Reviewerly/exHarmony huggingface dataset:
An expressive and tractable rule-based description language

Horn-DL is a rule language introduced by Nguyen, Nguyen, and Szałas for the Semantic Web. It has a PTime data complexity and is more expressive than Horn-SROIQ. These rule languages, however, do not allow PDL-like role constructors (i.e., union, sequential composition, the reflexive-transitive closure and the test operator). Using the transitive closure of a role r is not the same as using a transitive role that is a superrole of r. Furthermore, the test operator together with the other PDL-like role constructors would allow users to express sophisticated concepts. In this paper, we extend Horn-DL with PDL-like role constructors to obtain the rule language Horn-DL2. We modify the algorithm given by Nguyen, Nguyen, and Szałas (2015) to obtain another one that checks satisfiability of a knowledge base in Horn-DL2 instead of Horn-DL. The resulting algorithm also has a PTime data complexity and Horn-DL2 is therefore tractable.
PENERAPAN METODE K-NEAREST NEIGHBOR DAN INFORMATION GAIN PADA KLASIFIKASI KINERJA SISWA

Education is a very important problem in the development of a country. One way to reach the level of quality of education is to predict student academic performance. The method used is still using an ineffective way because evaluation is based solely on the educator's assessment of information on the progress of student learning. Information on the progress of student learning is not enough to form indicators in evaluating student performance and helping students and educators to make improvements in learning and teaching. K-Nearest Neighbor is an effective method for classifying student performance, but K-Nearest Neighbor has problems in terms of large vector dimensions. This study aims to predict the academic performance of students using the K-Nearest Neighbor algorithm with the Information Gain feature selection method to reduce vector dimensions. Several experiments were conducted to obtain an optimal architecture and produce accurate classifications. The results of 10 experiments with k values ​​(1 to 10) in the student performance dataset with the K-Nearest Neighbor method showed the largest average accuracy of 74.068 while the K-Nearest Neighbor and Information Gain methods obtained the highest average accuracy of 76.553. From the results of these tests it can be concluded that Information Gain can reduce vector dimensions, so that the application of K-Nearest Neighbor and Information Gain can improve the accuracy of the classification of student performance better than using the K-Nearest Neighbor method.
Pengaruh Semantic Expansion pada Naïve Bayes Classifier untuk Analisis Sentimen Tokoh Masyarakat

Sentiment analysis is a field of study that analyzes one's opinions, sentiments, evaluations, attitudes and emotions that are conveyed in written text. There are several factors that cause low accuracy results from sentiment analysis. These factors such as less optimal stemming process, word negation process that does not produce maximum results, writing errors in the dataset, and others. These problems can be overcome by optimizing the process of normalizing words, negation, stemming, and adding methods of semantic expansion. The purpose of adding the Semantic Expansion method and improvement in the process is to increase the accuracy value of the Sentiment Analysis process. This study aims to create a sentiment analysis model from public comments on a public figure (Ridwan Kamil) using the Naïve Bayes Classifier algorithm. Based on the test results in the sentiment analysis model using the Naïve Bayes Classifier method with the addition of the semantic expansion method it is proven that it can improve accuracy. The accuracy obtained using the semantic expansion method is 72%. While the value of accuracy without semantic expansion is 70%.
A Fast and Robust Template Matching Method with Rotated Gradient Features and Image Pyramid

In the field of industrial inspection, it is an urgent need to recognize and locate objects with similar transformation in real time. Current traditional methods cannot keep high robustness in illumination change, clutter, occlusion and other environments, and cannot match objects correctly when there is a rotation angle between the scene object and the given template. In order to solve the above problems, a fast and robust rotated template matching method is proposed in this paper. First, the algorithm uses the gradient of object edge points as the basic data of similarity measure function which has high robustness; Second, image pyramid technology is selected to reduce algorithm complexity; For edges, the maximum pyramid is designed to preserve the shape information to the greatest extent, and for gradients, the mean pyramid is designed to fully consider the neighborhood information. Third, down-sampling of feature points is adopted in accelerating the algorithm and combining rotated feature with image pyramid is adopted in solving rotated matching problem. Last, the least square adjustment is applied to improve the pose accuracy.
SVM-based Instruction Set Identification for Grid Device Firmware

Identifying the binary program instruction set is an important prerequisite for reverse analysis of firmware, but grid device firmware usually lacks a program description header, which poses a serious challenge to instruction set identification. This paper proposes an SVM-based instruction set recognition method(SVM-IBPS). First, the optimal feature list is obtained by the instruction set feature selection method of information gain, and then the SVM-IBPS model is trained by Support Vector Machine and tested in the dataset of 111,918 executable files, which is decompressed and extracted from the embedded device firmware. Finally, a comparative experiment was conducted with Binwalk and SVM-IBPS. The experimental results show that the accuracy of SVM-IBPS on the test dataset is 98.97%, which is 20.7% higher than that of Binwalk, and the time cost is only about one-ninth of it.
Explicitly Coordinated Policy Iteration

Coordination on an optimal policy between independent learners in fully cooperative stochastic games is difficult due to problems such as relative overgeneralization and miscoordination. Most state-of-the-art algorithms apply fusion heuristics on agents' optimistic and average rewards, by which coordination between agents can be achieved implicitly. However, such implicit coordination faces practical issues such as tedious parameter-tuning in real world applications. The lack of an explicit coordination mechanism may also lead to a low likelihood of coordination in problems with multiple optimal policies. Based on the necessary conditions of an optimal policy, we propose the explicitly coordinated policy iteration (EXCEL) algorithm which always forces agents to coordinate by comparing the agents' separated optimistic and average value functions. We also propose three solutions for deep reinforcement learning extensions of EXCEL. Extensive experiments in matrix games (from 2-agent 2-action games to 5-agent 20-action games) and stochastic games (from 2-agent games to 5-agent games) show that EXCEL has better performance than the state-of-the-art algorithms (such as faster convergence and better coordination).
System Analysis Of Inventory Information On Raw Material Companies

In the era of increasingly rapid development of information technology, the use of computers in every Indonesian company is very important to support information needs. This is definitely mandatory, because with a computerized system all processes ranging from data processing to other important documents can be arranged neatly so that it can facilitate data storage and search. Problems faced in the inventory information system at the company are usually lacking an accurate, fast, and precise information system so that it is less optimal and produces all accurate and relatively long reports. The purpose of the research conducted by the author is to analyze the information system that runs on the raw material company. The methodology used is the system development life cycle approach starting from analyzing the system that runs through UML (Unified Modeling Language) to describe the running system analysis and analysis of the proposed system so as to improve the quality of the standard company.&#x0D; &#x0D; Keywords: Inventory, company, Raw Materials, Management.
Automated versus handheld breast ultrasound examinations of suspicious breast masses: methodological errors in the reliability analysis

Ultrasonography (renamed from the Journal of Korean Society of Ultrasound in Medicine in January 2014), the official English-language journal of the Korean Society of Ultrasound in Medicine (KSUM), is an international peer-reviewed academic journal dedicated to practice, research, technology, and education dealing with medical ultrasound, Aims and Scope:Ultrasonography (renamed from the Journal of Korean Society of Ultrasound in Medicine in January 2014), the official English-language journal of the Korean Society of Ultrasound in Medicine (KSUM), is an international peer-reviewed academic journal dedicated to practice, research, technology, and education dealing with medical ultrasound.It is published four times per year: January 1, April 1, July 1, and October 1. Original articles, topical reviews, pictorial essays, and notable case reports are published in Ultrasonography covering state-of-the-art content. Ultrasonography also serves as a medium for cooperation among physicians and specialists from around the world who are focusing on various ultrasound technology and disease problems.
An Example of Application of CBM to Intersection-Union Hypotheses Testing

Kachiashvili KJ* Author Affiliations Faculty of Informatics and Control Systems of the Georgian Technical University I. Vekua Institute of Applied Mathematics of the Tbilisi State University Received: July 02, 2019 | Published: July 08, 2019 Corresponding author: Kachiashvili KJ, Georgian Technical University, I. Vekua Institute of Applied Mathematics of the Tbilisi State University DOI: 10.26717/BJSTR.2019.19.003304 Also View In:
AI Planning for Enterprise: Putting Theory Into Practice

In this paper, I overview a number of AI Planning applications for Enterprise and discuss a number of challenges in applying AI Planning in that setting. I will also summarize the progress made to date in addressing these challenges.
Hanada’s Text Leveling System (HTLS) from Text Engagement to Text Engagingness

Abstract Those who are involved in developing language curriculum in The Arab World face usually a challenging barrier related to texts selection. They are still in need of clear and justifiable criteria that may help them select and organize texts. In this article, I review critically Hanada Taha’s Text Leveling System (HTLS), the first Arabic attempt that addresses this issue. Developed by Hanada Taha, the system provides a procedural framework which will help us in selecting and organizing Arabic texts.
Intracranial Vascular Structure Extraction: A Machine Learning Approach

Extraction of brain blood vessels is an important issue for clinical assessment of intracranial vascular diseases. In this paper, the vessel extraction problem is formulated to a connected region classification problem. In processing images, an improved multi-scale filtering method is performed to improve vessel connectivity, and a post-processing step is added to harvest salient vessel candidates (SVC). Then, SVC is decomposed into connected regions and features are calculated and fed into a neural network classifier for training. For extraction, each connected region is individually analyzed using the trained neural network by considering the values of neighboring voxels that belong to its connection. The extraction results demonstrate the validity of the proposed approach.
Unsupervised deep feature embeddings for speaker diarization

Speaker diarization aims to determine ?who spoke when?? from multispeaker recording environments. In this paper, we propose to learn a set of high-level feature representations, referred to as feature embeddings, from an unsupervised deep architecture for speaker diarization. These sets of embeddings are learned through a deep autoencoder model when trained on mel-frequency cepstral coefficients (MFCCs) of input speech frames. Learned embeddings are then used in Gaussian mixture model based hierarchical clustering for diarization. The results show that these unsupervised embeddings are better compared to MFCCs in reducing the diarization error rate. Experiments conducted on the popular subset of the AMI meeting corpus consisting of 5.4 h of recordings show that the new embeddings decrease the average diarization error rate by 2.96%. However, for individual recordings, maximum improvement of 8.05% is acquired.
Symmetry Encoder-Decoder Network with Attention Mechanism for Fast Video Object Segmentation

Semi-supervised video object segmentation (VOS) has obtained significant progress in recent years. The general purpose of VOS methods is to segment objects in video sequences provided with a single annotation in the first frame. However, many of the recent successful methods heavily fine-tune the object mask in the first frame, which decreases their efficiency. In this work, to address this issue, we propose a symmetry encoder-decoder network with the attention mechanism for video object segmentation (SAVOS) requiring only one forward pass to segment the target object in a video. Specifically, the encoder generates a low-resolution mask with smoothed boundaries, while the decoder further refines the details of the segmentation mask and integrates lower level features progressively. Besides, to obtain accurate segmentation results, we sequentially apply the attention module on multi-scale feature maps for refinement. We conduct several experiments on three challenging datasets (i.e., DAVIS 2016, DAVIS 2017, and SegTrack v2) to show that SAVOS achieves competitive performance against the state-of-the-art.
Effects of Credit Memos on Performance Accountant on Uncollectible Receivables

In the current era we have entered the disruptive 4.0 era where sophistication has been applied in the finance of a company to record every expense incurred by an institution or company. In the transaction process as it is now does not rule out the possibility of several triggers for making a deposit such as the example of a sales or purchase return and overpayment if it is not handled immediately it will arise as a financial report problem. The Cloud Accounting system is equipped with a credit memo facility which is divided into 2 (two) parts, namely customer memo credit and supplier memo credit. The Cloud Accounting system is then equipped with a credit memo facility which is divided into 2 (two) parts, namely customer memo credit and supplier memo credit. Where customer memo credit is the customer entrusting a sum of money to the company, it will be used as a payment on the upcoming sales bill. While supplier memo credit is the balance that arises due to overpayment, then the deposit will be used as a discount on future sales bills. By using a memo credit based on Cloud Accounting, it can minimize crime or crime at the company because it has been certified by ISO / IEC 2700.&#x0D; &#x0D; Keywords: Online Accounting Software, Customer Memo Credit, and Supplier Memo Credit.
Learning K-way D-dimensional Discrete Embedding for Hierarchical Data Visualization and Retrieval

Traditional embedding approaches associate a real-valued embedding vector with each symbol or data point, which is equivalent to applying a linear transformation to ``one-hot" encoding of discrete symbols or data objects. Despite simplicity, these methods generate storage-inefficient representations and fail to effectively encode the internal semantic structure of data, especially when the number of symbols or data points and the dimensionality of the real-valued embedding vectors are large. In this paper, we propose a regularized autoencoder framework to learn compact Hierarchical K-way D-dimensional (HKD) discrete embedding of symbols or data points, aiming at capturing essential semantic structures of data. Experimental results on synthetic and real-world datasets show that our proposed HKD embedding can effectively reveal the semantic structure of data via hierarchical data visualization and greatly reduce the search space of nearest neighbor retrieval while preserving high accuracy.
Hybrid Spatio-Spectral Total Variation: A Regularization Technique for Hyperspectral Image Denoising and Compressed Sensing

We propose a new regularization technique, named Hybrid Spatio-Spectral Total Variation (HSSTV), for hyperspectral (HS) image denoising and compressed sensing. Regularization techniques based on total variation (TV) focus on local differences of an HS image to model its underlying smoothness and have been recognized as a popular approach to HS image restoration. However, existing TVs do not fully exploit underlying spectral correlation in their designs and/or require a high computational cost in optimization. Our HSSTV is designed to simultaneously evaluates two types of local differences: direct local spatial differences and local spatio-spectral differences in a unified manner with a balancing weight. This design resolves the said drawbacks of existing TVs. Then, we formulate HS image restoration as a constrained convex optimization problem involving HSSTV and develop an efficient algorithm based on the alternating direction method of multipliers (ADMM) for solving it. In the experiments, we illustrate the advantages of HSSTV over several state-of-the-art methods.
A New Unit Selection Optimisation Algorithm for Corpus-Based TTS Systems Using the RBF-Based Data Compression Technique

A major drawback of corpus-based speech synthesis systems is the use of large acoustic inventories, and currently one of the main challenges is the optimal representation of concatenation costs associated with units in the acoustic inventory. These concatenation costs are used to evaluate spectral mismatches between the acoustic units to be concatenated. The combinatorics of costs grows exponentially with the size of the acoustic inventories and can result in hundreds of millions or even billions of concatenation costs to be processed. Therefore, in this paper, we represent a novel unit selection optimization algorithm, which minimizes the size of concatenation costs through the vector quantization-based compression technique and tuple structures. Furthermore, the proposed optimization algorithm is designed to be used as an objective measure to optimize the performance of the unit selection cost function regarding the quality of the speech output, and to evaluate the effect of the vector quantization-based compression technique on its performance. The results obtained show that even when data compression is above 50%, the effect on the quality of the synthesized speech is negligible.
Behavior-driven development as an approach to improve software quality and communication across remote business stakeholders, developers and QA: two case studies

In this work, we describe two of our experiences applying BDD: one at a large publisher of financial information and business news, and the other in a secure collaboration platform vendor. The team composition involves both a local team and an external service provider. We analyze two adoption processes, one driven by the product team, and the other driven by the development team. We cover the communication benefits and impacts on areas such as stakeholder alignment, software design, code refactoring, and bug fixing. We also discuss lessons learned, caveats and how we plan to expand this practice in the future.
Not All FPRASs are Equal: Demystifying FPRASs for DNF-Counting (Extended Abstract)

The problem of counting the number of solutions of a DNF formula, also called #DNF, is a fundamental problem in AI with wide-ranging applications. Owing to the intractability of the exact variant, efforts have focused on the design of approximate techniques. Consequently, several Fully Polynomial Randomized Approximation Schemes (FPRASs) based on Monte Carlo techniques have been proposed. Recently, it was discovered that hashing-based techniques too lend themselves to FPRASs for #DNF. Despite significant improvements, the complexity of the hashing-based FPRAS is still worse than that of the best Monte Carlo FPRAS by polylog factors. Two questions were left unanswered in previous works: Can the complexity of the hashing-based techniques be improved? How do these approaches compare empirically? In this paper, we first propose a new search procedure for the hashing-based FPRAS that removes the polylog factors from its time complexity. We then present the first empirical study of runtime behavior of different FPRASs for #DNF, which produces a nuanced picture. We observe that there is no single best algorithm for all formulas and that the algorithm with one of the worst time complexities solves the largest number of benchmarks.
Randomized Greedy Search for Structured Prediction: Amortized Inference and Learning

In a structured prediction problem, we need to learn a predictor that can produce a structured output given a structured input (e.g., part-of-speech tagging). The key learning and inference challenge is due to the exponential size of the structured output space. This paper makes four contributions towards the goal of a computationally-efficient inference and training approach for structured prediction that allows to employ complex models and to optimize for non-decomposable loss functions. First, we define a simple class of randomized greedy search (RGS) based inference procedures that leverage classification algorithms for simple outputs. Second, we develop a RGS specific learning approach for amortized inference that can quickly produce high-quality outputs for a given set of structured inputs. Third, we plug our amortized RGS inference solver inside the inner loop of parameter-learning algorithms (e.g., structured SVM) to improve the speed of training. Fourth, we perform extensive experiments on diverse structured prediction tasks. Results show that our proposed approach is competitive or better than many state-of-the-art approaches in spite of its simplicity.
Real-Time Pedestrian Detection in Monitoring Scene Based on Head Model

Pedestrian detection is an essential technology in robotics, intelligent transportation system, and intelligent video surveillance. Pedestrians in the surveillance scene have the characteristics of dense crowds and high degree of occlusion, meanwhile, it needs to meet the requirements of real-time detection. To solve this problem, the method based on head model with YOLOv3 algorithm was proposed. This work re-selects the number and dimensions of anchor boxes by k-means method on the training set, fine-tuning the network and training it to get the optimal model. The experiment results show that this work effectively improves the detection performance and real-time of pedestrian detection in the surveillance scene.