Reviewerly/exHarmony huggingface dataset:
Systematic Rectification of Language Models via Dead-end Analysis

With adversarial or otherwise normal prompts, existing large language models (LLM) can be pushed to generate toxic discourses. One way to reduce the risk of LLMs generating undesired discourses is to alter the training of the LLM. This can be very restrictive due to demanding computation requirements. Other methods rely on rule-based or prompt-based token elimination, which are limited as they dismiss future tokens and the overall meaning of the complete discourse. Here, we center detoxification on the probability that the finished discourse is ultimately considered toxic. That is, at each point, we advise against token selections proportional to how likely a finished text from this point will be toxic. To this end, we formally extend the dead-end theory from the recent reinforcement learning (RL) literature to also cover uncertain outcomes. Our approach, called rectification, utilizes a separate but significantly smaller model for detoxification, which can be applied to diverse LLMs as long as they share the same vocabulary. Importantly, our method does not require access to the internal representations of the LLM, but only the token probability distribution at each decoding step. This is crucial as many LLMs today are hosted in servers and only accessible through APIs. When applied to various LLMs, including GPT-3, our approach significantly improves the generated discourse compared to the base LLMs and other techniques in terms of both the overall language and detoxification performance.
Architecting Peer-to-Peer Serverless Distributed Machine Learning Training for Improved Fault Tolerance

Distributed Machine Learning refers to the practice of training a model on multiple computers or devices that can be called nodes. Additionally, serverless computing is a new paradigm for cloud computing that uses functions as a computational unit. Serverless computing can be effective for distributed learning systems by enabling automated resource scaling, less manual intervention, and cost reduction. By distributing the workload, distributed machine learning can speed up the training process and allow more complex models to be trained. Several topologies of distributed machine learning have been established (centralized, parameter server, peer-to-peer). However, the parameter server architecture may have limitations in terms of fault tolerance, including a single point of failure and complex recovery processes. Moreover, training machine learning in a peer-to-peer (P2P) architecture can offer benefits in terms of fault tolerance by eliminating the single point of failure. In a P2P architecture, each node or worker can act as both a server and a client, which allows for more decentralized decision making and eliminates the need for a central coordinator. In this position paper, we propose exploring the use of serverless computing in distributed machine learning training and comparing the performance of P2P architecture with the parameter server architecture, focusing on cost reduction and fault tolerance.
Diacritic Recognition Performance in Arabic ASR

We present an analysis of diacritic recognition performance in Arabic Automatic Speech Recognition (ASR) systems. As most existing Arabic speech corpora do not contain all diacritical marks, which represent short vowels and other phonetic information in Arabic script, current state-of-the-art ASR models do not produce full diacritization in their output. Automatic text-based diacritization has previously been employed both as a pre-processing step to train diacritized ASR, or as a post-processing step to diacritize the resulting ASR hypotheses. It is generally believed that input diacritization degrades ASR performance, but no systematic evaluation of ASR diacritization performance, independent of ASR performance, has been conducted to date. In this paper, we attempt to experimentally clarify whether input diacritiztation indeed degrades ASR quality, and to compare the diacritic recognition performance against text-based diacritization as a post-processing step. We start with pre-trained Arabic ASR models and fine-tune them on transcribed speech data with different diacritization conditions: manual, automatic, and no diacritization. We isolate diacritic recognition performance from the overall ASR performance using coverage and precision metrics. We find that ASR diacritization significantly outperforms text-based diacritization in post-processing, particularly when the ASR model is fine-tuned with manually diacritized transcripts.
Internet Explorer: Targeted Representation Learning on the Open Web

Modern vision models typically rely on fine-tuning general-purpose models pre-trained on large, static datasets. These general-purpose models only capture the knowledge within their pre-training datasets, which are tiny, out-of-date snapshots of the Internet -- where billions of images are uploaded each day. We suggest an alternate approach: rather than hoping our static datasets transfer to our desired tasks after large-scale pre-training, we propose dynamically utilizing the Internet to quickly train a small-scale model that does extremely well on the task at hand. Our approach, called Internet Explorer, explores the web in a self-supervised manner to progressively find relevant examples that improve performance on a desired target dataset. It cycles between searching for images on the Internet with text queries, self-supervised training on downloaded images, determining which images were useful, and prioritizing what to search for next. We evaluate Internet Explorer across several datasets and show that it outperforms or matches CLIP oracle performance by using just a single GPU desktop to actively query the Internet for 30--40 hours. Results, visualizations, and videos at https://internet-explorer-ssl.github.io/
VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation

We propose a novel algorithm for offline reinforcement learning called Value Iteration with Perturbed Rewards (VIPeR), which amalgamates the pessimism principle with random perturbations of the value function. Most current offline RL algorithms explicitly construct statistical confidence regions to obtain pessimism via lower confidence bounds (LCB), which cannot easily scale to complex problems where a neural network is used to estimate the value functions. Instead, VIPeR implicitly obtains pessimism by simply perturbing the offline data multiple times with carefully-designed i.i.d. Gaussian noises to learn an ensemble of estimated state-action {value functions} and acting greedily with respect to the minimum of the ensemble. The estimated state-action values are obtained by fitting a parametric model (e.g., neural networks) to the perturbed datasets using gradient descent. As a result, VIPeR only needs $\mathcal{O}(1)$ time complexity for action selection, while LCB-based algorithms require at least $\Omega(K^2)$, where $K$ is the total number of trajectories in the offline data. We also propose a novel data-splitting technique that helps remove a factor involving the log of the covering number in our bound. We prove that VIPeR yields a provable uncertainty quantifier with overparameterized neural networks and enjoys a bound on sub-optimality of $\tilde{\mathcal{O}}( { \kappa H^{5/2} \tilde{d} }/{\sqrt{K}})$, where $\tilde{d}$ is the effective dimension, $H$ is the horizon length and $\kappa$ measures the distributional shift. We corroborate the statistical and computational efficiency of VIPeR with an empirical evaluation on a wide set of synthetic and real-world datasets. To the best of our knowledge, VIPeR is the first algorithm for offline RL that is provably efficient for general Markov decision processes (MDPs) with neural network function approximation.
Permutation-Invariant Set Autoencoders with Fixed-Size Embeddings for Multi-Agent Learning

The problem of permutation-invariant learning over set representations is particularly relevant in the field of multi-agent systems -- a few potential applications include unsupervised training of aggregation functions in graph neural networks (GNNs), neural cellular automata on graphs, and prediction of scenes with multiple objects. Yet existing approaches to set encoding and decoding tasks present a host of issues, including non-permutation-invariance, fixed-length outputs, reliance on iterative methods, non-deterministic outputs, computationally expensive loss functions, and poor reconstruction accuracy. In this paper we introduce a Permutation-Invariant Set Autoencoder (PISA), which tackles these problems and produces encodings with significantly lower reconstruction error than existing baselines. PISA also provides other desirable properties, including a similarity-preserving latent space, and the ability to insert or remove elements from the encoding. After evaluating PISA against baseline methods, we demonstrate its usefulness in a multi-agent application. Using PISA as a subcomponent, we introduce a novel GNN architecture which serves as a generalised communication scheme, allowing agents to use communication to gain full observability of a system.
Ensemble knowledge distillation of self-supervised speech models

Distilled self-supervised models have shown competitive performance and efficiency in recent years. However, there is a lack of experience in jointly distilling multiple self-supervised speech models. In our work, we performed Ensemble Knowledge Distillation (EKD) on various self-supervised speech models such as HuBERT, RobustHuBERT, and WavLM. We tried two different aggregation techniques, layerwise-average and layerwise-concatenation, to the representations of different teacher models and found that the former was more effective. On top of that, we proposed a multiple prediction head method for student models to predict different layer outputs of multiple teacher models simultaneously. The experimental results show that our method improves the performance of the distilled models on four downstream speech processing tasks, Phoneme Recognition, Speaker Identification, Emotion Recognition, and Automatic Speech Recognition in the hidden-set track of the SUPERB benchmark.
Role of Smart Librarian in Smart Library for E- Library Services in Academic Libraries of Kolar District: A Study

The maneuver of libraries occurs through three tier -modernization, automation and digitization.A quick library is an message centre with meshwork of many libraries and their office in a larger informational ecosystem around the orb.Smartness slavish that the unraveling of modern library e-weapon and avail are deliberate on the assessment of kingly expedient and users.Smart is more use-amicable than understanding.As every fresh exemplar, smartness sustain wager.In now's period, a library dress with 'dashy library' technology is to be admitting plentifully artless to library readers with no library stick.Technology fetters facilities for subdue and oversee of library buildings, conclude machinelike passage, lighting, car-office building, and any computers.The party of Smart Library demands three stuff i.e.Smart users, Smart Library stick and Smart Library benefit.Users are excluding more from the libraries particularly from the scholarly libraries not only for their mental consequence but also for their Time now instructive question which will sure thing extend Time by Time.The encore explain concisely low-level on suffer library, poignant office, dashy readers, stain benefit development 'showy librarians, office visibility, benefit orientation of quick scholarly library, support, characteristic, perception of Smart Library's, device for bump, showy arrangement, suffer library abode, showy contrivance, poignant library personnel, raw library construction, quick librarians and their e-office in digital period.This newspaper is nicely speculation supported notes which is portray by subcreator pinpointed concerning suffer library. .
Heterogeneous Ensemble Approaches for Robust Face Mask Detection in Crowd Scenes

Face masks are one of the effective tools to slow the spread of disease and reduce medical overload by protecting people from infectious diseases including COVID-19. To prevent infection from respiratory droplets, it is imperative to wear a mask that covers the nose and mouth completely. However, it is difficult to make it mandatory for crowds to wear masks in public places where many people gather. For example, detecting incorrect mask-wearing in crowded scenes is a tedious and attention-grabbing task. Therefore, the success of deep learning in computer vision motivates automated monitoring systems. However, deep learning-based detection models are unstable if the domain task is changed and may have different strengths and weaknesses. Therefore, in this study, we propose a heterogeneous ensemble-based detection model for robust face mask detection in crowd scenes. First, independent detection models such as You look Only Ones (YOLO) v6, YOLO v7, and Faster R-CNN are employed for the model ensemble. Second, the prediction results obtained from the detection models are post-processed such as merging, non-maximum suppression, and weighted box fusion. The experimental results show that the classification performance of our proposed model has an F1 score of about 90.5% and that the improvement of the generalization ability due to the ensemble strategy contributed to the improvement of the classification performance.
A Comparison of PCA and HOG for Feature Extraction and Classification of Human Faces

&lt;p&gt;This paper presents a simple analysis and comparison of the efficacy of feature extraction and generalization achieved using Histogram of Oriented Gradients (HOG) and Principal Component Analysis (PCA). A Naı̈ve Bayes classifier was trained with a dataset of pre-classified human male and female face images. The classifier was then presented with a fresh set of faces and was given the task of classifying the images as male or female. Also attempted, was an experiment of testing using the same images used for training. It was observed that PCA was more adaptive than HOG, in identifying new faces. HOG was more accurate in identifying the same faces, provided there were sufficient training samples. In all, PCA was found to be a better feature extractor, since it did not require many training examples. It only mattered that a sufficient number of principal components were used to obtain good classification accuracy.&lt;/p&gt;
ClusTop: An unsupervised and integrated text clustering and topic extraction framework

&lt;p&gt;Text clustering and topic extraction are two important tasks in text mining. Usually, these two tasks are performed separately. For topic extraction to facilitate clustering, we can first project texts into a topic space and then perform a clustering algorithm to obtain clusters. To promote topic extraction by clustering, we can first obtain clusters with a clustering algorithm and then extract cluster-specific topics. However, this naive strategy ignores the fact that text clustering and topic extraction are strongly correlated and follow a chicken-and-egg relationship. Performing them separately fails to make them mutually benefit each other to achieve the best overall performance. In this paper, we propose an unsupervised text clustering and topic extraction framework (ClusTop) which integrates text clustering and topic extraction into a unified framework and can achieve high-quality clustering result and extract topics from each cluster simultaneously. Our framework includes four components: enhanced language model training, dimensionality reduction, clustering and topic extraction, where the enhanced language model can be viewed as a bridge between clustering and topic extraction. On one hand, it provides text embeddings with a strong cluster structure which facilitates effective text clustering; on the other hand, it pays high attention on the topic related words for topic extraction because of its self-attention architecture. Moreover, the training of enhanced language model is unsupervised. Experiments on two datasets demonstrate the effectiveness of our framework and provide benchmarks for different model combinations under this framework.&lt;/p&gt;
6G goal-oriented communications: How to coexist with legacy systems?

&lt;p&gt;6G will connect heterogeneous intelligent agents to make them natively operate complex cooperative tasks. When connecting intelligence, two main research questions arise to identify how artificial intelligence and machine learning models behave depending on: i) their input data quality, affected by errors induced by interference and additive noise during wireless communication; ii) their contextual effectiveness and resilience to interpret and exploit the meaning behind the data. Both questions are within the realm of semantic and goal-oriented communications. With this paper we investigate on how to effectively share communication spectrum resources between a legacy communication systems (i.e., data-oriented) and a new goal-oriented edge intelligence one. Specifically, we address the scenario of an enhanced Mobile Broadband (eMBB) service, i.e., a user uploading a video stream to a radio access point, interfering with an edge inference system, in which a user uploads images to a Mobile Edge Host that runs a classification task. Our objective is to achieve, through cooperation, the eMBB service highest data rate, subject to a target goal-effectiveness of the edge inference service, namely the probability of confident inference on time. We first formalize a general definition of goal in the context of wireless communications. This includes the goal-effectiveness, (i.e., the goal achievability rate, or the probability of achieving the goal), as well as that of goal cost (i.e., the network resource consumption needed to achieve the goal with target effectiveness). We argue and show, through numerical evaluations, that communication reliability and goal-effectiveness are not straightforwardly linked. Then, after a performance evaluation aiming to clarify the difference between communication performance and goal-effectiveness, a long-term optimization problem is formulated and solved via Lyapunov stochastic network optimization tools, to guarantee the desired target performance. Finally, our numerical results assess the advantages of the proposed optimization, and the superiority of the goal-oriented strategy against baseline 5G compliant legacy approaches, under both stationary and non-stationary communication (and computation) environments. &lt;/p&gt;
Deterministic Decoupling of Global Features for Data Analysis

&lt;p&gt; We introduce a method for deterministic decoupling of global features with application to data analysis. We propose a new formalism that is based on defining transformations on submanifolds, by following trajectories along the features’ gradients. Through these transformations, we define a type of normalization that, we demonstrate, allows for decoupling differentiable features. We apply this to sampling moments, obtaining a quasi-analytic solution for the orthokurtosis, a normalized version of the kurtosis that is not just decoupled from mean and variance, but also from skewness. We also apply the proposed method, up to sixth-order moments, in the original data domain and at the output of a filter bank, to regression and texture classification problems, consistently obtaining strong improvements in performance as compared to using classical (non-decoupled) features &lt;/p&gt;
Pengembangan Media Pembelajaran Matematika berbasis QR Code untuk Kemampuan Berpikir Geometris Siswa Kelas IV SD

The purpose of this study was to design a mathematics learning media based on qr code to find out the level of geometric thinking of grade IV elementary school students. This research belongs to Research and Development (R&amp;D with ADDIE models, namely Analysis, Design, Development, Implementation, Implementation, and Evaluation). Learning media that have been developed will be assessed and validated by experts who are competent in their fields The instrument used in this study was a questionnaire in the form of teacher responses and student responses to the qr code-based mathematics learning media Results: 1) The author has produced a qr code-based mathematics learning media on the circumferential material and building area flat for grade IV SD / MI students, 2) Expert assessment in material validation, media validation, and language validation which gets a percentage of 96% at highly valid intervals, 3) responses of grade IV SD / MI students on the practicality of the media based on mathematics based learning qr code obtained an evaluation percentage of 87%. This value is at a very, very practical interval.
Pengaruh Model Kooperatif Tipe Talking Stick terhadap Hasil Belajar Tematik Terpadu Kelas V SD

The purpose of this study is to find out the effect of Cooperative Learning Model through Talking Stick on the integrated theatic learning outcomes of the fifth year elementary school students in Elementary School Cluster 4 in Padang Timur Sub-District, Padang. This study used a quasi-experimental design in the form of nonequivalent control group design. The sampling technique used was cluster random sampling. The sample of this study was the fifth year elemtary schol students in Elementary School Cluster 4 in Padang Timur Sub-District, Padang.18 students at Class VB of SDN 35 Parak Karakah were chosen as the experimental class, and 22 students of SDN 17Parak Karakah were chosen as the control class. The results of this study show that the average pretest score of the experimental class is 54,07, and the average pretest score of the control class is 54. Meanwhile, the average scores of the both the experimental class and control class are 84,07 and 64,94, respectively.furthermore, the results of the analysis of the hypothesis testing at a significance level of 5% are tcount&gt;ttable (6.13 &gt; 2,024), soit can be concluded that using Cooperative Learning Model through Talking Stick elementaryschool students in Elementary School Cluster 4 in Padang Timur Sub-District, Padang.
Peningkatan Hasil Belajar Keliling dan Luas Bangun Datar dengan Model Problem Based Learning di SD

The purpose of this research is to describe the learning outcomes improvement of circumference and area of two-dimentional figures by using problem based learning (PBL) models at Grade IV SDN 17 Manggis Ganting Bukittinggi. This type of research is Classroom Action Research using qualitative and quantitative approaches. The research subjects were teachers as observers, the researcher as practitioner, and 28 students of grade IV. The results of the first cycle study on the RPP an average of 82.81% (good), increased in the second cycle to 93.75% (very good). The implementation of the first cycle of teacher activity on average 82.14% (enough), increased in the second cycle to 96.42% (very good). Implementation of the first cycle of student activity on average 80.35% (enough), increased in the second cycle to 92.85% (very good). Student learning outcomes in the first cycle I meeting an average of 64.1 (C +), increased in the first cycle of meeting II to 69.2 (B-), increased in the second cycle to 76.25 (B +). Thus, it was concluded that the PBL model can improve mobile learning outcomes and flat build area in elementary schools.