Reviewerly/exHarmony huggingface dataset:
Travel Recommendation System Using Content and Collaborative Filtering - A Hybrid Approach

Tourism plays a major role in a country's economy. But there is still a lack of a platform that provides personalized information regarding tourist attractions. If there exists a system that can provide personalized accurate information to tourists about local attractions, food, and shopping it will be a huge benefit for tourists. In this paper, we are proposing a hybrid approach of recommended systems to recommend tourist attractions for users. This recommendation process involves a combination of both content and collaborative filtering approach. This Hybrid approach avoids the disadvantages in both the methods and provides users with accurate information. To calculate the similarity between items the cosine similarity method is adopted. We have applied a model-based collaborative filtering approach called SVD for better results. The weighted hybridization approach is used to combine the results of both methods. The data of tourist attractions and users have been collected for implementation. This approach has given better results compared to CB and CF filtering methods separately.
Classification of Mouse Lung Metastatic Tumor with Deep Learning

Traditionally, pathologists microscopically examine tissue sections to detect pathological lesions; the many slides that must be evaluated impose severe work burdens. Also, diagnostic accuracy varies by pathologist training and experience; better diagnostic tools are required. Given the rapid development of computer vision, automated deep learning is now used to classify microscopic images, including medical images. Here, we used a Inception-v3 deep learning model to detect mouse lung metastatic tumors via whole slide imaging (WSI); we cropped the images to 151 by 151 pixels. The images were divided into training (53.8%) and test (46.2%) sets (21,017 and 18,016 images, respectively). When images from lung tissue containing tumor tissues were evaluated, the model accuracy was 98.76%. When images from normal lung tissue were evaluated, the model accuracy ("no tumor") was 99.87%. Thus, the deep learning model distinguished metastatic lesions from normal lung tissue. Our approach will allow the rapid and accurate analysis of various tissues.
Information Extraction from CORD-19 Using Hierarchical Clustering and Word Bank

The Coronavirus pandemic has called for extensive research in the medical discipline. Since such disease outbreaks are about life and death of the patients, the doctors’ and biomedical scientists’ time is crucial. Research documents are usually comprehensive, often consuming the readers’ time. A solution to it would be to extract information from the research text resembling the most relevant parts of the original text so that their valuable time is saved. The problem of text summarization is to create a shortened piece of text that represents the most relevant information from a relatively larger piece of text. This paper aims to ease the burden of the doctors so won't have to read the extensive research documents by constructing a summary of the most relevant parts of a medical research paper. A text summarization algorithm always works by quantifying the sentences by some means and analyzing scores. We use the TF-IDF quantification which is a popular way to quantify sentences. We select sentences with a high score and exclude those with a lower score, compared to a threshold. In a medical research paper, several sentences might have a low score, but they might be important if they contain biomedical entities. We use a dataset which has been constructed upon biomedical and COVID-19 terminology to construct a much better summary than some existing tools. As new methods keep coming up, this simple, yet robust approach gives us an accuracy of over 78% for most CORD-19 research papers.
Interpersonal coordination in joint multiple object tracking.

People often perform visual tasks together, for example, when looking for a misplaced key. When performing such tasks jointly, people coordinate their actions to divide the labor, for example, by looking for the misplaced key in different rooms. This way, they tend to perform better together than individually-they attain a group benefit. A crucial factor determining whether (and to what extent) individuals attain a group benefit is the amount of information they receive about each other's actions and performance. We systematically varied, across 8 conditions, the information participant pairs received while jointly performing a visual task. We find that participants can attain a group benefit without receiving any information (and thus cannot coordinate their actions). However, actions are coordinated and the group benefit is enhanced if participants receive information about each other's actions or performance. If both types of information are received, participants are faster in creating efficient labor divisions. To create divisions, participants used the screen center as a reference to divide the labor into a left and right side. When participants cannot coordinate actions, they exhibit a bias toward choosing the same side, but they forgo this bias once action coordination is possible, thereby boosting group performance. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Locate Who You Are

Nowadays, users are encouraged to activate across multiple online social networks simultaneously. User identity linkage, which aims to reveal the correspondence among different accounts across networks, has been regarded as a fundamental problem for user profiling, marketing, cybersecurity, and recommendation. Existing methods mainly address the prediction problem by utilizing profile, content, or structural features of users in symmetric ways. However, encouraged by online services, information from different social platforms may also be asymmetric, such as geo-locations and texts. It leads to an emerged challenge in aligning users with asymmetric information across networks. Instead of similarity evaluation applied in previous works, we formalize correlation between geo-locations and texts and propose a novel user identity linkage framework for matching users across networks. Moreover, our model can alleviate the label scarcity problem by introducing external text-location pairs. Experimental results on real-world datasets show that our approach outperforms existing methods and achieves state-of-the-art results.
Classification of Blockchain Consensus Mechanisms Based on PBFT Algorithm

Blockchain is a solution for transmission, distribution and decentralized storage of data, the core of which lies in the way to reach consensuses among distributed nodes under trustless environments. Practical Byzantine Fault Tolerance (PBFT) is one of the most popular blockchain consensus mechanisms used nowadays, especially in consortium blockchain. To improve the performance of PBFT, many works have been done since early years. This paper proposes a novel classification approach of improved PBFT algorithms and summarizes several performance indicators of them. The work aims to offer reference for further innovation upon blockchain consensus mechanisms based on improved PBFT under variant circumstances.
FPGA Implementation of Deep Leaning Model for Video Analytics

In recent years, deep neural networks have become a fascinating and influential research subject, and they play a critical role in video processing and analytics. Since, video analytics are predominantly hardware centric, exploration of implementing the deep neural networks in the hardware needs its brighter light of research. However, the computational complexity and resource constraints of deep neural networks are increasing exponentially by time. Convolutional neural networks are one of the most popular deep learning architecture especially for image classification and video analytics. But these algorithms need an efficient implement strategy for incorporating more real time computations in terms of handling the videos in the hardware. Field programmable Gate arrays (FPGA) is thought to be more advantageous in implementing the convolutional neural networks when compared to Graphics Processing Unit (GPU) in terms of energy efficient and low computational complexity. But still, an intelligent architecture is required for implementing the CNN in FPGA for processing the videos. This paper introduces a modern high-performance, energy-efficient Bat Pruned Ensembled Convolutional networks (BPEC-CNN) for processing the video in the hardware. The system integrates the Bat Evolutionary Pruned layers for CNN and implements the new shared Distributed Filtering Structures (DFS) for handing the filter layers in CNN with pipelined data-path in FPGA. In addition, the proposed system adopts the hardware-software co-design methodology for an energy efficiency and less computational complexity. The extensive experimentations are carried out using CASIA video datasets with ARTIX-7 FPGA boards (number) and various algorithms centric parameters such as accuracy, sensitivity, specificity and architecture centric parameters such as the power, area and throughput are analyzed. These results are then compared with the existing pruned CNN architectures such as CNN-Prunner in which the proposed architecture has been shown 25% better performance than the existing architectures.
Segmentation of Rat Brains and Cerebral Hemispheres in Triphenyltetrazolium Chloride-Stained Images after Stroke

Ischemic stroke is one of the leading causes of death among the aged population in the world. Experimental stroke models with rodents play a fundamental role in the investigation of the mechanism and impairment of cerebral ischemia. For its celerity and veracity, the 2,3,5-triphenyltetrazolium chloride (TTC) staining of rat brains has been extensively adopted to visualize the infarction, which is subsequently photographed for further processing. Two important tasks are to segment the brain regions and to compute the midline that separates the brain. This paper investigates automatic brain extraction and hemisphere segmentation algorithms in camera-based TTC-stained rat images. For rat brain extraction, a saliency region detection scheme on a superpixel image is exploited to extract the brain regions from the raw complicated image. Subsequently, the initial brain slices are refined using a parametric deformable model associated with color image transformation. For rat hemisphere segmentation, open curve evolution guided by the gradient vector flow in a medial subimage is developed to compute the midline. A wide variety of TTC-stained rat brain images captured by a smartphone were produced and utilized to evaluate the proposed segmentation frameworks. Experimental results on the segmentation of rat brains and cerebral hemispheres indicated that the developed schemes achieved high accuracy with average Dice scores of 92.33% and 97.15%, respectively. The established segmentation algorithms are believed to be potential and beneficial to facilitate experimental stroke study with TTC-stained rat brain images.
RABERT: Relation-Aware BERT for Target-Oriented Opinion Words Extraction

Targeted Opinion Word Extraction (TOWE) is a subtask of aspect-based sentiment analysis, which aims to identify the correspondingopinion terms for given opinion targets in a review. To solve theTOWE task, recent works mainly focus on learning the target-aware context representation that infuses target information intocontext representation by using various neural networks. However,it has been unclear how to encode the target information to BERT,a powerful pre-trained language model. In this paper, we proposea novel TOWE model, RABERT (Relation-Aware BERT), that canfully utilize BERT to obtain target-aware context representations.To introduce the target information into BERT layers clearly, wedesign a simple but effective encoding method that adds targetmarkers indicating the opinion targets to the sentence. In addi-tion, we find that the neighbor word information is also importantfor extracting the opinion terms. Therefore, RABERT employs thetarget-sentence relation network and the neighbor-aware relationnetwork to consider both the opinion target and the neighbor wordsinformation. Our experimental results on four benchmark datasetsshow that RABERT significantly outperforms the other baselinesand achieves state-of-the-art performance. We also demonstrate theeffectiveness of each component of RABERT in further analysis
Federated matched averaging with information-gain based parameter sampling

Federated learning allows edge devices to learn a shared global model from the client’s model parameters while keeping the training data on the device. However, for large models, transmitting all model parameters imposes considerable communication costs, which can be a significant bottleneck in bandwidth-constrained deployments. We present a federated matched averaging algorithm with information-gain based sampling that considerably reduces the number of parameters to be sent by all clients in a federated learning paradigm. Experiments across five benchmark datasets, encompassing symbolic, image and audio data, suggest that our algorithm significantly reduces communication overhead in federated learning settings, without reducing classification accuracy.
Federated Machine Learning Architecture for Searching Malware

Modern technologies for searching viruses, cloud-edge computing, and also federated algorithms and machine learning architectures are shown. The architectures for searching malware based on the xor metric applied in the design and test of computing systems are proposed. A Federated ML method is proposed for searching for malware, which significantly speeds up learning without the private big data of users. A federated infrastructure of cloud-edge computing is described. The use of signature analysis and the assertion engine for searching malware is shown. The paradigm of LTF-computing for searching destructive components in software applications is proposed.
An Overview on Supervised Semi-structured Data Classification

Many collaboratively building resources, such as Wikipedia, Weibo and Quora, exist in the form of semi-structured data. The semi-structured data has been widely used in areas such as data integration, data distribution, data storage, data management, information retrieval and knowledge management. For large volumes of semi-structured data on the Web, semi-structured data classification technique can group them into different categories by their structure and/or content information. Supervised semi-structured data classification plays an important role in many applications. This paper provides an overview of the literature in the area of supervised semi-structured data classification. A general framework for semi-structured data classification is presented, which is mainly composed of two steps: feature extraction and model building. Several different representation models of semi-structured data are discussed, mainly including rooted labeled tree model, feature vector space model and feature set model. A large selection of semi-structured data classification approaches are reviewed in detail from two aspects: based on structure only and based on both structure and content. Finally, several future research directions for semistructured data classification are presented.
Fundamentals of Blockchain and New Generations of Distributed Ledger Technologies. Circular Economy Case Uses in Spain

The evolution of Distributed Ledger Technologies since the first blockchain constitutes an opportunity for digital transformation in many social and economic contexts. This transformation will become even more intense in the post–covid-19 economic scenario. One of the most significant changes in the transformation consumers have undergone in their perception of the environment and of the sustainability of our societies. The circular economy is an agent of change that helps minimize humankind’s impact on the planet. And this change must be aligned with the sustainable development goals of the 2030 Agenda. Since its inception, blockchain technology has represented an opportunity for transparency, immutability, and persistence in the processes in which it intervenes, allowing it to be incorporated into easily traceable, disintermediated networks. By implication, this suggests we can move towards a verifiable, reliable, circular economy. In this chapter we cover the fundamentals of blockchain technology including its history, the elements that define distributed ledger technologies, and how they have evolved into their most recent forms—e.g., Hashgraph, Direct Acyclic Graphs, Holochain or Neural Distributed Ledgers—which represent scalable, efficient, innovative solutions aligned with sustainability goals and facilitating the efficient, sustainable management of circular economy projects. Finally, drawing on striking use cases in Spain, we describe the use of blockchain technology in circular economy projects, and its operating status, as examples of ideas for the future development of models that may reach the market.
More-Than-Human Future Cities: From the design of nature to designing for and through nature.

This paper examines ways in which media architecture and information design can contribute to multispecies wellbeing and coexistence in urban and peri-urban environments. It argues that a radical shift is needed, from looking at design as a tool to plan tamed nature into urban fabric towards design actions for understanding and working with the unruliness of the natural world in its manifold forms. We present a review of existing literature and an analysis of a selection of projects-in-progress from the CUBIC research group at Lancaster University, UK. In doing so, we aim to propose a new approach for how we think of, and design for, more-than-human future cities. Media architecture is core to this endeavour because we need to find new ways to communicate these new knowledges and contribute toward a better understanding of the complex issues of multispecies coexistence and flourishing. Far from providing an established framework or guidelines, we share this provocation from the outset as a catalyst for emerging collaboration and through which further open-ended questions can be explored.
CARLA Simulated Data for Rare Road Object Detection

Labeled data are paramount for modern, deep learning object detection models. However, such data are not always available, either due to time and financial constraints or due to the rarity of certain objects. In this paper, we show that the CARLA simulator can be used effectively to provide automatic annotations for custom street-view objects, boosting datasets for objects with few labels. We evaluate our models on real world images and show that low-shot training data expanded by synthetic images rendered in CARLA can provide better performance than training models with low-shot examples alone. To overcome the sim-to-real domain gap, we perform domain randomization by taking advantage of CARLA's diverse simulations of weather conditions, actors, and maps. We train detectors on CARLA-generated images of two different object classes and evaluate them on publicly available datasets. We provide access to our synthetic fire hydrant <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> https://www.kaggle.com/xinhez/synthetic-fire-hydrants and crosswalk <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> https://www.kaggle.com/buvision/synthetic-crosswalks datasets as well as provide step-by-step instructions <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> https://www.github.com/xinhez/simulation-for-detection to generate custom datasets in CARLA.
Mind the Gap

Containerization is becoming increasingly popular, but unfortunately, containers often fail to deliver the anticipated performance with the allocated resources. In this paper, we first demonstrate the performance variance and degradation are significant (by up to 5x) in a multi-tenant environment where containers are co-located. We then investigate the root cause of such performance degradation. Contrary to the common belief that such degradation is caused by resource contention and interference, we find that there is a gap between the amount of CPU a container reserves and actually gets. The root cause lies in the design choices of today's Linux scheduling mechanism, which we call Forced Runqueue Sharing and Phantom CPU Time. In fact, there are fundamental conflicts between the need to reserve CPU resources and Completely Fair Scheduler's work-conserving nature, and this contradiction prevents a container from fully utilizing its requested CPU resources. As a proof-of-concept, we implement a new resource configuration mechanism atop the widely used Kubernetes and Linux to demonstrate its potential benefits and shed light on future scheduler redesign. Our proof-of-concept, compared to the existing scheduler, improves the performance of both batch and interactive containerized apps by up to 5.6x and 13.7x.
Artificial Intelligence (Ai) In The Food And Beverage Industry: Improves The Customer Experience

This study examine how Artificial Intelligence (AI) tools can help restaurants improve service quality and create a better customer experience. Robots, chatbots, facial recognition, voice-activated technology, sentiment analysis, and perceived usefulness have all been investigated. Questionnaires were used as the survey tool in this investigation. A total of 250 questionnaire sets were collected successfully. The findings demonstrate that all AI technologies have a favourable link with service quality, with Voice-Activated Technology having the strongest correlation.
Overcoming the Vanishing Gradient Problem during Learning Recurrent Neural Nets (RNN)

Artificial neural nets have been equipped with working out the difficulty that arises as a result of exploding and vanishing gradients. The difficulty of working out is worsened exponentially particularly in deep learning understanding. With gradient-oriented learning approaches the up-to-date error gesture has to “flow back in time” throughout the response links to previously feedbacks for designing suitable feedback storage. To address the gradient vanishing delinquent, adaptive optimization approaches are given. With adaptive learning proportion, the adaptive gradient classifier switches the constraint for substantial hyper factor fine-tuning. Based on the numerous outstanding advances that recurrent neural nets (RNN) have added in the erstwhile in the field of Deep Learning. The objective of this paper is to have a concise synopsis of this evolving topic, with a focus on how to over the vanishing gradient problems during learning RNN. There are four types of methods adopted in this study to provide solutions to the gradient vanishing problem and they include approaches that do not employ gradients; approaches that enforce larger gradients, approaches that work at a higher level, and approaches that make use of unique structures. The inaccuracy flow for gradient-oriented recurrent learning approaches was hypothetically examined. This analysis exhibited that learning to link long-term lags can be problematic. Cutting-edge approaches to solving the gradient vanishing difficulty were revealed, but these methods have serious disadvantages, for example, practicable only for discrete data. The study deep-rooted that orthodox learning classifiers for recurrent neural networks are not able to learn long-term lag complications at a reasonable interval.