Reviewerly/exHarmony huggingface dataset:
Comparison of particle swarm optimization and firefly algorithm in parameter estimation of Lotka-Volterra

Lotka-Volterra competition model has been applied in life sciences including competition between species, predicting the Aeromonas hydrophila growth on fish surface. The competition model also has been used in social sciences, including competition in the Korean stock market and competition between two types of bank, namely commercial bank and rural bank in Indonesia. It is well-known that the analytical solution of the Lotka-Volterra is unknown. Here gradient-base methods such as Newton method and Levenberg-Marquardt are difficult to be implemented to estimate paramaters of the model. In order to estimate parameters in the model, one need to use heuristic method such as genetic algorithm, particle swarm optimization, firefly algorithm or other heuristic methods. In this paper, we compared performance of particle swarm optimization and firefly algorithm in parameter estimation of Lotka-Volterra type competition model. Here we used the profit data of commercial bank and rural bank, where the data cited from literature. We found the mean absolute percentage error (MAPE) of firefly algorithm is a little bit smaller than the error of particle swarm optimization method. We also found variance of the error of firefly algorithm is lower than the particle swarm optimization method. Hence, for parameter estimation of Lotka-Volterra competition model, firefly algorithm is more beneficial than the particle swarm optimization method.
First International Workshop on Quantum Software Engineering (Q-SE 2020)

The First International Workshop on Quantum Software Engineering (Q-SE 2020), co-located with the 42nd International Conference on Software Engineering (ICSE 2020), was held between July 2 and July 3, 2020. The workshop was originally scheduled to be a physical event in May 2020. Due to the SARS-CoV-2, aligned with the main conference, the workshop was held virtually instead. This report summarizes the keynote speeches, the paper presentations in the workshop, and the ensuing discussions. IEEE and ACM publish the proceedings of the workshop as part of the ICSE 2020 Workshops Companion.
Frame Duplication Forgery Detection and Localization Algorithm Based on the Improved Levenshtein Distance

In this digital era of technology and software development tools, low-cost digital cameras and powerful video editing software (such as Adobe Premiere, Microsoft Movie Maker, and Magix Vegas) have become available for any common user. Through these softwares, editing the contents of digital videos became very easy. Frame duplication is a common video forgery attack which can be done by copying and pasting a sequence of frames within the same video in order to hide or replicate some events from the video. Many algorithms have been proposed in the literature to detect such forgeries from the video sequences through analyzing the spatial and temporal correlations. However, most of them are suffering from low efficiency and accuracy rates and high computational complexity. In this paper, we are proposing an efficient and robust frame duplication detection algorithm to detect duplicated frames from the video sequence based on the improved Levenshtein distance. Extensive experiments were performed on some selected video sequences captured by stationary and moving cameras. In the experimental results, the proposed algorithm showed efficacy compared with the state-of-the-art techniques.
An In-Network Parameter Aggregation using DPDK for Multi-GPU Deep Learning

In distributed deep neural network using remote GPU nodes, communication occurs iteratively between remote nodes for gradient aggregation. This communication latency limits the benefit of distributed training with faster GPUs. In distributed deep learning using the remote GPUs, workload of gradient aggregation is imposed on a host machine. In this paper, we therefore propose to offload the gradient aggregation to a DPDK (Data Plane Development Kit) based network switch between the host machine and remote GPUs. In this approach, the aggregation process is completed in the network using extra computation resources in the network switch. We evaluate the proposed switch when GPUs and the host communicate with a standard IP communication and a PCI Express (PCIe) over 40Gbit Ethernet (40GbE) product, respectively. The evaluation results using a standard IP communication show that the aggregation is accelerated by 2.2-2.5x compared to the aggregation executed by a host machine. The results using the PCIe over 40GbE product show that the proposed switch outperforms the aggregation done by the host machine by 1.16x. This approach is thus useful for distributed training with multiple GPUs.
Exploring the Role of BERT Token Representations to Explain Sentence Probing Results

Several studies have been carried out on revealing linguistic features captured by BERT.This is usually achieved by training a diagnostic classifier on the representations obtained from different layers of BERT.The subsequent classification accuracy is then interpreted as the ability of the model in encoding the corresponding linguistic property.Despite providing insights, these studies have left out the potential role of token representations.In this paper, we provide a more in-depth analysis on the representation space of BERT in search for distinct and meaningful subspaces that can explain the reasons behind these probing results.Based on a set of probing tasks and with the help of attribution methods we show that BERT tends to encode meaningful knowledge in specific token representations (which are often ignored in standard classification setups), allowing the model to detect syntactic and semantic abnormalities, and to distinctively separate grammatical number and tense subspaces. 1
Which is Better for Deep Learning: Python or MATLAB? Answering Comparative Questions in Natural Language

We present a system for answering comparative questions (Is X better than Y with respect to Z?) in natural language.Answering such questions is important for assisting humans in making informed decisions.The key component of our system is a natural language interface for comparative QA that can be used in personal assistants, chatbots, and similar NLP devices.Comparative QA is a challenging NLP task, since it requires collecting support evidence from many different sources, and direct comparisons of rare objects may be not available even on the entire Web.We take the first step towards a solution for such a task offering a testbed for comparative QA in natural language by probing several methods, making the three best ones available as an online demo.
Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yolóxochitl Mixtec

Transcription bottlenecks", created by a shortage of effective human transcribers are one of the main challenges to endangered language (EL) documentation.Automatic speech recognition (ASR) has been suggested as a tool to overcome such bottlenecks.Following this suggestion, we investigated the effectiveness for EL documentation of end-to-end ASR, which unlike Hidden Markov Model ASR systems, eschews linguistic resources but is instead more dependent on large-data settings.We open source a Yoloxóchitl Mixtec EL corpus.First, we review our method in building an end-to-end ASR system in a way that would be reproducible by the ASR community.We then propose a novice transcription correction task and demonstrate how ASR systems and novice transcribers can work together to improve EL documentation.We believe this combinatory methodology would mitigate the transcription bottleneck and transcriber shortage that hinders EL documentation.
<i>Guardian of the Snacks</i>: Toward designing a companion for mindful snacking

COVID-19 has brought significant changes to our lives and eating practices, where many of us are required to not only stay at home but also eat at home. This is particularly challenging for lone-person households as eating alone can be boring, less motivating and could lead to unhealthy behaviors such as mindless snacking or skipping of meals. To remedy such situations, we present Guardian of the Snacks, a tangible multimodal system that encourages mindful snacking by offering a playful companionship to snacking. The system is customizable to bear the shape of different animals and can be adapted to different snacking scenarios. Like a good companion, it encourages eating but moderates overeating through auditory feedback and playful nudging. In this article, we reflect on our design process and contribute ideas for the future development of technology-driven mindful snacking.
WITHDRAWN: An analysis on prediction of breast cancer using radius Nearest Neighbor algorithm over other classification algorithms

Breast cancer is the subsequent leading and high mortality rated disease among women worldwide in both developed and undeveloped countries. Breast cancer is categorized as benign and malignant with the symptoms of changes in size, skin textures of breasts, continuous pain in breasts, change of color into red etc. At the present time, machine learning algorithms are used for classifying breast cancer either malignant or benign. In this paper, we used Radius Nearest Neighbor classifier which is an extension of k-Nearest Neighbor algorithm to classify breast cancer and to calculate its accuracy, specificity and sensitivity. This algorithm performance metrics are compared with Decision Tree, k-Nearest Neighbor and Support Vector Machine algorithms and resulted that Radius Nearest Neighbor algorithm gives high performance metrics among all the compared algorithms with 98.8% accuracy, sensitivity 98.8% and specificity 97.9%.
An Improved Genetic Algorithm with 2-Opt Local Search for the Traveling Salesman Problem

The traveling salesman problem (TSP) is one of the classic and well-studied problems in optimization and computer science. Genetic algorithm (GA), an evolutionary algorithm, has been applied to solving TSP. However, GA has its own weaknesses, such as poor ability of local search and premature convergence. The resolution effectiveness of GA decreases in the large-scale TSP. In this paper, based on an novel crossover operator to expand the search space and 2-opt Local search, an improved genetic algorithm is proposed. The simulation indicates the proposed algorithm surpasses the standard GA in the efficiency.
Impact of K-Nearest Neighbour on Classification Accuracy in KNN Algorithm Using Machine Learning

Classification accuracy of the KNN algorithm is affected by the number of nearest neighbour for predicting points. The idea behind nearest neighbour classification consists in finding a number, i.e. the ‘k’—of training data point nearest in distance to a predicting data, which has to be labelled. The label of the new predicting data will be defined from these neighbours. Using a machine learning approach, the value of k will be fixed for model creation. In this paper, I analyse different numbers of neighbour so that classification accuracy can be maximized. Different distance metrics (Euclidean, Manhattan and Minkowski) can be used to measure the distance from the nearest data point.
Transformer Reasoning Network for Personalized Review Summarization

Review summarization aims to generate condensed text for online product reviews, and has attracted more and more attention in E-commerce platforms. In addition to the input review, the quality of generated summaries is highly related to the characteristics of users and products, e.g., their historical summaries, which could provide useful clues for the target summary generation. However, most previous works ignore the underlying interaction between the given input review and the corresponding historical summaries. Therefore, we aim to explore how to effectively incorporate the history information into the summary generation. In this paper, we propose a novel transformer-based reasoning framework for personalized review summarization. We design an elaborately adapted transformer network containing an encoder and a decoder, to fully infer the important and informative parts among the historical summaries in terms of the input review to generate more comprehensive summaries. In the encoder of our approach, we develop an inter- and intra-attention to involve the history information selectively to learn the personalized representation of the input review. In the decoder part, we propose to incorporate the constructed reasoning memory learning from historical summaries into the original transformer decoder, and design a memory-decoder attention module to retrieve more useful information for the final summary generation. Extensive experiments are conducted and the results show our approach could generate more reasonable summaries for recommendation, and outperform many competitive baseline methods.
Naive Bayes-based Experiments in Romanian Dialect Identification

This article describes the experiments and systems developed by the SUKI team for the second edition of the Romanian Dialect Identification (RDI) shared task which was organized as part of the 2021 VarDial Evaluation Campaign. We submitted two runs to the shared task and our second submission was the overall best submission by a noticeable margin. Our best submission used a character n-gram based naive Bayes classifier with adaptive language models. We describe our experiments on the development set leading to both submissions.
Deep learning based person search in aerial imagery

The increasing availability of drones and their flexible employment for surveillance tasks will lead to large amounts of aerial video data in the near future. Similar to camera network data, such large data volumes will pose a challenge when fast analysis of the data is required, for example after a security incident. Key automated tasks that can help make the data more easily navigable are detection and re-identification of persons. While both tasks pose a challenge in themselves, the combination of both, often called person search, can often be of greatest benefit to analysts. In this work we address the task of person search in aerial images on the newly available P-DESTRE dataset. In particular our work aims at investigating the suitability of existing methods for person detection and re-identification in the aerial domain and taking a look at how top performing methods can be combined to realize an aerial person search system. Besides evaluation of the individual components we focus on analyzing the interplay between the detection and re-identification methods. In particular, we look at wether errors from the detection stage, such as misaligned detections or false positive detection, strongly affect the re-identification accuracies.
A user-centric metadata model to foster sharing and reuse of multidisciplinary datasets in environmental and life sciences

The recent technological advancements and emergence of the open data in environmental and life sciences are opening new research opportunities while creating new challenges around data management. They make available an unprecedented amount of data that can be exploited for studying complex phenomena. However, new challenges related to data management need to be addressed to ensure effective data sharing, discovery and reuse, especially when dealing with interdisciplinary research contexts. These issues are magnified in interdisciplinary context, by the fact that each discipline has its practices, e.g., specific formats and metadata standards. Moreover, the majority of current data management practices do not consider semantic heterogeneity existing among disciplines. For this reason, we introduce a flexible metadata model that describes the datasets of various disciplines using a common paradigm based on the observation concept. It provides a key vision for articulating the user point of view and underlying scientific domains. In this study, we therefore decide to mainly reuse the SOSA lightweight ontology (Sensor, Observation, Sample, and Actuator) to efficiently leverage others existing ontologies to improve datasets discovery and reuse coming from Earth and life observation. The main benefit of the proposed metadata model is that it extends the technical description, usually provided by existing metadata models, with the observation context description enabling the need of a user viewpoint. Moreover, following the FAIR principles, the metadata model specifies the semantics of its elements using ontologies and vocabularies, and reuses as much as possible ontological and terminological existing resources. We show the benefit and applicability of the model through a case study we identified as representative after interviewing researchers in environmental and life sciences.
The Structural Relationship among Digital Information Usage Ability, Social Support, Self Directed Learning Ability, and Learning Immersion of Early Adult Learners: Focusing on the Untact Learning Environment

The purpose of this study is to identify how digital information usage ability of early adult learners influence on their learning immersion and to verify the mediation effect of social support and self directed learning ability focusing on the untact learning environment. For this, the research has been conducted with survey data of 242 early adult learners. Data analysis was performed by using SPSS22.0, AMOS21.0, and PROCESS macro program. Results of this study are as follows. First of all, the influence of social support on digital information usage ability turned out to be significantly positive, and the influence of self directed learning ability on social support also turned out to be significantly positive. In addition, the influence of learning immersion on self directed learning ability turned out to be significantly positive. On the other hand, the relationship between digital information usage ability and learning immersion was negative, and the relationship between social support and learning immersion was not significant. Lastly, it was confirmed that there was a mediation effect between digital information usage ability and learning immersion through social support and self directed learning ability as shown in the analysis of PROCESS macro. As shown above, it is suggested that education program shall be preceded on improving digital information usage ability, and also that specific programs for early adult learners shall be developed to enhance self directed learning ability as well as social support.
MLANet: Multi-Layer Anchor-free Network for generic lesion detection

In medical image processing, detecting lesions from computed tomography (CT) scans becomes an important research problem with increasing attention. However, this problem is nontrivial because lesions from different organs and parts reflect different characteristics as well as different sizes. Most conventional methods only use a single-scale architecture to detect lesion areas. To get rid of the drawbacks above in medical imaging, a multi-scale framework called MLANet is proposed. To deal with the scale imbalance problem, we design a new backbone—a mixed hourglass network, in which each hourglass module share different input sizes and orders to extract features from different scales. And then the information is sent to the proposed Strengthen Weighted Feature Pyramid Network (SWFPN), a multi-layer weighted feature fusion module, to combine more semantic and spatial information, especially for the case where the number of layers is small. Finally, a Center-to-Corner (C2C) transformation is proposed to deal with the inaccurate size prediction of lesions. It is a non-linear transformation function, aiming to make the predictions more stable and accurate. MLANet is an end-to-end network and is easy to train. In our experiment, it achieves 65.2% AP50, as well as 88.3% in the sensitivity of [email protected] on the DeepLesion dataset, which exceeds many state-of-the-art detectors.
The FPGA Hardware Implementation of the Gated Recurrent Unit Architecture

The long short-term memory (LSTM) is the most widely used recurrent neural network representation for modeling sequential data. However, it is assumed an expensive modeling architecture as it requires many hardware components to be implemented, which is inefficient in solving problems based on small datasets. Therefore, in this paper, we propose a novel hardware implementation of the Gated Recurrent Unit (GRU) as a smaller alternative recurrent structure of the LSTM. The GRU has a smaller structure than the LSTM, which simplifies hardware components' implementation and reduces overall power consumption. The proposed GRU hardware implementation can be perfectly fit problems that depend on small datasets.
Leveraging emoji to improve sentiment classification of tweets

Recent advances in the Natural Language Processing field have brought good results to a number of interesting tasks, for instance, Linguistic Acceptability, Question Answering, Reading Comprehension, Natural Language Inference, and Sentiment Analysis. Methods, such as ULMFiT, ELMo, BERT, and their derivatives, have achieved increasing success with these tasks, but often requiring substantial amounts of pre-training data and computational resources. We propose a novel methodology to classify the sentiment of tweets, based on BERT but focusing on emojis, treating them as an important source of sentiment as opposed to considering them simple input tokens. Additionally, it is possible to use a previously pre-trained BERT model to warm start ours, greatly reducing the training time required. Experiments on two Brazilian Portuguese datasets - TweetSentBR and 2000-tweets-BR - show that our methodology produces better results than BERT and outperforms the previously published results for both datasets, thus establishing new state-of-the-art results on TweetSentBR with accuracy of 0.7577 (4.8 percentage points absolute improvement) and F1 score of 0.7395 (8.4 percentage points absolute improvement); and on 2000-tweets-BR with accuracy of 0.8316 (15.2 percentage points absolute improvement) and F1 score of 0.8151 (24.5 percentage points absolute improvement).
ROSD: Refined Oriented Staged Detector for Object Detection in Aerial Image

Object detection in aerial image is a challenging task. Although many advanced methods based on the convolutional neural network were popular in natural scenes, the progress in aerial images is not so smooth. Unlike natural scenes, objects in aerial images have the characteristics of arbitrary orientation, densely distribution, and large scale variation, which leads to a series of problems such as feature misalignment, missed detection, and poor detection of large aspect ratio objects. In this paper, a Refined Oriented Staged Detector (ROSD) with the combination of refined horizontal detector and rotated detector is proposed to address these problems. In our refined horizontal detector, a multi-orientation Region of Interest (RoI) Align and Orientation Attention Module (OAM) are adopted to make use of the orientation information for obtaining the orientation-sensitive features in regression branch and produce the orientation-invariant features in classification branch. Considering the feature misalignment between horizontal and rotated anchors in rotated detector, Deform Inception Module (DIM) is proposed to deal with the geometric deformation problem caused by the location changes. Besides, we propose an aspect ratio guided loss which consists of a smooth L1 loss and an angular offset penalty loss to improve the detection performance of large aspect ratio objects. Comparison experiments on two public aerial images datasets (i.e., DOTA and HRSC2016) demonstrate that our method can achieve a competitive performance.