Reviewerly/exHarmony huggingface dataset:
Effectiveness of Detection-based and Regression-based Approaches for Estimating Mask-Wearing Ratio

Estimating the mask-wearing ratio in public places is important as it enables health authorities to promptly analyze and implement policies. Methods for estimating the mask-wearing ratio on the basis of image analysis have been reported. However, there is still a lack of comprehensive research on both methodologies and datasets. Most recent reports straightforwardly propose estimating the ratio by applying conventional object detection and classification methods. It is feasible to use regression-based approaches to estimate the number of people wearing masks, especially for congested scenes with tiny and occluded faces, but this has not been well studied. A large-scale and well-annotated dataset is still in demand. In this paper, we proposed two different methods for ratio estimation that are leveraged by either detection-based or regression-based approaches. For the detection-based approach, we improved a state-of-the-art face detector, RetinaFace, for the ratio estimation. For the regression-based approach, we utilized a baseline network, CSRNet, and finetuned it to estimate the density maps for masked and unmasked faces. We also proposed the first large-scale dataset, the “NFM,” which contains 581,108 face annotations extracted from 18,088 video frames in 17 street-view videos <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The annotations (bounding boxes and labels), and pre-trained models will be released with the publication of our paper. , Through experiments, the RetinaFace-based method achieves better accuracy under different situations, while the CSRNet-based method is superior in terms of operation time thanks to its compactness.
Community-Connect: COVID-19 Small Business Marketplace with Automated Regulation Matching and Company Lead Retrieval

Periods of unique economic distress such as the COVID-19 pandemic can be quite difficult for small businesses. Challenges acquiring the supplies necessary to adhere to safety regulations created in the wake of such events can introduce stress on these businesses. This is further exacerbated when supply chains have slowed down, leading to global shortages from most large suppliers. This paper proposes a platform to aid such businesses in procuring COVID-19 related supplies such as Personal Protective Equipment (PPE) from one another, leveraging advanced data acquisition, integration, and Natural Language Processing (NLP) methods. With the pandemic end in sight, the platform described in this paper can be reused for other emergencies such as hurricanes, floods, among others. The proposed platform supports business transactions within a Buyer's Club (BC), keyword-based sourcing of new businesses to join the platform, and matching products to relevant regulations using greater-than-word length encoding, helping businesses comply with the ever-changing regulatry landscape.
Reduced-Reference Stereoscopic Image Quality Assessment Using Gradient Sparse Representation and Structural Degradation

Reduced-reference stereoscopic image quality assessment (RRSIQA) models evaluate stereoscopic image quality degradation with partial information about the “ideal-quality” reference stereopair. On one hand, sparse representation in recent theoretical studies of visual cognition has been proved to resemble the strategy used to represent natural images in the primary visual cortex. On the other hand, the joint statistics of gradient magnitude (GM) and Laplacian of Gaussian (LOG) features are popularly utilized to form image semantic structures. Motivated by these findings, we present a new RRSIQA metric using gradient sparse representation and structural degradation in this paper. Concretely, the proposed metric is based on two main tasks: the first task extracts the distribution statistics of visual primitives by gradient sparse representation, while the second task measures structural degradation of stereoscopic image due to the presence of distortion by extracting the joint statistics of GM and LOG features. The former, so-called the binocular perceptual visual information (PVI), aims to effectively integrates the gradient maps that are sparser than the image itself. Especially, the process of binocular fusion is simulated by using the mutual information of the gradient-based visual primitives between left and right view’s images as binocular cue. Furthermore, the perceptual loss vectors are taken as the differences of binocular perceptual visual information and the structural degradation between reference and distorted stereopairs. Finally, the perceptual loss vector is utilized to calculate the quality score by a prediction function which is trained using kernel ridge regressing (KRR). The experiments are performed on the popular LIVE 3D IQA databases and Waterloo IVC 3D databases, and experimental results show highly competitive performance with the state-of-the-art algorithms. Moreover, in some challenging cases with particular asymmetric distortion types, the proposed metric can achieves the best quality prediction accuracy in LIVE 3D phase II and Waterloo IVC 3D Phase II.
A Statistical Test for Legal Interpretation: Theory and Applications

Abstract Many questions of legal interpretation hinge on whether two groups of people assign different meanings to the same word. For example: Do 18th- and 21st-century English speakers assign the same meaning to commerce? Do judges and laypersons agree on what makes conduct reasonable? We propose a new statistical test to answer such questions. In three applications, we use our test to (1) quantify differences in the meanings of specialized words from civil procedure, (2) identify statistically significant differences between judges and laypersons’ understandings of reasonable and consent, and (3) assess differences across various effort standards in commercial contracts (phrases like best effort and good faith effort). Our approach may be readily applied outside the law to quantify semantic disagreements between or within groups. (JEL C10, C12, C55, K12, K41).
Artificial Intelligence Imaging to Observe the Protective Effect of Hydrogen Sulfide on Acute Kidney Injury Caused by Urinary Sepsis

With the development of medical technology products and the rapid development of computer technology, medical AI has become a hotbed in scientific research and clinical practice. Some medical AI-assisted diagnosis has been applied to the clinic to assist doctors in formulating treatment plans. The traditional method of clinical diagnosis and treatment is that the physician makes an intentional diagnosis and then performs ancillary tests. The clinician performs diagnosis and treatment by identifying clinical symptoms and analyzing auxiliary examination results. Modern medical AI is based on big data collection and analyzes the test results through artificial intelligence and computer algorithms. It can output diagnostic results with high sensitivity and specificity for clinical tests. Acute kidney injury (AKI) is a common clinical emergency. The main clinical features are elevated blood creatinine, decreased urine output, and sharp decline in renal function within a short period of time, and it is a hot spot worldwide. In this experiment, a rabbit sepsis model was replicated by inoculating E. coli bacteria into the rabbit’s unilateral ureteral lumen and ligation. NaHS was used as an exogenous hydrogen sulfide donor to observe the effects of hydrogen sulfide on UTIs. The protective effect of oxidative stress and inflammatory response in acute kidney injury with hyperemia. In the experiment, the production of endogenous hydrogen sulfide was decreased in the Sepsis group, and the renal CSE activity was decreased, while the content of endogenous hydrogen sulfide in the NaHS group was higher than that of the Sepsis group, and the CSE activity of renal tissue was increased. It can be seen that the plasma hydrogen sulfide and renal tissue SCE levels in septic acute kidney injury increased after NaHS intervention, and the renal tissue damage was reduced, suggesting that hydrogen sulfide is mainly generated endogenously through the action of CSE, which causes damage to the kidneys. The expressions of iNOS and HO-1 in renal tissues of urinary sepsis are increased. H2S can play a certain protective effect on acute kidney injury in urinary sepsis by down-regulating iNOS and up-regulating the expression of HO-1.
Study of occupants’ perceptions and preferences of smart home system technology at The Eastern Green-LRT City Bekasi Apartment

Abstract The development of technology over the time is increasing rapidly in various fields including in the field of property that applies smart home system. During the Covid 19 Pandemic, residential with smart home system concept deserves to be an option because it can provide convenience for residents in activities and work by arranging electronic devices such as air conditioning, televisions, lights, access in and out of residence. One of them is The Eastern Green LRT City Bekasi Apartment which is a residence with the concept of smart home system. Apart from the purpose of knowing perception and preference of residents to smart home system in Eastern Green-LRT City Bekasi Apartment, there are certainly still weaknesses that are also a consideration for improvement so that smart home system is able to facilitate residents to improve the comfort of residents. This research uses quantitative method by collecting data through survey. The results showed there are several things that need to be useful evaluation materials to improve smart home system. It is expected that this article can provide feedback to realize a more comfortable residence based on smart home system in welcoming the era of society 5.0 and the new normal era.
Revisiting Contextual Toxicity Detection in Conversations

Understanding toxicity in user conversations is undoubtedly an important problem. Addressing “covert” or implicit cases of toxicity is particularly hard and requires context. Very few previous studies have analysed the influence of conversational context in human perception or in automated detection models. We dive deeper into both these directions. We start by analysing existing contextual datasets and find that toxicity labelling by humans is in general influenced by the conversational structure, polarity, and topic of the context. We then propose to bring these findings into computational detection models by introducing and evaluating (a) neural architectures for contextual toxicity detection that are aware of the conversational structure, and (b) data augmentation strategies that can help model contextual toxicity detection. Our results show the encouraging potential of neural architectures that are aware of the conversation structure. We also demonstrate that such models can benefit from synthetic data, especially in the social media domain.
The Total Number Calculation of States of Chinese Chess Based on the Dynamic Programming of Computer Games

The space complexity of Chinese chess is a primary index for analyzing the complexity of Chinese chess, which is a counting problem of calculating the number of states of Chinese chess. Given the features of Chinese chess, this problem can be divided into several subproblems that can be solved by dynamic programming to obtain a precise solution of the total number of states of Chinese chess. Our results show that the total number of states of Chinese chess mentioned in previous papers is inaccurate and much higher than the actual number of states. Finally, the main idea of the counting method was summarized based on dynamic programming, and illustrations for some uses of the method were provided.
Towards a Method for Characterizing and Improving Integration among Different Systems

One of the most challenging goals of SISSY (Self-improving System Integration) is to create methods for self-integrating systems to improve their integration. The purpose of this short position paper is to stimulate a community-wide discussion and eventual experimentation about what it means to measure the degree of integration between entities and then to improve or deepen integrations. To start this discussion, we first note the importance of having varying degrees and kinds of integration, and then define three ways in which an entity could improve integrations within itself and with other entities.
Same Sentence Prediction: A new Pre-training Task for BERT

In this paper, we explored the effects of BERT pre-training objectives on BERT's success. Various pre-training objectives have been applied to BERT and evaluated their success on downstream tasks such as text classification, sentiment analysis, masked word prediction and NER. Also, a new pretraining objective called SSP (Same Sentence Prediction) has been introduced. SSP predicts whether two segments are from the same sentence. Segments are created by splitting sentences into half. 50% of the first segments are replaced by a token sequence with the same length from the beginning of another sentence. The rest of the first parts remain unchanged. The main advantage of the proposed new pre-training task over NSP and SOP is that it uses the dataset more efficiently and creates more training input from a dataset. Thus, models can be trained with more training steps than NSP and SOP. Models trained with SSP and SOP achieved better results than models using NSP. This showed that using NSP in training is not sufficient and that the model can achieve better results by using other tasks. In addition, the models with SSP got close results to the models with SOP, while they outperformed the models with SOP in masked word prediction. This indicated that SSP could be an alternative to SOP. It has been observed that the auxiliary tasks to MLM used in the training phase do not sufficiently increase the success in sentiment analysis. It did not affect sentiment analysis as much as it affected success in text classification, named entity detection and masked word prediction. It has been shown that other tasks should be used in training to increase the success of sentiment analysis tasks.
Compare the Path Finding Algorithms that are Applied for Route Searching in Maps

The purpose of the conducted research is to find the most optimal algorithm in terms of finding the shortest path that are applied for route searching in maps. This research will compare the pathfinding algorithm such as Dijkstra, BFS, and A * algorithms in terms of time, path, and distance found, by considering the existing real life variables such as distance, weather, average speed, obstructions, and road width that are encountered by every road users to increase accuracy. With the characteristic and following the procedure of each algorithm, we finally manage to dive thoroughly about how the algorithm works by performing calculation and analyze the advantages and drawbacks of each algorithm. The comparison will be made by a system designed using Python Programming Language. This study concludes that Dijkstra's algorithm is 13.78% faster than A * Algorithm and 32.3% faster than Breadth First Search in terms of time but the paths found are not always optimal.
A Machine Learning Model Selection considering Tradeoffs between Accuracy and Interpretability

Using black box machine learning models (e.g., Deep Neural Networks) in high-stakes domains such as healthcare, criminal justice and real-time systems can cause serious problems due to their complexity and poor interpretability. Moreover, model selection with interpretability in addition to accuracy is one of emerging research areas with lack of model agnostic and quantitative interpretability metrics. In this work, we adopt a quantitative interpretability metric, and then, introduce a trade-offs methodology between accuracy and interpretability, which can be demonstrated by increasing interpretability of ML models while allowing accuracy to drop up to given thresholds. In our experimental results, interpretability in terms of simulatability operation count (SOC) is improved up to 76.2&#x0025; with minimal 2.3&#x0025; accuracy drop in a SVR estimator of the Auto MPG dataset (up to 64.3&#x0025; with minimal 1.9&#x0025; accuracy drop in the Forest Fire dataset of an MLP estimator).
Heuristic ensemble for unsupervised detection of multiple types of concept drift in data stream classification

Real-world data stream classification often deals with multiple types of concept drift, categorized by change characteristics such as speed, distribution, and severity. When labels are unavailable, traditional concept drift detection algorithms, used in stream classification frameworks, are often focused on only one type of concept drift. To overcome the limitations of traditional detection algorithms, this study proposed a Heuristic Ensemble Framework for Drift Detection (HEFDD). HEFDD aims to detect all types of concept drift by employing an ensemble of selected concept drift detection algorithms, each capable of detecting at least one type of concept drift. Experimental results show HEFDD provides significant improvement based on the z-score test when comparing detection accuracy with state-of-the-art individual algorithms. At the same time, HEFDD is able to reduce false alarms generated by individual concept drift detection algorithms.
An Integrated Impact of Blockchain Technology on Suppy Chain Management and the Logistics Industry

The benefits of blockchain technology for supply chain management and logistics have been authenticated in recent years. It can support transparency and guarantee eventual, system-wide consensus on the validity of an entire history of transactions. As Conte de Leon et al. defined, blockchains would cause disrupting transformations in all types of industries when used in supply chain management. Logistic companies should engage in blockchain technology to adapt rapidly to changing market conditions and needs in today's dynamic business environment. Thus, this chapter provides a comprehensive overview of blockchain technology and focuses on crucial supply chain capabilities to create such transparency, flexibility, and mutual trust between supply chain partners.
Live View Augmented Reality Technology Integrated IoT in Vocational Education Learning

The major purpose of this research is to build a smart learning package for the vocational area that is interactive, powerful, versatile, and user pleasant by combining live view technology with Augmented Reality and the Internet of Things (IoT). The development process in this study is based on five stages of ADDIE development, one of which enables for mass production, which is the researchers’ goal for the next two years. The concept of Live view was first developed by Google in 2020 for the Google Street AI mechanism, however this technology has been designed to be compatible with AR since 2017. Since 2017, researchers have been working on AR, VR, and mixed reality technologies that can smoothly integrate. Although there is a lot of IoT and Live View technology, there is still a lot of work to be done in the field of learning so that the technology may be used widely. The result of the development of live view AR is a product that can display objects in real time or objects that can be directly viewed using an AR camera. This development product has gone through a series of tests in expert materials with an average of 87.45 percent, media experts with an average of 84.67 percent, and active users with an average of 89.99 percent. It can be determined that the product’s development is sound and that it can be used by the general public. Although there are 1 out of 5 scores in the “Effectiveness and seamless use of AR live view applications” category that are in the typical standard of assessment, this is conceivable owing to late synchronization. The IoT-based AR live view concept has been implemented well, with a TSe value of 612 with results of 85.3 percent, UI/UX Design with a TSe of 815 with results of 85.1 percent, and Visual communication with a TSe of 513 with results of 87 percent in the final stage of testing. This demonstrates that Live View Augmented Reality Technology Integrated IoT in Vocational Education Learning is accurate and ready to use.
“Why did my AI agent lose?”: Visual Analytics for Scaling Up After-Action Review

How can we help domain-knowledgeable users who do not have expertise in AI analyze why an AI agent failed? Our research team previously developed a new structured process for such users to assess AI, called After-Action Review for AI (AAR/AI), consisting of a series of steps a human takes to assess an AI agent and formalize their understanding. In this paper, we investigate how the AAR/AI process can scale up to support reinforcement learning (RL) agents that operate in complex environments. We augment the AAR/AI process to be performed at three levels—episode-level, decision-level, and explanation-level—and integrate it into our redesigned visual analytics interface. We illustrate our approach through a usage scenario of analyzing why a RL agent lost in a complex real-time strategy game built with the StarCraft 2 engine. We believe integrating structured processes like AAR/AI into visualization tools can help visualization play a more critical role in AI interpretability.
Information-Directed Exploration via Distributional Deep Reinforcement Learning

Appropriate exploration strategy is crucial to the success of reinforcement learning tasks. One challenge for efficient explorations is to deal with noise in the reinforcement learning (RL), namely parametric uncertainty and intrinsic uncertainty. Researchers pointed out that intrinsic uncertainty may cause disaster to many common exploration strategies. The paper investigates an information-directed exploration strategy: Information Directed Sampling (IDS), which has been extended to general RL setting due to its merit of modeling both parametric uncertainty and intrinsic uncertainty. A modified version based on existing framework was proposed. Modified and original IDS were compared with in two Atari games: Asterix and Gravitar. It was observed that under the similar computational cost, modified method outperformed the original version in Asterix, and performed slightly worse in Gravitor but with much lower variance. Convincing justifications for the superior of modified method were also provided in the last part.
Intelligent Warehouse Information Management System Based on Hybrid Genetic Algorithm

Internet of things technology is an important item information management technology applied in the current social development, which has the characteristics of informationization, efficiency and precision. This paper analyzes the application of Internet of Things technology in logistics intelligent storage system, briefly expounds the technology of Internet of Things, puts forward the design of logistics intelligent storage information management system based on hybrid genetic algorithm, and realizes the effective management of mass logistics information by optimizing the functions and hardware structure of the system. Ensure intelligent storage information management and control effect.