Reviewerly/exHarmony huggingface dataset:
Accelerated Video Annotation driven by Deep Detector and Tracker

Annotating object ground truth in videos is vital for several downstream tasks in robot perception and machine learning, such as for evaluating the performance of an object tracker or training an image-based object detector. The accuracy of the annotated instances of the moving objects on every image frame in a video is crucially important. Achieving that through manual annotations is not only very time consuming and labor intensive, but is also prone to high error rate. State-of-the-art annotation methods depend on manually initializing the object bounding boxes only in the first frame and then use classical tracking methods, e.g., adaboost, or kernelized correlation filters, to keep track of those bounding boxes. These can quickly drift, thereby requiring tedious manual supervision. In this paper, we propose a new annotation method which leverages a combination of a learning-based detector (SSD) and a learning-based tracker (RE$^3$). Through this, we significantly reduce annotation drifts, and, consequently, the required manual supervision. We validate our approach through annotation experiments using our proposed annotation method and existing baselines on a set of drone video frames. Source code and detailed information on how to run the annotation program can be found at https://github.com/robot-perception-group/smarter-labelme
Pseudo Contrastive Learning for Graph-based Semi-supervised Learning

Pseudo Labeling is a technique used to improve the performance of semi-supervised Graph Neural Networks (GNNs) by generating additional pseudo-labels based on confident predictions. However, the quality of generated pseudo-labels has been a longstanding concern due to the sensitivity of the classification objective with respect to the given labels. To avoid the untrustworthy classification supervision indicating ``a node belongs to a specific class,'' we favor the fault-tolerant contrasting supervision demonstrating ``two nodes do not belong to the same class.'' Thus, the problem of generating high-quality pseudo-labels is then transformed into a relaxed version, i.e., identifying reliable negative pairs. To achieve this, we propose a general framework for GNNs, termed Pseudo Contrastive Learning (PCL). It separates two nodes whose positive and negative pseudo-labels target the same class. To incorporate topological knowledge into learning, we devise a topologically weighted contrastive loss that spends more effort separating negative pairs with smaller topological distances. Experimentally, we apply PCL to various GNNs, which consistently outperform their counterparts using other popular general techniques on five real-world graphs.
A Planning-Based Explainable Collaborative Dialogue System

Eva is a multimodal conversational system that helps users to accomplish their domain goals through collaborative dialogue. The system does this by inferring users' intentions and plans to achieve those goals, detects whether obstacles are present, finds plans to overcome them or to achieve higher-level goals, and plans its actions, including speech acts,to help users accomplish those goals. In doing so, the system maintains and reasons with its own beliefs, goals and intentions, and explicitly reasons about those of its user. Belief reasoning is accomplished with a modal Horn-clause meta-interpreter. The planning and reasoning subsystems obey the principles of persistent goals and intentions, including the formation and decomposition of intentions to perform complex actions, as well as the conditions under which they can be given up. In virtue of its planning process, the system treats its speech acts just like its other actions -- physical acts affect physical states, digital acts affect digital states, and speech acts affect mental and social states. This general approach enables Eva to plan a variety of speech acts including requests, informs, questions, confirmations, recommendations, offers, acceptances, greetings, and emotive expressions. Each of these has a formally specified semantics which is used during the planning and reasoning processes. Because it can keep track of different users' mental states, it can engage in multi-party dialogues. Importantly, Eva can explain its utterances because it has created a plan standing behind each of them. Finally, Eva employs multimodal input and output, driving an avatar that can perceive and employ facial and head movements along with emotive speech acts.
VQE-generated Quantum Circuit Dataset for Machine Learning

Quantum machine learning has the potential to computationally outperform classical machine learning, but it is not yet clear whether it will actually be valuable for practical problems. While some artificial scenarios have shown that certain quantum machine learning techniques may be advantageous compared to their classical counterpart, it is unlikely that quantum machine learning will outclass traditional methods on popular classical datasets such as MNIST. In contrast, dealing with quantum data, such as quantum states or circuits, may be the task where we can benefit from quantum methods. Therefore, it is important to develop practically meaningful quantum datasets for which we expect quantum methods to be superior. In this paper, we propose a machine learning task that is likely to soon arise in the real world: clustering and classification of quantum circuits. We provide a dataset of quantum circuits optimized by the variational quantum eigensolver. We utilized six common types of Hamiltonians in condensed matter physics, with a range of 4 to 16 qubits, and applied ten different ans\"{a}tze with varying depths (ranging from 3 to 32) to generate a quantum circuit dataset of six distinct classes, each containing 300 samples. We show that this dataset can be easily learned using quantum methods. In particular, we demonstrate a successful classification of our dataset using real 4-qubit devices available through IBMQ. By providing a setting and an elementary dataset where quantum machine learning is expected to be beneficial, we hope to encourage and ease the advancement of the field.
A One-Sample Decentralized Proximal Algorithm for Non-Convex Stochastic Composite Optimization

We focus on decentralized stochastic non-convex optimization, where $n$ agents work together to optimize a composite objective function which is a sum of a smooth term and a non-smooth convex term. To solve this problem, we propose two single-time scale algorithms: Prox-DASA and Prox-DASA-GT. These algorithms can find $\epsilon$-stationary points in $\mathcal{O}(n^{-1}\epsilon^{-2})$ iterations using constant batch sizes (i.e., $\mathcal{O}(1)$). Unlike prior work, our algorithms achieve comparable complexity without requiring large batch sizes, more complex per-iteration operations (such as double loops), or stronger assumptions. Our theoretical findings are supported by extensive numerical experiments, which demonstrate the superiority of our algorithms over previous approaches. Our code is available at https://github.com/xuxingc/ProxDASA.
Persian topic detection based on Human Word association and graph embedding

In this paper, we propose a framework to detect topics in social media based on Human Word Association. Identifying topics discussed in these media has become a critical and significant challenge. Most of the work done in this area is in English, but much has been done in the Persian language, especially microblogs written in Persian. Also, the existing works focused more on exploring frequent patterns or semantic relationships and ignored the structural methods of language. In this paper, a topic detection framework using HWA, a method for Human Word Association, is proposed. This method uses the concept of imitation of mental ability for word association. This method also calculates the Associative Gravity Force that shows how words are related. Using this parameter, a graph can be generated. The topics can be extracted by embedding this graph and using clustering methods. This approach has been applied to a Persian language dataset collected from Telegram. Several experimental studies have been performed to evaluate the proposed framework's performance. Experimental results show that this approach works better than other topic detection methods.
ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentation

We address a weakly-supervised low-shot instance segmentation, an annotation-efficient training method to deal with novel classes effectively. Since it is an under-explored problem, we first investigate the difficulty of the problem and identify the performance bottleneck by conducting systematic analyses of model components and individual sub-tasks with a simple baseline model. Based on the analyses, we propose ENInst with sub-task enhancement methods: instance-wise mask refinement for enhancing pixel localization quality and novel classifier composition for improving classification accuracy. Our proposed method lifts the overall performance by enhancing the performance of each sub-task. We demonstrate that our ENInst is 7.5 times more efficient in achieving comparable performance to the existing fully-supervised few-shot models and even outperforms them at times.
adaptive gamma-BSPE kernel density estimation for nonnegative heavy-tailed data

In this work, we consider the nonparametric estimation of the probability density function for nonnegative heavy-tailed (HT) data. The objective is first to propose a new estimator that will combine two regions of observations (high and low density). While associating a gamma kernel to the high-density region and a BS-PE kernel to the low-density region. Then, to compare the proposed estimator with the classical estimator in order to evaluate its performance. The choice of bandwidth is investigated by adopting the popular cross-validation technique and two variants of the Bayesian approach. Finally, the performances of the proposed and the classical estimators are illustrated by a simulation study and real data.
Truth as Consistent Assertion

This paper presents four key results. Firstly, it distinguishes between partial and consistent assertion of a sentence, and introduces the concept of an equivocal sentence, which is both partially asserted and partially denied. Secondly, it proposes a novel definition of truth, stating that a true sentence is one that is consistently asserted. This definition is immune from the Liar paradox, does not restrict classical logic, and can be applied to declarative sentences in the language used by any particular person. Thirdly, the paper introduces an epistemic model of language, known as assertional language, which is used to formalize the definition of truth. Finally, it provides an argument for the falsity of so-called Liar sentences. The paper also discusses Tarski's solution to the Liar paradox and argues for the abandonment of the Tarski Scheme: 'p' is true if and only if p, in the context of everyday language. The proposed definition of truth can be viewed as a formal account of the correspondence theory. The epistemic model is a powerful concept on its own that allows for combining different languages in a meaningful way. This model is uniquely capable of reflecting on epistemic inconsistencies, such as logical paradoxes, in a consistent manner.
A Complete Diagrammatic Calculus for Boolean Satisfiability

We propose a calculus of string diagrams to reason about satisfiability of Boolean formulas, and prove it to be sound and complete. We then showcase our calculus in a few case studies. First, we consider SAT-solving. Second, we consider Horn clauses, which leads us to a new decision method for propositional logic programs equivalence under Herbrand model semantics.
Development of Word Count Data Corpus for Hindi and Marathi Literature

Abstract India has a huge diversity of languages, and Hindi and Marathi are the most spoken languages in the northern and western parts of India. Hindi and Marathi have more than 528 million and 83 million speakers, respectively. The present paper describes the development of the Hindi Word Corpus (Hindi WordCorp) and the Marathi Word Corpus (Marathi WordCorp), reporting the frequency of single words (1-gram) used in written texts of the respective languages using the bag-of-words model (BoW). The word frequencies are provided for eleven decades, ranging from 1920 to 2020. Word frequency was separately computed for undated texts and texts published before 1920. These texts include books (fiction, non-fiction, history, autobiographies, etc.) and magazines. Academic and reference books were not used. Six hundred forty texts were used for the Hindi WordCorp, and 712 texts were used for the Marathi WordCorp. An analysis was employed to check whether the texts used were enough to stabilize the rank-order of the total frequencies of the words. Zipf’s and Heaps’ law coefficients were also estimated. Researchers in various areas like linguistics, social sciences, language sciences, text mining, machine learning analysis, etc., can use the dataset to answer research questions about language and culture. Some demonstrative examples are provided for using the datasets in the two languages. The dataset is made available on an open data repository. The paper is an account of dataset creation for Hindi and Marathi WordCorp. Hence, no empirical results or conclusions are made based on the data created. A web app named Indian Languages Word Corpus (ILWC) has been developed for users.
Revisiting Decidable Bounded Quantification, via Dinaturality

We use a semantic interpretation to investigate the problem of defining an expressive but decidable type system with bounded quantification. Typechecking in the widely studied System Fsub is undecidable thanks to an undecidable subtyping relation, for which the culprit is the rule for subtyping bounded quantification. Weaker versions of this rule, allowing decidable subtyping, have been proposed. One of the resulting type systems (Kernel Fsub) lacks expressiveness, another (System Fsubtop) lacks the minimal typing property and thus has no evident typechecking algorithm. We consider these rules as defining distinct forms of bounded quantification, one for interpreting type variable abstraction, and the other for type instantiation. By giving a semantic interpretation for both in terms of unbounded quantification, using the dinaturality of type instantiation with respect to subsumption, we show that they can coexist within a single type system. This does have the minimal typing property and thus a simple typechecking procedure. We consider the fragments of this unified type system over types which contain only one form of bounded quantifier. One of these is equivalent to Kernel Fsub, while the other can type strictly more terms than System Fsubtop but the same set of beta-normal terms. We show decidability of typechecking for this fragment, and thus for System Fsubtop typechecking of beta-normal terms.
PROMOTING SOCIAL EQUITY AND PREVENTING TEACHERS' MALADAPTIVE SCHEMES

Through this article we aim to highlight the importance of social and educational policies for promoting equity in the Romanian educational system. The paper also contains an investigative study aimed at evaluating the personality traits of teachers and their level of anxiety and the role that certain maladaptive cognitive mechanisms have in the manifestation of counterproductive professional behaviors. The data of the study were collected based on online questionnaires from 31 teachers from the "Kemal Ataturk" National College in Medgidia, Constanta County, Romania.
Google Translate Errors in Legal Texts: Machine Translation Quality Assessment

Machine translation received intensive research in different language pairs; yet, the quality of specialized translation; e.g., legal translation, from Arabic into English received little attention. The present study investigates the quality of machine translation of legal texts from Arabic into English. The paper aims at examining the errors found in the machine translation of legal texts from Arabic into English. It also studies the legal discourse features in machine-translation output. The research questions tackle the accuracy of machine translation of legal discourse and error categories and frequencies in machine translation. The researcher evaluated several factors to assess the quality of Google Translate; i.e., lexical, syntactic, and register-related errors. The study data consists of five legislative texts. The researcher conducted a manual error assessment and classification. To ensure the reliability of the error analysis, an existing human translation of the documents was used as a reference to ensure the reliability of the MT quality assessment and post-editing process. Later, the errors were classified, and their percentages and frequencies were calculated. A few examples of errors in each category were discussed and analysed. The highest error category was lexical errors scoring 43.4%. The last detected error category was deletion with a percentage of 1.7%. Syntactic errors constituted one-fourth of the errors found in the data. Legal register-related errors scored 30.2%. The subcategories of legal register-related errors varied in their occurrence; e.g., one-third of the errors related to legal discourse were legal terms. The study concluded that machine translation; though it provided a comprehensible output, could not translate legal structures and terminology perfectly.
The Challenges of Using Machine Translation While Translating Polysemous Words

The disparities between machine translation and human translation have shown to be quite challenging when translating polysemous words. This study examines the challenges of translating polysemous words between French and English by finding an easy way to translate the polysemous terms which is the key objective. It would also help in dealing with the most effective method of translating polysemous terms, while a few succeed in translating the contexts and others struggle to come up with the right interpretations. As many words or phrases have numerous meanings, a machine translator is still unable to handle the ambiguity issues that arise and determine what the given context means. One issue that machine translators have yet to solve is the phenomena of polysemy, or numerous meanings, though it certainly isn't the only one. In this paper, instances of these machine translations of texts from several text genres—including texts from the journalistic, medical, legal, and dictionary definition genres—are examined. To clear up any misunderstanding, one must consider the situation’s background. One must also acknowledge that the situation makes sense. In the concluding section of this study, we will be shown some examples of significant issues that have arisen as a result of a lack of understanding of the term, including misunderstanding, lack of comprehension, ambiguity, and limitations on the heedless use of technical definitions in favour of common ones.
Scene identification using visual semantic segmentation and supplementary classifier for resource-constrained edge systems

This paper presents a scene identification method employing semantic segmentation where the method provides real-time computation in resource-constrained edge devices. Scene identification could be crucial for intelligent systems (e.g., service robots, drone-based inspection, and visual surveillance) regarding a proper decision making of those systems. Existing methods focus on adopting a deep learning-based image classification for the identification. However, those approaches may provide wrong identification due to an overlap of spatial features when training dataset is limited.In this paper, we propose an accurate scene identification with a novel approach. Our method includes two-steps: 1) measurement of object class frequency with visual semantic segmentation; 2) scene classification using class frequencies. For fast computation, we build a lightweight backbone network for the segmentation model in addition to TensorRT-based optimization. From the experiments, we validate that our method improves the identification accuracy by 12% compared to conventional visual classification-based method. In terms of computation, we observe that the method enables real-time inference on resource- constrained devices (i.e., NVIDIA Jetsons).
A Enhancing Video Recommendation of Multi-Headed Self-Attentive with Multimodal Features

Abstract The exponential growth of the internet has led to the creation of a large volume of information, making it challenging for users to navigate through. As a solution to this challenge, recommendation systems have emerged. In video recommendation, besides applying some basic interaction data (including image data, behavior data, context data, etc.) to the recommendation model, many studies also try to apply the video content data to the model for video recommendation. In this paper, we propose different types of features for different modal data, and select the most suitable feature types according to different tasks. The internal representation of features is learned by a multi-headed self-attentive mechanism and the cross-representation of features is learned by attention. On the basis of this, the multimodal features of video data are represented and modeled in a unified way, which is the basis for the implementation of multi-view video recommendation. and based on this, we add multimodal video content to mine richer and more comprehensive descriptions, so as to provide more accurate and personalized recommendations. The results of experiments conducted on real data sets demonstrate the effectiveness of the model proposed in this paper.
“Conversione alla lirica”: uno studio su Giovanni Boine

The problem linked to the concrete and real feeling of faith and to the lively perception of the spirit and the divine is one of the fundamental issues of Giovanni Boine's thought, and has important repercussions on his personal consideration of ethics and morals.The writer's need for knowledge is initially set on the terrain of philosophical-religious investigation, but precisely this approach fails to offer Boine significant solutions to the problem.His path thus turns towards more strictly literary activities.This study deals, on the one hand, with the process that leads to this "conversion to poetry" and, on the other hand, with the Frantumi as a product of this process in relation to the writer's ideological presuppositions.Some of the most significant (non-lyrical) texts by Giovanni Boine will be considered and a brief comparison will be made between Boine's poetic theorization and that of Rebora, another writer close to the circle of the magazine "La Voce" and also intimately troubled by the existential conflict between faith and morals.In the last part of the essay, some aspects of the dialectic and style related to Boine's narrative production will also be mentioned.