Reviewerly/exHarmony huggingface dataset:
A Regularization-adaptive Non-negative Latent Factor Analysis-based Model For Recommender Systems

Non-negative latent factor analysis (NLFA) can high-efficiently extract useful information from high dimensional and sparse (HiDS) matrices often encountered in recommender systems (RSs). However, an NLFA-based model requires careful tuning of regularization coefficients, which is highly expensive in both time and computation. To address this issue, this study proposes an adaptive NLFA-based model whose regularization coefficients become self-adaptive via particle swarm optimization. Experimental results on two HiDS matrices indicate that owing to such self-adaptation, it outperforms an NLFA model in terms of both convergence rate and prediction accuracy for missing data estimation.
Faster Region Convolutional Neural Networks Applied to Ultrasonic Images for Breast Lesion Detection and Classification

According to the global cancer data report of the International Agency for Research on Cancer, breast cancer already becomes a common and widespread disease around the world and is also the leading cause of death from cancer in women. Compared with mammograms and magnetic resonance imaging, breast ultrasound imaging is a universal and inexpensive test method for breast lesion screening without additional risk from radiation. However, most radiologists still need support and assistance from the computer-aided diagnosis systems for the detection and classification of breast cancer in ultrasonic images by improving the differentiation between malignant and benign lesions. In this paper, we propose a lesion detection and classification algorithm for breast cancer using Faster Region Convolutional Neural Networks (Faster R-CNN) applied to ultrasonic images. The proposed algorithm is capable of locating breast lesions with a bounding box and characterizing the detected lesion as malignant or benign. An open-access series of ultrasonic data of breast lesions is used for training and testing the Faster R-CNN, and the examination result shows that the algorithm is capable of precisely locate and characterize the breast lesion with the accuracy of more than 95%.
VAE-BRIDGE: Variational Autoencoder Filter for Bayesian Ridge Imputation of Missing Data

The missing data issue is often found in real-world datasets and it is usually handled with imputation strategies that replace the missing values with new data. Recently, generative models such as Variational Autoencoders have been applied for this imputation task. However, they were always used to perform the entire imputation, which has presented limited results when comparing to other state-of-the-art methods. In this work, a new approach called Variational Autoencoder Filter for Bayesian Ridge Imputation is introduced. It uses a Variational Autoencoder at the beginning of the imputation pipeline to filter the instances that are later fitted to a Bayesian ridge regression used to predict the new values. The approach was compared to four state-of-the-art imputation methods using 10 datasets from the healthcare context covering clinical trials, all injected with missing values under different rates. The proposed approach significantly outperformed the remaining methods in all settings, achieving an overall improvement between 26% and 67%.
The role of e-business applications software in driving operational excellence: Impact of departments collaboration using sustainable software

The internet era in general, and information technology in particular, have enabled synthesis between different types of information systems across a company's divisions in ways that were previously impossible. EBA software is usually measured as a single construct, whereas this study has deconstructed the term, EBA software, to provide a comprehensive understanding of its roles from a theoretical perspective and the impact on operational excellence and collaboration between EBA software-enabled departments offers. This paper intends to investigate the roles of electronic business application software from three dimensions and explore if these dimensions affect collaboration between departments and operational excellence. An online survey was performed using Google forms to gather information from the fast-food restaurant sector, with SEM being performed to examine the study's structural and measurement model. The study results show that while some EBA software dimensions positively influenced operational excellence, some did not. The results also confirm that certain dimensions of EBA software have a significant effect on department collaboration. The hypothesis of the mediating impact of department collaboration using sustainable software on operational excellence was accepted. The study offers recommendations and suggestions for supervisors and managers in the fast-food sector in Jordan in terms of being potentially beneficial in implementing efficient and effective electronic business application software to attain operational excellence and sustain it. The study also suggests that applying sustainable software that has a beneficial impact on companies' achievements. This paper is recognized as the first to study the Jordanian e-business fast-food restaurant sector, which has witnessed an accelerated increase in investment in e-business solutions to support the processes of a company itself as well as its suppliers and partners. This research presents a valid and reliable measurement scale adjusted for the Jordanian fast-food restaurant sector. Furthermore, the significant impact that EBA software has on department collaboration relating to operational excellence adds to the various information offered by the more exhaustive research field of e-business.
Augmenting and Automating Corpus Enrichment

An agent in pursuit of a task may work with a reference library containing documents associated with additional data that provide location-specific explanations about the content. Faced with a new document, an agent has to decide whether to include the new document in its reference library. Basing the decision on words, topics, or entities has shown not to lead to a balanced performance for varying documents. In this paper, we present an approach for automatically enriching new documents with data associated to documents in a reference library. Additionally, we analyze these data to classify new documents into categories to help an agent in deciding whether to include the new document in its reference library.
Application-based Attention Mechanisms in Deep Learning

Attention is the process of reflecting intensely on several items when overlooking others. As a blooming aspect of machine learning, the field of natural language processing incorporated this cognitive process into one of its mechanisms. This mechanism has helped in terms of reducing computation power as it focuses on only the specific states. This paper presents the cognitive process applied to different other fields in deep learning along with challenges faced by the same.
Revealing the Secret Parameters of an FPGA-Based “True” Random Number Generator

A new response system is suggested to show the security vulnerabilities of an FPGA-based chaotic “true” random number generator (RNG). The auto-synchronization method is used to justify the success of the response system. The hidden parameter of the chaotic RNG is revealed when the design of the RNG is open to everyone and a limited scalar time series can be monitored from the chaotic RNG. Experimental results prove the applicability of the response system in a way of estimating next bit while the same bit stream of the RNG can be reproduced.
Automated Management of Collections of Autonomic Systems

Many applications have taken advantage of cloud provided autonomic capabilities, commonly auto-scaling, to harness easily available compute capacity to maintain performance against defined quality objectives. This has caused the management complexity of enterprise applications to increase. It is now common for an application to be a collection of autonomic sub-systems. Combining individual autonomic systems to create an application can lead to behaviors that negatively impact the global aggregate utility of the application and in some cases can be conflicting and self-destructive. Commonly, human administrators address these behaviors as part of a design time analysis of the situation or a run time mitigation of the undesired effects. However, the task of controlling and mitigating undesirable behaviors is complex and error prone. To handle the complexity of managing a collection of autonomic systems we have previously proposed an automated approach to the creation of a higher level autonomic management system, referred to as a Meta-Manager. In this paper, we improve upon prior work with a more streamlined and understandable formal representation of the approach, expand its capabilities to include global knowledge, and test its potential applicability and effectiveness by managing the complexity of a collection of autonomic systems in a case study of a major outage suffered by the Google Cloud Platform.
Efficient Time-Multiplexed Realization of Feedforward Artificial Neural Networks

This paper presents techniques and design structures to reduce the time-multiplexed hardware complexity of a feed-forward artificial neural network (ANN). After the weights of ANN are determined in a training phase, in a post-training stage, initially, the minimum quantization value used to convert the floating-point weights to integers is found. Then, the integer weights related to each neuron are tuned to reduce the hardware complexity in the time-multiplexed design avoiding a loss on the ANN accuracy in hardware. Also, at each layer of ANN, the multiplications of integer weights by an input variable at each time are realized under the shift-adds architecture using a minimum number of adders and subtractors. It is observed that the application of the post-training stage yields a significant reduction in area, latency, and energy consumption on the time-multiplexed designs including multipliers. Moreover, the multiplierless design of ANN whose weights are found in the post-training stage leads to a further reduction in area and energy consumption, increasing the latency slightly.
Creating a Feedback Loop Between Persona Development and User Research Towards Better Technology Acceptance

This work demonstrates the process of mapping real participants from the co-creation session in the user personas. The personas development that were used in co-creation sessions with older adults is presented. This paper provides also insights into how the ongoing user acceptance evaluation research has created a feedback loop into the development and enhancement of personas. The feedback collected improved the real users versus persona distribution while at the same time could provide early enough insights for the market research and drive the exploitation map.
Pose Guided Person Image Generation Based on Pose Skeleton Sequence and 3D Convolution

This paper proposes a novel approach for pose transfer, which aims at transferring the pose of a given person to a target pose. Unlike previous works directly simulating the target pose, we emphasize the pose geometric constraint in our approach to tackle this task. To capture the geometric constraints among the condition pose and target pose, we generate a sequence of different intermediate pose skeleton. Moreover, we introduced the 3D convolution into the generator of our Generative Adversarial Network (GAN) in order to learn spatiotemporal features from the generated pose skeleton sequence. The final target pose will be estimated and refined by a pose transfer block and a series of residual blocks based on the spatiotemporal features. Compared with the existing works, our generated person pose images achieved better performance in terms of Inception Score and Structure Similarity. Extensive experiments on dataset Market-1501 demonstrate the effectiveness of the pose skeleton sequence on pose transfer.
Web API Search: Discover Web API and Its Endpoint with Natural Language Queries

In recent years, Web Application Programming Interfaces (APIs) are becoming more and more popular with the development of the Internet industry and software engineering. Many companies provide public Web APIs for their services, and developers can greatly accelerate the development of new applications by relying on such APIs to execute complex tasks without implementing the corresponding functionalities themselves. The proliferation of web APIs, however, also introduces a challenge for developers to search and discover the desired API and its endpoint. This is a practical and crucial problem because according to ProgrammableWeb, there are more than 22,000 public Web APIs each of which may have tens or hundreds of endpoints. Therefore, it is difficult and time-consuming for developers to find the desired API and its endpoint to satisfy their development needs. In this paper, we present an intelligent system for Web API searches based on natural language queries by using a two-step transfer learning. To train the model, we collect a significant amount of sentences from crowdsourcing and utilize an ensemble deep learning model to predict the correct description sentences for an API and its endpoint. A training dataset is built by synthesizing the correct description sentences and then is used to train the two-step transfer learning model for Web API search. Extensive evaluation results show that the proposed methods and system can achieve high accuracy to search a Web API and its endpoint.
A Convolutional Neural Network Architecture for Camera Model Identification with Small Datasets

We consider the problem of camera model identification using small datasets, motivated by the fact that a large number of training samples from newer camera models are difficult to obtain. In particular, we propose a custom convolutional neural network (CNN) architecture - without relying on pre-trained models - which performs both feature extraction and classification. Therefore, our approach is completely data-driven. We utilise well-known techniques such as data augmentation, dropout and batch normalization in our design to eliminate the problem of overfitting, and to compensate for the small dataset used in our training. Through an extensive experimental study performed on about 27,500 images from cameras on various smartphones, we establish the superior performance of our proposed CNN-based technique over a recently proposed CNN-driven SVM classification approach, in terms of improved classification accuracy and decreased loss. Furthermore, we provide insights on the effect of patch size and other design parameters on the performance, in terms of the loss, accuracy and the confusion matrix. Therefore, the model proposed herein is a practical solution to the important problem of camera model identification, due to its efficacy in utilizing small training data, smaller training time and low complexity.
Multi-Scale Features Joint Rain Removal For Single Image

The presence of rain and haze often cause degradation of images. Therefore, it is important to remove rain or haze and recover the background in outdoor vision systems. Due to the limited size of the network acceptance domain, the pixel value of each spatial position can only be inferred from the surrounding small local area; thus, it is often difficult to remove long rain streaks using existing methods. Therefore, we propose a feature joint dense network (FJDN) to extract multi-scale aggregation features. First, we design a multiscale feature extraction module that uses four dilated convolutional layers to extract multi-scale features. These multi-scale features are then combined into one feature map. We also aggregate three multi-scale features in feature joint dense block (FJDB). By using multi-scale features, we can effectively detect rain streaks of different lengths. Finally, we perform multiple experiments to visually and quantitatively compare our method with several existing methods, demonstrating its superiority. The proposed method is also applied to image dehazing.
An Early Warning Detection System of Terrorism in Indonesia from Twitter Contents using Naïve Bayes Algorithm

Aware on the benefits of social media as the networking platform, the extremist organization is utilized social media to spread the ideology, recruit new member and guided a suicide bomber alike. There are opportunities to analyze the content of document texts in social media including the terrorism detection and intention by extracting the content evident in their post, comment etc. The objective of this research is to analyze content posted in Twitter and to review whether post and conversation on Twitter will be highly related to terrorism intention or another way around. This study deployed Naïve Bayes classification technique which identified Twitter contents in Indonesian national language. The method has been processed text pre-processing, and dataset divided with hold out technique. Result of F-measure value indicates that 76% and 77% of texts are associated with the accuracy level of terrorism based on macro-averaging and micro-averaging indicators. The finding is contributed to the scanty literature on the early warning detection method in Indonesian language and assist the government to target the extremists' organizations.
Variational Auto-Regularized Alignment for Sim-to-Real Control

General-purpose simulators can be a valuable data source for flexible learning and control approaches. However, training models or control policies in simulation and then directly applying to hardware can yield brittle control. Instead, we propose a novel way to use simulators as regularizers. Our approach regularizes a decoder of a variational autoencoder to a black-box simulation, with the latent space bound to a subset of simulator parameters. This enables successful encoder training from a small number of real-world trajectories (10 in our experiments), yielding a latent space with simulation parameter distribution that matches the real-world setting. We use a learnable mixture for the latent prior/posterior, which implies a highly flexible class of densities for the posterior fit. Our approach is scalable and does not require restrictive distributional assumptions. We demonstrate ability to recover matching parameter distributions on a range of benchmarks, challenging custom simulation environments and several real-world scenarios. Our experiments using ABB YuMi robot hardware show ability to help reinforcement learning approaches overcome cases of severe sim-to-real mismatch.
A Hybrid Deep Contractive Autoencoder and Restricted Boltzmann Machine Approach to Differentiate Representation of Female Brain Disorder

Deep Learning approach dragged the full attention of researcher in medical images due to their superior literature reported success and promising directions. Concluding the most discriminative set of features greatly represents a valuable guide for achieving the satisfaction performance of a medical diagnosing system. Despite, many methods proposed for such objective, the restricted Boltzmann machines outperform as they learn features directly from data, however they lack the optimal classification performance due to data complexity. Additionally, the contractive au-toencoder offers regularized term that explicitly increases the robustness of features representation and enhancement in overall performance. This paper proposes a novel deep learning framework for diagnosing female brain disorder from fMRI scans. The configuration combines the contractive autoencoder and the discriminative restricted Boltz-mann machine (DRBM) as we seek an improvement for the classification of fMRI. The demonstrated effectiveness of the contractive autoencoder supports fitting the probability distribution model of the DRBM and transfer learning to a deeper level. Our experimental indicates that the proposed model is able to detect female brain disorder with an accuracy of 88.17% and improved literature reported results on common issues.
Learning to Classify Text Complexity for the Italian Language Using Support Vector Machines

Natural language processing is undoubtedly one of the most active fields of research in the machine learning community. In this work we propose a supervised classification system that, given in input a text written in the Italian language, predicts its linguistic complexity in terms of a level of the Common European Framework of Reference for Languages (better known as CEFR). The system was built by considering: (i) a dataset of texts labeled by linguistic experts was collected, (ii) some vectorisation procedures which transform any text to a numerical representation, and (iii) the training of a support vector machine’s model. Experiments were conducted following a statistically sound design and the experimental results show that the system is able to reach a good prediction accuracy.
Multilayer Probabilistic Knowledge Transfer for Learning Image Representations

Probabilistic Knowledge Transfer (PKT) aims to transfer the knowledge encoded in the representations extracted from a layer of a large and complex neural network (teacher) into a smaller and faster one (student). However, PKT only transfers the knowledge between two layers of the networks, ignoring the potentially useful information encoded by the previous ones, reducing in this way the efficiency of PKT and the performance of the student model. In this paper, we propose a novel efficient multilayer PKT method that is capable of transferring the knowledge between the student and teacher networks by employing the representations extracted from multiple layers. The ability of the proposed multilayer PKT method to improve the knowledge transfer and increase the performance of the student model over other state-of-the-art methods is demonstrated using two image datasets.
PRESTO: a latency-aware power-capping orchestrator for cloud-native microservices

Power consumption is a major concern for cloud data-centers. In this context, cloud-native applications emerged in the last few years and fostered the adoption of the cloud computing model across many organizations. Cloud-native workloads are highly heterogeneous, co-located and latency-sensitive and are able to scale to a high number of machines. To properly manage their power consumption, within this paper we propose Power REgulator for Service Time Optimization (PRESTO), a latency-aware power-capping orchestrator. PRESTO defines an Observe Decide Act (ODA) loop to manage power consumption and average latency of microservice-based workloads by considering all the network interactions between microservices in the cluster. PRESTO reduces the power consumption by 37.13% on average with a control error that is below 12.5% and below 1.5ms on average w.r.t. an unconstrained execution.
Pathnet: Learning To Generate Trajectories Avoiding Obstacles

This paper presents a novel approach to solving 2D motion planning problems using deep neural networks, which we refer to as PathNet. PathNet first takes a 2D environment map composed of obstacle zone and free zone and compresses it to a latent vector. The latent vector is afterward concatenated with the start and goal positions to generate a trajectory connecting those positions. Our learning-based neural planner can solve motion planning problems in unseen environments and is computationally efficient as it only needs one single pass in our network to generate trajectories.