Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis to Support Marketing Decision Making Process: A Hybrid Model

Marketers aim to understand what influences people’s decisions when purchasing products and services, which has been proven to be based on natural instincts that drive humans to follow the behavior of others. Thus, this research is investigating the use of sentiment analysis techniques and proposes a hybrid approach that combines lexicon-based and machine learning-based approaches to analyze customers’ review a major e-commerce platform. The lexicon approach was firstly applied at a word-level to explore the reviews and provide some preliminary results about the most frequent words used in the reviews in a form of word-clouds. Then, the lexicon approach was applied to sentence-level to obtain sentiment polarity results, which was used to train machine learning models. Next, the trained models were tested on un-labelled reviews (test data); proving that Naïve Bayes (NB) outperformed other classifiers. The hybrid model described in this research can offer organizations a better understanding of customers’ attitudes towards their products.
Personas revisited: Extending the Use of Personas to Enhance Participatory Design

Personas are a well-established tool in interactive system development, largely used with designers and developers. They can also be included in work with potential and actual users, particularly in eliciting user needs and concerns. We present three case studies in which personas are part of the research with potential users of interactive systems. Blind participants in research on assistive technologies often spontaneously generated personas who would benefit more from the technology than they themselves would. This was a way of being able to discuss potential needs and concerns, without admitting to having them oneself. Older participants exploring the potential of indoor drones generated personas who were older than themselves and would need the system more. Finally, personas were created to aid discussions with forestry workers about fatigue and safety issues that they might not want to admit to having themselves. From these case studies we draw recommendations for how personas can be deployed in working with potential and actual users of interactive systems in a variety of ways.
Attention-Based High-Order Feature Interactions to Enhance the Recommender System for Web-Based Knowledge-Sharing Service

Providing personalized online learning services has become a hot research topic. Online knowledge-sharing services represents a popular approach to enable learners to use fragmented spare time. User asks and answers questions in the platform, and the platform also recommends relevant questions to users based on their learning interested and context. However, in the big data era, information overload is a challenge, as both online learners and learning resources are embedded in data rich environment. Offering such web services requires an intelligent recommender system to automatically filter out irrelevant information, mine underling user preference, and distil latent information. Such a recommender system needs to be able to mine complex latent information, distinguish differences between users efficiently. In this study, we refine a recommender system of a prior work for web-based knowledge sharing. The system utilizes attention-based mechanisms and involves high-order feature interactions. Our experimental results show that the system outperforms known benchmarks and has great potential to be used for the web-based learning service.
Understanding and handling alert storm for online service systems

Alert is a kind of key data source in monitoring system for online service systems, which is used to record the anomalies in service components and report to engineers. In general, the occurrence of a service failure tends to be along with a large number of alerts, which is called alert storm. However, alert storm brings great challenges to diagnose the failure, since it is time-consuming and tedious for engineers to investigate such an overwhelming number of alerts manually. To help understand alert storm, we conduct the first empirical study of alert storm based on large-scale real-world alert data and gain some valuable insights. Based on the findings, we propose a novel approach to handling alert storm. Specifically, this approach includes alert storm detection which aims to identify alert storm accurately, and alert storm summary which aims to recommend a small set of representative alerts to engineers for failure diagnosis. Our experimental study on real-world dataset demonstrates that our alert storm detection can achieve high F1-score (larger than 0.9). Besides, our alert storm summary can reduce the number of alerts that need to be examined by more than 98% and discover useful alerts accurately.
Improved Brain Segmentation Using Pixel Separation and Additional Segmentation Features

Brain segmentation is key to brain structure evaluation for disease diagnosis and treatment. Much research has been invested to study brain segmentation. However, prior research has not considered separating actual brain pixels from the background of brain images. Not performing such separation may (a) distort brain segmentation models and (b) introduce overhead to the modeling performance. In this paper, we improve the performance of brain segmentation using 3D, fully Convolutional Neural Network (CNN) models. We use (i) infant and adult datasets, (ii) a multi-instance loss method to separate actual brain pixels from the background and (iii) Gabor filter banks and K-means clustering to provide additional segmentation features. Our model obtains dice coefficients of \(87.4\%\)–\(94.1\%\) (i.e., an improvement of up to 11% to the results of five state-of-the-art models). Unlike prior studies, we consult experts in medical imaging to evaluate our segmentation results. We observe that our results are fairly close to the manual reference. Moreover, we observe that our model is 1.2x–2.6x faster than prior models. We conclude that our model is more efficient and accurate in practice for both infant and adult brain segmentation.
Real-Time Object Detection Based on Convolutional Block Attention Module

Object detection is one of the most challenging problems in the field of computer vision, the practicality of object detection requires accuracy and real-time. YOLOv3 is a good real-time object detection algorithm, but with insufficient recall rate and insufficient positioning accuracy. The Attention Mechanism in deep learning is similar to the attention mechanism of human vision, which is to focus attention on important points in many information, select key information, and ignore other unimportant information. In this paper, we integrate Convolutional Block Attention Module (CBAM) in YOLOv3 in order to improves the detection accuracy and keep real-time. Compared to a conventional YOLOv3, we experimentally show the effectiveness and accuracy of the proposed method on the PASCAL VOC and MS-COCO datasets.
Flexible versus structured support for reasoning: enhancing analytical reasoning through a flexible analytic technique

Structured analytic techniques (SATs) help the intelligence community reduce flaws in cognition that lead to faulty reasoning. To ascertain whether SATs provide benefits to reasoning we conducted an experiment within a web-based application, comparing three conditions: 1) unaided reasoning, 2) a prototypical order-based SAT and 3) a flexible, process-based SAT that we call TRACE. Our findings suggest that the more flexible SAT generated higher quality reasoning compared to the other conditions. Consequently, techniques and training that support flexible analytical processes rather than those that require a set sequence of steps may be more beneficial to intelligence analysis and complex reasoning. Keywords: structured analytical techniques, Analysis of Competing Hypotheses, tradecraft, cognitive biases, experiments.
A New Approach To Estimate Depth Of Cars Using A Monocular Image

Predicting scene depth from RGB images is a challenging task. Since the cameras are the most available, least restrictive and cheapest source of information for autonomous vehicles; in this work, a monocular image has been used as the only source of data to estimate the depth of the car within the frontal view. In addition to the detection of cars in the frontal image; a convolutional neural network (CNN) has been trained to detect and localize the lights corresponding to each car. This approach is less sensitive to errors due to the disposition of bounding boxes. An enhancement on the COCO dataset has also been provided by adding the car lights labels. Simulation results show that the proposed approach outperforms those who only use the height and width of bounding boxes to estimate the depth.
Optical Character Recognition for Hangul Character using Artificial Neural Network

Korean language is one of the languages that are becoming widely known in the world, along with the occupation of Korean music (K-POP). Hangul is a character used to write Korean, which is not like Latin, which is relatively more easily understood by the majority of people in this world. This research aims to analyze the performance of an Artificial Neural Network (ANN) in recognizing Hangul characters with a simplified optical character recognition (OCR) method. The OCR process is carried out by entering characters in a 15x13 tile area, then the characters that enter fully on the tile will be changed to value 1 while others become 0 values so that a binary image is generated. The next step is the character pattern crafting process towards the field. The results were recognized by ANN, in an experiment using four types of training data composition: testing, namely 50%: 50%, 60%: 40%, 70%: 30%, and 80%: 20%. The dataset used is 40 Hangul characters in which there are 10 sample data each, so in total there are 400 data. Based on testing, the highest accuracy is produced with a composition of 50%: 50% where the accuracy is 97%.
mmLSH: A Practical and Efficient Technique for Processing Approximate Nearest Neighbor Queries on Multimedia Data

Many large multimedia applications require efficient processing of nearest neighbor queries. Often, multimedia data are represented as a collection of important high-dimensional feature vectors. Existing Locality Sensitive Hashing (LSH) techniques require users to find top-k similar feature vectors for each of the feature vectors that represent the query object. This leads to wasted and redundant work due to two main reasons: 1) not all feature vectors may contribute equally in finding the top-k similar multimedia objects, and 2) feature vectors are treated independently during query processing. Additionally, there is no theoretical guarantee on the returned multimedia results. In this work, we propose a practical and efficient indexing approach for finding top-k approximate nearest neighbors for multimedia data using LSH called mmLSH, which can provide theoretical guarantees on the returned multimedia results. Additionally, we present a buffer-conscious strategy to speed up the query processing. Experimental evaluation shows significant gains in performance time and accuracy for different real multimedia datasets when compared against state-of-the-art LSH techniques.
A Workload-Driven Document Database Schema Recommender (DBSR)

Database schema design requires careful consideration of the application’s data model, workload, and target database technology to optimize for performance and data size. Traditional normalization schemes used in relational databases minimize data redundancy, whereas NoSQL document-oriented databases favor redundancy and optimize for horizontal scalability and performance. Systematic NoSQL schema design involves multiple dimensions, and a database designer is in practice required to carefully consider (i) which data elements to copy and co-locate, (ii) which data elements to normalize, and (iii) how to encode data, while taking into account factors such as the workload and data model. In this paper, we present a workload-driven document database schema recommender (DBSR), which takes a systematic, search-based approach in exploring the complex schema design space. The recommender takes as main inputs the application’s data model and its read workload, and outputs (i) the suggested document schema (featuring secondary indexing), (ii) query plan recommendations, and (iii) a document utility matrix that encodes insights on their respective costs and relative utility. We evaluate recommended schema in MongoDB using YCSB, and show significant benefits to read query performance.
Fuzzy Systems Interpretability: What, Why and How

Interpretability has been always present in Machine Learning and Artificial Intelligence. However, it is difficult to measure it (even to define it), and quite commonly it collides with other properties as accuracy, with a clear meaning and well defined metrics. This situation has reduced its influence in the area. But due to different external reasons, interpretability is now gaining importance in Artificial Intelligence, and particularly in Machine Learning. This new situation has two effects on the field of fuzzy systems. First, considering the capability of the fuzzy formalism to describe complex phenomena in terms that are quite close to human language, fuzzy systems have gained significant presence as an interpretable modeling tool. Second, the attention paid to interpretability of fuzzy systems, that grew during the first decade of this century and then experienced a certain decay, is growing again. The present paper will consider four questions regarding interpretability: what is, why is it important, how to measure it, and how to achieve it. These questions will be first introduced in the general framework of Artificial Intelligence, to be then focused from the point of view of fuzzy systems.
ProphetNet-Ads: A Looking Ahead Strategy for Generative Retrieval Models in Sponsored Search Engine

In a sponsored search engine, generative retrieval models are recently proposed to mine relevant advertisement keywords for users' input queries. Generative retrieval models generate outputs token by token on a path of the target library prefix tree (Trie), which guarantees all of the generated outputs are legal and covered by the target library. In actual use, we found several typical problems caused by Trie-constrained searching length. In this paper, we analyze these problems and propose a looking ahead strategy for generative retrieval models named ProphetNet-Ads. ProphetNet-Ads improves the retrieval ability by directly optimizing the Trie-constrained searching space. We build a dataset from a real-word sponsored search engine and carry out experiments to analyze different generative retrieval models. Compared with Trie-based LSTM generative retrieval model proposed recently, our single model result and integrated result improve the recall by 15.58% and 18.8% respectively with beam size 5. Case studies further demonstrate how these problems are alleviated by ProphetNet-Ads clearly.
3D Pyramid Pooling Network for Abdominal MRI Series Classification

Recognizing and organizing different series in an MRI examination is important both for clinical review and research, but it is poorly addressed by the current generation of picture archiving and communication systems (PACSs) and post-processing workstations. In this paper, we study the problem of using deep convolutional neural networks for automatic classification of abdominal MRI series to one of many series types. Our contributions are three-fold. First, we created a large abdominal MRI dataset containing 3717 MRI series including 188,665 individual images, derived from liver examinations. 30 different series types are represented in this dataset. The dataset was annotated by consensus readings from two radiologists. Both the MRIs and the annotations were made publicly available. Second, we proposed a 3D pyramid pooling network, which can elegantly handle abdominal MRI series with varied sizes of each dimension, and achieved state-of-the-art classification performance. Third, we performed the first ever comparison between the algorithm and the radiologists on an additional dataset and had several meaningful findings.
Balancing the user-driven feature selection and their incidence in the clustering structure formation

The feature selection represents a key step in mining high-dimensional data: the significance of features in maintaining the data structure while ignoring the feature redundancy is crucial to improve the final performance of classification methods. At the same time, an accurate understanding of feature domains may need human intervention to balance the importance of structure-based features with those one dictated by human expertise. To address this issue, this work introduces a human-driven feature selection method for data clustering. The algorithm, called Feature Selection EFCM (FS-EFCM in short), aims at supporting the relevance of some features from the domain of interest, but preserving their incidence in the natural clustering structure. The relevance and incidence of each feature are measure assessed during the FS-EFCM execution, in order to find a balance between the human suggestions about the feature importance and the by feature incidence in the natural cluster-based data distribution. Experimental results and comparisons highlight how the algorithm is robust in the presence of not very significant features, and the classification performance shows the effectiveness of the proposed feature selection method compared with the well-known feature selection algorithms.
Optical Character Recognition for Audio-Visual Broadcast Transcription System

This paper investigates the use of optical character recognition (OCR) for system of audio-visual broadcast transcription. Characters were recognized from video frames by open-source program OCR Tesseract. The OCR in this program (from version 4) is based on Recurrent Neural Networks (RNN) and it uses text post-processing by bigram language model. However, the resulting recognized text contains a number of errors. In some images, the text is not detected and recognized correctly or it is not detected at all. We have designed and tested image pre-processing and text post-processing methods for OCR error reduction. The word error rate (WER) was reduced from 29,4% to 15,4%.
Video Quality Assessment: Some Remarks on Selected Objective Metrics

The paper presents selected objective video quality assessment metrics. The authors discuss the usefulness of the PSNR, NQI and SSIM metrics in the face of the disparity of the results which sometimes occur during tests. The measurements were performed in the lossless environment for different videos and using modern VP9 and AV1 codecs with different compression ratios. Based on the high correlation between the results of the objective and subjective quality assessment the PSNR and SSIM metrics were chosen as the objective metrics that well describe the quality of the tested videos. One of the main contributions of the paper is an indication of high usefulness of the presented metrics as tools that may support creation of proper encoding ladders for specific videos. Such ladders define sets of configurations (i.a. data rates and resolutions) for display on a range of end user devices over a range of connection speeds. The tests have confirmed that AV1 codec allows to achieve the same video quality with generating up to 40% smaller file size. However, a several dozens larger processing time that was observed in the case of AV1 codec, indicates that currently it may not be used for live streaming, but only for ‘on demand’ services.
Echo: Analyzing Gameplay Sessions by Reconstructing Them From Recorded Data

Games user research (GUR) is centered on ensuring games deliver the experience that their designers intended. GUR researchers frequently make use of playtesting to evaluate games. This often requires watching back hours of video footage after the session to ensure that they did not miss anything important. Analytics have been used to help improve this process, providing visualizations of the underlying gameplay data. Yet, many of these game analytics tools provide static visualizations which do not accurately capture the dynamic aspects of modern video games. To address this problem, we have created Echo, a tool that uses gameplay data to reconstruct the original session with in-game assets, instead of abstracting them away. Echo has been designed to help bridge the gap between static gameplay data representation and video footage, with the goal of providing the best of both. A user study revealed that participants found Echo less frustrating to use compared to videos for gameplay analysis and also ranked it higher for efficiency, among others. It revealed that participants felt less cognitive load when using Echo as well. Qualitative results were also promising as participants employed several distinct workflows while using Echo. We received numerous suggestions for building upon the current state of the tool, including support for multiple viewports, live annotations, and visible gameplay metrics.
Data Profiling in Property Graph Databases

Property Graph databases are being increasingly used within the industry as a powerful and flexible way to model real-world scenarios. With this flexibility, a great challenge appears regarding profiling tasks due to the need of adapting them to these new models while taking advantage of the Property Graphs’ particularities. This article proposes a set of data profiling tasks by integrating existing methods and techniques and an taxonomy to classify them. In addition, an application pipeline is provided while a formal specification of some tasks is defined.
Short Paper: Secure Multiparty Logic Programming

Logic Programming (LP) is considered to be relatively simple for non-programmers, and allows the developer to focus on developing facts and rules of a logical derivation, and not on algorithms. Secure multiparty computation (MPC) is a methodology that allows several parties to process private data collaboratively without revealing the data to any party. In this paper, we bring together the notions of MPC and LP, allowing users to write privacy-preserving applications in logic programming language.
Sentiment analysis using semi-supervised learning with few labeled data

Sentiment analysis has been widely explored in many text domains, including tweets, movie reviews, shop/restaurant reviews, product reviews, and peer reviews for scholarly papers. However, it is very costly to manually label the training data for sentiment analysis. We focus on the problem and presents an approach for leveraging contextual features from unlabeled movie and restaurant reviews with a neural-network-based learning model, Ladder network. The experimental results by using two benchmark datasets, IMDb and YelpNYC, show that our model outperforms the baseline models including LSTM and SVM. Especially we verified that our model is better performance gaining on limited training datasets with 1% data labeled. Our source codes are available online. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> Our source code can be obtained from https://github.com/jepyh/sentiment_analysis_few_labeled.