Reviewerly/exHarmony huggingface dataset:
Hierarchical Graph Neural Network Based on Semi-Implicit Variational Inference

Graph neural network (GNN) has obtained outstanding achievements in relational data. However, these data have uncertain properties, for example, spurious edges may be included. Recently, variational graph autoencoder (VGAE) has been proposed to solve this problem. However, the distributional assumptions in the variational family restrict the variational inference (VI) flexibility and they define variational families using mean field, which can not capture complex posterior distributional. To solve the above question, in this article, we proposed a novel GNN model based on semi-implicit VI (SIVI), which can embed the node to the latent space to improve VI flexibility and enhance VI expressiveness with mixing distribution. Specifically, to approximate the true posterior, a variational posterior was given utilizing a semi-implicit hierarchical variational framework, which can model complex posterior. Moreover, an iterative decoder is used to better capture graph properties. Besides, due to the hierarchical structure in our model, it can incorporation neighbor information between nodes. Experiments on multiple data sets, Our method has achieved state-of-the-art results compared to other similar methods. Particularly, on the citation data set Citeseer without features, our method outperforms VGAE by 9%.
Team Stanford ACMLab at SemEval 2022 Task 4: Textual Analysis of PCL Using Contextual Word Embeddings

We propose the use of a contextual embedding based-neural model on strictly textual inputs to detect the presence of patronizing or condescending language (PCL).We finetuned a pretrained BERT model to detect whether or not a paragraph contained PCL (Subtask 1), and furthermore finetuned another pre-trained BERT model to identify the linguistic techniques used to convey the PCL (Subtask 2).Results show that this approach is viable for binary classification of PCL, but breaks when attempting to identify the PCL techniques.Our system placed 32/79 for subtask 1, and 40/49 for subtask 2.
Traffic Control System Development Based on Computer Vision

In 2020, the number of Brazil inhabitants was approximately 212 millions, whereas the number of vehicles jumped from 104 to 107 millions. It is observed, therefore, that there is 1 vehicle for every 2 inhabitants. That same year, the population in urban areas went to 80%. Managing traffic in big cities is becoming a huge challenge. Traffic lights operating with Fixed Time Signal to control vehicle flux are no longer efficient in all traffic scenarios. Technological advances in Computer Vision, moving objects detection and classification techniques and the demanding of little computer power to manage these tasks allowed the development of TEOP, a CV-based traffic control system. This low cost solution was implemented to advance the Fixed Time Signal system, cameras and logical network infrastructure already in use in Brazilian cities, transforming regular traffic lights into smart traffic lights (STL). An application was developed and installed in a computer to capture images of traffic, count vehicles and calculate time needed for them to pass through. Raspberry controlled traffic lights. In comparison to regular traffic lights, STLs improved traffic flow by 33%, allowing a wait of just 4 s in cases where there is no vehicle flow at the competing traffic light and reducing the crossing time of 46 vehicles from 152 to 116 s when there were 12 vehicles on the competing side, a significant gain. It was also capable of reporting traffic jams and creating a database that could be used for decision-taking by agencies responsible for each route.
LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognition

Processing complex and ambiguous named entities is a challenging research problem, but it has not received sufficient attention from the natural language processing community. In this short paper, we present our participation in the English track of SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition. Inspired by the recent advances in pretrained Transformer language models, we propose a simple yet effective Transformer-based baseline for the task. Despite its simplicity, our proposed approach shows competitive results in the leaderboard as we ranked 12 over 30 teams. Our system achieved a macro F1 score of 72.50% on the held-out test set. We have also explored a data augmentation approach using entity linking. While the approach does not improve the final performance, we also discuss it in this paper.
Tsch-Varba – Dynamic Channel Allocation with Variable Bandwidth in Tsch Networks

The transmission and reception of packets in wireless networks suffer from interference, packet collisions, and bottlenecks that increase latency and reduce network throughput. The high demand for a set of links can turn the network inefficient and even dead. The TSCH networks try to avoid collisions by scheduling channels and time. However, there is still the presence of bottlenecks, and an improvement is required. Channels with wider bandwidth can improve the network in bottleneck regions. Therefore, this paper proposes a scheduling algorithm to dynamically allocate channels with variable bandwidth to reduce bottlenecks and improve network performance. The scheduling and channel allocation happen using the conflict mapping method where congestion is identified. A set of nodes compete for the same communication channel resources and sending time, organizing them to improve the network performance. This work proposes a procedure for allocating channels with bandwidth proportional to the demand for packets per link. We implemented the algorithm in the Contiki-NG platform. Results show a reduction in the slot time duration and improvement in packet delivery.
Lacuna Reconstruction: Self-Supervised Pre-Training for Low-Resource Historical Document Transcription

We present a self-supervised pre-training approach for learning rich visual language representations for both handwritten and printed historical document transcription.After supervised fine-tuning of our pre-trained encoder representations for low-resource document transcription on two languages, (1) a heterogeneous set of handwritten Islamicate manuscript images and (2) early modern English printed documents, we show a meaningful improvement in recognition accuracy over the same supervised model trained from scratch with as few as 30 line image transcriptions for training.Our masked language modelstyle pre-training strategy, where the model is trained to be able to identify the true masked visual representation from distractors sampled from within the same line, encourages learning robust contextualized language representations invariant to scribal writing style and printing noise present across documents.
Bitcoin ETFs: Measuring the Performance of This New Market Niche

The use of futures to track BTC may introduce a difference between the return of the ETF and that of its underlying benchmark. The standard deviation of this difference – the so-called “tracking error” – is the typical measure of performance for an ETF relative to its index and can be of considerable magnitude, which is troubling, since these ETFs have already attracted billions in investment dollars.1 Furthermore, tracking error alone may be insufficient as a measure of ETF performance. The less commonly used “tracking difference” metric provides additional insight into BTC-benchmarked ETF that should also be taken into consideration. In this article, we disentangle the various components of tracking error, and explain the use of the additional tracking difference metric to provide an enhanced framework for evaluating the Bitcoin ETFs performance.
Domain Adaptive Bootstrap Aggregating

When there is a distributional shift between data used to train a predictive algorithm and current data, performance can suffer. This is known as the domain adaptation problem. Bootstrap aggregating, or bagging, is a popular method for improving stability of predictive algorithms, while reducing variance and protecting against over-fitting. This article proposes a domain adaptive bagging method coupled with a new iterative nearest neighbor sampler. The key idea is to draw bootstrap samples from the training data in such a manner that their distribution equals that of new testing data. The proposed approach provides a general ensemble framework that can be applied to arbitrary classifiers. We further modify the method to allow anomalous samples in the test data corresponding to outliers in the training data. Theoretical support is provided, and the approach is compared to alternatives in simulations and real data applications.
Distributed Nonblocking Commit Protocols for Many-Party Cross-Blockchain  Transactions

The interoperability across multiple blockchains would play a critical role in future blockchain-based data management paradigm. Existing techniques either work only for two blockchains or requires a centralized component to govern the cross-blockchain transaction execution, neither of which would meet the scalability requirement. This paper proposes a new distributed commit protocol, namely \textit{cross-blockchain transaction} (CBT), for conducting transactions across an arbitrary number of blockchains without any centralized component. The key idea of CBT is to extend the two-phase commit protocol with a heartbeat mechanism to ensure the liveness of CBT without introducing additional nodes or blockchains. We have implemented CBT and compared it to the state-of-the-art protocols, demonstrating CBT's low overhead (3.6\% between two blockchains, less than $1\%$ among 32 or more blockchains) and high scalability (linear scalability on up to 64-blockchain transactions). In addition, we developed a graphic user interface for users to virtually monitor the status of the cross-blockchain transactions.
The Human Visual System and Adversarial AI

This paper applies theories about the Human Visual System to make Adversarial AI more effective. To date, Adversarial AI has modeled perceptual distances between clean and adversarial examples of images using Lp norms. These norms have the benefit of simple mathematical description and reasonable effectiveness in approximating perceptual distance. However, in prior decades, other areas of image processing have moved beyond simpler models like Mean Squared Error (MSE) towards more complex models that better approximate the Human Visual System (HVS). We demonstrate a proof of concept of incorporating HVS models into Adversarial AI.
Chemo Creatures in a Digital Ocean!

The three-channel video installation In Search of Chemozoa (boredomresearch 2020a) offers an alternative register for health, one which poetically grapples with the restless balancing acts between cells and their milieux and between communities and their resources. The video portrays a fictitious organism that feeds off toxic algae to regulate its inherently cancerous rate of growth, thereby emulating the rhythms of cancer care. Art duo boredomresearch conceived the Chemozoa and its underwater world through interactions with scientists from the Arizona Cancer Evolution Center (ACE) at the Biodesign Institute, Arizona State University (ASU). This chapter explores how the interdisciplinary project uses digital technologies and media conventions to simulate paradigms for life capable of producing emergent and affective ecological relations.
Nonconvex variational approach for simultaneously recovering cartoon and texture images

Many real-world applications in image processing and computer vision require splitting an input image into a cartoon component and a texture component. We propose a nonconvex variational image decomposition model for simultaneously recovering cartoon and texture images. To induce the sparsity of gradient norms of the cartoon image more strongly than the classical total variation regularization, we applied the nonconvex firm penalty function as a regularizer for the cartoon image. The nonconvex firm penalty regularizer function has a better ability to separate the piecewise constant component with neat edges. The G-norm was used as an oscillating prior for the texture image. Converting the proposed optimization model to a constrained problem by variable splitting, we addressed it with the alternating direction method of multipliers. Experimental results and comparisons were given to verify the superiority of existing state-of-the-art methods in terms of correlation, peak signal-to-noise ratio, structural similarity, and visual quality. Finally, we demonstrated the effectiveness of the proposed model by several applications such as image abstraction and pencil sketching, artifact removal, image denoising, image composition, and detail enhancement.
Competency of Adult Learners at Lifelong Education System of University

The purpose of this study was to explore support strategies according to extra-curricular program with needs analysis of adult learners in the lifelong education system participating in the lifelong education system support project of university. To this end, a survey was conducted for demand analysis, and the results of analyzing the collected data through the t-test and the The Focus for Focus model are as follows. First, among all adult learners, the priority of comparative and support competencies was in the order of life competency, basic learning competency, convergence, and start-up competency. Second, by age of adult learners, those in their 20s and 60s showed life competency as the top priority, those in their 30s and 50s showed basic learning competency as the top priority, and those in their 40s showed convergence and global competency as the top priority. Third, by major, life and basic learning capabilities of the construction environment convergence engineering department, basic learning capabilities and learning capabilities of the sports welfare department, convergence and start-up capabilities of the welfare management department, and smart energy engineering were the top areas. Through the above research results, several implications for the ability to support adult learners in the college's lifelong learning system department were derived. This study is significant in that it derived an adult-friendly comparative department support plan that meets the needs of adult learners in the lifelong education system. The results of this study are expected to be used as useful data for supporting comparative programs tailored to the needs of adult learners at universities operating the university lifelong learning system.
Phoneme analysis for multiple languages with fuzzy‐based speaker identification

Most voice biometric systems are dependent on the language of the user. However, if the idea is to create an all-inclusive and reliable system that uses speech as its input, then they should be able to recognise people regardless of language or accent. Thus, this paper investigates the effects of languages on speaker identification systems and the phonetic impact on their performance. The experiments are performed using three widely spoken languages which are Portuguese, English, and Chinese. The Mel-Frequency Cepstrum Coefficients and its Deltas are extracted from those languages. Also, this paper expands the research study of fuzzy models in the speaker recognition field, using a Fuzzy C-Means and Fuzzy k-Nearest Neighbours and comparing them with k-Nearest Neighbours and Support Vector Machines. Results with more languages decreases the accuracy from 92% to 85.59%, but further investigation suggests it is caused by the number of classes. A phonetic investigation finds no relation between the phonemes and the results. Finally, fuzzy methods offer more flexibility and in some cases, even better results compared to their crisp version. Therefore, the biometric system presented here is not affected by multilingual environments.
Vision-Based Walking Style Recognition in the Wild

Human recognition at distance has ever been an attractive solution for many applications. However, the challenges associated with this task, especially in the wild, are difficult to overcome. Most gait recognition methods do not take into account the shape of the body as a whole, but also a walking style, which helps to recognize a person more accurately. We offer a method for recognizing walking style at a reasonable distance of the camera so that the visual information of a walking person can be captured and analyzed. Method is based on the latest achievements in deep object detection and tracking, as well as deep networks for human action recognition, adapted for walking style recognition. We study six main categories of walking style based on head position, torso position, arm swing, stride length, and walking speed. Additionally, we analyze several clips from each video sequence for accurate recognition. Experiments show that sometimes we cannot explicitly assign a walking style to one category, but two categories such as driver and influencer describe human walking in the wild well. We achieved recognition results 82.4% Top-1 and 94.7% Top-2.
Potentials of Blockchain Technologies in Supply Chain Management—Empirical Results

This paper focuses on Blockchain Technologies in Supply Chain Management (SCM). It is based on the qualitative study “Potentials of Blockchain Technologies in Supply Chain Management—A Conceptual Model” by Härting et al. (Science Direct. 24th International conference on knowledge-based and intelligent information & engineering systems. Elsevier B.V., pp 1–9, 2020 [1]). The paper aims to investigate the potentials of Blockchain Technology in Supply Chain Management based on the conceptual model with six influencing factors and four moderators. A quantitative study is used to gain further insights and to examine relevant factors influencing the potentials of Blockchain Technologies in SCM. The results of the quantitative research approach show that the factors efficiency and scalability positively and significantly influence the potentials of Blockchain Technologies in SCM.
Assured Multi-agent Reinforcement Learning with Robust Agent-Interaction Adaptability

Multi-agent reinforcement learning facilitates agents learning to solve complex decision-making problems requiring collaboration. However, reinforcement learning methods are underpinned by stochastic mechanisms, making them unsuitable for safety-critical domains. To solve this issue, approaches such as assured multi-agent reinforcement learning, which utilises quantitative verification to produce formal guarantees of safety requirements during the agents learning process, have been developed. However, this approach relies on accurate knowledge about the environment to be effectively used which can be detrimental if this knowledge is inaccurate. Therefore, we developed an extension to assured multi-agent reinforcement learning called agent interaction driven adaptability, an automated process to securing reliable safety constraints, allowing inaccurate and missing knowledge to be used without detriment. Our preliminary results showcase the ability of agent interaction driven adaptability to allow safe multi-agent reinforcement learning to be utilised in safety-critical scenarios.
RESEARCH ON BEHAVIORAL FEATURES OF INTERNET USERS WHO ARE INTERESTED IN PERSONAL TESTS

We have studied the behavioral characteristics of Internet users who are interested in psychological questionnaires and use the Google Internet for this matter. The representative sample consisted of 440 respondents, each of whom was assigned to one of 16 types according to the author’s method of "Social Roles". The socio-psychological characteristics of each type, presented according to this method, and their quantitative distribution allowed us to analyze the socio-psychological structure of the general population. The respondents had most often the type related with psychological competencies and the opposite type which described the opposite need in these traits. This proposed structure of behavioral characteristics can be useful for researchers performing similar Internet research projects. The method scales were standardized with beta-distribution as a statistical basis for the test result data. This approach is an alternative to the method transforming the original data in order to comply with the normal distribution, which leads to arbitrary changes in data. The statistical structure of the "influence" scale was analyzed; it reflected the features of the general population and the structure of communicative competencies existing in modern society. The "influence" scale had a significant asymmetry, so that a number of expert types was much smaller compared to that of the types focused on motivation of others, which reflects the existing social shortage of specialists in technical and other profiles. A cluster analysis with the data obtained from the survey of 1,038 respondents was also conducted, which allowed us to identify 12 clusters and analyze their compliance with the examined types.
An Application Framework for Blockchain on Smart Factory Locations Using a Datacenter Approach

We offer an in-pocket platform which allows data providers, virtual servers and AI designers to collaborate for machine knowledge representations in a permission less AI marketplace. The information is a valuable numerical tool that is important for group's perspectives. Our initiative assists data owners in protecting data access and security while supporting AI developers’ use of their data for training. Comparably, AI developers can use the calculation tools from the cloud provider against relinquishing power or privacy. Our framework protocols are designed to allow all three entities data owners, cloud vendors and AI developers to legitimately increase their behavior in the public system to test and approve of misconduct or conflict arbitration with the blockchain system. The Hyperledger Fabric is an analogy to centralized AI networks that do not have protection for information prior to modeling. We present investigational outcomes which show dormancy in various access networks where blockchain colleagues are accessible via dissimilar data centers. Our findings specify that the planned approach is well tailored to numerous data. Also, model owners can educate up to 70 models to a 12-peer un-optimized blockchain system and some 30 prototypes to a 24-peer framework.
Consideration for NoSQL Databases Technical Consolidation

In the era of business analytics, enterprises are processing and persisting data from a variety of sources. Data sources include structured, unstructured, and semi-structured. Some data sources are schema driven and many are schema-agnostic. NoSQL databases serve the need of persisting, processing, and managing unstructured as well as structured data in different ways. It is important to select the right set of NoSQL for analytics-driven organizations. In the paper, the intention is to determine guidelines and consideration that can help enterprises to select the right set of NoSQL and consolidate NoSQL technologies for optimization. The right combination of NoSQL databases can help homogeneous technology footprint along with cost benefits & effort benefits for an analytics-driven enterprise. Design principals to help select between graph databases, document databases, and in-memory databases and in memory.