Reviewerly/exHarmony huggingface dataset:
Half Quadratic Dual Learning for Fuzzy Multiconcepts of Partially-Observed Images

For partially-observed data with large continuous occlusion or specific outlier noise, it is difficult to represent them by using L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -, L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -, and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norms, not to mention learning multiconcept mapping from them. Furthermore, multiconcept learning (i.e., multilabel learning) and sample-correlation learning are inseparable as they provide guiding information to each other. Nonetheless, when most sample relations are unknown/biased, detecting multiconcepts associated with partially-observed data becomes even more difficult. In view of such challenges, this study presents a dual learning model that can simultaneously learn multiconcepts, sample relations, and data representation while partially-observed data and partially-observed sample relations are present. Moreover, half quadratic (HQ) loss functions along with half quadratic optimization and their gradient forms are developed in this study to model unobserved information. Different types of partially-observed sources are jointly hinged together to compensate each other during learning. This allows the system to estimate accurate multiconcepts, sample relations, and data representation. Finally, fuzzy scores for multiconcepts can be learnt and generated. Thus, a labelled data instance can utilize such fuzzy scores to rank its concepts. Experiments on open images datasets were carried out for evaluating the performance. Analytical results showed that the proposed model was capable of conquering multiconcept learning problems while data were partially observed, better than the baselines, thereby demonstrating the effectiveness of the proposed idea.
Sentiment Analysis on Bengali Movie Reviews using Multinomial Naïve Bayes

Opinion mining of consumers has become one of the undeniable approaches for finding gaps in businesses’ marketing strategies. For today’s gently growing film industry of Bangladesh, sentiment mining of viewers feedback on their specified work has become inevitably a dire need for the production company and also for the audiences to take decision about watching a film. With the availability of such a great extent of e-content on film reviews, it becomes handy to analyze the viewers’ sentiment on any film. Because of the lack of structured research work on Bengali movie review sentiment analysis, we are interested to focus this kind of research work. We collect 3000 Bengali movie reviews and extract TF-IDF features by using uni-gram, bi-gram, and tri-gram models. We use several machine learning classifiers for performing classification solutions on extracted TF-IDF features of the corpus. Experimental results show that the Multinomial Naïve Bayes classifier provides the highest accuracy, 86%, on uni-gram features of the validation data.
Local density peaks clustering with small size distance matrix

Clustering by using density peaks (DPC) is a powerful clustering method. The cluster centers in DPC are characterized by a higher density than their neighbors and by a relatively large distance from points with higher densities, which leads to the storage of a distance matrix with the same size of sample size. In this paper, to deduce the storage of the distance matrix, we propose a local density peaks clustering (LDPC) by introducing the local region by reverse nearest neighbors. Compared with DPC, the potential cluster centers and the potential haloes are defined, and the potential cluster centers distance matrix is introduced to replace the storage of the sample distance matrix, which makes it more practical for large scale data. At the same time, the local information is used to construct the high densities, leads to more robust to the data structure. Further, the second order differential operator is used to give the number of clustering automatically. Preliminary computational results demonstrate the effectiveness of proposed LDPC over DPC on artificial datasets and face image considered.
Retrieval of Multiple Atmospheric Environmental Parameters From Images With Deep Learning

Retrieving atmospheric environmental parameters such as atmospheric horizontal visibility and mass concentration of aerosol particles with a diameter of 2.5 or 10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mu }\text{m}$ </tex-math></inline-formula> or less (PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> , PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> , respectively) from digital images provides new tools for horizontal environmental monitoring. In this study, we propose a new end-to-end convolutional neural network (CNN) for the retrieval of multiple atmospheric environmental parameters (RMEPs) from images. In contrast to other retrieval models, RMEP can retrieve a suite of atmospheric environmental parameters including atmospheric horizontal visibility, relative humidity (RH), ambient temperature, PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> , and PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> simultaneously from a single image. Experimental results demonstrate that: 1) it is possible to simultaneously retrieve multiple atmospheric environmental parameters; 2) spatial and spectral resolutions of images are not the key factors for the retrieval on the horizontal scale; and 3) RMEP achieves the best overall retrieval performance compared with several classic CNNs such as AlexNet, ResNet-50, and DenseNet-121, and the results are based on experiments on images extracted from webcams located in different continents (test <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> values are 0.63, 0.72, and 0.82 for atmospheric horizontal visibility, RH, and ambient temperature, respectively). Experimental results show the potential of utilizing webcams to help monitor the environment. Code and more results are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/cvvsu/RMEP</uri> .
Context-Aware Augmented Reality with 5G Edge

Augmented Reality (AR) provides immersive user experiences by overlaying digital information on physical environments. Context-awareness is crucial for delivering relevant augmentations that best suit users&#x0027; requirements and their en-vironments. In this article, we combine context-aware reasoning with emerging AR applications to provide the most relevant infor-mation according to user and environment contexts. To support the best possible quality of experience, 5G edge computing enables the distribution of computation-intensive AR tasks to edge servers through 5G networks. We develop ConAR, a context-aware head-mounted display AR system that is deployed on the edge and cloud leveraging both environmental sensors and user profile context for navigation. ConAR is composed of a HoloLens application and a paired mobile client, which contains a context model for air quality forecasting, and rendering recommendations on holograms through a HoloLens 2 device. We evaluate our system performance by deploying our proposed air quality prediction algorithm on the edge and cloud while communicating to them using 5G and LTE connections. We measure network quality metrics and find the deployment on the edge with 5G connections significantly outperforms alternative solutions. Our results demonstrate that the 5G edge computing is suitable for supporting latency-sensitive analysis tasks for context-aware AR.
Reduplication in Assamese: Identification and Modeling

Reduplication is a productive morphological process widely used in a substantial number of languages in the world. Reduplication is a well-studied phenomenon, and several typological works have provided evidence for different types of reduplication in most of the languages around the world. Addressing reduplication plays a vital role in the efficiency of POS tagger, sentiment analysis, as well as other NLP tasks. However, it is an understudied area in computational linguistics, especially in low-resource languages like Assamese. This article first describes different types of reduplication and their shapes that occur in Assamese. Second, an exhaustive set of reduplication formation rules is compiled that is incorporated to build a system to identify reduplication in Assamese text. The results of the experiments performed on three different domain datasets showed that the rule-based system can identify reduplicated expressions with an average precision, recall, and F1 scores of 94.19%, 98.07%, and 96.07%, respectively. Third, it is shown that the Assamese reduplication processes can be captured through a two-way finite-state transducer (2-way FST). Finally, two broad categories of reduplicative processes along with their corresponding 2-way FST model are presented.
Entanglement, irritation and routinisation: the embodied pedagogy of digital activity tracking

The paper explores activity tracking as a postdigital phenomenon and provides a detailed account of the embodied pedagogy of its practices. Drawing on a case-based, ethnographically inspired, participatory research project with German university students and following a praxeologic approach of transformative Bildung, an empirically grounded heuristic framework is presented that helps to understand how embodied entanglements of person and wearable develop over time and how dynamics of irritation and routinisation that characterise these developments open up processes of learning or Bildung. We discuss how this contributes to researching vital pedagogical questions about self-tracking, and more broadly about yet uncertain futures of postdigital physical culture and society, such as how we constitute ourselves as embodied subjects and continuously develop relations to ourselves and to the world through entangling with digital technology.
Learning from Limited Data for Speech-based Traumatic Brain Injury (TBI) Detection

Due to the high cost of collecting data especially for conditions that afflict a small percentage of the population, data is often scarce in healthcare. Inadequate data presents a challenge for training Deep Neural Networks (DNNs). Many Traumatic Brain Injury (TBI) patients require long periods of recovery with unexpected setbacks and possible rehospitalization, making continuous monitoring important. While speech-based TBI assessment has been found to be effective, available datasets are too small for DNNs. To solve the limited TBI speech data problem, we explore three Learning from Limited Data (LLD) methods (transfer, multi-task and meta-learning) that augment a small primary TBI dataset by learning from external non-TBI datasets to improve DNN performance. We found that all three LLD methods mitigate overfitting, improving binary TBI classification accuracy by 33.6%, 36.7%, and 26.4% respectively. External datasets with scripted speech improved the TBI detection accuracy of all three learning methods the most. Using a few-shot learning approach, we extrapolated results on real data to estimate the full trajectory of expected performance for various amounts of data.
A Hierarchal Approach for Arabic Phonemes Classification

Phonemes are defined as the smallest unit of sound that make a semantic change within language words. By capturing and recognizing the distinctive phonemes within the signal, we would be able to recognize the words uttered in the given sample. In this research, we present a new methodology for recognizing Arabic phonemes using a classification approach. The presented approach adopted a hierarchical based technique for Arabic phonemes classification. The results showed that the hierarchical based classification method has outperformed the traditional flat classification approaches.
Is cut-free logic fit for unrestricted abstraction?

Without cut the paradoxes of unrestricted abstraction do not yield the empty sequent, so would it be a viable option to give up cut in order to be able to accommodate unrestricted abstraction without the risk of triviality? My answer will be in the negative and that by applying a system LKλ− of cut-free logic with unrestricted abstraction as a meta theory to a rudimentary formal system Σc (“C” for Curry) which is tailored to prove a version of Curry's paradox with a minimum of technicalities. Now, a proof predicate for Σc can be simulated in LKλ− confirming the existence of a deduction of ⊥ in Σc while, on the other hand, a kind of reflection principle can be established for Σc which asserts that every deduction in Σc actually implies the wff of which it is a deduction. This then leads to the result that the deduction of ⊥ is not a deduction in Σc. In view of the primitive recursive character of the proof predicate for Σc we thus arrive at a provable contradiction in LKλ− far more devastating than those established by Russell's or Curry's paradox because it concerns (simulated) primitive recursive predicates, not just a naive notion of set.
Reduction of Glare in Images with Saturated Pixels

In this paper, we propose a deconvolution-based glare reduction method for HDR images. Specifically, we consider glared images containing saturated pixels and estimate the true radiance of these pixels followed by the deconvolution with the known glare spread function (GSF) to recover a glare-free image. The proposed method combines the image glare model with a dark channel prior for an accurate estimation of true radiance. The GSF is estimated separately in an offline calibration step. The qualitative and quantitative analysis of results on a variety of real and simulated glare images demonstrate the effectiveness of the proposed method.
NoPeek-Infer: Preventing face reconstruction attacks in distributed inference after on-premise training

For models trained on-premise but deployed in a distributed fashion across multiple entities, we demonstrate that minimizing distance correlation between sensitive data such as faces and intermediary representations enables prediction while preventing reconstruction attacks.Leakage (measured using distance correlation between input and intermediate representations) is the risk associated with the reconstruction of raw face data from intermediary representations that are communicated in a distributed setting.We demonstrate on face datasets that our method is resilient to reconstruction attacks during distributed inference while maintaining information required to sustain good classification accuracy.We share modular code for performing NoPeek-Infer at http://tiny.cc/nopeekalong with corresponding trained models for benchmarking attack techniques.
An ASP-based Approach to Answering Natural Language Questions for Texts

Abstract An approach based on answer set programming (ASP) is proposed in this paper for representing knowledge generated from natural language texts. Knowledge in a text is modeled using a Neo Davidsonian-like formalism, which is then represented as an answer set program. Relevant commonsense knowledge is additionally imported from resources such as WordNet and represented in ASP. The resulting knowledge-base can then be used to perform reasoning with the help of an ASP system. This approach can facilitate many natural language tasks such as automated question answering, text summarization, and automated question generation. ASP-based representation of techniques such as default reasoning, hierarchical knowledge organization, preferences over defaults, etc., are used to model commonsense reasoning methods required to accomplish these tasks. In this paper, we describe the CASPR system that we have developed to automate the task of answering natural language questions given English text. CASPR can be regarded as a system that answers questions by “understanding” the text and has been tested on the SQuAD data set, with promising results.
Image-based malicious Internet content filtering method for child protection

Children and teenagers are among Internet users and they encounter harmful data in the global network. Young users often become the potential victims of pornographic images. Avoiding pornographic images harmful to the child audience is an important research task in the field of detection, computer vision and multimedia. Malicious content can be prevented using various methods. Current paper presents a ChildNet model that filters harmful image content. The pixels of the digital images are used as a data source for recognition of nudity in the images. For each class, a multi-layer deep neural network architecture with five convolution blocks is developed to study the color patterns of undesirable image pixels. The developed neural network consists of 21 layers; the size of the filters is specified as (3 × 3). The filter's size is reduced to increase the accuracy of pixel recognition. The efficiency of the proposed method is tested on real datasets for evaluation purposes and the superior results are obtained from the proposed method in comparison with classical CNN.
A Critical Race Theory Perspective on Assaultive Speech in U.S. Campus Communities

US college and university administrators are reluctant to regulate racialized assaultive speech by members of their campus communities, even when the effect and objective of such speech is to demean, degrade, ostracize, and threaten Black, Indigenous, and other people of color. My critical race theory analysis reveals how two US Supreme Court cases commonly used to defend assaultive speech in campus communities, have been misapplied. Both cases were about students expressing political speech—a different legal realm from hate speech that has no educational value and, in my view, merits regulation because of its harmful effect. In this theoretical article, I urge institutional leaders and researchers who are committed to equity in education to challenge the white legal logic that for too long has protected assaultive speech in educational settings at the expense of marginalized communities. I recommend that institutions of higher education establish robust, unapologetic standards and policies for incidents of assaultive speech to compellingly affirm the dignity and inclusion of all campus members.
Asymptotic Continuity of Additive Entanglement Measures

We study rates of asymptotic transformations between entangled states by local operations and classical communication and a sublinear amount of quantum communication. It is known that additive asymptotically continuous entanglement measures provide upper bounds on the rates that are achievable with asymptotically vanishing error. We show that for transformations between pure states, the optimal rate between any pair of states can be characterized as the infimum of such upper bounds provided by fully additive asymptotically continuous entanglement measures.
State estimation of MEMS electromagnetic micromirror systems by unscented Kalman filter

In this paper, an unscented Kalman filter (UKF) is proposed for state estimation of Micro Electromechanical System (MEMS) electromagnetic micromirror systems. In the electromagnetic micromirror systems, there exists hysteresis. And internal states in the systems cannot be measured, directly, so classical Kalman filter cannot be used to estimate states of the nonlinear system. Thus, it is challenging for the micromirror system to estimate the internal states. It is an advantage for UKF to reduce Gaussian noise and estimate the states of nonlinear systems. Therefore, an UKF is applied to estimate the states of electromagnetic micromirror systems. Moreover, the simulation results show the validation of the UKF for the micromirror systems.
LPP2KL: Online Location Privacy Protection Against Knowing-and-Learning Attacks for LBSs

When user-centric location privacy protections are used by the public (e.g., published as a mobile application to Mac app store), they are found to be vulnerable to a novel kind of potential inference attacks. These attacks can utilize the open-use protection’s input–output pairs to closely study the protection’s structure and mechanism. We name this kind of attacks as knowing-and-learning (KL) attacks. Targeted at such potential but threatening deep learning-based KL attacks in local search services, we propose a novel location privacy protection framework LPP2KL. LPP2KL aims to generate an obfuscated location, which is robust to the aforementioned attacks, so that the user’s current location (real) can be replaced by the generated location (pseudo) when submitted to the untrusted search server. To achieve this, we first depict a feasible net structure to implement the deep KL attack and verify its power with extensive experiments. Second, to simulate the attack-and-defense process, we develop a novel network structure with an adversary net and a protection net. These nets play a mini–max game toward privacy inference in an adversarial manner until an equilibrium is reached. Meanwhile, the protection net also achieves the balance between preserved privacy and quality of service through utility-constrained privacy optimization. We introduce a novel penalty function to handle the utility-constrained privacy optimization problem. Extensive experiments are conducted on Yelp and FoursSwarm datasets, which validate the generality of our proposed LPP2KL in protection handling and illustrate its effectiveness in utility control under the acceptable quality of service.
Subspace-based self-weighted multiview fusion for instance retrieval

Most existing subspace-based multiview learning algorithms treat multiview features equally in the process of subspace reconstruction without distinguishing the discrepant contributions from different views. This limitation adversely affects subspace embedding, particularly when significant variances in view-specific feature characteristics are present. In this paper, a unified subspace-based self-weighted multiview learning framework (SSMVL) is proposed for instance retrieval. In contrast to the previous methods in which the variances in multiview features are ignored, the self-weighted learning mechanism is integrated into the multiview subspace learning framework such that the weights of different views are adaptively learned instead of empirically assigned. In addition, the proposed SSMVL approach falls into the unsupervised learning category and is thus independent of massive amounts of labeled data resulting from labor-intensive annotation. In this study, the extension of SSMVL to the Hamming subspace learning paradigm is also explored for efficient retrieval. Experiments on five public benchmarks reveal that the self-weighted learning strategy plays a beneficial role in multiview fusion, and our method achieves superior performance in comparison to state-of-the-art methods.