Reviewerly/exHarmony huggingface dataset:
Feature-driven 3D range geometry compression via spatially-aware depth encoding

The ever-growing variety of capture methods and applications for 3D range geometry continually increases the need for efficient methods of data storage and transmission.Compression techniques address this need by offering reduced file sizes while maintaining the precision needed for a particular application.Several such compression methods use phase-shifting principles to encode the 3D data into a 2D RGB image.In some applications, such as telepresence, high precision may only be required in a particular region within a scan.This paper proposes a feature-driven compression method that provides a way to encode regions of interest at higher precision while encoding the remaining data at lower precision to reduce file sizes.This method supports both lossless and lossy compression, enabling even greater file size savings and a wider range of applications.In the case of a depth scan of a bust, an extracted bounding box of the face was used to create an encoding distribution such that the facial region was encoded at higher precisions.When using JPEG 80, the global RMS reconstruction accuracy of this novel encoding was 99.72%; however, in the region of interest, the accuracy was 99.88%.This feature-driven encoding achieved a 26% reduction in compressed file size compared to a fixed, high precision encoding.
Hyperbolic Disentangled Representation for Fine-Grained Aspect Extraction

Automatic identification of salient aspects from user reviews is especially useful for opinion analysis. There has been significant progress in utilizing weakly supervised approaches, which require only a small set of seed words for training aspect classifiers. However, there is always room for improvement. First, no weakly supervised approaches fully utilize latent hierarchies between words. Second, each seed word’s representation should have different latent semantics and be distinct when it represents a different aspect. In this paper we propose HDAE, a hyperbolic disentangled aspect extractor in which a hyperbolic aspect classifier captures words’ latent hierarchies, and an aspect-disentangled representation models the distinct latent semantics of each seed word. Compared to previous baselines, HDAE achieves average F1 performance gains of 18.2% and 24.1% on Amazon product review and restaurant review datasets, respectively. In addition, the embedding visualization experience demonstrates that HDAE is a more effective approach to leveraging seed words. An ablation study and a case study further attest the effectiveness of the proposed components.
On Trusting a Cyber Librarian: How rethinking underlying data storage infrastructure can mitigate risks of automation

INTRODUCTION:The increased ability of Artificial Intelligence (AI) technologies to generate and parse texts will inevitably lead to more proposals for AI's use in the semantic sentiment analysis (SSA) of textual sources.We argue that instead of focusing solely on debating the merits of automated versus manual processing and analysis of texts, it is critical to also rethink our underlying storage and representation formats.Specifically, we argue that accommodating multivariate metadata is an example of how underlying data storage infrastructure can reshape the ethical debate surrounding the use of such algorithms.In other words, a system that employs automated analysis may typically require manual intervention to assess the quality of its output, or demand that we select between multiple competing NLP algorithms.Settling on whichever algorithm or ensemble can produce the best results, this is a decision that need not be made a priori at all.OBJECTIVES: An underlying storage and representation system that allows for the existence and evaluation of multiple variants of the same source data, while maintaining attribution to the individual sources of each variant, would be an example of a much-needed enhancement to existing storage technologies, especially in anticipation of the proliferation of AI semantic analysis technologies.METHODS: To this end, we take the view of AI in SSA as a sociotechnical system, and describe a possible novel solution that would allow for safer cyber curation.This can be done by allowing multiple different annotations to coexist within a single publishing ecosystem (whether those different annotations are the result of competing algorithmic models, or varying degrees of human intervention).RESULTS: We discuss the feasibility of such a scheme, using our own infrastructure model (MultiVerse) as an illustrative model for such a system, and analyse the ethical implications.CONCLUSION: Considering an underlying storage and representation system that allows for the existence and evaluation of multiple variants of the same source data, while maintaining attribution to the individual sources of each variant within a single publishing ecosystem helps mitigate risks of automation and enhances AI (semantic) explainability.
Distributed Fusion Tracking Estimation Under Range-Only Measurement

This paper is concerned with the distributed fusion estimation problem of range-only target tracking system with unknown but bounded noises, where the linear and nonlinear motion models are both considered.Particularly, a kind of nonlinear transformation is used to convert the nonlinear distance measurement model into a linear one, which eliminates the corresponding linearization errors in the design of estimation error system.In spite of the transformed measurement noise becomes more complicated, while it is still bounded.Moreover, for the nonlinear target motion model, the state linearization error caused by the Taylor expansion is modeled by the state dependent matrix with uncertainty bounded matrix.In this case, based on the bounded recursive optimization algorithm, two kinds of convex optimization problems are established to determine the gains of the local/fusion estimators, and the stability of the designed estimators also can be guaranteed.Finally, two different range-only target tracking systems are presented to show the effectiveness and advantages of the proposed methods.
ACQUISITION OF EARLY CHILDHOOD PHONOLOGY : A CASE STUDY OF A 24 MONTHS TODDLER IN PALEMBANG

This research aimed to describe the language acquisition of a toddler at the age of 24 months, seen from the phonological aspects, such as consonants, vowels, and diphthongs. This research was conducted in Palembang, on November – December 2021. The method used in this research was qualitative method through case study approach. The data were obtained from a toddler whose initial name was “MSM” as the respondent. The techniques for collecting the data were through observation and recording. The results of the analysis showed that the respondent was able to pronounce various phonemes existed in Bahasa Indonesia and Bahasa Palembang (Indonesian and Palembang Language). These phonemes consisted of 14 consonants which were /p/, /b/, /m/, /t/, /d/, /s/, /n/, /l/, /?/, /t?/, /j/, /g/, /k/, and /?/. The respondent also has been able to produce the vowels phonemes such as /a/, /i/, /u/, /e/, /?/ and /?/ and some kinds of diphthongs which were /a?/, /e?/, /a?/, and /??/.
A Comparative Study of Data-Driven Models for Travel Destination Characterization

Characterizing items for content-based recommender systems is a challenging task in complex domains such as travel and tourism. In the case of destination recommendation, no feature set can be readily used as a similarity ground truth, which makes it hard to evaluate the quality of destination characterization approaches. Furthermore, the process should scale well for many items, be cost-efficient, and most importantly correct. To evaluate which data sources are most suitable, we investigate 18 characterization methods that fall into three categories: venue data, textual data, and factual data. We make these data models comparable using rank agreement metrics and reveal which data sources capture similar underlying concepts. To support choosing more suitable data models, we capture a desired concept using an expert survey and evaluate our characterization methods toward it. We find that the textual models to characterize cities perform best overall, with data models based on factual and venue data being less competitive. However, we show that data models with explicit features can be optimized by learning weights for their features.
Performance analysis of different intonation models in Kannada speech synthesis

Text &lt;span lang="EN-US"&gt;to speech (TTS) is a system that generates artificial speech from text input. The prosodic models used improve the quality of the synthesized speech especially naturalness and intelligibility. The prosody involves intonation, intonation refers to the variations in the pitch frequency (F0) with respect to time in an utterance. This work mainly concentrates on building feedback neural network model to predict F0 contour in the utterances using Fujisaki intonation model parameters as the input features to the network since the Fujisaki intonation model is data driven and not a rule based one. In this work we have built 4-layer feedback neural network in the festival framework. Finally, the synthetically generated Kannada speech using the neural network model, is compared for its performance with the classification and regression tree (CART) model and Tilt model. Database of simple declarative Kannada sentences created by Carnegie Mellon University have been deployed in this work. From the study it is very clear that F0 contours can be accurately predicted using CART and neural network models, whereas naturalness and intelligibility is high in CART model rather than neural network model.&lt;/span&gt;
ESL Learners’ Obstacles in Learning Speaking Skills

English is regarded as a second language in Malaysia and is part of the compulsory learning subject in primary and secondary school. However, the learning span of six years in primary school and five years in secondary school does not assure that the students will be able to master their speaking skills. Many of our students are still struggling to speak in English with correct sentence structures and pronunciations. Therefore, this paper aims to discuss the obstacles faced by ESL learners in their speaking skills. Learning perception, anxiety, and social-related problems as well as learning related problems, were the focus of issues discussed in this paper. This study engaged a quantitative approach to collect the data. The online survey, through “Google Form” was used to obtain data from the 196 tertiary level ESL respondents at one of the higher learning institutions in Ipoh. An adapted questionnaire was used for this study. The questionnaire was constructed in the form of a 5-point Likert Scale from which the choices range from strongly agree to strongly disagree. The items on the questionnaire were analyzed using SPSS 26 and the mean score for each item constructed was calculated accordingly. Findings indicated that the respondents experienced interference of L1 when it comes to difficulty in speaking. The respondents also expected the teacher to correct every error that they say and want the teacher to use a variety of methods and latest approaches to teach speaking skills to boost their interest. This study suggests that ESL learners need to be given more motivation to develop their confidence in using the English language and teachers need to be more creative in implementing strategies that stimulate learners’ interest to speak in English.
Cosine similarity-based algorithm for social networking recommendation

Social media have become a discussion platform for individuals and groups. Hence, users belonging to different groups can communicate together. Positive and negative messages as well as media are circulated between those users. Users can form special groups with people who they already know in real life or meet through social networking after being suggested by the system. In this article, we propose a framework for recommending communities to users based on their preferences; for example, a community for people who are interested in certain sports, art, hobbies, diseases, age, case, and so on. The framework is based on a feature extraction algorithm that utilizes user profiling and combines the cosine similarity measure with term frequency to recommend groups or communities. Once the data is received from the user, the system tracks their behavior, the relationships are identified, and then the system recommends one or more communities based on their preferences. Finally, experimental studies are conducted using a prototype developed to test the proposed framework, and results show the importance of our framework in recommending people to communities.
Expression-Preserving Face Frontalization Improves Visually Assisted Speech Processing

Face frontalization consists of synthesizing a frontal view from a profile one. This paper proposes a frontalization method that preserves non-rigid facial deformations, i.e. facial expressions. It is shown that expression-preserving frontalization boosts the performance of visually assisted speech processing. The method alternates between the estimation of (i) the rigid transformation (scale, rotation, and translation) and (ii) the non-rigid deformation between an arbitrarily-viewed face and a face model. The method has two important merits: it can deal with non-Gaussian errors in the data and it incorporates a dynamical face deformation model. For that purpose, we use the Student's t-distribution in combination with a Bayesian filter in order to account for both rigid head motions and time-varying facial deformations, e.g. caused by speech production. The zero-mean normalized cross-correlation score is used to evaluate the ability of the method to preserve facial expressions. The method is thoroughly evaluated and compared with several state of the art methods, either based on traditional geometric models or on deep learning. Moreover, we show that the method, when incorporated into speech processing pipelines, improves word recognition rates and speech intelligibility scores by a considerable margin.
Text Is No More Enough! A Benchmark for Profile-Based Spoken Language Understanding

Current researches on spoken language understanding (SLU) heavily are limited to a simple setting: the plain text-based SLU that takes the user utterance as input and generates its corresponding semantic frames (e.g., intent and slots). Unfortunately, such a simple setting may fail to work in complex real-world scenarios when an utterance is semantically ambiguous, which cannot be achieved by the text-based SLU models. In this paper, we first introduce a new and important task, Profile-based Spoken Language Understanding (ProSLU), which requires the model that not only relies on the plain text but also the supporting profile information to predict the correct intents and slots. To this end, we further introduce a large-scale human-annotated Chinese dataset with over 5K utterances and their corresponding supporting profile information (Knowledge Graph (KG), User Profile (UP), Context Awareness (CA)). In addition, we evaluate several state-of-the-art baseline models and explore a multi-level knowledge adapter to effectively incorporate profile information. Experimental results reveal that all existing text-based SLU models fail to work when the utterances are semantically ambiguous and our proposed framework can effectively fuse the supporting information for sentence-level intent detection and token-level slot filling. Finally, we summarize key challenges and provide new points for future directions, which hopes to facilitate the research.
The Interactive Method for Language Science and Some Salient Results

The use of information technology in linguistic research gave rise in the 1950s to what is known as Natural Language Processing, but that framework was created without paying due attention to the need for logical reconstruction of linguistic concepts which were borrowed directly from barely (or even not at all) formalised structural linguistics. The Computer-aided Acquisition of Semantic Knowledge project (CASK) based on the Knowledge Discovery in Databases technology (KDD) enabled us to interact with computers while gathering and improving our knowledge about languages.&#x0D; Thus, with the help of data mining tools, as a result of revisiting two sorts of generally admitted linguistic theories (the Predicate Argument Structure theory and Information Structure theory), we succeeded in improving these local linguistic approaches by proposing to unify the Associative Semantics (AS) theory (in which we introduced the concept of ortho-information) with the Meta-Informative Centering (MIC) theory (in which we described the meta-informative layer of natural languages). The resulting Distributed Grammar (DG) program (sketched out in this paper) treats, in addition to the above types of information, the third one, para-information (concerning identity and alikeness with respect to context and language ontology) which – despite many studies – had no uniform theoretical background in general linguistics. This DG program aims to lay the foundations for creating the theoretical background of Conceptual Linguistics.&#x0D;
Learning to Drive (L2D) as a Low-Cost Benchmark for Real-World Reinforcement Learning

We present Learning to Drive (L2D), a low-cost benchmark for real-world reinforcement learning (RL). L2D involves a simple and reproducible experimental setup where an RL agent has to learn to drive a Donkey car around three miniature tracks, given only monocular image observations and speed of the car. The agent has to learn to drive from disengagements, which occurs when it drives off the track. We present and open-source our training pipeline, which makes it straightforward to apply any existing RL algorithm to the task of autonomous driving with a Donkey car. We test imitation learning, state-of-the-art model-free, and model-based algorithms on the proposed L2D benchmark. Our results show that existing RL algorithms can learn to drive the car from scratch in less than five minutes of interaction. We demonstrate that RL algorithms can learn from sparse and noisy disengagement to drive even faster than imitation learning and a human operator.
Simulating Modern CPU Vulnerabilities on a 5-stage MIPS Pipeline Using Node-RED

This paper proposes a simulation of the 5-stage pipelined MIPS processor using Node-RED and illustrates the basic effects of modern CPU vulnerabilities. Demonstrated in this study are Spectre vulnerability attack and load value injection (LVI) transient-execution attack. The storing of secret data within the cache is shown for Spectre, and through the use of an attacker’s injected page number after a page fault has occurred, we demonstrate LVI’s ability to access the host secrets via simulated memory hierarchy. The persistence of the secret data in the cache can also be observed in the case of both attacks. The characteristics of such security vulnerabilities are successfully simulated with the proposed Node-RED-based processor simulator.
OCR Studymate

Abstract: As we are familiar that there are many languages around the world, which many of us might even not be aware of but due to globalization and digitalization we have got to see and learn many languages. During those days of colonization, English became an integral part of many people’s language and still continues to be in dominance. Hence, inarguably English is one of the prominent languages that is spoken worldwide. But we also cannot deny the fact that there are still many people across the world who aren’t familiar with this language and face difficulties when most of the texts are written in this particular language. For instance, Indian being one of the countries where there are more than 121 languages and every state has its own dialects. So, it becomes difficult for the people who are less familiar with English or any other language that they aren’t familiar with. To overcome these issues and help people who are from vernacular medium or whose native language is other than English, there are already many applications and systems around the world which is appreciable. Most of the existing systems that are present currently have built separate application for each and every step which could be little overwhelming for someone who is not very mobile friendly specifically people with age group above 60. So, the applications are divided according to the steps like capturing text image through camera, then another application for translation which could be done through any translation API and lastly for scanning we have Optical Character Recognition (OCR) scanner for text. But problem is that people expect that the application that they are using should be easy to use, simple to reach and does everything in one go. So, the application that we have proposed makes it easy for people and provides them with a new idea to translate the unfamiliar language (specifically English) into their native language or the languages that are known to them. So, this mobile application captures images which are text based and has important messages or any documents from real world that is important to be translated, and once capturing of image is done then the text is extracted and at the same time identification of language is done of extracted text, and then using Google translation (we have used this translation api) language is translated into their chosen language( we have more than 50 languages to choose) and then the result is displayed on the screen. Keywords: Text recognition, Text Translation, Language Identification, Natural Language Processing, Android, ML kit
Design and Simulation of ANFIS based Brushless DC Motor Control

BLDC motor control with ANFIS control methods was investigated and analysed, in this work. The results show that the ANFIS approach is simple, efficient and achieves the desired speed and torque values. The approach was developed and used in both industrial and literary applications. The ANFIS controller was compared with Fuzzy and PI controllers. ANFIS has proved its reliability and good performance. Besides its swift execution, speed control has demonstrated its capability to maintain a minimum cost and commitment track of the target speed values. The method is a basic method with light steps and no reference processing or synchronisation is required.
Kubernetes-Native Pipelines

So far, we have written code, compiled, tested, and debugged our application. We ran it in a container and on Kubernetes. Moreover, in the last chapter, we explored several approaches to make use of the Kube-apiserver and its extensibility to make the implementation of our Local News application more expressive and maintainable. Until now, we haven’t left the inner loop of development, and even in the “Dev-to-K8s” part of Chapter 3, which explained how to develop with Kubernetes, we were still running this cycle in our own environment: changing code, recompiling, and watching the results as we can see in the center of Figure 5-1.