Reviewerly/exHarmony huggingface dataset:
A Weighted Critic Update Approach to Multi Agent Twin Delayed Deep Deterministic Algorithm

Multi-Agent Reinforcement Learning (MARL) has gained a lot more focus in the recent years because of its widespread use in multi-agent systems like autonomous vehicles, smart grids, traffic control management. Despite seeing significant developments in algorithmic design, problems such as overestimation of bias and instability in learning are still prevalent in MARL algorithms. Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm shapes policies of every agent irrespective of the setting- i.e. fully cooperative, fully competitive, and mixed setting. In its attempt to maximise the overall return of all agents, it suffers from bias overestimation which subsequently slows the convergence. Multi-Agent Twin Delayed Deep Deterministic (MATD3) was proposed to overcome the bias overestimation problem encountered in MADDPG algorithm. In this paper we propose a modification for the critic loss calculation of MATD3 algorithm which when applied, performs better than the original MATD3 algorithm. The proposed algorithm, termed MATD3-WCU in the paper, further stabilises the learning process and speeds up convergence. The proposed algorithm is tested on three scenarios of the state-of-art Multi-agent Particle Environment (MPE). The results against the experiment demonstrate that the MATD3-WCU algorithm performs better and provides a more stabilised learning with respect to the original MATD3 algorithm.
Generating High-quality Movie Tags from Social Reviews: A Learning-driven Approach

As a kind of keywords to describe video contents, tags are extremely beneficial for viewers to locate videos when they search the site. Additionally, tags also help video platform operators to better organize and recommend videos to platform users. Previous approaches are mainly based on manually tagging or tag propagation via video content analysis, which is time-consuming and resource-consuming. Especially, given the high volume of fresh movies generated per year, it is difficult to accurately tag all the movies manually. Moreover, video content analysis is also not easy considering typical features (e.g., long duration, complicated scenarios) of commercial movies. In this paper, we propose an automatic tagging algorithm called TagRec that exploits crowdsourced user reviews to generate accurate movie tags. We observe that user reviews contain rich information about movies (e.g., quality, actors) which can be learned to generate high-quality movie tags. Inspired by the above observation, we choose to transform the movie video tagging problem into a tag recommendation problem, in which tags are recommended to different movies by extracting knowledge from crowdsourced movie reviews. We take latent topics, tag co-occurrence probability and tag semantics into account, and formulate the problem as a recommendation optimization problem. We evaluate the performance of our pro-posed TagRec algorithm with a large-scale real-world dataset. Extensive experiments demonstrate that TagRec achieves 7.1 &#x0025; and 9.6&#x0025; improvement compared with other state-of-the-art methods in terms of Hit Ratio and Normalized Discounted Cumulative Gain respectively.
Multi-dimensional indexes for point and range queries on outsourced encrypted data

We present an approach for indexing encrypted data stored at external providers to enable provider-side evaluation of queries. Our approach supports the evaluation of point and range conditions on multiple attributes. Protection against inferences from indexes is guaranteed by clustering tuples in boxes that are then mapped to the same index values, so to ensure collisions for individual attributes as well as their combinations. Our spatial-based algorithm partitions tuples to produce such a clustering in a way to ensure efficient query execution. Query translation and processing require the client to store a compact map. The experiments, evaluating query performance and client-storage requirements, confirm the efficiency enjoyed by our solution.
A differential privacy preserving algorithm for greedy decision tree

In recent years, the contradiction between data application and privacy protection has become increasingly prominent, and differential privacy is considered an effective technology to resolve this contradiction. Therefore, we propose a differential privacy protection algorithm for greedy decision trees. Firstly, the privacy budget is allocated according to the structural characteristics of the tree. Secondly, the algorithm meets ϵ-differential privacy by adding Laplacian perturbation to the leaf nodes and exponential mechanism perturbation to the intermediate nodes. Finally, The integration strategy is used to construct random forest to avoid the instability of single tree. Experimental results show that this algorithm achieves a better balance between data availability and invisibility compared with other algorithms.
SmartZ COVID-19 Smart Door

During the ongoing COVID-19 pandemic, it is imperative that we take measures to safeguard the lives of people. Wearing face masks, temperature screening, and regular sanitization of hands have become necessary in these times. However, the guards stationed at the entrances of various establishments face an added risk of infection while enforcing these regulations. This paper proposes to automate the abovementioned processes by means of a smart door, which performs three important functions. Firstly, face mask detection is performed by a Convolutional Neural Network model which is built using the MobileNetV2 architecture. Secondly, contactless temperature screening is performed by the infrared temperature sensor MLX90614 in coordination with electronic circuitry controlled by the Arduino Uno microcontroller. Thirdly, user-friendly audio and visual instructions guide the users throughout the screening process, and the door is then opened to allow the user to pass through. Although all the required technologies currently exist independently, the proposed smart door aims to integrate these functionalities into one automated system. This project demonstrates how smart systems can be successfully deployed, with the integration of PC and microcontroller boards, driven by artificial intelligence and machine learning technologies. The deployment of the said smart door would help in minimizing the risk of infection during the pandemic, thereby benefiting the society at large.
MCLA: Research on cumulative learning of Markov Logic Network

Compared with other probabilistic semantic learning algorithms, Markov Logic Network (MLN) learning can integrate existing knowledge fragments. However, the knowledge fragments increase as the MLN learning algorithm traverses candidate objects, making the execution time of the MLN learning algorithm long. To shorten the execution time, an MLN Cumulative Learning Algorithm (MCLA) that combines the MLN learning algorithm and cumulative learning is proposed. MCLA can unify the existing knowledge and the new–old knowledge, perform multi-task learning under appropriate circumstances, and directly call the existing knowledge to learn the new knowledge. This paper applies MCLA to single-resident indoor activity scenarios to verify the effectiveness of the learning algorithm. Experiments have proved that MCLA not only ensures the accuracy and improves the versatility of knowledge, but also dramatically decreases the learning time and effectively manages the knowledge memory of the MLN.
Projection as a way of thinking to find factors in factor space

With the development of artificial intelligence, statistical learning methods are widely used. Among all artificial intelligence problems, obtaining excellent training models through massive data mining and huge computing power is currently a hot topic. Although such statistical learning methods have shown great potential in academic and industrial applications, the causal logic of machine learning still has poor interpretability. In the factor space theory proposed in 1982, Professor Peizhuang Wang first proposed causal reasoning to find the relationship between causation and effect and created factor space to illustrate causal relationships. Factors play a vital role in the factor space, which can be applied in various scenarios. The projection method provides a way of thinking and perspective for finding factors by projecting the vectors in the n-dimensional real vector space Rn into subspace, and this method has great value in knowledge representation and causal reasoning.
Towards Intelligent Reading through Multimodal and Contextualized Word LookUp

This paper presents Koob, an eBook Reader app that coalesces three key ideas to enhance students’ language learning, specifically for ambiguous words. The first idea is to improve the effectiveness of word lookup functionality through contextualization – by incorporating word sense disambiguation (WSD) techniques to show the contextually relevant definition at the top. The second idea is to augment WSD results with crowd-sourcing solutions. The last idea seeks to reinforce students’ learning by augmenting textual information with a visual aid, pictures related to the word, as part of the word lookup functionality. An empirical evaluation demonstrates that existing WSD techniques can successfully employed to dynamically reorder definitions such that the most relevant definition is at the top of the list for more than 80% of the instances.
Learning Hierarchical Video Graph Networks for One-Stop Video Delivery

The explosive growth of video data has brought great challenges to video retrieval, which aims to find out related videos from a video collection. Most users are usually not interested in all the content of retrieved videos but have a more fine-grained need. In the meantime, most existing methods can only return a ranked list of retrieved videos lacking a proper way to present the video content. In this paper, we introduce a distinctively new task, namely One-Stop Video Delivery (OSVD) aiming to realize a comprehensive retrieval system with the following merits: it not only retrieves the relevant videos but also filters out irrelevant information and presents compact video content to users, given a natural language query and video collection. To solve this task, we propose an end-to-end Hierarchical Video Graph Reasoning framework (HVGR) , which considers relations of different video levels and jointly accomplishes the one-stop delivery task. Specifically, we decompose the video into three levels, namely the video-level, moment-level, and the clip-level in a coarse-to-fine manner, and apply Graph Neural Networks (GNNs) on the hierarchical graph to model the relations. Furthermore, a pairwise ranking loss named Progressively Refined Loss is proposed based on prior knowledge that there is a relative order of the similarity of query-video, query-moment, and query-clip due to the different granularity of matched information. Extensive experimental results on benchmark datasets demonstrate that the proposed method achieves superior performance compared with baseline methods.
Breadth-First Coupled Sensor Configuration and Path-Planning in Unknown Environments

We present a breadth-first sensor configuration strategy to find near-optimal placement and sensor field of view (FoV). The strategy couples the sensor configuration procedure directly with the decision making task of planning a path for an agent in an unknown static environment comprised of threats. This coupled sensor configuration and path-planning (CSCP) strategy iteratively uses Gaussian Process Regression to construct a threat field estimate and find a candidate optimal path with minimum threat exposure. The strategy utilizes a unique task-driven information gain (TDIG) metric, which yields the sensor configurations when maximized. Due to the non-convex and non-submodular nature of the problem, we present an approximation for the optimization of the TDIG metric. Finally, we discuss the performance of the breadth-first strategy in contrast to a standard and depth-first strategy as well as traditional information-maximization.
Bounded Rational RRT-QX: Multi-Agent Motion Planning in Dynamic Human-Like Environments Using Cognitive Hierarchy and Q-Learning

This paper presents a multi-agent motion planning algorithm for human-like navigation in dynamic environments. A cognitive hierarchy approach is used to model the motion of autonomous agents. We discuss potential levels of rationality and introduce a method to predict them in real-time. The rationality level prediction is achieved by observing the kinody-namic distance (KD) of other agents. An offline training phase is required to learn the maximum KD from multiple boundary value problems. Collision avoidance is ensured by introducing artificial obstacles in the environment based on the predicted levels of rationality. The motion planning is then carried out using RRT-Q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sup> . The effectiveness of the bounded rational motion planning algorithm is illustrated in simulations.
Cognitive Internet of Things (IoT) and computational intelligence for mental well-being

In the current world of competition and constant struggle, taking care of mental well-being is of the upmost importance. With hundreds of millions of people suffering from mental disorders like depression, Alzheimer disease, schizophrenia, etc. each year, intelligent systems are needed that can diagnose, track, and manage the mental well-being of individuals. Computers have been widely used in various clinical applications such as clinical text analysis and medical image analysis. Computers have found uses in various medical domains such as cancer diagnosis, tumor analysis, etc. Computational intelligence has been used in building smart predictive models that can be of huge significance for diseases that require early clinical intervention. Depression, Alzheimer disease, etc. are mental disorders that worsen with time when not treated appropriately. Thus, there should be systems to identify mental disorders at an early stage. In this chapter, various methods in which mental well-being can be taken care of using computational intelligence are discussed.
Secure PAAS environment over hybrid cloud using load-balanced Docker containers

The purpose of this research is to provide cloud service providers (CSP) a secure platform that can be accessed for configuring secure, silent software deployment on the hybrid cloud using load-balanced Docker Containers (LBDC) that are configured with different PAAS software solutions. As many researchers in the past have worked on providing PASS platforms using either a public cloud or private cloud less research is carried out on the hybrid cloud systems and researchers who have worked on the hybrid cloud have followed a different approach to providing PAAS to organizations in need but lack in securing the PAAS platform. This research will provide cloud service providers a secure deployment of the PAAS platform for the developers on the go and will reduce the overhead of CSP to deploy the PAAS services from scratch. Compared to the other researcher’s contribution this research work provides a load-balanced containerized secure PASS platform that can be scaled on-demand and be secure while scaling the resources for peak workloads. The admins of the hybrid cloud can access and deploy the Docker containers based on the requirement laid down by the developers. Once the Admin configures the Docker container with the required PAAS services he can then easily invoke the LBDC in the hybrid cloud and deploy the PAAS platform for the developers to work on in seconds. LBDC’s will help in scalable deployment of Secure PAAS services for different locations and machines simultaneously thus reducing the cost and time required for hybrid virtual machines to get up and running for the development process by the developers of the company. CSP’s can achieve less turnaround time for deployment of secure PAAS environments for organizations.
Aspect Oriented Suggestion Extraction from Online Reviews

In the world of business, products need to evolve adjusting to the needs of the customer to ensure customer satisfaction. The abundance of opinionated text on the internet contains suggestions made by users that can be used to improve products. These suggestions are important to multiple stakeholders; as product improvements for businesses or as tips and advice to consumers. Until now, the extraction of suggestions has usually been defined as a problem of classifying sentences into suggestion and non-suggestion classes. No work has attempted to differentiate suggestions on the basis of intended receiver. Extracting these suggestions with respect to the aspects that reviewers are not satisfied with, could be used as a potential solution to improve products. To address these shortcomings, this study proposes a novel task decomposition called Aspect Oriented Suggestion Extraction to identify product improvements. It contains three main subtasks: suggestion classification, beneficiary classification and aspect extraction. The proposed approach proved to be very effective in determining suggestions from non-suggestions, achieving an F-score of 91% with BERT pretrained language model, outperforming state-of-the-art methods on suggestion mining. Experimental results on hotel reviews show the effectiveness of the techniques. The proposed framework, being the first of its nature, yields promising results.
An Improved K-means Clustering Algorithm for Multi-dimensional Multi-cluster data Using Meta-heuristics

k-means is the most widely used clustering algorithm which is an unsupervised technique that needs assumptions of centroids to begin the process. Hence, the problem is NP-hard and needs careful consideration and optimization to get a better quality of clusters of data. In this work, a meta-heuristic based genetic algorithm is proposed to optimize the centroid initialization process. The proposed method includes tournament selection, probability-based mutation, and elitism that leads to finding the optimal centroids for the clusters of a given dataset. Nine different and diversified datasets were used to test the performance of the proposed method in terms of the davies-bouldin index and it performed better in all the datasets than the standard k-means and minibatch k-means algorithm.
Research on the Method of Landscape Image Style Transfer based on Semantic Segmentation

The traditional style transfer technique in landscape image has the problem of semantic mismatching in different scenes. In this paper, based on the convolution and landscape style transfer algorithm of neural network, and analyses the semantic segmentation algorithm in theory, based on the normalized statistics of image style transfer algorithm (BN-NST) algorithm was designed on the basis of landscape style transfer algorithm based on semantic segmentation (BS-NST), solve the problem of semantic matching accuracy in different scenarios. In order to verify the algorithm, this paper uses the improved Deeplabv3+ network model and the convolutional neural network algorithm to process the input content image and style image, and the obtained target image has the advantages of authenticity and appreciation of landscape photos.
Classification of Activities and Falls within a Multimodal Dataset

Accurately predicting fall detection from wearable sensor data has many implications. Detecting falls and other activities from wearable data provide a method by which it assists those who need it. Multiple methods were employed to predict activities from wearable data. One method was using a Recurrent Neural Network(RNN) known as a Long Short Term Memory network (LSTM). In addition, a traditional machine learning approach was explored with the use of a Random Forest Classifler(RFC). This work was adapted from a few previous works, as the dataset being used was that of the Challenge Up competition. Despite utilizing previous methods and works, the highest accuracy attained was 72% which lends itself to the potential difficulty of predicting rare events from time-series data.
Modelling Symbolic Knowledge Using Neural Representations

Symbolic reasoning and deep learning are two fundamentally different approaches to building AI systems, with complementary strengths and weaknesses. Despite their clear differences, however, the line between these two approaches is increasingly blurry. For instance, the neural language models which are popular in Natural Language Processing are increasingly playing the role of knowledge bases, while neural network learning strategies are being used to learn symbolic knowledge, and to develop strategies for reasoning more flexibly with such knowledge. This blurring of the boundary between symbolic and neural methods offers significant opportunities for developing systems that can combine the flexibility and inductive capabilities of neural networks with the transparency and systematic reasoning abilities of symbolic frameworks. At the same time, there are still many open questions around how such a combination can best be achieved. This paper presents an overview of recent work on the relationship between symbolic knowledge and neural representations, with a focus on the use of neural networks, and vector representations more generally, for encoding knowledge.
Acceptance of Tourism Blockchain Based on Utaut and Connectivism Theory

With advances in internet technologies, innovative industrialization applying big data-based artificial intelligence is leading Industry 4.0. However, opportunities for the public to learn about virtual cryptocurrency or utilities based on blockchain technology are limited, except for Bitcoin. This lack of opportunities generates misunderstandings. This study ascertained and examined the determinants affecting the acceptance of new technologies on Jeju Island, Korea, where tourism is the dominant business. A total of 537 Jeju-do residents and potential visitors from the mainland were analyzed by conducting a partial least squares (PLS)- structural equation modeling (SEM) and one-group pretestâ€“posttest design through a survey after observing a video clip on blockchain implementation on YouTube. The empirical analysis results showed the effect of blockchain trust transparency on the performance and effort expectancy. Facilitating conditions were found to be the most influential in blockchain technology acceptance. After a video experiment introducing blockchain implementation as a Jeju tourism currency, a significant positive change towards blockchain was observed. This paper presents a concrete application of blockchain technology and shows the possibility of using social media (YouTube) to elicit user awareness and potential interest in the domestic tourism market.
Enhanced CNN Architecture for Invasive Ductal Carcinoma Detection in Breast Histopathology Images

Invasive Ductal Carcinoma (IDC) is considered the most frequently occurring breast cancer subtypes, and the early detection of IDC is crucial for treatment plan decision and improve therapy outcomes. Therefore, IDC’s automated identification will help the pathologists in diagnosis and provide a valuable second opinion. This study uses a dataset of Breast Histopathology Images, which is publicly accessible on Kaggle for the IDC classification task. This dataset includes 277,524 patches, 198,738 of which are IDC-negative, and 78,786 are IDC-positive images. We trained a novel architecture based on deep convolutional neural networks, and we also trained some predefined deep learning architectures for comparison. The proposed model outperforms the remaining models and achieves 89.5 % accuracy, 89 % F1-score, and the area under the receiver operating characteristic curve for detecting IDC (AUC) is 0.96 on the testing dataset, which is a new state-of-the-art result compared with the latest published approaches of IDC classification.