Reviewerly/exHarmony huggingface dataset:
Diverse Scenario Exploration in Model Finders Using Graph Kernels and Clustering

Complex software systems can be described using modeling notations such as UML/OCL or Alloy. Then, some correctness properties of these systems can be checked using model finders, which compute sample scenarios either fulfilling the desired properties or illustrating potential faults. Such scenarios allow designers to validate, verify and test the system under development. Nevertheless, when asked to produce several scenarios, model finders tend to produce similar solutions. This lack of diversity impairs their effectiveness as testing or validation assets. To solve this problem, we propose the use of graph kernels, a family of methods for computing the (dis)similarity among pairs of graphs. With this metric, it is possible to cluster scenarios effectively, improving the usability of model finders and making testing and validation more efficient.
Uni-image: Universal image construction for robust neural model

Deep neural networks have shown high performance in prediction, but they are defenseless when they predict on adversarial examples which are generated by adversarial attack techniques. In image classification, those attack techniques usually perturb the pixel of an image to fool the deep neural networks. To improve the robustness of the neural networks, many researchers have introduced several defense techniques against those attack techniques. To the best of our knowledge, adversarial training is one of the most effective defense techniques against the adversarial examples. However, the defense technique could fail against a semantic adversarial image that performs arbitrary perturbation to fool the neural networks, where the modified image semantically represents the same object as the original image. Against this background, we propose a novel defense technique, Uni-Image Procedure (UIP) method. UIP generates a universal-image (uni-image) from a given image, which can be a clean image or a perturbed image by some attacks. The generated uni-image preserves its own characteristics (i.e. color) regardless of the transformations of the original image. Note that those transformations include inverting the pixel value of an image, modifying the saturation, hue, and value of an image, etc. Our experimental results using several benchmark datasets show that our method not only defends well known adversarial attacks and semantic adversarial attack but also boosts the robustness of the neural network.
Towards Verifying Logic Programs in the Input Language of clingo

We would like to develop methods for verifying programs in the input language of the answer set solver clingo using software created for the automation of reasoning in first-order theories. As a step in this direction, we extend Clark’s completion to a class of clingo programs that contain arithmetic operations as well as intervals and prove that every stable model is a model of generalized completion. The translator anthem calculates the completion of a program and represents it in a format that can be processed by first-order theorem provers. Some properties of programs can be verified by anthem and the theorem prover vampire, working together.
Dynamic aberrations correction of Roll-Nod conformal seeker based on the diffraction surface and anamorphic asphere surface

Traditional optical domes are spherical, which have a large air resistance coefficient. In order to reduce the coefficient of air resistance, conformal optical technology was proposed, which used a streamlined design of the outer surface of the dome. However, conformal domes generate dynamic aberrations varying significantly with look angles in the field of regard (FOR). Thus, correcting the dynamic aberrations is the core task of conformal optics. This Letter presented a correcting method of dynamic aberrations based on the diffraction surface and anamorphic asphere surface. This method is derived from the arch corrector and can only be used on the Roll-Nod gimbal. For the seeker with a Roll-Nod gimbal, the arch corrector is replaced with a diffractive surface superimposed on the inner surface of the conformal dome. To correct astigmatism, which is the main aberration that needs to be corrected, anamorphic asphere surfaces are used in the imaging system. Compared with the arch corrector, this method can reduce the size of the correction element while retaining sufficient design freedom. Design results show that this method can well correct the dynamic aberrations in a larger FOR. With a simpler form in structure, this method can improve the reliability of conformal optical systems and promote the application of conformal optical technology.
Analysis of the Tensile Strength of Composite Material from Fiber Bags

The use of composite materials in Indonesia, as a nonmetal alternative material has begun to increase. Composite use has grown from water tanks from fiberglass materials, household appliances, children's toys, sports equipment, furniture/interior design to automotive components and aircraft. Many of the advantages of this composite material include hand, strong, anti-corrosion, finely tuned finish. Its mild nature has a direct impact on energy savings. The prospect of composite materials as a non-metal alternative material is rapidly growing and its use. Various types of reinforce used can be influenced by the prices available, so seek alternatives as a commonly used glass fiber substitute woven rowing. The substitute used is a sack of gourds of the same kind as woven rowing, from which the same type of fiber is tried to find its strength by undergoing a pull test. And as a result of the test the mateial can know its power.
On the benefit of logic-based machine learning to learn pairwise comparisons

In recent years, many daily processes such as internet web searching, e-mail filter-ing, social media services, e-commerce have benefited from machine learning tech-niques (ML). The implementation of ML techniques has been largely focused on blackbox methods where the general conclusions are not easily interpretable. Hence, theelaboration with other declarative software models to identify the correctness and com-pleteness of the models is not easy to perform. On the other hand, the emerge of somelogic-based machine learning techniques with their advantage of white box approachhave been proven to be well-suited for many software engineering tasks. In this paper,we propose the use of a logic-based approach to learn user preference in the form ofpairwise comparisons. APARELL as a novel approach of inductive learning is able tomodel the user’s preferences in description logic representation. This offers a rich, re-lational representation which is then can be used to produce a set of recommendations.A user study has been performed in our experiment to evaluate the implementation ofpairwise preference recommender system when compared to a standard list interface.The result of the experiment shows that the pairwise interface was significantly betterthan the other interface in many ways.
How to Recognize Actionable Static Code Warnings (Using Linear SVMs)

Static code warning tools often generate warnings that programmers ignore. Such tools can be made more useful via data mining algorithms that select the warnings; i.e. the warnings that are usually not ignored. But what is the best way to build those selection algorithms? To answer that question, we learn predictors for 5,675 actionable warnings seen in 31,058 static code warnings from FindBugs. Several data mining methods perform very well on this task. For example, linear Support Vector Machine achieved median recalls of 96%; median false alarms of 2%; and AUC(TNR, TPR) of over 99%. Other learners (tree-based methods and deep learning) achieved very similar results (usually, within 4% or less). On investigation, we found the reason for all these learners performing very well: the data was intrinsically very simple. Specifically, while our data sets have up to 58 raw features, those features can be approximated by less than two underlying dimensions. For such intrinsically simple data, many different kinds of learners can generate useful models with similar performance. Based on the above, we conclude that it is both simple and effective to use data mining algorithms for selecting warnings from static code analysis tools. Also, we recommend using linear SVMs to implement that selecting process (since, at least in our sample, that learner ran relatively quickly and achieved the best all-around performance). Further, for any analytics task, it important to match the complexity of the inference to the complexity of the data. For example, we would not recommend deep learning for finding actionable static code warnings since our data is intrinsically very simple.
Simulation-based Approximate Graph Pattern Matching

Graph pattern matching is a fundamental problem in analyzing attributed graphs, that is to search the matches of a given query graph in a large data graph. However, existing algorithms either encounter with the performance issues or cannot capture reasonable matches. In this paper, we propose a simulation-based approximate pattern matching algorithm that is not only efficient to compute, but also able to capture those reasonable matches (missed by existing algorithms)
Time for Change: Implementation of Aksentijevic-Gibson Complexity in Psychology

Given that complexity is critical for psychological processing, it is somewhat surprising that the field was dominated for a long time by probabilistic methods that focus on the quantitative aspects of the source/output. Although the more recent approaches based on the Minimum Description Length principle have produced interesting and useful models of psychological complexity, they have not directly defined the meaning and quantitative unit of complexity measurement. Contrasted to these mathematical approaches are various ad hoc measures based on different aspects of structure, which can work well but suffer from the same problem. The present manuscript is composed of two self-sufficient, yet related sections. In Section 1, we describe a complexity measure for binary strings which satisfies both these conditions (Aksentijevic–Gibson complexity; AG). We test the measure on a number of classic studies employing both short and long strings and draw attention to an important feature—a complexity profile—that could be of interest in modelling the psychological processing of structure as well as analysis of strings of any length. In Section 2 we discuss different factors affecting the complexity of visual form and showcase a 2D generalization of AG complexity. In addition, we provide algorithms in R that compute the AG complexity for binary strings and matrices and demonstrate their effectiveness on examples involving complexity judgments, symmetry perception, perceptual grouping, entropy, and elementary cellular automata. Finally, we enclose a repository of codes, data and stimuli for our example in order to facilitate experimentation and application of the measure in sciences outside psychology.
The Implementation of Reading Aloud in Developing Students' Speaking Skill

This research focuses on the implementation of reading aloud in developing students’ speaking skill. It aims to find out whether the implementation of reading aloud develops the students’ speaking skill. This research used correlation research design in which questionnaire and speaking test are the instruments of this research. The population of this research is the first semester students of English Language Education Study Program, Faculty of Teacher Training and Education, Cokroaminoto Palopo University. The sample of this research is selected through purposive sampling technique. One class represented as the sample that is class B. The result of this research shows that reading aloud can be effective way in teaching speaking class since there is a significant relationship between reading aloud and the students’ speaking skill. It is determined by p value of 0,027 which is below 0,05. Thus, the implementation of reading aloud significantly contributed to develop students’ speaking skill.
Pose estimation of excavators

In current pose estimation studies, bottom-up approaches estimating the pose from detected parts of the bodies and their relations have been major methods. This approach enabled us fast and accurate pose estimation. In other hands, there are many fields that the pose estimation could be applied. In this paper, we propose a bottom-up method for excavator pose estimation. For pose estimation, we generate ground truth confidence maps according to the annotation and evaluate loss function for training. We evaluated the model by calculating the loss between the estimated map and true map.
RETRACTED: Multi-neural network-based sentiment analysis of food reviews based on character and word embeddings

Sentiment analysis becomes one of the most active research hotspots in the field of natural language processing tasks in recent years. However, the inability to fully and effectively use emotional information is a problem in present deep learning models. A single Chinese character has different meanings in different words, and the character embeddings are combined with the word embeddings to extract more precise meaning information. In this paper, a single Chinese character and word are used as input units to train. Based on BLSTM, the attention mechanism based on vocabulary semantics in food field is introduced to realize distance-related sequence semantic feature extraction. CNN is used to realize semantic sentiment classification of sequence semantic features. Therefore, a model based on multi-neural network for sentiment information extraction and analysis is proposed. Experiments show that the model has excellent characteristics in sentiment analysis and obtains high accuracy and F value.
MakeSense

There has been increasing interest in deploying Internet of Things (IoT) devices to study human behavior in locations such as homes and offices. Such devices can be deployed in a laboratory or “in the wild” in natural environments. The latter allows one to collect behavioral data that is not contaminated by the artificiality of a laboratory experiment. Using IoT devices in ordinary environments also brings the benefits of reduced cost, as compared with lab experiments, and less disturbance to the participants’ daily routines, which in turn helps with recruiting them into the research. However, in this case, it is essential to have an IoT infrastructure that can be easily and swiftly installed and from which real-time data can be securely and straightforwardly collected. In this article, we present MakeSense, an IoT testbed that enables real-world experimentation for large-scale social research on indoor activities through real-time monitoring and/or situation-aware applications. The testbed features quick setup, flexibility in deployment, the integration of a range of IoT devices, resilience, and scalability. We also present two case studies to demonstrate the use of the testbed: one in homes and one in offices.
Dynamic Stopword Removal for Sinhala Language

In the modern era of information retrieval, text summarization, text analytics, extraction of redundant (noise) words that contain a little information with low or no semantic meaning must be filtered out. Such words are known as stopwords. There are more than 40 languages which have identified their language specific stopwords. Most researchers use various techniques to identify their language specific stopword lists. But most of them try to define a magical cut-off point to the list, which they identify without any proof. In this research, the focus is to prove that the cut-off point depends on the source data and the machine learning algorithm, which will be proved by using Newton's iteration method of root finding algorithm. To achieve this, the research focuses on creating a stopword list for Sinhala language using the term frequency-based method by processing more than 90000 Sinhala documents. This paper presents the results received and new datasets prepared for text preprocessing.
Semantic Guidance of Dialogue Generation with Reinforcement Learning

Neural encoder-decoder models have shown promising performance for human-computer dialogue systems over the past few years. However, due to the maximum-likelihood objective for the decoder, the generated responses are often universal and safe to the point that they lack meaningful information and are no longer relevant to the post. To address this, in this paper, we propose semantic guidance using reinforcement learning to ensure that the generated responses indeed include the given or predicted semantics and that these semantics do not appear repeatedly in the response. Synsets, which comprise sets of manually defined synonyms, are used as the form of assigned semantics. For a given/assigned/predicted synset, only one of its synonyms should appear in the generated response; this constitutes a simple but effective semantic-control mechanism. We conduct both quantitative and qualitative evaluations, which show that the generated responses are not only higher-quality but also reflect the assigned semantic controls.
Composable Sketches for Functions of Frequencies: Beyond the Worst Case

Recently there has been increased interest using machine learning techniques to improve classical algorithms. In this paper we study when it is possible to construct compact, composable sketches for weighted sampling and statistics estimation according to functions of data frequencies. Such structures are now central components of large-scale data analytics and machine learning pipelines. However, many common functions, such as thresholds and $p$th frequency moments with $p>2$, are known to require polynomial size sketches the worst case. We explore performance beyond the worst case under two different types of assumptions. The first is having access to noisy advice on item frequencies. This continues the line of work of Hsu et al. (ICLR 2019), who assume predictions are provided by a machine learning model. The second is providing guaranteed performance on a restricted class of input frequency distributions that are better aligned with what is observed practice. This extends the work on heavy hitters under Zipfian distributions a seminal paper of Charikar et al. (ICALP 2002). Surprisingly, we show analytically and empirically that in practice small polylogarithmic-size sketches provide accuracy for hard functions.
The Indonesian National Scientific Repository: A Case Study of Research Data Sharing

Abstract This study discusses the sharing of research data through the Repositori Ilmiah Nasional, the Indonesian national scientific repository, which is managed by the Center for Scientific Data and Documentation, Indonesian Institute of Sciences (Pusat Dokumentasi dan Informasi Ilmiah, Lembaga Ilmu Pengetahuan Indonesia, known by the abbreviation PDDI-LIPI). The purpose of this study is to describe the process of research data sharing and identify supporting factors and obstacles faced in that process. This study uses a qualitative approach, with a case study method. Data collection techniques included field observations and observations on the repository system; semi-structured interviews with several informants, including researchers as well as development and librarian teams; and, analysis of policy documents and guidelines. Through these investigations, we discovered that while the Center has developed a new DataVerse repository system to enable research data sharing, there are still several issues that impede the repository from meeting institutional goals for increased data access. There is a need for additional training and socialization of researchers, to encourage and motivate them to share their research data through this service. Additionally, staff members need to gain competence in the management and curation of data. Researchers and librarians involved in research data sharing activities still face various obstacles in the areas of policy, service visibility, and promotion. This research is expected to increase the awareness of researchers, librarians, and repository development teams about each other’s needs and to aid them in collaborating with each other to optimize the sharing of research of data through the repository.
An OpenAPI-Based Testing Framework to Monitor Non-functional Properties of REST APIs

REST APIs have become key assets for any company willing to have online presence and provide access to its services. Several approaches have been proposed to describe this kind of APIs, being OpenAPI the dominant proposal in the last years. OpenAPI allows any consumer to understand the operations and data elements of a REST API. However, it does not cover any kind of non-functional properties, such as performance and availability. In this paper we present Gadolinium, a framework that leverages the OpenAPI specification to test non-functional properties of REST APIs. Gadolinium automatically tests performance and availability in different geographical locations by means of a master/slave architecture. The results of the test can eventually be injected in the original OpenAPI definition of the REST API. Demo: http://hdl.handle.net/20.500.12004/1/C/ICWE/2020/001
Enhancing the Performance of Spatial Queries on Encrypted Data Through Graph Embedding

Most online mobile services make use of location data to improve customer experience. Mobile users can locate points of interest near them, or can receive recommendations tailored to their whereabouts. However, serious privacy concerns arise when location data is revealed in clear to service providers. Several solutions employ Searchable Encryption (SE) to evaluate spatial predicates directly on location ciphertexts. While doing so preserves privacy, the performance overhead incurred is high. We focus on a prominent SE technique in the public-key setting – Hidden Vector Encryption (HVE), and propose a graph embedding technique to encode location data in a way that significantly boosts the performance of processing on ciphertexts. We show that finding the optimal encoding is NP-hard, and provide several heuristics that are fast and obtain significant performance gains. Our extensive experimental evaluation shows that our solutions can improve computational overhead by a factor of two compared to the baseline.
Influence of New Media Technology on the Reading Habits of Contemporary College Students

Abstract As the continuous development of society, the level of science and technology in our country is constantly improving, the development speed of new media technology is constantly accelerating, and the influence on people’s production and life is becoming more and more tremendous. New media technology has influenced people’s reading. Different from paper reading, new ways of reading emerge in an endless stream. New media reading and traditional reading forms blend with each other, creating a new reading pattern, which has both advantages and disadvantages. In order to ensure the quality of college students’ reading, relevant parties should deeply analyze the influence of new media technology on college students’ reading habits, and actively exert the advantages of new media technology to ensure the quality and effect of college students’ reading.