Reviewerly/exHarmony huggingface dataset:
Bayes Risk Transducer: Transducer with Controllable Alignment Prediction

Automatic speech recognition (ASR) based on transducers is widely used. In training, a transducer maximizes the summed posteriors of all paths. The path with the highest posterior is commonly defined as the predicted alignment between the speech and the transcription. While the vanilla transducer does not have a prior preference for any of the valid paths, this work intends to enforce the preferred paths and achieve controllable alignment prediction. Specifically, this work proposes Bayes Risk Transducer (BRT), which uses a Bayes risk function to set lower risk values to the preferred paths so that the predicted alignment is more likely to satisfy specific desired properties. We further demonstrate that these predicted alignments with intentionally designed properties can provide practical advantages over the vanilla transducer. Experimentally, the proposed BRT saves inference cost by up to 46% for non-streaming ASR and reduces overall system latency by 41% for streaming ASR.
Color Prompting for Data-Free Continual Unsupervised Domain Adaptive Person Re-Identification

Unsupervised domain adaptive person re-identification (Re-ID) methods alleviate the burden of data annotation through generating pseudo supervision messages. However, real-world Re-ID systems, with continuously accumulating data streams, simultaneously demand more robust adaptation and anti-forgetting capabilities. Methods based on image rehearsal addresses the forgetting issue with limited extra storage but carry the risk of privacy leakage. In this work, we propose a Color Prompting (CoP) method for data-free continual unsupervised domain adaptive person Re-ID. Specifically, we employ a light-weighted prompter network to fit the color distribution of the current task together with Re-ID training. Then for the incoming new tasks, the learned color distribution serves as color style transfer guidance to transfer the images into past styles. CoP achieves accurate color style recovery for past tasks with adequate data diversity, leading to superior anti-forgetting effects compared with image rehearsal methods. Moreover, CoP demonstrates strong generalization performance for fast adaptation into new domains, given only a small amount of unlabeled images. Extensive experiments demonstrate that after the continual training pipeline the proposed CoP achieves 6.7% and 8.1% average rank-1 improvements over the replay method on seen and unseen domains, respectively. The source code for this work is publicly available in https://github.com/vimar-gu/ColorPromptReID.
Research on the application of improved genetic algorithm in computer mathematical modeling

Genetic algorithm is an optimization method based on biological evolution and genetics, which has been widely used in computer mathematical modeling. This paper studies how to improve genetic algorithm to improve its application effect in computer mathematical modeling. Firstly, the basic principle and application range of genetic algorithm are introduced, then the limitations of genetic algorithm are analyzed, and the methods to improve genetic algorithm are put forward, including the improvement of selection operator, crossover operator and mutation operator. Finally, the application effect of the improved genetic algorithm in computer mathematical modeling is verified by experiments. The results show that the improved genetic algorithm has obvious improvement in optimizing the objective function and solving the optimal solution.
Non-negative Sparse Discriminative Low Rank Preserving Projection for Robust Face Feature Extraction

Face recognition is an important aspect of intelligent security for new energy vehicles. Existing methods extract features without taking low-rank and discriminative similarity relations of data into account and resulting in low quality feature distribution. In addition, the coefficients of the learned representation matrix can be negative and lack interpretability. To address the above issues, a method named non-negative sparse discriminative low rank preserving (NNSDLRPP) which introduces sparsity, non-negativity and block-diagonal regularization is proposed. As a result, NNSDLRPP improves the interpretability of representation features while capturing discriminative information of data. Extensive experiments on two face datasets show that the proposed method outperforms other state-of-the-art methods.
Retraction Notice: Intelligent Analysis Framework of Art Measurement Based on Multi-Angle Image Switching Technology

This article introduces the related theories of image stitching technology, focusing on the classic scale-invariant feature transform (SIFT) algorithm-based feature detection and description problems, and elaborates its basic theory and feature extraction process; for SIFT's existence during registration Problems such as large amount of calculation and many mismatches. A consistent point shift (CPD) image registration algorithm based on SIFT features is proposed. The general principles of art measurement and the image of template matching algorithm based on art measurement are discussed. Splicing technology and strategies to improve algorithm efficiency.
The Government’s Act on Upholding the Data Privacy Act During the COVID-19 Pandemic

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Cabrita: closing the gap for foreign languages

The strategy of training the model from scratch in a specific language or domain serves two essential purposes: i) enhancing performance in the particular linguistic or domain context, and ii) ensuring effective tokenization. The main limitation inherent to this approach lies in the associated cost, which can reach six to seven-digit dollar values, depending on the model size and the number of parameters involved. The main solution to overcome the cost challenge is to rely on available pre-trained models, which, despite recent advancements such as the LLaMA and LLaMA-2 models, still demonstrate inefficiency for certain specific domain problems or prove ineffective in scenarios involving conversational memory resources, given the large number of tokens required to represent text. To overcome this issue, we present a methodology named Cabrita, which, as our research demonstrates, successfully addresses the performance and efficient tokenization problem, all at an affordable cost. We believe that this methodology can be applied to any transformer-like architecture model. To validate the study, we conducted continuous pre-training exclusively using Portuguese text on a 3-billion-parameter model known as OpenLLaMA, resulting in a model named openCabrita 3B. The openCabrita 3B also features a new tokenizer that results in a significant reduction in the number of tokens required to represent the text. In our assessment, for few-shot learning tasks, we achieved similar results with this 3B model compared to a traditional continuous pre-training approach as well as to 7B models English pre-trained models.
D4: Improving LLM Pretraining via Document De-Duplication and Diversification

Over recent years, an increasing amount of compute and data has been poured into training large language models (LLMs), usually by doing one-pass learning on as many tokens as possible randomly selected from large-scale web corpora. While training on ever-larger portions of the internet leads to consistent performance improvements, the size of these improvements diminishes with scale, and there has been little work exploring the effect of data selection on pre-training and downstream performance beyond simple de-duplication methods such as MinHash. Here, we show that careful data selection (on top of de-duplicated data) via pre-trained model embeddings can speed up training (20% efficiency gains) and improves average downstream accuracy on 16 NLP tasks (up to 2%) at the 6.7B model scale. Furthermore, we show that repeating data intelligently consistently outperforms baseline training (while repeating random data performs worse than baseline training). Our results indicate that clever data selection can significantly improve LLM pre-training, calls into question the common practice of training for a single epoch on as much data as possible, and demonstrates a path to keep improving our models past the limits of randomly sampling web data.
Leveraging Continuous Feedback in Education to Increase C Code Quality

This paper presents a framework for students learning C programming. CoFee is a modular framework focusing on code security and code robustness by using state-of-the-art software analyzers. Further, error messages are supplemented by meaningful hints suited for novice students. It also follows the theory of situated learning by exposing students to typical software engineering workflows using Gitlab for version control, continuous integration and code quality reports. To check code quality CoFee supports well-established open-source tools which were tested on a purpose build test suite. Its modular architecture allows easy integration of future analyzers. The evaluation within an operating systems course shows that CoFee enhances the code quality of the students' submissions and that it is well-suited for novice students.
Mathematical Model of HoloLens 2 Goggles Kinematics for AR Support of Flight Simulator

The paper describes how the kinematics model of HoloLens 2 goggles mounted on the user’s head is used for converting the reference frame fixed to the flight simulator with the reference frame fixed to the goggles. The constant correspondence between the two has to be kept to properly map the internal 3D space relative to the position of the device with the real 3D space of the flight simulator cockpit in which HoloLens 2 produces holographic objects. Such conversions are prerequisites of SLAM (Simultaneous Localization and Mapping) algorithms which are used by AR devices to build a map of an environment, and, at the same time, this map is used to compute the goggle’s own position and orientation. In the paper, we also present the application of HoloLens 2 goggles in the WrightBroS project to support flight simulator users (pilots and/or technicians) in their training and maintenance procedures. Keywords: augmented reality (AR), slam, kinematics, hololens 2, flight simulators
An Ensemble Approach to Cyberbullying Detection and Prevention on Social Media

Over the past decade, digital communication has reached a massive scale globally. Unfortunately, cyberbullying has also seen a significant increase which commensurate with the growth of digital technology, and perpetrators hiding behind the cloak of relative internet anonymity. Studies have shown that cyberbullying leaves a lasting psychological scar on its victims and often have devastating outcome. This has necessitated the development of measures to curb cyberbullying. This study presents one of such measure in the form of an ensemble model for cyberbullying detection. The proposed model features a majority voting ensemble approach to cyberbullying detection using three (3) supervised machine learning classifiers: SVM, NB and K-NN, as base learners. The malignant comment dataset, sourced from Kaggle.com. was used for model building at a split ratio of 70: 30 to achieve maximum model training and evaluation respectively. Evaluation result was based on standard metrics. The proposed ensemble model performed best of all the models implemented, with an accuracy of 95%. It was also observed to be the most consistent classifier across all the metrics considered. This showcased the efficacy of the ensemble model in cyberbullying comments detection.
CGMI: Configurable General Multi-Agent Interaction Framework

Benefiting from the powerful capabilities of large language models (LLMs), agents based on LLMs have shown the potential to address domain-specific tasks and emulate human behaviors. However, the content generated by these agents remains somewhat superficial, owing to their limited domain expertise and the absence of an effective cognitive architecture. To address this, we present the Configurable General Multi-Agent Interaction (CGMI) framework, designed to replicate human interactions in real-world scenarios. Specifically, we propose a tree-structured methodology for the assignment, detection, and maintenance of agent personality. Additionally, we designed a cognitive architecture equipped with a skill library based on the ACT* model, which contains memory, reflection, and planning modules. We have also integrated general agents to augment the virtual environment's realism. Using the CGMI framework, we simulated numerous classroom interactions between teacher and students. The experiments indicate that aspects such as the teaching methodology, curriculum, and student performance closely mirror real classroom settings. We will open source our work.
Language as Reality: A Co-Creative Storytelling Game Experience in 1001 Nights using Generative AI

In this paper, we present "1001 Nights", an AI-native game that allows players lead in-game reality through co-created storytelling with the character driven by large language model. The concept is inspired by Wittgenstein's idea of the limits of one's world being determined by the bounds of their language. Using advanced AI tools like GPT-4 and Stable Diffusion, the second iteration of the game enables the protagonist, Shahrzad, to realize words and stories in her world. The player can steer the conversation with the AI King towards specific keywords, which then become battle equipment in the game. This blend of interactive narrative and text-to-image transformation challenges the conventional border between the game world and reality through a dual perspective. We focus on Shahrzad, who seeks to alter her fate compared to the original folklore, and the player, who collaborates with AI to craft narratives and shape the game world. We explore the technical and design elements of implementing such a game with an objective to enhance the narrative game genre with AI-generated content and to delve into AI-native gameplay possibilities.
MENINGKATKAN KEMAMPUAN KOSAKATA BERBAHASA PADA KOSAKATA BERBICARA ANAK DENGAN PENERAPAN METODE BERMAIN PERAN DI KELOMPOK A TK RESTU IBU KOTA DEPOK

This research was conducted to determine the speaking vocabulary skills of early childhood using the group A role playing method at Restu Ibu Kindergarten. The purpose of this learning improvement research was to look at the speaking vocabulary abilities of early childhood using the group A role playing method at Restu Ibu Kindergarten. In this research activity, a classroom action research model was carried out collaboratively between teachers and colleagues in class A. This research involved 20 children in group A. The data collection process was carried out by researchers by collecting the results of documentation and observation with analytical techniques, namely in the form of descriptive quantitative. The tool used is an observation instrument sheet. There is a success target in this study, namely achieving 75% of the 20 children who have language vocabulary skills which are carried out in cycle 1 and cycle 2 which shows the achievement results in the Pre-action stage with a percentage of 25%, in Cycle 1 as much as 50%, and in Cycle 2 with a success percentage of 85%. This proves that the research carried out has achieved indicators of success in children's early language vocabulary skills that have been applied by researchers. Based on the results of the study which showed an increase of more than 85%, it can be concluded that the Role Playing method can improve vocabulary skills in children's speaking vocabulary by applying the role playing method in group A, Kindergarten, Restu, Ibu Depok.
STRUCTURES OF VORTEXES NEAR THE POLES OF PLANETS OF THE SOLAR SYSTEM

In the vicinity of the poles of the planets of the solar system, ordered crystals of vortices and intermittency are observed. The formation of vortex structures in the vicinity of the poles of the planets of the solar system is investigated. The conditions for the formation of vortexes are considered. A nonlinear equation was found describing the vortex dynamics and structures were studied.
Hybrid extreme learning machine-based approach for IDS in smart Ad Hoc networks

Abstract In recent years, intrusion detection systems (IDSs) have increasingly come to be regarded as a significant method due to their potential to develop into a key component that is necessary for the safety of computer networks. This work focuses on the usage of extreme learning machines, which are also known as ELMs, with the purpose of spotting prospective intrusions and assaults. The proposed method combines the self-adaptive differential evolution method for optimising network input weights and hidden node biases and multi-node probabilistic approach with the extreme learning machine for deriving network output weights. This body of work presents an innovative method of learning that can be put into practice in order to determine whether or not an incursion has taken place in the system that is the focus of the investigation that is being carried out by this body of work. A hybrid extreme learning machine is used in the execution of this strategy. When there is one thousand times more traffic on a network, the ability of regular IDS systems to detect malicious network intrusions is lowered by a factor of one hundred. This is because there are less opportunities to detect the intrusions. This is due to the fact that there are less probabilities to identify potential dangers. This paper lays the groundwork for a novel methodology for identifying malicious network breaches. The findings of the simulation demonstrated that putting into practice the approach that was proposed resulted in an improvement in the accuracy of the scenario's classification while it was being investigated. The implementation of the method seems to have produced the desired results.
Removal of Turbidity from Raw water by Novel Coagulant Based on PVC Pipe Waste and Sodium Alginate

In the current study, waste polyvinyl chloride pipes were used as a primary material in the reactions, which are widely used for water supply and drainage systems where the waste pipes were prepared by cleaning them from suspended materials and grinding them into small particles, then reacting natural polymers from a plant; Alginic acid. We have obtained modified polymer (PVC/Alginate) or AL-PVC which have been grinded to obtain a powder. Four methods were used to characterize the properties of natural and prepared polymers: Fourier infrared spectroscopy (FTIR), scanning electron microscopy (SEM) X-ray diffraction (XRD), and thermogravimetry (TGA-TG). The effectiveness of AL-PVC polymer as a coagulant flocculant in the treatment of surface water has been studied. Tests were carried out in the laboratory on two kinds of water (50 NTU and 100 NTU). The first one is the synthesis of water mixed with high and low concentrations of bentonite, simulating thus turbid water. The second one is raw water from the treatment station of the Shatt Al-Arab River. The performance of the coagulation-flocculation process has been assessed by measuring the supernatant turbidity for different pH, doses, and flocculation times of the AL-PVC polymer. The obtained results show that AL-PVC polymer effectiveness was strongly dependent on pH and the doses of AL-PVC polymer, the initial turbidity, and the water quality. The physical and chemical methods used in the present study of water treatments are mainly due to their simplicity, ease of use, low cost as well as excellent removal efficiency.
Decoupled Structure for Improved Adaptability of End-to-End Models

Although end-to-end (E2E) trainable automatic speech recognition (ASR) has shown great success by jointly learning acoustic and linguistic information, it still suffers from the effect of domain shifts, thus limiting potential applications. The E2E ASR model implicitly learns an internal language model (LM) which characterises the training distribution of the source domain, and the E2E trainable nature makes the internal LM difficult to adapt to the target domain with text-only data To solve this problem, this paper proposes decoupled structures for attention-based encoder-decoder (Decoupled-AED) and neural transducer (Decoupled-Transducer) models, which can achieve flexible domain adaptation in both offline and online scenarios while maintaining robust intra-domain performance. To this end, the acoustic and linguistic parts of the E2E model decoder (or prediction network) are decoupled, making the linguistic component (i.e. internal LM) replaceable. When encountering a domain shift, the internal LM can be directly replaced during inference by a target-domain LM, without re-training or using domain-specific paired speech-text data. Experiments for E2E ASR models trained on the LibriSpeech-100h corpus showed that the proposed decoupled structure gave 15.1% and 17.2% relative word error rate reductions on the TED-LIUM 2 and AESRC2020 corpora while still maintaining performance on intra-domain data.
Camouflaged cryptosystem based on optical diffractive imaging

Abstract A camouflaged cryptosystem based on optical diffractive imaging is proposed in this paper. During the encryption process, a camouflaged image is first encoded into an intensity image by optical diffractive imaging, which is stored and transmitted as a fake ciphertext. Then a secret image is encoded into two generated phase masks( GPM s) with the fake ciphertext as the amplitude constraint by cascading phase encoding. The ciphertext and random phase masks( RPM s) used in the optical diffractive imaging are transmitted to the receivers via a common channel, while GPM s are transmitted via a private channel. The common channel can be accessed by both authorized receivers and potential illegal intruders. Therefore, illegal intruders can just obtain the camouflaged image by the fake ciphertext and RPM s, while only authorized receivers with the fake ciphertext and GPM s can retrieve the secret image. The decoded camouflaged image would confuse the illegal users and provide additional security protection for the secret image. The feasibility and security of this cryptosystem would by verified by several simulation experiments.
MM-AU:Towards Multimodal Understanding of Advertisement Videos

Advertisement videos (ads) play an integral part in the domain of Internet e-commerce as they amplify the reach of particular products to a broad audience or can serve as a medium to raise awareness about specific issues through concise narrative structures. The narrative structures of advertisements involve several elements like reasoning about the broad content (topic and the underlying message) and examining fine-grained details involving the transition of perceived tone due to the specific sequence of events and interaction among characters. In this work, to facilitate the understanding of advertisements along the three important dimensions of topic categorization, perceived tone transition, and social message detection, we introduce a multimodal multilingual benchmark called MM-AU composed of over 8.4K videos (147 hours) curated from multiple web sources. We explore multiple zero-shot reasoning baselines through the application of large language models on the ads transcripts. Further, we demonstrate that leveraging signals from multiple modalities, including audio, video, and text, in multimodal transformer-based supervised models leads to improved performance compared to unimodal approaches.