Reviewerly/exHarmony huggingface dataset:
Evaluating Machine Learning Techniques on Human Activity Recognition Using Accelerometer Data

Human activity recognition is gaining increasing importance because of its implication in remote monitoring application including security, health and fitness apps. This paper provides an analysis of different machine learning techniques for recognizing human activity. Firstly, all the recent work related to human activity recognition using accelerometer data is analyzed and presented in the paper. In this study the accelerometer used in smartphones as well as those embedded in wearable devices are compared and recognition methodologies applied on both the devices are presented. The dataset used in this project is a transformed version of "Activity Recognition using Cell Phone Accelerometers," by the Wireless Sensor Data Mining WSDM. Some important features were extracted from the data and based on it different models were assessed using Matlab Classification Learner App. Four distinct machine learning techniques were applied on the dataset, namely, linear regression, logistic regression, support vector machine and neural network. For the purposed of applying classifier Weka tool is used. The results of these algorithms are compared and presented in the form of tables and graphs and Bagged Tree is identified to be the best algorithm based on accuracy results.
An evaluation of 3D printing for the manufacture of a binaural recording device

With increasing availability of low-cost, high fidelity 3D printers, complex designs which previously proved costly to manufacture can be made readily available for research and public engagement. This work reports on the development of a binaural recording device which is tested for compatibility on 4 commercial 3D printers, each offering different print capabilities. The primary objective is to demonstrate the potential for open access designs that can be reliably 3D printed but maintain a high fidelity on the recorded HRTF. The influence of both the overall manufacturing strategy and pinna print material were investigated. A dummy head was printed and used to generate a database of HRTF’s in the horizontal plane. The HRTF’s are compared per material and relative effects are observed. A numerical simulation for the rigid boundary case provides validation data and serves as verification of the manufacturing strategy and acts as a baseline to inspect influence of pinna print material.
Unsupervised Image Decomposition in Vector Layers

Deep image generation is becoming a tool to enhance artists and designers creativity potential. In this paper, we make the generation process more structured and easier to interact with. We propose a new deep image reconstruction paradigm where the outputs are composed from simple layers, defined by their color and a vector transparency mask. This presents a number of advantages compared to the commonly used convolutional network architectures. In particular, our layered decomposition allows simple user interaction, for example to update a given mask, or change the color of a selected layer. From a compact code, our architecture also generates vector images with a virtually infinite resolution, the color at each point in an image being a parametric function of its coordinates. We validate the efficiency of our approach by comparing reconstructions with state-of-the-art baselines given similar memory resources on CelebA and ImageNet datasets. We demonstrate several applications of our new image representation obtained in an unsupervised manner, including editing, vectorization and image search.
Line-segment Feature Analysis Algorithm Using Input Dimensionality Reduction for Handwritten Text Recognition

Recently, demand for handwriting recognition, such as automation of mail sorting, license plate recognition, and electronic memo pads, has exponentially increased in various industrial fields. In addition, in the image recognition field, methods using artificial convolutional neural networks, which show outstanding performance, have been applied to handwriting recognition. However, owing to the diversity of recognition application fields, the number of dimensions in the learning and reasoning processes is increasing. To solve this problem, a principal component analysis (PCA) technique is used for dimensionality reduction. However, PCA is likely to increase the accuracy loss due to data compression. Therefore, in this paper, we propose a line-segment feature analysis (LFA) algorithm for input dimensionality reduction in handwritten text recognition. This proposed algorithm extracts the line segment information, constituting the image of input data, and assigns a unique value to each segment using 3 × 3 and 5 × 5 filters. Using the unique values to identify the number of line segments and adding them up, a 1-D vector with a size of 512 is created. This vector is used as input to machine-learning. For the performance evaluation of the method, the Extending Modified National Institute of Standards and Technology (EMNIST) database was used. In the evaluation, PCA showed 96.6% and 93.86% accuracy with k-nearest neighbors (KNN) and support vector machine (SVM), respectively, while LFA showed 97.5% and 98.9% accuracy with KNN and SVM, respectively.
On visualization techniques comparison for large social networks overview: A user experiment

Visualizing social networks, especially an overview emphasizing their structure, i.e., communities and their interconnections, is known to be a challenging problem. In this paper, we present a set of design rationales to build such overview visualizations of social networks and our solution called Jasper. We evaluate its performances against two of the most wide-spread visualization techniques (matrix and node-link diagram) in a human–computer controlled experiment based on community-related tasks. While none of the techniques emerge as the overwhelming winner, Jasper appears to be one of the best methods for each task; a fact sustained by the marks given by the users. Overall, Jasper can be seen as an all-encompassing solution for quickly producing legible and compact overviews of large social networks on a single modern computer.
Complete Weight Enumerators of Linear Codes Based on Weil Sums

With a proper defining set, we define a class of linear codes and determine their complete weight enumerators and weight enumerators using Weil sums. They only have two or three nonzero weights, and some of them are optimal with respect to the Griesmer bound or Markus Grassl's code tables. So they are suitable for applications in strongly regular graphs and secret sharing schemes.
The impact of adding perspective-taking to spatial referencing during human–robot interaction

For effective verbal communication in collaborative tasks, robots need to account for the different perspectives of their human partners when referring to objects in a shared space. For example, when a robot helps its partner find correct pieces while assembling furniture, it needs to understand how its collaborator perceives the world and refer to objects accordingly. In this work, we propose a method to endow robots with perspective-taking abilities while spatially referring to objects. To examine the impact of our proposed method, we report the results of a user study showing that when the objects are spatially described from the users’ perspectives, participants take less time to find the referred objects, find the correct objects more often and consider the task easier.
Cryptography Encryption Technique Using Circular Bit Rotation in Binary Field

In the digital age when technology has advanced so much that communication between people is just a text away no matter where they are geographical. But with every advancement, the technology must be secure enough so that no eavesdropping can take place. That's where cryptography comes into play, Cryptography is necessary when communicating over any untrusted medium. Cryptography is an important field in information security, and, in this modern age, cryptography is used everywhere from surfing the internet to phone calls. The requirement for a better cryptosystem keeps increasing as the advancement of modern computers outpaces the old cryptosystems. In cryptography, the plain text is the original text and ciphertext is the encrypted text. So, the proposed technique in this paper encrypts plain text into ciphertext that is unrecognizable that makes the ciphertext unidentifiable when compared to plain text. First, we generate a very large random number that is at least 1024 bits which will be the key. Encrypting the plaintext is based on the key, first the plain text is divided into blocks of 9 bytes then the key divided into array of `n/2' number of pairs, the first digit of pair being position in the block and second digit being the number of bits to shift right, the shift is circular from the position of the selected character. In the decryption the left shift is performed to the blocks, the start of decryption begins from the end of the array of key pairs.
Double-Attentive Principle Component Analysis

This letter proposes a double-attentive principle component analysis (DA-PCA) model for image processing. Compared to the previous PCA-based works that cannot deal with normal images and outliers effectively, the proposed DA-PCA model performs a double-attentive mechanism to sever the connections with outliers and hold the effectiveness of normal images. To solve the proposed DA-PCA model, we propose an efficiently iterative algorithm and provide strict convergence analysis for it. Moreover, in the simulations, we conduct the reconstruction and classification experiments on several real datasets and the experimental results demonstrate the superb performance of our proposal.
Kl-Divergence-Based Region Proposal Network For Object Detection

The learning of the region proposal in object detection using the deep neural networks (DNN) is divided into two tasks: binary classification and bounding box regression task. However, traditional RPN (Region Proposal Network) defines these two tasks as different problems, and they are trained independently. In this paper, we propose a new region proposal learning method that considers the bounding box offset’s uncertainty in the objectness score. Our method redefines RPN to a problem of minimizing the KL-divergence, difference between the two probability distributions. We applied KLRPN, which performs region proposal using KL-Divergence, to the existing two-stage object detection framework and showed that it can improve the performance of the existing method. Experiments show that it achieves 2.6% and 2.0% AP improvements on MS COCO test-dev in Faster R-CNN with VGG-16 and R-FCN with ResNet-101 backbone, respectively.
Author classification using transfer learning and predicting stars in co‐author networks

Summary The vast amount of data is key challenge to mine a new scholar that is plausible to be star in the upcoming period. The enormous amount of unstructured data raise every year is infeasible for traditional learning; consequently, we need a high quality of preprocessing technique to expand the performance of traditional learning. We have persuaded a novel approach, Authors classification algorithm using Transfer Learning (ACTL) to learn new task on target area to mine the external knowledge from the source domain. Comprehensive experimental outcomes on real‐world networks showed that ACTL, Node‐based Influence Predicting Stars, Corresponding Authors Mutual Influence based on Predicting Stars, and Specific Topic Domain‐based Predicting Stars enhanced the node classification accuracy as well as predicting rising stars to compared with contemporary baseline methods.
Investigating the Roles of Effective Communication Among Stakeholders in Collaborative Software Development Projects

The development of software includes multiple stakeholders since it is a multidisciplinary collaboration. Processes in software development demonstrate many problems relating to multidisciplinary collaboration. Communication has been recognized as an important factor for success in software development projects because previous researches on stakeholder analysis and collaboration has demonstrated the importance of communication. Hence, this study investigated and analyzed communication between stakeholders in collaborative software development projects with references to team activities as a social and intellectual practice. The objective of the study was to define factors that would influence and recognize the process of collaboration during software development. The study focused on communication between the software developers (Students) and project managers (lecturers) during the software requirements phase, interdisciplinary collaboration, team process, and inter-team, particularly between software engineers and technical communicators. First, a survey questionnaire was developed to gather data and analyze the effects and sources of communication among the involved stakeholders in software development projects. An experimental analysis was also performed to further test the supporting impact of client documents on requirements gathering. An experimental study was conducted to further test the supporting effect on requirement gathering of client documents. The results revealed that project managers are vital to software development progress, because they are the one that will provide the team with documents that support requirements gathering, thus improving team productivity and efficiency. Finally, software teams ensured that participants from all professional backgrounds were actively involved in the projects with a relatively centralized team structure.
Consumer Behavior Analytics using Machine Learning Algorithms

User-generated content in the form of reviews, ratings, and comments can be analyzed for greater insights for enterprise use. The analysis of such consumer behavior is helpful to understand the consumer's requirements and predict their future intentions towards the service. Through this cognitive study, E-commerce Organizations can track the usage and sentiments attached to their products and take appropriate marketing approaches to provide a personalized shopping experience for their consumers, thereby increasing their organizational profit. This paper aims to employ data-driven marketing tools, such as data visualization, natural language processing, and machine learning models that help in understanding the demographics of an organization. We also build recommender systems through collaborative filtering, neural networks, and sentiment analysis.
Attentive Task-Net: Self Supervised Task-Attention Network for Imitation Learning using Video Demonstration

This paper proposes an end-to-end self-supervised feature representation network named Attentive Task-Net or AT-Net for video-based task imitation. The proposed AT-Net incorporates a novel multi-level spatial attention module to highlight spatial features corresponding to the intended task demonstrated by the expert. The neural connections in AT-Net ensure the relevant information in the demonstration is amplified and the irrelevant information is suppressed while learning task-specific feature embeddings. This is achieved by a weighted combination of multiple intermediate feature maps of the input image at different stages of the CNN pipeline. The weights of the combination are given by the compatibility scores, predicted by the attention module for respective feature maps. The AT-Net is trained using a metric learning loss which aims to decrease the distance between the feature representations of concurrent frames from multiple view points and increase the distance between temporally consecutive frames. The AT-Net features are then used to formulate a reinforcement learning problem for task imitation. Through experiments on the publicly available Multi-view pouring dataset, it is demonstrated that the output of the attention module highlights the task-specific objects while suppressing the rest of the background. The efficacy of the proposed method is further validated by qualitative and quantitative comparison with a state-of-the-art technique along with intensive ablation studies. The proposed method is implemented to imitate a pouring task where an RL agent is learned with the AT-Net in Gazebo simulator. Our findings show that the AT-Net achieves 6.5% decrease in alignment error along with a reduction in the number of training iterations by almost 155k over the state-of-the-art while satisfactorily imitating the intended task.
Dependent Scalar Quantization For Neural Network Compression

Recent approaches to compression of deep neural networks, like the emerging standard on compression of neural networks for multimedia content description and analysis (MPEG-7 part 17), apply scalar quantization and entropy coding of the quantization indexes. In this paper we present an advanced method for quantization of neural network parameters, which applies dependent scalar quantization (DQ) or trellis-coded quantization (TCQ), and an improved context modeling for the entropy coding of the quantization indexes. We show that the proposed method achieves 5.778% bitrate reduction and virtually no loss (0.37%) of network performance in average, compared to the baseline methods of the second test model (NCTM) of MPEG-7 part 17 for relevant working points.
Improving Abstractive Text Summarization with History Aggregation

Recent neural sequence to sequence models have provided feasible solutions for abstractive summarization. However, such models are still hard to tackle long text dependency in the summarization task. A high-quality summarization system usually depends on strong encoder which can refine important information from long input texts so that the decoder can generate salient summaries from the encoder's memory. In this paper, we propose an aggregation mechanism based on the Transformer model to address the challenge of long text representation. Our model can review history information to make encoder hold more memory capacity. Empirically, we apply our aggregation mechanism to the Transformer model and experiment on CNN/DailyMail dataset to achieve higher quality summaries compared to several strong baseline models on the ROUGE metrics.
Toward a Task-driven Intelligent GUI Adaptation by Mixed-initiative

Adapting the user interface (UI) to the changing context of use is intended to support the interaction effectiveness and sustain UI usability. However, designing and/or processing UIs adaptation at design time does not encompass real situation requirements. Adaptation should have a cross-cutting and low-cost impact on software patterning and appearance with regard to the situation and the ambient-context. To meet this requirement, we present TADAP proposal for run-time adaptive and adaptable UI based user feedbacks and machine learning. It allows a task-driven adaptation of the user interface (UI) at runtime by mixed-initiative. The particularity of TADAP is the utilization of Machine Learning potential to support context-aware runtime adaptation within model-driven UI. Further, TADAP allows the UI adaptation by mixed-initiative (User and System) considering the user preferences (implicit and explicit) during an interaction. Such a mixed-initiative runtime UI-adaptation tool provides recommendations on how to personalize the UI. Further, it has the ability to track real-time users’ interventions and learn their preferences. Diverse tests were performed and showed TADAP as a promising initiative for intelligent model-driven UI adaptation.
Building a Machine Learning Model for Unstructured Text Classification: Towards Hybrid Approach

Text analytics is one of the major research domains. Many researchers are working on business intelligence to create decision-making systems of high accuracy. We are enjoying the result from these machine learning models but these systems are hungry for data. Too much data is available to deal with as it is arriving at incredible speed. Problem is that 90% of data is unstructured, and it is very difficult to tackle this raw data. Data scientists are trained to deal with categorical and numerical data only. The idea is to create a machine learning model for unstructured text categorization. The proposed model is going to use the k-means algorithm for text clustering followed by a deep neural network for classification. This hybrid clustering and classification (HCC) model is a combination of state of the art algorithm k-means and very hot concept Deep Learning. The main focus of this research is the unstructured text that can be utilized for various natural language processing (NLP) applications.
Multi-Objective Information Retrieval-Based NSGA-II Optimization for Requirements Traceability Recovery

Abtract-The recovery of requirements traceability links assists multiple software development activities, including analyzing impact of changes, validating requirements, maintenance and evolution of software. Poor communication and time pressure problems often have resulted in organizations being unsuccessful in implementing traceability practices. A number of solutions for automatic recovery of links across software artifacts have been developed, including the application of textual analysis and information retrieval techniques. Link recovery with information retrieval has seen some success, but there is a need for methods that further enable the automatic generation of links. We investigate the requirements traceability recovery problem as an optimization problem; we study it as a multi-objective search in which we assign one-to-many relationships between each requirement to a code element by considering the semantic similarity between the textual content of requirements and the source code. We adapt the Non-dominated Sorting Genetic Algorithm, NSGA-II, to seek the optimal solution between two objective functions based on textual similarity using the similarity measures of Jaccard similarity and term frequency (TF-idf) combined with cosine similarity. Our IR-based NSGA-II returned values on average above 75% for both precision and recall for the recovered links. High precision and recall are positive results for our approach to requirements traceability recovery.
Hyperbolic Embeddings for Hierarchical Multi-label Classification

Hierarchical multi-label classification (HMC) is a practically relevant machine learning task with applications ranging from text categorization, image annotation and up to functional genomics. State of the art results for HMC are obtained with ensembles of predictive models, especially ensembles of predictive clustering trees. Predictive clustering trees (PCTs) generalize decision trees towards HMC and can be combined into ensembles using techniques such as bagging and random forests. There are two major issues that influence the performance of HMC methods: (1) the computational bottleneck imposed by the size of the label hierarchy that can easily reach tens of thousands of labels, and (2) the sparsity of annotations in the label/output space. To address these limitations, we propose an approach that combines graph node embeddings and a specific property of PCTs (descriptive, clustering and target attributes can be specified arbitrarily). We adapt Poincaré hyperbolic node embeddings to obtain low dimensional label set embeddings, which are then used to guide PCT construction instead of the original label space. This greatly reduces the time needed to construct a tree due to the difference in dimensionality. The input and output space remain the same: the tests in the tree use original attributes, and in the leaves the original labels are predicted directly. We empirically evaluate the proposed approach on 9 datasets. The results show that our approach dramatically reduces the computational cost of learning and can lead to improved predictive performance.