Reviewerly/exHarmony huggingface dataset:
Depression Prediction: A Clinical Questionnaire and Machine Learning Based Approach

Depression is specified as a mental health disease that leads to continual gloomy mood and detached interest in daily tasks, which a person was able to do easily and showed much interest normally, thus crippling a person’s daily life. It’s important to identify the symptoms of mental health problems early in a person as there are many conditions for which effective treatments work best during the early stages thus preventing the aggravation of the disease. Over the past decade, Machine learning techniques have become immensely popular for examining the medically available data and identifying the root issue. In this work, we also use a machine learning based approach to propose a response-based classifier that uses just a quick questionnaire to diagnose and predict the early onset of depression in a person being screened. The questionnaire used in our approach has been designed strictly based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM- 5). In particular, our approach uses the responses made by the person being screened to predict depression, using a non-linear support vector machine (SVM) classifier. In general, the classifier is straightforward, computationally lightweight, and can predict depression with 83.87% accuracy. We also test our proposed method on a Kashmir-based dataset.
Mizo to English Machine Translation: An Evaluation Benchmark

Speech is the most natural method for people to convey emotions and communicate. Traditional input techniques are used for machine communication. Communication across distinct regional languages is always unavoidable and takes significant technological effort to make meaningful and effective understanding. Methods of Machine Translation (MT) have been developed in an effort to circumvent this obstacle. Neural Machine Translation (NMT) is one of the projects that has lately seen a significant improvement in terms of human judgement compared to traditional methods such as phrase-based machine translation (PMT) and statistical machine translation (SMT). Therefore, developments in NMT have an influence on the perceived difficulty of humans. Numerous internet translation and mobile application solutions, such as Google Translate, etc., were created to solve this issue. However, such simple translation methods are not accurate for the parallel MizoEnglish languages. In this research, we have attempted to improve language translation by using the superior capacity of NMT to boost the Mizo-to-English translation in the BiLingual Evaluation Understudy (BLEU) measure and achieved 42.65 BLEU Score on the 4 grams. Publicly available Mizo-English corpus was developed.
A Comparative Study of Feature Extraction Techniques and Similarity Measures for Image Retrieval

With the growing popularity of using massive amount image database in several applications, it is critical to develop an autonomous and efficient retrieval system to search the relevant images from entire database. The method of obtaining the relevant images from huge image libraries by extracting their content features is known as content-based image retrieval (CBIR). In this paper, a comparative study is performed while acquiring various methods of traditional feature extraction, such as Color moment, Gabor wavelet, Discrete wavelet transform (DWT), Local binary pattern (LBP), Gray level co-occurrence matrix (GLCM), and Histogram of orientation (HOG), to present an efficient and more accurate CBIR system. The experiment is demonstrated on two benchmark datasets, namely Wang (color images) and Medical MNIST (grayscale images), with different visual effects. To retrieve relevant images of a query image, three distinct distance metrics, such as Cosine, City block, and Euclidean, are used to examine the similarity between the query image and the database images. The experiment is evaluated using two performance metrics: precision and recall, to compare the efficacy of various approach. We achieve the best results as average precision of 65.65% and average recall of 6.57 on a scale of 10 using Color moment features via Euclidean distance metric in case of WANG dataset, while 99.89% and 9.99 on a scale of 10 for average precision and average recall using HOG features via City block distance metric in case of Medical MNIST dataset.
A Deep Hybrid Learning Model for Classification of Code-Mixed Text

Social media consists of many tweets. These tweets consist of emotions like positive, negative. Many people use the bilingual sentences it means using two languages in a sentence. The main aim of the project is to develop a classification algorithm that can be used on social media data to classify it as positive or negative. Social media consists of code-mixed data which means more than one language has been used by the users. To classify the code-mixed data, we use classifiers to produce the emotions for the bilingual sentences. This project involves natural language processing, deep learning and machine learning algorithms. By mixing different classifying algorithms output we are going to increase the accuracy. The datasets are available for the linguistic languages. There are classifiers like CNN, LSTM and other machine learning classifiers like Adaptive boost are used to increase the accuracy and efficiency of the model.
Human Activity Detection Using Pose Net

Human detection and tracking is one of the important study fields that has attracted a lot of attention recently.Despite the fact that commercially available technologies for human identification and counting are now available, further experimentation is required in order to address the challenges. Abnormality detection, commonly called automated video surveillance, is the way of noticing and describing human behaviour and interactions in a crowded setting. People must be discovered and tracked in order to maintain reliability, welfare, and site management. Object identification is a critical stage in abnormality detection and automated video surveillance. Background subtraction is a technique for detecting human actions in video segments. The frequent technique for distinguishing moving objects from motionless images Background subtraction is the technique of detaching foreground objects from the background in a sequence of video frames, as the label implicit. In this scenario, the primary purpose of abnormality detection is to recognise and track a moving object using video recorded images and pose net.
GAL: Graph-Induced Adaptive Learning for Weakly Supervised 3D Object Detection

Weakly Supervised 3D Object Detection (WS3DOD) aims to perform 3D object detection with little reliance on 3D labels, which greatly reduces the cost of 3D annotations. In recent literature, the pseudo-label-based approach brings impressive performance, which generates 3D pseudo-labels from 2D bounding boxes. Despite their success, two key issues remain unresolved that reduce the quality of 3D pseudo-labels: 1) the existing local object locating algorithm can not capture complete clusters of points globally, and 2) the existing algorithm can not capture sparse points caused by the unevenly distributed points obtained by LiDAR cameras. Hence, we propose GAL, a Graph-induced Adaptive Learning algorithm, to generate 3D pseudo-labels. First, we propose the Cluster Locating algorithm based on the Minimum Spanning Tree (MST) to globally locate the objects, which can leverage the characteristic that points inside an object are compact while points between objects are discrete. Second, we propose a density-guided adaptive learning algorithm to optimise the Cluster Locating algorithm, named Cuboid Drift. Cuboid Drift considers the inhomogeneous distribution of reflected points on different reflective surfaces of LiDAR imaging. Finally, 3D pseudo-labels generated by GAL are leveraged to train 3D detectors. Extensive experiments on the challenging KITTI and DAIR-V2X-V dataset demonstrate that GAL without 3D labels can be comparable with strongly supervised approaches and outperforms the previous state-of-the-art WS3DOD methods. Moreover, our method saves 88% of the time spent on pseudo-label generation.
Focus on Hard Samples: Hierarchical Unbiased Constraints for Cross-Domain 3D Model Retrieval

Cross-domain 3D model retrieval facilitates the management of explosively emerging unlabeled 3D models with conveniently available 2D images or RGB-D objects, which has attracted more and more attention. The modality gap between query samples (2D images or RGB-D objects) and 3D models makes the task challenging, and adversarial domain adaptation techniques have achieved success in narrowing such gaps. However, existing methods always pay excessive attention to the samples with high discriminability and transferability, whereas the hard samples with rich information are neglected. Accordingly, we propose hierarchical unbiased constraints to make full use of data at semantic level, sample level and feature level to improve the retrieval performance. At semantic level, we utilize maximum F-norm loss to constrain the semantic prediction results of target domain, which takes advantage of more hard samples to reduce ambiguous predictions and enhance discriminability. At sample level, we propose an adaptive triplet center loss to assign less confident samples with a farther negative class, which reliably compacts samples within the same class and expands the distance across different classes. At feature level, we perform SVD (singular value decomposition) for both source features and target features and suppress the relative value of the largest singular value, so that the information of other eigenvectors can be fully utilized to improve transferability. Experiments on two public datasets validate the superiority of the proposed method, and the ablation study analyzes different roles played by these hierarchical unbiased constraints.
Using Machine Learning to Establish the Concerns of Persons With HIV/AIDS During the COVID-19 Pandemic From Their Tweets

There are millions of People Living with HIV/AIDS (PLWHA) globally and over the years, addressing their concerns has been topical for many stakeholders.It is a well-known and established fact that PLWHA are at increased risk of victimization and stigmatization.Unfortunately, the world experienced an outbreak of the COVID-19 pandemic that has led to strict social measures in many states.Thus, it is the goal of this research to study the impact that the outbreak and its mitigation measures have had on the PLWHA.Specifically, we sought to highlight their concerns from sentiments expressed on social media based on posted tweets.By combining machine learning (ML) techniques such as textual mining and thematic analysis, we determined 14 major themes as factors that are worth exploring.In this work, we originally extracted 2,839,091 tweets related to HIV/AIDS posted from March 2020 to April 2022.After initially doing data cleaning and preprocessing, we performed topic modeling using the Latent Dirichlet Allocation (LDA) topic model to extract 25 topics that are made up of 30 keywords each.The topics were then narrowed into 14 themes.The paper details the negative, positive, and neutral sentiment polarities which we highlight as concerning.These sentiments were determined using the Valence Aware Dictionary and sEntiment Reasoner (VADER) Sentiment Analysis Library with a 90% F1-score compared to TextBlob which showed a 53% F1score.The research findings highlight issues affecting PLWHA during and post-pandemic such as high cost of medical care, late diagnosis of HIV, limited access to medications, stigmatization and victimization, absence of testing kits in hospitals, and lack of urgency in the development of vaccines or cure for HIV.
MPL-TransKR: Multi-Perspective Learning Based on Transformer Knowledge Graph Enhanced Recommendation

The emergence of recommender system is aimed at solving the problems brought by information explosion to human life and even the development of human society. As a traditional recommendation technique, collaborative filtering often encounters sparsity and cold start problems in many recommendation scenarios. Therefore, researchers have found that the introduction of side information can solve these problems to a certain extent and improve the performance of recommender systems. The knowledge graph is a heterogeneous graph that contains rich semantic relationships among items. The Multi-Perspective Learning based on Transformer Knowledge Graph Enhanced Recommendation (MPL-TransKR) proposed in this paper uses the knowledge graph as the side information for input and introduces the multi-head self-attention mechanism by reasonably combining the transformer idea. While learning the high-order neighborhood information of the items, the long-distance information between the items is captured, and the weight value is assigned to the user through the attention mechanism to strengthen the user representation to realize user-item multi-perspective learning to enhance the performance of the recommendation model. Through extensive experiments using public datasets, we demonstrated that MPL-TransKR performs well in book and music recommendations, surpassing state-of-the-art baselines on several metrics.
A Privacy-Preserving Data Aggregation Scheme Based on Chinese Remainder Theorem in Mobile Crowdsensing System

Mobile crowdsensing provides a large-scale reliable data, since sensing nodes support wide distribution, flexible mobility, and opportunistic connectivity. The generated data often contains the sensing node's sensitive information. Existing schemes dedicated to protecting the privacy of perception data, but these schemes cannot balance the computational and communication overheads well. Therefore, we propose a privacy-preserving data aggregation scheme based on the Chinese remainder theorem. Using blinding factor and Paillier homomorphic encryption technology, which not only ensures the privacy of perceived data but also improves the robustness of the system. Specially, the introduction of secure multicast communication technology based on the Chinese remainder theorem protects task privacy and ensures only designated sensing nodes can obtain the task. In addition, we design an efficient signature scheme to ensure data integrity. Detailed security analysis shows that our scheme is existentially unforgeable against adaptively chosen message attack. Extensive experiments and performance analysis show that our scheme is efficient and has a low communication overhead.
Long-Term Privacy-Preserving Aggregation With User-Dynamics for Federated Learning

Privacy-preserving aggregation protocol is an essential building block in privacy-enhanced federated learning (FL), which enables the server to obtain the sum of users’ locally trained models while keeping local training data private. However, most of the work on privacy-preserving aggregation provides privacy guarantees for only one communication round in FL. In fact, as FL usually involves long-term training, i.e., multiple rounds, it may lead to more information leakages due to the dynamic user participation over rounds. In this connection, we propose a long-term privacy-preserving aggregation (LTPA) protocol providing both single-round and multi-round privacy guarantees. Specifically, we first introduce our batch-partitioning-dropping-updating (BPDU) strategy that enables any user-dynamic FL system to provide multi-round privacy guarantees. Then we present our LTPA construction which integrates our proposed BPDU strategy with the state-of-the-art privacy-preserving aggregation protocol. Furthermore, we investigate the impact of LTPA parameter settings on the trade-off between privacy guarantee, protocol efficiency, and FL convergence performance from both theoretical and experimental perspectives. Experimental results show that LTPA provides similar complexity to that of the state-of-the-art, i.e., an additional cost of around only 1.04X for a 100,000-user FL system, with an additional long-term privacy guarantee.
ScriptNet: A Two Stream CNN for Script Identification in Camera-Based Document Images

Script identification is an essential part of a document image analysis system, since documents written in different scripts may undergo different processing methods. In this paper, we address the issue of script identification in camera-based document images, which is challenging since the camera-based document images are often subject to perspective distortions, uneven illuminations, etc. We propose a novel network called ScriptNet that is composed of two streams: spatial stream and visual stream. The spatial stream captures the spatial dependencies within the image, while the visual stream describes the appearance of the image. The two streams are then fused in the network, which can be trained in an end-to-end manner. Extensive experiments demonstrate the effectiveness of the proposed approach. The two streams have been shown to be complementary to each other. An accuracy of $$99.1\%$$ has been achieved by our proposed network, which compares favourably with state-of-the-art methods. Besides, the proposed network achieves promising results even when it is trained with non-camera-based document images and tested on camera-based document images.
BPMCF: Behavior Preference Mapping Collaborative Filtering for Multi-behavior Recommendation

Traditional recommendation methods usually consider one single behavior of users, such as purchasing on e-commerce platforms. But users usually have more than one kind of behavior, such as browsing and adding shopping carts. All behavior data of users will have an impact on their interests. However, in multi-behavior scenario, users also have different preference for behaviors, which contributes to capturing users' real interests. In this work, we propose a new solution named Behavior Preference Mapping Collaborative Filtering (BPMCF), which mines users preference for behaviors by modeling the deep semantic relations among behaviors. In particular, we propose to stimulates the deep semantic relations among different behaviors by the way of a spatial attention mapping mechanism. And we propose the concept of user behavior interaction matrix for the first time in the multi-behavior scenario, which plays an important role in capturing users preference for behaviors. We treat the optimization on a behavior as a task and make a joint optimization to correlate them. Extensive experiments on two real-world datasets demonstrate that BPMCF significantly outperforms state-of-the-art recommender systems designed to learn from both single-behavior data and multi-behavior data.
Robust Clustered Federated Learning

Federated learning (FL) is a special distributed machine learning paradigm, where decentralized clients collaboratively train a model under the orchestration of a global server while protecting users' data privacy. Concept shift across clients as a specific type of data heterogeneity challenges the generic federated learning methods, which output the same model for all clients. Clustered federated learning is a natural choice for addressing concept shift. However, we empirically show that existing state-of-the-art clustered federated learning methods cannot match some personalized learning methods. We attribute it to the fact they group clients based on their entangled signals, which results in poor clustering. To tackle the problem, in this paper, we devise a lightweight disentanglement mechanism, which explicitly captures client-invariant and client-specific patterns. Incorporating the disentanglement mechanism into clients' local training, we propose a robust clustered federated learning framework (RCFL), which groups the clients based on their client-specific signals. We conduct extensive experiments on three popular benchmark datasets to show the superiority of RCFL over the competitive baselines, including personalized federated learning methods and clustered federated learning methods. Additional experiments demonstrate its robustness against several sensitive factors. The ablation study verifies the effectiveness of our introduced components in RCFL.
Cost-Effective Clustering by Aggregating Local Density Peaks

Hierarchical clustering algorithms that provide tree-shaped results can be regarded as data summarization and thus play an important role in the application of knowledge discovery and data mining. However, such structured result also brings a challenge, i.e., a difficult trade-off between complexity (time and space) and quality. To tackle of this issue, we propose a newly designed agglomerative algorithm for hierarchical clustering in this paper, which merges data points into tree-shaped sub-clusters via the operations of nearest-neighbor chain searching and determines the proxy of each sub-cluster by the process of local density peak detection. Extensive experimental studies on real-world and synthetic datasets show that our method performs well by outperforming other baselines in accuracy, response time, and memory footprint. Meanwhile, our method can scale to half a million data points on a personal computer, further verifying its cost-effectiveness.
AntTune: An Efficient Distributed Hyperparameter Optimization System for Large-Scale Data

Selecting the best hyperparameter configuration is crucial for the performance of machine learning models over large-scale data. To this end, the automation of hyperparameter optimization (HPO) has been widely applied in many automated machine learning (AutoML) frameworks. However, without the effective mechanisms of early stopping and prior knowledge leveraging, such automation is often time-consuming and even inefficient. To improve efficiency, we introduce AntTune, a distributed HPO system that includes parallel optimization, distributed evaluation, tensor cache, etc. Specifically, in AntTune, a time-saving and lightweight mechanism of early stopping is designed to process multiple trials simultaneously. Also, a tree-based meta-learning approach is developed to leverage knowledge from prior tasks and thus it can speed up current HPO tasks. The extensive experiments on both public and industrial datasets demonstrate that our AntTune can improve the state-of-the-art HPO platforms by an average of 3.26% in terms of the effectiveness metrics and 26.25% in terms of tuning time.
Deep User and Item Inter-matching Network for CTR Prediction

CTR prediction plays an important role in increasing company revenue and user experience, and many efforts start with historical behavior to uncover user interest. There are two main problems with previous works: (1) When most previous works mined interests from users' historical behaviors, they only focus on implicit or explicit interests. (2) When most previous works mined user interests through the relationship between target users and similar users, the representation of target users was not rich and accurate enough, resulting in less accurate item recommendations. Therefore the Deep User and Item Inter-Matching Network (DUIIN) is proposed in this paper to solve the above problems. First, we design Item-to-Item Network (IIN), using two different sub-networks Evolving Interest Network (EIN) and Feature Interaction Network (FIN) to mine the hidden interests and explicit interests shown by users' historical behaviors, respectively. Then the User-to-User Network (UUN) is designed to mine user interests through the relationship between target users and similar users after representing the target users more accurately and richly. The experimental results show that the DUIIN model proposed in this paper performs better than other state-of-the-art models.