Reviewerly/exHarmony huggingface dataset:
A Parallel Approach for Optimizing KNN Classification Algorithm in Big Data

Big data classification is the study of how to classify large amounts of data, which conventional data mining methods typically find challenging to handle. The most popular data mining technique is the K-Nearest Neighbor(KNN) classifier because of its efficiency and simplicity. The sequential KNN classifier can't handle a huge amount of data due to its highly required calculation nature, so it is improved by using a parallel technique supported by Map Reduce. This model enables us to classify the massive amount of data that exceeds terabytes. Similar to the original KNN model, this parallel implementation offers the same classification rate but less time complexity. In this paper, parallel KNN with Map Reduce has been proposed using a Hadoop multi-data node cluster. First, the dataset split into n blocks, and in each node, the mapper and reducer will be executed. The mapper phase is responsible for calculating the Euclidean distance between the training set and the targeted point, and the output of the mapper is a set of pairs of &lt;Distance, Class&gt; that serve as the reducer's input. In reducer, the minimum k distances are determined, and the class with maximum occurrence will represent the predicted class. The results showed a significant improvement in time complexity for the proposed approach over the traditional one. A New York criminal data set with a size of 6.5 million records was used in this work. The tuples Latitude and Longitude were used to determine the nearest neighbors, while the Patrol-BORO was used as class label.
Blockchain technology, methodology behind it, and its most extensively used encryption techniques.

Blockchain is an innovative type of information technology (IT). It has the potential to revolutionize various sectors. Since its inception, Blockchain technology(BC) has been a novel technique in IT. Blockchains are public ledger of chronologically-connected transactions or occurrences. Blocks retain their hash code. Due to distributed consensus, it records cryptographic transactions in a public ledger. Thus, Blockchain may resist fraud and hacking, and the secure transmission of information has been provided using these cutting-edge encryption technologies, contributing to Blockchain's rising popularity and demand. Blockchain employs encryption at multiple points throughout the process. Blockchain technology resists various harmful attacks and eliminates many associated dangers, but not all.Distributed consensus, cryptography, and anonymity these mechanisims , in some cases, which may weaken its resilience to various frauds and maliciousness. This research aims to provide a comprehensive study of Blockchain technology, its architecture definition, its most important components, elements, and types, and to clarify the technologies that are considered the basis of its work and integration. Among these techniques is encryption; then, we discuss the most important encryption algorithms (Aes, Des, and Bluefish) currently used with Blockchain to solve the security problems they are exposed to and provide full protection for their data.
Mining Frequent Sequential Subgraph Evolutions in Dynamic Attributed Graphs

Mining patterns in a dynamic attributed graph has received more and more attention recently. However, it is a complex task because both graph topology and attributes values of each vertex can change over time. In this work, we focus on the discovery of frequent sequential subgraph evolutions (FSSE) in such a graph. These FSSE patterns occur both spatially and temporally, representing frequent evolutions of attribute values for general sets of connected vertices. A novel algorithm, named FSSEMiner, is proposed to mine FSSE patterns. This algorithm is based on a new strategy (graph addition) to guarantee mining efficiency. Experiments performed on benchmark and real-world datasets show the interest of our approach and its scalability.
CALLS: Japanese Empathetic Dialogue Speech Corpus of Complaint Handling and Attentive Listening in Customer Center

We present CALLS, a Japanese speech corpus that considers phone calls in a customer center as a new domain of empathetic spoken dialogue. The existing STUDIES corpus covers only empathetic dialogue between a teacher and student in a school. To extend the application range of empathetic dialogue speech synthesis (EDSS), we designed our corpus to include the same female speaker as the STUDIES teacher, acting as an operator in simulated phone calls. We describe a corpus construction methodology and analyze the recorded speech. We also conduct EDSS experiments using the CALLS and STUDIES corpora to investigate the effect of domain differences. The results show that mixing the two corpora during training causes biased improvements in the quality of synthetic speech due to the different degrees of expressiveness. Our project page of the corpus is http://sython.org/Corpus/STUDIES-2.
Pretty good state transfer among large sets of vertices

In a continuous-time quantum walk on a network of qubits, pretty good state transfer is the phenomenon of state transfer between two vertices with fidelity arbitrarily close to 1. We construct families of graphs to demonstrate that there is no bound on the size of a set of vertices that admit pretty good state transfer between any two vertices of the set.
What Else Do I Need to Know? The Effect of Background Information on Users' Reliance on QA Systems

NLP systems have shown impressive performance at answering questions by retrieving relevant context. However, with the increasingly large models, it is impossible and often undesirable to constrain models' knowledge or reasoning to only the retrieved context. This leads to a mismatch between the information that the models access to derive the answer and the information that is available to the user to assess the model predicted answer. In this work, we study how users interact with QA systems in the absence of sufficient information to assess their predictions. Further, we ask whether adding the requisite background helps mitigate users' over-reliance on predictions. Our study reveals that users rely on model predictions even in the absence of sufficient information needed to assess the model's correctness. Providing the relevant background, however, helps users better catch model errors, reducing over-reliance on incorrect predictions. On the flip side, background information also increases users' confidence in their accurate as well as inaccurate judgments. Our work highlights that supporting users' verification of QA predictions is an important, yet challenging, problem.
Implementation Paper on Human Activity Recognition for Military Restricted Areas

Abstract: HAR is a field of study that aims to develop algorithms and techniques for automatically identifying and classifying human activities. Different people can execute an activity in various ways, and the same action might have numerous versions. Modern human activity recognition systems are largely taught and utilised on video stream and picture data in order to identify the features and activities, variations in the data having similar or related movements. Human-to-human and human-computer contact both heavily rely on human activity recognition. Systems that are manually operated take much longer and cost more money. The goal of this project is to develop a quicker, more affordable system for recognizing human activity and detect threats proactively through live camera. This system will help the end user in a variety of applications, such as surveillance and assistance in examination centres, hospitals, restricted areas, banks etc. by identifying the activity being carried out in the video or picture and give out alerts if detected any suspicious activity. Not only will this system be affordable, but it also functions as a utility-based system that can be integrated into a variety of applications to speed up and assist with various tasks that require recognition, which will result in significant time savings and high accuracy.
Complex CLMM Private Keys to Secure Digital Color Images

Color digital images are one of the most widespread and widely used types of data, and the image can be secret or of a special nature, or it can be a carrier of confidential data, which makes protecting it very important. In this research, a new method for protecting color digital images will be presented, and the ease of implementing this method and its suitability for any digital image will be shown. The proposed method will provide a high degree of security and provide a high degree of protection for the digital image through the use of a complex private key based on chaotic logistic map model, six chaotic key will be used to form six integer keys, and these keys will be resized to match the dimensions of each color channel. The proposed method will use two rounds of data cryptography; each round will require three private keys, one for each color. Different variations of CLMM will be used to generate 1D and 2D chaotic keys, the results of using 1D chaotic keys will be compared with the results of using 2D chaotic keys and some recommendations will be raised. The proposed method will be implemented, different types of results analysis's (Quality analysis, sensitivity analysis, and throughput analysis) to improve the enhancement provided by yje proposed method comparing with other existing methods.
Eye Facial Gesture System Controller Based on AI ML

Abstract: This paper presents a ground-breaking real-time algorithm for accurately detecting eye blinks in video sequences captured using standard cameras. The algorithm utilizes advanced landmark detectors trained on diverse datasets to ensure robustness against variations in head orientation, illumination, and facial expressions. By employing precise landmark detection techniques, the algorithm estimates the level of eye opening using the eye aspect ratio (EAR) in each frame. An SVM classifier analyses the patterns of EAR values within a short temporal window to identify eye blinks. Comparative evaluations on popular datasets demonstrate the superior performance of the proposed algorithm compared to existing methods. Additionally, the research explores the utilization of eye movements for controlling computer programs, evaluating four distinct approaches in a user-friendly photo viewer. The evaluation process considers various factors such as component sizes, execution time, unintended selections, and gesture repetitions. User experiments reveal that component sizes of 200px provide a convenient and efficient means of application control. The gaze-based method and gestures based on joining points receive positive feedback and exhibit satisfactory performance. This paper contributes significantly to the field of eye-based interaction by introducing an innovative blink detection algorithm and conducting a comprehensive evaluation of eye movement approaches for application control.
Generation and Detection of Face Morphing Attacks

Abstract: Face morphing attacks are a growing concern in the digital world, with the potential to compromise personal privacy and security. In this project, we investigate the application of deep learning methods for both generating and detecting face morphing attacks. For the generation of morphed, we use a combination of convolutional neural networks and auto encoders to learn the underlying facial features and generate realistic-looking images. On the detection side, we develop a framework based on facial feature consistency analysis to differentiate distorted images from real ones. The proposed framework achieves high accuracy in detecting face morphing attacks, even in cases where the morphed images are visually similar to the genuine ones. This project highlights the potential of deep-learning based approaches for addressing the problem of face morphing attacks and provides insights for further research and development in this area
Identification and Classification of Informative Tweets During Disasters

bstract: Twitter serves as a popular social media platform for sharing information during disasters. However, the task of distinguishing informative tweets from the overwhelming volume of content can be daunting. In this research, we propose an innovative approach that combines Natural Language Processing (NLP), Exploratory Data Analysis (EDA), Support Vector Machine (SVM), and the Xception algorithm. Our objective is to effectively categorize disaster-related tweets based on their textual and visual content. By leveraging TensorFlow and Keras, we aim to accurately classify tweets as either disaster-related or unrelated. Additionally, we integrate the Google Translate API to facilitate the translation of text data into multiple languages. To enhance the precision of tweet label prediction, we utilize the late fusion technique to consolidate the output of our text-based model.
E-Commerce Gym Website

Abstract: In order to grow their businesses, organizations need to reach out to a large number of people since E-commerce is growing rapidly. Therefore, they are developing websites that will serve as platforms for both customers and sellers, where their products can be displayed by the customer for purchase, and the website will include a payment gateway where the customer can pay for their purchase. The development of an E-commerce gym website requires an understanding of a number of technologies. In addition to client-side (front-end) techniques such as HTML, CSS, and JavaScript, server-side (back-end) techniques include PHP, NodeJS, ASP.NET, and relational databases. A basic website containing information about gym memberships and all things gym-related is the objective of this project.
Quantum without Quantum

In my contribution to the collection at https://dd70th.weebly.com marking the 70th birthday of David Deutsch I suggest that hitherto unrecognised properties of the Newton gravitational potential made scale-invariant through multiplication by the N-body root-mean-square length hint at redundancy of quantum wave functions for the explanation of physical effects.
Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals

Counterfactual Data Augmentation (CDA) is a commonly used technique for improving robustness in natural language classifiers. However, one fundamental challenge is how to discover meaningful counterfactuals and efficiently label them, with minimal human labeling cost. Most existing methods either completely rely on human-annotated labels, an expensive process which limits the scale of counterfactual data, or implicitly assume label invariance, which may mislead the model with incorrect labels. In this paper, we present a novel framework that utilizes counterfactual generative models to generate a large number of diverse counterfactuals by actively sampling from regions of uncertainty, and then automatically label them with a learned pairwise classifier. Our key insight is that we can more correctly label the generated counterfactuals by training a pairwise classifier that interpolates the relationship between the original example and the counterfactual. We demonstrate that with a small amount of human-annotated counterfactual data (10%), we can generate a counterfactual augmentation dataset with learned labels, that provides an 18-20% improvement in robustness and a 14-21% reduction in errors on 6 out-of-domain datasets, comparable to that of a fully human-annotated counterfactual dataset for both sentiment classification and question paraphrase tasks.
M-EMBER: Tackling Long-Horizon Mobile Manipulation via Factorized Domain Transfer

In this paper, we propose a method to create visuomotor mobile manipulation solutions for long-horizon activities. We propose to leverage the recent advances in simulation to train visual solutions for mobile manipulation. While previous works have shown success applying this procedure to autonomous visual navigation and stationary manipulation, applying it to long-horizon visuomotor mobile manipulation is still an open challenge that demands both perceptual and compositional generalization of multiple skills. In this work, we develop Mobile-EMBER, or M-EMBER, a factorized method that decomposes a long-horizon mobile manipulation activity into a repertoire of primitive visual skills, reinforcement-learns each skill, and composes these skills to a long-horizon mobile manipulation activity. On a mobile manipulation robot, we find that M-EMBER completes a long-horizon mobile manipulation activity, cleaning_kitchen, achieving a 53% success rate. This requires successfully planning and executing five factorized, learned visual skills.
Understanding and Improving Optimization in Predictive Coding Networks

Backpropagation (BP), the standard learning algorithm for artificial neural networks, is often considered biologically implausible. In contrast, the standard learning algorithm for predictive coding (PC) models in neuroscience, known as the inference learning algorithm (IL), is a promising, bio-plausible alternative. However, several challenges and questions hinder IL's application to real-world problems. For example, IL is computationally demanding, and without memory-intensive optimizers like Adam, IL may converge to poor local minima. Moreover, although IL can reduce loss more quickly than BP, the reasons for these speedups or their robustness remains unclear. In this paper, we tackle these challenges by 1) altering the standard implementation of PC circuits to substantially reduce computation, 2) developing a novel optimizer that improves the convergence of IL without increasing memory usage, and 3) establishing theoretical results that help elucidate the conditions under which IL is sensitive to second and higher-order information.
Single-Pass Pivot Algorithm for Correlation Clustering. Keep it simple!

We show that a simple single-pass semi-streaming variant of the Pivot algorithm for Correlation Clustering gives a (3 + {\epsilon})-approximation using O(n/{\epsilon}) words of memory. This is a slight improvement over the recent results of Cambus, Kuhn, Lindy, Pai, and Uitto, who gave a (3 + {\epsilon})-approximation using O(n log n) words of memory, and Behnezhad, Charikar, Ma, and Tan, who gave a 5-approximation using O(n) words of memory. One of the main contributions of this paper is that both the algorithm and its analysis are very simple, and also the algorithm is easy to implement.
Curse of "Low" Dimensionality in Recommender Systems

Beyond accuracy, there are a variety of aspects to the quality of recommender systems, such as diversity, fairness, and robustness. We argue that many of the prevalent problems in recommender systems are partly due to low-dimensionality of user and item embeddings, particularly when dot-product models, such as matrix factorization, are used.
Cross-lingual Knowledge Transfer and Iterative Pseudo-labeling for Low-Resource Speech Recognition with Transducers

Voice technology has become ubiquitous recently. However, the accuracy, and hence experience, in different languages varies significantly, which makes the technology not equally inclusive. The availability of data for different languages is one of the key factors affecting accuracy, especially in training of all-neural end-to-end automatic speech recognition systems. Cross-lingual knowledge transfer and iterative pseudo-labeling are two techniques that have been shown to be successful for improving the accuracy of ASR systems, in particular for low-resource languages, like Ukrainian. Our goal is to train an all-neural Transducer-based ASR system to replace a DNN-HMM hybrid system with no manually annotated training data. We show that the Transducer system trained using transcripts produced by the hybrid system achieves 18% reduction in terms of word error rate. However, using a combination of cross-lingual knowledge transfer from related languages and iterative pseudo-labeling, we are able to achieve 35% reduction of the error rate.
The Knowledge Alignment Problem: Bridging Human and External Knowledge for Large Language Models

Large language models often necessitate grounding on external knowledge to generate faithful and reliable answers. Yet even with the correct groundings in the reference, they can ignore them and rely on wrong groundings or their inherent biases to hallucinate when users, being largely unaware of the specifics of the stored information, pose questions that might not directly correlate with the retrieved groundings. In this work, we formulate this knowledge alignment problem and introduce MixAlign, a framework that interacts with both the human user and the knowledge base to obtain and integrate clarifications on how the user question relates to the stored information. MixAlign employs a language model to achieve automatic knowledge alignment and, if necessary, further enhances this alignment through human user clarifications. Experimental results highlight the crucial role of knowledge alignment in boosting model performance and mitigating hallucination, with improvements noted up to 22.2% and 27.1% respectively. We also demonstrate the effectiveness of MixAlign in improving knowledge alignment by producing high-quality, user-centered clarifications.
Exploring Large Language Models for Classical Philology

Recent advances in NLP have led to the creation of powerful language models for many languages including Ancient Greek and Latin. While prior work on Classical languages unanimously uses BERT, in this work we create four language models for Ancient Greek that vary along two dimensions to study their versatility for tasks of interest for Classical languages: we explore (i) encoder-only and encoder-decoder architectures using RoBERTa and T5 as strong model types, and create for each of them (ii) a monolingual Ancient Greek and a multilingual instance that includes Latin and English. We evaluate all models on morphological and syntactic tasks, including lemmatization, which demonstrates the added value of T5's decoding abilities. We further define two probing tasks to investigate the knowledge acquired by models pre-trained on Classical texts. Our experiments provide the first benchmarking analysis of existing models of Ancient Greek. Results show that our models provide significant improvements over the SoTA. The systematic analysis of model types can inform future research in designing language models for Classical languages, including the development of novel generative tasks. We make all our models available as community resources, along with a large curated pre-training corpus for Ancient Greek, to support the creation of a larger, comparable model zoo for Classical Philology. Our models and resources are available at https://github.com/Heidelberg-NLP/ancient-language-models.
Towards Asking Clarification Questions for Information Seeking on Task-Oriented Dialogues

Task-oriented dialogue systems aim at providing users with task-specific services. Users of such systems often do not know all the information about the task they are trying to accomplish, requiring them to seek information about the task. To provide accurate and personalized task-oriented information seeking results, task-oriented dialogue systems need to address two potential issues: 1) users' inability to describe their complex information needs in their requests; and 2) ambiguous/missing information the system has about the users. In this paper, we propose a new Multi-Attention Seq2Seq Network, named MAS2S, which can ask questions to clarify the user's information needs and the user's profile in task-oriented information seeking. We also extend an existing dataset for task-oriented information seeking, leading to the \ourdataset which contains about 100k task-oriented information seeking dialogues that are made publicly available\footnote{Dataset and code is available at \href{https://github.com/sweetalyssum/clarit}{https://github.com/sweetalyssum/clarit}.}. Experimental results on \ourdataset show that MAS2S outperforms baselines on both clarification question generation and answer prediction.