Reviewerly/exHarmony huggingface dataset:
Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers

This paper introduces Timers and Such, a new open source dataset of spoken English commands for common voice control use cases involving numbers. We describe the gap in existing spoken language understanding datasets that Timers and Such fills, the design and creation of the dataset, and experiments with a number of ASR-based and end-to-end baseline models, the code for which has been made available as part of the SpeechBrain toolkit.
Matrix Factorization Based Recommendation Algorithm for Sharing Patent Resource

As scientific and technological resources are experiencing information overload, it is quite expensive to find resources that users are interested in exactly. The personalized recommendation system is a good candidate to solve this problem, but data sparseness and the cold starting problem still prevent the application of the recommendation system. Sparse data affects the quality of the similarity measurement and consequently the quality of the recommender system. In this paper, we propose a matrix factorization recommendation algorithm based on similarity calculation(SCMF), which introduces potential similarity relationships to solve the problem of data sparseness. A penalty factor is adopted in the latent item similarity matrix calculation to capture more real relationships furthermore. We compared our approach with other 6 recommendation algorithms and conducted experiments on 5 public data sets. According to the experimental results, the recommendation precision can improve by 2% to 9% versus the traditional best algorithm. As for sparse data sets, the prediction accuracy can also improve by 0.17% to 18%. Besides, our approach was applied to patent resource exploitation provided by the wanfang patents retrieval system. Experimental results show that our method performs better than commonly used algorithms, especially under the cold starting condition.
Survey on Data Mining Techniques, Process and Algorithms

Abstract The term “Data Mining” is refers to the extraction of patterns and knowledge from large amounts of raw data and often defined as finding hidden information in a database. It insinuate analyzing data patterns in large volume of data using one or more software. Data mining involves effective data collection and warehousing as well as computer processing.
Perbandingan Algoritma SVM dan SVM Berbasis Particle Swarm Optimization Pada Klasifikasi Beras Mekongga

Rice is the most important staple food in Indonesia. There are various types of varieties available, one of them is Inpari Mekongga variety. In Karawang, Mekongga rice type is the most popular and superior compared to others. However, this type of rice is often mixed with the other types because there are too many varieties and various other problems. Classifying varieties of rice types can be done to identify the types of rice. The classification of rice varieties in this research is divided into 2 classes, Mekongga and not Mekongga. The method that used in this reserach is Support Vector Machine (SVM) and Particle Swarm Optimatizon (PSO). SVM method was chosen because it basically handles the classification of two classes. Meanwhile, PSO method used to optimize the accuracy level of the SVM method. Combination from the two methods is very well used in classification data because it can increase the level of accuracy better. The purpose of this reserach is compare the accuracy of the 2 methods that used. The results from research is mekongga rice classification with Support Vector Machine has accuracy value 46.67% and AUC value 0.475. Meanwhile, using Support Vector Machine based on Particle Swarm Optimization (PSO) can help improve the classification of this mekongga rice with accuracy value 70.83% and AUC value 0.671.
Scene-Specific Dark Channel Prior for Single Image Fog Removal

Extensive presence of fog in outdoor images severely alters the scene appearance and hence reduces the visibility. Image processing based defogging algorithms are used to restore the details and colour in a single foggy image. Performances of the previous defogging approaches are considerably low since they fail to consider the image-specific cues. In this paper, a novel and simple defogging approach is proposed based on the estimation of depth map by considering the density of fog in local image regions. The proposed approach uses the scene-specific depth map information to compute the dark channel and transmission. The quality of recovered image is further improved by a post-processing technique. Experimental evaluation performed on FRIDA and FRIDA2 benchmark datasets demonstrates the proposed defogging framework outperforms state-of-the-art approaches. The code and the results of this work are open-sourced for reproducibility (https://github.com/RPRO5/Defogging).
Multi-level Emotion Cause Analysis by Multi-head Attention Based Multi-task Learning

Emotion cause analysis (ECA) aims to identify the potential causes behind certain emotions in text. Lots of ECA models have been designed to extract the emotion cause at the clause level. However, in many scenarios, only extracting the cause clause is ambiguous. To ease the problem, in this paper, we introduce multi-level emotion cause analysis, which focuses on identifying emotion cause clause (ECC) and emotion cause keywords (ECK) simultaneously. ECK is a more challenging task since it not only requires capturing the specific understanding of the role of each word in the clause but also the relation between each word and emotion expression. We observe that ECK task can incorporate the contextual information from the ECC task, while ECC task can be improved by learning the correlation between emotion cause keywords and emotion from the ECK task. To fulfill the goal of joint learning, we propose a multi-head attention based multi-task learning method which utilizes a series of mechanisms including shared and private feature extractor, multi-head attention, emotion attention and label embedding to capture features and correlations between the two tasks. Experimental results show that the proposed method consistently outperforms the state-of-the-art methods on a benchmark emotion cause dataset.
Text Summarization using Extractive and Abstractive Methods

Text Summarization is a process where a huge text file is converted into summarized version which will preserve the original meaning and context. The main aim of any text summarization is to provide a accurate and precise summary. One approach is to use a sentence ranking algorithm. This comes under extractive summarization. Here, a graph based ranking algorithm is used to rank the sentences in the text and then top k-scored sentences are included in the summary. The most widely used algorithm to decide the importance of any vertex in a graph based on the information retrieved from the graph is Graph Based Ranking Algorithm. TextRank is one of the most efficient ranking algorithms which is used for Web link analysis that is for measuring the importance of website pages. Another approach is abstractive summarization where a LSTM encoder decoder model is used along with attention mechanism which focuses on some important words from the input. Encoder encodes the input sequence and decoder along with attention mechanism gives the summary as the output.
Detecting abnormal electricity usage using unsupervised learning model in unlabeled data

Smart-home systems achieved great popularity in the last decade as they increase the comfort and quality of life. Reduction of energy consumption became a very important desiderate in the context of the explosive technological development of modern society with a major impact on the future development of mankind. Moreover, due to the large amount of data available from smart meters installed in households. It makes leverage to able to find data abnormalities for better monitoring and forecasting. Detecting data anomalies helps in making a better decision for reducing energy usage wasted. In recent years, machine learning models are widely used for developing intelligent systems. Currently, researchers’ main focus is on developing supervised learning models for predicting anomalies. However, there are challenges to train models with unlabeled data indicating data anomaly or not. In this paper, abnormalities are detected in electricity usage using unsupervised learning and evaluated using Excess Mass. The unsupervised anomaly detection model is based on Gaussian Mixture Model (GMM) and Isolation Forest (iForest). The models are compared with Local Outlier Factor (LOF) and One-class support vector machine (OCSVM). The proposed framework is tested with actual electricity usage and temperature data obtained from Numenta Anomaly Benchmark (NAB), which contains normal and anomaly data in time series. Finally, it has been observed that the iForest out-performed as the detection model for the selected use case. The outcome showed that the iForest can quickly detect anomalies in electricity usage data with only a sequence of data without feature extraction. The proposed model is suitable for the Smart Home Energy Management System's practical requirement and can be implemented in various houses independently. The proposed system can also be extended with the various use cases having similar data types.
EVALITA4ELG: Italian Benchmark Linguistic Resources, NLP Services and Tools for the ELG Platform

Starting from the first edition held in 2007, EVALITA is the initiative for the evaluation of Natural Language Processing tools for Italian.This paper describes the EVALITA4ELG project, whose main aim is at systematically collecting the resources released as benchmarks for this evaluation campaign, and making them easily accessible through the European Language Grid platform.The collection is moreover integrated with systems and baselines as a pool of web services with a common interface, deployed on a dedicated hardware infrastructure.
A Novel Sequence-to-Subgraph Framework for Diagnosis Classification

Text-based diagnosis classification is a critical problem in AI-enabled healthcare studies, which assists clinicians in making correct decision and lowering the rate of diagnostic errors. Previous studies follow the routine of sequence based deep learning models in NLP literature to deal with clinical notes. However, recent studies find that structural information is important in clinical contents that greatly impacts the predictions. In this paper, a novel sequence-to-subgraph framework is introduced to process clinical texts for classification, which changes the paradigm of managing texts. Moreover, a new classification model under the framework is proposed that incorporates subgraph convolutional network and hierarchical diagnostic attentive network to extract the layered structural features of clinical texts. The evaluation conducted on both the real-world English and Chinese datasets shows that the proposed method outperforms the state-of-the-art deep learning based diagnosis classification models.
Gambiarra and Techno-Vernacular Creativity in NIME Research

Over past editions of the NIME Conference, there has been a growing concern towards diversity and inclusion.It is relevant for an international community whose vast majority of its members are in Europe, the USA, and Canada to seek a richer cultural diversity.To contribute to a decolonial perspective in the inclusion of underrepresented countries and ethnic/racial groups, we discuss Gambiarra and Techno-Vernacular Creativity concepts.We believe these concepts may help structure and stimulate individuals from these underrepresented contexts to perform research in the NIME field.similar to race, social similarity, or religion, as shown by a study in the USA [5].In a global community such as NIME, the concept of ethnicity and race is undoubtedly even more complex and should be a topic for further discussion.Nevertheless, it is alarming that no one was from the African continent or wrote anything related to African descent.Surveys are helpful tools to help us understand how we can improve diversity in many ways.Efforts to broaden its range and improve its precision will certainly direct our community for the better.
Improving Text Generation with Dynamic Masking and Recovering

Due to different types of inputs, diverse text generation tasks may adopt different encoder-decoder frameworks. Thus most existing approaches that aim to improve the robustness of certain generation tasks are input-relevant, and may not work well for other generation tasks. Alternatively, in this paper we present a universal approach to enhance the language representation for text generation on the base of generic encoder-decoder frameworks. This is done from two levels. First, we introduce randomness by randomly masking some percentage of tokens on the decoder side when training the models. In this way, instead of using ground truth history context, we use its corrupted version to predict the next token. Then we propose an auxiliary task to properly recover those masked tokens. Experimental results on several text generation tasks including machine translation (MT), AMR-to-text generation, and image captioning show that the proposed approach can significantly improve over competitive baselines without using any task-specific techniques. This suggests the effectiveness and generality of our proposed approach.
Asymptotic Tracking Control for Uncertain MIMO Systems: A Biologically Inspired ESN Approach

In this study, a biologically inspired echo state network (ESN)-based method is established for the asymptotic tracking control of a class of uncertain multi-input multi-output (MIMO) systems. By mimicking the characters of real biological systems, a diversified multiclustered echo state network (DMCESN) is proposed in this work and then it is applied to deal with the modeling uncertainties and coupling nonlinearities in the control systems. Different from the most existing neural network (NN)-based control methods that only ensure the uniform ultimate boundedness result, the proposed method can allow the tracking error to achieve asymptotic convergence through rigorous theoretical analysis. The effectiveness of the proposed method is also confirmed by numerical simulation by comparing with multilayer feedforward network-based control scheme and traditional ESN-based control, admitting better tracking performance of the proposed control.
Finetuned YOLOv3 for Getting Four Times the Detection Speed

Object detection is an exciting research area in computer vision, widely used in autonomous driving, face recognition, and drones. Due to the limitation of mobile devices, the size of object detection models is limited, and it is not easy to achieve the ideal balance between detection accuracy and detection speed. So reducing the model size and increasing the model detection speed becomes a trendy research topic. The Slice-Concat structure proposed in this paper does not require too many changes to YOLOv3 and YOLOv3-SPP, and the four times improvement in detection speed is obtained only by changing the width and height of the input feature map. While getting a 4-fold FPS improvement, the network model parameters remain essentially unchanged, and the computational effort becomes a quarter of that of the original network. The improved YOLO model can also obtain a larger batch size with the same image size, providing an excellent solution for training models on hardware-constrained devices.
Online and Offline Android Based Mobile Application for Mapping Health Facilities Using Google Map API. Case Study: Tanzania and Kenya Borders

The evolution of technology in 1990s resulted in the enormous growth of smartphones and the propagation of mobile applications (App) that marked new opportunities for healthcare centers and medical education.Apps have altered health services from patient's health monitoring to specialist's appointments and consultations from specialized health facilities.It can be argued that a healthy society can bring forth sustainable economic development to its full potential while an unhealthy society cannot.However, a free movement of people, labour and right to residence which was built across East Africa (EA) borders enabled Tanzania and Kenya borders to have enormous interactions.Subsequently, increase the risk of highly communicable diseases such as Tuberculosis and Sexually transmitted infections in such a way that medical attention is unavoidable along the borders.Statistically, Android Operating System (OS) owns 83% of Africa's mobile OS market.In addition, 25,794,560 internet users reported by Tanzania Communications Regulatory Authority (TCRA) together with the 22.86 million internet users provided by Kenya Digital which is equivalent to 46% and 43% of internet penetration in year 2020, disclose the need for Android mobile application for mapping health facilities both online and offline using Google map API, which will solve residents' need to healthcare services on the presence or shortage of internet connections; using either Swahili or English language via Smartphone devices.The App incorporates Monitoring and Evaluation (M & E) tool for tracking application usage which will ease Admin's task to generate daily and monthly reports in Excel and Comma-Separated Values (CSV) formats.The developed system received positive feedback from EA citizens and residents in
Hybrid Descriptor and Patches Optimization for Face Recognition

This work focuses on the system optimization for face recognition (FR) in controlled and uncontrolled environments. The strategy adopted in this article represents the contribution of hybrid and face patches descriptors for the extraction of facial features. The extracted features are optimize by the selection of optimal characteristics. In order to significantly improve the performance of system based on a single descriptor using the combination of several pieces of information and their optimization the Particle Swarm Optimization (PSO) algorithm method is used for optimize information. In this work, the problem of multiclass patches recognition is converted into face recognition. For each patch, a new feature optimization model based patch optimization is proposed to represent the specificity of the face expression. The patch representation-based approach is then proposed to detect the active region of interest of the testing sample for better generalization. The algorithm achieved competitive accuracies of real-world data: Labeled Faces in the Wild (LFW), as well as on Databases "Extended Yale face database B" and give a good performance.
Cyber Security and Privacy Protection During Coronavirus Pandemic

The coronavirus pandemic has brought a new reality to people around the world, who are facing uncertainty, fear of disease, sudden change of habits and lifestyles, while physical distance, social isolation, restriction of freedom of movement, mandatory self-isolation and other health measures during the epidemic instructed people to transfer their everyday habbits and professional obligations to cyberspace.The right to privacy is one of the fundamental human rights, which is recognized by the United Nations Declaration of Human Rights, the International Covenant on Civil and Political Rights, and other international and regional treaties.This right includes privacy of personal data or information, the protection of human body, protection of personal living space and the privacy of communications.Data security aims to ensure that any personal information that is collected, used, or stored is protected from unauthorized use.In order to fight Covid-19, the laws on the state of emergency are promulgated in the countries of Central and Southeast Europe, which has led to an increasing number of arbitrary arrests, surveillance, wiretapping and violations of privacy.
A Parallel Apriori Algorithm and FP- Growth Based on SPARK

Frequent Itemset Mining is an important data mining task in real-world applications. Distributed parallel Apriori and FP-Growth algorithm is the most important algorithm that works on data mining for finding the frequent itemsets. Originally, Map-Reduce mining algorithm-based frequent itemsets on Hadoop were resolved. For handling the big data, Hadoop comes into the picture but the implementation of Hadoop does not reach the expectations for the parallel algorithm of distributed data mining because of its high I/O results in the transactional disk. According to research, Spark has an in-memory computation technique that gives faster results than Hadoop. It was mainly acceptable for parallel algorithms for handling the data. The algorithm working on multiple datasets for finding the frequent itemset to get accurate results for computation time. In this paper, we propose on parallel apriori and FP-growth algorithm to finding the frequent itemset on multiple datasets to get the mining itemsets using the Apache SPARK framework. Our experiment results depend on the support value to get accurate results.
Security Improvement and Privacy Preservation in E-Health

In today’s world, all things are connected and influencing the existing applications. The E-Health domain extensively adopts IoT and presents new healthcare services and medical facilities. However, the major hurdle is improving security and preserving the patients’ privacy. Many security/privacy-preserving models and protocols are proposed but prone to adversarial attacks. Our objective is to improve their security and ensure lightweight complexity for the low-powered and limited memory device. In this paper, we improve security using four critical steps. The effectivity of random number generation is improved, which defines the current security level in cryptography. A new technique is proposed utilising timestamp for handling a replay attack. We ensure strong forward security using the Elliptic Curve Discrete Logarithm Problem (ECDLP), making it challenging for an adversary to decode the security parameters. Finally, it is ensured that the hash function’s bits maintain the entropy of the key involved in the security model. Thus, the proposed model preserves privacy as well as improves the security of the E-Health model.
Klasifikasi Pengguna Shopee Berdasarkan Promosi Menggunakan Naïve Bayes

Online shopping is a transaction of buying and selling goods or services through intermediary media, namely social networks. There has been a change in consumption patterns and the way people spend their money, which was originally conventional to switch to E-Commerce services due to several factors, namely the increasing public interest in online shopping due to the COVID-19 virus outbreak, and throughout 2019 E-Commerce users who made transactions reached 168.3 million people. . Based on iprice report data in 2020, Shopee is the most visited E-Commerce with a total of 129,320,800 visitors. Shopee is only a third party that provides a place to sell and payment facilities, therefore Shopee is not responsible for marketing the products sold. To attract consumers, sellers need attractive promotions. Therefore, research is needed to classify E-Commerce users. The purpose of this research is to classify E-Commerce users based on the promotion used using the Naïve Bayes algorithm with the Knowledge Discovery in Database (KDD) methodology. Nine test scenarios were carried out with cross validation which showed that the best performance was a test scenario using 3 folds which resulted in performance with an accuracy value of 88.73%, and with a kappa value of 0.451 which was included in the moderate category. Based on these results, the model generated by the Naïve Bayes algorithm is quite consistent.