Reviewerly/exHarmony huggingface dataset:
Igloo: soundly linking compositional refinement and separation logic for distributed system verification

Lighthouse projects like CompCert, seL4, IronFleet, and DeepSpec have demonstrated that full system verification is feasible by establishing a refinement between an abstract system specification and an executable implementation. Existing approaches however impose severe restrictions on the abstract system specifications due to their limited expressiveness or versatility, or on the executable code due to their use of suboptimal code extraction or inexpressive program logics. We propose a novel methodology that combines the compositional refinement of event-based models of distributed systems with the verification of full-fledged program code using expressive separation logics, which support features of realistic programming languages like heap data structures and concurrency. Our main technical contribution is a formal framework that soundly relates event-based system models to program specifications in separation logics. This enables protocol development tools to soundly interoperate with program verifiers to establish a refinement between the model and the code. We formalized our framework, Igloo, in Isabelle/HOL. We report on three case studies, a leader election protocol, a replication protocol, and a security protocol, for which we refine formal requirements into program specifications that we implement in Java and Python and prove correct using the VeriFast and Nagini tools.
Mobile Application Design for Heritage Tourism Uses Gamification Approach in Indonesia

Heritage tourism is a trip traveling in certain areas that have historical value and ancestral heritage, such as temples, museums, palaces, etc. Indonesia is a country that has diverse historical heritages that have the potential to be developed be-cause there are historical sites and are considered as tourism potential. Technolo-gy plays a vital role in the development of heritage tourism to facilitate the deliv-ery of information to tourists, one of which is a mobile application. The proposed application design is a mobile application design with a gamification approach to facilitate tourists to obtain information and travel experiences to explore exciting tourist attractions. The gamification approach is used as a unique attraction where tourists are wrongly venturing for the concept of the game in conveying infor-mation using element games. This research was conducted in 3 temples, namely Gedongsongo temple, Prambanan temple, and Borobudur temple. The prototype design test was conducted on 100 tourists who visited 3 of the temple's tourist at-tractions. The results showed 86% of users agreed with the proposed prototype design. Based on the 95% confidence scale, shows that this research was suc-cessful in designing a prototype of a heritage tourism mobile application to ex-plore temple tourism. This application design is suitable for users based on four variables: Usefulness, Ease of Use, Ease of Learning, and Satisfaction.
Opposition-Based Enhanced Grey Wolf Optimization Algorithm for Feature Selection in Breast Density Classification

Breast Cancer is the most widespread cancer amongst women in the world.An increase in the density of breast may lead to an increase in the risk of breast cancer.For the accurate diagnosis of breast cancer, these days Machine learning-based computer-aided diagnosis systems are widely used to assist the radiologists.However, there is still a scope of improvement in the computer-aided diagnosis system for the feature selection problem, which is considered as an optimization problem.Optimization techniques are the methods where the finest solution to a problem is found by using random search mechanism.Grey wolf optimization is one of the most recent optimization algorithms.This paper presents an enhanced version of Grey Wolf Optimization algorithm and tests the performance of the proposed version of the algorithm on thirteen different benchmark functions and the binary version of the proposed algorithm is used for selecting optimal number of features for breast density classification problem.
Person-level Action Recognition in Complex Events via TSD-TSM Networks

The task of person-level action recognition in complex events aims to densely detect pedestrians and individually predict their actions from surveillance videos. In this paper, we present a simple yet efficient pipeline for this task, referred to as TSD-TSM networks. Firstly, we adopt the TSD detector for the pedestrian localization on each single keyframe. Secondly, we generate the sequential ROIs for a person proposal by replicating the adjusted bounding box coordinates around the keyframe. Particularly, we propose to conduct straddling expansion and region squaring on the original bounding box of a person proposal to widen the potential space of motion and interaction and lead to a square box for ROI detection. Finally, we adapt the TSM classifier on the generated ROI sequences to perform action classification and further adopt late fusion to promote the prediction. Our proposed pipeline achieved the 3rd place in the ACM-MM 2020 grand challenge, i.e., Large-scale Human-centric Video Analysis in Complex Events (Track-4), obtaining final 15.31% [email protected] and 20.63% [email protected] on the testing set.
Development of arabic sign language dictionary using 3D avatar technologies

&lt;span&gt;The arabic sign language (ArSL) is the natural language of the deaf community in Arabic countries. ArSL suffers from a lack of resources such as unified dictionaries and corpora. In this work, a dictionary of Arabic language to ArSL has been constructed as a part of a translation system. The Arabic words are converted into hamburg notation system (HamNoSys) using eSign editor Software. HamNoSys was used to create manual parameters (handshape, hand orientation, hand location, and hand movement), while non-manual parameters (facial expressions, shoulder raising, mouthing gesture, head tilting, and body movement) added by using (mouth, face, and limbs) in the eSign editor software. The sign then converted to the sign gesture markup language (SiGML) file, and later 3D avatar interprets the SiGML file scripts to the animated sign. The constructed dictionary has three thousand signs; therefore, it can be adopted for the translation system in which written text can be transformed into sign language and can be utilized for the education of deaf people. The dictionary will be available as a free resource for researchers. It is hard and time-consuming work, but it is an essential step in machine translation of whole Arabic text to ArSL with 3D animations. &lt;/span&gt;
Intrinsic Quality Assessment of Arguments

Several quality dimensions of natural language arguments have been investigated.Some are likely to be reflected in linguistic features (e.g., an argument's arrangement), whereas others depend on context (e.g., relevance) or topic knowledge (e.g., acceptability).In this paper, we study the intrinsic computational assessment of 15 dimensions, i.e., only learning from an argument's text.In systematic experiments with eight feature types on an existing corpus, we observe moderate but significant learning success for most dimensions.Rhetorical quality seems hardest to assess, and subjectivity features turn out strong, although length bias in the corpus impedes full validity.We also find that human assessors differ more clearly to each other than to our approach.
Joint Entity-Relation Extraction via Improved Graph Attention Networks

Joint named entity recognition and relation extraction is an essential natural language processing task that aims to identify entities and extract the corresponding relations in an end-to-end manner. At present, compared with the named entity recognition task, the relation extraction task performs poorly on complex text. To solve this problem, we proposed a novel joint model named extracting Entity-Relations viaImproved Graph Attention networks (ERIGAT), which enhances the ability of the relation extraction task. In our proposed model, we introduced the graph attention network to extract entities and relations after graph embedding based on constructing symmetry relations. To mitigate the over-smoothing problem of graph convolutional networks, inspired by matrix factorization, we improved the graph attention network by designing a new multi-head attention mechanism and sharing attention parameters. To enhance the model robustness, we adopted the adversarial training to generate adversarial samples for training by adding tiny perturbations. Comparing with typical baseline models, we comprehensively evaluated our model by conducting experiments on an open domain dataset (CoNLL04) and a medical domain dataset (ADE). The experimental results demonstrate the effectiveness of ERIGAT in extracting entity and relation information.
Application of PRISMA satellite hyperspectral imagery to mineral alteration mapping at Cuprite, Nevada, USA

The PRISMA hyperspectral imaging satellite of the Italian Space Agency was launched into orbit on March 22, 2019. The PRISMA is a pushbroom sensor that records 250 hyperspectral bands in the 0.4-2.5 μm wavelength region at a spatial resolution of 30 m. The swath of the hyperspectral imagery is 30 km. This study evaluates the application of the PRISMA hyperspectral imagery to mineral exploration. The study area is the Cuprite in Nevada, USA. Cuprite has served as test-site for a number of airborne and spaceborne remote sensing imaging systems. The Cuprite PRISMA hyperspectral data were analyzed with the Advanced Coherence Estimator algorithm. The analysis of the hyperspectral imagery accurately mapped the spatial distribution of alunite, kaolinite, hydrated silica, muscovite and buddingtonite. The study shows that the PRISMA hyperspectral imagery is a useful tool for mineral exploration projects in arid and semi-arid environments.
Data Deduplication System Based on Content-Defined Chunking Using Bytes Pair Frequency Occurrence

Every second, millions of data are being generated due to the use of emerging technologies. It is very challenging to store and handle such a large amount of data. Data deduplication is a solution for this problem. It is a new technique that eliminates duplicate data and stores only a single copy of data, reducing storage utilization and the cost of maintaining redundant data. Content-defined chunking (CDC) has been playing an important role in data deduplication systems due to its ability to detect high redundancy. In this paper, we focused on deduplication system optimization by tuning relevant factors in CDC to identify chunk cut-points and introduce an efficient fingerprint using a new hash function. We proposed a novel bytes frequency-based chunking (BFBC) algorithm and a new low-cost hashing function. To evaluate the efficiency of the proposed system, extensive experiments were done using two different datasets. In all experiments, the proposed system persistently outperformed the common CDC algorithms, achieving a better storage gain ratio and enhancing both chunking and hashing throughput. Practically, our experiments show that BFBC is 10 times faster than basic sliding window (BSW) and approximately three times faster than two thresholds two divisors (TTTD). The proposed triple hash function algorithm is five times faster than SHA1 and MD5 and achieves a better deduplication elimination ratio (DER) than other CDC algorithms. The symmetry of our work is based on the balance between the proposed system performance parameters and its reflection on the system efficiency compared to other deduplication systems.
Intent Classification in Question-Answering Using LSTM Architectures

Question-answering (QA) is certainly the best known and probably also one of the most complex problem within Natural Language Processing (NLP) and artificial intelligence (AI). Since the complete solution to the problem of finding a generic answer still seems far away, the wisest thing to do is to break down the problem by solving single simpler parts. Assuming a modular approach to the problem, we confine our research to intent classification for an answer, given a question. Through the use of an LSTM network, we show how this type of classification can be approached effectively and efficiently, and how it can be properly used within a basic prototype responder.
PENGEMBANGAN DAN PENDAMPINGAN SISTEM INFORMASI PENGOLAHAN PENDAPATAN JASA PADA PT. DMS KONSULTAN BANDAR LAMPUNG

PT DMS Konsultan Bandar Lampung is a company engaged in tax consulting services from cooperation with companies to calculate how much obligations the company must pay. Currently processing income data by recording by the administration in the income book. This has resulted in several problems, including resulting in a higher error rate, if data is needed again it will take time to search the data and risk damage and loss of data. For this reason, community service is carried out by developing an information system for processing income data and providing training to employees regarding how to use the application.
Neural Style Transfer and Geometric Transformations for Data Augmentation on Balinese Carving Recognition using MobileNet

The preservation of Balinese carving data is a challenge in recognition of Balinese carving. Balinese carvings are a cultural heritage found in traditional buildings in Bali. The collection of Balinese carving images from public images can be a solution for preserving cultural heritage. However, the lousy quality of taking photographs, e.g., skewed shots, can affect the recognition results. Research on the Balinese carving recognition has existed but only recognizes a predetermined image. We proposed a Neural Style Geometric Transformation (NSGT) as a data augmentation technique for Balinese carvings recognition. NSGT is combining Neural Style Transfers and Geometric Transformations for a small dataset solution. This method provides variations in color, lighting, rotation, rescale, zoom, and the size of the training dataset, to improve recognition performance. We use MobileNet as a feature extractor because it has a small number of parameters, which makes it suitable to be applied on mobile devices. Eight scenarios were tested based on image styles and geometric transformations to get the best results. Based on the results, the proposed method can improve accuracy by up to 16.2%.
Learning with continuous piecewise linear decision trees

In this paper, we propose a piecewise linear decision tree and its generalized form, namely the (G)PWL-DT, which introduces piecewise linearity and overcomes the discontinuity of the existing piecewise constant decision trees (PWC-DT). The proposed (G)PWL-DT inherits the basic topology and interpretability of decision trees by recursively partitioning the domain into subregions, which are represented by leaf nodes. Rather than the indicator function, the (G)PWL-DT employs rectifier linear units (ReLU) to interpret domain partitions, where the nested ReLUs are combined to formulate the corresponding PWL decision rules. Due to the piecewise linearity of each leaf node, additional boundaries among linear areas are obtained to approach greater flexibility than the existing PWC-DT under the same tree structure, where the continuity can also be guaranteed. Then, an optimization algorithm is constructed analogously based on the second-order approximation. The proposed (G)PWL-DT can be flexibly applied as a novel decision tree in different tree learning methods and it can also be regarded as a simple extension of ReLUs to the framework of tree learning. Numerical experiments verify the effectiveness of the proposed (G)PWL-DT and its potential as an alternative of the existing PWC-DT to approach better performance even with more concise structures.
An Improved Differential Evolution Algorithm Based on Dual-Strategy

In recent years, Differential Evolution (DE) has shown excellent performance in solving optimization problems over continuous space and has been widely used in many fields of science and engineering. How to avoid the local optimal solution and how to improve the convergence performance of DE are hotpot problems for many researchers. In this paper, an improved differential evolution algorithm based on dual-strategy (DSIDE) is proposed. The DSIDE algorithm has two strategies. (1) An enhanced mutation strategy based on “DE/rand/1,” which takes into account the influence of reference individuals on mutation and has strong global exploration and convergence ability. (2) A novel adaptive strategy for scaling factor and crossover probability based on fitness value has a positive impact on population diversity. The DSIDE algorithm is verified with other seven state-of-the-art DE variants under 30 benchmark functions. Furthermore, Wilcoxon sign rank-sum test, Friedman test, and Kruskal–Wallis test are utilized to analyze the results. The experiment results show that the proposed DSIDE algorithm can significantly improve the global optimization performance.
Three-Dimensional Multi-Mission Planning of UAV Using Improved Ant Colony Optimization Algorithm Based on the Finite-Time Constraints

factor Finite-time constraints A B S T R A C TAn improved ant colony optimization (IACO) is proposed to solve three-dimensional multi-task programming under finitetime constraints.The algorithm introduces the artificial preemptive coefficient matrix into the transfer probability formula, which makes results convergence and also reduces the convergence time of the algorithm.Following the principle that there is no pheromone on the path where the ants are just beginning to forage in reality, the pheromone is initially zero, and the ant's self-guided ability is fully utilized, which enhances the random exploration ability of the ant algorithm for the entire solution space.By introducing the variable dimension vector coefficient and the time adaptive factor of transfer probability, the search probability in the inferior solution set is reduced and the convergence speed of the algorithm is increased.Finally, through the simulation on the random map and comparison with the traditional ant colony optimization, particle swarm optimization, and tabu search algorithm, the superiority of the IACO proposed in this paper is demonstrated.
Beyond NP: Quantifying over Answer Sets

Abstract Answer Set Programming (ASP) is a logic programming paradigm featuring a purely declarative language with comparatively high modeling capabilities. Indeed, ASP can model problems in NP in a compact and elegant way. However, modeling problems beyond NP with ASP is known to be complicated, on the one hand, and limited to problems in $\[\Sigma _2^P\]$ on the other. Inspired by the way Quantified Boolean Formulas extend SAT formulas to model problems beyond NP, we propose an extension of ASP that introduces quantifiers over stable models of programs. We name the new language ASP with Quantifiers (ASP(Q)). In the paper we identify computational properties of ASP(Q); we highlight its modeling capabilities by reporting natural encodings of several complex problems with applications in artificial intelligence and number theory; and we compare ASP(Q) with related languages. Arguably, ASP(Q) allows one to model problems in the Polynomial Hierarchy in a direct way, providing an elegant expansion of ASP beyond the class NP.
Teachers’ Perceptions of the Use of ICTs in the Educational Response to Students with Disabilities

The educational response to students with specific needs for educational support associated with disability could not be understood within the current educational landscape without addressing how Information and Communication Technology (ICT) is used within those responses. ICT support is not a matter of fashion or one-off use due to the current circumstances brought about by the effects of the COVID-19 pandemic. Programmes and materials are already being developed to enable such application. Even from the Universal Learning Design approaches, it is precisely the use of technologies in learning processes, with special emphasis on educational inclusion processes, which are a key focus. This study deals with the perception that teachers have of this use of ICT with students with disabilities: How are the resources available to them and how good is their training in this respect? To do this, a section of a questionnaire was used, which forms part of the R + D + I project awarded under a national call in Spain, and which in one of its categories precisely focuses attention on this use of ICT. In turn, this category, according to the confirmatory factor analysis carried out, is subdivided into three subcategories: didactic use, spaces and resources, and finally teacher training. We are also interested in knowing if there are differences of opinion between population groups grouped according to sex, ownership of the education centre and location of the centre (urban, or rural). The results indicate that although there is good use and good preparation of teachers, the level of resources is low and teacher training needs to be reinforced.
Token‐curated registry in a scholarly journal: Can blockchain support journal communities?

In this paper, we propose a novel framework for a scholarly journal – a token‐curated registry (TCR). This model originates in the field of blockchain and cryptoeconomics and is essentially a decentralized system where tokens (digital currency) are used to incentivize quality curation of information. TCR is an automated way to create lists of any kind where decisions (whether to include N or not) are made through voting that brings benefit or loss to voters. In an academic journal, TCR could act as a tool to introduce community‐driven decisions on papers to be published, thus encouraging more active participation of authors/reviewers in editorial policy and elaborating the idea of a journal as a club. TCR could also provide a novel solution to the problems of editorial bias and the lack of rewards/incentives for reviewers. In the paper, we discuss core principles of TCR, its technological and cultural foundations, and finally analyse the risks and challenges it could bring to scholarly publishing.
Dual Low-Rank Multimodal Fusion

Tensor-based fusion methods have been proven effective in multimodal fusion tasks.However, existing tensor-based methods make a poor use of the fine-grained temporal dynamics of multimodal sequential features.Motivated by this observation, this paper proposes a novel multimodal fusion method called Fine-Grained Temporal Low-Rank Multimodal Fusion (FT-LMF).FT-LMF correlates the features of individual time steps between multiple modalities, while it involves multiplications of high-order tensors in its calculation.This paper further proposes Dual Low-Rank Multimodal Fusion (Dual-LMF) to reduce the computational complexity of FT-LMF through low-rank tensor approximation along dual dimensions of input features.Dual-LMF is conceptually simple and practically effective and efficient.Empirical studies on benchmark multimodal analysis tasks show that our proposed methods outperform the state-of-the-art tensorbased fusion methods with a similar computational complexity.
Zero-Shot Crosslingual Sentence Simplification

Sentence simplification aims to make sentences easier to read and understand.Recent approaches have shown promising results with encoder-decoder models trained on large amounts of parallel data which often only exists in English.We propose a zero-shot modeling framework which transfers simplification knowledge from English to another language (for which no parallel simplification corpus exists) while generalizing across languages and tasks.A shared transformer encoder constructs language-agnostic representations, with a combination of task-specific encoder layers added on top (e.g., for translation and simplification).Empirical results using both human and automatic metrics show that our approach produces better simplifications than unsupervised and pivot-based methods.