Reviewerly/exHarmony huggingface dataset:
PASAR - Planning as Satisfiability with Abstraction Refinement

One of the classical approaches to automated planning is the reduction to propositional satisfiability (SAT). Recently, it has been shown that incremental SAT solving can increase the capabilities of several modern encodings for SAT-based planning. In this paper, we present a further improvement to SAT-based planning by introducing a new algorithm named PASAR based on the principles of counterexample guided abstraction refinement (CEGAR). As an abstraction of the original problem, we use a simplified encoding where interference between actions is generally allowed. Abstract plans are converted into actual plans where possible or otherwise used as a counterexample to refine the abstraction. Using benchmark domains from recent International Planning Competitions, we compare our approach to different state-of-the-art planners and find that, in particular, combining PASAR with forward state-space search techniques leads to promising results.
Pengembangan Inventori Motivasi Belajar Untuk Siswa Sekolah Menengah Kejuruan

The purpose of this research resulted learning motivation inventory for vocational high school students that meet the requirements of validity and reliability, so that it is able to measure the learning motivation of students that would become the guidelines on teacher guidance and counselling in providing tutoring services as well as to arouse the learning motivation of students. This research is the development of research or research and development model of the Borg and Gall, by involving 317 students. The population of this research is the vocational high school students aged 15-19 years. The taking of the sample of this study using cluster random sampling, while the analysis used product moment pearson, cronbach alpha, test for normality and an analysis of the factors. This research resulted learning motivation inventory for vocational high school students consisting of 59 items statement declared valid, has a high level of reliability, data is normally distributed and has 15 correlating factors on each particular item. Thus this study motivation inventory product can be used to identify learning problems of vocational high school students.
Salient Object Detection via Recurrently Aggregating Spatial Attention Weighted Cross-Level Deep Features

This paper proposes a novel deep saliency detection network by recurrently aggregating and refining features in a cross-level and spatial attention-aware manner. In this way, the features integrated from multiple layers can be used to refine layer-wise features step by step and the complementary information in different layers can be fully captured for detecting salient objects with different scales, i.e., the features integrated from low-level layers can serve to refine the details of detected salient objects while the features integrated from high-level layers with semantic information can benefit the locating of salient objects. In addition, by considering that only partial regions of an image are salient, we embed a spatial attention-aware module to suppress the non-salient regions and highlight salient objects. Finally, different saliency detection results from different layers are fused to generate the final saliency map. Experimental results on five benchmark datasets demonstrate that our proposed method outperforms other 14 state-of-the-art competitors.
Systematic Approach Towards Sentiment Analysis in Online Review’s

Ever Since its birth in the late 60's, Internet has been widely and mainly used for interaction purpose only; but over the period of time, its application has changed significantly. Now a days' Internet is no longer use only for communication purpose. Its use is spread over wide variety of applications' and E-Commerce is one of them. The most important part in e-commerce, from consumer perspective is, the reviews associated with products. Most of the people do their decision making, based on these online reviews about products or services. These reviews not only help user to know the product or service thoroughly but also affect user's decision making ability to a great extent and also divert the sentiments about the product positively or negatively. As a result, there have been attempts made, to change the product sentiments positively or negatively by manipulating the online reviews artificially to gain the business benefits. Ultimately, affect the genuine business experience of the user. Therefore in this paper, we have dealt with this particular problem of e-commerce field, specifically online reviews' in particular and sentiment analysis domain as a whole, in general. A ton of work has been already done in this domain since last decade. In this paper, we will see cumulative study of all this work and how one should approach to deep dive into sentiment analysis field and start research from the scratch. This paper will provide insight of sentiment analysis domain, its general workflow and systematic approach towards solving problems in this domain.
Learning to Transfer Relational Representations through Analogy

We propose a novel approach to learn representations of relations expressed by their textual mentions. In our assumption, if two pairs of entities belong to the same relation, then those two pairs are analogous. We collect a large set of analogous pairs by matching triples in knowledge bases with web-scale corpora through distant supervision. This dataset is adopted to train a hierarchical siamese network in order to learn entity-entity embeddings which encode relational information through the different linguistic paraphrasing expressing the same relation. The model can be used to generate pre-trained embeddings which provide a valuable signal when integrated into an existing neural-based model by outperforming the state-of-the-art methods on a relation extraction task.
Robust Over-the-Air Adversarial Examples Against Automatic Speech Recognition Systems.

Automatic speech recognition (ASR) systems are possible to fool via targetedadversarial examples. These can induce the ASR to produce arbitrarytranscriptions in response to any type of audio signal, be it speech,environmental sounds, or music. However, in general, those adversarial examplesdid not work in a real-world setup, where the examples are played over the airbut have to be fed into the ASR system directly. In some cases, where theadversarial examples could be successfully played over the air, the attacksrequire precise information about the room where the attack takes place inorder to tailor the adversarial examples to a specific setup and are nottransferable to other rooms. Other attacks, which are robust in an over-the-airattack, are either handcrafted examples or human listeners can easily recognizethe target transcription, once they have been alerted to its content. In thispaper, we demonstrate the first generic algorithm that produces adversarialexamples which remain robust in an over-the-air attack such that the ASR systemtranscribes the target transcription after actually being replayed. For theproposed algorithm, guessing a rough approximation of the room characteristicsis enough and no actual access to the room is required. We use the ASR systemKaldi to demonstrate the attack and employ a room-impulse-response simulator toharden the adversarial examples against varying room characteristics. Further,the algorithm can also utilize psychoacoustics to hide changes of the originalaudio signal below the human thresholds of hearing. We show that theadversarial examples work for varying room setups, but also can be tailored tospecific room setups. As a result, an attacker can optimize adversarialexamples for any target transcription and to arbitrary rooms. Additionally, theadversarial examples remain transferable to varying rooms with a highprobability.
THE CHALLENGES OF TOTAL QUALITY MANAGEMENT ON EDUCATION QUALITY IN UAE

Today, educational institutions all over the world are facing many challenges to deliver high quality education for students. Total quality management (TQM) represents a set of practices to sustain quality in service deliver for modern organization. The aim of this study is to investigate the effect of total quality practice on education quality in UAE universities and colleges. This study used quantitative approaches and SEM modeling technique to investigate the multiple regression effect of TQM practices. The empirical evidences provided in the study will help leaders in higher education institutions to improve the education process significantly. The result of this study indicates that educational institutions need to practice all principles of TQM such as customer focus, leadership, engagement of people, quality process, continuous improvement, decision making, and relationships management to achieve high education quality. This study recommends that universities in U.A.E should establish a a quality framework based on TQM principles with a continuous following-up and monitoring to application of TQM on the long-term. JEL: M11, I21, I22 Article visualizations:
Inferring Missing Attributes of Users in Large-Scale Social networks

User attribute inference plays an important role in personalized recommendation and precision marketing. However, in large-scale social networks, user attributes are often missing. To address the problem, this paper introduces an inference framework for deriving missing attributes of users in largescale social networks. We use Sina Weibo as our experimental platform. The framework leverages various collaborative filtering methods and a similarity learning scheme to infer missing user attribute values. Experimental results demonstrate the proposed framework is able to generate satisfactory inference results.
Research on College Students' Deep Learning Behavior Under the Guidance of Cognitive Theory

Deep learning behavior of college students plays an important role not only in improving learning quality, but also in affecting the quality of talent training. This paper puts forward the characteristics of deep learning of college students, establishes the framework of deep learning behavior of college students under the guidance of cognitive theory, and formulated a questionnaire on deep learning behavior of college students according to the framework. This result of questionnaire surveys shows that there are three main problems in the behavior of deep learning of the selected college student, which is only few deep learning behavior which are based on practice, or coexistence and knowledge.
Generative Adversarial Networks: A Survey of Techniques and Methods

Generative Adversarial Networks (GANs) are a class of deep learning algorithms which are based on two neural networks competing with each other. They are capable of learning representations from data that isn't well annotated and these deep representations can be quite useful in a variety of applications, including image classification, generation, and in deblurring and creating super-resolution images. This paper is an attempt to explore a few different types of GANs, and to discuss issues and solutions in training methods. We also attempt to explore various applications of GANs and the methods we can use these learned deep representations.
Identification of representative terms of datasets

The development of open data portals is increasingly widespread internationally, and they are high demanded by software applications' developers. However, when trying to use similar data from different portals, they found the problem of lack of homogenization. This article collects techniques to analyze the terms that appear in the description of the datasets and represent their content semantically. With these techniques, a model is proposed to identify the most suitable terms, with the aim that they can be used in datasets' selection or unification processes.
The Development of E-Atlas Learning Media Based on Mobile Learning on Cells Structure Material in SMA N 1 Kandangserang

Cells are the one of hard-to-understand material for students, because it cannot be seen directly by students therefore it requires detailed visualization of images to explain the structure and processes that occur in it. Adequate visualization is needed to study better the structure and function of cells. This is the reason why we need to develop a mobile learning media. The media is an E-atlas of cell structure and function. This product is then analyzed its suitability as a learning media in SMA N 1 Kandangserang. Students of classes XI MIA 1 and XI MIA 2 academic year 2018/2019 were selected as the samples of this research using saturated sampling techniques. This is a research and development (R &amp; D). Research shows that E-atlas is suitable as learning media. E-atlas affects the learning outcomes, where the N-gain value shows an increase with an average of 0.72 (categorized as high) with classical completeness of 83%. It is concluded that E-atlas mobile learning is suitable as a teaching learning medium for students.
A Complex-Valued Firefly Algorithm

In order to improve the diversity of population and convergence rate further, a firefly algorithm (FA) based on the complex-valued encoding (CFA) is proposed. In CFA, each variable could be encoding by complex number (the real part and the imaginary part). These parts could be optimized in parallel. The amplitude and angle of complex number are calculated for individual evaluation. Three real-valued benchmark functions are utilized to test the performance of CFA. The experiment results show that CFA has better convergence speed and accuracy than FA because it increases the diversity of population.
Stability of Linear Structural Equation Models of Causal Inference.

We consider the numerical stability of the parameter recovery problem in Linear Structural Equation Model ($\LSEM$) of causal inference. A long line of work starting from Wright (1920) has focused on understanding which sub-classes of $\LSEM$ allow for efficient parameter recovery. Despite decades of study, this question is not yet fully resolved. The goal of this paper is complementary to this line of work; we want to understand the stability of the recovery problem in the cases when efficient recovery is possible. Numerical stability of Pearl's notion of causality was first studied in Schulman and Srivastava (2016) using the concept of condition number where they provide ill-conditioned examples. In this work, we provide a condition number analysis for the $\LSEM$. First we prove that under a sufficient condition, for a certain sub-class of $\LSEM$ that are \emph{bow-free} (Brito and Pearl (2002)), the parameter recovery is stable. We further prove that \emph{randomly} chosen input parameters for this family satisfy the condition with a substantial probability. Hence for this family, on a large subset of parameter space, recovery is numerically stable. Next we construct an example of $\LSEM$ on four vertices with \emph{unbounded} condition number. We then corroborate our theoretical findings via simulations as well as real-world experiments for a sociology application. Finally, we provide a general heuristic for estimating the condition number of any $\LSEM$ instance.
Demo: functors and music

We present work-in-progress on two projects whose combination enables live coding music in Haskell: cnoidal, a library for representing and transforming music, and HyperHaskell, a Haskell interpreter with a worksheet interface and graphical output. The library represents music as a collection of time intervals tagged with values, a data structure known as temporal media. Parametric polymorphism suggests various functor instances, like Applicative Functor, which we find to be highly useful for live coding. However, a lawful Monad instance can only be defined for some variants of the data type. We stress that these projects are not a specialized music environment, instead we compose a library with a general purpose interpreter.
Multiple Underwater Target Search Path Planning Based on GBNN

For the underwater target search problem of Autonomous Underwater Vehicle (AUV), this paper proposes a search path planning method for multiple underwater targets based on GBNN. Firstly, the underwater two-dimensional environment discrete grid map is constructed. Secondly, the corresponding two-dimensional GBNN model is constructed according to the grid map. Finally, the GBNN model is used to adaptively suppress the obstacle and adaptively attract the target search area. AUV can search and detect underwater targets in close proximity according to the activity output values of neural network neurons. The simulation results show that AUV can avoid obstacles autonomously and search and detect underwater targets.
Intelligent Traffic Light Controller: A Solution for Smart City Traffic Problem

Traffic congestion has been causing many setbacks and challenges in the major developing and developed smart cities in India and all over the world. The typical traffic light sequence works on the specific switching of Red, Green and Yellow lights in a particular way with stipulated time irrespective of peak traffic hour and situation, this makes typical traffic light sequence method inefficient for present situation where density of traffic is exponentially increasing over the time. In this work, we propose and develop a design of Intelligent Traffic Light Control System to manage the road traffic according to the real time traffic situation. This work is intended to propose methods of dynamic time allocation for traffic lights according to traffic density and situation. In this paper, we acquired real time traffic density using image sensors and obstacle detector, where according to the traffic density, traffic controller dynamically generates “ON” and “OFF” time of traffic light sequence. The EDGE Spartan 6 FPGA (Field Programmable Gate Array) development board is programmed as controller to implement the proposed work and MATLAB programming in used to capture real time traffic image and generate dynamic time for the traffic light sequence to control the traffic situation in real time.
MSMC-Net: Image Inpainting using Deep Multi-scale and Multi-connection Networks

Image inpainting is a hot research topic via deep learned features that can generate more reasonable structures and textures compared with traditional inpainting methods. In this work, we propose a generative adversarial network with multi-scale and multi-connection structure for image inpainting. Specifically, the proposed network consists of an edge generation network and a multi-scale image completion network with multi-connection structure. Experimental results show that our method outperforms state-of-the-arts on both ICME 2019 Grand Challenge and Places2 dataset.
Acquisition of Word-Object Associations from Human-Robot and Human-Human Dialogues

Past work on acquisition of word-object associations in robots has focused on either fast instruction-based methods which accept highly constrained input or gradual cross-situational learning methods, but not a mixture of both. In this paper, we present an integrated robotic system which allows for a combination of these methods to contribute to the task of learning the labels of objects in AI agents. We demonstrate the expanded word learning capabilities in the outcome system and how learning from both human-human and human-robot dialogues can be achieved in one integrated system.
An Enhanced Image Encryption Scheme Based on ECC and PWLCM

Elliptic curve cryptography (ECC) is an effective approach to protect privacy and security of information. Digital Image encryption is an important issue widely used to protect the data and to ensure the security. Several encryption and decryption cryptosystems are available to keep image secure from unauthorized user. Elliptic Curve Cryptography (ECC) has proven to be the best solution for public key encryption. It provides a good level of security with smaller key size. In this paper we attempt to develop an enhanced Image Encryption Scheme based on ECC and PWLCM (Piecewise Linear Chaotic Map). Here, we generate a key image to enhance data security using ECC and PWLCM. From the experiment results and security analysis, we prove that our scheme cannot only achieve good encryption, but also resist the exhaustive, statistical and differential attacks.
An Approach to Policy Gradient Reinforcement Learning with Multiple Evaluation Metrics

The development of technology in which robots autonomously learn the environment is remarkable. Although rewards are used for machine learning, robots operating in various environments require multiple evaluation metrics. This paper proposes a method to apply multiple evaluation metrics to policy gradient reinforce learning which is known as one of machine learning.
Radar signal sorting method based on support vector clustering and grey correlation degree index

Radar signal sorting method based on traditional clustering algorithm takes on a high time complexity and has poor accuracy. Aimed at the problem, a new sorting method is researched based on improved cone cluster labelling (ICCL) method for SVC algorithm and grey correlation degree (GCD) index. The ICCL method relies on approximate coverings both in feature space and data space. By using ICCL the calculation of adjacency matrix is avoided. This method is interpreted in theory and is modified for lower complexity and high accuracy by handling the outliers. And a new cluster validity index, grey correlation degree (GCD) index, is proposed which assesses the compactness and separation of clusters using average grey relational degree. In this paper, the SVC sorting model is constructed to obtain the cluster division firstly with ICCL method. Secondly, grey correlation degree is designed to measure the similarity between clusters, and to verify the clustering validity. Thirdly, the clustering parameters are adjusted adaptively to achieve the best clusters division. Finally, clustering validity sorting experiments of interleaved pulse streams is implemented. The results show that this method can obtain better clustering results.
Correction to: An ontological foundation for ocular phenotypes and rare eye diseases

Professor Michael Larsen, who is a member of the ERN-EYE Ontology Study Group and co-chair of Workgroup on Retinal Rare Eye Diseases (WG1), was inadvertently omitted from the author list in the Acknowledgements section of the original article [1].
Improved Cuckoo Search Algorithm Based on Exponential Function

The Cuckoo Search Algorithm (CS) is a novel swarm intelligence optimization algorithm inspired by biology. An improved cuckoo algorithm with adaptive adjustment of discovery probability and step size control factor is proposed to solve the problem of slow convergence speed and low calculation accuracy of CS algorithm. The algorithm uses exponential curves to simulate the changing trend of $$ \alpha $$ and $$ P_{a} $$ , and establishes the dynamic adjustment model of the two parameters mentioned above, so as to effectively improve the global search ability in the initial stage of iteration and accelerate the local search in the later stage of iteration to achieve stable convergence. Finally, the cuckoo algorithm and improved cuckoo algorithm are compared and analyzed under several common benchmark functions. The experimental results show that the cuckoo algorithm of exponential curve adaptive parameter model converges faster and calculates more accurately.