Sustainable Education System

The study explores the nature of the relationship between the strategy of managing the learning crisis and the effectiveness of e-learning in light of the COVID-19 epidemic to continue the educational process and keep abreast of technological developments at the University of Kufa. Using a questionnaire survey of a sample consisting of (265) teachers from various colleges of the University of Kufa, we explore the effect of the strategy of e. learning crisis management on the effectiveness of LMS. Accordingly, (265) questionnaires were distributed, there are (14) questionnaires that were not received, (4) questionnaires incomplete answers, and therefore (247) questionnaires were approved for statistical analysis. The results show that the learning crisis management strategy has a significant and positive impact on the effectiveness of e-learning, given the importance of the learning crisis management strategy in developing the effectiveness of e-learning considering the COVID-19 epidemic. To continue and expand the educational process and increase the scientific sobriety in universities would raise higher education in Iraq. We suggest for future studies the use of different methodologies to test society’s behavioral and cultural dimensions on the ability of organizations to digital transformation in times of crisis. © 2021
Fast and Accurate Graph Learning for Huge Data via Minipatch Ensembles

Gaussian graphical models provide a powerful framework for uncovering conditional dependence relationships between sets of nodes; they have found applications in a wide variety of fields including sensor and communication networks, physics, finance, and computational biology. Often, one observes data on the nodes and the task is to learn the graph structure, or perform graphical model selection. While this is a well-studied problem with many popular techniques, there are typically three major practical challenges: i) many existing algorithms become computationally intractable in huge-data settings with tens of thousands of nodes; ii) the need for separate data-driven hyperparameter tuning considerably adds to the computational burden; iii) the statistical accuracy of selected edges often deteriorates as the dimension and/or the complexity of the underlying graph structures increase. We tackle these problems by developing the novel Minipatch Graph (MPGraph) estimator. Our approach breaks up the huge graph learning problem into many smaller problems by creating an ensemble of tiny random subsets of both the observations and the nodes, termed minipatches. We then leverage recent advances that use hard thresholding to solve the latent variable graphical model problem to consistently learn the graph on each minipatch. Our approach is computationally fast, embarrassingly parallelizable, memory efficient, and has integrated stability-based hyperparamter tuning. Additionally, we prove that under weaker assumptions than that of the Graphical Lasso, our MPGraph estimator achieves graph selection consistency. We compare our approach to state-of-the-art computational approaches for Gaussian graphical model selection including the BigQUIC algorithm, and empirically demonstrate that our approach is not only more statistically accurate but also extensively faster for huge graph learning problems.
use of imperative sentences in hotels website

The use of imperative sentences is very important especially in attracting customers for any business. Especially for the business in the tourism industry how to attract customers is very important. That is why this research is very important to be conducted therefore the people who have I have businesses in the tourism industries know how to attract people using the imperative sentences in their website. The research will divide the imperative sentences into rationalization, identification, giving advice, confirmation, compensation, projection, and a replacement found on the website of the hotels and resorts in the village of Kenderan. It is found out that the hotels and resorts in the Village use these sentences to attract more visitors to stay in their hotel or resort. So it is suggested that the business owner, especially the owner of the hotels and feel like in this village. Keep using imperative sentences on the other website to attract more customers. For example, the technique of rationalization is very useful to attract more customers because the customer feels that they can feel or they image in the situation that they will have if they stay in the resort.
Interactive Experiences

The first edition of the Interactive Experiences (IEs) at CHItaly welcomed prototypes and installations that explored, represented, and challenged the boundaries of technology-mediated interactions. We received a total of 15 high-quality submissions and we accepted 11 of them. The submissions were peer-reviewed by members of the committee, which provided insightful comments and suggestions to the authors. The selected works addressed topics such as machine learning in user profiling, and technology obsolescence in creative, playful, and provocative ways. All these works will be part of the IEs exhibition that will take place during the main conference.
APLIKASI PEMESANAN DAN PENJUALAN ONLINE PADA TOKO BAKUNG GORDEN DAN WALLPAPER

Bakung Curtains and Wallpaper store is a shop engaged in the sale of curtains and wall wallpapers. In conveying the promotional media in this shop the owner only gives brochures to one consumer to another and word of mouth. The purpose of this research is the formation of an online ordering and selling application at the Bakung Gorden Shop and Wallpaper so that it can develop and improve existing businesses, making it easier for admins to recap the sales and ordering reports of items dated, monthly, and annually. In making the Online Ordering and Sales Application program at the Bakung Gorden Shop and Wallpaper using the PHP programming language and MySQL database. The input needs of this application are in the form of item data, customer data, order data to be entered and for the needs of the output in the form of order data and sales data reports. The need for data processing taken from the Bakung Gorden and Wallpaper store is the process of recording purchase data and the process of recording sales data. The results of this research are expected to be able to help and solve the problems that exist in the Store Bakung Curtains and Wallpaper, making it easier for shop owners to promote these products widely through the internet media.
Forecasting and Analysis of Contribution channel for e-commerce

E-commerce business is a new type of business in the modern times which can reach many groups of consumers and generate a lot of revenue for entrepreneurs. However, E- Commerce entrepreneurs have not been successful in evaluating the distribution channels efficiently. This research aims to study the distribution channels in E-Commerce businesses to analyze and forecast the distribution channels of products in E- Commerce business Including creating models and evaluating the effectiveness of the models. This research methodology uses K- Mean clustering algorithm to segment E- Commerce entrepreneurs using Linear Regression Analysis in forecasting of sales channels and using the Cross-Validation in evaluating the effectiveness of the model. The research results show that the segmentation of large E- Commerce entrepreneurs uses product distribution channels as Facebook, Shopee, and Weloveshopping significantly need to sell food and beverages, sports equipment, travel equipment and miscellaneous products. Medium- sized E-Commerce entrepreneurs uses product distribution channels as Weloveshopping, and JD Central significantly need to sell sports equipment, travel equipment, stationery, office equipment and miscellaneous products. Small E- Commerce entrepreneurs uses Facebook, Shopee, JD Central significantly need to sell food and beverages, stationery, office equipment, and cosmetics. The results of the evaluation of the efficiency of the model for forecasting sales channels in E-Commerce businesses has an accuracy of 96.25%.
CYUT at ROCLING-2021 Shared Task: Based on BERT and MacBERT.

This paper present a description for the ROCLING 2021 shared task in dimensional sentiment analysis for educational texts. We submitted two runs in the final test. Both runs use the standard regression model. The Run1 uses Chinese version of BERT as the base, and in Run2 we use the early version of MacBERT that Chinese version of RoBERTa-like BERT model, RoBERTa-wwm-ext. Using powerful pre-training model of BERT for text embedding to help train the model.
Sinkformers: Transformers with Doubly Stochastic Attention

Attention based models such as Transformers involve pairwise interactions between data points, modeled with a learnable attention matrix. Importantly, this attention matrix is normalized with the SoftMax operator, which makes it row-wise stochastic. In this paper, we propose instead to use Sinkhorn's algorithm to make attention matrices doubly stochastic. We call the resulting model a Sinkformer. We show that the row-wise stochastic attention matrices in classical Transformers get close to doubly stochastic matrices as the number of epochs increases, justifying the use of Sinkhorn normalization as an informative prior. On the theoretical side, we show that, unlike the SoftMax operation, this normalization makes it possible to understand the iterations of self-attention modules as a discretized gradient-flow for the Wasserstein metric. We also show in the infinite number of samples limit that, when rescaling both attention matrices and depth, Sinkformers operate a heat diffusion. On the experimental side, we show that Sinkformers enhance model accuracy in vision and natural language processing tasks. In particular, on 3D shapes classification, Sinkformers lead to a significant improvement.