Reviewerly/exHarmony huggingface dataset:
Interview Bot with Automatic Question Generation and Answer Evaluation

Resume screening and interview chatbots are the basic requirements for today’s hiring processes for a given job post. There are a large number of candidates who apply and manually sorting these resumes as per the job requirement is a tedious task for shortlisting the candidates. The motivation for the project lies in solving the challenge of automating the process of recruitment from resume screening to the first technical test of the candidate. The challenge for screening candidates’ resumes would be to get structured data from differently structured resumes using Natural Language Processing and get all the keywords consisting of programming languages. The other challenge lies in the generation of questions and evaluation of answers given by the candidates using Natural Language Processing techniques. The challenge of proctoring the candidate during the test is the detection of eye movements of candidates during the interview and lip reading to check for any malpractice. The idea here is that the candidate’s resume would be scanned for all the keywords consisting of programming languages to the concept words extracted from the resume. Then based on that, the candidate would be assigned a job role that suits his/her skill set which reduces the time required to find the relevant candidate for a given Job Description. The candidate’s interview would be conducted to know the candidate’s technical skills to some extent. Multiple Choice Questions based tests could be used for the initial screening. Still, they don’t assess the in-depth knowledge of the candidate as much as the short answer questions can. Hence generation of short answer questions and evaluation is important. The question generated for the candidate would be either based on sentence-level information or paragraph-level information; the sentences and paragraphs would be of different technical questions on the programming languages and concepts. The questions would be selected both randomly and based on candidates’ responses. During the interview, the camera would be used to proctor the exam so that there should be no more than one person in front of the camera and the candidate should not use any other device during the exam. The final score generated for the candidate would be based on the match with the job description and candidate interview.
Efficient Object Detection on Sparse-to-Dense Depth Prediction

Detecting objects has been an important and intricate field for many years, particularly in the safety and healthcare sectors as well as the automotive industry. Much research on identifying objects has been shown to be a challenging one. When compared to other existing algorithms, You Only Look Once (YOLO) provides superior power and speed for this task. Semantic segmentation is a computer vision challenge in which the different parts of an image are sorted into classes that can be understood based on their meaning. When it is said that a class is semantically subject to interpretation, it means that it has some significance in the real world. The scope of the work is to detect the objects in an image and draw a bounding box around the object with its class and confidence score. The experimental results show that the objects are detected, classified, and localized with their confidence score.
A Comprehensive Comparison of Machine Learning Algorithms for Breast Cancer Prediction

The root cause of death for women is breast cancer. Early detection is essential for rectifying cancer. In this study, we explored the use of machine learning models for predicting breast cancer. We evaluated logistic regression, K-neighbors classifier, support vector machine, decision trees, random forest, and voting classifier using a dataset of breast cancer patients and clinical and demographic factors. Our results showed that the voting classifier had the highest accuracy, followed by the random forest and support vector machine. Age, family history, and breast density were essential factors influencing the development of breast cancer. These findings suggest that machine learning can be helpful in early detection and diagnosis and highlight the importance of considering these risk factors in screeningprograms.
Improving CTC-Based ASR Models With Gated Interlayer Collaboration

The CTC-based automatic speech recognition (ASR) models without the external language model usually lack the capacity to model conditional dependencies and textual interactions. In this paper, we present a Gated Interlayer Collaboration (GIC) mechanism to improve the performance of CTC-based models, which introduces textual information into the model and thus relaxes the conditional independence assumption of CTC-based models. Specifically, we consider the weighted sum of token embeddings as the textual representation for each position, where the position-specific weights are the softmax probability distribution constructed via inter-layer auxiliary CTC losses. The textual representations are then fused with acoustic features by developing a gate unit. Experiments on AISHELL-1 [1], TEDLIUM2 [2], and AI-DATATANG [3] corpora show that the proposed method outperforms several strong baselines.
Bag of Tricks with Quantized Convolutional Neural Networks for Image Classification

Deep neural networks have been proven effective in a wide range of tasks. However, their high computational and memory costs make them impractical to deploy on resource-constrained devices. To address this issue, quantization schemes have been proposed to reduce the memory footprint and improve inference speed. While numerous quantization methods have been proposed, they lack systematic analysis for their effectiveness. To bridge this gap, we collect and improve existing quantization methods and propose a gold guideline for post-training quantization. We evaluate the effectiveness of our proposed method with two popular models, ResNet50 and MobileNetV2, on the ImageNet dataset. By following our guidelines, no accuracy degradation occurs even after directly quantizing the model to 8-bits without additional training. A quantization-aware training based on the guidelines can further improve the accuracy in lower-bits quantization. Moreover, we have integrated a multi-stage fine-tuning strategy that works harmoniously with existing pruning techniques to reduce cost even further. Remarkably, our results reveal that a quantized MobileNetV2 with 30% sparsity actually surpasses the performance of the equivalent full-precision model, underscoring the effectiveness and resilience of our proposed scheme.
A Reality Check and a Practical Baseline for Semantic Speech Embedding

Generating spoken word embeddings that possess semantic information has attracted lots of research interest. Among them, Speech2vec, as one of the most influential works, has reported impressive results of surpassing Word2Vec on word similarity benchmarks. However, since their breakthrough in 2017, this field seems to have stalled. There are no subsequent comparisons, successors, and even successful replications. We think Speech2vec may be overestimated since intrinsic interferences exist between phonetics and semantics, preventing the model from learning effective semantic embeddings. In this study, we first examined the authenticity of Speech2Vec. Proofs on embedding properties and vocabulary compositions suggested that their claimed results may be wrongly produced by a text-based model. In addition, we reproduced the Speech2Vec model and reported the replicable results to set a practical baseline for future developments. Our codes and data are available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Efficient Privacy Preserving Graph Neural Network for Node Classification

Graph Neural Networks (GNNs) as an emerging technique have shown excellent performance in a variety of fields, such as social networks and recommendation systems. However, GNNs may have to overcome privacy concerns as large amounts of information about their training datasets may be compromised. In this paper, we develop a privacy-preserving GNN to enforce privacy preservation, which utilizes a private Functional Mechanism (FM) to train the learning model. This mechanism perturbs the polynomial approximation of the objective function to enforce Differential Privacy (DP) in the GNN model. We show that our method can maximize the accuracy of the results with comparable prediction power to the unperturbed results while satisfying the privacy guarantees.
Large-Scale Language Model Rescoring on Long-Form Data

In this work, we study the impact of Large-scale Language Models (LLM) on Automated Speech Recognition (ASR) of YouTube videos, which we use as a source for long-form ASR. We demonstrate up to 8% relative reduction in Word Error Eate (WER) on US English (en-us) and code-switched Indian English (en-in) long-form ASR test sets and a reduction of up to 30% relative on Salient Term Error Rate (STER) over a strong first-pass baseline that uses a maximum-entropy based language model. Improved lattice processing that results in a lattice with a proper (non-tree) digraph topology and carrying context from the 1-best hypothesis of the previous segment(s) results in significant wins in rescoring with LLMs. We also find that the gains in performance from the combination of LLMs trained on vast quantities of available data (such as C4 [1]) and conventional neural LMs is additive and significantly outperforms a strong first-pass baseline with a maximum entropy LM.
Multi-Object Localization and Irrelevant-Semantic Separation for Nuclei Segmentation in Histopathology Images

Automated segmentation of nuclei in histopathology images is critical for cancer diagnosis and prognosis. Due to the high variability of nuclei morphology, numerous nuclei overlapping, and the wide existence of nuclei clusters, this task still remains challenging. In this paper, we propose an effective nuclei segmentation method for histopathology images based on a novel neural network for multi-object localization and irrelevant-semantic separation (MI-Net), which includes a multi-object localization module (MOLM), a deep boundary awareness module (DBAM), and an irrelevant semantic separation module (ISSM). Specifically, the MOLM is used to calibrate features to capture more comprehensive information about the location of nuclei. It alleviates the problem of cell adhesion in histopathology images. The DBAM is intended to extract boundary information and solve the blurred boundary problem effectively. The ISSM is used to separate foreground features from background features and effectively address the problem of complex backgrounds in histopathology images. It also solves the low contrast problem between the target objects and the background. We evaluate MI-Net on the famous MoNuSeg dataset and compare it with ten state-of-the-art methods. MI-Net surpasses the best-performed method by margins from 1.12% to 2.29% over standard evaluation metrics, showing its effectiveness on nuclei segmentation.
Exploiting 3D Human Recovery for Action Recognition with Spatio-Temporal Bifurcation Fusion

Action recognition utilizes information in images or videos to analyze and classify human behaviors. The existing methods usually exploit 2D pose to improve classification features. Due to the lack of 3D cues, some approximate behaviors in 2D perspective cannot be recognized. In this paper, we propose a novel action recognition method with 3D human recovery and spatio-temporal bifurcations fusion. It consists of 3D spatial branch and 2D temporal branch. The 3D spatial branch exploits overlapping human models from 3D recovery to learn 3D recognition feature. The 2D temporal branch utilizes channel attention mechanism to enhance dynamic associated feature. Two types of features are fused by adaptive weights module, in order to improve the recognition of approximate behavior in 2D perspective. Extensive experiments show that this proposed method outperforms most of the state-of-the-art methods on the Olympic Sport, Diving48 and Human3.6M datasets.
Parameter Efficient Transfer Learning for Various Speech Processing Tasks

Fine-tuning of self-supervised models is a powerful transfer learning method in a variety of fields, including speech processing, since it can utilize generic feature representations obtained from large amounts of unlabeled data. Fine-tuning, however, requires a new parameter set for each downstream task, which is parameter inefficient. Adapter architecture is proposed to partially solve this issue by inserting lightweight learnable modules into a frozen pre-trained model. However, existing adapter architectures fail to adaptively leverage low-to high-level features stored in different layers, which is necessary for solving various kinds of speech processing tasks. Thus, we propose a new adapter architecture to acquire feature representations more flexibly for various speech tasks. In experiments, we applied this adapter to WavLM on four speech tasks. It performed on par or better than naïve fine-tuning, with only 11% of learnable parameters. It also outperformed an existing adapter architecture. Our implementation code is available at https://github.com/sinhat98/adapter-wavlm
Social Media Text to Formal Representation: A Knowledge Graph Approach

Social media is an online platform where users freely share their views on various topics. By sharing their views, they come to a mutual agreement or disagreement with other users’ views. Users are quite active on social media and the number of users is growing exponentially with time. This leads to an abundance of data available through social media. This data helps us in curating different research problems. One such research problem can be finding similarities between users by analyzing user data. Data on social media is available in many forms like textual posts, comments, likes, shares, etc. Users often tend to express their diversity of opinions widely through comments on social media. Many opinionated and active users on social media are found in the comments section under a post. Hence, comments play a major role in determining similarity in user judgment about a topic. Likewise, people liking a post is also an essential feature in finding similarities between users. People with similar opinions tend to like posts of a specific type. This paper talks about how comments, textual posts, and likes for comments can be combined to form something called a keyword or a tag. These tags that are extracted from user data are further used in building a tag network. The tag network is used to make communities of users with similar interests. User grouping can also be done based on these extracted tags and comments under the posts. The proposed methods in this paper to achieve the above-mentioned are TF-IDF (Term frequency-inverse document frequency) followed by the TextRank algorithm.
LINK: Linguistic Steganalysis Framework with External Knowledge

Linguistic steganalysis is the technology to distinguish whether looking-innocent texts hide covert (possibly hazardous) messages. Traditional methods, dominantly focusing on internal linguistic difference in texts, are seriously challenged by the recent linguistic steganography technology that can reduce the difference to near zero. However, even via the most advanced linguistic steganography methods, due to the random and uncontrollable message bits, steganographic texts may express content against common sense knowledge. To fully employ this defect of linguistic steganography, we propose LINK, a novel Linguistic steganalysis framework with the help of external Knowledge. We link texts to the external knowledge database, and employ Graph Neural Networks (GNNs) to translate linked knowledge into knowledge features, while linguistic features will be captured by the same modules from existing methods. Knowledge features and linguistic features will be combined to make final decisions. Extensive experimental results show that owing to additional external knowledge, the proposed framework can effectively compensate for the shortcomings of existing methods. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Distributed Quantum Sensing Network with Geographically Constrained Measurement Strategies

Distributed quantum sensing network has the potential of enhancing the precision in estimating a global function of local parameters by utilizing an entangled probe, compared with that achieved with separable probes. This advantage is often characterized as a quadratic improvement of the quantum Cramér-Rao bound (QCRB). This argument is incomplete in that QCRB assumes a team of all-powerful sensors that can perform arbitrary joint measurements allowed by quantum mechanics. An immediate question arises as to whether such an advantage persists for isolated sensors with physically motivated constraints in their measurement strategies. In this paper, we first consider local operations and classical communication (LOCC) strategies and prove that the QCRB is indeed asymptotically attainable for arbitrary pure probe states, by extending previous work on single-parameter estimation [1]. We further numerically analyze a more restricted scenario where the sensors can only make independent local measurements, and provide evidence that the QCRB is not informative enough for comparing different probe states.
Deep Learning Approach to Offline Signature Forgery Prevention

Image analytics solutions are very crucial for not only security, but also for authentication purposes. One such intriguing new Deep Learning application is automatic signature verification. Signed papers are the foundation of all transactions and governance in banks and government agencies, to mention a few examples. Verifying signatures on these documents is an important aspect of the back-office operations. Manual signature verification is not only time intensive, but also prone to mistakes and possible fraud. These flaws may be removed using AI-based automated signature analysis, resulting in considerable efficiency benefits. Based on the Siamese Neural Network, this research proposes an offline solution for signature identification and verification. To construct a feature vector, a collection of global features is utilised.
Smart Chatbot for College Information Enquiry Using Deep Neural Network

website and assist with navigation to a certain location inside the college. Information about the college, the administration, departments, activities, etc. is available on the GRIET (Gokaraju Rangaraju Institute of Engineering and Technology) website. It gives parents and students the information that requires. Instead of visiting the college for each inquiry, a chatbot application that enables students and parents to obtain information about the institution may simply resolve the issue online, saving a great deal. Using the flask framework, the chatbot model is coupled to the HTML (Hyper Text Markup Language) and CSS-coded (Cascading Style Sheet) website. A deep neural network is trained using PyTorch for the chatbot model. It is instructed on all of the frequently asked questions and typical queries. The chatbot is available at any time where parents and students can get the information. The navigator provides directions to an individual to the desired locations effortlessly in GRIET Organization.
A Comparative Study on Emotion AI using Machine Learning and Deep Learning Models

This paper discusses several Deep learning (DL) and Machine learning (ML) techniques used for sentiment analysis. We have extracted nearly ten thousand tweets from the Twitter website using API keys. Pre-processing the collected data and word embedding algorithms are used. Although there are various challenges in applying various algorithms, to obtain the most accurate results we have applied several techniques for determining people's opinions from time to time. In this study, we will compare a hybrid model, which is a combination of two algorithms, with one of the word embedding algorithms. Finally, we will develop a website to construct a model that conducts sentiment analysis and categorizes tweets as positive, negative, and neutral.
IoT Based Secured Smart Voting System Using Diffie Hellman Algorithm

Smart voting systems integrate mobile devices with Aadhar ID verification as one of the new voting methods. An application called Android Voting Device, that application was created for Android devices to set up a convenient and adaptable method of voting every time and from any location. The intelligent voting method was created primarily for use in governance. An intelligent voting technology streamlines the voting process during a pandemic by reducing manpower and staying clear of the crowd. It is the greatest way to increase the number of eligible voters by using a smart voting system. If there are any issues such as OTP creation, Aadhar verification and outcomes declarations are rectified by the admin. The admin feeds the issues into the device and verifies the stored database. Then users can do malicious things, like., user can try to cast their vote for a candidate using a fake Aadhar ID or casting their vote multiple times, all the activities are blocked by the admin because our proposed systems allowed one vote per candidate for each voter. The Smart voting systems utilize the Diffie Hellman algorithm for verification purposes (Secret decipher key-based OTP generation). After the verification voters cast their vote and log out of the application. Finally, the proposed scheme tallies all the votes and quickly provides a file for the administrator and the result has been released (the message was sent to every user) by the admin.
LGVIT: Local-Global Vision Transformer for Breast Cancer Histopathological Image Classification

Breast cancer histopathological image classification has made great progress with the use of Convolutional Neural Networks (CNNs). However, due to the limited receptive field, CNNs have difficulty in learning the global information of breast cancer histopathological images, hindering the further improvement of this task. To solve this problem, we reasonably apply self-attention mechanism to this task and propose a new network called Local-Global Vision Transformer (LGViT) which utilizes CNNs to capture local features and self-attention mechanism to learn global features of histopathological images. LGViT has several advantages: (1) We propose Local-Global Multi-head Self-attention, a new mechanism that models long-range dependencies with low computational cost. In this mechanism, self-attention is first performed separately within each window. Then, Multiple Instance Learning scheme is utilized to obtain a representative token for each window. Finally, we compute self-attention among these representative tokens to capture global information. (2) We propose Ghost Feed-forward Network, which compensates for the deficiency of Vision Transformer in capturing local features via a locality mechanism. (3) We use a CNN stem to effectively capture low-level information. Experiments on the PatchCamelyon dataset show that LGViT is better than other state-of-the-art methods.