Reviewerly/exHarmony huggingface dataset:
Distributed Attention for Grounded Image Captioning

We study the problem of weakly supervised grounded image captioning. That is, given an image, the goal is to automatically generate a sentence describing the context of the image with each noun word grounded to the corresponding region in the image. This task is challenging due to the lack of explicit fine-grained region word alignments as supervision. Previous weakly supervised methods mainly explore various kinds of regularization schemes to improve attention accuracy. However, their performances are still far from the fully supervised ones. One main issue that has been ignored is that the attention for generating visually groundable words may only focus on the most discriminate parts and can not cover the whole object. To this end, we propose a simple yet effective method to alleviate the issue, termed as partial grounding problem in our paper. Specifically, we design a distributed attention mechanism to enforce the network to aggregate information from multiple spatially different regions with consistent semantics while generating the words. Therefore, the union of the focused region proposals should form a visual region that encloses the object of interest completely. Extensive experiments have demonstrated the superiority of our proposed method compared with the state-of-the-arts.
Using immersive video technology for construction management content delivery: a pilot study

Construction management is considered a hands-on field of study which requires good spatial and visual cognitive ability. Virtual reality and other innovative immersive technologies have been used to facilitate experiential learning and to improve students’ spatial cognitive abilities. Virtual environments have been criticized due to the gamified look of the environment. Static panorama pictures have been previously used to bring a better sense of reality and immersion at the same time in construction education. However, they cannot provide a continuous experience, and the sense of presence (immersion) is not ideal either. Immersive videos such as 360-degree videos can address this shortfall by providing a continuous experience and a better sense of presence. The use of this technology in construction education field is very limited. As a result, this study investigated a pilot experiment where a combination of 360, 180 3D, and flat videos was incorporated as an educational instrument in delivering construction management content. The content was recorded using different configurations from different body postures to further investigate the optimal way of utilizing this technology for content delivery. The content of the videos was focused on construction means and methods. Students reviewed the content using head-mounted display devices and laptop screens and answered a survey designed to capture their perception and experience of using this technology as an educational tool in the construction management field. The results show a positive perception toward using immersive videos in construction education. Furthermore, the students preferred the head-mounted display as their favorite delivery method. As a result, the prospect of incorporating immersive videos to enhance construction management education is promising.
Functionality, Emotion, and Acceptance of Artificial Intelligence Virtual Assistants

This paper aims to expand the acceptance of the AI Virtual Assistant model from the perspective of user’s cognition. Based on the 240 samples, we used multi-layer regression analysis to investigate the influencing factors and differential effects of users' acceptance of AI Virtual Assistant. The results show that functional cognition and emotional cognition of users are important influencing factors for an artificial intelligence virtual assistant. This provides a new perspective for user acceptance processes of the AI Virtual Assistant. We also examined the moderating effect of social norms between user cognition and AI Virtual Assistant. At last, a new AI acceptance model of AI Virtual Assistant was established.
Evolution of bitcoin as a Financial Asset

The cryptocurrency market debate resumed in 2020 with renewed vigour as the price of Bitcoin surpassed late 2017 highs. This study aims to analyse possible factors of Bitcoin’s pricing at various cryptocurrency market development stages — before the 2017 price bubble, after and during the COVID-19 pandemic. The main method of analysis is a generalized autoregressive conditional heteroskedasticity model with conditional generalized error distribution (GARCHGED). Two groups of indicators are used as possible factors related to the Bitcoin dynamics. The first group consists of various quantitative indicators directly related to Bitcoin (the so-called internal factors) — the volume of exchange trade, the volume of transactions in the Bitcoin blockchain, the number of new and active wallets, hash rate, the sum of fees paid in the blockchain, as well as the dynamics of Google Trends search queries. The second group is the return on various financial assets — stock and bond indexes, commodities, and currency markets. The results of the analysis demonstrate the absence of a stable correlation between any of the factors under consideration and Bitcoin returns in all the periods that we focus on. In the period before the 2017 price bubble, the internal factors and Bitcoin returns showed generally co-directional dynamics, but the situation changed in 2018. In early 2021, the correlation between Bitcoin and traditional financial assets returns has increased significantly. We can conclude that Bitcoin is becoming a popular means of diversification as a high-risk asset, which, however, follows the pattern of a speculative bubble at the beginning of 2021. The increased demand for the need to invest in Bitcoin using various exchange-traded instruments (ETFs for cryptocurrencies) may soon lead to a further increase in the price of this cryptocurrency if such instruments are registered on the exchange.
10 Years of Technical Debt Research and Practice: Past, Present, and Future

Delivering increasingly complex software-reliant systems demands better ways to manage the long-term effects of short-term expedients. The technical debt (TD) metaphor has gained significant traction as a way to understand and communicate such issues. Almost 25 years after the term was coined in 1992 by Ward Cunningham, and more than 10 years after the first edition of the TechDebt workshop/conference series, we take a brief look at the past, present, and future of TD.
An Approach for Context-Aware Cyber-Physical Automation Systems

Future automation systems, operating under increasing dynamic conditions need to be equipped with methods to enhance their adaptability accordingly. Context-aware systems model and apply contextual parameters influencing the interpretation of situations to adapt their operation accordingly. Designing context-aware automation systems requires methods to acquire and process heterogeneous and distributed data in a dynamic and continuous manner. In this contribution, a methodology for processing multimodal data in a flexible and reusable context-model is demonstrated, using concepts of data aggregation and integration, semantic labeling and graph modeling. Based on this approach, the use case of a context-aware medication assistance system is subsequently presented.
Expand-and-Randomize: An Algebraic Approach to Secure Computation

We consider the secure computation problem in a minimal model, where Alice and Bob each holds an input and wish to securely compute a function of their inputs at Carol without revealing any additional information about the inputs. For this minimal secure computation problem, we propose a novel coding scheme built from two steps. First, the function to be computed is expanded such that it can be recovered while additional information might be leaked. Second, a randomization step is applied to the expanded function such that the leaked information is protected. We implement this expand-and-randomize coding scheme with two algebraic structures - the finite field and the modulo ring of integers, where the expansion step is realized with the addition operation and the randomization step is realized with the multiplication operation over the respective algebraic structures.
Face Detection and Recognition Using OpenCV

Intel's OpenCV is a free and open-access image and video processing library. It is linked to computer vision, like feature and object recognition and machine learning. This paper presents the main OpenCV modules, features, and OpenCV based on Python. The paper also presents common OpenCV applications and classifiers used in these applications like image processing, face detection, face recognition, and object detection. Finally, we discuss some literary reviews of OpenCV applications in the fields of computer vision such as face detection and recognition, or recognition of facial expressions such as sadness, anger, happiness, or recognition of the gender and age of a person.
Quantum computing without quantum computers: Database search and data processing using classical wave superposition

Quantum computing is an emerging field of science which will eventually lead us to new and powerful logic devices with capabilities far beyond the limits of current transistor-based technology. There are certain types of problems which quantum computers can solve fundamentally faster than the tradition digital computers. There are quantum algorithms which require both superposition and entanglement (e.g. Shor algorithm). But neither the Grover algorithm nor the very first quantum algorithm due to Deutsch and Jozsa need entanglement. Is it possible to utilize classical wave superposition to speedup database search? This interesting question was analyzed by S. Lloyd. It was concluded that classical devices that rely on wave interference may provide the same speedup over classical digital devices as quantum devices. There were several experimental works using optical beam superposition for emulating Grover algorithm. It was concluded that the use of classical wave superposition comes with the cost of exponential increase of the resources. Since then, it is widely believed that the use of classical wave superposition for quantum algorithms is inevitably leading to an exponential resources overhead (number of devices, power consumption, precision requirements). In this work, we describe a classical Oracle machine which utilizes classical wave superposition for database search and data processing. We present experimental data on magnetic database search using spin wave superposition. The data show a fundamental speedup over the digital computers without any exponential resource overhead. We argue that in some cases the classical wave-based approach may provide the same speedup in database search as quantum computers.
Geologic map of the State of Hawaii

First posted November 2, 2021 For additional information, contact: Contact HVOHawaiian Volcano ObservatoryU.S. Geological Survey1266 Kamehameha AvenueSuite A-8Hilo, HI 96720 This geologic map and its digital databases present the geology of the eight major islands of the State of Hawaiʻi. The map should serve as a useful guide to anyone studying the geologic setting and history of Hawai‘i, including ground- and surface-water resources, economic deposits, and landslide or volcanic hazards. Its presentation in digital format allows the rapid application of geologic knowledge when conducting field work; analyzing land-use or engineering problems; studying onshore or nearshore biologic communities; or simply understanding the relation between the geology, scenery, and cultural history of the Hawaiian paradise.The map includes a Description of Map Units, which describes the lithologic characteristics and distribution of the geologic deposits. A Correlation of Map Units shows how the different geologic formations are related to each other stratigraphically. A fairly complete geospatial database of the radiometric ages and geochemical analyses has been compiled from findings published over the past 100 years by numerous Earth scientists working across the island chain.
On Improving Temporal Consistency for Online Face Liveness Detection System

In this paper, we focus on improving the online face liveness detection system to enhance the security of the downstream face recognition system. Most of the existing frame-based methods are suffering from the prediction inconsistency across time. To address the issue, a simple yet effective solution based on temporal consistency is proposed. Specifically, in the training stage, to integrate the temporal consistency constraint, a temporal self-supervision loss and a class consistency loss are proposed in addition to the softmax cross-entropy loss. In the deployment stage, a trainingfree non-parametric uncertainty estimation module is developed to smooth the predictions adaptively. Beyond the common evaluation approach, a video segment-based evaluation is proposed to accommodate more practical scenarios. Extensive experiments demonstrated that our solution is more robust against several presentation attacks in various scenarios, and significantly outperformed the state-ofthe-art on multiple public datasets by at least 40% in terms of ACER.
Multiscale Supervision-Guided Context Aggregation Network for Single Image Dehazing

End-to-end learning-based image dehazing methods tend to overdehaze or underdehaze in real scenes due to inefficient feature extraction and feature fusion. In this letter, we propose a multiscale supervision-guided context aggregation network (MSGCAN) based on two principles: improving feature extraction and enhancing feature mapping. To improve feature extraction, an attention-guided context aggregation (AGCA) module is adopted to merge context features extracted by several residual dense blocks (RDB). Moreover, we output these aggregated context features on each scale and form multiscale supervision to enhance feature mapping and ensure that the extracted features on each scale contain more realistic details. The experimental results show that the proposed MSGCAN performs better than other state-of-the-art dehazing methods in both synthetic and real-world scenes.
Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning

The recent algorithms for math word problems (MWP) neglect to use outside knowledge not present in the problems.Most of them only capture the word-level relationship and ignore to build hierarchical reasoning like the human being for mining the contextual structure between words and sentences.In this paper, we propose a Reasoning with Pre-trained Knowledge and Hierarchical Structure (RPKHS) network, which contains a pre-trained knowledge encoder and a hierarchical reasoning encoder.Firstly, our pretrained knowledge encoder aims at reasoning the MWP by using outside knowledge from the pre-trained transformer-based models.Secondly, the hierarchical reasoning encoder is presented for seamlessly integrating the wordlevel and sentence-level reasoning to bridge the entity and context domain on MWP.Extensive experiments show that our RPKHS significantly outperforms state-of-the-art approaches on two large-scale commonly-used datasets, and boosts performance from 77.4% to 83.9% on Math23K, from 75.5 to 82.2% on Math23K with 5-fold cross-validation and from 83.7% to 89.8% on MAWPS.More extensive ablations are shown to demonstrate the effectiveness and interpretability of our proposed method.
Smart Educational Learning Strategy with the Internet of Things in Higher Education System

Smart Educational Learning (SEL) has recently opened its ways in various changes in scientific discoveries, informatics, globalization, astronautics production, robotics, and artificial intelligence in the Higher Education System. In such an educational system, managing resources to increase education quality with an effective interactive environment has been considered a significant challenging factor for the Students and Teachers. Hence, in this paper, the internet of things assisted Interactive system (IoT-IS) for Smart Learning is used to measure the teachers’ and students’ performance analysis in the SEL platform. The psychometric processes with standards for effective teaching using smart educational learning tools have been discussed based on the higher education system requirements. Furthermore, an active learning strategy with an attention scoring method promotes students’ performance assessment in the higher education system using the interactive system. Facial expression detection and analysis are used and applied in online classroom videos in the SEL. Based on this detection and analysis, the attention of the students is observed. The experimental results show that the method enhances the student performance ratio of 98.5%, an accuracy ratio of 95.3%, an efficiency ratio of 96.7%, a reliability ratio of 93.2%, and a probability ratio of 94.5% compared to other existing methods.
T3-Vis: visual analytic for Training and fine-Tuning Transformers in NLP

Transformers are the dominant architecture in NLP, but their training and fine-tuning is still very challenging.In this paper, we present the design and implementation of a visual analytic framework for assisting researchers in such process, by providing them with valuable insights about the model's intrinsic properties and behaviours.Our framework offers an intuitive overview that allows the user to explore different facets of the model (e.g., hidden states, attention) through interactive visualization, and allows a suite of built-in algorithms that compute the importance of model components and different parts of the input sequence.Case studies and feedback from a user focus group indicate that the framework is useful, and suggest several improvements.Our framework is available at:
Exploring the Effect of Visual Cues on Eye Gaze During AR-Guided Picking and Assembly Tasks

In this paper, we present an analysis of eye gaze patterns pertaining to visual cues in augmented reality (AR) for head-mounted displays (HMDs). We conducted an experimental study involving a picking and assembly task, which was guided by different visual cues. We compare these visual cues along multiple dimensions (in-view vs. out-of-view, static vs. dynamic, sequential vs. simultaneous) and analyze quantitative metrics such as gaze distribution, gaze duration, and gaze path distance. Our results indicate that visual cues in AR significantly affect eye gaze patterns. Specifically, we show that the effect varies depending on the type of visual cue. We discuss these empirical results with respect to visual attention theory.
Implicit Sentiment Analysis with Event-centered Text Representation

Implicit sentiment analysis, aiming at detecting the sentiment of a sentence without sentiment words, has become an attractive research topic in recent years.In this paper, we focus on event-centric implicit sentiment analysis that utilizes the sentiment-aware event contained in a sentence to infer its sentiment polarity.Most existing methods in implicit sentiment analysis simply view noun phrases or entities in text as events or indirectly model events with sophisticated models.Since events often trigger sentiments in sentences, we argue that this task would benefit from explicit modeling of events and event representation learning.To this end, we represent an event as the combination of its event type and the event triplet <subject, predicate, object>.Based on such event representation, we further propose a novel model with hierarchical tensor-based composition mechanism to detect sentiment in text.In addition, we present a dataset 1 for event-centric implicit sentiment analysis where each sentence is labeled with the event representation described above.Experimental results on our constructed dataset and an existing benchmark dataset show the effectiveness of the proposed approach.
A Comparative Study of Energy Big Data Analysis for Product Management in a Smart Factory

Energy has been obtained as one of the key inputs for a country's economic growth and social development. Analysis and modeling of industrial energy are currently a time-insertion process because more and more energy is consumed for economic growth in a smart factory. This study aims to present and analyse the predictive models of the data-driven system to be used by appliances and find out the most significant product item. With repeated cross-validation, three statistical models were trained and tested in a test set: 1) General Linear Regression Model (GLM), 2) Support Vector Machine (SVM), and 3) boosting Tree (BT). The performance of prediction models measured by R2 error, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Variation (CV). The best model from the study is the Support Vector Machine (SVM) that has been able to provide R2 of 0.86 for the training data set and 0.85 for the testing data set with a low coefficient of variation, and the most significant product of this smart factory is Skelp.
Cybersecurity knowledge and skills for port facility security officers of international seaports: Perspectives of IT and security personnel

Cyberattacks on worldwide port facilities have highlighted the urgent need for port facility security officers (PFSOs) to upgrade their cybersecurity knowledge and skills. This study used the survey data from all international container ports in Thailand and analyzed the results from two perspectives, i.e., IT and security officers. Based on 73 responses, cybersecurity knowledge and skill would become essential for PFSOs than ever before because the port digitalization and automation would shape the PFSOs career to a technically-oriented specialist rather than a multi-skilled generalist. Furthermore, the responsibilities of PFSOs would extend to cover cyber risk management, which enables them to prevent the digital port facilities from emerging cyber threats. Therefore, they should learn how to incorporate the existing risk management process with cyber risk management and cybersecurity knowledge because these would be the foundation for PFSOs to practice cybersecurity skills. At the end of the learning process, PFSOs could also gain cybersecurity competence once they have mature knowledge and skill, which would be the vital element of port security hygiene.
Hierarchical Multi-label Text Classification with Horizontal and Vertical Category Correlations

Hierarchical multi-label text classification (HMTC) deals with the challenging task where an instance can be assigned to multiple hierarchically structured categories at the same time.The majority of prior studies either focus on reducing the HMTC task into a flat multi-label problem ignoring the vertical category correlations or exploiting the dependencies across different hierarchical levels without considering the horizontal correlations among categories at the same level, which inevitably leads to fundamental information loss.In this paper, we propose a novel HMTC framework that considers both vertical and horizontal category correlations.Specifically, we first design a loosely coupled graph convolutional neural network as the representation extractor to obtain representations for words, documents, and, more importantly, level-wise representations for categories, which are not considered in previous works.Then, the learned category representations are adopted to capture the vertical dependencies among levels of category hierarchy and model the horizontal correlations.Finally, based on the document embeddings and category embeddings, we design a hybrid algorithm to predict the categories of the entire hierarchical structure.Extensive experiments conducted on real-world HMTC datasets validate the effectiveness of the proposed framework with significant improvements over the baselines.