Reviewerly/exHarmony huggingface dataset:
Tell Me Where to Go Next: Improving POI Recommendation via Conversation

Next Point-of-Interest (POI) recommendation estimates user preference on POIs according to past check-in history, suffering from the intrinsic limitation of obtaining dynamic user preferences. Conversational Recommendation System (CRS), which can collect dynamic user preferences through conversation, brings a solution to the above limitation. However, none of the existing CRS methods consider the spatio-temporal factors in the action selection phase, which are essential for POI conversational recommendation. In this paper, we propose a new Spatio-Temporal Conversational Recommendation System (STCRS) to fuse the spatio-temporal and dialogue information for next POI recommendation. Specifically, STCRS first learns the spatio-temporal information in the user's check-in history. Then reinforcement learning is used to decide which action (asking for an attribute or recommending POIs) to take at the next turn to achieve successful POI recommendation within as few turns as possible. Finally, our extensive experiments on two real-world datasets demonstrate significant improvements over the state-of-the-art methods.
Design of employee presence system using Radio Frequency Identification technology

Abstract Employee attendance is important data at a college, because it is related to employee performance. Information technology for presence systems exists that uses magnetic cards and biometric measurements. Lately there is a technology of Radio Frequency Identification (RFID) devices that use radio signals to identify objects. Such RFID characteristics enable this technology to be used on employee cards connected to a computer-based presence system. Based on these conditions, this study aims to design a presence system using RFID technology. The system design uses the Rational Unified Process method, the Unified Modelling Language, with PHP and MySQL as builder software. The hardware used, in addition to a set of computers with specifications tailored to the needs, is also equipped with RFID devices. The presence system produced includes an RFID card reader, and a presence data processor. The presence system has a menu for adding employee data, employee attendance, delay identification, time management, monthly attendance recapitulation for each employee, and attendance recapitulation for all employees. This system has been trained by operators in the presence section with a very good absorption of 82% with a two hour training period. This shows that the system is relatively easy to implement.
Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.
Visualization of google mobility data for provinces in Indonesia using seaborn python programming package

Abstract One important data needed in handling the COVID-19 pandemic is human mobility data. Based on mobility data provided by Google, analysis can be done as a basis for decision making. This research produces several visualization methods that provide insight to decision makers. Visualization is done using Python Programming.
A Hybrid Approach to Temporal Pattern Matching

Temporal graphs represent relationships and interactions among entities over time, such as those occurring among users in social, transaction, and telecommunication networks. The analysis of their temporal structure help us understand, and predict the behavior of their entities. A typical analysis task in graph networks is the finding of all appearances of an input graph pattern query. Such appearances are called matches. In this paper, we are interested in finding all matches of an interaction pattern query within temporal graphs. To this end, we propose a hybrid approach that achieves effective filtering of potential matches based both on structure and time. Our approach exploits a graph representation where edges are ordered by time. We present experiments with real datasets that illustrate the efficiency of our approach.
Predicting Early Readmission of Diabetic Patients: Toward Interpretable Models

Hospital readmission among diabetic patients is a common phenomenon throughout the world. Predicting such patients with a high risk of readmission at the time of discharge even before can help us to provide better health care to them. It can minimize the cost associated with readmission too. This study aims at creating a decision support system that can find diabetic patients who are prone to early readmission. To do that, several data mining techniques have been used. Two regular classifiers using the decision tree and random forest have been developed. After that, two rule-based classifiers using Repeated Incremental Pruning to Produce Error Reduction (RIPPER) and PART algorithms have been developed to provide better interpretability and understandability of the support system. Between two regular classifiers, the random forest has shown a better performance with 89.5% accuracy and 89.5% recall. And, between rule-based classifiers, PART has demonstrated promising performance with 86.6% accuracy and 84.6% recall. Using these classifiers, smart and improved health care can be ensured.
Interactive, Efficient and Creative Image Generation Using Compositional Pattern-Producing Networks

In contrast to most recent models that generate an entire image at once, the paper introduces a new architecture for generating images one pixel at a time using a Compositional Pattern-Producing Network (CPPN) as the generator part in a Generative Adversarial Network (GAN), allowing for effective generation of visually interesting images with artistic value, at arbitrary resolutions independent of the dimensions of the training data. The architecture, as well as accompanying (hyper-) parameters, for training CPPNs using recent GAN stabilisation techniques is shown to generalise well across many standard datasets. Rather than relying on just a latent noise vector (entangling various features with each other), mutual information maximisation is utilised to get disentangled representations, removing the requirement to use labelled data and giving the user control over the generated images. A web application for interacting with pre-trained models was also created, unique in the offered level of interactivity with an image-generating GAN.
On the Use of the SSIM Algorithm for Detecting Intellectual Property Copying in Web Design

Copied images as well as website designs are a substantial concern in digital rights management. These issues often lead to litigation. Unfortunately, judgements regarding similarity are often arrived at with imprecise methods. What is needed and what is described in this current study is a mathematically precise method for determining design similarity. The Structural Similarity Index (SSIN), provides a method for comparing images, including images from web design. This produces a quantifiable method for evaluating similarities in design and in imagery. The SSIN has already been well established for analyzing images in other contexts.
Tested Success Tips for Simulation Project Excellence

How can you make your projects successful? Modeling can certainly be fun, but it can also be quite challenging. With the new demands of Smart Factories, Digital Twins, and Digital Transformation, the challenges multiply. You want your first and every project to be successful, so you can justify continued work. Unfortunately, a simulation project is much more than simply building a model -- the skills required for success go well beyond knowing a particular simulation tool. A 35-year veteran who has done hundreds of successful projects shares important insights to enable project success. He also shares some cautions and tips to help avoid common traps leading to failure and frustration.
An efficient and global interactive optimization methodology for path planning with multiple routing constraints

Path planning problem is attracting wide attention in autonomous system and process industry system. The existed research mainly focuses on finding the shortest path from the source vertex to the termination vertex under loose constraints of vertex and edge. However, in realistic, the constraints such as specified vertexes, specified paths, forbidden paths and forbidden vertexes have to be considered, which makes the existing algorithms inefficient even infeasible. Aiming at solving the problems of complex path planning with multiple routing constraints, this paper organizes transforms the constraints into appropriate mathematical analytic expressions. Then, in order to overcome the defects of existing coding and optimization algorithms, an adaptive strategy for the vertex priority is proposed in coding, and an efficient and global optimization methodology based on swarm intelligence algorithms is put forward, which can make full use of the high efficiency of the local optimization algorithm and the high search ability of the global optimization algorithm. Moreover, the optimal convergence condition of the methodology is proved theoretically. Finally, two experiments are inducted, and the results demonstrated its efficiency and superiority.
Autonomous Spiral Motion by a Small-Type Robot on an Obstacle-Available Surface

Several robot-related studies have been conducted in recent years; however, studies on the autonomous travel of small mobile robots in small spaces are lacking. In this study, we investigate the development of autonomous travel for small robots that need to travel and cover the entire smooth surface, such as those employed for cleaning tables or solar panels. We consider an obstacle-available surface and target this travel on it by proposing a spiral motion method. To achieve the spiral motion, we focus on developing autonomous avoidance of obstacles, return to original path, and fall prevention when robots traverse a surface. The development of regular travel by a robot without an encoder is an important feature of this study. The traveled distance was measured using the traveling time. We achieved spiral motion by analyzing the data from multiple small sensors installed on the robot by introducing a new attitude-control method, and we ensured that the robot returned to the original spiral path autonomously after avoiding obstacles and without falling over the edge of the surface.
STRISA: A New Regulation Architecture to Enforce Travel Rule

FATF introduces a new regulation framework with Travel Rule in 2019, many crypto exchanges have incorporated the TRISA (Travel rule information sharing architecture) system as a part of their compliance mechanisms. However, the TRISA system may not meet the regulation requirements for all the countries. This paper proposes another version of TRISA, STRISA, that is compatible with the TRISA, but with four new features: 1) track individual digital wallets; 2) support a BDL (Blockchain Data Lake) platform that can interoperate with various blockchain (BC) systems for analysis; 3) supply a regulation enforcement framework using smart contracts with automated machine learning techniques; 4) Most of data are stored in associated BCs to ensure that no one including regulators should try to alter the data stored to protect the integrity of entire regulation compliance process. This paper also discusses other design and protocols to support these new features including unique storage scheme, double-interlocking mechanism. STRISA has been implemented and is available for research and experimentation.
Exploring the Role of AI in K12

The purpose of this chapter is to discover the role of artificial intelligence (AI) in K12. How AI, in particular robot teachers, are taking over online learning is the key objective of this study. A focus group interview was conducted online to measure the phenomenon. Findings from focus group interview indicated that the traditional method of teaching and learning had been changed dramatically due to numerous reasons, especially due to market turbulence like COVID-19. As a result, the role of AI in K12 received massive attention in society. The study contained a limited number of respondents that may affect the generalizability of the study. Future studies with mixed methodology may shed light on the undiscovered phenomenon of AI in the context of K12.
Trusted Data Management for E-learning System Based on Blockchain

During the epidemic outbreak of COVID-19, all universities, middle schools and primary schools in China adopted the form of online teaching. It brings convenience, but it also raises some security and privacy issues. The challenge to solve the issues is how to achieve effective access control and how to ensure the authenticity of online data. Because blockchain has the characteristics of openness, unforgeability and decentralization, we present a trusted data management scheme for e-learning system based on blockchain. In the scheme, large amounts of resource data are stored in a distributed storage system; the evidences of uploaded data are stored in blockchain network established by high credible institutions. Once the data in the distributed storage are tampered, it will be discovered by looking up blockchain transactions. In order to make up for the lack of privacy protection in blockchain, we adopt attribute encryption. For different resources, different ciphertext policies are given. Only the users whose attributes satisfy the policy can decrypt the data. Further, a fine-grained access control scheme for e-learning is designed. In addition, the scheme can prevent collusion attacks; even if multiple users collude, they will not get more resources.
On intermediate inquisitive and dependence logics: An algebraic study

This article provides an algebraic study of intermediate inquisitive and dependence logics. While these logics are usually investigated using team semantics, here we introduce an alternative algebraic semantics and we prove it is complete for all intermediate inquisitive and dependence logics. To this end, we define inquisitive and dependence algebras and we investigate their model-theoretic properties. We show they are elementary structures axiomatised by Horn sentences and we use our completeness result to state a version of algebraisability for inquisitive and dependence logics. We then focus on finite, core-generated, well-connected inquisitive and dependence algebras: we show they witness the validity of formulas true in inquisitive algebras, and of formulas true in well-connected dependence algebras. Finally, we obtain Representation Theorems for finite, core-generated, well-connected, inquisitive and dependence algebras and we prove some results connecting team and algebraic semantics.
A Study of Maintainability in Evolving Open-Source Software

Our study is focused on an evaluation of the maintainability characteristic in the context of the long-term evolution of open-source software. According to well established software quality models such as the ISO 9126 and the more recent ISO 25010, maintainability remains among key quality characteristics alongside performance, security and reliability. To achieve our objective, we selected three complex, widely used target applications for which access to their entire development history and source code was available. To enable cross-application comparison, we restricted our selection to GUI-driven software developed on the Java platform. We focused our examination on released versions, resulting in 111 software releases included in our case study. These covered more than 10 years of development for each of the applications. For each version, we determined its maintainability using three distinct quantitative models of varying complexity. We examined the relation between software size and maintainability and studied the main drivers of important changes to software maintainability. We contextualized our findings using manual source code examination. We also carried out a finer grained evaluation at package level to determine the distribution of maintainability issues within application source code. Finally, we provided a cross-application analysis in order to identify common as well as application-specific patterns.
How do education resources respond to the quality of local governance in Africa?

Abstract We empirically investigate the extent to which weak local governance such as corruption, lack of effectiveness, and responsiveness in local government offices in Africa affects the likelihood that citizens experience educational resource challenges in their local public schools. We consider the challenges of lack of textbooks, poor school facilities, expensive school fees, teacher absenteeism, overcrowded classrooms, and poor teaching quality. Our perception‐based indicators of local governance are measured at the regional/provincial level, the smallest geographical location in our pooled Afrobarometer data set across 33 African countries. We find that local government officials' corrupt behaviors and ineffectiveness increase the local inhabitants' probability of experiencing challenges in their local public schools, even after controlling for government expenditure on education. The cross‐region analysis with instrumental variables reports that a one‐point increase in the percentage of citizens who perceived their local government officials as corrupt yields an increase that ranges between 0.4 and 0.9 in the percentage of people who face poor human or physical school resources in their local public schools. These values range between 0.2 and 0.4 for a one‐point percentage increase in the measure of local government ineffectiveness.
Smart Education and Informatization Teaching Practice ‐‐‐ A case of English Teaching

Under the background of Smart Education, the modern information technologies has become a hotspot in teaching theory and practical research. This article points out the informatization teaching research and practice by the means of cloud space online courses, online writing correction network, foreign language intelligent learning platform. In the case teaching of English, from the three stages of teaching analysis, teaching strategy, and teaching implementation, modern information teaching methods were put into practice, such as flipped classroom, mind mapping, virtual reality training. Studies have shown that modern information education technology has a significant role in promoting knowledge transfer and improving students' quality and ability.
Conservative confidence intervals for the intraclass correlation coefficient for clustered binary data

Asymptotic approaches are traditionally used to calculate confidence intervals for intraclass correlation coefficient in a clustered binary study. When sample size is small to medium, or correlation or response rate is near the boundary, asymptotic intervals often do not have satisfactory performance with regard to coverage. We propose using the importance sampling method to construct the profile confidence limits for the intraclass correlation coefficient. Importance sampling is a simulation based approach to reduce the variance of the estimated parameter. Four existing asymptotic limits are used as statistical quantities for sample space ordering in the importance sampling method. Simulation studies are performed to evaluate the performance of the proposed accurate intervals with regard to coverage and interval width. Simulation results indicate that the accurate intervals based on the asymptotic limits by Fleiss and Cuzick generally have shorter width than others in many cases, while the accurate intervals based on Zou and Donner asymptotic limits outperform others when correlation and response rate are close to their boundaries.
Soft Taylor Pruning for Accelerating Deep Convolutional Neural Networks

Networking pruning is widely utilized for accelerating the inference procedure of deep models in low-resource settings. In this paper, a novel Gradient-based method, Soft Taylor Pruning(STP), are proposed to reduce the network complexity in dynamic way. Given a global compression rate, all filters are categorized into two parts by the gradient-based evaluation criterion in the pruning process. Then, two types of filters are remixed and updated in the training epoch. When a pruning error occurs, the model can correct the pruning error by re-active pruned filters in the next training epoch. In this way, the capacity of the model channel space remains the same until the network structure converges. In order to reduce the impact of large-weighted filters on criterion, We take the absolute value of the product of the feature map and the gradient as the evaluation criterion. So as to reduce the model pruning time, STP allows simultaneous pruning on multiple layers by controlling the opening and closing of multiple mask layers. Moreover, STP can be applied to various advanced CNNs, such as MobileNet. The features of our method are: 1) maintain the integrity of the model channel space; 2) less time cost of model compression; 3)less dependence on the pretrained model.
Visualization of neural network activity in the human brain based on fractal analysis

Abstract Methods for the quantitative assessment of signals based on functional magnetic resonance imaging data when studying the activity of neural networks in the human brain are presented. The algorithm of computational and analytical methods is implemented in the MATLAB environment, fractal analysis methods are selected as methods. Based on the developed algorithm, the FracLab software complex was created, with the help of which the signals received during the recognition of stimuli by 13 subjects were processed.