Reviewerly/exHarmony huggingface dataset:
A Platform based on HLS to Implement a Generic CNN on an FPGA

The fast progress in modern applications based on convolution neural network poses new challenges, such as higher precision and real-time response. On the other hand, advances of Field Programmable Gate Arrays tools allows designs based on High-Level Synthesis, allowing a faster and an easier implementation on hardware of solutions for complex problems. However, a significant amount of time and still some level of hardware design expertise are required to implement a convolution neural network on hardware. To solve this difficulty a platform to emulate a generic parameterizable-based convolution neural network on a programmable gate arrays is developed, giving the freedom to specify the network topology and tune the parameterization. This platform, developed in C language and synthetized through High-Level Synthesis, is prepared to configure a floating-point convolution neural network as a “lego”. This approach became attractive because the designer focuses on the network topology, without in-depth understanding of the underlying hardware a requirement.
Sentiment Analysis and Classification of Photos for 2-Generation Conversation in China

Appropriate photos can help the Chinese emptynest elderly and young volunteers find common topics to promote communication.However, there are little researches on such photo in China.This paper used 40 online photos with 160 sessions for the conversation experiment for the Chinese elderly and young people to analyze these photos and classify them.Sentiment analysis of Chinese conversational texts was used to estimate the speaker's attitude towards these photos.We collected the data set from the average value of sentiment analysis, the number of words uttered by the speakers, the pulse of the elderly, and the stress level of the youth for each photo.Principal Component Analysis (PCA) was carried out as a data preprocessing step to improve classification accuracies, and we selected four Principal Components (PCs) that account for 85.20% of total variance in the data.Next, we normalized these four PCs scores for Hierarchical Clustering Analysis (HCA) of the photos, and we got four clusters with different features.The results showed that photos in cluster2 were only optimal for the youth; cluster3 only made the elderly participants speak more; cluster1 and cluster4 was not suitable for the elders and the young people.This paper firstly classified the photos for 2-generation conversation and describing their features in China.Although, we did not find any photos suitable for both the elderly and the youth, this empirical study took a step forward in the investigation of photos for 2generation conversation in China.
Modeling Dialogues in FL Class

The article gives a brief review on how to improve students’ speaking skills via modeling dialogues in FL class. This study can be considered the continuation of the research undertaken by the authors in2018, inwhich the authors put forward the hypothesis that students’ speaking skills improve provided their ability to match speech patterns to behavioral patterns is developed. An aggressive behavioral pattern was researched. In this study the authors evolve that idea and analyze two more behavioral patterns: friendly and neutral. The researchers claim that the better students know different behavioral patterns, the more effectively they model dialogues. Watching videos and commenting on the conversation strategies, and modeling dialogues are chosen to be the leading teaching methods approbated in the multi-stage experiment to favor the researchers’ idea. The obtained results indicate the high potential of the chosen teaching methods for the improvement of students’ speaking skills in FL class and can substantially help FL teachers to adopt the most effective teaching styles, based on their course learning objectives.
Reliable Fault Tolerance System for Service Composition in Mobile Ad Hoc Network

&lt;p&gt;&lt;span lang="EN-US"&gt;A Due to the rapid development of smart processing mobile devices, Mobile applications are exploring the use of web services in MANETs to satisfy the user needs. Complex user needs are satisfied by the service composition where a complex service is created by combining one or more atomic services. Service composition has a significant challenge in MANETs due to its limited bandwidth, constrained energy sources, dynamic node movement and often suffers from node failures. These constraints increase the failure rate of service composition. To overcome these, we propose Reliable Fault Tolerant System for Service Composition in MANETs (RFTSC) which makes use of the checkpointing technique for service composition in MANETs. We propose fault policies for each fault in service composition when the faults occur. Failure of services in the service composition process is recovered locally by making use of Checkpointing system and by using discovered services which satisfies the QoS constraints. A Multi-Service Tree (MST) is proposed to recover failed services with O(1) time complexity. Simulation result shows that the proposed approach is efficient when compared to existing approaches.&lt;/span&gt;&lt;/p&gt;
Evaluation of Stacked Embeddings for Bulgarian on the Downstream Tasks POS and NERC

This paper reports on experiments with different stacks of word embeddings and evaluation of their usefulness for Bulgarian downstream tasks such as Named Entity Recognition and Classification (NERC) and Part-of-speech (POS) Tagging.Word embeddings stay in the core of the development of NLP, with several key language models being created over the last two years like FastText (Bojanowski et al., 2017), ElMo (Peters et al., 2018), BERT (Devlin et al., 2018) and Flair (Akbik et al., 2018).Stacking or combining different word embeddings is another technique used in this paper and still not reported for Bulgarian NERC.Well-established architecture is used for the sequence tagging task such as BI-LSTM-CRF, and different pre-trained language models are combined in the embedding layer to decide which combination of them scores better.
PML-LocNet: Improving Object Localization With Prior-Induced Multi-View Learning Network

This paper introduces a new model for Weakly Supervised Object Localization (WSOL) problems where only image-level supervision is provided. The key to solve such problems is to infer the object locations accurately. Previous methods usually model the missing object locations as latent variables, and alternate between updating their estimates and learning a detector accordingly. However, the performance of such alternative optimization is sensitive to the quality of the initial latent variables and the resulted localization model is prone to overfitting to improper localizations. To address these issues, we develop a Prior-induced Multi-view Learning Localization Network (PML-LocNet) which exploits both view diversity and sample diversity to improve object localization. In particular, the view diversity is imposed by a two-phase multi-view learning strategy, with which the complementarity among learned features from different views and the consensus among localized instances from each view are leveraged to benefit localization. The sample diversity is pursued by harnessing coarse-to-fine priors at both image and instance levels. With these priors, more emphasis would go to the reliable samples and the contributions of the unreliable ones would be decreased, such that the intrinsic characteristics of each sample can be exploited to make the model more robust during network learning. PML-LocNet can be easily combined with existing WSOL models to further improve the localization accuracy. Its effectiveness has been proved experimentally. Notably, it achieves 69.3% CorLoc and 50.4% mAP on PASCAL VOC 2007, surpassing the state-of-the-arts by a large margin.
Sistem Pakar Diagnosa Kerusakan Mesin Sepeda Motor Transmission Automatic dengan Metode Forward Chaining Studi Kasus: AHASS 00955 Mitra Perdana

Implementation of Expert System Diagnosis of motorcycle engine damage is a solution of motorcycle service at AHASS 00955 Mitra Perdana, Expert System is a system that is related to the knowledge possessed by experts and technology users to solve a problem. This article, discusses the design of decision-making applications that have an important role in the process of diagnosing motor engine damage. Because the system is currently running the user only hands over the motorcycle to the technician. The use of computer-based artificial intelligence can be a solution to problems in structured decision making. The design of this expert system is designed using the forward tracking inference method and is designed in the form of a website to facilitate users in using it.
Menara Masjid Al-Aqsha Kudus antara Situs Hindu atau Islam

The purpose of this article is to answer of question on the origin of the minaret of Al Aqsha Mosque of Kudus whether its is Hinduism-Buddhism or Islamic site. Data are collected through interview, observation and literature review by means of descriptive analytic method of analysis. This paper shows that archeological study classified the minaret as an Islamic building based on the Javanese symbols (candra sengkala) engraved in the pillars gapura rusak ewahing jagad. Gapura (gate) refers to 9, rusak means 0, ewahing is 6 and jagad means 1. Read from the last, it refers to the year 1609. The year was the era of Walisongo when Hinduism was declining in Kudus. Hindus and Buddhists people consider the minaret similar to temple based on the architecture and oral tradition. Therefore, further study incorporating history and archeology need to be conducted.
Learning Strategies for Real-Time Strategy Games with Genetic Programming

Planning in real-time strategy (RTS) games is challenging due to their very large state and action spaces. Action abstractions have shown to be a promising approach for dealing with this challenge. Previous approaches induce action abstractions from a small set of hand-crafted strategies, which are used by algorithms to search only on the actions returned by the strategies. Previous works use a set of expert-designed strategies for inducing action abstractions. The main drawback of this approach is that it limits the agent behaviour to the knowledge encoded in the strategies. In this research, we focus on learning novel and effective strategies for RTS games, to induce action abstractions. In addition to being effective, we are interested in learning strategies that can be easily interpreted by humans, allowing a better understanding of the workings of the resulting agent.
Overview on NLP Techniques for Content-Based Recommender Systems for Books

Recommender systems are an essential part of today's largest websites.Without them, it would be hard for users to find the right products and content.One of the most popular methods for recommendations is contentbased filtering.It relies on analysing product metadata, a great part of which is textual data.Despite their frequent use, there is still no standard procedure for developing and evaluating content-based recommenders.In this paper, we first examine current approaches for designing, training and evaluating recommender systems based on textual data for books recommendations for the GoodReads website.We examine critically existing methods and suggest how natural language techniques could be employed for the improvement of content-based recommenders.
An Improved Differential Evolution Algorithm for Data Stream Clustering

A Few algorithms were actualized by the analysts for performing clustering of data streams. Most of these algorithms require that the number of clusters (K) has to be fixed by the customer based on input data and it can be kept settled all through the clustering process. Stream clustering has faced few difficulties in picking up K. In this paper, we propose an efficient approach for data stream clustering by embracing an Improved Differential Evolution (IDE) algorithm. The IDE algorithm is one of the quick, powerful and productive global optimization approach for programmed clustering. In our proposed approach, we additionally apply an entropy based method for distinguishing the concept drift in the data stream and in this way updating the clustering procedure online. We demonstrated that our proposed method is contrasted with Genetic Algorithm and identified as proficient optimization algorithm. The performance of our proposed technique is assessed and cr eates the accuracy of 92.29%, the precision is 86.96%, recall is 90.30% and F-measure estimate is 88.60%.
A performance of comparative study for semi-structured web data extraction model

&lt;span lang="EN-US"&gt;The extraction of information from multi-sources of web is an essential yet complicated step for data analysis in multiple domains. In this paper, we present a data extraction model based on visual segmentation, DOM tree and JSON approach which is known as Wrapper Extraction of Image using DOM and JSON (WEIDJ) for extracting semi-structured data from biodiversity web. The large number of information from multiple sources of web which is image’s information will be extracted using three different approach; Document Object Model (DOM), Wrapper image using Hybrid DOM and JSON (WHDJ) and Wrapper Extraction of Image using DOM and JSON (WEIDJ). Experiments were conducted on several biodiversity website. The experiment results show that WEIDJ approach promising results with respect to time analysis values. WEIDJ wrapper has successfully extracted greater than 100 images of data from the multi-source web biodiversity of over 15 different websites.&lt;/span&gt;
Lexicon-based Bot-aware Public Emotion Mining and Sentiment Analysis of the Nigerian 2019 Presidential Election on Twitter

Online social networks have been widely engaged as rich potential platforms to predict election outcomes' in several countries of the world.The vast amount of readilyavailable data on such platforms, coupled with the emerging power of natural language processing algorithms and tools, have made it possible to mine and generate foresight into the possible directions of elections' outcome.In this paper, lexicon-based public emotion mining and sentiment analysis were conducted to predict win in the 2019 presidential election in Nigeria.224,500 tweets, associated with the two most prominent political parties in Nigeria, People's Democratic Party (PDP) and All Progressive Congress (APC), and the two most prominent presidential candidates that represented these parties in the 2019 elections, Atiku Abubakar and Muhammadu Buhari, were collected between 9th October 2018 and 17th December 2018 via the Twitter's streaming API.tm and NRC libraries, defined in the 'R' integrated development environment, were used for data cleaning and preprocessing purposes.Botometer was introduced to detect the presence of automated bots in the preprocessed data while NRC Word Emotion Association Lexicon (EmoLex) was used to generate distributions of subjective public sentiments and emotions that surround the Nigerian 2019 presidential election.Emotions were grouped into eight categories (sadness, trust, anger, fear, joy, anticipation, disgust, surprise) while sentiments were grouped into two (negative and positive) based on Plutchik's emotion wheel.Results obtained indicate a higher positive and a lower negative sentiment for APC than was observed with PDP.Similarly, for the presidential aspirants, Atiku has a slightly higher positive and a slightly lower negative sentiment than was observed with Buhari.These results show that APC is the predicted winning party and Atiku as the most preferred winner of the 2019 presidential election.These predictions were corroborated by the actual election results as APC emerged as the winning party while Buhari and Atiku shared very close vote margin in the election.Hence, this research is an indication that twitter data can be appropriately used to predict election outcomes and other offline future events.Future research could investigate spatiotemporal dimensions of the prediction.
Message Processing-based Steganographic Algorithm using Karhunen-Loève Transform

Steganography is the science that can hide messages inside images, videos or sounds where the hiding process is known by the sender and the receiver of the message. In this paper we present the Message Processing-based Steganographic Algorithm (MPSA), a new technique to conceal the secret message in a pseudo-random way inside of the cover object using the Karhunen-Loève transform. The paper describes the algorithm and presents an analysis of the results obtained from experimental tests.
Road flood warning system with information dissemination via social media

&lt;span&gt;Flooding is one of the most devastating natural disasters in the world. It occurs when water from heavy rains exceeds the capacity of the river banks into which it runs. Some roads are also being affected when this natural phenomenon happens which lead to closure of roads. This closure, however, are often dissiminated late to the people that are passing through the closed roads. For this purpose, a Road Flood Warning System with Information Dissemination via Social Media has been developed to provide timely flood alert information to the public. The system determines the depth of the water through an ultrasonic sensor and disseminates the data gathered to the Internet through the use of social media sites specifically Twitter and Facebook. Moreover, the system has a LED matrix and light emitting diode to display whether the roads are passable during the occurrence of floods. The test results on the performance of ultrasonic sensor showed an accuracy of 98.69% while dissemination of information was 90% successful.&lt;/span&gt;
Evaluating Optimum Conditions for Fatigue Life of NR-based Rubber Compounds Using the Taguchi Method

In the present work, four relevant factors, temperature, time, humidity, and storage condition (Oxygen-flow rate) were selected, and to monitor the extent of degradation, the effect of each factor was studied at three different levels on the ozone resistant and fatigue life time of rubber compounds.The optimal combination of physical parameters based on the Taguchi protocol and maximum fatigue life was obtained.Comparison between the confirmation run and prediction from optimal factor settings illustrate that the Taguchi parameter design is very practicable in optimizing the factors influencing aging of natural rubber based compounds.Taguchi method stresses the importance of studying response variation using the signal-to-noise (S/N) ratio, resulting in minimization of quality characteristic variations due to uncontrollable parameters.Therefore, S/N ratio and ANOVA were applied to investigate a set of parameters.The optimum conditions implicated for maximum ozone resistance and fatigue life of aged rubber compounds based on NR were time: one day; temperature: 25˚C; storage condition:
Grafting for combinatorial binary model using frequent itemset mining

Abstract We consider the class of linear predictors over all logical conjunctions of binary attributes, which we refer to as the class of combinatorial binary models (CBMs) in this paper. CBMs are of high knowledge interpretability but naïve learning of them from labeled data requires exponentially high computational cost with respect to the length of the conjunctions. On the other hand, in the case of large-scale datasets, long conjunctions are effective for learning predictors. To overcome this computational difficulty, we propose an algorithm, GRAfting for Binary datasets (GRAB) , which efficiently learns CBMs within the $$L_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>L</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math> -regularized loss minimization framework. The key idea of GRAB is to adopt weighted frequent itemset mining for the most time-consuming step in the grafting algorithm, which is designed to solve large-scale $$L_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>L</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math> -RERM problems by an iterative approach. Furthermore, we experimentally showed that linear predictors of CBMs are effective in terms of prediction accuracy and knowledge discovery.
A Method for Designing Domain-Specific Document Retrieval Systems using Semantic Indexing

Using domain knowledge and semantics to conduct effective document retrieval has attracted great attention from researchers in many different communities.Ultilizing that approach, we presents the method for designing domain-specific document retrieval systems, which manages semantic information related to document content and supports semantic processing in search.The proposed method integrates components such as an ontology describing domain knowledge, a database of document repository, semantic representations for documents; and advanced search techniques based on measuring semantic similarity.In this article, a model of domain knowledge for various information retrieval tasks, called The Classed Keyphrase based Ontology (CK-ONTO), will be presented in details.We also present graph-based models for representing documents together measures for evaluating the semantic relevance for usage in searching.The above methodology has been used in designing many real-world applications such as the Job-posting retrieval system.Evaluation with real-world inspired dataset, our methods showed noticeable improvements over traditional retrieval solutions.
A Simple and Effective Method for Injecting Word-Level Information into Character-Aware Neural Language Models

We propose a simple and effective method to inject word-level information into characteraware neural language models.Unlike previous approaches which usually inject wordlevel information at the input of a long shortterm memory (LSTM) network, we inject it into the softmax function.The resultant model can be seen as a combination of characteraware language model and simple word-level language model.Our injection method can also be used together with previous methods.Through the experiments on 14 typologically diverse languages, we empirically show that our injection method, when used together with the previous methods, works better than the previous methods, including a gating mechanism, averaging, and concatenation of word vectors.We also provide a comprehensive comparison of these injection methods.