Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey Paper on Data Mining Algorithms Applied for Recommendation System

: As a standout amongst the best ways to deal with building recommender frameworks, collaborative filtering (CF) utilizes the known inclinations of a gathering of clients to make suggestions or forecasts of the obscure inclinations for different clients. In this paper, we initially present CF undertakings and their primary difficulties, for example, information sparsity, adaptability, synonymy, security insurance, and so forth, and their conceivable arrangements. We at the point present three principle algorithms of data mining strategies: content based filtering, user based filtering, agglomerative clustering (that join CF with other suggestion systems), with models for delegate calculations of every classification, and examination of their prescient execution and their capacity to address the difficulties. From fundamental strategies to the cutting edge, we endeavor to show a complete review for CF systems, which can be filled in as a guide for research and practice around there.
Scalable simulation of rate-coded and spiking neural networks on shared memory systems

The size and complexity of the neural networks investigated in computational neuroscience are increasing, leading to a need for efficient neural simulation tools to support their development. Several neuro-simulators have been developed over the years by the community, all with different scopes (rate-coded, spiking, mean-field), target platforms (CPU, GPU, clusters) or modeling principles (fixed model library, code generation). We compare here the current version of the neuro-simulator ANNarchy against other state-of-the-art simulators on ratecoded and spiking benchmarks with a focus on their parallel performance.
From Online Political Posting to Mansplaining: The Gender Gap and Social Media in Political Discussion

The gender dynamics of political discussion are important. These dynamics shape who shares their political views and how they share their views and reactions to these views. Using representative survey data from the United States and the UK, we investigate how social media platforms shape the gender dynamics of political posting. We find that on Facebook, gender does not predict political posting, whereas on Twitter, the gender gap is more pronounced. We also examine the concept of “mansplaining”—a term used to describe a patronizing form of communication directed at women by men. Firstly, we find that posting about political issues to Twitter is more likely to result in being an explainee but also being an explainer of political issues. Furthermore, posting to Twitter increases the likelihood of men reporting having been accused of mansplaining and women reporting having experienced it. In general, more than half of the women say they have experienced mansplaining, especially those who are younger, well educated, and left-leaning. We argue that the possibility of being mansplained affects who is willing to post their opinions online, and as such, caution should be exercised when using digital trace data to represent public opinion.
Threat Management in Data-centric IoT-Based Collaborative Systems

In this article, we propose a threat management system (TMS) for Data-centric Internet-of-Things-based Collaborative Systems (DIoTCSs). In particular, we focus on tampering attacks that target shared databases and can affect the execution of the DIoTCS services. The novelty of the proposed system is to isolate the damage caused by tampering attacks into data partitions. We formulate the partitioning problem as a cost-driven optimization problem, prove its NP-hardness, and propose two polynomial-time heuristics. We evaluate a TMS experimentally and demonstrate that intelligent partitioning of the database improves the overall availability of the DIoTCS.
Variance Reduction in Bipartite Experiments through Correlation Clustering

Causal inference in randomized experiments typically assumes that the units of randomization and the units of analysis are one and the same. In some applications, however, these two roles are played by distinct entities linked by a bipartite graph. The key challenge in such bipartite settings is how to avoid interference bias, which would typically arise if we simply randomized the treatment at the level of analysis units. One effective way of minimizing interference bias in standard experiments is through cluster randomization, but this design has not been studied in the bipartite setting where conventional clustering schemes can lead to poorly powered experiments. This paper introduces a novel clustering objective and a corresponding algorithm that partitions a bipartite graph so as to maximize the statistical power of a bipartite experiment on that graph. Whereas previous work relied on balanced partitioning, our formulation suggests the use of a correlation clustering objective. We use a publicly-available graph of Amazon user-item reviews to validate our solution and illustrate how it substantially increases the statistical power in bipartite experiments.
Technical characteristics’ determination of crumb rubber product by using quality function deployment (QFD) phase I

Products that have the best quality are products that are produced by researching consumer’s need. Product indicators that have the best quality are the linkages between technical characteristics and consumer’s need. PT XYZ is a company engaged in the production of crumb rubber products. However, the products produced are not by the needs of consumers because there are differences between the indicators required and the indicators produced by the company. The purpose of this study is to identify technical characteristics that are by consumer needs. The method that can be used is the Quality Function Deployment (QFD) method. The QFD method is a method that relates the relationship between consumer needs and product characteristics to obtain the products quality. In other words, by using the QFD method, consumer needs can be translated into technical characteristics so that improvements can be made to prioritized technical characteristics. The results obtained from this study are stating that the priority characteristics obtained are the speed of production with a difficulty level is 4, the degree of importance is 21%, and the estimated cost is 18%.
Research on Evaluation Method of LDA Topic Model in Mail Classification

The LDA topic model is a document generation model, which is an unsupervised machine learning technique that can be used in keyword extraction, topic classification, and so on. The main purpose of this paper is to effectively evaluate the number of optimal topics of the LDA topic model in the message subject classification and extract the importance of the topic, so that the LDA topic model can be highly readable after subject classification. Therefore, this paper proposes a keyword matching and subjective statistical value word comparison (KM-SSVW) for the subject classification of emails, the keyword matching technique in this method uses the TF-IDF technique. This method can accurately evaluate the extracted keywords and optimize the number of topics. The data in this article is mainly from the mail gate, a total of 7,000 messages that Hillary communicates with others. The empirical results show that the proposed method of keyword matching and subjective statistical value word comparison has a good effect on subject quantity optimization and subject word readable evaluation. However, there are still some limitations in this paper. In the experiment, new methods are not validated for other types of data sets, such as microblog short text, XML documents, and WeChat public platform articles.
Complete Optimizer for Dispatching Schedule of Ready Mix Concrete Plant

Dispatching Ready Mixed Concrete (RMC) to construction sites is an important task of the RMC batch plant manager. The RMC batch plant manager has to prepare an efficient schedule of dispatching RMC trucks, which will optimizes the waiting time of RMC trucks at the construction sites and also at the batch plant. The existing dispatching schedule mainly depends on the experiences of the dispatching manager and preferences from site. See if, the RMC plant manager dispatches more and more RMC trucks to the required construction site, it results in the lineup of RMC trucks at one site and keeping other sites waiting for the arrivals of RMC trucks, called as ‘interruption’. Also when the RMC trucks are dispatched as per the industry logic, more number of trucks are required than optimized value. In this paper attempt is made to reduce the interruption time by applying evolutionary Genetic Algorithm (GA) optimization techniques and also to reduce number of trucks to optimum value. A user-friendly computer program is developed in MATLAB environment to help the plant manager to decide un-interrupted dispatching schedule.
Inflectional Regularity In Modern Standard Arabic Broken Plural Nouns

This study aims to investigate the inflectional regularity in Modern Standard Arabic broken plural nouns because Arabic broken plurals have recently attracted the attention of many linguists. It is based on data qualitatively collected from English and Arabic materials on Arabic broken plurals, reference dictionaries and supplemented by data obtained from native speakers’ competence. Inflectional regularity refers to the use of the rules of a language to form new forms of a lexeme used in a grammatical context. In Arabic plural nouns, semantic regularity is the norm even where forms differ. The Arabic plural nouns are morphologically classified into two types: sound plurals formed by the addition of affixes and broken plurals formed by modifying the stem. It has been observed that Arabic broken plurals show a large number of variation in forms because of stem changes. However, these changes are not random or arbitrary. They are regular and productive. This result processes lines to further research on regularity in Arabic broken plurals and the relationship between phonology and morphology in forming broken plurals.
Nonsense!: Quality Control via Two-Step Reason Selection for Annotating Local Acceptability and Related Attributes in News Editorials

Annotation quality control is a critical aspect for building reliable corpora through linguistic annotation. In this study, we present a simple but powerful quality control method using two-step reason selection. We gathered sentential annotations of local acceptance and three related attributes through a crowdsourcing platform. For each attribute, the reason for the choice of the attribute value is selected in a two-step manner. The options given for reason selection were designed to facilitate the detection of a nonsensical reason selection. We assume that a sentential annotation that contains a nonsensical reason is less reliable than the one without such reason. Our method, based solely on this assumption, is found to retain the annotations with satisfactory quality out of the entire annotations mixed with those of low quality.
Time Series Modelling of Market Price in Real-time Bidding

Real-Time-Bidding (RTB) is one of the most popular online advertisement selling mechanisms. Modeling the highly dynamic bidding environment is crucial for making good bids. Market prices of auctions ﬂuctuate heavily within short time spans. State-of-the-art methods neglect the temporal dependencies of bidders’ behaviors. In this paper, the bid requests are aggregated by time and the mean market price per aggregated segment is modeled as a time series. We show that the Long Short Term Memory (LSTM) neural network outperforms the state-of-the-art univariate time series models by capturing the nonlinear temporal dependencies in the market price. We further improve the predicting performance by adding a summary of exogenous features from bid requests.
CMIB: Unsupervised Image Object Categorization in Multiple Visual Contexts

Object categorization in images is fundamental to various industrial areas, such as automated visual inspection, fast image retrieval, and intelligent surveillance. Most existing methods treat visual features (e.g., scale-invariant feature transform) as content information of the objects, while regarding image tags as their contextual information. However, the image tags can hardly be acquired in completely unsupervised settings, especially when the image volume is too large to be marked. In this article, we propose a novel contextual multivariate information bottleneck (CMIB) method to conduct unsupervised image object categorization in multiple visual contexts. Unlike using manual contexts, the CMIB method first automatically generates a set of high-level basic clusterings by multiple global features, which are unprecedentedly defined as visual contexts since they can provide overall information about the target images. Then, the idea of the data compression procedure for object category discovery is proposed, in which the content and multiple visual contexts are maximally preserved through a “bottleneck. ” Specifically, two Bayesian networks are initially built to characterize the relationship between data compression and information preservation. Finally, a novel sequential information-theoretic optimization is proposed to ensure the convergence of the CMIB objective function. Experimental results on seven real-world benchmark image datasets demonstrate that the CMIB method achieves better performance than the state-of-the-art baselines.
EUSP: An Easy-to-Use Semantic Parsing PlatForm

Semantic parsing aims to map natural language utterances into structured meaning representations. We present a modular platform, EUSP (Easy-to-Use Semantic Parsing PlatForm), that facilitates developers to build semantic parser from scratch. Instead of requiring a large amount of training data or complex grammar knowledge, in our platform developers can build grammar-based semantic parser or neural-based semantic parser through configure files which specify the modules and components that compose semantic parsing system. A high quality grammar-based semantic parsing system only requires domain lexicons rather than costly training data for a semantic parser. Furthermore, we provide a browser-based method to generate the semantic parsing system to minimize the difficulty of development. Experimental results show that the neural-based semantic parser system achieves competitive performance on semantic parsing task, and grammar-based semantic parsers significantly improve the performance of a business search engine.
LightCert: On designing a lighter certificate for resource‐limited Internet‐of‐Things devices

Internet‐of‐Things (IoT) devices are typically resource constrained in terms of computing capabilities and battery power. Despite the efforts from the Internet Engineering Task Force (IETF) to established standards for IoT such as IPv6 over low‐power wireless personal area networks (6LoWPAN), routing protocol for low‐power lossy networks (RPL), and constrained application protocol (CoAP), certificate‐based Internet security protocols have not been fully addressed yet.
Planning and communicating prototype tests for the Nano Membrane Toilet: A critical review and proposed visual tool

Urban sanitation in growing cities of the Global South presents particular challenges, like the speed of their growth, the high population density, and, often, the lack of existing wastewater infrastructure. This led to the Bill & Melinda Gates Foundation’s Reinvent The Toilet Challenge, a call to develop novel, non-sewered sanitation technologies, which sparked the development of various inventions, like the Nano Membrane Toilet. Complex technologies like this entail an extensive product development process, including various iterations of prototype tests. While there is an abundance of literature discussing how to build prototypes, and the optimal number of tests, there has been little focus on how to plan, communicate, and conduct tests, especially in a product development endeavour of this complexity. Multiple aspects of testing prototypes are reviewed. A visual test planning tool is proposed that encompasses the entire product development process and can be used to plan and communicate prototype tests for the Nano Membrane Toilet to ultimately achieve compliance with international standards.
Learning Fairness in Multi-Agent Systems

Fairness is essential for human society, contributing to stability and productivity. Similarly, fairness is also the key for many multi-agent systems. Taking fairness into multi-agent learning could help multi-agent systems become both efficient and stable. However, learning efficiency and fairness simultaneously is a complex, multi-objective, joint-policy optimization. To tackle these difficulties, we propose FEN, a novel hierarchical reinforcement learning model. We first decompose fairness for each agent and propose fair-efficient reward that each agent learns its own policy to optimize. To avoid multi-objective conflict, we design a hierarchy consisting of a controller and several sub-policies, where the controller maximizes the fair-efficient reward by switching among the sub-policies that provides diverse behaviors to interact with the environment. FEN can be trained in a fully decentralized way, making it easy to be deployed in real-world applications. Empirically, we show that FEN easily learns both fairness and efficiency and significantly outperforms baselines in a variety of multi-agent scenarios.
Multi-Task Learning of Depth from Tele and Wide Stereo Image Pairs

In this paper, we introduce the problem of estimating the real world depth of elements in a scene captured by two cameras with different field of views, where the first field of view (FOV) is a Wide FOV obtained by a lens with 1 × the optical zoom, and the second FOV is contained in the first FOV and corresponds to a tele zoom lens with 2 × the optical zoom. Traditional stereo matching techniques can estimate the stereo disparity, and hence the depth, in the overlapping FOV between both cameras only, which corresponds to the Tele FOV. We refer to the problem of estimating the disparity, or inverse depth, for the union of FOVs as ‘Tele-Wide disparity estimation’. We propose different deep learning solutions to establish baseline performances. We trained a single-image inverse-depth estimation (SIDE) network to estimate the inverse depth from the image corresponding to the Wide FOV only. We also trained a stereo image disparity estimation network to estimate the disparity for the overlapping Tele FOV only, and another tele-wide stereo matching network (TW-SMNet) for estimating the disparity for the union Wide FOV. We further propose an end-to-end multi-task tele-wide stereo matching deep neural network (MT-TW-SMNet) which attempts to do stereo matching in the overlapped Tele FOV and SIDE in the union Wide FOV. Experimental results on KITTI and the SceneFlow datasets establish baseline performances for the tele-wide stereo matching and demonstrate that multitask tele-wide stereo matching provides a reasonable solution to the Tele-Wide depth estimation problem.
Enhancing Local Feature Extraction with Global Representation for Neural Text Classification

For text classification, traditional local feature driven models learn long dependency by deeply stacking or hybrid modeling. This paper proposes a novel Encoder1-Encoder2 architecture, where global information is incorporated into the procedure of local feature extraction from scratch. In particular, Encoder1 serves as a global information provider, while Encoder2 performs as a local feature extractor and is directly fed into the classifier. Meanwhile, two modes are also designed for their interaction. Thanks to the awareness of global information, our method is able to learn better instance specific local features and thus avoids complicated upper operations. Experiments conducted on eight benchmark datasets demonstrate that our proposed architecture promotes local feature driven models by a substantial margin and outperforms the previous best models in the fully-supervised setting.
Feature-Attentioned Object Detection in Remote Sensing Imagery

In this work, we introduce a novel feature-attentioned object detection framework to boost its performance in remote sensing imagery, which can focus on learning these intrinsic representations from different aspects in an end-to-end framework. Firstly, when fusing multi-scale visual features of backbone network, we adopt the channel-wise and pixel-wise attentions to enhance these object-related representations and weaken the background/noise information. Secondly, an adaptive multiple receptive fields attention mechanism is employed to generate horizontal region proposals under the special situation where objects in the remote sensing imagery are always with different aspect ratios. Finally, the proposal-level feature attention is proposed to better consider both multi-layer convolutional and apparent representations so that the region of interest network can better predict the object-wise category and its corresponding location information. Comprehensive evaluations on DOTA and UCAS-AOD datasets well demonstrate the effectiveness of our feature-attentioned network for object detection in remote sensing imagery.
Fruit Image Classification Using Convolutional Neural Networks

Convolutional neural networks (CNN) are the most popular class of models for image recognition and classification task nowadays. Most of the superstores and fruit vendors resort to human inspection to check the quality of the fruits stored in their inventory. However, this process can be automated. We propose a system that can be trained with a fruit image dataset and then detect whether a fruit is rotten or fresh from an input image. We built the initial model using the Inception V3 model and trained with our dataset applying transfer learning.
Utilization of Efficient Features, Vectors and Machine Learning for Ranking Techniques

Pandey, Gaurav Utilization of Efficient Features, Vectors and Machine Learning for Ranking Techniques Jyväskylä: University of Jyväskylä, 2019, 63 p.(+included articles) (JYU Dissertations ISSN 2489-9003; 100) ISBN 978-951-39-7806-8 (PDF) Finnish summary Diss. Document ranking systems and recommender systems are two of the most used applications on the internet. Document ranking systems search for documents in response to a query given by the user. On the other hand, recommender systems suggest items to the users on the basis of their previously expressed preferences. Both document ranking systems and recommender systems make use of ranking techniques, since they typically present their results in the form of a ranked list. The order of the results is important because the users expect the most useful results at the top of these ranked lists. Improvements in algorithms used by document ranking systems and recommender systems, including the utilization of advanced machine learning techniques, lead to the generation of improved rankings. Moreover, advanced document ranking systems often use features collected from the documents to generate rankings. Similarly, vectors generated for the users as well as items are utilized by the recommender systems. Therefore, generation of features and vectors of good quality is instrumental for ranking techniques. This dissertation makes the following contributions to explore the improvements in ranking techniques using efficient features, vectors and machine learning: a) Creation of a feature extraction algorithm for learning to rank tasks in document ranking, b) Creation of pairwise preference vectors of ratings on items by using neural embeddings that can be utilized in machine learning tasks including recommender systems, c) Utilization of deep neural networks and transfer learning for serendipitous recommendations, d) Recommendations using ranking probabilities and non-negative matrix factorization and e) Application of neural embeddings to search for cities and tours, taking user’s travel interests into account.