Reviewerly/exHarmony huggingface dataset:
Unifying Decidable Entailments in Separation Logic with Inductive Definitions

Abstract The entailment problem $$\upvarphi \models \uppsi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>φ</mml:mi><mml:mo>⊧</mml:mo><mml:mi>ψ</mml:mi></mml:mrow></mml:math> in Separation Logic [12, 15], between separated conjunctions of equational ( $$x \approx y$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>x</mml:mi><mml:mo>≈</mml:mo><mml:mi>y</mml:mi></mml:mrow></mml:math> and $$x \not \approx y$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>x</mml:mi><mml:mo>≉</mml:mo><mml:mi>y</mml:mi></mml:mrow></mml:math> ), spatial ( $$x \mapsto (y_1,\ldots ,y_\upkappa )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>x</mml:mi><mml:mo>↦</mml:mo><mml:mo>(</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mi>κ</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math> ) and predicate ( $$p(x_1,\ldots ,x_n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>p</mml:mi><mml:mo>(</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>n</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math> ) atoms, interpreted by a finite set of inductive rules, is undecidable in general. Certain restrictions on the set of inductive definitions lead to decidable classes of entailment problems. Currently, there are two such decidable classes, based on two restrictions, called establishment [10, 13, 14] and restrictedness [8], respectively. Both classes are shown to be in $$\mathsf {2\text {EXPTIME}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>2</mml:mn><mml:mtext>EXPTIME</mml:mtext></mml:mrow></mml:math> by the independent proofs from [14] and [8], respectively, and a many-one reduction of established to restricted entailment problems has been given [8]. In this paper, we strictly generalize the restricted class, by distinguishing the conditions that apply only to the left- ( $$\upvarphi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>φ</mml:mi></mml:math> ) and the right- ( $$\uppsi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ψ</mml:mi></mml:math> ) hand side of entailments, respectively. We provide a many-one reduction of this generalized class, called safe , to the established class. Together with the reduction of established to restricted entailment problems, this new reduction closes the loop and shows that the three classes of entailment problems (respectively established, restricted and safe) form a single, unified, $$\mathsf {2\text {EXPTIME}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>2</mml:mn><mml:mtext>EXPTIME</mml:mtext></mml:mrow></mml:math> -complete class.
Offensive Language Detection in Nepali Social Media

Social media texts such as blog posts, comments, and tweets often contain offensive languages including racial hate speech comments, personal attacks, and sexual harassments.Detecting inappropriate use of language is, therefore, of utmost importance for the safety of the users as well as for suppressing hateful conduct and aggression.Existing approaches to this problem are mostly available for resource-rich languages such as English and German.In this paper, we characterize the offensive language in Nepali, a low-resource language, highlighting the challenges that need to be addressed for processing Nepali social media text.We also present experiments for detecting offensive language using supervised machine learning.Besides contributing the first baseline approaches of detecting offensive language in Nepali, we also release human annotated data sets to encourage future research on this crucial topic.
Fast pixel-matching for video object segmentation

Video object segmentation, aiming to segment the foreground objects given the annotation of the first frame, has been attracting increasing attentions. Many state-of-the-art approaches have achieved great performance by relying on online model updating or mask-propagation techniques. However, most online models require high computational cost due to model fine-tuning during inference. Most mask-propagation based models are faster but with relatively low performance due to failure to adapt to object appearance variation. In this paper, we are aiming to design a new model to make a good balance between speed and performance. We propose a model, called NPMCA-net, which directly localizes foreground objects based on mask-propagation and non-local technique by matching pixels in reference and target frames. Since we bring in information of both first and previous frames, our network is robust to large object appearance variation, and can better adapt to occlusions. Extensive experiments show that our approach can achieve a new state-of-the-art performance with a fast speed at the same time (86.5% IoU on DAVIS-2016 and 72.2% IoU on DAVIS-2017, with speed of 0.11s per frame) under the same level comparison. Source code is available at https://github.com/siyueyu/NPMCA-net.
Neural complex luminaires

Complex luminaires, such as grand chandeliers, can be extremely costly to render because the light-emitting sources are typically encased in complex refractive geometry, creating difficult light paths that require many samples to evaluate with Monte Carlo approaches. Previous work has attempted to speed up this process, but the methods are either inaccurate, require the storage of very large lightfields, and/or do not fit well into modern path-tracing frameworks. Inspired by the success of deep networks, which can model complex relationships robustly and be evaluated efficiently, we propose to use a machine learning framework to compress a complex luminaire's lightfield into an implicit neural representation. Our approach can easily plug into conventional renderers, as it works with the standard techniques of path tracing and multiple importance sampling (MIS). Our solution is to train three networks to perform the essential operations for evaluating the complex luminaire at a specific point and view direction, importance sampling a point on the luminaire given a shading location, and blending to determine the transparency of luminaire queries to properly composite them with other scene elements. We perform favorably relative to state-of-the-art approaches and render final images that are close to the high-sample-count reference with only a fraction of the computation and storage costs, with no need to store the original luminaire geometry and materials.
LARGE: Latent-Based Regression through GAN Semantics

We propose a novel method for solving regression tasks using few-shot or weak supervision. At the core of our method is the fundamental observation that GANs are incredibly successful at encoding semantic information within their latent space, even in a completely unsupervised setting. For modern generative frameworks, this semantic encoding manifests as smooth, linear directions which affect image attributes in a disentangled manner. These directions have been widely used in GAN-based image editing. In this work, we leverage them for few-shot regression. Specifically, we make the simple observation that distances traversed along such directions are good features for downstream tasks - reliably gauging the magnitude of a property in an image. In the absence of explicit supervision, we use these distances to solve tasks such as sorting a collection of images, and ordinal regression. With a few labels - as little as two - we calibrate these distances to real-world values and convert a pre-trained GAN into a state-of-the-art few-shot regression model. This enables solving regression tasks on datasets and attributes which are difficult to produce quality supervision for. Extensive experimental evaluations demonstrate that our method can be applied across a wide range of domains, leverage multiple latent direction discovery frame-works, and achieve state-of-the-art results in few-shot and low-supervision settings, even when compared to methods designed to tackle a single task. Code is available on our project website.
ALGORITHMIC THINKING IN EARLY CHILDHOOD EDUCATION: OPPORTUNITIES AND SUPPORTS IN THE PORTUGUESE CONTEXT

ALGORITHMIC THINKING IN EARLY CHILDHOOD EDUCATION: OPPORTUNITIES AND SUPPORTS IN THE PORTUGUESE CONTEXT
Secure Semantic-Aware Search Over Dynamic Spatial Data in VANETs

Vehicular Ad hoc Networks (VANETs) play an increasingly important role in a number of applications, particularly those associated with location-based services (e.g., spatial keyword searches – SKS). However, there is a need to strike a balance between privacy guarantee and search efficiency, and existing SKS solutions cannot be directly implemented in VANETs. In addition, existing schemes may also lack support for semantic-awareness in the dynamic setting. To address these limitations, we propose a Secure Semantic-aware Spatial Keyword Search scheme that supports Dynamic update (3SKSD). Specifically, we leverage the Latent Dirichlet Allocation (LDA) topic model and secure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> Nearest Neighbor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN) method to help us achieve both efficiency and security. We also construct an encrypted R-tree structure to facilitate SKS and dynamic update. Moreover, we propose an advanced scheme with forward security on the basis of 3SKSD, with the aim of minimizing privacy leakage due to dynamic updates. Our formal security analysis verifies the validity and security of 3SKSD, and findings from the experimental evaluation demonstrate its high search accuracy and efficiency.
An Algebraic Approach to the Rank Support Learning Problem

Rank-metric code-based cryptography relies on the hardness of decoding a random linear code in the rank metric. The Rank Support Learning problem (RSL) is a variant where an attacker has access to N decoding instances whose errors have the same support and wants to solve one of them. This problem is for instance used in the Durandal signature scheme [5]. In this paper, we propose an algebraic attack on RSL which often outperforms the previous attacks to solve this problem when the number of instances is of the order of the one used in Durandal. We build upon [8], where similar techniques are used to solve MinRank and RD. However, our analysis is simpler and overall our attack relies on very elementary assumptions compared to standard Gröbner bases attacks. In particular, our results show that key recovery attacks on Durandal are more efficient than was previously thought, especially in a parameter range where the RD attack from [8] is not the most powerful.
A Novel Sensor-Based Human Activity Recognition Method Based on Hybrid Feature Selection and Combinational Optimization

In recent years, sensor-based human activity recognition (HAR) has become a hot topic due to the advancement of sensing technologies, wireless communication technologies and nano-technologies.Since the sensor signals are usually non-stationary and quite noisy, both selecting the discriminant feature representations and finding out the optimal parameters for recognition algorithm play an important role for the enhanced performance and robustness of an HAR system.However, most of the previous research focused on one of them ignoring their interactions.Very few studies focused on these two aspects simultaneously.Considering the two factors separately may lead to inferior HAR performance.This paper presents a novel HAR framework which can optimize the feature set and the parameters of recognition algorithm synchronously for robust and optimal system performance.A new hybrid feature selection methodology using game-theory based feature selection (GTFS) and binary firefly algorithm (BFA), called GTFS-BFA, is proposed.GTFS-BFA is a hybrid methodology combining evidence from both filter and wrapper feature selection methods.It consists of two phases, namely pre-selection phase and re-selection phase.Pre-selection phase relies on game-theory-based filter method, while the re-selection phase uses binary firefly algorithm (BFA) as a wrapper method.The popular and efficient algorithm kernel extreme learning machine (KELM) is utilized as a classifier.The experimental results indicate that the proposed method can obtain better comprehensive performance in terms of four performance measures through a comparison to other existing methods on daily activity dataset from five body positions.
Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video

We present Non-Rigid Neural Radiance Fields (NR-NeRF), a reconstruction and novel view synthesis approach for general non-rigid dynamic scenes. Our approach takes RGB images of a dynamic scene as input (e.g., from a monocular video recording), and creates a high-quality space-time geometry and appearance representation. We show that a single handheld consumer-grade camera is sufficient to synthesize sophisticated renderings of a dynamic scene from novel virtual camera views, e.g. a `bullet-time' video effect. NR-NeRF disentangles the dynamic scene into a canonical volume and its deformation. Scene deformation is implemented as ray bending, where straight rays are deformed non-rigidly. We also propose a novel rigidity network to better constrain rigid regions of the scene, leading to more stable results. The ray bending and rigidity network are trained without explicit supervision. Our formulation enables dense correspondence estimation across views and time, and compelling video editing applications such as motion exaggeration. Our code will be open sourced.
Flesch-Kincaid is Not a Text Simplification Evaluation Metric

Sentence-level text simplification is evaluated using both automated metrics and human evaluation.For automatic evaluation, a combination of metrics is usually employed to evaluate different aspects of the simplification.Flesch-Kincaid Grade Level (FKGL) is one metric that has been regularly used to measure the readability of system output.In this paper, we argue that FKGL should not be used to evaluate text simplification systems.We provide experimental analyses on recent system output showing that the FKGL score can easily be manipulated to improve the score dramatically with only minor impact on other automated metrics (BLEU and SARI).Instead of using FKGL, we suggest that the component statistics, along with others, be used for posthoc analysis to understand system behavior.
Minimum Word Error Rate Training with Language Model Fusion for End-to-End Speech Recognition

Integrating external language models (LMs) into end-to-end (E2E) models remains a challenging task for domain-adaptive speech recognition. Recently, internal language model estimation (ILME)-based LM fusion has shown significant word error rate (WER) reduction from Shallow Fusion by subtracting a weighted internal LM score from an interpolation of E2E model and external LM scores during beam search. However, on different test sets, the optimal LM interpolation weights vary over a wide range and have to be tuned extensively on well-matched validation sets. In this work, we perform LM fusion in the minimum WER (MWER) training of an E2E model to obviate the need for LM weights tuning during inference. Besides MWER training with Shallow Fusion (MWER-SF), we propose a novel MWER training with ILME (MWER-ILME) where the ILME-based fusion is conducted to generate N-best hypotheses and their posteriors. Additional gradient is induced when internal LM is engaged in MWER-ILME loss computation. During inference, LM weights pre-determined in MWER training enable robust LM integrations on test sets from different domains. Experimented with 30K-hour trained transformer transducers, MWER-ILME achieves on average 8.8% and 5.8% relative WER reductions from MWER and MWER-SF training, respectively, on 6 different test sets
DALC: the Dutch Abusive Language Corpus

As socially unacceptable language become pervasive in social media platforms, the need for automatic content moderation become more pressing.This contribution introduces the Dutch Abusive Language Corpus (DALC v1.0), a new dataset with tweets manually annotated for abusive language.The resource address a gap in language resources for Dutch and adopts a multi-layer annotation scheme modeling the explicitness and the target of the abusive messages.Baselines experiments on all annotation layers have been conducted, achieving a macro F1 score of 0.748 for binary classification of the explicitness layer and .489for target classification.
An Object Detection Framework Based on Deep Features and High-Quality Object Locations

Objection detection has long been a fundamental issue in computer vision. Despite being widely studied, it remains a challenging task in the current body of knowledge. Many researchers are eager to develop a more robust and efficient mechanism for object detection. In the extant literature, promising results are achieved by many novel approaches of object detection and classification. However, there is ample room to further enhance the detection efficiency. Therefore, this paper proposes an image object detection and classification, using a deep neural network (DNN) for based on high-quality object locations. The proposed method firstly derives high-quality class-independent object proposals (locations) through computing multiple hierarchical segments with super pixels. Next, the proposals were ranked by region score, i.e., several contours wholly enclosed in the proposed region. After that, the top-ranking object proposal was adopted for post-classification by the DNN. During the post-classification, the network extracts the eigenvectors from the proposals, and then maps the features with the softmax classifier, thereby determining the class of each object. The proposed method was found superior to traditional approaches through an evaluation on Pascal VOC 2007 Dataset.
Towards Streamlined Single-Image Super-Resolution: Demonstration with 10 m Sentinel-2 Colour and 10–60 m Multi-Spectral VNIR and SWIR Bands

Higher spatial resolution imaging data are considered desirable in many Earth observation applications. In this work, we propose and demonstrate the TARSGAN (learning Terrestrial image deblurring using Adaptive weighted dense Residual Super-resolution Generative Adversarial Network) system for Super-resolution Restoration (SRR) of 10 m/pixel Sentinel-2 “true” colour images as well as all the other multispectral bands. In parallel, the ELF (automated image Edge detection and measurements of edge spread function, Line spread function, and Full width at half maximum) system is proposed to achieve automated and precise assessments of the effective resolutions of the input and SRR images. Subsequent ELF measurements of the TARSGAN SRR results suggest an averaged effective resolution enhancement factor of about 2.91 times (equivalent to ~3.44 m/pixel for the 10 m/pixel bands) given a nominal SRR upscaling factor of 4 times. Several examples are provided for different types of scenes from urban landscapes to agricultural scenes and sea-ice floes.
CONNER: A Cascade Count and Measurement Extraction Tool for Scientific Discourse

This paper presents our contribution to SemEval 2021 Task 8: MeasEval.The purpose of this task is identifying the counts and measurements from clinical scientific discourse, including quantities, entities, properties, qualifiers, units, modifiers, and their mutual relations.This task can be induced to a joint entity and relation extraction problem.Accordingly, we propose CONNER, a cascade count and measurement extraction tool that can identify entities and the corresponding relations in a two-step pipeline model.We provide a detailed description of the proposed model hereinafter.Furthermore, the impact of the essential modules and our in-process technical schemes are also investigated.
Can Transformer Language Models Predict Psychometric Properties?

Transformer-based language models (LMs) continue to advance state-of-the-art performance on NLP benchmark tasks, including tasks designed to mimic human-inspired "commonsense" competencies.To better understand the degree to which LMs can be said to have certain linguistic reasoning skills, researchers are beginning to adapt the tools and concepts of the field of psychometrics.But to what extent can the benefits flow in the other direction?I.e., can LMs be of use in predicting what the psychometric properties of test items will be when those items are given to human participants?We gather responses from numerous human participants and LMs (transformerand non-transformer-based) on a broad diagnostic test of linguistic competencies.We then use the responses to calculate standard psychometric properties of the items in the diagnostic test, using the human responses and the LM responses separately.We then determine how well these two sets of predictions match.We find cases in which transformerbased LMs predict psychometric properties consistently well in certain categories but consistently poorly in others, thus providing new insights into fundamental similarities and differences between human and LM reasoning. 1
Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms

The curse of dimensionality problem occurs when the data are high-dimensional. It affects the learning process and reduces the accuracy. Feature selection is one of the dimensionality reduction approaches that mainly contribute to solving the curse of the dimensionality problem by selecting the relevant features. Irrelevant features are the dependent and redundant features that cause noise in the data and then reduce its quality. The main well-known feature-selection methods are wrapper and filter techniques. However, wrapper feature selection techniques are computationally expensive, whereas filter feature selection methods suffer from multicollinearity. In this research study, four new feature selection methods based on outlier detection using the Projection Pursuit method are proposed. Outlier detection involves identifying abnormal data (irrelevant features of the transpose matrix obtained from the original dataset matrix). The concept of outlier detection using projection pursuit has proved its efficiency in many applications but has not yet been used as a feature selection approach. To the author’s knowledge, this study is the first of its kind. Experimental results on nineteen real datasets using three classifiers (k-NN, SVM, and Random Forest) indicated that the suggested methods enhanced the classification accuracy rate by an average of 6.64% when compared to the classification accuracy without applying feature selection. It also outperformed the state-of-the-art methods on most of the used datasets with an improvement rate ranging between 0.76% and 30.64%. Statistical analysis showed that the results of the proposed methods are statistically significant.