Reviewerly/exHarmony huggingface dataset:
Male and Female Employment in Agriculture and Agricultural Productivity in Nigeria

Abstract The contribution of women to labour in African agriculture is quoted regularly in the range of 60–80%. Using available statistical data, to compare the level of employment and productivity of both male and female on an individual basis, and consequently proffer ways of improve that with a lower turn-out is the one major aim of the study. The contribution of females in the agricultural sector is substantially lower in Nigeria (37%), and some other developing countries. In order to get appropriate estimation of the model, the Augmented Dicky-Fuller and the Phillips-Perron test are used to check the variables level of stationarity. This also helps to explain the existing relationship between and among the following variable: employment in agriculture, male, employment in agriculture, female, trade and inflation. The result informs the need to enhance the employment of female in the Nigerian economy for higher agricultural productivity. Therefore the study suggests that policies geared towards female empowerment in agriculture and services alongside other concerned sectors should be promoted.
A Lightweight Motional Object Behavior Prediction System Harnessing Deep Learning Technology for Embedded ADAS Applications

This paper proposes a lightweight moving object prediction system to detect and recognize pedestrian crossings, vehicles cutting-in, and vehicles ahead applying emergency brakes based on a 3D Convolution network for behavior prediction. The proposed design significantly improves the performance of the conventional 3D convolution network (C3D) adapted to predict the behaviors employing behavior recognition network capable of performing object localization, which is pivotal in detecting the numerous moving objects’ behaviors, combining and verifying the detected objects with the results of the YOLO v3 detection model with that of the proposed C3D model. Since the proposed system is a lightweight CNN model requiring far lesser parameters, it can be efficiently realized on an embedded system for real-time applications. The proposed lightweight C3D model achieves 10 frames per second (FPS) on a NVIDIA Jetson AGX Xavier and yields over 92.8% accuracy in recognizing pedestrian crossing, over 94.3% accuracy in detecting vehicle cutting-in behavior, and over 95% accuracy for vehicles applying emergency brakes.
Construction of wavelet dictionaries for ECG modeling

Technical details, algorithms, and MATLAB implementation for a method advanced in the paper ``Wavelet Based Dictionaries for Dimensionality Reduction of ECG Signals'', are presented. This work aims to be the companion of that publication, in which an adaptive mathematical model for a given ECG record is proposed. The method comprises the following building blocks. Construction of a suitable redundant set, called 'dictionary', for decomposing an ECG signal as a superposition of elementary components, called 'atoms', selected from that dictionary. Implementation of the greedy strategy Optimized Orthogonal Matching Pursuit (OOMP) for selecting the atoms intervening in the signal decomposition. The use of the software and the power of the technique is illustrated by reducing the dimensionality of ECG records taken from the MIT-BIH Arrhythmia Database. The MATLAB software has been made publicly available on a dedicated website. We provide the explanations, algorithms and software for the construction of scaling functions and wavelet prototypes for 17 different wavelet families. The procedure is designed to allow for straightforward extension of the software by the inclusion of additional options for the wavelet families.
Fine-Grained, Aspect-Based Sentiment Analysis on Economic and Financial Lexicon

The last two decades have seen a tremendous increase in the adoption of Semantic Web technologies as a result of the availability of big data, the growth in computational power and the advancement of artificial intelligence (AI) technologies. Cutting-edge semantic techniques are now able to capture sentiments more accurately in various practical applications, including economic and financial forecasting. In particular, the extraction of sentiment from news text, social media and blogs for the prediction of economic and financial variables has attracted attention in recent years. Despite many successful applications of sentiment analysis (SA) in these domains, the range of semantic techniques employed is still limited and mostly focused on the detection of sentiment at a coarse-grained level, that is, whether the sentiment expressed by the entire text of a sentence is either positive or negative. This paper proposes a novel methodology for Fine-Grained Aspect-based Sentiment (FiGAS) analysis. The aim of the approach is to identify the sentiment associated to specific topics of interest in each sentence of a document and assigning real-valued polarity scores between -1 and +1 to those topics. The proposed approach is completely unsupervised and customized to the economic and financial domains by using a specialized lexicon make available along with the source code of FiGAS. Our lexicon-based SA approach relies on a detailed set of semantic polarity rules that allow understanding the origin of sentiment, in the spirit of the recent trend on \textit{Interpretable AI}. We provide an in-depth comparison of the performance of the FiGAS algorithm relative to other popular lexicon-based SA approaches in predicting a humanly annotated data set in the economic and financial domains. Our results indicate that FiGAS statistically outperforms the other methods by providing a sentiment score that is closer to one of the human annotators.
Secure and Efficient Data Sharing Scheme in Cloud for Protecting Data in Smart Cities

In recent years, smart cities’ data have been affected by a lot of security problems. Protecting the smart cities’ data is a major problem in the development of smart cities. Therefore, the data is to be stored into the cloud. Because cloud computing is an emanating paradigm to distinguish the user’s data has to be stored into cloud server remotely. Cloud data service provides an extensible and convergent way for data shared across multiple users, which brings greater conveniences for the users. However, the users’ data are stored and shared with trustworthy cloud providers. It will raise a lot of security concerns. Attribute-based encryption (ABE) is a popular mathematical technique in the cloud storage system to resolve security concerns. In the new CP-ABE method, however, key escrow problem cannot be solved. A new Ciphertext Policy Attribute-Based Encryption with Key Hashing Scheme is implemented to overcome this problem. The mechanism’s key role avoids key escrow and reverses secrecy. Therefore, by analyzing security and performance, the proposed method allows for efficient and secure data sharing in cloud computing.
CryptoRec: Novel Collaborative Filtering Recommender Made Privacy-Preserving Easy

With the explosive growth of user data, recommenders have become increasingly complicated. State-of-the-art algorithms often have high computational complexity and heavily use non-linear transformations. This fact makes the privacy-preserving problem more challenging, despite the significant advances in cryptography. To alleviate this problem, we propose a privacy-friendly recommender, CryptoRec. It only relies on additions and multiplications, which are efficiently supported by most cryptographic primitives. Different from others, in CryptoRec, the parameter space only contains item features (user features can be directly computed from the item features). This property allows CryptoRec to, (1) naturally achieve transferability if two datasets share the same item entries, which can benefit differential privacy protection; (2) directly estimate the preference of new users whose data is not included in the training set, drastically improving recommendation efficiency. We first evaluate CryptoRec on three real-world datasets. The evaluation results show that the accuracy is competitive with state-of-the-art. Then, we build differential privacy into CryptoRec and leverage its transferability property to reduce the overall privacy loss. Lastly, we demonstrate the simplicity and efficiency of using CryptoRec to construct secure recommendation protocols based on homomorphic encryption schemes. Our results show that CryptoRec outperforms existing solutions in terms of both accuracy and efficiency.
Robust Recursive Least-Squares Finite Impulse Response Predictor in Linear Discrete-Time Stochastic Systems with Uncertain Parameters

This paper newly proposes the robust RLS Wiener FIR prediction algorithm based on the innovation theory for the linear stochastic systems including with parameters. In the robust RLS Wiener predictor, the following information is used. (1) The system matrices for the signal and the degraded signal. (2) The observation matrices for the signal and the degraded signal. (3) The variance of the state for the degraded signal. (4) The cross-variance of the state for the signal with the state. (5) The variance of the observation noise. As a step to obtain the robust RLS Wiener FIR prediction algorithm, this paper presents the robust prediction algorithm of the signal using the covariance information etc. In the predictor, the following information is used. (1) The observation matrices for the signal and the degraded signal. (2) The variance of the state for the degraded signal. (3) The auto-covariance information of the state for the degraded signal. (4) The cross-covariance information of the state for the signal with that for the degraded signal. (5) The variance of the observation noise. The estimation accuracy of the proposed robust RLS Wiener FIR predictor is superior to the existing RLS Wiener FIR predictor.
hSOM: Visualizing Self-Organizing Maps to Accomodate Categorical Data

Kohonen's self-organizing map is an unsupervised machine learning method designed to preserve the topology of its input space. Although this method has been used to efficiently summarize multidimensional data, the visualization of its constituent data has received less attention. We propose a method of addressing the visualization problem by augmenting a classical self-organizing map visualization to include an embedded histogram and evaluate its utility in depicting the self-organizing maps's constituents categorized by a discrete variable.
Facial Recognition using Enhanced Facial Features k-Nearest Neighbor (k-NN) for Attendance System

This paper discusses the developments of employee attendance system via face detection and facial recognition, using the enhanced featured supervised learning technique. The main goal of the proposed system, FaceAuth is to uniquely identify a person without the use of Internet connection and cloud services. The developed system can identify the face of a user using the k-nearest neighbors (k-NN) algorithm for both training and testing phases. Then, a suitable distance threshold is configured to exclude the unregistered person from the prediction. Meanwhile, it marks the attendance of individual, whenever the person's face has trained within five seconds. For 40 participants in this experiment, the developed system can achieve 92.5% in accuracy, without false positive error occur.
The Use of Whatsapp Gateway for Automatic Notification System

Abstract Whatsapp is a messaging application from facebook which many people use it. For using whatsapp, someone must have a mobile number. The mobile number will be account in whatsapp application. In addition for chat, whatsapp can be a tool for notification system. The notification system can broadcast the messages that called as Whatsapp Gateway. Whatsapp gateway can be implemented for the school notification system where the parents of students can recieve notification from school. The Whatsapp gateway developed consists of fingerprint device, sender engine, management information system, and whatsapp gateway application. When someone checked their finger through fingerprint device, the fingerprint device will captured date and time then save it. The sender engine will checked data from fingerprint as periodically and send data to whatsapp gateway application through Application Programming Interface (API). To prevent blocking from whatsapp, whatsapp gateway will checked handphone number or whatsapp account from inbox message. If the handphone number or whatsapp account was found in inbox message, whatsapp gateway application will send message as notification.
Jodani: A spell checking and suggesting tool for Gujarati language

The spell checker is used in the pre-processing phase of Natural Language Processing systems for applications like Opinion Mining, Text Summarization, Machine Translation, Chatbot etc. A traditional spell checker tool compares the inputted string with an available dictionary of correct words. But as it works on string matching concepts, it is not able to handle inflected words. So, there is a need for a spell checker that overcomes this limitation. In this paper, the Gujarati spell checker tool <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> Jodani <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">,</sup> is proposed which works on root words, uses string similarity measures to identify wrongly spelled words, and attempts to auto-correct the word or suggest syntactically relevant words.
Teacher Readiness for Distance Learning

The article is devoted to the study of the readiness of teachers and students of an engineering university to implement distance learning in modern conditions. The study of the phenomenon of preparedness involves focusing on a number of problems, which are simultaneously factors that impede the formation of readiness. Both positive and negative aspects of the use of distance technologies were identified.
On the design of Bayesian principled algorithms for imbalanced classification

A principled methodology for solving imbalanced binary classification problems has been recently introduced. It permits to obtain high performance designs avoiding the risks of degradation that other procedures suffer from. The corresponding paper Benítez-Buenache et al. (2019) shows evidence of these facts by applying direct versions, using just one of the possible rebalancing techniques and applying full rebalancing. In this contribution, we extend the above study for maximizing the performance of the resulting designs. To this end, we combine principled techniques in order to taking benefit from their different characteristics. The combination weights as well as the rebalance degree are selected by means of a simple (cross-validation) search. A number of experiments with different kinds of databases shows significant performance improvements. At the same time, the database characteristics that limit the performance improvements −such as small size and noisy samples− are detected.
Knowledge forest: a novel model to organize knowledge fragments

With the rapid growth of knowledge, it shows a steady trend of knowledge fragmentization. Knowledge fragmentization manifests as that the knowledge related to a specific topic in a course is scattered in isolated and autonomous knowledge sources. We term the knowledge of a facet in a specific topic as a knowledge fragment. The problem of knowledge fragmentization brings two challenges: First, knowledge is scattered in various knowledge sources, which exerts users' considerable efforts to search for the knowledge of their interested topics, thereby leading to information overload. Second, learning dependencies which refer to the precedence relationships between topics in the learning process are concealed by the isolation and autonomy of knowledge sources, thus causing learning disorientation. To solve the knowledge fragmentization problem, we propose a novel knowledge organization model, knowledge forest, which consists of facet trees and learning dependencies. Facet trees can organize knowledge fragments with facet hyponymy to alleviate information overload. Learning dependencies can organize disordered topics to cope with learning disorientation. We conduct extensive experiments on three manually constructed datasets from the Data Structure, Data Mining, and Computer Network courses, and the experimental results show that knowledge forest can effectively organize knowledge fragments, and alleviate information overload and learning disorientation.
Encoding Broad Learning System : An Effective Shallow Model For Anti-fraud

The criminal behavior of telecom fraud is increasing rapidly with the development of the communication industry, causing huge losses every year. The commonly used traditional fraud detection methods are less flexible. Currently, a more accurate and timely method is needed to deal with the evolving fraud. However, with the development of deep learning, more and more complex structures and a large number of parameters lead to the more time-consuming training process and poor interpretability which are unacceptable in the field of anti-fraud. Therefore, this paper proposes a shallow model called Encoding Broad Learning by incorporating the denoising autoencoder into the Broad Learning System (BLS). We use the first 15 seconds of the call content to process the text data of the fraudulent call by constructing TF-IDF, adding Gaussian noise to it, and combining with the denoising autoencoder to learn more general and robust features in the data. Then it is transformed into a neural network based on BLS, and fraudulent calls are identified on this model. This method is extremely suitable for fraud identification systems with few data features and high real-time training requirements. At the same time, in order to further improve the training efficiency and solve the potential memory explosion problem, we propose an integrated learning algorithm for parallel training of EBLS. Experiments and detailed analysis of the above methods are carried out. The results show that compared with the existing classic network algorithms, this method has a faster training speed, can ensure the accuracy and timeliness of online fraud identification, and help quickly identify fraudulent calls. The paper visualized the training results of these algorithms after processing the relevant parameters of EBLS training. These algorithms have strong interpretability and better meet the high security requirements in the field of anti-fraud.
PCA Tail as the Anomaly Indicator

Nowadays, tools based on machine learning becomes an integral part of education. Propper application of these tools brings benefits, but misuse can be dangerous. The pattern recognition system always indicates the class most similar to the submitted pattern based on the features extracted from the training set. Designers optimise recognisers for specific training set classes. Still, users may not be familiar with its preparation methodology, and thus, they may apply the recognition system to samples incompatible to the training set (outliers, novelties, anomalies). This paper analyses a tail remaining after linear principal component analysis as an anomaly indicator. A nonlinear approach based on generative adversarial networks (GAN) is also presented. In addition to the result of the recognition, the user also gets a level of its credibility categorised into three classes: accept, do not decide, reject. For example, Fashion-MNIST queries were submitted to the recogniser trained on the MNIST database. The proposed linear misuse detector refused all of them; the neural network-based detector failed in 4.81 % of queries. For a more detailed analysis, MNIST samples corrupted by Gaussian noise were admitted presented to the misuse detector trained on the noiseless MNIST dataset. The experiments revealed a sharp border between acceptance and non-acceptance (no decision or rejection) decisions.
Offline Dynamic Grid Generation for Automotive Environment Perception Using Temporal Inference Methods

The validation of online perception algorithms in automotive systems requires a large amount of ground-truth data. Since manual labeling is inefficient and error-prone, an automatic generation of accurate and reliable reference data is desirable. We present a post-processing approach based on a particle-based dynamic occupancy grid representation of the environment. In contrast to existing online dynamic grid algorithms, our estimation additionally utilizes future measurements by applying offline smoothing algorithms. Our proposed concept uses a two-filter procedure for smoothing the occupancy states of the grid cells. We further introduce two methods based on particle reweighting and two-filter smoothing to improve the velocity estimates. We show that our approach enhances the situational awareness and thus provides a more precise environment model. We demonstrate these benefits using lidar data from real-world experiments.
Exploiting Data Entropy for Neural Network Compression

Convolutional neural networks (CNN) achieves tremendous success in computer vision. However, due to the increasing number of parameters and the limitation of hardware/software resources, model compression has become an important issue, so we should reduce the size of CNN's and improve the train and inference speed. This paper focuses on channel pruning, a model compression technique that evaluates the importance of channels within a convolution layer, and prune away the less important ones.In this paper, we propose a mutual information metric to prune the network. By measuring the entropy of feature maps, we can estimate how much information goes through each channel during the label recognition and prune away those that have the least information. We compute the mutual information between feature maps and labels, which is the only information relevant to the label classification.We also propose a weighted mutual information metric that further improves the accuracy. We observe from our experiments that the weighted mutual information metric achieves better accuracy than the classic L1-norm metric [1] and the original entropy metric [2]. We also discover that the classic L1-norm pruning metric can be improved by computing the L1-norm of output filter weights (denoted as output L1) instead of input filter weights (denoted as input L1).We test our channel pruning algorithms on the SVHN, the CIFAR-10, and the CIFAR-100 datasets using Simplenet [3]. When we prune away 70% parameters for all convolution layers, our weighted mutual information method has 1.52%, 13.24%, and 7.90% higher accuracy than the output L1 metric on these three datasets. In the global pruning experiment, our weighted mutual in-formation metric has about 2% higher accuracy than the output L1 metric when we removed 55% of parameters from the SVHN dataset. On the CIFAR-100 dataset, our metric is 1.5% more accurate than the output L1 metric when there are only 53% of the parameters remain. The only exception is the CIFAR-10 dataset, where our metric is 5% less accurate than the output L1 metric when there are 40% of the parameters remain.