Reviewerly/exHarmony huggingface dataset:
Approach for Improving User Interface Based on Gesture Recognition

Gesture recognition technology based on visual detection to acquire gestures information is obtained in a non-contact manner. There are two types of gesture recognition: independent and continuous gesture recognition. The former aims to classify videos or other types of gesture sequences that only contain one isolated gesture instance in each sequence (e.g., RGB-D or skeleton data). In this study, we review existing research methods of visual gesture recognition and will be grouped according to the following family: static, dynamic, based on the supports (Kinect, Leap…etc), works that focus on the application of gesture recognition on robots and works on dealing with gesture recognition at the browser level. Following that, we take a look at the most common JavaScript-based deep learning frameworks. Then we present the idea of defining a process for improving user interface control based on gesture recognition to streamline the implementation of this mechanism.
Web Page Classification Using Convolutional Neural Network (CNN) Towards Eliminating Internet Addiction

In the modern world, everyone has access to the internet as a source of information by surfing the web pages. The most popular web page surf is on Game and Online Video Streaming. Users who are spending too much time on these kinds of web pages may lead to a negative impact on Internet addiction. To overcome the internet addiction problem, access to Game and Online Video Streaming web pages needs to be restricted. Thus, a mechanism that can classify the category of the incoming web page based on the web page content is needed. This paper is proposing a web page classification model using a Convolutional Neural Network (CNN) to classify the web page, then identify whether it is a Game or Online Video Streaming based on the pattern of words in the word cloud image taken from the web page text content. The proposed web page classification model has achieved 82.22 % accuracy to detect the pre-classifled web pages.
CJ-2050: Body Cooling Wearable Technology

Due to the climate change effects, the average temperature in the 2100s might be between 2 and 9.7°F warmer- an excessive heat wave beyond human tolerance. CJ-2050 is a speculative wearable device that provides body cooling through a jewelry design when in direct contact with the skin. Cold water constantly travels through dedicated tubes to reduce the body's temperature at key points in the circulation system. A Peltier module structure has been utilized to keep the water cold and a pump system irrigates the water through the tubes. This project aims at reimagining our daily jewelry in a dystopia that may not be too far away with a wearable design that intertwines body heating regulation, speculative jewelry design, and aesthetics.
Spatial Data Infrastructure Integrated with Geospatial Artificial Intelligence: A Systematic Literature Review

In the era of industrial revolution 4.0, artificial intelligence has became a driver in the high technology sustainability. It is important to acceleration of development planning and decision making for stakeholder based on region zone, point of society and distribution an object with system coordinates. The linkage of information and communications technology (ICT) with spatial data, located in the use of data sharing technology, architecture and data dissemination. The availability of spatial data is increasingly numerous and varied requires a high computing technology that users can access and use spatial data from various sources of spatial data providers. Therefore, the Spatial Data Infrastructure (SDI) was built to facilitate access and exchange of spatial data from various sources of spatial data providers, both government and private, which doing an important role in conducted system and platform interoperability as spatial services. The government of Indonesia has made regulation for national spatial data infrastructure (NSDI) in Law Of Presidential Number is 27 of 2014 and and strengthened by onemap policy. The advantages using geospatial artificial intelligence (GeoAI) can reduce duplication and analysis spatial processing of big data spatial. The systematic literature review process contain of regulation, method for harvesting spatial data in SDI and several GeoAI model research from 2016 to this year. The conclusion in this paper is find possibility ellaborate the artificial intelligence technology with spatial data infrastructure development from past studies in other country.
Sports Video Target Tracking Algorithm Based on Optimized Particle Swarm Algorithm

With the rapid development of computer technology and the continuous improvement of sensor performance, the role of sports video target tracking technology in practical applications such as intelligent sports competitions and international sports stadium security monitoring systems has become increasingly prominent. It has become the current research hotspot and difficulty in the field of computer sports vision and artificial intelligence. In order to achieve long-term, stable, accurate and efficient tracking of targets in complex environments, it is of great significance to establish an effective adaptive model and research on sports video target tracking algorithms. The purpose of this paper is to study the sports video target tracking algorithm based on the optimized particle swarm algorithm. This paper proposes a particle filter video target tracking algorithm based on optimized particles. Based on the particle filter framework, this algorithm optimizes the particles before resampling by using the particle swarm optimization algorithm, keeping particles with larger weights still, and allowing particles with smaller weights to continuously approach particles with larger weights, so that most of the particles are in the high-likelihood area, thereby reducing the particle elimination rate in the re-sampling process and improving the problem of particle shortage. This paper uses the least squares-based trajectory prediction intervening particle filter algorithm at the level of the algorithm process framework, which makes the improved tracking algorithm more robust in dealing with target occlusion, especially in the case of long-term occlusion and background mutation. Experimental research shows that at the 27th frame, because the target is temporarily occluded by the foreground person, when the target reappears from behind the occluded foreground, the target cannot be tracked correctly, but the foreground occlusion is mistakenly regarded as the target for tracking iterations leading to the tracking process Failure, because the target keeps moving forward while the foreground target remains relatively stationary, so the tracking error percentage gradually increases.
Quantum Computing and Post Quantum Cryptography

The present knowledge we had in quantum computer and the most possible architecture of a quantum computer might be able to break RSA 2048 in future. In classical computer two bits represents any one of four bit information whereas in quantum due to superposition it can be represent all four states. For ‘n’ qubits system is analogous to 2n classical bits. Quantum teleportation, quantum entanglement and other makes it possible to break present cryptosystem. Shor’s Algorithm is used for integer factorization which is polynomial time for quantum computer. This can be threat for RSA security. In this paper matlab implementation of Shor’s algorithm is presented. Used classical way for getting period of function because classical computers not engage quantum phenomena. As numbers of iterations grow, probability of getting exact factor of ‘n’ acutely increased. This paper also discusses popular methods for making qubits like Silicon based Qubits in which electron is put inside nano material which is used as a transistor. In Superconducting circuit method insulator is used as a sandwich in between two metal layers. Used by Google, IBM, Intel, Microsoft. In Flux qubits method very small size loop of superconducting metal is used. This paper also discusses Quantum Proof Algorithm like Lattice-based cryptography used concept of good and bad base. In Learning with errors method if we have more equation then variable, it is over defined system. In Code based cryptography some matrixes allow for efficient error correction (good matrix) but most matrix’s does not (bad matrix) concept is used. In Hash based signatures scheme have long signatures or keys, but they are secure. Also discuss Multivariate Quantum proof algorithm. The abstract should contain maximum of 300 words. No abbreviation should be mentioned in the abstract. Give a brief summary of your research work.
Learning to Binarize Convolutional Neural Networks with Adaptive Neural Encoder

The high computational complexity and memory consumption of the deep Convolutional Neural Networks (CNNs) restrict their deployability in resource-limited embedded devices. To address this challenge, emerging solutions are proposed for neural network quantization and compression. Among them, Binary Neural Networks (BNNs) show their potential in reducing computational and memory complexity; however, they suffer from considerable performance degradation. One of the major causes is their non-differentiable discrete quantization implemented using a fixed sign function, which leads to output distribution distortion. In this paper, instead of using the fixed and naive sign function, we propose a novel adaptive Neural Encoder (NE), which learns to quantize the full-precision weights as binary values. Inspired by the research of neural network distillation, a distribution loss is introduced as a regularizer to minimize the Kullback-Leibler divergence between the outputs of the full-precision model and the encoded binary model. With an end-to-end backpropagation training process, the adaptive neural encoder, along with the binary convolutional neural network, could reach convergence iteratively. Comprehensive experiments with different network structures and datasets show that the proposed method can improve the performance of the baselines and also outperform many state-of-the-art approaches. The source code of the proposed method is publicly available at https://github.com/CQUlearningsystemgroup/LearningToBinarize.
HIN2Grid: A disentangled CNN-based framework for heterogeneous network learning

Recently, graph convolutional networks (GCNs) have been applied to heterogeneous information network (HIN) learning and have shown promising performance. However, the performance of GCNs degrades attributed to the recursive propagation, which leads to an indistinguishable embedding for the distinctly heterogeneous node. Besides, the inherently coupled paradigm of GCNs limits their applications on large-scale graphs. In this paper, we tackle these problems by proposing a disentangled framework named Heterogeneous Information Network to Grid (HIN2Grid) for heterogeneous network learning. We innovatively design an effective and efficient strategy to transform the graph data into semantic-specific grid-like data, which can be effectively processed by convolutional neural networks (CNNs), thus explicitly overcoming the drawbacks of the inherent paradigm of GCNs. Such a CNN-based learning scheme also contributes to extracting more expressive features and consume less time and memory. We further propose dual attention mechanisms to capture the importance of various grid-like data and heterogeneous semantics, thus providing interpretability and robustness for HIN2Grid. We conduct experiments on four datasets and the results show that HIN2Grid significantly outperforms the state-of-the-art methods, gaining a improvement on node classification of about 2% to 10% and a 2 to 5 times promotion on running speed.
RecSys 2021 Challenge Workshop: Fairness-aware engagement prediction at scale on Twitter’s Home Timeline

The workshop features presentations of accepted contributions to the RecSys Challenge 2021, organized by Politecnico di Bari, ETH Zürich, Jönköping University, and the data set is provided by Twitter. The challenge focuses on a real-world task of tweet engagement prediction in a dynamic environment. For 2021, the challenge considers four different engagement types: Likes, Retweet, Quote, and replies. This year's challenge brings the problem even closer to Twitter's real recommender systems by introducing latency constraints. We also increases the data size to encourage novel methods. Also, the data density is increased in terms of the graph where users are considered to be nodes and interactions as edges. The goal is twofold: to predict the probability of different engagement types of a target user for a set of Tweets based on heterogeneous input data while providing fair recommendations. In fact, multi-goal optimization considering accuracy and fairness is particularly challenging. However, we believed that the recommendation community was nowadays mature enough to face the challenge of providing accurate and, at the same time, fair recommendations. To this end, Twitter has released a public dataset of close to 1 billion data points, > 40 million each day over 28 days. Week 1 − 3 will be used for training and week 4 for evaluation and testing. Each datapoint contains the tweet along with engagement features, user features, and tweet features. A peculiarity of this challenge is related to keeping the dataset updated with the platform: if a user deletes a Tweet, or their data from Twitter, the dataset is promptly updated. Moreover, each change in the dataset implied new evaluations of all submissions and the update of the leaderboard metrics. The challenge was well received with 578 registered users, and 386 submissions.
TimeRadar: Visualizing the Dynamics of Multivariate Communities via Timeline Views

Analyzing temporal event sequences play an important role in many application domains, such as workload behavior analysis, hardware fault diagnosis, and natural disaster resilience. As data volume keeps growing, real-world temporal event sequences are often noisy, missing, and complex, thus making it a daunting task to convey much of the information from a comprehensive overview for analysts. This work proposes a visual approach based on clustering and superimposing to construct a high-level overview of sequential event data while balancing the amount of information and its cardinality. We also implement an interactive prototype, called TimeRadar, that allows domain analysts to simultaneously analyze sequence clustering, extract distribution patterns, drill multiple levels of detail to accelerate the analysis. This work aims to provide an abstracted view of temporal event sequences where significant events are highlighted. The TimeRadar is demonstrated through case studies with real-world temporal datasets of various sizes.
Object Relationship Graph Reasoning for Object Detection of Remote Sensing Images

In recent years, deep learning has been successfully applied in object detection of remote sensing images, due to its powerful feature extraction and representation capabilities. However, it usually ignores the relationships among different objects, which greatly hinders the further improvement of the detection precision. In this paper, a novel object relationship graph reasoning deep learning method is proposed for object detection of remote sensing images. The reasoning is carried out on a graph constructed by region proposals. It mainly contains three modules for relationship generation in consideration of different factors that related to object detection: 1) Location-based relationship generation module (LRGM) aims to find spatial relationships among objects according to their positions; 2) Feature-based relationship generation module (FRGM) focuses on mining semantic relationships among objects according to their appearances that implied in feature maps and 3) Class-dependent relationship generation module (CRGM) introduces class dependencies into the relationship generation process. Class dependencies are the prior knowledge that is obtained by computing class co-occurrence. The experimental results on a public twenty-class remote sensing object detection dataset have shown that the proposed method has better detection performance compared to other object detection deep networks with no considering object relationships.
Coresets Application in Channel Pruning for Fast Neural Network Slimming

Pruning reduces neural networks' parameters and accelerates inferences, enabling deep learning in resource-limited scenarios. Existing saliency-based pruning methods apply characteristics of feature maps or weights to judge the importance of neurons or structures, where weights' characteristics based methods are data-independent and robust for future input data. This paper proposes a coreset based pruning method for the data-independent structured compression, aiming to improve the construction efficiency of pruning. The first step of our method is to prune channels, according to the channel coreset merged from multi-rounds coresets constructions. Our method adjusts the importance function utilized in the random probability sampling during coresets construction procedures to achieve data-independent channel selections. The second step is recovering the precision of compressed networks through solving the compressed weights reconstruction by linear least squares. Our method is also generalized to implementations on multi-branch networks such as SqueezeNet and MobileNet-v2. In tests on classification networks like ResNet, it is observed that our method performs fast and achieves an accuracy decline as small as 0.99% when multiple layers are pruned without finetuning. As shown in evaluations on object detection networks, our method acquires the least decline in mAP indicator compared to comparison schemes, due to the advantage of data-independent channel selections of our method in preserving precision.
Prognosis Prediction of Breast Cancer Based on CGAN

Breast cancer is the malignancy with the highest morbidity and mortality rate in women worldwide, and prognosis prediction of breast cancer is of great practical importance for both patients and clinical practitioners. In this paper, we use a modified Conditional Generative Adversarial Networks (CGAN) to train the generators in a GAN into a predictive model that can perform prognosis of breast cancer on clinical data from patients and compare it with a multi-factor Cox proportional hazards model. In this paper, the accuracy of the prognostic model using CGAN was 0.950 with an AUC of 0.915; the AUC value of prognosis obtained using the multi-factor Cox proportional hazards model was 0.837. The experimental results demonstrated that the breast prognostic model based on CGAN can more accurately quantify and assess the prognosis of patients.
Approach to Patient Assessment Based on a Spatial-Temporal Model for Decision Support Systems in Cardiology

Cardiovascular (CV) diseases represent a significant proportion of all diseases and fatal events worldwide. With the development of screening and digital technologies, as well as smart medical gadgets, the number of sources and volume of medical data characterizing CV health is increasing. It creates conditions for the development of patient support systems for the early detection and prevention of the development of CV diseases. The article presents the theoretical provisions of the data-driven approach to the automatic assessment of individual CV health status according to a variety of indicators, including the spatial-temporal assessment model and the method for obtaining it. The proposed model of patient is focused on describing the state and changes in CV health using linguistic propositions. The conducted experimental study showed the effectiveness of the proposed approach for identifying problems with CV health of patients.
A Sentiment Classification Model Based on Deep Learning

Sentiment classification is an interesting and crucial research topic of opinion mining and sentiment analysis, which is used to obtain sentiment types from the text documents of numerous sources. Sentiment classification has the problems of insufficient semantic feature extraction and ignoring context information. This paper proposes a MSCNN-BiGRU model for text sentiment classification by utilizing multi-scale convolution kernels to extract rich semantic features and BiGRU to extract features containing text context information. The experimental results on the Chinese Weibo text dataset and the English e-commerce reviews dataset show that the classification accuracy of this model is 3.41% better than CNN and 3.59% better than RNN. The model proposed in this paper is not only suitable for text sentiment classification, but also helpful for expression classification research with time series information.
Dynamic Narrowing of VAE Bottlenecks Using GECO and L<sub>0</sub> Regularization

When designing variational autoencoders (VAEs) or other types of latent space models, the dimensionality of the latent space is typically defined upfront. In this process, it is possible that the number of dimensions is under- or overprovisioned for the application at hand. In case the dimensionality is not predefined, this parameter is usually determined using time- and resource-consuming cross-validation. For these reasons we have developed a technique to shrink the latent space dimensionality of VAEs automatically and on-the-fty during training using Generalized ELBO with Constrained Optimization (GECO) and the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{0}$</tex> -Augment-REINFORcE-Merge ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{0}$</tex> -ARM) gradient estimator. The GECO optimizer ensures that we are not violating a predefined upper bound on the reconstruction error. This paper presents the algorithmic details of our method along with experimental results on five different datasets. We find that our training procedure is stable and that the latent space can be pruned effectively without violating the GECO constraints.
Design and Verification of Multi-Agent Systems with the Use of Bigraphs

Widespread access to low-cost, high computing power allows for increased computerization of everyday life. However, high-performance computers alone cannot meet the demands of systems such as the Internet of Things or multi-agent robotic systems. For this reason, modern design methods are needed to develop new and extend existing projects. Because of high interest in this subject, many methodologies for designing the aforementioned systems have been developed. None of them, however, can be considered the default one to which others are compared to. Any useful methodology must provide some tools, versatility, and capability to verify its results. This paper presents an algorithm for verifying the correctness of multi-agent systems modeled as tracking bigraphical reactive systems and checking whether a behavior policy for the agents meets non-functional requirements. Memory complexity of methods used to construct behavior policies is also discussed, and a few ways to reduce it are proposed. Detailed examples of algorithm usage have been presented involving non-functional requirements regarding time and safety of behavior policy execution.
On-board cable attitude measurement and controller for outdoor aerial transportation

Abstract Deploying quadcopters for aerial transportation can be cost effective in impromptu material handling applications. However, such applications are limited mainly due to the requirement of onboard localization sensors and associated computation. The current work presents a human-controlled modality to successfully execute spontaneous outdoor flight of a quadcopter with a cable-suspended payload. Stable and smooth flights are achieved through an onboard integration of a custom-built sensor system and a controller to minimize payload oscillations. The feasibility of the proposed modality is demonstrated by conducting outdoor experiments and a case study in an unstructured environment.