Reviewerly/exHarmony huggingface dataset:
Zero Trust Security

Zero Trust security is an IT security model that requires strict identity verification for every person and device trying to access resources on a private network, whether they are inside or outside the perimeter of the network. No specific technology is associated with the Zero Trust architecture; It is a holistic approach to network security that incorporates several different principles and technologies. The traditional security of the IT network is based on the „castle-and-moat” concept. In that model, it's hard to gain access from outside the network, but everyone on the network is trusted by default. The problem with this approach is that once an attacker gains access to the network, he has free rein over everything inside.
A Comparative Analysis of Neural Network and Decision Tree Model for Detecting Result Anomalies

The decision tree and neural network models are considered as one of the fastest and easy-to-use techniques having the ability to learn from classified data patterns.These models can be employed in detecting result anomalies measurable under normal circumstances on the bases that the student is healthy, had no problem and sat for exams.The existing techniques lack merit and integrity to efficiently detect irregularities found between student continuous assessments and exam scores.The addition of weights and calibrated values aided the learning process and addressed the problem facing the existing methods in operation.This provided an instance of having suitable control over the objective function in overcoming the identified problem.The added calibrated value helped control wrongly classified data patterns and improved the intelligence of the model.In this paper, the K-fold cross-validation test was employed to have a better classification report with the best split.This research was aimed to provide a comparative analysis of neural network and decision tree model for detecting result anomalies.The functionality of both models was used as a measure to check against result anomalies.This resulted in 96% and 91% accuracy with feed-forward multi-layered neural network and decision tree technique.
Potential of Transforming Sodalite from Synthesis Kaolin with a Mild Condition of the Hydrothermal Method

The hydrothermal synthesis method is one of the successful methods for transforming kaolin into sodalite with various parameters considered. The variation of alkalinity source of 2-3 Molarity of sodium hydroxide and crystallization time (12-16 hours) was considered an important parameter that influences the formation of sodalite. It is reported in this research that the process of synthesizing sodalite to transform into an amorphous stage (Metakaolinization phase) began with beneficiation of kaolin to remove the impurities and calcination by 6500C for four hours. The synthesis was done through an aging process (400C, 24 hours), and the crystallization temperature was 1000C. The kaolin was characterized by XRD, FESEM, FTIR, PSA, TGA, followed with metakaolin by XRD, FESEM, FTIR, and the end-product by XRD, FESEM, FTIR, and PSA. The crystallinity percentage of sodalite slightly increased by increasing the molarity and crystallization time, but quartz remains in the end-product.
Teaching BERT to Wait: Balancing Accuracy and Latency for Streaming Disfluency Detection

In modern interactive speech-based systems, speech is consumed and transcribed incrementally prior to having disfluencies removed.This post-processing step is crucial for producing clean transcripts and high performance on downstream tasks (e.g. machine translation).However, most current state-of-theart NLP models such as the Transformer operate non-incrementally, potentially causing unacceptable delays.We propose a streaming BERT-based sequence tagging model that, combined with a novel training objective, is capable of detecting disfluencies in real-time while balancing accuracy and latency.This is accomplished by training the model to decide whether to immediately output a prediction for the current input or to wait for further context.Essentially, the model learns to dynamically size its lookahead window.Our results demonstrate that our model produces comparably accurate predictions and does so sooner than our baselines, with lower flicker.Furthermore, the model attains state-of-the-art latency and stability scores when compared with recent work on incremental disfluency detection.
Using Explainable AI to Measure Feature Contribution to Uncertainty

The application of artificial intelligence techniques in safety-critical domains such as medicine and self-driving vehicles has raised questions regarding its trustworthiness and reliability. One well-researched avenue for improving trust in and reliability of deep learning is uncertainty quantification. \textit{Uncertainty} measures the algorithm’s lack of trust in its predictions, and this information is important for practitioners using machine learning-based decision support. A variety of techniques exist that produce uncertainty estimations for machine learning predictions; however, very few techniques attempt to explain why that uncertainty exists in the prediction. Explainable Artificial Intelligence (XAI) is an umbrella term that encompasses techniques that provide some level of transparency to machine learning predictions. This can include information on which inputs contributed to or detracted from the algorithm’s prediction. This work focuses on applying existing XAI techniques to deep neural networks to understand how features contribute to epistemic uncertainty. Epistemic uncertainty is a measure of confidence in a prediction given the training data distribution upon which the neural network was trained. In this work, we apply common feature attribution XAI techniques to efficiently deduce explanations of epistemic uncertainty in deep neural networks.
On Tracking Dialogue State by Inheriting Slot Values in Mentioned Slot Pools

Dialogue state tracking (DST) is a component of the task oriented dialogue system. It is responsible for extracting and managing slots, where each slot represents a part of the information to accomplish a task, and slot value is updated recurrently in each dialogue turn. However, many DST models cannot update slot values appropriately. These models may repeatedly inherit wrong slot values extracted in previous turns, resulting in the fail of the entire DST task. They cannot update indirectly mentioned slots well, either. This study designed a model with a mentioned slot pool (MSP) to tackle the update problem. The MSP is a slot specific memory that records all mentioned slot values that may be inherited, and our model updates slot values according to the MSP and the dialogue context. Our model rejects inheriting the previous slot value when it predicates the value is wrong. Then, it extracts the slot value from the current dialogue context. As the contextual information accumulates, the new value is more likely to be correct. It also can track the indirectly mentioned slot by picking a value from the MSP. Experimental results showed our model reached state of the art DST performance on MultiWOZ datasets.
Differentially private multivariate time series forecasting of aggregated human mobility with deep learning: Input or gradient perturbation?

This paper investigates the problem of forecasting multivariate aggregated human mobility while preserving the privacy of the individuals concerned. Differential privacy, a state-of-the-art formal notion, has been used as the privacy guarantee in two different and independent steps when training deep learning models. On one hand, we considered gradient perturbation, which uses the differentially private stochastic gradient descent algorithm to guarantee the privacy of each time series sample in the learning stage. On the other hand, we considered input perturbation, which adds differential privacy guarantees in each sample of the series before applying any learning. We compared four state-of-the-art recurrent neural networks: Long Short-Term Memory, Gated Recurrent Unit, and their Bidirectional architectures, i.e., Bidirectional-LSTM and Bidirectional-GRU. Extensive experiments were conducted with a real-world multivariate mobility dataset, which we published openly along with this paper. As shown in the results, differentially private deep learning models trained under gradient or input perturbation achieve nearly the same performance as non-private deep learning models, with loss in performance varying between $$0.57$$ and $$2.8\%$$ . The contribution of this paper is significant for those involved in urban planning and decision-making, providing a solution to the human mobility multivariate forecast problem through differentially private deep learning models.
An Introduction to Graph Neural Networks

Graph Neural Networks (GNNs) are considered a subset of deep learning methods designed to extract important information and make useful predictions on graph representations. Researchers have been working to adapt neural networks to operate on graph data for more than a decade. Most practical applications come from the areas of physics simulations, object detection and recommendation systems. Given the extended application areas, GNNs are one of fastest growing and most active research topic, that attracts increasing attention not only from the machine learning and data science community, but from the larger scientific community as well. The materials for this tutorial will be selected and organized for researchers with no prior knowledge of GNNs. Further reading, applications and most popular software packages and frameworks will also be discussed.
Comparative Analysis of Transformers to Support Fine-Grained Emotion Detection in Short-Text Data

Understanding a person’s mood and circumstances by way of sentiment or finer-grained emotion detection can play a significant role in AI systems and applications, such as in chat dialogue or reviews. Analysis of emotion from text typically requires specialized text or document understanding, and recent work has focused on transformer learning approaches. Common models of these transformers (e.g. BERT, RoBERTa, ELECTRA, XLM-R, and XLNet) have been pre-trained using longer texts of well-written English; however, many application contexts align more directly with social media content or have a shorter format more akin to social media, where texts often bend or violate standard language conventions. To understand the applicability and tradeoffs among common transformers within such contexts, our research investigates accuracy and efficiency considerations in fine-tuning transformers for granular emotion detection in short-text data. This paper presents a comparative study investigating the performance of five common transformers as applied in the specific context of multi-category emotion detection in short-text Twitter data. The study explores different considerations for hyperparameter settings in this context. Results show significant fine-tuning benefits in comparison to recommended baselines for the approaches and provide guidance for fine-tuning to support fine-grained emotion detection in short texts.
Factored Beliefs for Machine Agents in Decentralized Partially Observable Markov Decision Processes

A shared mental model (SMM) is a foundational structure in high performing, task-oriented teams and aid humans in determining their teammate's goals and intentions. Higher levels of mental alignment between teammates can reduce the direct dialogue required for team success. For decision-making teams, a transactive memory system (TMS) offers team members a map of specialized knowledge, indicating source of knowledge and the source's credibility. SMM and TMS formulations aid human-agent team performance in their intended team types. However, neither improve team performance with a project team--one that requires both behavioral and knowledge integration. We present a hybrid cognitive model (HCM) for machine agents that subsumes the integrated portions of a team's transactive memory in an SMM. The unified structure of the HCM enables contextual switches during execution for machine agents, over the two cognitive formulations with comparable computational complexity of a single cognitive model. Results in a multi-agent project environment demonstrates how the HCM provides machine agents with a generalizable cognitive structure that is able to maintain fully factored belief states with minimal inter-agent communication.
Comprehensive quantitative malignant risk prediction of pure grouped amorphous calcifications: clinico-mammographic nomogram

Pure grouped amorphous calcifications are classified as Breast Imaging Reporting and Data System (BI-RADS) category 4B suspicious calcifications and recommended for biopsy. However, the biopsies often reveal benign findings, especially in screening mammograms (92.4-97.2%).Mammograms of 699 pure grouped amorphous calcifications with final pathological results were analyzed in this retrospective study. The maximum span (MS) of the group of calcifications and the MS of the parallel/vertical direction of the mammary duct (MPS/MVS) were measured, and the MPS to MVS ratio was calculated. Based on the MS and ratio, 2 prediction nomograms with other clinic-mammographic features were developed. The discrimination performance of the models was assessed and compared by the area under the receiver operating characteristic curve (AUC). Scatterplots were created to determine the cutoff values with fewer misdiagnoses of malignant calcifications and fewer false positives.Ultimately, 2 prediction models were successfully developed based on the 4 risk factors of age, purpose of the mammogram, whether multiple or single calcifications, and the MS [odds ratio (OR) =1.06, P=0.02]/ratio (OR =6.05, P<0.001). Both models had good discrimination. The ratio model performed better than the MS model in the training cohort (AUC of 0.875 and 0.834, respectively, P=0.003) and validation cohort (AUC 0.908 and 0.867, respectively, P=0.047). For the group with probably benign calcifications (as detected by the ratio nomogram), the malignancy rates were 2.7% [95% confidence interval (CI): 1.00% to 6.53%] and 1.19% (95% CI: 0.06% to 7.37%) in the training and validation cohorts, respectively, and 44.12% and 47.70% of benign biopsies were detected in the training and validation cohorts, respectively.The clinico-mammographic quantitative malignancy risk prediction nomogram showed favorable discrimination and calibration performance. The ratio model showed better diagnostic efficiency than the MS model, and identified >40% of benign biopsies.
On the Sustainability of Virtual Platforms: A Behavioral Intervention

The energy required to supply data centers today is estimated to be around 1% of the global demand of electricity, with the cloud computing paradigm being the main driver of computing demand.Leading cloud providers are already making efforts to reduce energy expenditure of their data centers.However, the role that online platform operators and end-users can play towards a more sustainable cloud is still unclear.In this article, we raise the question whether making end users aware of their impact on cloud energy expenditure leads to more efficient use of the platforms.Focusing on non-retail platforms, we have run an A/B test in the Virtual Campus of the Universitat Oberta de Catalunya (UOC), one of the biggest online universities worldwide.In this intervention, we show the test group real-time information about the energy consumption of the platform, as well as tips on how to reduce it.Alongside, we monitor user behavior in terms of session duration and volume of traffic generated.Our results reveal that users who received this information did not change their behavior significantly.This result encourages us to find alternative ways to reduce the energy impact associated with the platform without the active participation of the end user, such as a more intelligent session management in conjunction with auto-scaling tools.
Intelligent Integrative Platform for Sharing Heterogenuous Stem Cell Research Data

Recent studies demonstrated that comparative analysis of stem cell research data sets originating from multiple studies can produce new information and help with hypotheses generation. Effective approaches for incorporating multiple diverse heterogeneous data sets collected from stem cell projects into a harmonized project-based framework have been lacking. Here, we provide an intelligent informatics solution for integrating comprehensive characterizations of stem cells with research subject and project outcome information. Our platform is the first to seamlessly integrate information from iPSCs and cancer stem cell research into a single platform, using a multi-modular common data element framework. Heterogeneous data is validated using predefined ontologies and stored in a relational database, to ensure data quality and ease of access. Testing was performed using 103 published, publicly-available iPSC and cancer stem cell projects conducted in clinical, preclinical and in vitro evaluations. We validated the robustness of the platform, by seamlessly harmonizing diverse data elements, and demonstrated its potential for knowledge generation through the aggregation and harmonization of data. Future aims of this project include increasing the database size using crowdsourcing and natural language processing functionalities. The platform is publicly available at https://remedy.mssm.edu/.
Visual-perception-driven Urban Three-dimensional Scene Data Scheduling Method

Toward solving the problems of low data scheduling efficiency and relative delay in rendering when constructing complex urban three-dimensional (3D) scenes, we propose a visualperception-driven strategy based on scene graphs.According to the spatial distribution characteristics of 3D scene data, this strategy uses a scene graph to organize the local 3D scene of a city.It uses a level-of-detail simplification algorithm to simplify the 3D model of the city into four resolution levels.On this basis, a visual-perception-driven strategy based on scene graphs is designed.This strategy utilizes the good relationship attributes between geographical entities provided by scene graphs to construct a visual-perception evaluation model and help constrain the adaptive scheduling of models at different detail levels.Experimental results show that this method can effectively improve the data scheduling efficiency and accelerate the construction of local 3D scenes.
Prediction of the COVID-19 Vaccination Target Achievement with Exponential Regression

The achievement of the national COVID-19 vaccination target in Indonesia is often reported to be uncertain with various existing obstacles. Prediction with exponential regression modeling is done by adopting part of the SKKNI Data Science with the stages of Data Understanding, Data Preparation, Modeling, Model Evaluation. The vaccination dataset from the Ministry of Health of the Republic of Indonesia for the period from January 13, 2021 to October 10, 2021, was randomly separated into training data of 0.8 parts and testing data of 0.2 parts. The optimal parameters of the exponential function are found using the scipy.optimize library in IPython. The model obtained was evaluated using MAE, RMSE, and R-Squared metrics on normalized training data, training data, test data, and recent data for seven days from 11 to 17 October 2021. The prediction results show that the vaccination target will be achieved 100 percent on January 18, 2022, while on December 31, 2021, only 80 percent will be achieved. From the recent data, it appears that more acceleration is needed, especially if it is desired to be achieved in December 2021 as determined by President Joko Widodo, there will be a shortfall of 20 percent based on the prediction results.
The Reality of Cybersecurity and its Challenges in Saudi Arabia

Recent technological developments have led to an increasingly wide use of electronic devices, platforms and applications. These technologies are now an important factor in the digital transformations taking place in many governmental agencies. In parallel with these technological advancements, new cybercrime techniques have been developed, making them a complex and highly prioritized national security issue for many countries around the world. To address this challenge, the Saudi Arabian government, through a royal decree, has urged government agencies to create internal cybersecurity departments to safeguard their information and technical assets. This makes cybersecurity a critical element of the Kingdom’s 2030 Vision. This study aims to shed light on the reality of cybersecurity and its challenges in the Kingdom of Saudi Arabia. The researcher designed a questionnaire to measure several dimensions, including cybersecurity management, addressing cyber threats and risks, security techniques, and future (strategic) challenges. A sample of 98 IT employees working in different government agencies participated in this study. The results of the data analysis show that the level of cybersecurity against cyber threats and risks is high in the government agencies of Saudi Arabia. KEYWORDS Cybersecurity, cyberspace, cyber risks management, cyber threats and risks, cyber warfare
Constraint Sampling Reinforcement Learning: Incorporating Expertise for Faster Learning

Online reinforcement learning (RL) algorithms are often difficult to deploy in complex human-facing applications as they may learn slowly and have poor early performance. To address this, we introduce a practical algorithm for incorporating human insight to speed learning. Our algorithm, Constraint Sampling Reinforcement Learning (CSRL), incorporates prior domain knowledge as constraints/restrictions on the RL policy. It takes in multiple potential policy constraints to maintain robustness to misspecification of individual constraints while leveraging helpful ones to learn quickly. Given a base RL learning algorithm (ex. UCRL, DQN, Rainbow) we propose an upper confidence with elimination scheme that leverages the relationship between the constraints, and their observed performance, to adaptively switch among them. We instantiate our algorithm with DQN-type algorithms and UCRL as base algorithms, and evaluate our algorithm in four environments, including three simulators based on real data: recommendations, educational activity sequencing, and HIV treatment sequencing. In all cases, CSRL learns a good policy faster than baselines.
Pengembangan Media Pembelajaran Fisika Berbasis Web Menggunakan Google Sites Pada Materi Gelombang Bunyi

Distance learning used in physics teaching and learning activities is indeed difficult to apply properly, this is caused by the many formulas and theories that must be explained well and in detail and the difficulty of explaining to students so that students are not able to understand concepts, materials and theories properly. good. Based on the background described previously, the purpose of this study is to test the feasibility of web-based physics learning media using google sites on sound wave material in helping students to learn easily and independently during distance learning. Learning media using Google sites aims to help students learn physics independently, especially in learning physics with sound wave material. In this study, research and development (R&amp;D) methods were used. Website-based learning media developed using Google Sites on the subject of Sound Waves received an average validation score of 50.50% by media experts and 63.50% by material experts. Both the average score results by media experts and material experts are included in the "feasible" criteria to be used and tested for students, and can develop Google Sites and disseminate the results of Google Sites development.