Reviewerly/exHarmony huggingface dataset:
Sequence-Aware Factored Mixed Similarity Model for Next-Item Recommendation

Sequence-aware next-item recommendation has recently been studied because of the noteworthy usefulness of the sequential information integrated into recommendation algorithms. Following the development thread of sequential recommendation methods, especially the factored Markov chains segment, we propose a novel sequence-aware factored mixed similarity model (S-FMSM) based on a well-designed fusing similarity model with factored high-order Markov chains (i.e., Fossil). Unlike the baseline methods that model the general representation and the sequential representation in two divided factorization components, we use a factored mixed similarity model that unites the general similarity and the sequential relationship between two successive items for their sequential representation learning. Experiments on six datasets show that our newly introduced general similarity can notably improve the results of the recommended ranking lists. And a study on tuning the prior tradeoff parameter indicates the importance of the general similarity on different datasets.
Auto-Classification of Government Department-Specific News Articles

The purpose of this study is to propose an unsupervised learning-based method for automatic classification of news articles using a dictionary, incorporating the attributes of each administrative department. The results of auto-classification of news articles for individual departments showed 71% accuracy. A classification technique using unsupervised learning may be utilized to automatically classify documents without labels when gathering policy issues for individual administrative departments.
A Multi-view Learning Approach for Glioblastoma Image Contrast Enhancement

Image enhancement technique is able to enhance structure of a lesion and filter out irrelevant information through an image processing method. It can make contrast of an image stronger, and therefore enhance diagnostic accuracy. Current image enhancement techniques apply global enhancement with some strategies like histogram equalization. However, local contrast information may be lost because of the use of a global enhancement. And, global enhancement may bring unnecessary information on irrelevant background tissues. For this reason, local contrast information needs to be incorporated in the global enhancement procedure. We propose a multi-view learning approach for glioblastoma image contrast enhancement. Each local contrast is accomplished by a single-view learning and therefore final enhancement is a result of multi-view learning. Experimental results demonstrate that the proposed method is able to outperform traditional global contrast enhancement techniques.
Automated Feature Engineering using Kernel Functions

We investigate the potential of using the various kernel functions common to Support Vector Machine(SVM) based modeling from a feature-engineering perspective. Recently, with the advent of Automated Machine Learning, significant research efforts have been placed in areas such as Neural Architecture Search, Hyper Parameter Optimization, Meta Learning, Automated Data Cleaning and Automated Feature Engineering(Auto FE). Auto FE has had some research around automated linear and nonlinear systems feature generation.We have taken a concrete approach towards feature generation taking cue from SVM's kernel tricks which intend to move features from non-linear relationships in lower dimensions to linear relationships in higher dimensions. While the approach was developed for better classification of features in a linear hyperspace, the same technique could be used to assist other classification techniques by auto generating features which add lift to existing feature space.Our approach has had positive uplift for some of the simplistic kernels within SVM literature and has had negative uplift for the more complicated kernel functions. The baseline and experimental tests were done on standard datasets available for research and both datasets were passed through a standard gradient boosting model to handle multi-collinearity.
Heterogeneous Cross Project Defect Prediction – A Survey

In the testing phase of Software Development Life Cycle (SDLC), Software Defect Prediction (SDP) is one of the pivotal task which finds the modules that are more vulnerable to defects and therefore need substantial testing for the early identification of these defects. A lot of work has been done on Cross - Project Defect Prediction (CPDP) that aims to predict defects in the target project lacking in historical defect prediction data or having limited defect data to build an effective generalized defect prediction model. Mostly, CPDP approaches predict the defects in target project on the basis of similar metrics found between source and target project. This paper focuses on the prediction of defects using a Heterogeneous metric set such that no common metrics exist between the source and the target projects. In this paper, a systematic literature study has been done to quote the main findings about CPDP from year 2002 to 2019. The main purpose of this survey is to put forward the adequate content in front of computer science researchers for exploring the specific area and to provide various future directions in this field.
Incorporating Scenarios and States Definitions on Real-Time Entity Monitoring in PAbMM

Data are continuously arriving the current markets, and the data stream engines are an alternative for this kind of applications. PAbMM is a real-time processing architecture specialized on measurement projects and mounted on Apache Storm. The project definitions are loaded based on a measurement and evaluation framework. In this framework, the indicator is the way in which each measure is interpreted based on associated decision criteria for making real-time decisions. However, the decision criteria are susceptible to be influenced by the context, the different states related to the entity under monitoring and the specific indicator. Thus, a new schema is introduced as a compliment for the project definition, allowing incorporating multiple scenarios, and the entity’s states in relation to the indicators for supporting the multi-criteria decision making. This allows each indicator with its corresponding decision criteria can be interpreted by a specific scenario and an entity’s current state. In addition, the cincamipd library was extended for supporting the complementary schema, jointly with its interchanging under the JSON and XML data formats, using optionally the ZIP compression. Because the library is open source and available on GitHub, the underlying idea is to foster the interoperability between measurement systems. A discrete simulation is introduced for describing the times and sizes associated with the new schema when the volume of the projects to update grow-up. The results of the discrete simulation are very promising, only 0.308 seconds were necessary for updating 1000 active projects.
Machine Learning Based Bounding Box Regression for Improved Pedestrian Detection

The most of the studies on pedestrian and passenger detection focus on end-to-end learning by considering either improvement of features to be used or the enhancement of the detectors. One of the important steps of these systems is non-maximum suppression (NMS), which aims reducing proposed bounding boxes that supposed to belong the same target through a greedy regional search and clustering. In order to improve the performance of NMS, recent approaches consider using only bounding boxes and their scores. By following this path with a novel approach, in this study, a machine learning based bounding box regression approach is proposed. During the training phase, proposed system uses position, size and confidence scores of bounding boxes as features and the same information of the corresponding ground truth (except score) as the desired output. By this way, a pattern between initially generated bounding boxes and the ground truth is revealed. Several tests and experiments have been performed and the results show that the developed system can be particularly effective when correct decisions are needed with low overlapping ratios (such as applications with strong occlusion) without increasing false positives.
DeepOSM-3D: recognition in aerial LiDAR RGBD imagery

In this paper, we present a pipeline and prototype vision system for near-real-time semantic segmentation and classification of objects such as roads, buildings, and vehicles in large high-resolution wide-area real-world aerial LiDAR point-cloud and RGBD imagery. Unlike previous works, which have focused on exploiting ground- based sensors or narrowed the scope to detecting the density of large objects, here we address the full semantic segmentation of aerial LiDAR and RGBD imagery by exploiting crowd-sourced labels that densely canvas each image in the 2015 Dublin dataset.1 Our results indicate important improvements to detection and segmentation accuracy with the addition of aerial LiDAR over RGB imagery alone, which has important implications for civilian applications such as autonomous navigation and rescue operations. Moreover, the prototype system can segment and search geographic areas as big as 1km2 in a matter of seconds on commodity hardware with high accuracy (_ 90%), suggesting the feasibility of real-time scene understanding on small aerial platforms.
Neuro-fuzzy logic for parts-based reasoning about complex scenes in remotely sensed data

In this article, we explore the role and usefulness of neuro-fuzzy logic in the context of automatically reasoning under uncertainty about complex scenes in remotely sensed data. Specifically, we consider a first order Takagi- Sugeno-Kang (TSK) adaptive neuro-fuzzy inference system (ANFIS). First, we explore the idea of embedding an experts knowledge into ANFIS. Second, we explore the augmentation of this knowledge via optimization relative to training data. The aim is to explore the possibility of transferring then improving domain performance on tedious but important and challenging tasks. This route was selected, versus the popular modern thinking of learning a neural solution from scratch in an attempt to maintain interpretability and explainability of the resultant solution. An additional objective is to observe if the machine learns anything that can be returned to the human to improve their individual performance. To this end, we explore the task of detecting construction sites, an abstract concept that has a large amount of inner class variation. Our experiments show the usefulness of the proposed methodology and it sheds light onto future directions for neuro-fuzzy computing, both with respect to performance, but also with respect to glass box solutions.
Generation of stereo images for Mastcam imagers

There are two multispectral Mastcam imagers in the Mars Science Laboratory (MSL) onboard the Mars rover Curiosity. The left imager has low resolution whereas the right imager is just the opposite. Most of the time, the two imagers work independently. It will be interesting to explore the possibility of fusing the two left and right images to produce stereo images. However, the extremely short baseline of the stereo images challenges the stereo 3D reconstruction. In this paper, we tested the feasibility of using Mastcam images for stereo 3D reconstruction. We took a five-point algorithm to perform the epipolar rectification and then performed a census-based semi-global matching algorithm on the rectified stereo pairs to produce disparity maps. Preliminary tests using Mastcam images of two scenes were performed to test the robustness of the processing pipeline.
Working with Numbers

Computers are designed and built to crunch numbers. The programs you write will take advantage of the computational power of computers to provide experiences to users that are completely dependent on your ability to understand and utilize the tools provided by C++ to manipulate numbers. C++ provides support for different types of numbers. This support includes whole numbers and real numbers as well as multiple different ways of storing and representing these.
Spectral Decomposition By Schur for Medical Ultrasound Image Denoising

This study presents an effective approach based on Schur decomposition in frequency domain for removing speckle noise from ultrasound images. The proposed scheme has been tested on both simulated and real ultrasound images, and is compared with different benchmark schemes including the Schur Regular, PNLM and Lee. The theme of proposed approach is to segment a speckle noise corrupted image into various overlapping blocks of a small size, and generate the global covariance matrix by averaging the covariances of individual blocks. The Fourier transform of global covariance matrix is taken and Schur decomposition is applied to generate eigenvectors. These frequency domain orthogonal vectors are arranged in a descending order, and a subset is used to create the Feature matrix, which is used for speckle noise removal. The proposed approach shows better performance than benchmark techniques in terms of both Peak Signal to Noise Ratio (PSNR) and Signal to Noise Ratio (SNR), which are regarded as key parameters in the despeckling area.
PENERAPAN SISTEM INFORMASI PENJUALAN DENGAN PLATFORM E-COMMERCE PADA PERUSAHAAN DAERAH APOTEK SARI HUSADA DEMAK

Sari Husada Pharmacy is one of the regional companies engaged in drug sales in Demak Regency. The company prioritizes innovation in information technology to face globalization and modern business competition in the industrial era 4.0. Improved services are needed so they can add regular customers and provide comfort to the existence of e-commerce. E-commerce solutions will overcome the problem of some customers who do not easily reach the location of the pharmacy because the transaction activities do not have to be in one place and it is difficult to check the stock of the drug, so that information systems are made more effective and efficient. The method used uses SDLC application development, namely waterfall. This method consists of five sequences, including: analysis, design, coding, testing, and implementation. This information system is created using PHP programming with CodeIgniter, Javascript, CSS (Bootstrap) and Sublime Text frameworks and database management using PHPMyAdmin which is installed together in Xampp, Apache Web Server and MySQL. The testing technique uses Blackbox and User Acceptance Test (UAT). The results of calculations from the User Acceptance Test (UAT) is 85,4% determine that the information system is very good to implemented at Sari Husada Pharmacy.
Impact of Artificial Intelligence 2.0 on Teaching and Learning

Artificial intelligence (AI) has experienced more than 60 years of development and accumulated a strong technical foundation. In the past ten years, AI has gone through computational intelligence and perceptual intelligence, and is rapidly moving towards cognitive intelligence. AI has stepped from the 1.0 era, which is only recognized and used by elite minority groups, to the 2.0 era, which is widely used by all walks of life. With the rapid development and application of natural language understanding, facial expression recognition, educational data, virtual reality and robotics, the new intelligent teaching system after introducing these technologies is carrying out intelligent transformation and reconstruction of knowledge representation and learning process, and capturing and perceiving and analyzing learners' knowledge in real time. Learning state, automatic assessment, learning analysis, emotional perception, simulation teaching and intelligent companionship become the proper functions of intelligent teaching system. AI 2.0 will bring about great changes in the field of education. Teaching and learning are undergoing a series of changes, ranging from the generation and flow of knowledge, the form and interaction of learning, the form and construction of learning resources, the organization and implementation of teaching content, expert system, ubiquitous learning to the evaluation and management of teaching performance.
A Statistical Decision-Theoretic Approach for Measuring Privacy Risk and Utility in Databases

In this paper, we deal with the problem of database statistics publishing with privacy and utility guarantees. While various privacy and utility metrics have been proposed, purposes of using the statistics for a user and an adversary and their background knowledge about the database have not been specified. We model the user and the adversary from two perspectives. First, we model their background knowledge: knowledge of statistics of the database and knowledge of distribution for the database. Then we model the purposes of them as decision functions in statistical decision theory. Privacy and utility metrics are defined based on risk functions. Comparison of the statistical decision-theoretic framework we propose and differential privacy framework is made through a numerical example.
Modeling Causal Impact of Textual Style on a Targeted Goal

The consumption characteristics of a textual piece are influenced by both the core-content (i.e., what is being conveyed) and its stylistic attributes (i.e., how it is being conveyed). We present an approach to model stylistic attributes in text and leverage a multi-cause deconfounder model to estimate the causal effect of stylistic attributes towards a targeted goal. We show that our approach can identify causally significant attributes along with the ones considered important by conventional supervised approaches. Furthermore, we demonstrate using performance comparison on classification tasks that our approach does not compromise on the modeling capabilities. We believe that such a model can be valuable towards providing statistical feedback to an author to improve on certain style attributes to better achieve a target objective.
Towards a Digital IUPAC

Abstract Communicating chemical knowledge is at the core of the IUPAC mission and underlies the success of the chemistry enterprise. In the global economy of the 21st century, this involves exchange among computer systems along with the expert scientists who use them. To enable the application of IUPAC outputs in the digital environment, IUPAC must augment its efforts to enable accessibility and interpretation by machines as well as humans. The Union must adapt to a digital work culture to engage in its mission of sustainable development, common language and free exchange of scientific information. [Frey 2014, https://doi.org/10.1515/ci.2014.36.1.14 ]
Upaya Literasi Digital dalam Membangun SDM Unggul pada SMPN 7 Amarasi Barat di Desa Tooboun, Amarasi Barat, Kupang, NTT

There are many regions in Indonesia that should be paid more attention by the government. Without us knowing and realizing, apparently there are still many people who really need it, whether it's clothing, food and shelter, and proper education. This study uses descriptive qualitative data in the eastern part of Indonesia, namely the village of Tooboun, Amarasi Barat, NTT. The houses are also not as good as in the area of Java. The existence of NTT communities like this, will cause a dehumanization of its own, due to lack of access to all needs, and ultimately lead to a decrease in Human Resources (HR) from the community itself. The existence of demographics like this, makes them feel discriminated from certain groups. So, in this study, the writer wants to do how to develop the human resources of the village community of Tooboun, Amarasi Barat, NTT by using Media Literacy as the feasibility of life as social beings, each of whom has independence in life. This research was created to provide a basis, evaluate media literacy efforts and contextualize them relative to the current scope of media. Keywords: NTT Society, Media Literacy, Socio-Cultural Values
Crawler Framework for Category Search Engine

Due to the increasing in size of web information, it is needed to make a crawler with good scalability. Category based search engines are going to be the next generation search engines. Crawlers with good load balancing are required for Category based search engines. In this paper, we present a distributed web crawler architecture that holds goods for category support Search engine. Our category based search engine crawler splits the crawled data based upon the web page metadata into categories and distributes their storage and computing resources, with minimized communication cost. We also analyze the performance and scalability of the category based web crawler over traditional distributed web crawlers.
Design and implementation of intelligent environment monitoring system based on OneNET platform

With the development of electronic technology, remote monitoring has become the main development direction of data management. The monitoring of indoor environmental data is our main research content. The OneNET platform provided by China mobile communications corporation is used as the coordination command center of the intelligent monitoring application system for terminal design and development. OneNET platform provides standardized access terminal and the background monitor template type design method of the staff through constructing a set of environmental data acquisition and monitoring system based on OneNET, implementation of indoor environment parameters, such as temperature, temperature and concentration of PM2.5 real-time reporting and management operation, with abnormal data of automatic alarm. The system shows that the design based on the open platform can effectively improve the generation efficiency of new business and promote the development of Internet of things business.
Motion Classification Based on Noisy Micro-Doppler Signatures

Movement-induced micro-Doppler signatures are not easy to be distinguished from noise in a chaotic environment. In this work, we develop a new convolutional neural network (CNN) to classify human motions on noisy micro-Doppler spectrograms. The network is validated on both simulated and measured data and comparative experiments are also carried out. The experimental results prove the network’s effectiveness under different levels of noise circumstances.
The Struggle is Real: Adoption Insights from a Supply Chain Blockchain Case

Despite the anticipated benefits and the numerous announcements of pilot cases, we have seen very few successful implementations of blockchain technology (BCT) solutions in supply chains. Little is empirically known about the obstacles to blockchain adoption, particularly in a supply chain’s inter-organizational setting. In supply chains, blockchains’ benefits, for example BCT-based tracking and tracing, are dependent on a critical mass of supply chain actors’ adopting the technology. While previous research has mainly been conceptual and has lacked both theory and empirical data, we propose a theory-based model for inter-organizational adoption of BCT. We use the proposed model to analyze a unique, in-depth revelatory case study. Our case study confirms previous conceptual work and reveals a paradox as well as several tensions between drivers for and against (positive and negative determining factors, respectively) of BCT adoption that must be managed in an inter-organizational setting. In this vertical context, the adoption and integration decision of one supply chain actor recursively affects the adoption and integration decisions of the other supply chain actors. This paper contributes mid-range theory on BCT in supply chain management (SCM), future research directions, and managerial insights on BCT adoption in supply chains.