Reviewerly/exHarmony huggingface dataset:
NNK-Means: Data summarization using dictionary learning with non-negative kernel regression

An increasing number of systems are being designed by gathering significant amounts of data and then optimizing the system parameters directly using the obtained data. Often this is done without analyzing the dataset structure. As task complexity, data size, and parameters all increase to millions or even billions, data summarization is becoming a major challenge. In this work, we investigate data summarization via dictionary learning (DL), leveraging the properties of recently introduced non-negative kernel regression (NNK) graphs. Our proposed NNK-Means, unlike previous DL techniques, such as kSVD, learns geometric dictionaries with atoms that are representative of the input data space. Experiments show that summarization using NNK-Means can provide better class separation compared to linear and kernel versions of kMeans and kSVD. Moreover, NNK-Means is scalable, with runtime complexity similar to that of kMeans.
Fine-Grained Domain Adaptation for Aspect Category Level Sentiment Analysis

Aspect category level sentiment analysis aims to identify the sentiment polarities towards the aspect categories discussed in a sentence. It usually suffers from a lack of labeled data. A popular solution is to transfer knowledge from a labeled source domain to an unlabeled target domain by unsupervised domain adaptation. However, most domain adaptation methods in sentiment analysis are coarse-grained, considering the source or target domain as a whole during the adaptation. We argue that these single-source single-target methods are inefficient since they ignore the difference between different aspect categories. In this paper, we propose a fine-grained domain adaptation method to address the aspect category level sentiment analysis task by considering the adaptation between subdomains. Specifically, the source/target domain is divided into multiple subdomains according to the hierarchical structure of the aspect categories. We then design a multi-source multi-target transfer network to achieve fine-grained transfer. Extensive experimental results demonstrate the effectiveness of our fine-grained domain adaptation method on aspect category level sentiment analysis.
FEW-Shot Object Detection with Foreground Augment and Background Attenuation

Few-shot object detection has made prominent progress in the development of object detection tasks owning to its ability of detection under extremely few annotated data. In this paper, we put forth a novel few shot object detection method which consists of foreground augment and background attenuation module. This approach is proposed to alleviate the impact of irrelevant contextual information in novel categories. It has proven to be a powerful foreground augment and background attenuation framework for few-shot object detection. Comprehensive experiments on PASCAL VOC benchmarks demonstrate the effectiveness of our approach.
Performance Evaluation of ANU block cipher on 8-bit AVR for IoT node security

Recent advancement in IoT with next generation wireless systems produces a data security challenge due to bulky encryption standards which are not capable to provide efficient hardware performance on tiny and low-end IoT devices. Numerous lightweight ciphers are proposed to address data security in constrained IoT environment. It is essential to evaluate the hardware performance of these designed ciphers which are proved their robustness against various cryptanalysis technique. This paper implements and analyses the new block cipher ANU on tiny and low-end device with the aim of secured IoT node development. Additionally, we implement popular block cipher PRESENT which was designed with consideration of constrained IoT applications such as Radio Frequency Identification (RFID). Finally, we compare our achieved results of ANU with PRESENT for performance metrics including flash memory, code size, machine cycle and execution time. Both designs are evaluated and analysed on 8-bit <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu$</tex> -controller (AVR ATmega32) simulation platform.
Storytelling to Visualize Changes in Regions Based on Social Inclusion Indicators

This paper shows an application of data science in the healthcare system by using the Social Inclusion Indicators (ISS) of each entity in Mexico for 25 years to make a clustering based on the lack of primary healthcare. Multiple procedures were applied, like cleaning and transformation of open data published by the Mexican Health Department, the imputation of missing values. With the complete information, data was scaled, and then one of the most common clustering algorithms was applied, which is K-Means. This algorithm was initialized with previously defined centroids to make it more standardized and make it easier to notice changes amongst the classes through the years. Six clusters were defined using previous works. All the implementations were made in Python using the Scikit-Learn library to apply the algorithms and measure performance, like K-Means and Mean Squared Error respectively. Results obtained were displayed using Tableau to observe in a more interactive way, how the classes had changed over the years.
Real-Time Hand Gesture Detection Based on YOLOv5s

Since the breakout of Corona Virus Disease 2019 (COVID-19), the global fight against influenza has begun. Var-ious technologies have been developed to support the fast-growing contactless service market, and hence contactless services are rapidly becoming a new growth strategy. In particular, the retail service industry most urgently needs contactless service technology. A representative technical case is the self-checkout machine, which can reduce labor costs and provide customer satisfaction. We present a solution in this article. We propose a hand gesture recognition contactless self-checkout system, which is a hand gesture recognition model based on YOLOv5s. The hand gesture recognition mAP (0.5) value reaches 0.995, the mAP (0.5:0.95) value reaches 0.865, and the Fl score is 0.96, together with the accuracy and recall rate is close to 1. Compared with the excellent algorithm YOLOx-s, the FPS value of YOLOv5s can reach 123 (YOLOx-s is 108). In addition, the model can be used to detect recorded static and dynamic hand gestures in real-time. Practical results show that the YOLOv5s can effectively recognize hand gestures and realize the contactless checkout process.
Shift- ViT: Siamese Vision Transformer using Shifted Branches

In this paper, we propose a novel classification method to overcome the limitation of Vision Transformer (ViT). Inspired by Transformers that performed state-of-the-art in NLP (Natural Language Process), research on ViT that applied a learning mechanism to vision tasks is being actively conducted. However, ViT has no inductive bias fundamentally unlike conventional Convolution Neural Network (CNN) models. For that reason, previous ViTs are limited to handle sensitively to small changes in the image. Therefore, in this paper, we propose Shift-ViT, a new algorithm that adaptively learns image changes by extending the conventional ViT. Our proposed model attempts to overcome the limitations of the existing ViT by using Siamese encoders to learn both horizontal and vertical features of the raw input image.
Reasoning over personalized healthcare knowledge graph: a case study of patients with allergies and symptoms

Background: Current health applications (e.g., Google Fit) based on devices (e.g., Fitbit) often are limited to data visualization, summarization, or statistics-based models to understand and explore the patient data. The data must be more meaningful for the patient in a contextualized and personalized manner. Objective: A knowledge-based reasoning architecture is designed to collect, manage, integrate, and analyze multimodal health data from sources such as smart phone applications, wearable devices, Internet of Things (IoT) devices, and environmental sensors. The architecture and associated methods with several use cases are demonstrated in the chapter. Methods: A personalized health knowledge graph is developed to capture personalized and contextualized patient data from smartphone applications, wearables, and environmental sensors. It converts the data into knowledge using relevant medical knowledge from existing ontologies. The personalized health knowledge graph and its use cases consists of (1) Kno.e.sis asthma ontology, whose core model (e.g., patient) is generic enough to be reused for other use cases, and (2) a rule-based reasoning engine, which infers high-level abstractions from data annotated with Kno.e.sis asthma ontology to provide suggestions to patients. Results: The designed architecture is comprised of components compliant with each other: (1) A personalized health knowledge graph, which integrates cross-domain knowledge (asthma, weather, W3C SOSA SSN, smart home), (2) an automatic semantic annotation engine, (3) a rule (IF THEN ELSE) data set, which reuses domain expertise, (4) a rule-based inference engine to automatically infer new abstractions, and (5) a generic query engine to query inferred data and provides suggestions for a better health management. The architecture reuses and integrates knowledge in a machine-processable form to replace human interpretation as a long-term goal. Conclusion: The knowledge graph based reasoning is comprised of a set of tutorials with SPARQL queries and end-to-end scenarios (e.g., asthma, obesity, allergies to food). The methodology used to develop the knowledge graph can be generalized, refined, and reused for other diseases or even to other domains such as agriculture with smart irrigation to deal with different crop types, robotics in smart home for ageing people, smart energy, etc. The health knowledge graph can be used as a gold standard to design KG made with Machine Learning algorithms automatically.
Exploring the Influence of an Immersive Virtual Reality Environment on Training Pre-service Teachers' Inquiry-based Instruction

Virtual reality has the great potential to be used in education and specially in teacher training. This paper described an experiment to examine the influence of VR environment on pre-service teachers' training on inquiry-based instruction. The instructors guided pre-service teachers to follow the principle of inquiry-based instruction and conducted teaching activities in the virtual system. Through qualitative data analysis, the results reveled that the pre-service teachers' performances of using inquiry-based instruction could be effectively improved in the immersive VR environment. However, challenges also emerged during the training. Discussion and implications were addressed in this study.
Unsupervised Medical Image Registration Based on Multi-scale Cascade Network

As a core technique of medical image analysis task, image registration is the process of finding the non-linear spatial correspondence among the input images. Comparing with supervised learning methods, unsupervised learning methods can ease the burden of manual annotation by exploiting unlabeled data without supervision. For high-resolution medical images, existing methods would maintain the consistency of the global structure and have limited matching accuracy in local details. Moreover, the intensity distribution of the warped image tends to the fixed image, which is quite different from the moving image. To solve the above problems, in this paper, we propose a multi-scale cascade network based on unsupervised end-to-end network registration method. We cascade the registration subnetworks with multi-scale strategy, which can extract local features more comprehensively and realize the registration from coarse to fine. The cascading procedure can further maintain the consistency of both global structure and local details for high-resolution medical images. The cyclic consistency loss is introduced to ensure the content consistency between the moving image and the warped image, and the structural consistency loss is used to ensure the structural consistency between the fixed image and the warped image. Experiments on three datasets demonstrate that our algorithm achieves state-of-the-art performance in medical image registration. The source code would be available online.
A Systematic Literature Review of Law Enforcement Image Recognition Methods based on Generative Adversarial Networks Framework

Image recognition research based on the Generative Adversarial Networks (GAN) has been widely used in various specialized technical fields because of its universality, adaptability, and scalability of the adversarial framework. The framework achieves the tasks of image generation, synthesis, and transformation to improve the image resolution and recognition rate, which is consistent with the purpose of the police in the image recognition work. However, the task of applying the GAN method to police image recognition is not widely explored. In this paper, we systematically review related research to understand the current applications of GAN in the field of image recognition and summarize their contributions. We analyze 35 academic papers after 2018 to provide a state-of-the-art research stream. According to the GAN applications, we divided the dataset into three domains, which are image-to-image translation, image augmentation, and mixed model. The results show that there is little difference in the number of articles in the three fields. In addition, most of the papers use image conversion methods from more than two domains, which indicates that GAN is flexible in designing the framework according to the research tasks. Based on the methods and challenges in the literature, we further propose future research directions.
Solving the QoS Issues of Video Streaming in IP Networks

The quality of service in IP networks is an important part that is responsible for stable data transmission and maintaining the user experience at the required level. The paper describes the research, which deals with the approximation and verification of the benefits of service quality and its impact on the quality of the transmitted video. Achieving the set goals was realized through the design of a real test network and test scenarios, which helped verify the benefits of service quality and their impact on the quality of the transmitted video. The results were obtained by comparing the measured values during the test scenarios using measurement software with video no-reference objective metrics.
A Causality Network Construction Method for High Dimensional Time Series Big Data

How to infer the causal relationship between variables in high-dimensional time series data and construct the causal network have always been a difficulty of research. The two major challenges are dimension reduction and causal inference. This paper proposes a causality network construction method for high-dimensional sequential big data. In this method, the dimensionality of feature variables is reduced by fuzzy C-means clustering twice and feature variables with similar and specific patterns are screened out. Then transfer entropy is used to infer causal relationships among variables and construct the time-series data network of causal relationships among variables. The proposed method was applied to a high-dimensional time-series data set which contains 32884 rats' gene expression values at 480 time points to infer the causal regulatory relationships among the genes, and efficiently construct the regulatory network of rats' rhythm genes.
Apples, Oranges &amp; Fruits – Understanding Similarity of Software Repositories Through The Lens of Dissimilar Artifacts

Open-source repositories have facilitated developers to reuse existing software artifacts to develop and maintain new or similar kinds of software. However, finding similar repositories is a challenging task as the notion of similarity varies depending on multiple contexts, and most of the existing approaches tend to find similar repositories by comparing similar software artifacts. This paper aims to determine "whether dissimilar artifacts can be used as one of the criteria to find similar repositories?" Even though, there could be dissimilarity between two similar artifacts, there could also be similarities between two dissimilar artifacts. We define the notion of similarity by defining two categories of similar repositories. Four text-based artifacts are selected for the experiment, i.e., pull-requests, issues, commits, and readme files. The textual similarity is computed between different artifacts. The results show that similarity does exist in dissimilar artifacts. We observed that 10-20% of dissimilar artifact pairs could be used in searching similar repositories. The preliminary results show promising directions where dissimilar artifacts can also be considered while searching for similar repositories motivating the need for further research.
Experimentally Realizable Continuous-variable Quantum Neural Networks

The continuous-variable (CV) quantum computing has shown a great potential for building neural network models. These neural networks can have different levels of quantum- classical hybridization depending on the complexity of the problem. The previous work on CV neural network protocols requires the implementation of non-Gaussian operators in the network. These operators are used to introduce non-linearity which is an essential feature of neural networks. Due to these operators, the protocols become hard to execute experimentally. We built a CV hybrid quantum-classical neural network protocol that can be realized experimentally. Our protocol uses Gaussian gates only with the addition of ancillary qumodes. We achieve non-linearity through measurements on ancillary qumodes. Our gates can be implemented with squeezers and beam splitters; hence our protocol can be realized experimentally. We study two canonical machine learning problems in a supervised learning setting - curve fitting and binary classification to test our neural network. For the curve fitting problem, we used a fully quantum neural network which produced exceptional results as discussed in the paper. The second problem was more complicated in that we found the fraudulent transaction in the credit card data. After training, our model was able to identify genuine data with almost 92% accuracy. After studying both the problem, we can confidently conclude that our protocol gives good results with a great advantage that it can be implemented experimentally with current photonic quantum hardware.
Data Synthesis and Iterative Refinement for Neural Semantic Parsing without Annotated Logical Forms

Semantic parsing aims to convert natural language utterances to logical forms. A critical challenge for constructing semantic parsers is the lack of labeled data. In this paper, we propose a data synthesis and iterative refinement framework for neural semantic parsing, which can build semantic parsers without annotated logical forms. We first generate a naive corpus by sampling logic forms from knowledge bases and synthesizing their canonical utterances. Then, we further propose a bootstrapping algorithm to iteratively refine data and model, via a denoising language model and knowledge-constrained decoding. Experimental results show that our approach achieves competitive performance on Geo, ATIS and Overnight datasets in both unsupervised and semi-supervised data settings.
Improving Robustness Verification of Neural Networks with General Activation Functions via Branching and Optimization

Robustness verification of neural networks (NNs) is a challenging and significant problem, which draws great attention in recent years. Existing researches have shown that bound propagation is a scalable and effective method for robustness verification, and it can be implemented on GPUs and TPUs to get parallelized. However, the bound propagation methods naturally produce weak bound due to linear relaxations on the neurons, which may cause failure in verification. Although tightening techniques for simple ReLU networks have been explored, they are not applicable for NNs with general activation functions such as Sigmoid and Tanh. Improving robustness verification on these NNs is still challenging. In this paper, we propose a Branch-and-Bound (BaB) style method to address this problem. The proposed BaB procedure improves the weak bound by splitting the input domain of neurons into sub-domains and solving the corresponding sub-problems. We propose a generic heuristic function to determine the priority of neuron splitting by scoring the relaxation and impact of neurons. Moreover, we combine bound optimization with the BaB procedure to improve the weak bound. Experimental results demonstrate that the proposed method gains up to 35% improvement compared to the state-of-art CROWN method on Sigmoid and Tanh networks.
Mnemosyne: Privacy-Preserving Ride Matching With Collusion-Resistant Driver Exclusion

Ride-Hailing Service (RHS) has drawn plenty of attention as it provides transportation convenience for riders and financial incentives for drivers. Despite these benefits, riders risk the exposure of sensitive location data during ride requesting to an untrusted Ride-Hailing Service Provider (RHSP). Our motivation arises from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">repetitive matching, i.e., the same driver is repetitively assigned to the same rider</i> . Meanwhile, we introduce a driver exclusion function to protect riders' location privacy. Existing work on privacy-preserving RHS overlooks this function. While Secure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> Nearest Neighbor (S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN) facilitates efficient matching, the state-of-the-art neglects a collusion attack. To solve this problem, we formally define repetitive matching and strong location privacy, and propose Mnemosyne: privacy-preserving ride matching with collusion-resistant driver exclusion. We extend the simple integration of equality checking and item exclusion to a dynamic integration. We concatenate each prefix of an acceptable identity range to each location code when generating a ride request, i.e., secure mix index. We process each prefix of the driver identity to generate a ride response, i.e., a mix token. We build an indistinguishable Bloom-filter as an index to query the token. When matching riders with drivers, the colluding parties cannot distinguish identity prefixes from location codes. We build a prototype of Mnemosyne based on servers, smartphones, and a real-world dataset. Experimental results demonstrate that Mnemosyne outperforms existing work regarding strong location privacy and computational costs.
Topological modelling of helm graph in terms of order

Material science has witnessed many applications of Mathematics involving Graph theory and Topology. In this paper some concepts of Graph theory and Topology has been linked together. The order of points of a topological space with topology as incidence topology generated by Comb graph and Helm graph has been presented in this paper. Also, the order of edges with respect to respective incidence topology of these special graphs have been studied. Various results based on connectedness and separation axioms of the incidence topology generated by these graphs have been discussed. Moreover, order of these nodes and edges with respect to the incidence topology has been established and generalized for these special graphs and supporting examples are illustrated along with the results.