Reviewerly/exHarmony huggingface dataset:
Autonomic computing based trustworthiness attributes weight estimation using Electre-Tri method

Abstract With the advancement in autonomic computing technology, quality assurance for autonomic software is another big challenge to be faced by IT industries. Autonomic software is quite different in working compared to non-autonomic software. So, quality estimation parameters will also be different. Quality assurance method for autonomic software has not been standardized yet but quality parameters based on autonomic features have been suggested by researchers. Based on the functioning of autonomic software and autonomic capabilities, Autonomicity and Trustworthiness are considered to be the major quality estimation metrics. In this paper, trustworthiness estimation is performed using six attributes. To contribute in this area further, attributes are ranked based on the weights estimated using Electre-Tri multi-criteria outranking method. The attributes weights can be used further to estimate trustworthiness for any autonomic software. With this, the quality estimation task can also be processed for autonomic software.
A Certified Functional Nominal C-Unification Algorithm

The nominal approach allows us to extend first-order syntax and represent smoothly systems with variable bindings, using nominal atoms instead of variables and dealing with renaming through permutations of atoms. Nominal unification is, therefore, the extension of first-order unification modulo $$\alpha $$-equivalence by taking into account this nominal setting. In this work, we present a specification of a nominal C-unification algorithm (nominal unification with commutative operators) in PVS and discuss the proofs of soundness and completeness. Additionally, the algorithm has been implemented in Python. In relation to the only known specification of nominal C-unification, there are two novel features in this work: first, the formalization of a functional algorithm that can be directly executed (not just a set of non-deterministic inference rules); second, simpler proofs of termination, soundness and completeness, due to the reduction in the number of parameters of the lexicographic measure, from four parameters to only two.
Enhanced Algorithm of Superpixel Segmentation Using Simple Linear Iterative Clustering

Image segmentation process represents the main stage for most computer vision systems. This paper presents an improved algorithm based on simple linear iterative clustering (SLIC) to reduce the number of used seeds for threshold estimation as well as the entire execution time of image segmentation. These is achieved by using split and merge stages for the location, number of seeds as well as other parameters of the seed points. The obtained results showed the possibility of using various threshold levels instead of a single one which represented a challenge due to the estimation complexity. The independent of the threshold-level estimation can contribute significantly in improving performance of the overall image segmentation process).
Automatic recognition of hand gestures

Developing and implementing a software algorithm for detecting, recognizing and interpreting hand gestures. The project involves an automatic realtime image analysis system that recognizes several predefined movements. The analysis system first involves locating and separating the areas of the image associated with the hand, followed by the motion classification (recognition). These movements will be transposed into commands to an integrated device.
Social Media Dark Side Content Detection using Transfer Learning Emphasis on Hate and Conflict

Although online content continues to grow, the prevalence of dark side content such as hate, misinformation, disinformation, conflicting, fake, and so on continues to grow and has become a problem for online and offline society. Consequently, work into automated analytical and detection methods has gained much attention. The scarcity of the labeled dataset has, however, become one of the major challenges in both machine and deep learning to develop an effective supervised learning model. As a result, most State-of-the-Art (SOTA) approaches focus on English languages for the detection of such content. The identification task of such content has become a problem due to the diversity of languages used on social media platforms. We propose transfer learning since it needs only access to a large unlabeled text available on social media platforms. Since we use data from Amharic Language, which is in the low-resource language family for machine leaarning, transfer learning is found effective. First, we prepare a topic and word embedding models using Facebook data as a task-specific and a general corpus from different web domains respectively. Second, we combine topic embedding and word embedding and then send the features to a fully-connected Recurrent Neural Networks (RNNs). Our preliminary experimental results from the newly proposed attention-based topic model combined with word embedding outperform the baselines.
Computing Centroidal Voronoi Tessellation Using the GPU

We propose a novel algorithm to compute centroidal Voronoi tessellation using the GPU. It is based on the iterative approach of Lloyd's method while having good considerations to address the two major challenges of achieving fast convergence with few iterations, and at the same time achieving fast computation within each iteration. Our implementation of the algorithm can complete the computation for a large image in the order of hundreds of milliseconds and is faster than all prior work on a state-of-the-art GPU. As such, it is now easier to integrate centroidal Voronoi tessellations into interactive applications.
Solving Multiple Traveling Salesmen Problem using Discrete Pigeon Inspired Optimizer

Multiple Traveling Salesmen Problem (MTSP) is an extended version of the popular traveling salesman problem. In MTSP there are m salesmen that should travel n cities with minimum cost (distance). All the salesmen start their trips from a depot city and back to the same city, each city must be visited once. MTSP is an optimization problem that researchers tried to solve using meta-heuristic algorithms. In this paper, a discrete pigeon inspired optimizer is proposed to solve MTSP. The proposed algorithm evaluated using five instances from TSPLIB benchmark and compared its results with four algorithms from state-of-the-art. The results indicate that the proposed algorithm outperformed the examined algorithms in most cases.
Keeping Their Words: Direct and Indirect Chinese Quote Attribution from Newspapers

Quote attribution plays an important role in emerging research topics such as fact checking, stance detection, and argument mining. This work explores Chinese quote attribution from newspapers. Both direct and indirect quotes are addressed by a text-encoder based sequence labeling model. We create a dataset for empirical analysis. Experimental results show the effectiveness of our model.
How Can I Contribute?

Developers collaboratively implement large-scale industrial and open-source projects. Such projects pose several challenges for developers, as they require considerable knowledge about the project and its development processes, for instance, to fix bugs or implement new features. Understanding what information developer communities codify on how to contribute to their project is crucial, for example, to onboard new developers or for researchers to scope analysis techniques. In this paper, we report the results of a qualitative analysis of 25 Unix-like distributions, focusing on what information the communities codify publicly on contributing. The results reveal no dedicated strategies to codify information on contribution or development practices. Still, non-technical contributions are easy to identify, while information on the development is hard to collect---and mostly concerned with versioning and bug reporting. Our insights help to understand information-provisioning strategies, identify information sources, and scope analyses.
Bernoulli Filter for Track-Before-Detect: Swerling-1 Target in K-distributed Clutter

In this paper, we investigate the problem of detecting and tracking small targets in sea clutter using the Bernoulli Track-Before-Detect filter. This filter is an optimal recursive Bayesian detector / estimator to determine the state of a single target and its presence in noise. Typically, clutter amplitude fluctuations are modelled using the Rayleigh distribution. This assumption, however, is not valid in the maritime domain, where sea-clutter is often spikier with a longer distribution tail. In this work, we develop the Bernoulli filter for a compound Gaussian clutter model with thermal noise and model the target with a Swerling 1 fluctuation. To demonstrate the performance improvement, we model the texture as a gamma distribution, giving an overall K plus noise distribution for the clutter plus noise. The detection and tracking improvement is then demonstrated using Monte Carlo simulation.
An efficient methodology to estimate the parameters of a two-dimensional chirp signal model

In various capacities of statistical signal processing two-dimensional (2-D) chirp models have been considered significantly, particularly in image processing—to model gray-scale and texture images, magnetic resonance imaging, optical imaging etc. In this paper we address the problem of estimation of the unknown parameters of a 2-D chirp model under the assumption that the errors are independently and identically distributed (i.i.d.). The key attribute of the proposed estimation procedure is that it is computationally more efficient than the least squares estimation method. Moreover, the proposed estimators are observed to have the same asymptotic properties as the least squares estimators, thus providing computational effectiveness without any compromise on the efficiency of the estimators. We extend the propounded estimation method to provide a sequential procedure to estimate the unknown parameters of a 2-D chirp model with multiple components and under the assumption of i.i.d. errors we study the large sample properties of these sequential estimators. Simulation studies and two synthetic data analyses have been performed to show the effectiveness of the proposed estimators.
Probabilities of Causation: Three Counterfactual Interpretations and Their Identification

chapter Share on Probabilities of Causation: Three Counterfactual Interpretations and Their Identification Author: Judea Pearl Search about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 317–372https://doi.org/10.1145/3501714.3501735Online:04 March 2022Publication History 0citation7DownloadsMetricsTotal Citations0Total Downloads7Last 12 Months7Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Unifying Parsing and Reflective Printing for Fully Disambiguated Grammars

Language designers usually need to implement parsers and printers. Despite being two closely related programs, in practice they are often designed separately, and then need to be revised and kept consistent as the language evolves. It will be more convenient if the parser and printer can be unified and developed in a single program, with their consistency guaranteed automatically. Furthermore, in certain scenarios (like showing compiler optimisation results to the programmer), it is desirable to have a more powerful reflective printer that, when an abstract syntax tree corresponding to a piece of program text is modified, can propagate the modification to the program text while preserving layouts, comments, and syntactic sugar. To address these needs, we propose a domain-specific language BiYacc, whose programs denote both a parser and a reflective printer for a fully disambiguated context-free grammar. BiYacc is based on the theory of bidirectional transformations, which helps to guarantee by construction that the generated pairs of parsers and reflective printers are consistent. Handling grammatical ambiguity is particularly challenging: we propose an approach based on generalised parsing and disambiguation filters, which produce all the parse results and (try to) select the only correct one in the parsing direction; the filters are carefully bidirectionalised so that they also work in the printing direction and do not break the consistency between the parsers and reflective printers. We show that BiYacc is capable of facilitating many tasks such as Pombrio and Krishnamurthi’s ‘resugaring’, simple refactoring, and language evolution.
City-Scale Distance Sensing via Bispectral Light Extinction in Bad Weather

In this paper, we propose a novel city-scale distance sensing algorithm based on atmosphere optics. The suspended particles, especially in bad weather, would attenuate the light at almost all wavelengths. Observing this fact and starting from the light scattering mechanism, we derive a bispectral distance sensing algorithm by leveraging the difference of extinction coefficient between two specifically selected near infrared wavelengths. The extinction coefficient of the atmosphere is related to both wavelength and meteorological conditions, also known as visibility, such as the fog and haze day. To account for different bad weather conditions, we explicitly introduce visibility into our algorithm by incorporating it into the calculation of extinction coefficient, making our algorithm simple yet effective. To capture the data, we build a bispectral imaging system that is able to take a pair of images with a monochrome camera and two narrow band-pass filters. We also present a wavelength selection strategy that allows us to accurately sense distance regardless of material reflectance and texture. Specifically, this strategy determines two distinct near infrared wavelengths by maximising the extinction coefficient difference while minimizing the influence of building’s reflectance variance. The experiments empirically validate our model and its practical performance on the distance sensing for the city-scale buildings.
Mixed Modified Recurring Rogers-Szego Polynomials Neural Network Control with Mended Grey Wolf Optimization Applied in SIM Expelling System

Due to a good ability of learning for nonlinear uncertainties, a mixed modified recurring Rogers-Szego polynomials neural network (MMRRSPNN) control with mended grey wolf optimization (MGWO) by using two linear adjusted factors is proposed to the six-phase induction motor (SIM) expelling continuously variable transmission (CVT) organized system for acquiring better control performance. The control system can execute MRRSPNN control with a fitted learning rule, and repay control with an evaluated rule. In the light of the Lyapunov stability theorem, the fitted learning rule in the MRRSPNN control can be derived, and the evaluated rule of the repay control can be originated. Besides, the MGWO by using two linear adjusted factors yields two changeable learning rates for two parameters to find two ideal values and to speed-up convergence of weights. Experimental results in comparisons with some control systems are demonstrated to confirm that the proposed control system can achieve better control performance.
Bottom-Up and Top-Down Graph Pooling

Pooling layers are crucial components for efficient deep representation learning. As to graph data, however, it’s not trivial to decide which nodes to retain in order to represent the high-level structure of a graph. Recently many different graph pooling methods have been proposed. However, they all rely on local features to conduct global pooling over all nodes, which contradicts poolings in CNNs that only use local features to conduct local pooling. We analyze why this may hinder the performance of graph pooling, then propose a novel graph pooling method called Bottom-Up and Top-Down graph POOLing (BUTDPool). BUTDPool aims to learn a more fine-grained pooling criterion based on coarse global structure information produced by a bottom-up pooling layer, and can enhance local features with global features. Specifically, we propose to use one or multiple pooling layers with a relatively high retain ratio to produce a coarse high-level graph. Injecting the high-level information back into low-level representation, BUTDPool enhances learning a better pooling criterion. Experiments demonstrate the superior performance of the proposed method over compared methods.
Improving the Efficiency of Independent Work in the Study of a Second Foreign Language by Undergraduates

The analysis of the theoretical base allows us to examine the features of studying a foreign language as a whole, as well as the peculiarities of teaching a second foreign language, and thereby provides independent work on learning for the effectiveness and success of the entire educational process and formulates the principles of independent work of a student while studying a second foreign language in the conditions of distance educational process because of shortage of academic hours. A developed online course on the Moodle platform has demonstrated high efficiency and revealed potential, which was confirmed by the results of final tests of experimental groups, compared with the results of control groups.State policy to higher education institutions, information processing, the reduction of academic hours and audit of independent work is becoming an important aspect of the educational process. Therefore, the correct and effective educational operation is becoming an increasingly urgent task requiring immediate solutions.In our opinion, autonomous courses on various online platforms that do not practically require the participation of a professor and take into account the interests of students can significantly increase the productivity of independent work of linguistics undergraduates, especially concerning a second foreign language. As students should work with new material, they need to constantly access information and this process is time-consuming, so online courses allow students to organize successfully their individual independent work and focus only on the educational process.
Spa-neg: An Approach for Negation Detection in Clinical Text Written in Spanish

Electronic health records contain valuable information written in narrative form. A relevant challenge in clinical narrative text is that concepts commonly appear negated. Several proposals have been developed to detect negation in clinical text written in Spanish. Much of these proposals have adapted the Negex algorithm to Spanish, but obtained results indicating lower performance than NegEx implementations in other languages. Moreover, in most of these proposals, the validation process could be improved using a shared test corpus focused on negation in clinical text. This paper proposes Spa-neg, an approach to improve negation detection in clinical text written in Spanish. Spa-neg combines three elements: (i) an exploratory data analysis of how negation is written in the clinical text, (ii) use of regular expressions best adapted to the way in which negation is expressed in Spanish, (iii) experiments, and validation using a shared annotated corpus focused on negation. Our findings suggest that the combination of these elements improves the process of negation detection. The tests performed have shown 92% F-Score using IULA Spanish, an annotated corpus for negation in clinical text.
PoCo

Existing security-policy-specification languages allow users to specify obligations, but challenges remain in the composition of complex obligations, including effective approaches for resolving conflicts between policies and obligations and allowing policies to react to other obligations. This paper presents PoCo, a policy-specification language and enforcement system for the principled composition of atomic-obligation policies. PoCo enables policies to interact meaningfully with other policies' obligations, thus preventing unexpected and insecure behaviors that can arise from partially executed obligations or obligations that execute actions in violation of other policies.
Collaborative Recommendation of Temporally-Discounted Tag-Based Expertise for Community Question Answering

We propose an innovative approach to finding experts for community question answering (CQA). The idea is to recommend answerers, who are credited the highest expertise under question tags at routing time. The expertise of answerers under already replied question tags is intuitively discounted by accounting for the observed tags, votes and temporal information of their answers. Instead, the discounted expertise under not yet replied tags is predicted via a latent-factor representation of both answerers and tags. These representations are inferred by means of Gibbs sampling under a new Bayesian probabilistic model of discounted user expertise and asking-answering behavior. The devised model unprecedentedly explains the latter two CQA aspects as the result of a generative process, that seamlessly integrates probabilistic matrix factorization and network behavior characterization. An extensive comparative experimentation over real-world CQA data demonstrates that our approach outperforms several-state-of-the-art competitors in recommendation effectiveness.
Analysis of Contextual Teaching and Learning (CTL) learning method in speaking skill of BIPA students

This paper aims to find out the application of Contextual Teaching and Learning (CTL) method in BIPA BINUS 2018 students' speaking learning. The research method used is a qualitative method with case study technique. The sample of this research is 32 students of BIPA BINUS of academic year 2017/2018. The results showed that: (1) there was an increase of average learning result of BIPA BINUS students' speaking skill from 68.7 to 76.2 by using CTL learning method; (2) 21 out of 32 BIPA get high rank (istimewa to madya) and 11 low rank (semenjana to terbatas) in their speaking skill outcomes; (3) 22 out of 32 students of BIPA BINUS stated strongly that CTL learning method is motivating, fun, meaningful, and helpful to improve the speaking skill of BIPA students; (4) CTL learning method is done through technique of educational visit, cultural visit, and business visit; (5) education for sustainable development can be done through the application of motivational and student-centered learning methods, one of which is the CTL learning method. CTL learning method has advantages and weaknesses that can be used as input for BIPA teachers as an alternative to be creative in BIPA teaching.
A distributed location obfuscation method for online route planning

A novel location obfuscation method for online route planning is proposed which is robust to privacy inferences by the service provider regarding route source and destination. This is achieved by performing the task of route computation in a distributed manner. Specifically, the client decomposes the required route into a sequence of shorter routes between intermediate locations. These routes are subsequently requested from independent online route planners with the results being integrated by the client to give the route originally required. Robustness to privacy inferences is a consequence of the fact that, without significant coordination and sharing of information, an individual online route planner cannot infer with high probability the true route source or destination. An evaluation of the proposed method is performed in the context of route planning within the street network of Boston. This evaluation demonstrates that the proposed method offers robustness to privacy inferences while exhibiting a reasonable reduction in quality of service.