Reviewerly/exHarmony huggingface dataset:
HyFea: Winning Solution to Social Media Popularity Prediction for Multimedia Grand Challenge 2020

Social Media Popularity (SMP) prediction focuses on predicting the social impact of a given post from a specific user in social media, which is crucial for online advertising, social recommendation, and demand prediction. In this paper, we present HyFea, our winning solution to the Social Media Prediction (SMP) Challenge for multimedia grand challenge of ACM Multimedia 2020. To address the multi-modality and personality issues of this challenge, HyFea carefully considers multiple feature types and adopts a tree-based ensembling method, i.e., CatBoost, which is shown to perform well in prediction. Specifically, HyFea involves the features related to Image, Category, Space-Time, User Profile, Tag, and Others. We conduct several experiments on the Social Media Prediction Dataset (SMPD), verifying the positive contributions of each type of features.
Perfect state transfer in NEPS of complete graphs

Perfect state transfer in graphs is a concept arising from quantum physics and quantum computing. Given a graph G with adjacency matrix AG, the transition matrix of G with respect to AG is defined as HAG(t)=exp(−itAG), t∈R,i=−1. We say that perfect state transfer from vertex u to vertex v occurs in G at time τ if u≠v and the modulus of the (u,v)-entry of HAG(τ) is equal to 1. If the moduli of all diagonal entries of HAG(τ) are equal to 1 for some τ, then G is called periodic with period τ. In this paper we give a few sufficient conditions for NEPS of complete graphs to be periodic or exhibit perfect state transfer.
Deep Approaches on Malicious URL Classification

Malicious URLs are one of the biggest threats to this digital world and preventing it is one of the challenging tasks in the domain of cyber security. Previous research to tackle malicious URLs using hard-coded features have proven good indeed, but it comes with the limitation that these features are non-exhaustive and therefore detection algorithms fail to recognize new or unseen malicious URLs. However, with the deep learning revolution, this problem can be easily solved, since deep learning models extract features of their own by learning from patterns occurring in such URLs. In this paper, we have shown a comparative study of deep learning based architectures - simple RNN, simple LSTM and CNN-LSTM and how these methods can be effective for classifying URLs as malicious or benign. We have compared their performances on the basis of accuracy, precision and recall, where CNN-LSTM architecture outperforms the other two with an accuracy of 93.59%.
Reactive Video

Videos are a convenient platform to begin, maintain, or improve a fitness program or physical activity. Traditional video systems allow users to manipulate videos through specific user interface actions such as button clicks or mouse drags, but have no model of what the user is doing and are unable to adapt in useful ways. We present adaptive video playback, which seamlessly synchronises video playback with the user's movements, building upon the principle of direct manipulation video navigation. We implement adaptive video playback in Reactive Video, a vision-based system which supports users learning or practising a physical skill. The use of pre-existing videos removes the need to create bespoke content or specially authored videos, and the system can provide real-time guidance and feedback to better support users when learning new movements. Adaptive video playback using a discrete Bayes and particle filter are evaluated on a data set collected of participants performing tai chi and radio exercises. Results show that both approaches can accurately adapt to the user's movements, however reversing playback can be problematic.
Siamese Networks for Few-Shot Learning on Edge Embedded Devices

Edge artificial intelligence hardware targets mainly inference networks that have been pretrained on massive datasets. The field of few-shot learning looks for methods that allow a network to produce high accuracy even when only a few samples of each class are available. Siamese networks can be used to tackle few-shot learning problems and are unique because they do not require retraining on the new samples of the new classes. Therefore they are suitable for edge hardware accelerators which often do not include on-chip training capabilities. This work describes improvements to a baseline Siamese network and benchmarking of the improved network on edge platforms. The modifications to the baseline network included adding multi-resolution kernels, a hybrid training process as well a different embedding similarity computation method. This network shows an average accuracy improvement of up to 22% across 4 datasets in a 5-way, 1-shot classification task. Benchmarking results using three edge computing platforms (NVIDIA Jetson Nano, Coral Edge TPU and a custom convolutional neural network accelerator) show that a Siamese classifier can run on these devices at reasonable frame rates for real-time performance, between 3 frames per second (FPS) on Jetson Nano and 60 FPS on the Edge TPU. By increasing the weight sparsity during training, the inference time of a network with 25% weight sparsity increases by 10 FPS but with only 1% drop in accuracy.
Leveraging Computer Vision for Emergency Vehicle Detection-Implementation and Analysis

Recent advances in Computer Vision technology have revolutionized the field of Intelligent Transportation Systems. The applications are far reaching- right from traffic monitoring systems to self-driving cars. Most applications entail at least simple, if not advanced image or video analytics at a fundamental level. This paper is an attempt to examine the use of object detection and instance segmentation for emergency vehicle detection, which is indispensable to any Intelligent Transportation System. More particularly, emergency vehicle detection can be programmed into autonomous vehicles as well as traffic signal controllers for preferential signal switching upon encountering emergency vehicles. The architectures implemented are Faster RCNN for object detection and Mask RCNN for instance segmentation. The computational results of these implementations, their accuracies and most importantly, their suitability for emergency vehicle detection in disordered traffic conditions are deliberated. Additionally, the object detection model is contrasted with instance segmentation and the merits and demerits of each are identified, again in the context of emergency vehicle detection.
Utilization of the Blockchain Network in The Public Community Health Center Medicine Supply Chain

Distribution of medicine in the public health community centers has been regulated and monitored in an official distribution channel. However, counterfeit medications still circulate in the community. Circulation of counterfeit medicines is caused by factors from businesses, consumers, and medicine distribution supervision. The use of blockchain technology in supply-chain has shown new breakthroughs to overcome these problems since it ensures immutability, transparency, and decentralization of data. In this paper, we proposed a new blockchain-based Medicine Supply Chain Management (MSCM) system. The proposed system has been applied to medicine distribution channels between public community health centers in Indonesia. To evaluate the proposed system, we used JMeter as a benchmarking tool to measure the system performance in terms of transactions per second, latency, and the utilization of resources.
The economic prospect of rooftop photovoltaic (PV) system in the commercial buildings in Bangladesh: a case study

The research investigates the financial and environmental implications of rooftop PV installation in a case study of commercial buildings in Bangladesh. With annual horizontal solar radiation of 4.65 kWh/m2/day, Bangladesh has a great potential to avail sustainable solar energy which would have environmental and economic ramifications. To accomplish this, the simulation has been carried out using the RETScreen software. To perform the calculation, the climatic data from NASA for the selected location have been considered. Numerous economic indices encompassing net present value, payback period, internal rate of return and benefit–cost ratio have been taken into consideration as a measure of performance indicator for the enacted project. The determined economic outcome of the case study points out a net present value of $756,896 with 14.2% internal rate of return, 10.1 years simple payback time, 8.1 years equity payback and 1.5 benefit–cost ratio which implies that the project is remarkably appealing. In addition, the sensitivity analysis of the project highlights that the profitability of the investment is protected in most of the cases for ± 30% variation in input parameters. For example, initial and fuel costs of the proposed case may generate a negative net present value beyond 15% variations. However, a positive net present value within the wide sensitivity ranges for fuel costs base case, operation and maintenance costs, fuel cost escalation and discount rate, debt ratio and debt term imply that economic feasibility is still obvious. This may encourage the building owner to incorporate solar PV in commercial buildings in Bangladesh.
A Winograd-Based CNN Accelerator with a Fine-Grained Regular Sparsity Pattern

Field-Programmable Gate Array (FPGA) is a high-performance computing platform for Convolution Neural Networks (CNNs) inference. Winograd transformation and weight pruning are widely adopted to reduce the storage and arithmetic overhead in matrix multiplication of CNN on FPGAs. Recent studies strive to prune the weights in the Winograd domain, however, resulting in irregular sparse patterns and leading to low parallelism and reduced utilization of resources. In this paper, we propose a regular sparse pruning pattern in the Winograd-based CNN, namely Sub-Row-Balanced Sparsity (SRBS) pattern, to overcome the above challenge. Then, we develop a 2-step hardware co-optimization approach to improve the model accuracy using the SRBS pattern. Finally, we design an FPGA accelerator that takes advantage of the SRBS pattern to eliminate low-parallelism computation and irregular memory accesses. Experimental results on VGG16 and Resnet-18 with CIFAR-10 and Imagenet show up to 4.4x and 3.06x speedup compared with the state-of-the-art dense Winograd accelerator and 52% (theoretical upper-bound is 72%) performance enhancement compared with the state-of-the-art sparse Winograd accelerator. The resulting sparsity ratio is 80% and 75% and the loss of model accuracy is negligible.
Conditional random fields as message passing mechanism in anchor-free network for multi-scale pedestrian detection

Many deep learning detectors have been proposed to address the scale variation issue in pedestrian detection. However, it is still far from being fully solved because most of these detectors handle the issue by directly fusing multiple extracted features with the weighted average or concatenation. Therefore, this paper designs a novel and effective anchor-free network (namely MPAF-Net) for multi-scale pedestrian detection. Specifically, we first improve Res2Net as our backbone network by designing a novel bottleneck block to make the proposed detector has a strong multi-scale representation ability for pedestrians. Furthermore, based on Conditional Random Fields (CRFs), the multi-scale features obtained from three different feature pyramid levels of backbone network are refined with a designed message passing mechanism. By doing so, the complementary information from the features at other scales can be dynamically passed to enhance the scale-specific feature. Finally, pedestrian detection is simplified as a straightforward center and scale prediction task by performing convolution on the concatenation of refined multi-scale convolution features. This way, the proposed MPAF-Net can be trained in an end-to-end fashion. Experimental results show that MPAF-Net achieves state-of-the-art performance on CityPersons, Caltech, and CrowdHuman benchmarks.
Providing Direct Answers in Search Results: A Study of User Behavior

To study the impact of providing direct answers in search results on user behavior, we conducted a controlled user study to analyze factors including reading time, eye-tracked attention, and the influence of the quality of answer module content. We also studied a more advanced answer interface, where multiple answers are shown on the search engine results page (SERP). Our results show that users focus more extensively than normal on the top items in the result list when answers are provided. The existence of the answer module helps to improve user engagement on SERPs, reduces user effort, and promotes user satisfaction during the search process. Furthermore, we investigate how the question type -- factoid or non-factoid -- affects user interaction patterns. This work provides insight into the design of SERPs that includes direct answers to queries, including when answers should be shown.
Based on the application of AI technology in resume analysis and job recommendation

This study adopted machine learning- and text mining technology-based artificial intelligence and current big data technology to analyze the trendiness of online discussion. Developing a system that can be applied in large job fairs, where numerous job applicants seek to match with the maximum of job vacancies provided by companies possible. The developed system conducts personal competitiveness analysis, personality trait analysis, and gives job vacancy recommendations according to the electronic resumes job applicants submit. In addition, the system generates a talent recommendation list for the companies. The experimental results verified that the job vacancies recommended by the developed system desirably met job applicants’ expectation.
Personalized News Recommendation and Simulation Based on Improved Collaborative Filtering Algorithm

Faced with massive amounts of online news, it is often difficult for the public to quickly locate the news they are interested in. The personalized recommendation technology can dig out the user’s interest points according to the user’s behavior habits, thereby recommending the news that may be of interest to the user. In this paper, improvements are made to the data preprocessing stage and the nearest neighbor collection stage of the collaborative filtering algorithm. In the data preprocessing stage, the user-item rating matrix is filled to alleviate its sparsity. The label factor and time factor are introduced to make the constructed user preference model have a better expression effect. In the stage of finding the nearest neighbor set, the collaborative filtering algorithm is combined with the dichotomous K-means algorithm, the user cluster matching the target user is selected as the search range of the nearest neighbor set, and the similarity measurement formula is improved. In order to verify the effectiveness of the algorithm proposed in this paper, this paper selects a simulated data set to test the performance of the proposed algorithm in terms of the average absolute error of recommendation, recommendation accuracy, and recall rate and compares it with the user-based collaborative filtering recommendation algorithm. In the simulation data set, the algorithm in this paper is superior to the traditional algorithm in most users. The algorithm in this paper decomposes the sparse matrix to reduce the impact of data sparsity on the traditional recommendation algorithm, thereby improving the recommendation accuracy and recall rate of the recommendation algorithm and reducing the recommendation error.
In-depth analysis of SVM kernel learning and its components

The performance of support vector machines in nonlinearly separable classification problems strongly relies on the kernel function. Toward an automatic machine learning approach for this technique, many research outputs have been produced dealing with the challenge of automatic learning of good-performing kernels for support vector machines. However, these works have been carried out without a thorough analysis of the set of components that influence the behavior of support vector machines and their interaction with the kernel. These components are related in an intricate way and it is difficult to provide a comprehensible analysis of their joint effect. In this paper, we try to fill this gap introducing the necessary steps in order to understand these interactions and provide clues for the research community to know where to place the emphasis. First of all, we identify all the factors that affect the final performance of support vector machines in relation to the elicitation of kernels. Next, we analyze the factors independently or in pairs and study the influence each component has on the final classification performance, providing recommendations and insights into the kernel setting for support vector machines.
Data mining privacy preserving: Research agenda

Abstract In the modern days, the amount of the data and information is increasing along with their accessibility and availability, due to the Internet and social media. To be able to search this vast data set and to discover unknown useful data patterns and predictions, the data mining method is used. Data mining allows for unrelated data to be connected in a meaningful way, to analyze the data, and to represent the results in the form of useful data patterns and predictions that help and predict future behavior. The process of data mining can potentially violate sensitive and personal data. Individual privacy is under attack if some of the information leaks and reveals the identity of a person whose personal data were used in the data mining process. There are many privacy‐preserving data mining (PPDM) techniques and methods that have a task to preserve the privacy and sensitive data while providing accurate data mining results at the same time. PPDM techniques and methods incorporate different approaches that protect data in the process of data mining. The methodology that was used in this article is the systematic literature review and bibliometric analysis. This article identifieds the current trends, techniques, and methods that are being used in the privacy‐preserving data mining field to make a clear and concise classification of the PPDM methods and techniques with possibly identifying new methods and techniques that were not included in the previous classification, and to emphasize the future research directions. This article is categorized under: Commercial, Legal, and Ethical Issues &gt; Security and Privacy
An Improved UAV Path Planning method Based on RRT-APF Hybrid strategy

Autonomous path planning in complex environments is a hot topic for current UAV mission planning researchers, and there is usually no distinction between threats and obstacles. In order to solve the problem of unknown threat and obstacle characteristics in the existing algorithms, a UAV penetration path planning algorithm based on the artificial potential field and the RRT algorithm is proposed. The RRT algorithm makes it possible to distinguish between the characteristics of threats and obstacles in the penetrating algorithm. The problem of too strong randomness of RRT algorithm is solved by increasing the adaptive target gravity when the growth of nodes is guided to the target direction. The simulation results show that the algorithm can avoid the threat area quickly and effectively, and a better trajectory is obtained to meet the demand of UAV real-time path planning.
Building Community Knowledge In Online Competitions

Knowledge building is a prevalent feature in open online systems, but it is challenging to motivate participants to contribute and to maintain quality in the participants' contributions. Open online competitions, where participants compete for prizes with knowledge artifacts, offer a potential design model for online systems to incentivize community knowledge building activities. However, while there is evidence that participants contribute to public knowledge and share it during competitions, it remains unclear how and why they do so. In this study, through interviews with 14 participants in Kaggle Competitions, we investigate participants' motivation, practices, and challenges when contributing to community knowledge under a competitive structure. We find that competitive mechanisms impact expert and beginner participants very differently in their public knowledge building behaviors. Experts contribute to shared knowledge in order to compete for reputation, while they tend to form their own niches and only share knowledge artifacts that are abstract and not usable by less experienced participants. Beginners are often driven away from contributing to shared knowledge because of their vulnerable social image. We leverage Scardamalia's framework for Knowledge Building Communities to discuss the different challenges and opportunities that competitive design brings to expert and beginner participants. We offer design implications for effectively implementing competitive mechanisms that could benefit both expert and beginner participants in future knowledge building systems.
I Am/We Are: Exploring the Online Self-Avatar Relationship

Research into how online avatars are used has mostly concentrated on the agentic user controlling the passive avatar. With the proliferation, variety, and increasing uptake of online representations now available to individuals, however, we might more productively examine the relationship between what I call the avatar-persona and the user as one in which the avatar-persona also plays a part. Due to the affordances of parasociality and self-presence, a dialectic interaction develops between the user and the avatar as extended and extraordinary self. This dialectic enables the avatar-persona to express a certain degree of influence over the user in some circumstances. My discussion of these dynamics is illustrated from observations made during an extended participant observation in a multi-user virtual environment Second Life.
Exploit Multilingual Language Model at Scale for ICD-10 Clinical Text Classification

The automatic ICD-10 classification of medical documents is actually an unresolved issue, despite its crucial importance. The existence of machine learning approaches de-voted to this task is in contrast with the lack of annotated resources, especially for languages different from English. Recent Transformer-based multilingual neural language models at scale have provided an innovative approach for dealing with cross lingual Natural Language Processing tasks. In this paper, we present a preliminary evaluation of the Cross-lingual Language Model (XLM) architecture, a recent multilingual Transformer-based model presented in literature, tested in the cross lingual ICD-10 multilabel classification of short medical notes. In detail, we analysed the performances obtained by fine tuning the XLM model on English language training data and tested for ICD-10 codes prediction of an Italian test set. The obtained results show that the use of the novel XLM multilingual neural language architecture is very promising and it can be very useful in case of low resource languages.