Reviewerly/exHarmony huggingface dataset:
A discriminative method for global query expansion and term reweighting using co-occurrence graphs

This article presents a new query expansion (QE) method aiming to tackle term mismatch in information retrieval (IR). Previous research showed that selecting good expansion terms which do not hurt retrieval effectiveness remains an open and challenging research question. Our method investigates how global statistics of term co-occurrence can be used effectively to enhance expansion term selection and reweighting. Indeed, we build a co-occurrence graph using a context window approach over the entire collection, thus adopting a global QE approach. Then, we employ a semantic similarity measure inspired by the Okapi BM25 model, which allows to evaluate the discriminative power of words and to select relevant expansion terms based on their similarity to the query as a whole. The proposed method includes a reweighting step where selected terms are assigned weights according to their relevance to the query. What’s more, our method does not require matrix factorisation or complex text mining processes. It only requires simple co-occurrence statistics about terms, which reduces complexity and insures scalability. Finally, it has two free parameters that may be tuned to adapt the model to the context of a given collection and control co-occurrence normalisation. Extensive experiments on four standard datasets of English (TREC Robust04 and Washington Post) and French (CLEF2000 and CLEF2003) show that our method improves both retrieval effectiveness and robustness in terms of various evaluation metrics and outperforms competitive state-of-the-art baselines with significantly better results. We also investigate the impact of varying the number of expansion terms on retrieval results.
RingFIR: A Large Volume Earring Dataset for Fashion Image Retrieval

Fashion image retrieval (FIR) is a challenging task, which involves similar item searching from a massive collection of fashion products based on a query image. FIR in different garments and shoes are popular in literature. More complex fashion products such as ornaments are getting less attention. Here, we introduce a new earring dataset, namely, RingFIR. The dataset is a collection of ( $$\sim $$ 2.6K) high-quality images collected from major India based jewellery chains. The dataset is labelled in 46 classes in a structured manner. We have benchmarked the dataset using state-of-the-art image retrieval methods. We believe that the dataset is challenging and will attract computer vision researchers in the future. The dataset is available publicly ( https://github.com/skarifahmed/RingFIR ).
“AI News Anchor” With Deep Learning-Based Speech Synthesis

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep learning-based text to speech (TTS) is used in various situations, and the sound quality is close to that of humans. We previously developed a news-specific deep learning-based TTS (DL-TTS) system and implemented it with our AI news anchor for live broadcast programs and automatic news-speech distribution services. We also developed our DL-TTS system for the control of speaking style and speech rate, pitch, intonation, and volume to facilitate the creation of various programs. More specifically, this method enables the changing of specific speaking styles, such as news style, which mimics the style of news reporters, and conversation style. The purpose of creating this system was to eliminate the discomfort due to differences in speech and speaking styles. Controlling speaking style is important in news speech because a mismatched speaking style does not appropriately convey news articles. For this study, we conducted an evaluation experiment on the conveying of simple news articles for language learners regarding speaking-style control and found appropriate speaking styles for automatically generated news speech. We conducted another evaluation experiment on whether synthetic speech generated from our system for “easy news” for Japanese language learners can help people understand the news in Japanese. We also discuss practical applications of our system. Our news-specific deep neural network-based TTS system was found to effectively provide news services to broadcast stations. In the future, we will consider various use cases of flexible production by using a cloud system. The coronavirus pandemic has forced broadcasters to adopt new working styles. Thus, we will explore a new production system, such as a cloud-based system, for news-speech automation for this new normal</i> .
Optimizing Non-Markovian Information Gain Under Physics-Based Communication Constraints

In many exploration scenarios, it is important for robots to efficiently explore new areas and constantly communicate results. Mobile robots inherently couple motion and network topology due to the effects of position on wireless propagation-e.g. distance or obstacles between network nodes. Information gain is a useful measure of exploration. However, finding paths that maximize information gain while preserving communication is challenging due to the non-Markovian nature of information gain, discontinuities in network topology, and zero-reward local optima. We address these challenges through an optimization and sampling-based algorithm. Our algorithm scales to 50% more robots and obtains 2-5 times more information relative to path cost compared to baseline planning approaches.
On a Factorial Knowledge Architecture for Data Science-powered Software Engineering

Given the data-intensive and collaborative trend in science, the software engineering community also pays increasing attention to obtaining valuable and useful insights from data repositories. Nevertheless, applying data science to software engineering (e.g., mining software repositories) can be blindfold and meaningless, if lacking a suitable knowledge architecture (KA). By observing that software engineering practices are generally recorded through a set of factors (e.g., programmer capacity, different environmental conditions, etc.) involved in various software project aspects, we propose a factor-based hierarchical KA of software engineering to help maximize the value of software repositories and inspire future software data-driven studies. In particular, it is the organized factors and their relationships that help guide software engineering knowledge mining, while the mined knowledge will in turn be indexed/managed through the relevant factors and their interactions. This paper explains our idea about the factorial KA and concisely demonstrates a KA component, i.e. the early-version KA of software product engineering. Once fully scoped, this proposed KA will supplement the well-known SWEBOK in terms of both the factor-centric knowledge management and the coverage/implication of potential software engineering knowledge.
Privacy-Preserving Market for Smart Grid Data Trading

Smart grid and smart city, propelled by Internet of Things (IoT) technologies, are generating big data of an unprecedented scale. The smart grid data plays an important role in managing the smart grid. However, the present smart grid infrastructure lacks in managing and trading of such big data. This paper addresses the issue of data exchange between different entities of the smart grid. By combining homomorphic encryption (HE) with one-time padding procedure, a secure data trading framework has been proposed. The proposed data trading framework is not only providing security but also privacy preserving in nature.
A Machine Learning Approach for the Classification of Methamphetamine Dealers on Twitter in Thailand

This research presents a method to classify messages from Twitter (tweet) related to Methamphetamine. The messages are classified into three classes: normal, seller, buyer. The models presented in this research are Multinomial Naive Bayes, Multi-Class LSTM, and Hierarchical LSTM. Model training uses a balanced and imbalanced dataset. The text used for Model training is tokenized from four tokenizers: Tlex+, Lexto+, Attacut, and Deepcut. To study the model performance's effect, we divide the data with a different dataset and tokenizer. The results showed that all models could classify the messages into the three classes. The most effective model built from a balanced dataset is the Hierarchical LSTM model using the Lexto+ Tokenizer provides the highest Accuracy, and the most effective model build from an imbalanced dataset is the Multi-Class LSTM model using the Lexto+ Tokenizer. This model gave the highest Accuracy, but the Fl-Score of the Hierarchical LSTM model gave better Accuracy in each class.The creation of a text classification model related to Methamphetamine uses Twitter messages. Most of them are Thai grammatical errors and has many slang usage. We found that Lexto+ is the best tokenizer to build a model. However, it is not much different from other tokenizers. On the other hand, the best dataset to build the model is a balanced dataset that significantly affects model performance.
Ground-truth or DAER: Selective Re-query of Secondary Information

Many vision tasks use secondary information at inference time—a seed—to assist a computer vision model in solving a problem. For example, an initial bounding box is needed to initialize visual object tracking. To date, all such work makes the assumption that the seed is a good one. However, in practice, from crowdsourcing to noisy automated seeds, this is often not the case. We hence propose the problem of seed rejection—determining whether to reject a seed based on the expected performance degradation when it is provided in place of a gold-standard seed. We provide a formal definition to this problem, and focus on two meaningful subgoals: understanding causes of error and understanding the model’s response to noisy seeds conditioned on the primary input. With these goals in mind, we propose a novel training method and evaluation metrics for the seed rejection problem. We then use seeded versions of the viewpoint estimation and fine-grained classification tasks to evaluate these contributions. In these experiments, we show our method can reduce the number of seeds that need to be reviewed for a target performance by over 23% compared to strong baselines.
Impact of COVID-19 contact tracing on human resources for health – A Caribbean perspective

• Border reopening will increase burden on contact tracing (CT) resources. • Governments of tourism-based economies must build quality health workforce capacity. • Prevalence of COVID-19 in the source country predicted CT capacity needed. • Mobile app technology will facilitate only a small decrease in workforce needs.
Improving Distributed Neuroevolution Using Island Extinction and Repopulation

Neuroevolution commonly uses speciation strategies to better explore the search space of neural network architectures. One such speciation strategy is the use of islands, which are also popular in improving the performance of distributed evolutionary algorithms. However, islands may experience stagnation, which prevents their convergence towards better solutions and can result in wasted computation. This work evaluates utilizing an island extinction and repopulation mechanism to avoid premature convergence using Evolutionary eXploration of Augmenting Memory Models (EXAMM), an asynchronous island based neuroevolution algorithm that progressively evolves recurrent neural networks (RNNs). In island extinction and repopulation, all members of the worst performing island are erased periodically and repopulated with mutated versions of the global best RNN. This island based strategy is additionally compared to NEAT's (NeuroEvolution of Augmenting Topologies) speciation strategy. Experiments were performed using two different real-world time series datasets (coal-fired power plant and aviation flight data). With statistical significance, results show that in addition to being more scalable, this island extinction and repopulation strategy evolves better global best genomes than both EXAMM's original island based strategy and NEAT's speciation strategy. The extinction and repopulation strategy is easy to implement, and can be generically applied to other neuroevolution algorithms.
Filter pruning-based two-step feature map reconstruction

Abstract In deep neural network compression, channel/filter pruning is widely used for compressing the pre-trained network by judging the redundant channels/filters. In this paper, we propose a two-step filter pruning method to judge the redundant channels/filters layer by layer. The first step is to design a filter selection scheme based on $$\ell _{2,1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msub> </mml:math> -norm by reconstructing the feature map of current layer. More specifically, the filter selection scheme aims to solve a joint $$\ell _{2,1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msub> </mml:math> -norm minimization problem, i.e., both the regularization term and feature map reconstruction error term are constrained by $$\ell _{2,1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msub> </mml:math> -norm. The $$\ell _{2,1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msub> </mml:math> -norm regularization plays a role in the channel/filter selection, while the $$\ell _{2,1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msub> </mml:math> -norm feature map reconstruction error term plays a role in the robust reconstruction. In this way, the proposed filter selection scheme can learn a column-sparse coefficient representation matrix that can indicate the redundancy of filters. Since pruning the redundant filters in current layer might dramatically influence the output feature map of the following layer, the second step needs to update the filters of the following layer to assure output of feature map approximates to that of baseline. Experimental results demonstrate the effectiveness of this proposed method. For example, our pruned VGG-16 on ImageNet achieves $$4\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>4</mml:mn> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> speedup with 0.95% top-5 accuracy drop. Our pruned ResNet-50 on ImageNet achieves $$2\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> speedup with 1.56% top-5 accuracy drop. Our pruned MobileNet on ImageNet achieves $$2\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> speedup with 1.20% top-5 accuracy drop.
Active Exploration for Unsupervised Object Categorization Based on Multimodal Hierarchical Dirichlet Process

This paper describes an effective active exploration method for multimodal object categorization using a multimodal hierarchical Dirichlet process (MHDP). MHDP is a type of multimodal latent variable models, e.g., multimodal latent Dirichlet allocation and multimodal variational autoencoder, that enables a robot to perform unsupervised multimodal object categorization on the basis of different types of sensor information. The goal of the active exploration is to reduce the number of actions executed to collect multimodal sensor information from a variety of objects to acquire knowledge on object categories. The active exploration method employing the information gain (IG) criterion for MHDP is described by extending the IG-based active perception method. Exploiting the submodular property of IG in MHDP, greedy and lazy greedy algorithms with a certain theoretical guarantee of performance are proposed. The effectiveness of the proposed method is evaluated in a robot experiment. Results show that the proposed active exploration method with the greedy algorithm works well, and it significantly reduces the step for exploration. Further, the performance of the lazy greedy algorithm is found to deteriorate at times, due to the estimation error in the IG, differently from that of active perception.
Automatic Text-to-SQL Machine Translation for Scholarly Publication Database Search

A Database Management System (DBMS) is typically accessed using a Structured Query Language (SQL). Here, we investigate a structured method to parse questions in natural language. Our work is a text-to-SQL translator for scholarly publication database search. The work includes implementation of a phrase-based information retrieval and a Text-to-SQL translator. The information retrieval is emphasizes on the recognition of Named Entity (NE), Named Entity Attribute (NEA), Question Intention (QI) and Focus Recognition. NE is a database entry and NEA is the attribute or column name of the corresponding NE entry. QI is the type of a question. The proposed translator is a domain-specific Text-to-SQL translator. Therefore the scope for named entities is limited to the database values. Thus, we use the database itself as a knowledge-base. We derive rules comprised of phrases, predicates and substitution strings. The phrases are regular expressions and the predicates are stop-words. The usual practice of natural language processing removes stop words. Instead, we make use of many stop words as predicates, thus helping the recognition of NE, NEA and QI. The text cosine similarity measure is used to validate NE, NEA and QI. The rule-based approach is computationally effective and does not require any exhaustive training on the dataset, while providing precise translation.
Uncertainty-Aware Representations for Spoken Question Answering

This paper describes a spoken question answering system that utilizes the uncertainty in automatic speech recognition (ASR) to mitigate the effect of ASR errors on question answering. Spoken question answering is typically performed by transcribing spoken content with an ASR system and then applying text-based question answering methods to the ASR transcriptions. Question answering on spoken documents is more challenging than question answering on text documents since ASR transcriptions can be erroneous and this degrades the system performance. In this paper, we propose integrating confusion networks with word confidence scores into an end-to-end neural network-based question answering system that works on ASR transcriptions. Integration is performed by generating uncertainty-aware embedding representations from confusion networks. The proposed approach improves F1 score in a question answering task developed for spoken lectures by providing tighter integration of ASR and question answering.
Deep two-way matrix reordering for relational data analysis

Matrix reordering is a task to permute the rows and columns of a given observed matrix such that the resulting reordered matrix shows meaningful or interpretable structural patterns. Most existing matrix reordering techniques share the common processes of extracting some feature representations from an observed matrix in a predefined manner, and applying matrix reordering based on it. However, in some practical cases, we do not always have prior knowledge about the structural pattern of an observed matrix. To address this problem, we propose a new matrix reordering method, called deep two-way matrix reordering (DeepTMR), using a neural network model. The trained network can automatically extract nonlinear row/column features from an observed matrix, which can then be used for matrix reordering. Moreover, the proposed DeepTMR provides the denoised mean matrix of a given observed matrix as an output of the trained network. This denoised mean matrix can be used to visualize the global structure of the reordered observed matrix. We demonstrate the effectiveness of the proposed DeepTMR by applying it to both synthetic and practical datasets.
Development of Integrated Online Learning Content Distribution Module Based on Social Media for Beginners Online Teachers in Creating Learning Content Due to The Covid-19 Pandemic

Abstract The Covid-19 pandemic has brought many changes in the field of education, and the need for teaching for teachers or lecturers has increased. The habit of teaching directly in the classroom changed by having to create learning content and dealing with social media to distribute the content. Instead of modernizing education, many are confused and only teach by giving a sign that there is an adequate explanation of the material. Guidelines that are easy to use even for teachers or lecturers who are lay and fast so that the ongoing Covid-19 pandemic is balanced with learning that also continues. The guideline contains procedures for using the screencast O-Matic application and AZ screen recorder then using YouTube media to post content and WhatsApp as a communication medium. This study uses the ADDIE type development research method with the validity testing phase of experts and testing 32 teachers to determine the feasibility and effectiveness of the learning system guidelines. The results showed that the quality of the content was 81.8%, 83.3% showed easy language to understand, 80% showed interest in using illustrations, 76.7% showed easy interaction on WhatsApp, 70% showed clear task instructions, and 70% showed the system is easy to access.
Glioma Classification Using Multimodal Radiology and Histology Data

Gliomas are brain tumours with a high mortality rate. There are various grades and sub-types of this tumour, and the treatment procedure varies accordingly. Clinicians and oncologists diagnose and categorise these tumours based on visual inspection of radiology and histology data. However, this process can be time-consuming and subjective. The computer-assisted methods can help clinicians to make better and faster decisions. In this paper, we propose a pipeline for automatic classification of gliomas into three sub-types: oligodendroglioma, astrocytoma, and glioblastoma, using both radiology and histopathology images. The proposed approach implements distinct classification models for radiographic and histologic modalities and combines them through an ensemble method. The classification algorithm initially carries out tile-level (for histology) and slice-level (for radiology) classification via a deep learning method, then tile/slice-level latent features are combined for a whole-slide and whole-volume sub-type prediction. The classification algorithm was evaluated using the data set provided in the CPM-RadPath 2020 challenge. The proposed pipeline achieved the F1-Score of 0.886, Cohen's Kappa score of 0.811 and Balance accuracy of 0.860. The ability of the proposed model for end-to-end learning of diverse features enables it to give a comparable prediction of glioma tumour sub-types.