Reviewerly/exHarmony huggingface dataset:
Prediction Of Customers at Bank Rakyat Indonesia Using Backpropagation Algorithm

This study aims to see the development of the number of BRI bank customers in the following year. With this prediction, it is hoped that it can help bank bri in making policies related to customers who save more easily. The data source is obtained from Bank Rakyat Indonesia (BRI). In this study, researchers used the Backpropagation Algorithm. Backpropagation Algorithm is an algorithm that functions to reduce the error rate by adjusting the weight based on the desired output and target. The benefit of this research is to determine the increase or decrease in savings customers at the BRI bank in the following year. And artificial neural networks using the backpropogation algorithm can be applied in analyzing the increase in the number of bank bri customers by determining the best architectural model from a series of training and testing processes carried out.
Deciphering the Morphology of Tumor-Stromal Features in Invasive Breast Cancer Using Artificial Intelligence

Tumor-associated stroma in breast cancer (BC) is complex and exhibits a high degree of heterogeneity. To date, no standardized assessment method has been established. Artificial intelligence (AI) could provide an objective morphologic assessment of tumors and stroma, with the potential to identify new features not discernible by visual microscopy. In this study, we used AI to assess the clinical significance of (1) stroma-to-tumor ratio (S:TR) and (2) the spatial arrangement of stromal cells, tumor cell density, and tumor burden in BC. Whole-slide images of a large cohort (n = 1968) of well-characterized luminal BC cases were examined. Region and cell-level annotation was performed, and supervised deep learning models were applied for automated quantification of tumor and stromal features. S:TR was calculated in terms of surface area and cell count ratio, and the S:TR heterogeneity and spatial distribution were also assessed. Tumor cell density and tumor size were used to estimate tumor burden. Cases were divided into discovery (n = 1027) and test (n = 941) sets for validation of the findings. In the whole cohort, the stroma-to-tumor mean surface area ratio was 0.74, and stromal cell density heterogeneity score was high (0.7/1). BC with high S:TR showed features characteristic of good prognosis and longer patient survival in both the discovery and test sets. Heterogeneous spatial distribution of S:TR areas was predictive of worse outcome. Higher tumor burden was associated with aggressive tumor behavior and shorter survival and was an independent predictor of worse outcome (BC-specific survival; hazard ratio: 1.7, P = .03, 95% CI, 1.04-2.83 and distant metastasis-free survival; hazard ratio: 1.64, P = .04, 95% CI, 1.01-2.62) superior to absolute tumor size. The study concludes that AI provides a tool to assess major and subtle morphologic stromal features in BC with prognostic implications. Tumor burden is more prognostically informative than tumor size.
Binary Diversification Characterizes Exact Capacities

In this paper we provide the first axiomatic characterization of preferences that can be represented by a Choquet integral with respect to an exact capacity. The characterizing axiom, Binary Diversification, is novel and states a preference for mixtures resulting in bets on events, i.e., binary acts. Furthermore, we show that the three capacity classes balanced, exact and convex fully exhaust all levels of what we call "hierarchy of ambiguity aversion", a unifying framework for the two most influential definitions of ambiguity aversion suggested by Ghirardato and Marinacci (2002) and Schmeidler (1989).
A participant selection based vehicle platoon asynchronous federated learning framework

Abstract In the vehicular network, federated learning is an emerging paradigm to train deep learning models safely. However, the non‐identically independent distributed data collected by intelligent connected vehicles, expensive training overhead, and the existence of malicious participants significantly affect the efficiency of training and constrain the development of federated learning in the vehicular network. To address the above issues, this paper proposes a participant selection‐based asynchronous federated learning framework for an intelligent connected vehicle platoon. We proposed a platoon‐based asynchronous federated learning framework that effectively improves training efficiency through data share and asynchronous optimization. Also, We designed a signaling game‐based participant selection strategy that accurately identifies the potentially malicious participants by analyzing the Perfect Bayesian Equilibrium. The comparison experiment result shows the training model using our proposed scheme converges faster than the baseline scheme and gains approximately 10% higher accuracy.
Clustering-Based Filter Pruning Approach for Efficient ConvNets

Convolutional Neural Networks (CNN) or ConvNets have been popularly used in real-world applications related to Computer Vision, Natural Language Processing (NLP) and various similar domains, accompanied by an increased computational cost. Deploying CNN models on resource-constrained devices has become a challenging task. In order to solve this, one can produce a compact structure by compressing the model. This work proposes a novel cluster-based filter pruning approach to compress the model and accelerate the inference performance of ConvNets. We use a structured filter pruning method, which removes the redundant filters from each convolution layer of the CNN model and thus reduces the size and the computation cost needed for the inference. First, we determine the optimal number of clusters to be formed using the Silhouette score and perform clustering of filters based on the K-Means++ algorithm in each layer. Next, we apply a filter selection strategy that selects only suitable high-impact filters as representatives of each cluster to achieve the pruning effect. With this strategy, we obtain a Binary Filter Mask (BFM), which is further used to decide which filters need to be retained and which need to be discarded. Finally, we apply BFM to our CNN model to perform the pruning. We have demonstrated this method on the VGG network model and the experimental results show that by using this method, on CIFAR-10, we are able to prune at an average of more than 83% of parameters resulting in 54% of FLOP reduction and 94% size reduction. We also compare the pruning effect of the proposed approach with the original unpruned and the L1-Norm pruned model and it is found that the proposed pruning method outperforms the other two methods.
Scam Alert: Can Cryptocurrency Scams Be Detected Early?

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SMS and E-mail Spam Classification Using Natural Language Processing and Machine Learning

Billions of messages are sent daily over the Internet, out of which a majority part of them is spam. These spam messages have become a primary cause of distraction and security threat for users as their number keeps on increasing day by day. Many researchers have addressed this problem, and there are different approaches to it. In this present study, Machine Learning algorithms such as Naïve Bayes, Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, Gradient Boosting, and Extra Trees Classifier have been utilized to predict whether an incoming message or e-mail is spam. The model performance has been evaluated based on accuracy, precision, F1-score, and confusion matrix. Three different datasets including two SMS datasets and one e-mail dataset has been used, and a maximum F1-score of 96.06% and accuracy of 99.12% with the Extra Trees Classifier are achieved, which is 0.02% higher than the highest value of accuracy ever achieved for the SMS Spam Collection Dataset.
Distributed Randomized Multiagent Policy Iteration in Reinforcement Learning

We propose a distributed randomized policy iteration algorithm for infinite horizon dynamic programming problems for which the control at each stage is m-dimensional. The traditional policy iteration algorithm involves performing a minimization over an m-dimensional constraint set and has a computational complexity that increases exponentially in m, resulting in an intractable combinatorial search problem. In each iteration, our algorithm performs a series of sequential minimizations followed by policy evaluation and policy improvement using the policy that attains the minimum cost over the sequential minimizations. Our algorithm is well-suited for parallel computation, has a complexity that increases linearly in m, and converges to an agent-by-agent optimal policy. We characterize sufficient conditions for which our algorithm generates a globally optimal policy that coincides with that obtained from standard policy iteration.
What Beyond Implementing ‘eOffice’ the Digital Workplace in Government

India has already traversed all levels of digital transformation. Workflows are already automated, across department data are seamlessly exchanged or shared in real time. These public service delivery workflows are comprising repeating work function and are generating structured and organized data. The eOffice, a single collaborative workspace for the Government with paperless electronic workflows environment in government offices, has also been implemented in large number of offices. Now tactical and strategic decisions within Government are being taken in file or eFile of eOffice. The contents of eFile are majorly in text format (word format, pdf format). Decision situations having similar context are repeating frequently in eFile, and precedence of similar decisions can be leverage in current situation towards enhancing the effectiveness of decision making. Use of Natural Language Parsing (NLP) and AI inferencing are game changer. For eFile NLP applications, dependency relations will be of prime importance. Stanford System that automatically extracts typed dependency parses from phrase structure can be utilized for eFile NLP case. Many ‘English’ parser including some with open source exhibited to achieve 94% accuracy that is very close to human accuracy considered in between 96 and 97% range. In the current regime of open data, deluge of data from social sites has already enabled environment for evolving automated decision making (ADM). Algorithms to make decisions automatically with varying degree of human intervention are exhibiting success in vivid contexts. Knowledge required to take a decision already exists in eFile and can be leveraged to enhance the effectiveness of decision once intelligence is incorporated in place within eOffice. Availability of NLP technology enables to develop intelligent model inferencing context matching decisions along with values of associated decision parameters from eFiles. Modules empowered with Natural Language Parsing (NLP) and AI inferencing search, explore and set similar Decision Match Cases. The decision being taken is compared with qualified match cases for assessing the consistency and accordingly Maturity Index is assigned to the decision option(s). Intelligent Inferencing Model is outlined which would pave way for developing precedence supported decision system that would have competence to self-enhance its intelligence along with its usage.
Virtual History-Expanding the Learning of Students in a Modern Age

Most students love to learn by physically doing activities rather than just reading theoretically. The aim of this work is to create a proof-of-concept VR application that would enhance the children learning experience of different historical places and activities. Specifically, in this work, we look at teaching VR in the case of history which can be an asset as with learning kinaesthetic for some students is their way of learning something, and it may not be feasible for some students to be able to afford to go to another country or place to see a place of historical significance. To setup this environment in the game, we created the Californian Goldrush scene which was build using VR Unity Game Engine and the Oculus Rig which is used to provide the camera and interaction within the virtual world. The user acceptance testing has been performed, and the overall, feedback of this VR game has been impressive.
Assembled LSTM Technique Used for Phonetic-Based Algorithm for Demographical Data

Demographic information is qualitatively gathered data that depicts various aspects of the population. Numerous phonetic-based retrieval methods are employed, but they are ineffective when attempting to appeal Indian name records. This paper is based on data records obtained from demographic databases in order to identify Indian names with similar accents but different spellings. The phonetic-based pronunciation algorithm, long short-term memory (LSTM), and K-Mean algorithm cross-breed LSTM technique are all included in this research proposal to recognize Indian names from the databases. For the purpose of evaluating prediction accuracy for Indian names, the proposed cross-breed LSTM technique is put up against a recurring unit (GRU)-based algorithm. The K-Mean technique and the LSTM technique are combined to produce the cross-breed LSTM model. When retrieving Indian Names records from databases, the assembled LSTM method gives greater accuracy. If, as is the case at hand, the demographic data gathered from Indian names in demographic databases contains inconsistent data due to managed to misspell names, the suggested approach can be used to reduce data redundancies and obtain precise data.
Does Cycling Reveal Insights About You? Investigation of User and Environmental Characteristics During Cycling

Smartwatches are increasingly being used as fitness and health trackers. To provide such a service, these devices have to collect and process movement data gathered by built-in accelerometers and gyroscopes. Based on these data, existing studies leveraging smartphones have shown that it is possible to distinguish users when they (1) walk, (2) perform different hand gestures, or (3) pick up their phone from the table. However, to the best of our knowledge, the case of cycling has not been addressed yet. The goal of this paper is to close this gap by investigating whether it is possible to infer information about users wearing a smartwatch coupled with their smartphone when cycling, their bike type, seat height, gear, and the terrain. In addition, we explore whether it is possible to distinguish individual users based on their movement patterns that may lead to their (re)identification. To this end, we conducted a user study with 17 participants, equipped with a smartphone and a smartwatch, who had to ride along a bike road for two km. Among others, our results show that it is possible to infer the four characteristics bike type, gear, seat height, and terrain with accuracies of 93.05%, 92.23%, 95.76%, 94.24% respectively and distinguish participants with a probability of 99.01%.
The Problem and Its Key Characteristics

We start with the description of general context and with formulation of the problem that we address. In this manner we set a framework for both the particular issues that we deal with on a technical level, described in the consecutive parts of the book, and for the potential implications thereof, some of them forwarded as more general conclusions or hypotheses.
Identification of Abnormal Behavior in Activities of Daily Life Using Novelty Detection

The world population is aging at a rapid pace. According to the WHO (World Health Organization), from 2015 to 2050, the proportion of elderly people will practically double, from 12 to 22%, representing 2.1 billion people. From the individual’s point of view, aging brings a series of challenges, mainly related to health conditions. Although, seniors can experience opposing health profiles. With advancing age, cognitive functions tend to degrade, and conditions that affect the physical and mental health of the elderly are disabilities or deficiencies that affect Activities of Daily Living (ADL). The difficulty of carrying out these activities within the domestic context prevents the individual from living independently in their home. Abnormal behaviors in these activities may represent a decline in health status and the need for intervention by family members or caregivers. This work proposes the identification of anomalies in the ADL of the elderly in the domestic context through Machine Learning algorithms using the Novelty Detection method. The focus is on using available ADL data to create a baseline of behavior and using new data to classify them as normal or abnormal daily. The results obtained using the E-Health Monitoring database, using different Novelty Detection algorithms, have an accuracy of 91% and an F1-Score of 90%.
InnerEye: A Tale on Images Filtered Using Instagram Filters - How Do We Interact with them and How Can We Automatically Identify the Extent of Filtering?

Even though digitally filtered images are taking over the Internet for their aesthetic appeal, general people often feel betrayed if they are dealt with filtered images. Our study, comprising a series of structured surveys over images filtered using Instagram filters, reveals that different people perceive the filtered images differently. However, people have a common need for an automated tool to help them distinguish between original and filtered images. Accordingly, we develop an automated tool named ‘InnerEye’, which is capable of identifying how far an image is filtered or not. InnerEye utilizes a novel analytical design of a Neural Network Model that learns from a diverse set of images filtered using Instagram filters. Rigorous objective and subjective evaluations confirm the efficacy of InnerEye in identifying the extent of filtering in the images.
Low Contrast Medical Image Enhancement Technique Using Fuzzy-Based Two Class Pixels Partial Contrast Stretching

This paper presents a new fuzzy based low contrast medical image enchantment technique.This technique is very simple and effective regarding the object domain. It is a global partial contrast stretching technique for enhancement. It involves two stages like pixel classification and pixel range stretching. Pixel classification is based on fuzzy system. The pixels are classified based on the fuzzy membership degree of the pixels using threshold value into two classes. Each class is applied different stretching function. Since the application is point operation, the result causes global contrast and brightness to the output image, and it is also very fast compared to other related techniques. As compared with the other contrast stretching technique, it outperforms with respect to result as well as speed.
Feature Encoding by Location-Enhanced Word2Vec Embedding for Human Activity Recognition in Smart Homes

Human Activity Recognition (HAR) in Smart Homes (SH) is the basis of providing automatic and comfortable living experience for occupants, especially for the elderly. Vision-based approaches could violate occupants’ privacy and wearable sensors based approaches could be intrusive with their daily activities. In this study, we proposed an NLP-based feature encoding for HAR in smart homes by using the Word2Vec word embedding model and incorporating location information of occupants. We used the NLP approach to generate semantic and automatic features directly from the raw data that significantly reduced the workload of feature encoding. The results showed that both Word2Vec embedding and location-enhanced sequences can significantly improve the classification performance. Our best model which used both Word2Vec embedding and location-enhanced sequences achieved an accuracy of 81% and a weighted average F1 score of 77% on the test data with Sensor Event Windows (SEW) size of 25. This size is considered as a small SEW size which can be applied better to real-time classification due to the short latency.
The Hybrid Cluster-And-Classify Approach

When dealing with challenging, real-world datasets, one may turn to hybrid data processing approaches that join several kinds of data analysis algorithms. The hybrid data analysis techniques are popular in the literature, where we may find fusions of optimization and classification algorithms, clustering and classification algorithms.
Clustering Analysis

This chapter is devoted to presentation of results, which were obtained for the data considered with the use of clustering algorithms. Clustering consists in grouping of similar observations, while separating the dissimilar ones. (The groups obtained therefrom are referred to, exactly, as clusters.) Hence, we might suspect that the observations we analyse should fall into different clusters, and that first of all depending upon whether they represent “bot” or “human” behavior, based on the apparently obvious assumption that bots are more similar to (at least some) bots than to humans and that humans are more similar to (at least some) humans than to bots. Therefrom the attempts we report on in the present chapter.
Behavioral Anomaly Detection of Older People Living Independently

Older people living independently represent one significant part of the population nowadays. Most of them have family or friends interested in being informed about changes in their routine. Considering these changes signal some physical or mental problem, they can trigger a contact or action from the interested persons to provide support. This paper presents an approach for non-intrusive monitoring of older people to send alerts after detecting anomalous behaviors. An analysis of seven months of data gathered using PIR sensors in a couple’s living house has shown regularities in their presence in compartments along the day. We validated the adequacy of an outlier detection algorithm to build a model of the persons’ behavior, exhibiting just 3.6% of outliers interpreted as false positives.
Book Genre Classification System Using Machine Learning Approach: A Survey

The amount of complicated texts and documents that require a deeper understanding of machine learning techniques has expanded rapidly in recent decades. Several machine learning approaches have shown exceptional results in NLP. Complex models and non-linear data correlations help these learning systems function well. It is difficult for academics to discover appropriate text classification structures, designs, and procedures. Manually reading and classifying a book's category would be tedious. It takes a long time for a language beginner to absorb the complete text in order to discern its genre. Natural Language Processing (NLP), which is commonly used nowadays, can help overcome this issue. It includes text classification and summarization. The book synopsis is input to the Machine Learning algorithms, which output the genre. This review includes text feature extraction, dimensionality reduction, algorithms, and evaluation. Finally, the limitations of each strategy are assessed, as well as their practicality.
Police Interventions as a Context-Aware System. A Case of a Contextual Data Modelling

Smart systems which operate in Intelligent Environments (IE) are complex. They analyse the large volumes of various contextual data on-line and often in real time to obtain, autonomously and reliably, the required pro-activeness of a system which operates pervasively. We proposed both a development framework for context-aware systems and a context-based decision making scheme for the system of managing police interventions, focusing on providing support for police patrols in life threatening situations. This system, owing to the symultaneous collection of rich contextual information from many police officers, which constitute the mobile network, as well as the complex processes of contextual reasoning, takes automatic decisions on supporting officers in emergency. We implemented the initial, yet not trivial, simulations of the system behaviour within the whole city. The results obtained prove the feasibility of the framework.