Reviewerly/exHarmony huggingface dataset:
Adaptive Landmark-Based Spectral Clustering for Big Datasets

Clustering has emerged as an effective tool for the processing and assessment of the vast data generated by modern applications; its primary aim is to classify data into clusters in which the items are grouped into a given category. However, various challenges, such as volume, velocity, and variety, occur during the clustering of big data. Different algorithms have been proposed to enhance the performance of clustering. The landmark-based spectral clustering (LSC) technique has been proven to be efficient in clustering big datasets. In this study, an algorithm called adaptive landmark-based spectral clustering (ALSC) is proposed for clustering big datasets. The proposed algorithm comprises the adaptive competitive learning neural network (ACLNN) algorithm, which can be efficiently used to determine the number of clusters and the LSC technique. The ACLNN algorithm can also be used with small datasets. Thus, in our implementation, the original big dataset is split into N small sub datasets, which run in parallel by N copies of the ACLNN algorithm. To evaluate the performance of the proposed algorithm, two distinctive datasets, namely, Fashion-MNIST and United States Postal Service are used. The experiments show that the proposed ALSC algorithm produces high clustering accuracy with the identification of the number of clusters. Results reveal that the normalized mutual information and adjusted Rand index of the proposed algorithm outperform state-of-the-art models.
Sports Video Tracking Technology Based on Mean Shift and Color Histogram Algorithm

Abstract With the development of society, people pay more and more attention to sports. In this context, sports video tracking technology has become a research hotspot. At present, a lot of research has been done on target tracking technology at home and abroad, and great breakthroughs have been made. However, there are still some deficiencies in the existing video tracking algorithms. On this basis, this paper combines the mean shift algorithm with color histogram algorithm, and proposes a new sports video tracking technology algorithm. In the study, in order to verify the effectiveness of this method, we select two videos to do experiments. In order to highlight the advantages of this method, we will use the mean shift algorithm for sports video tracking method and the color histogram algorithm for sports video tracking method as a comparison. The results show that, overall, the tracking accuracy of the proposed method is the highest, with an average tracking accuracy of 95.9%, followed by the mean shift algorithm for sports video tracking. The average tracking accuracy of this method is 90.35%. Finally, the color histogram algorithm is used to track sports video, and the tracking accuracy of this method is accurate the rate was 89.8%. In addition, the tracking speed of the proposed method is better than the other two methods. It can be seen that this method has good performance and can track sports video quickly and accurately.
Semi-Supervised Few-Shot Learning with Pseudo Label Refinement

Few-shot classification aims at recognising novel categories with very limited labelled samples. Although substantial achievements have been obtained, few-shot classification remains challenging due to the scarcity of labelled examples. Recent studies resort to leveraging unlabelled data to expand the training set using pseudo labelling, but this strategy often yields significant label noise. In this work, we introduce a new baseline method for semi-supervised few-shot learning by iterative pseudo label refinement to reduce noise. Then, we investigate the label noise propagation problem and improve the baseline with a denoising network to learn distributions of clean and noisy pseudo-labelled examples via a mixture model. This helps to estimate confidence values of pseudo labelled examples and to select the reliable ones with less noise for iteratively refining a few-shot classifier. Extensive experiments on three widely used benchmarks, minilma- genet, tieredImagenet and CIFAR-FS, show the superiority of the proposed methods over the state-of-the-art methods.
A Novel Trojan Attack against Co-learning Based ASR DNN System

ASR (Automatic Speech Recognition) technology is a key technology for human-computer interaction. Especially the DNN models of wake-up-word speech recognition, which enables the smart device to recognize wake-up words spoken by users when they are in the sleep or lock screen state, allowing the device to directly enter the wait command state, and start the first step of voice interaction. ASR technology no wonder provides great convenience for people's daily life, but it's security problem has always been a hot topic of further research. The rapid development of ASR models has made these models very vulnerable which seriously affect their performance in real scenarios. This paper proposes a new backdoor attack method TNN (Trojan Neural Network) for deep learning models in voice wake-up scenarios. The attacker leaves backdoor in the deep learning model of the ASR. Besides specific wakeup words, attackers can also use other words to force the device to wake up and get the IoT smart device's highest privileges. And when the smart device is in the awake state, any audio containing a particular vocabulary will be recognized as a specific command and executed. In this papaer, we propose a novel method to attack ASR model, and without affecting the performance of clean samples, by applying the attack method, the success rate in compulsory recognition of specific words can be up to 100%. The experimental results prove that our attack method is very effective and poses a great security problem of the voice-interactive IoT device.
Transform-based phase retrieval techniques from a single off-axis interferogram

Optical phase retrieval (OPR) methods are important because they are used to obtain the transverse phase profile information of a beam. Interference methods are extensively used to convert the phase information into an intensity pattern, which can then be processed further to retrieve the unknown phase. The most widely used interference method involves the interference of the unknown object beam and a known reference beam with an angle between them. There are several algorithms that retrieve the phase information from such a single off-axis interference pattern. For a particular application, the choice of an algorithm for OPR is very important. Therefore, it is necessary to choose between them, depending on the requirements. Three entirely different noniterative, transform-based algorithms, namely the Fourier transform (FT) method, the continuous wavelet transform (CWT) method, and the Hilbert transform (HT) method, are explained in detail. A quantitative comparison is made using a combination of rms error and standard structural similarity measure. The advantages of using a standard unwrapping algorithm are also validated using the same combination of comparison metrics. We show that the HT method has a better response with object beam with higher spatial frequency content, but with the penalty of affected noise. The FT method and CWT method have better noise immunity, but have the limitation of the spatial frequency range of the object beam. The different constraints, advantages, and some practical limitations of the methods are discussed with the help of a quantitative phase imaging experiment of monodispersed polymethyl methacrylate beads.
Sublinear-Time Attraction Force Computation for Large Complex Graph Drawing

Recent works in graph visualization attempt to reduce the runtime of repulsion force computation of force-directed algorithms using sampling, however they fail to reduce the runtime for attraction force computation to sublinear in the number of edges.We present new sublinear-time algorithms for the attraction force computation of force-directed algorithms and integrate them with sublinear-time repulsion force computation.Extensive experiments show that our algorithms, operated as part of a fully sublinear-time force computation framework, compute graph layouts on average 80% faster than existing linear-time force computation algorithm, with surprisingly significantly better quality metrics on edge crossing and shape-based metrics.
Low-light Image Enhancement based on Joint Decomposition and Denoising U-Net Network

In the process of low-light image enhancement, how to handle the noise effectively is an important yet challenging problem. Based on Retinex decomposition theory, this paper proposes a deep learning method for low-light image enhancement with joint decomposition and denoising. First, we propose a new joint decomposition and denoising enhancement U-Net network (JDEU). The network can be trained with low-light images only, and high quality normal-light images are used as reference guided decomposition network to decompose the desired reflectance component for noise removal. Then, the decomposition result is used as the input of the enhanced network to adjust the illumination component brightness. Finally, the reflectance and the adjusted illumination are reconstructed to produce the enhanced image. Experiments based on LOL dataset show that our method not only performs well in texture details and denoising, but also reduces color distortion and greatly improves the overall visual effect.
High Dimensional Text Document Clustering and Classification using Machine Learning Methods

Although High Dimensional documents are used for classification, document dimensions are a major concern and a worrying sign. The effects of Dimensional reduction can be both the positive and negative aspects. If a dimensional reduction is not in a correct format, classification using lower-dimensional documents will not produce the desired results. Our research studies centered on dimensional reduction using the fundamental similarity property, but they haven't yet discussed text classification. This approach discusses the classification and clustering task by providing various algorithms. We used clustering and classification methods on various datasets in this paper. It also suggested improving the efficiency of the K-means clustering and the Naive Bayes classification technique. Popular parameters like precision, recall, and F-score are used to evaluate the recommended method's performance. The results of the experiments would demonstrate that the proposed model outperforms current algorithms.
WITHDRAWN: Sentimental analysis in student-teacher communication for effective learning

The sentimental analysis relates to structural detection, extraction, quantification, and evaluation of effects and knowledge in natural language processing, text analysis, computer-language sociology, and biometric data. Many emotional hurdles prevent student-teacher communication's intellectual progress that can promote healthy feelings towards the class environment. Therefore, in this paper, sentimental analysis assisted student-teacher communication (SAA-STC) has been proposed for effective learning in higher education purposes. Using sentiment analysis, feelings were drawn from the students' teaching records based on the concurrent exploratory approach. STC is introduced, which can manage numerous e-learning fields to check the student-teacher communication for effective learning. STC allows any initial checks to be completed, focusing on a few transparent challenges to enable the framework to continue expanding higher education learning communication.
Product Recommendation System Using Opinion Mining on Vietnamese Reviews

Opinion mining (also known as sentiment analysis) through customers’ reviews or feedbacks which can identify the user opinion about different product features has received a lot of attention exhibited in numerous studies. The majority of recommender systems have recommended products based only on overall evaluation and primarily based on expert’s evaluation. In this work, we propose a method to explore Vietnamese reviews extracted from e-commerce websites in Vietnam to provide suggestions in products selection based on products’ features/functions. The proposed approach introduces a topic-based model to identify products’ features which are mentioned in customer comments/reviews. The proposed system is implemented with the integration combining the VietSentiWordnet to calculate the importance scores for the features of each product. We also construct a product recommendation database which can store customers preference and purchases history. The work is analysed on more than 2,000 Vietnamese comments/reviews about laptop products and is expected to be feasible to apply in practical cases.
Evaluating the Scalability of a Multi-Object Detector Trained with Multiple Datasets

Background Thresholding is a method used to improvise the training of a model with multiple datasets. The primary objective of this paper is to test the scalability of the Background Thresholding approach incorporated with a proposed object detection model for multiple datasets. The proposed research work has taken the Bosch traffic light dataset for traffic light detection, Tsinghua-Tencent 100K Dataset for Traffic sign detection and Udacity annotated driving dataset for car detection. Different algorithms and deep learning frameworks are experimented to provide a detailed analysis as to which model performs the best for the particular task at hand. The proposed research work is concluded with the Faster-RCNN (Regional-CNN) algorithm, which is coupled with Inception V2 to remain as the best for our approach.
Web-Based Application Design based on Usability Attributes for Sport Reservation Places: FUTSALKUY Apps

Abstract Along with the growing world of information technology, website application become extremely popular as medium and channel that offers variety of ways in presenting information. It offers high interoperability and accessibility, which provide ease of use and learn despite different of user demographic. Therefore, the web development often faces several problems such as requirement are unclear and less defined, frequent changes in each phase, hard to launch due to budget or time, not ready to scale incomplete input validation and so on. Thus, this study utilize design thinking in the pre-development to offer complementary approach to the problem-solving through encouraging active participation from users and iterative cycle of phases.
Self-adaptive Graph Traversal on GPUs

GPU's massive computing power offers unprecedented opportunities to enable large graph analysis. Existing studies proposed various preprocessing approaches that convert the input graphs into dedicated structures for GPU-based optimizations. However, these dedicated approaches incur significant preprocessing costs as well as weak programmability to build general graph applications. In this paper, we introduce SAGE, a self-adaptive graph traversal on GPUs, which is free from preprocessing and operates on ubiquitous graph representations directly. We propose Tiled Partitioning and Resident Tile Stealing to fully exploit the computing power of GPUs in a runtime and self-adaptive manner. We also propose Sampling-based Reordering to further optimize the memory efficiency of SAGE through a lightweight and effective node reordering technique on the fly. Extensive experiments demonstrate that SAGE can achieve superior graph traversal performance over existing approaches under different architectural scenarios, i.e., single-GPU, out-of-core, and multi-GPU.
An Efficient Ciphertext Policy Attribute-Based Encryption Scheme from Lattices and Its Implementation

Lattice-based Attribute-based encryption is a well-known cryptographic mechanism that can resist quantum attacks and has the ability of fine-grained access control, and it has a wide range of application scenarios in current Internet of Thing (IoT) era. However, lack of efficiency and existing the problem of large ciphertext expansion rate are the main disadvantages impede the applications of this mechanism. Thus, we propose an efficient and practical ciphertext policy attribute-based encryption (CP-ABE) scheme from lattices in the paper. In this scheme, to make the secret key reusable, we adjust access tree and propose a basic access tree structure, which can be converted from disjunctive normal form, and combine it with a light post-quantum scheme of Kyber. In addition, the compression method and plaintext expansion method are introduced to optimize the scheme. Our CP-ABE scheme is secure against chosen plaintext attack under the hardness of module learning with errors problem. We implement our scheme and compare it with three recent related schemes in terms of security, function and communication cost. Experiments and comparisons show that our CP-ABE scheme has advantages in high encryption efficiency, small matrix dimension, small key sizes, and low ciphertext expansion rate, which has some merit in practice.
Differential Translation for Japanese Partially Amended Statutory Sentences

We propose a differential translation method that targets statutory sentences partially modified by amendments. After a statute is amended, we need to promptly update its translations for international readers. We must focus on the focality of translation. In other words, we should modify only the amended expressions in the translation and retain the others to avoid causing misunderstanding of the amendment’s contents. To generate focal, fluent, and adequate translations, our method incorporates neural machine translation (NMT) and template-aware statistical machine translation (SMT). In particular, our method generates n-best translations by an NMT model with Monte Carlo dropout and chooses the best one by comparing them with the SMT translation. This complements the weaknesses of each method: NMT translations are usually fluent but they often lack focality and adequacy, while template-aware SMT translations are rather focal and adequate but not fluent. In our experiments, we showed that our method outperformed both the NMT-only and SMT-only methods.
YOLO-Tight: an Efficient Dynamic Compression Method for YOLO Object Detection Networks

Deep learning algorithms perform well in the field of object detection. Object detection networks represented by YOLO, SSD and faster-RCNN have achieved excellent performance on public datasets such as VOC and COCO. However, deep learning models are difficult to deploy on the edge computing platform with less computing resources due to its huge amount of parameters and computation. In this paper, we propose an efficient dynamic sparsity method to help the network quickly mine important parameters, and then prune the unimportant weight channels, which makes the network model more compact and consumes less computation. In the case of high sparsity, our method is more robust than L1 regularization and other regularization forms, and can achieve better sparsity and pruning effects. Through this method, we can prune the YOLOv3 network and the enhanced YOLOv3-SPP3 network by up to 90%. This allows the network to achieve 5× reduction in FLOPs and maintain an accuracy loss of less than 1% on the BDD100k dataset.
A novel method for in-home Gait Speed estimation in Health Monitoring Using Bluetooth Low Energy

Gait speed (GS) is a crucial parameter in the evolution and diagnosis of degenerative illnesses. Nowadays, it is measured in clinical environments where it is impossible to keep track of all patients due to the lack of resources. The development of GS measurement systems for in-home environments could solve this problem, but current methods present, at least, one of these problems: they require expensive hardware, are intrusive for the patient, or are imprecise. A novel method to measure GS in these scenarios using Bluetooth Low Energy and smart wearable devices is proposed in this work. The proposed system is inexpensive, non-intrusive, and its precision is comparable to the current state of the art methods. This system could be commercialized as part of an in-home health monitoring system.
Latent Low-rank Graph Learning for Multimodal Clustering

Multimodal clustering has become a fundamental and important problem in the data mining community since the development of multimedia technology over the last two decades has led to a tremendous increase in unlabeled multimodal data. Although a panoply of multimodal subspace clustering methods shows promising performance via fusing information from different views of multimodal data, most of them consist of two sequential steps, i.e., learning a consensus affinity matrix from the original data and then feeding the resulting affinity matrix into the framework of spectral clustering. However, this leads to the suboptimal clustering performance due to the following limitations: 1) the two steps of learning the affinity matrix and clustering are carried out independently; 2) the affinity matrix may be unreliable; 3) the post-processing requirement, such as K-means. To address these issues, we propose a novel multimodal subspace clustering method via adaptively learning a similarity graph on a latent low-rank representation space. In particular, the number of connected components of the learned graph is precisely equal to the number of clusters, i.e., the optimal solution of the associated problem directly reveals the clustering structure of data. Extensive evaluations on several benchmark multimodal datasets demonstrate that the proposed approach outperforms state-of-the-art methods.
SMIX(λ): Enhancing Centralized Value Functions for Cooperative Multiagent Reinforcement Learning

Learning a stable and generalizable centralized value function (CVF) is a crucial but challenging task in multiagent reinforcement learning (MARL), as it has to deal with the issue that the joint action space increases exponentially with the number of agents in such scenarios. This article proposes an approach, named SMIX( λ ), that uses an OFF-policy training to achieve this by avoiding the greedy assumption commonly made in CVF learning. As importance sampling for such OFF-policy training is both computationally costly and numerically unstable, we proposed to use the λ -return as a proxy to compute the temporal difference (TD) error. With this new loss function objective, we adopt a modified QMIX network structure as the base to train our model. By further connecting it with the Q(λ) approach from a unified expectation correction viewpoint, we show that the proposed SMIX( λ ) is equivalent to Q(λ) and hence shares its convergence properties, while without being suffered from the aforementioned curse of dimensionality problem inherent in MARL. Experiments on the StarCraft Multiagent Challenge (SMAC) benchmark demonstrate that our approach not only outperforms several state-of-the-art MARL methods by a large margin but also can be used as a general tool to improve the overall performance of other centralized training with decentralized execution (CTDE)-type algorithms by enhancing their CVFs.