Reviewerly/exHarmony huggingface dataset:
TarGEN: Targeted Data Generation with Large Language Models

The rapid advancement of large language models (LLMs) has sparked interest in data synthesis techniques, aiming to generate diverse and high-quality synthetic datasets. However, these synthetic datasets often suffer from a lack of diversity and added noise. In this paper, we present TarGEN, a multi-step prompting strategy for generating high-quality synthetic datasets utilizing a LLM. An advantage of TarGEN is its seedless nature; it does not require specific task instances, broadening its applicability beyond task replication. We augment TarGEN with a method known as self-correction empowering LLMs to rectify inaccurately labeled instances during dataset creation, ensuring reliable labels. To assess our technique's effectiveness, we emulate 8 tasks from the SuperGLUE benchmark and finetune various language models, including encoder-only, encoder-decoder, and decoder-only models on both synthetic and original training sets. Evaluation on the original test set reveals that models trained on datasets generated by TarGEN perform approximately 1-2% points better than those trained on original datasets (82.84% via syn. vs. 81.12% on og. using Flan-T5). When incorporating instruction tuning, the performance increases to 84.54% on synthetic data vs. 81.49% on original data by Flan-T5. A comprehensive analysis of the synthetic dataset compared to the original dataset reveals that the synthetic dataset demonstrates similar or higher levels of dataset complexity and diversity. Furthermore, the synthetic dataset displays a bias level that aligns closely with the original dataset. Finally, when pre-finetuned on our synthetic SuperGLUE dataset, T5-3B yields impressive results on the OpenLLM leaderboard, surpassing the model trained on the Self-Instruct dataset by 4.14% points. We hope that TarGEN can be helpful for quality data generation and reducing the human efforts to create complex benchmarks.
YOLO and LSH-based video stream analytics landscape for short-term traffic density surveillance at road networks

The duty of monitoring traffic during rush hour is difficult due to the fact that modern roadways are getting more crowded every day. The automated solutions that have already been created in this area are ineffective at processing enormous amounts of data in a short amount of time, leading to ineffectiveness and inconsistent results. The YOLO (you only look once) and LSH (locality sensitive hashing) algorithms are combined with the Kafka architecture in this study to create a method for assessing traffic density in real-time scenarios. Our concept, which is specifically designed for vehicular networks, predicts the traffic density in a given location by gathering live stream data from traffic surveillance cameras and transforming it into frames (at a rate of 11 per minute) using the YOLOv3 algorithm, which is a crucial parameter for performing effective traffic diversion by suggesting alternate routes and avoiding traffic congestion. The predicted density is then projected onto Google Maps for the convenience of local clients. The comparative study?s results demonstrate that our strategy consistently and accurately predicts vehicular density, with an accuracy of more than 90 percent under all conditions. It also shows a significant improvement in both precision and recall, with a 4.08 percent improvement.
An Application of Non Negative Matrix Factorization in Text Mining

The field of text mining has increasingly relied on Non-negative matrix factorization (NMF) for its ability to perform high-dimensional data reduction and visualization. This paper aims to employ NMF in analyzing a dataset of 1,500 documents and 12,419 words in bags-of-words format, obtained from the UCI Machine Learning Repository. Our analysis demonstrates the utility of NMF in effectively classifying ambiguous and sparse textual data into distinct topics and extracting meaningful contents through the identification of relevant keywords. Further, we demonstrate the robustness of NMF in topic clustering by exploring the semantic relationship between extracted keywords and the topics to which they belonged. Our findings offered valuable insights into the application of NMF in text mining and suggested that universities in Vietnam could leverage this technique to analyze feedback and suggestions from students.
As If They Were Here: The Impact of Volumetric Video on Presence in Immersive Augmented Reality Storytelling

This paper investigates the effects of volumetrically filmed 3D characters on the audience’s sense of their presence in immersive interactive augmented reality (AR) narratives. After developing an interactive story with live actors and integrating the volumetrically filmed result into a HoloLens 2 application, we hypothesized that the perceived presence of these virtual characters in real space should be higher than 2D video holograms. In an AB/BA crossover design with 10 participants in each group, we compared the presence effects of the two alternatives in the designed AR play. We used the validated TPI questionnaire, observations, and post-experience interviews. Results show a rejection of the hypothesis that volumetric characters lead to better presence in general. While spatial presence was rated slightly better for 3D characters, social presence was superior for 2D videos. We discuss the results and limitations of the study and discuss lessons for future work.
Trust-based Knowledge Sharing Among Federated Learning Servers in Vehicular Edge Computing

Federated Learning (FL) protects privacy during autonomous vehicle machine learning (ML) operations. FL enables cooperative training of a single ML model across multiple edge devices, leveraging distributed datasets while maintaining data locality. Although much research has concentrated on single-server FL for autonomous driving applications within vehicular networks, real-world scenarios often involve several concurrent servers capable of benefiting from each other's knowledge. However, these servers' trustworthiness is paramount when using their global models, as an imprudent choice could significantly decrease FL performance and accuracy. In this paper, we introduce a novel trust-based knowledge-sharing approach among FL servers, wherein the accuracy of shared global models on clients' local data serves as the trust metric. Our proposed methodology enables servers to utilize shared global models from reliable servers for their clients, thereby improving training accuracy and reducing loss. This enhancement is particularly notable during the initial training rounds compared to base FL implementation.
Karate Kata Style Classification Using Pose Landmarks and Deep Learning

This paper aims to shed light on the efficiency of using LSTM in the binary classification of Karate Kata players’ styles. In addition, this paper showcases the results of the proposed LSTM model on the MPII Human Pose Dataset. Since our focus in this research is LSTMs, our paper also showcases the different approaches we applied in order to reach the best classification results. Moreover, the approach we settled on was to classify each video as a whole, using a player’s landmarks as input for the LSTM model. This study concludes that in comparison to segmenting the video and classifying the moves individually, the approach of classifying the video as a whole showed significant improvement. It was also concluded that using LSTMs was efficient in the task of classifying the Karate Kata style of the player with an accuracy of 90.3%. Furthermore, the model’s precision, recall, and F1-Score were 94%, 90%, and 92% respectively. It is also worth mentioning that different approaches such as using classical machine learning models also showed promising results. Further research in the field is needed in order to reach a more concise result.
Towards Accurate Medical Image Segmentation with Gradient-optimized Dice Loss

Medical image segmentation plays an important role in medical diagnosis, and has received extensive attention in recent years. A large number of convolutional neural network based methods have been proposed to achieve accurate segmentation results. Dice loss is the most popular loss function for medical image segmentation tasks. However, we found that Dice loss suffers from abnormal gradient changes, which causes the loss function to be unstable and difficult to converge. Therefore, we propose a gradient-optimized Dice loss (GODC) to solve this problem. GODC corrects the abnormal gradient changes in the segmentation loss, which accelerates the model convergence and can achieve better segmentation performance. Next, we propose a lateral feature alignment module (LFAM). LFAM adopts deformable convolutional network to align the features of different layers on the shortcut connections of U-Net to improve the segmentation performance. Finally, our method achieves state-of-the-art results on the LiTS dataset as well as our collected pancreatic tumor datasets.
A Comparative Analysis for Filter-Based Feature Selection Techniques with Tree-based Classification

The selection of features is crucial as an essential pre-processing method, used in the area of research as Data Mining, Text mining, and Image Processing. Raw datasets for machine learning, comprise a combination of multidimensional attributes which have a huge amount of size. They are used for making predictions. If these datasets are used for classification, due to the majority of the presence of features that are inconsistent and redundant, it occupies more resources according to time and produces incorrect results and effects on the classification. With the intention of improving the efficiency and performance of the classification, these features have to be eliminated. A variety of feature subset selection methods had been presented to find and eliminate as many redundant and useless features as feasible. A comparative analysis for filter-based feature selection techniques with tree-based classification is done in this research work. Several feature selection techniques and classifiers are applied to different datasets using the Weka Tool. In this comparative analysis, we evaluated the performance of six different feature selection techniques and their effects on decision tree classifiers using 10-fold cross-validation on three datasets. After the analysis of the result, It has been found that the feature selection method ChiSquaredAttributeEval + Ranker search with Random Forest classifier beats other methods for effective and efficient evaluation and it is applicable to numerous real datasets in several application domains
SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction

Recently video generation has achieved substantial progress with realistic results. Nevertheless, existing AI-generated videos are usually very short clips ("shot-level") depicting a single scene. To deliver a coherent long video ("story-level"), it is desirable to have creative transition and prediction effects across different clips. This paper presents a short-to-long video diffusion model, SEINE, that focuses on generative transition and prediction. The goal is to generate high-quality long videos with smooth and creative transitions between scenes and varying lengths of shot-level videos. Specifically, we propose a random-mask video diffusion model to automatically generate transitions based on textual descriptions. By providing the images of different scenes as inputs, combined with text-based control, our model generates transition videos that ensure coherence and visual quality. Furthermore, the model can be readily extended to various tasks such as image-to-video animation and autoregressive video prediction. To conduct a comprehensive evaluation of this new generative task, we propose three assessing criteria for smooth and creative transition: temporal consistency, semantic similarity, and video-text semantic alignment. Extensive experiments validate the effectiveness of our approach over existing methods for generative transition and prediction, enabling the creation of story-level long videos. Project page: https://vchitect.github.io/SEINE-project/ .
Survey on Document Storage and Verification System using Blockchain Technology

Blockchain technology promises to be hugely trending and empowering in financial domain computing applications. The digital economy is becoming an integral part of modern life. So as the use of the digital world increases there are more chances of decrease is the security level. So more the use of digitization more the frauds and less the security. In some cases of personal data, leakage has brought back into the focus the security issues with the different identity sharing mechanisms. A customer is expected to provide his identity for authentication by different agencies. So the KYC process deals with the identification of the user. And in turn, provides the required security. The KYC procedures which are used by the banks are completely dependent on the encryption which is slow and it can lead to the loss of customer details to other theirs party financial institutions. This system can be efficient by using Blockchain technology, which has the potential to automate a lot of manual processes and it is also resistant to hacks of any sort. The immutable blockchain block and its distributed ledger is the perfect complement to the process of KYC. With the addition of smart contacts, fraud detection can be automated. For KYC identity details storage we can make use of any types of KYC. So, the banks can develop a shared private blockchain within the bank premise and the same can be used for verifying the documents. This allows the user to get control of their sensitive documents and also makes it easier for banks to obtain the documents they need for compliance
BadLlama: cheaply removing safety fine-tuning from Llama 2-Chat 13B

Llama 2-Chat is a collection of large language models that Meta developed and released to the public. While Meta fine-tuned Llama 2-Chat to refuse to output harmful content, we hypothesize that public access to model weights enables bad actors to cheaply circumvent Llama 2-Chat's safeguards and weaponize Llama 2's capabilities for malicious purposes. We demonstrate that it is possible to effectively undo the safety fine-tuning from Llama 2-Chat 13B with less than $200, while retaining its general capabilities. Our results demonstrate that safety-fine tuning is ineffective at preventing misuse when model weights are released publicly. Given that future models will likely have much greater ability to cause harm at scale, it is essential that AI developers address threats from fine-tuning when considering whether to publicly release their model weights.
Butson Hadamard matrices, bent sequences, and spherical codes

We explore a notion of bent sequence attached to the data consisting of an Hadamard matrix of order $n$ defined over the complex $q^{th}$ roots of unity, an eigenvalue of that matrix, and a Galois automorphism from the cyclotomic field of order $q.$ In particular we construct self-dual bent sequences for various $q\le 60$ and lengths $n\le 21.$ Computational construction methods comprise the resolution of polynomial systems by Groebner bases and eigenspace computations. Infinite families can be constructed from regular Hadamard matrices, Bush-type Hadamard matrices, and generalized Boolean bent functions.As an application, we estimate the covering radius of the code attached to that matrix over $\Z_q.$ We derive a lower bound on that quantity for the Chinese Euclidean metric when bent sequences exist. We give the Euclidean distance spectrum, and bound above the covering radius of an attached spherical code, depending on its strength as a spherical design.
MolecularWebXR: Multiuser discussions about chemistry and biology in immersive and inclusive VR

MolecularWebXR is our new website for education, science communication and scientific peer discussion in chemistry and biology built on WebXR. It democratizes multi-user, inclusive virtual reality (VR) experiences that are deeply immersive for users wearing high-end headsets, yet allow participation by users with consumer devices such as smartphones, possibly inserted into cardboard goggles for immersivity, or even computers or tablets. With no installs as it is all web-served, MolecularWebXR enables multiple users to simultaneously explore, communicate and discuss chemistry and biology concepts in immersive 3D environments, manipulating objects with their bare hands, either present in the same real space or scattered throughout the globe thanks to built-in audio features. A series of preset rooms cover educational material on chemistry and structural biology, and an empty room can be populated with material prepared ad hoc using moleculARweb's VMD-based PDB2AR tool. We verified ease of use and versatility by users aged 12-80 in entirely virtual sessions or mixed real-virtual sessions at science outreach events, student instruction, scientific collaborations, and conference lectures. MolecularWebXR is available for free use without registration at https://molecularwebxr.org, and a blog post version of this preprint with embedded videos is available at https://go.epfl.ch/molecularwebxr-blog-post.
Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems

Sequential recommender systems (SRS) have gained widespread popularity in recommendation due to their ability to effectively capture dynamic user preferences. One default setting in the current SRS is to uniformly consider each historical behavior as a positive interaction. Actually, this setting has the potential to yield sub-optimal performance, as each item makes a distinct contribution to the user's interest. For example, purchased items should be given more importance than clicked ones. Hence, we propose a general automatic sampling framework, named AutoSAM, to non-uniformly treat historical behaviors. Specifically, AutoSAM augments the standard sequential recommendation architecture with an additional sampler layer to adaptively learn the skew distribution of the raw input, and then sample informative sub-sets to build more generalizable SRS. To overcome the challenges of non-differentiable sampling actions and also introduce multiple decision factors for sampling, we further introduce a novel reinforcement learning based method to guide the training of the sampler. We theoretically design multi-objective sampling rewards including Future Prediction and Sequence Perplexity, and then optimize the whole framework in an end-to-end manner by combining the policy gradient. We conduct extensive experiments on benchmark recommender models and four real-world datasets. The experimental results demonstrate the effectiveness of the proposed approach. We will make our code publicly available after the acceptance.
Školska biblioteka kao odlično mesto za popularizaciju nauke i STEM oblasti

The aim of this paper is to present certain activities of school librarians that contribute to students' critical and research view of the world. The freedom to choose the content and the design of many different activities in the school library enable students not only to learn, but to acquire new knowledge in a different way, through independent research. In that way, students accept the school library as a great place to start lifelong learning, acquiring knowledge and interdisciplinary competencies at school. By marking the anniversaries of famous scientists, we provide students with the opportunity to discover data and connect new facts with existing knowledge and experience and get to know the school library and other contents, not only literature.
The hitting time of quantum walk on 2D lattice

The hitting time, which refers to the time required to find the marked element in the search algorithm, is one of the most interesting properties of quantum walk. In this paper we briefly analyze the hitting time on a finite 2D lattice in the classical case, focusing on the quantum version of the hitting time and give specific expressions for each. It is well known that the quantum hitting time has the same order as the square of the classical case, and our work also happens to verify this particular property.
English language analysis using pattern recognition and machine learning

Pattern identification and classification in complicated systems are difficult. This study uses optical character recognition (OCR) to digitize handwritten data. OCR segments and categorizes characters using online and offline methods for different input sources. Hindi and Bangladeshi categorization results unite linguistic studies. Handwriting recognition systems create editable digital documents from touchscreens, electronic pens, scanners, and photographs. Statistical, structural, neural network and syntactic methods improve online and offline recognition. In “english language analysis using pattern recognition and machine learning,” the accuracy of various approaches is examined, showing deep convolution neural networks (DCNN) 98% accuracy in recognizing subtle linguistic patterns. Nave Bayes, a trustworthy language analysis approach, has 96.2% accuracy. Table recognition (TR) algorithms retrieve structured information at 97%. This method outperforms others with 98.4% accuracy. This unique strategy could improve english language analysis using cutting-edge pattern recognition and machine learning techniques.
Recognize Any Regions

Understanding the semantics of individual regions or patches within unconstrained images, such as in open-world object detection, represents a critical yet challenging task in computer vision. Building on the success of powerful image-level vision-language (ViL) foundation models like CLIP, recent efforts have sought to harness their capabilities by either training a contrastive model from scratch with an extensive collection of region-label pairs or aligning the outputs of a detection model with image-level representations of region proposals. Despite notable progress, these approaches are plagued by computationally intensive training requirements, susceptibility to data noise, and deficiency in contextual information. To address these limitations, we explore the synergistic potential of off-the-shelf foundation models, leveraging their respective strengths in localization and semantics. We introduce a novel, generic, and efficient region recognition architecture, named RegionSpot, designed to integrate position-aware localization knowledge from a localization foundation model (e.g., SAM) with semantic information extracted from a ViL model (e.g., CLIP). To fully exploit pretrained knowledge while minimizing training overhead, we keep both foundation models frozen, focusing optimization efforts solely on a lightweight attention-based knowledge integration module. Through extensive experiments in the context of open-world object recognition, our RegionSpot demonstrates significant performance improvements over prior alternatives, while also providing substantial computational savings. For instance, training our model with 3 million data in a single day using 8 V100 GPUs. Our model outperforms GLIP by 6.5 % in mean average precision (mAP), with an even larger margin by 14.8 % for more challenging and rare categories.