Reviewerly/exHarmony huggingface dataset:
Frequent Itemsets Based Partitioning Approach to Decision Tree Classifier

Decision tree is a classification technique which is widely used in many real world applications. It suffers from few challenges like structural instability, overfitting, curse of dimensionality etc. To address some of these issues, vertical partitioning paradigm is used in the literature. In vertical partitioning paradigm, the feature set is split into multiple subsets and these subsets are used for subsequent processing instead of original feature set. In this paper, we propose a novel partitioning approach using highest-size frequent itemsets. The efficiency of the method is evaluated using 5 standard datasets from UCI repository. The proposed method achieves significant improvement in classification accuracy and demonstrates better or competitive structural stability as compared to classical decision tree methods. The statistical significance of the results obtained by the proposed method is demonstrated using t-test, wilcoxon signed rank and pearson correlation tests.
Modelling of Incoming Longwave Radiation Under Foggy Sky over Multiple Agro-Climate Settings of India

Net surface radiation defines the availability of radiative energy on and near the surface to drive many physical, physiological and eco-hydrological processes such as latent heat, sensible heat fluxes and evapotranspiration. Incoming longwave radiation (LWin) is one of the key components of net longwave radiation. One of the prime challenges of modelling radiation budget is estimation of surface incoming longwave radiation. Estimation of incoming longwave radiation in cloudy and foggy conditions has always been a challenge due to the lack of instrumentation and regular measurements at different spatial and temporal scales. In this study, two neural network models (daytime and night-time) were developed for estimation of incoming longwave radiation under foggy sky using half-hourly LWin, and other meteorological parameters such Ta, RH etc. The model provided high correlation of 0.85 (daytime) and 0.86 (night-time) with Root Mean Square Error (RMSE) of 4.9% for both daytime and night-time.
WITHDRAWN: Biometric finger print based voting machine using ATmega328P microcontroller

In this paper, an approach of designing of a finger print based voting system using fingerprint module and ATmega328P is presented. First we are having one database which will store all the valid information of user such as Aadhar Card Number, finger print (linked with Aadhar Card) & Voter card number. Now during the Voting time, using the individual login credentials the user will fill all his/her details in registration form. This registration details will be verified by the details feed earlier in database, if all the details of the voter matches with the server feed information the user can move for voting process otherwise it will end the process. After reaching at voting process, user will first verify his/her fingerprint pattern and then he can vote. Here the user will be recognized by his/her unique fingerprint pattern. Since the finger print pattern is not same for any two persons. So, the voter can’t vote twice or more. This system also give permission to admin to get the result just after the election completes. And allow admin to change/delete the data after completing the election result.
A Data Indexing Technique to Improve the Search Latency of AND Queries for Large Scale Textual Documents

Boolean AND queries (BAQ) are one of the most important types of queries used in text searching. In this paper, a graph-based indexing technique is proposed to improve the search latency of BAQ. It shows how a graph structure represented using a hash table can reduce the number of intersections needed for the execution of BAQ. The performance of the proposed technique is compared with one of the most widely used index structures for textual documents called Inverted Index. A detailed performance analysis is performed through prototyping and measurement on a system subjected to a synthetic workload. To get further performance insights, the proposed graph-based indexing technique is also compared with an enterprise-level search engine called Elasticsearch which uses Inverted Index at its core. The analysis shows that the graph-based indexing technique can reduce the latency for executing BAQ significantly in comparison to the other techniques.
Poster: Radiometric Signatures for Wireless Device Identification over Dynamic Channels

Radiometric signatures have been shown effective in identifying wireless devices based on imperfections in their electronics, also known as fingerprinting.Previous work mainly considered static channel conditions.In this work, we experimentally study the impact of movement and dynamic channel conditions on the radiometric signatures.We demonstrate the feasibility of fingerprinting when channels are dynamic.
Conclusion and Future Research

In this conclusion, we summarize the main steps of this research. We describe the approaches and conclusions of each step and suggest new research avenues.
High Throughput CNN Inference and Training with In-Cache Computation

We present an architecture for CNN training and batched inference with in-cache computing. Specifically targeting the high throughput requirements of the training and inference workloads, we propose novel resource partitioning and work scheduling strategies to balance the in-place computing and data storage requirements on the last level cache. Further, we propose a compression mechanism to reduce the data movement between the cache and the main memory. For ResNet-50, the proposed architecture achieves 2.2× better throughput, compared to a state-of-the-art CNN inference engine with in-cache computing [1] with a baseline scheduling policy, and a training throughput of 65.4 images per second.
Myanmar Text-to-Speech System based on Tacotron (End-to-End Generative Model)

The main motivation of this paper is to improve the naturalness of Myanmar text-to-speech system using an end-to-end generative model called Tacotron. We introduce the open-source implementation for Myanmar text-to-speech system with very high natural-sounding. In this paper, there are four main parts: speech corpus creation, data pre-processing, applying end-to-end generative model, and speech synthesis. Firstly, we develop a speech corpus of 8k sentences from a large set of news articles, novel books, daily usages and travel-related expressions for corpus creation. Secondly, we use a syllable segmenter and text normalizer for data pre-processing. Thirdly, we apply end-to-end generative model called Tacotron that synthesizes speech directly from the sequence of text characters. Finally, we use Griffin-Lim algorithm to convert the corresponding text into the output speech. For the subjective evaluation, we compare our synthesized speech output with the original recording speech in both intelligibility and naturalness by using mean opinion score (MOS). The experimental results show that we can obtain the synthesized speech comparable to the similar state-of-the-art synthsizers for other languages.
Recommendation by Joining a Human Conversation

We propose a novel way of the conversational recommendation where instead of asking user questions to acquire her preferences, the recommender tracks her conversations with other people, including customer support agents (CSA) and joins the conversation only when there is something important to recommend and the time is correct to do so. Building a recommender that joins a human conversation (RJC), we propose information extraction, discourse and argumentation analyses, as well as dialogue management techniques to compute a recommendation for a product and service that is badly needed by the customer, as inferred from the conversation. A special case of such conversations is considered where the customer raises his problem with a CSA in an attempt to resolve it, along with receiving a recommendation for a product with features addressing this problem. The performance of RJC is evaluated in a number of human–human and human-chatbot dialogues and demonstrates that RJC is an efficient and less intrusive way to provide high relevance and persuasive recommendations.
Target Recognition Framework and Learning Mode Based on Parallel Images

In the application of deep learning algorithms based on large-scale data sets, some problems, such as insufficient samples, imperfect sample quality, and high cost of building large data sets, emerge and restrict algorithm performance. In this paper, a target recognition framework and learning mode based on parallel images are proposed, and the application verification is carried out by taking the insulator target recognition in the transmission line as an example. This paper uses the artificial image generation technology to establish the insulator data set NCEPU-J, and then proposes the target recognition framework PITR and three learning modes, namely OriPITR, TrsPITR, and MutiPITR. The insulator strings with the piece number of 7, 11 and 14 are verified, and the recognition accuracy is significantly improved. The results show that the target recognition framework and learning mode based on parallel images are feasible and effective.
Development of Web Crawler to Build Indonesian Text Corpus

Abstract Recent improvement in natural language understanding research can be attributed to the availability of large scale datasets. Those datasets are mainly in English. In this work, we develop a web crawler with the purpose of extracting Indonesian news content from the DetikNews website and building a large dataset of texts. The web crawler is developed by following the waterfall model using Python, Scrapy, and BeautifulSoup4. It collects more than 790k news from DetikNews, spanning from 2011 to 2020, which consists of a total number of more than 190 million words, almost 2 million unique words, and more than 14 million sentences.
Learning Robust Multi-label Sample Specific Distances for Identifying HIV-1 Drug Resistance

Acquired immunodeficiency syndrome (AIDS) is a syndrome caused by the human immunodeficiency virus (HIV). During the progression of AIDS, a patient’s the immune system is weakened, which increases the patient’s susceptibility to infections and diseases. Although antiretroviral drugs can effectively suppress HIV, the virus mutates very quickly and can become resistant to treatment. In addition, the virus can also become resistant to other treatments not currently being used through mutations, which is known in the clinical research community as cross-resistance. Since a single HIV strain can be resistant to multiple drugs, this problem is naturally represented as a multi-label classification problem. Given this multi-class relationship, traditional single-label classification methods usually fail to effectively identify the drug resistances that may develop after a particular virus mutation. In this paper, we propose a novel multi-label Robust Sample Specific Distance (RSSD) method to identify multi-class HIV drug resistance. Our method is novel in that it can illustrate the relative strength of the drug resistance of a reverse transcriptase sequence against a given drug nucleoside analogue and learn the distance metrics for all the drug resistances. To learn the proposed RSSDs, we formulate a learning objective that maximizes the ratio of the summations of a number of $$\ell _1$$ -norm distances, which is difficult to solve in general. To solve this optimization problem, we derive an efficient, non-greedy, iterative algorithm with rigorously proved convergence. Our new method has been verified on a public HIV-1 drug resistance data set with over 600 RT sequences and five nucleoside analogues. We compared our method against other state-of-the-art multi-label classification methods and the experimental results have demonstrated the effectiveness of our proposed method.
Automatic Fetal Head Candidate Localization from 2D Ultrasound Images using Haar Cascade Classifier and Enhanced Localization Algorithm

Fetal head circumference (HC) is one of the fetal biometrics that is often used to determine gestational age and monitor the fetal growth in the womb. Nowadays, head circumference measurement from ultrasound images is performed manually by a doctor or sonographer by drawing a line or forming an ellipse to surround the fetal head. However, manual annotations are prone to human error and intra-observer as well as inter-observer variabilities. In this research, an automatic fetal head candidate localization was implemented using Haar Cascade Classifier (HCC) and further optimized by Enhanced Localization Algorithm (ELA). The combination of HCC and ELA was evaluated on 703 ultrasound images of the second trimester and 141 ultrasound images of the third trimester using the Jaccard Index (JI), Dice Similarity Coefficient (DSC), and Overlapped Area Ratio (OAR). The localization results showed that the HCC + ELA produced an average JI of 90.5%, DSC of 94.58%, OAR of 97.77% for the second trimester and an average JI of 88.17%, DSC of 93.33%, OAR of 96.97% for the third trimester. Based on the three evaluation parameters, we analyzed the factors affecting the accuracy of the localization algorithm and the correspondence of the localization results with the ellipse fitting outcome as the final process to determine the fetal head circumference.
A Fault Detection Scheme for Reversible Circuits using -Ve Control k-CNOT Based Circuit

The reversible logic circuit is a prominent research area for its low-power design, and also quantum computing. The development of synthesis and optimization is a well-known problem in the reversible circuits. For ensuring the high reliability and integrity performance of these circuits, the proper testing technique will be required to detect and locate the faults. In this paper, we consider the problem of reversible circuit testing, specifically targeting the fault detection for the missing-gate fault model in the k-CNOT based reversible circuit. It has been shown that n number of test vectors is sufficient for the detection of all single missing-gate faults (SMGFs), repeated-gate faults (RGFs), and partial missing-gate faults (PMGFs) of the proposed fault detection scheme in a reversible circuit with n inputs. Finally, we provide our experimental results based on several benchmark circuits and also show the comparative analysis with existing methods.
Color restoration algorithm of Rubik's cube color block based on SVM classification

Under different color temperatures, the image will change color to different degrees during the process from collection to display. In the process of acquisition and digital conversion, the image will be disturbed by ambient noise and light environment, resulting in errors. The change of illumination environment and color difference will reduce the success rate of color recognition. This paper proposes an color restoration algorithm of Rubik's cube color block based on SVM classification to solve the above problems. Firstly, the image edge is processed through OpenCV gaussian filter, and then the image is processed through RGVHSV color domain conversion. The classification model of the six colors of Rubik's cube was established by studying the SVM classifier algorithm, so as to improve the color capture accuracy of Rubik's cube. At the same time, according to the overall structure of the Rubik's cube, the corner, edge and center block patching algorithm is proposed, the SVM category model is combined with the color patching algorithm, and the color of each cube block is obtained according to the color value predicted by the classifier and the color patching algorithm. Experimental simulation shows that the algorithm can effectively improve the speed and accuracy of the system to identify the color of Rubik's cube and accurately identify the color of Rubik's cube.
Robust End-to-End Speaker Verification Using EEG

In this paper we demonstrate that performance of a speaker verification system can be improved by concatenating electroencephalography (EEG) signal features with speech signal features or only using EEG signal features. We use state-of-the-art end-to-end deep learning model for performing speaker verification and we demonstrate our results for noisy speech. Our results indicate that EEG signals can improve the robustness of speaker verification systems, especially in noiser environment.
A Deep Learning Decoder for Long-Range Communication Systems

Long-range communication systems require receivers that can detect and decode messages in spite of strong distorting effects. However, classical decoders often fail at coping with complex effects such as interference or multipath propagation. Deep learning has shown strong generalization and adaptation capabilities and is a promising approach for improving decoding systems. In this work, we study the specific case of aircraft communication and build a purely deep learning-based receiver. It detects incoming messages, finds the exact starting point and then decodes their message bits. We demonstrate the performance of our system and show that it can decode 45% more messages than a classical baseline decoder. Our approach is general and can be adapted to many communication protocols.
Importance of the Diagnostic Test in the Supervision of Future Physical Science Teachers

According to the new teacher engineering in Morocco in accordance with the lawestablishing the Regional Centres for Education and Training (CRMEF), teachers train and supervise futureteachers throughout their training. The latter, based on the competency-based approach, implies a bettercoherence of the modules and ensures the professionalization of teaching practices. Our issue is part of astrategy to improve the performance of the results of the of future physical science teachers at theCRMEF in Casablanca-Settat. To respond to our problematic, we have thought about an innovativepedagogical strategy based on diagnostic evaluation.The aim of this work is to study the results of a diagnostic test consisting of all the elements of the chemistrymodules, validated at the beginning of 2018. The diagnostic test is written in the form of a multiple-choiceMCQ questionnaire. It is made up of 26 items divided into 7 Chemistry themes. Analysis of the resultsobtained by calculating the overall averages of the success score for each theme of our diagnostic testshowed that the theme pH and strength of an was more successful than Structure of the atom andthe latter was less difficult than Dosing a weak acid with a strong base. On the other hand, the largepercentages of false answers corresponded to the topics thermochemistry, crystallography, chemicalkinetics and concentrated HCl solution respectively. The results of the diagnostic test enabled us toclassify the future teachers by level of mastery of the items, to identify the causes that may hinder learningand thus to better plan their initial at the CRMEF for the complementary training module.Keywords: Diagnostic test, MCQ, Future teachers, Training, Regional Education and Training Trade Centre(CRMEF)
A New Adaptive Learning algorithm to train Feed-Forward Multi-layer Neural Networks, Applied on Function Approximation Problem

Slow convergence and inverse hessian calculation respectively, are the major drawbacks of first-order and second-order learning algorithms. This paper presents a new efficient algorithm to train feed-forward Multi-Layered Perceptron (MLP) neural network that doesn't require explicit computation of the inverse Hessian matrix. Due to the use of mathematical adaptive learning rates in the purposed approach, the rating speed is improved significantly compared to the first-order algorithms. The proposed method is applied to some function approximation problems and compared with backpropagation and modified backpropagation.
Research on segmentation of sports images based on GMMLS method

The transmission is a major economic game broadcast, not just playing games, video files, and can be tracked by image segmentation. However, from the current situation, it can see the video segmentation technology is still immature. Image segmentation is a technique that divides the original image into several different categories. Many of the possible solutions, it can be made available for image segmentation into different motion image segmentation qualities and each, some of the categories. The first step in the preprocessing is to remove the noise from the image. The second level is designed to be based on the proposed Gaussian Mixed Model Level Set (GMMLS), which uses multi-level threshold technology for image segmentation. The GMMLS is expected to realize an image processing method because it is substantially parallel to and consistent with the regional image division processing. And the third process, feature extraction, completion and final motion image classification. Experiments have demonstrated the feasibility of artificial life methods in both luminance and color images. Experimental results show that the algorithm provides fast segmentation with high perceptual segmentation quality while moving the image split view.
Language-agnostic Injection Detection

Formal languages are ubiquitous wherever software systems need to exchange or store data. Unparsing into and parsing from such languages is an error-prone process that has spawned an entire class of security vulnerabilities. There has been ample research into finding vulnerabilities on the parser side, but outside of language specific approaches, few techniques targeting unparser vulnerabilities exist. This work presents a language-agnostic approach for spotting injection vulnerabilities in unparsers. It achieves this by mining unparse trees using dynamic taint analysis to extract language keywords, which are leveraged for guided fuzzing. Vulnerabilities can thus be found without requiring prior knowledge about the formal language, and in fact, the approach is even applicable where no specification thereof exists at all. This empowers security researchers and developers alike to gain deeper understanding of unparser implementations through examination of the unparse trees generated by the approach, as well as enabling them to find new vulnerabilities in poorly-understood software. This work presents a language-agnostic approach for spotting injection vulnerabilities in unparsers. It achieves this by mining unparse trees using dynamic taint analysis to extract language keywords, which are leveraged for guided fuzzing. Vulnerabilities can thus be found without requiring prior knowledge about the formal language, and in fact, the approach is even applicable where no specification thereof exists at all. This empowers security researchers and developers alike to gain deeper understanding of unparser implementations through examination of the unparse trees generated by the approach, as well as enabling them to find new vulnerabilities in poorly-understood software.
Predicting Buying Behavior using CPT+: A Case Study of an E-commerce Company

: Recently, predicting the buying behaviour of customers on e-commerce websites is a very critical issue in business management. This could help merchants understand the tendencies of consumers in choosing and buying products. It has become increasingly common these days that predicting buying behaviour on online systems. Although this is a challenging task, it is an exciting and hot topic for researchers. This article intends to be proposed as a predictive model for buying behaviour on online systems. This model may be represented as a two-stage process. First, a sequence database is built from a shopping cart. Second, the prediction will be performed by using the CPT+, which is an improved model of CPT (Compact Prediction Tree)). The main contribution of this paper is that we proposed a solution for predicting buying behaviour in the e-commerce context (a case study of an e-commerce company). The core prediction is mainly based on sequence prediction, in particularly, CPT+ (Compact Prediction Tree).