Reviewerly/exHarmony huggingface dataset:
Extractive Document Summarization using Non-negative Matrix Factorization

Effectiveness of Non-negative Matrix Factorization (NMF) in mining latent semantic structure of text has motivated its use for single document summarization. Initial promise shown by the method provokes further research in this field to advance state-of-the-art. In this paper, we propose two methods to improve the performance of NMF based document summarization method for mining important sentences from the text to construct summary. We use Non-negative Double Singular Value Decomposition (NNDSVD) method to initialize NMF factor matrices, which begets summary stability and improves quality. Next, we propose two novel sentence scoring methods that use parts-based representation of text obtained after NMF decomposition. Both variations exploit information contained in feature and co-efficient matrices to achieve improvement in summary quality. Quality of summaries mined by the proposed methods is evaluated for four public data-sets using standard ROUGE tool. The proposed method is unsupervised, agnostic to the language of the document and does not use external knowledge. It is also generic, independent of domain and collection. These features of NMF based summarization along with additional advantage of speed make our method a potent candidate for online extractive summarization tool.
A Novel Artificial Bee Colony Algorithm with Division of Labor for Solving CEC 2019 100-Digit Challenge Benchmark Problems

As a relatively new paradigm of evolutionary algorithms, artificial bee colony (ABC) algorithm has shown attractive performance in solving optimization problems. However, for some complex optimization problems, its performance is still not satisfactory. To address this concerning issue, in this paper, we designed an improved ABC variant, called DLABC, based on the division of labor theory. In the DLABC, both of the employed bee phase and the onlooker bee phase are modified by introducing new solution search equations. For the modified employed bee phase, two new control parameters are introduced in the new solution search equation, which aims to control the frequency of perturbation and the magnitude of perturbation, respectively. For the modified onlooker bee phase, a novel depth-first search framework is used which tends to allocate more computing resources to elite solutions for accelerating convergence rate. By combing these two modified phases, we attempt to balance the exploration and exploitation capabilities of ABC. Numerical experiments are conducted on the CEC 2019 100-digit challenge benchmark suite, and the DLABC is compared with the basic ABC and a recently well-established ABC variant (DFSABC_elite). The total scores of these included algorithms have shown that our proposed DLABC algorithm performed best.
Multi-UAS Path-Planning for a Large-scale Disjoint Disaster Management

A UAS-based disaster management method has been adopted to monitor the disaster impact and protect human lives since it can be rapidly deployed, execute an aerial imaging mission, and provide a cost-efficient operation. In the case of a wildfire disaster, a disaster management is highly complex because of large-scale wildfires that can occur simultaneously and disjointly in a large area. In order to effectively manage these large-scale wildfires, it requires multiple UAS with multiple ground stations. However, conventional UAS-based management methods relies on a single ground station that can have a limitation to handle the large-scale wildfire problem. This paper presents a new path-planning framework for UAS operations including a fleet of UAVs and multiple ground stations. The framework consists of two parts: creating coverage paths for each wildfire and optimizing routes for each UAV. To test the developed framework, this paper uses representative wildfire scenarios in the State of California.
Applications and Advancements of Firefly Algorithm in Classification: An Analytical Perspective

Nature-inspired algorithms, particularly those located on swarm intelligence and population-based, have dragged much attention in the last few years. Firefly algorithm is one of the leading swarm-based algorithms, which came into view about 10 years ago. It has become progressively an essential appliance of swarm intelligence that has been enforced in many vicinities of optimization along with the engineering practice. Abundant difficulties from distinctive regions have been solved effectively by using firefly algorithm and its development. In this paper, we conducted a brief study on the applications and advancements of firefly algorithm in the area of data classification. Various classification areas, such as image classification, text classification, neural network-based classification and some other classifications, are taken into consideration for this study. Special attention has been paid toward the usage level and implementation issues of firefly algorithm in different classification domains. The main aim of this survey is to inspire researchers toward further research of firefly algorithm in several other application areas other than the intended area.
Hybrid Approach for Face Recognition from a Single Sample per Person by Combining VLC and GOM

Abstract This paper proposes a new face recognition system based on combining two feature extraction techniques: the Vander Lugt correlator (VLC) and Gabor ordinal measures (GOM). The proposed system relies on the execution speed of VLC and the robustness of GOM. In this system, we applied the Tan and Triggs and retina modeling enhancement techniques, which are well suited for VLC and GOM, respectively. We evaluated our system on the standard FERET probe data sets and on extended YaleB database. The obtained results exhibited better face recognition rates in a shorter execution time compared to the GOM technique.
Correlation power analysis and effective defense approach on light encryption device block cipher

Abstract Recently many works have shown that one of the weaknesses of cryptographic algorithms is power analysis attacks on its hardware implementation. This side channel analysis (SCA) is of interest because such attack can leak information of cryptographic algorithms. So, developing attack resistant mechanism is important for hardware security modules. This paper investigates the vulnerability of light encryption device (LED) block cipher, against correlation power analysis. LED is a lightweight block cipher targeted for compact hardware implementation to protect ubiquitous computing data. We use Chipwhisperer capture hardware to capture power traces during encryption of randomly generated plain texts using the same secret key. Our proposed attack successfully retrieves all 64 bits of secret key correctly using only 300 power traces and 256 key guesses. Furthermore, we propose a simple but effective mitigation technique to prevent such attacks.
Quantum Differential Evolution: an Investigation

Several research studies have been carried out on the integration of quantum operators and evolutionary algorithms. However, the performance of such integration remains questionable and needs further research for drawing a useful conclusion. Therefore, this paper takes a step forward to analyze the effect of integrating quantum entanglement and quantum NOT gate with the well-known differential evolution algorithm. The performance of different algorithm designs has been evaluated by solving a number of unconstrained real parameter single objective optimization problems. Although the integration of quantum entanglement helps to get competitive results, the overall experimental results with quantum operators are not convincing enough, especially when there are no inter-dependency among decision variables. This in turn opens up new research directions to deepen the knowledge in the design of quantum evolutionary algorithms.
Potential of Technological Enablement for Agile Automotive Product Development

This qualitative study investigates the potential of generative design to enable agile automotive product development which is subject to both constraints of physicality and challenges of scale. These limitations are caused by the product's physicality and the complex development process, distributed between silos and teams, which imply multiple dependencies compared to software development. The research revealed that an agile framework based on Generative Design significantly reduced the impact of constraints of physicality and avoided some challenges of scale. In general, successful technology integration requires the agile framework to be tailored to both technology and development context. Otherwise both sides do not interlock and the benefits cannot be realized on overall product development level. Therefore applicability of standard agile methods is limited.
Triangulated Sentiment Analysis of Tweets for Social CRM

High resolution data from social media platforms like Twitter presents an unprecedented opportunity to organisations for social customer relationship management (Social CRM) by analysing the ongoing discussion about business events such as a service outage. Text based sentiment analysis has been widely researched utilising mainly lexicon-based and machine learning approaches to uncover customers' opinions. They are similar in the sense that the machine learning approach relies on an initial lexical model on which the learning is based. Both methods view sentiment as either positive, neutral, or negative. This is not the case for the psycholinguistic approach following which text sentiment is more continuous. We compare these three approaches with a Twitter dataset collected during a service outage. Contrary to our expectation, we find that the language used in tweets is not very negative or emotionally intense. This research therefore contributes to the sentiment analysis discussion by dissecting three methods and illustrating how and why they arrive at differing results. The selected research context provides an illuminating case about service failure and recovery.
Neural attention model with keyword memory for abstractive document summarization

Summary Abstractive summarization is the task of creating summaries by generating a set of novel sentences based on the information extracted from the original document, while most of summarization researches are based on extractive or compressive approaches. These approaches extract phrases from the original document and concatenate them by post‐processing and cannot truly encapsulate the contents of summaries, because they only reuse the phrases in the given document. Moreover, there are limits for paraphrasing and re‐organizing of the original contents with the current Natural Language Processing (NLP) techniques. With these reasons, we propose a novel abstractive summarization method. The main goal of our paper is to generate a long sequence of words with coherent sentences by reflecting the key concepts of the original document and the contents of summaries. To achieve this goal, we propose an attention mechanism that uses Document Content Memory for learning the language model effectively. To evaluate its effectiveness, the proposed methods are compared with other language models and an extractive summarization method. We demonstrate that our proposed methods improve summarization results in ROUGE score using ACL dataset. The experimental results show that our proposed methods using keyword memory are effective to generate long sequence summary.
Investigating Joint CTC-Attention Models for End-to-End Russian Speech Recognition

We propose an application of attention-based models for automatic recognition of continuous Russian speech. We experimented with three types of attention mechanism, data augmentation based on a tempo and pitch perturbations, and a beam search pruning method. Moreover we propose a using of sparsemax function for our task as a probability distribution generator for an attention mechanism. We experimented with a joint CTC-Attention encoder-decoders using deep convolutional networks to compress input features or waveform spectrograms. Also we experimented with Highway LSTM model as an encoder. We performed experiments with a small dataset of Russian speech with total duration of more than 60 h. We got the recognition accuracy improvement by using proposed methods and showed better performance in terms of speech decoding speed using the beam search optimization method.
Fully Convolutional Network for 3D Human Skeleton Estimation from a Single View for Action Analysis

Elderly health monitoring is important nowadays. This paper proposes to extract 3D skeleton for elderly for monitoring their daily behavior (such as walk, fall-down, etc.). Our technique uses two "Fully Convolutional Networks" (FCNs) to estimate 3D Human pose (skeleton) from the corresponding 2D pose (skeleton), which can be estimated from a single RGB-image. Our FCNs contain two-stages: the 1st stage is to estimate a 3D Anchor Pose (i.e., the most similar and frequently occurring pose in the dataset) from the 2D skeleton, while the 2nd stage is to further regress/refine the 3D anchor pose to its final state (parameters). We also apply global or object-centered normalization methods as a pre-processing step so as to be applicable when cameras of different FOV (Field of View), focal length, or object distances, are encountered. According to experiments, our two-stage FCNs are capable of achieving an MPJPE (Mean per joint position error) of 38.84 mm (better than other methods in comparison, 45.5 mm) when a 2D ground truth pose is used as the input. When cascaded with other 2D pose estimator (e.g., stacked Hourglass model), the average MPJPE is about 67.71 mm. Tis can be further improved if a 17-joints human skeleton model is adopted and re-trained based on the dataset H36M.
ScholarSight: Visualizing Temporal Trends of Scientific Concepts

In this paper, we present a system for exploring the temporal trends of scientific concepts. Scientific concepts were captured by extracting noun phrases and entities from all computer science papers of arXiv.org. Our system allows users to review the time series of numerous concepts and to identify positively and negatively trending concepts. By applying clustering techniques and cluster analysis visualizations, it can also present concepts which share the same usage patterns over time. Our system can be beneficial for both ordinary researchers of any field and for researchers working in bibliometrics and scientometrics in order to investigate the evolution of scientific concepts.
A Hybrid Approach for Artwork Recommendation

In this article, we give an overview of the particularities that surround artwork Recommender Systems in museums. Unlike other domains, such as movies, where the user selects only one movie to watch, in the museum domain it is necessary to recommend a sequence of items to consume, in which the order and relationships among them might affect the recommendation. Furthermore, curators determine a place for each artwork in the museum, generating a distribution over the museum's rooms that imposes constraints about the way visitors walk through it. However, nowadays it is also possible to access to their digital collection, gaining independence from the physical distribution, allowing any visitor to be his/her own curator. Because of that, in this article we analyze and discuss different approaches to exploit the particularities of this domain. Specifically, we focus on one aspects, namely: item modeling. First, we summarize different approaches to represent and extract features from artworks that could be relevant for the recommendation task. Finally, we propose a novel approach to model artworks that could be useful in the recommendation task.
Comparing Three Approaches to Micro in RTS Games

We compare three promising approaches to micromanaging units in real-time strategy games. These approaches span the range from easily understandable meta-search, which uses genetic algorithms to search through the space of parameters of a human specified control algorithm to pure potential fields, which searches through a space of less human understandable potential field parameter values, to neuro-evolution of augmented topologies which evolves an opaque difficult to understand neural network. All three approaches use a two-objective pareto optimal fitness function that maximizes damage done to opponent units and minimizes damage received by friendly units. We first show that all three approaches can quickly evolve micro superior to the default AI for Starcraft 2, a popular real-time strategy game and research testbed. We then manually co-evolve micro against previously evolved micro to produce micro that plays well against good (gold level) human Starcraft 2 players. Furthermore, we can integrate the micro produced by different approaches to control a single group of units composed from multiple types. These results indicate that we may choose our approach based on our need to understand unit control behavior and thus provides another bridge to transferring research results from autonomous units in simulation games to autonomous agents (robots) in the real world.
Towards Meta-learning of Deep Architectures for Efficient Domain Adaptation

This paper proposes an efficient domain adaption approach using deep learning along with transfer and meta-level learning. The objective is to identify how many blocks (i.e. groups of consecutive layers) of a pre-trained image classification network need to be fine-tuned based on the characteristics of the new task. In order to investigate it, a number of experiments have been conducted using different pre-trained networks and image datasets. The networks were fine-tuned, starting from the blocks containing the output layers and progressively moving towards the input layer, on various tasks with characteristics different from the original task. The amount of fine-tuning of a pre-trained network (i.e. the number of top layers requiring adaptation) is usually dependent on the complexity, size, and domain similarity of the original and new tasks. Considering these characteristics, a question arises of how many blocks of the network need to be fine-tuned to get maximum possible accuracy? Which of a number of available pre-trained networks require fine-tuning of the minimum number of blocks to achieve this accuracy? The experiments, that involve three network architectures each divided into 10 blocks on average and five datasets, empirically confirm the intuition that there exists a relationship between the similarity of the original and new tasks and the depth of network needed to fine-tune in order to achieve accuracy comparable with that of a model trained from scratch. Further analysis shows that the fine-tuning of the final top blocks of the network, which represent the high-level features, is sufficient in most of the cases. Moreover, we have empirically verified that less similar tasks require fine-tuning of deeper portions of the network, which however is still better than training a network from scratch.
Adoption of Machine Learning Techniques in Software Effort Estimation: An Overview

Abstract Nowadays the significant trend of the effort estimation is in demand. It needs more data to be collected and the stakeholders require an effective and efficient software for processing, which makes the hardware and software cost development becomes steeply increasing. This scenario is true especially in the area of large industry, as the size of a software project is becoming more complex and bigger, the complexity of estimation is continuously increased. Effort estimation is part of the software engineering economic study on how to manage limited resources in a way a project could meet its target goal in a specified schedule, budget and scope. It is necessary to develop or adopt a useful software development process in executing a software development project by acting as a key constraint to the project. The accuracy of estimation is the main critical evaluation for every study. Recently, the machine learning techniques are becoming widely used in many effort estimation problems but there are limitations in some of the models and the variation research is still not enough. This paper presents an overview of the effort estimation using machine learning techniques and will be useful for researchers to provide future direction in the field of machine learning adoption in software effort estimation.
Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings.
KnobSlider: Design of a Shape-Changing Parameter Control UI and Study of User Preferences on Its Speed and Tangibility

Professionals such as sound engineers or aircraft pilots heavily use physical knobs and sliders on their interfaces. The interfaces have advantages over touchscreen interfaces, especially when the users need to quickly and eyes-freely respond to changing situations such as when musicians are improvising, or there is smoke in a cockpit. However, unlike touchscreen interfaces, the physical interfaces are often bulky and crowded and lack of adaptability to user preferences or small spaces. To have advantages from both physical and touchscreen control interfaces, we explore design space of control interfaces and suggest design guidelines in the following steps. We first conduct a formative study with eight professionals who use knobs and sliders. Based on their feedback, we propose design requirements for future parameter control interfaces. We then introduce the design of the KnobSlider, a shape-changing device that combines the advantages of a physical knob and a slider in a time- and space-multiplexing way. To increase users' acceptance on shape-changing control interfaces, we investigate subjective preference on speed of shape-changes by using pairwise comparison with different maximum speeds. We also investigate how tangibility – showing KnobSlider on a video or showing it in the physical world – affects users preference and suggest speed design guidelines for future studies.