Reviewerly/exHarmony huggingface dataset:
Extraction and classification of risk-related sentences from securities reports

With the drastically changing business environment, it is difficult even for experts to properly extract and classify risk statements from securities reports, which contain large volumes and unstructured information. Several methods have been proposed, but the existing methods face difficulty in dealing with flexible risk expressions. This study presents an open-domain risk-analysis framework that combine the strengths of both humans and machines. They include defining appropriate business risks and constructing supervised data based on those definitions. Risks were then extracted and classified from the securities reports of a representative group of Japanese companies. We confirmed the limitations of pattern matching, and the usefulness of contextual analysis methods. We also confirmed the importance of constructing supervised data based on appropriate guidelines of data classification. This study presents a framework that quickly and effectively derives the risk structure of a given industry or company from vast and unstructured information.
UAV-based magnetometry — Practical considerations, performance measures, and application to magnetic anomaly detection

Interest in ubiquitous low-cost unmanned aerial vehicles (UAVs) for use in aeromagnetic surveying has grown dramatically over the past decade. While their appeal is alluring, caution is called for as high-quality airborne magnetometry requires diligent system design and performance qualification. This paper discusses considerations and trade-offs in UAV-based magnetometry, standard measures to qualify performance, and application to magnetic anomaly detection (MAD). The apparent simplicity of towed-bird installations needs careful consideration. Logistical complexities, stability, and safety issues aside, critical compensation for time-varying swing effects is seldom, if at all, standard practice. While well-compensated fixed-mount sensor installations are preferable, they require careful attention to a number of unique aspects including the complex magnetic signatures of typical UAVs. The paper introduces a novel anomaly detection method that is based on the entropy of the total-field magnetometer signal, gated by an analogous measure obtained from a vector magnetometer. Two field studies using a fixed-mount single-magnetometer configuration on a helicopter UAV empirically demonstrate the application of the performance measures and the performance of the MAD method. Notably, the latter clearly illustrates the importance of sound aeromagnetic compensation and enhances the output of an earlier entropy-based detection method.
Memorial design as information creation: Honoring the past through co-production of an informing aesthetic

Information behavior scholars are increasingly exploring information creation; however, these studies focus primarily on individuals' processes. The Melbourne Korean War Memorial (MKWM) used an interdisciplinary co-creation process, combining expertise in architecture, industrial design, engineering, and history. Over a two-year period, the MKWM research/design team worked with government officials and community members, from ideation and prototype design, through construction and installation of the completed memorial. The finished product informs the viewer about historic events, while also meeting architectural and design best practices. The co-authors' analysis of the information co-creation process constructs a new model of an informing aesthetic, where the memorial design embeds three types of information: explicit (e.g., photographs); implicit (e.g., national flowers); and embodied (e.g., pathway elevation). By combining research expertise with community feedback, while meeting technical and government design needs, the memorial informs about the war through a combination of statistics, storytelling, and visual knowledge representations.
Mapping of the silica-rich rocks and serpentinites using newly defined thermal indices from Advanced Spaceborne Thermal Emission and Reflection Radiometer thermal infrared data of Udaipur-Rakhabdev region, Rajasthan, India

Advanced Spaceborne Thermal Emission and Reflection Radiometer L1T data were used for the derivation of the new indices for the identification and mapping of the silica-rich and mafic-ultramafic rocks of the Udaipur-Rakhabdev region. The newly derived silica-rich rock index Ronak Jain (QI-RJ) was used in four bands except band 10 as [((band 13 – sqrt(band 11))/band 14)/band 12]. The mafic-rich rocks were also defined by a new index (MI-RJ), as (band 10 + band 14) / band 13. The new indices had advantages in accurate mapping compared with those given by Ninomiya (QI-N), Rockwell and Hofstra (QI-RH), and Guha and Vinod Kumar (QI-AV) for mapping of silica-rich rocks by removing striping noise. Similarly, mafic minerals were mapped by indices given by Ninomiya (MI-N) and Guha and Vinod Kumar (MI-AV) for ultramafics, but they have not mapped the discontinuous outcrops of serpentinites. The statistical regression analysis approach was used for the prediction of the goodness of fit between the new indices and already existing indices. The silica index (QI-RJ) is highly correlated with the QI-RH and QI-AV and represents low values for QI-N. In a similar way, MI-RJ shows a high correlation with the MI-N and MI-AV indices. The regression analyses for the QI-RJ with MI-RJ, MI-N, and MI-AV, show a negative trend but have high values of regression correlation. The generated maps from the newly defined index were also compared with the indices maps generated from the existing indices and were found to have better maps of litho-units. Field collected data and petrological studies also validated the presence of silica-rich rocks and ultramafics of the region mapped with the help of newly defined indices, and a better spatial accuracy of maps was obtained. The new mafic index was more suitable for mapping serpentinites and their alteration products exposed in the area.
Tracking an Underwater Object with Unknown Sensor Noise Covariance Using Orthogonal Polynomial Filters

In this manuscript, an underwater target tracking problem with passive sensors is considered. The measurements used to track the target trajectories are (i) only bearing angles, and (ii) Doppler-shifted frequencies and bearing angles. Measurement noise is assumed to follow a zero mean Gaussian probability density function with unknown noise covariance. A method is developed which can estimate the position and velocity of the target along with the unknown measurement noise covariance at each time step. The proposed estimator linearises the nonlinear measurement using an orthogonal polynomial of first order, and the coefficients of the polynomial are evaluated using numerical integration. The unknown sensor noise covariance is estimated online from residual measurements. Compared to available adaptive sigma point filters, it is free from the Cholesky decomposition error. The developed method is applied to two underwater tracking scenarios which consider a nearly constant velocity target. The filter's efficacy is evaluated using (i) root mean square error (RMSE), (ii) percentage of track loss, (iii) normalised (state) estimation error squared (NEES), (iv) bias norm, and (v) floating point operations (flops) count. From the simulation results, it is observed that the proposed method tracks the target in both scenarios, even for the unknown and time-varying measurement noise covariance case. Furthermore, the tracking accuracy increases with the incorporation of Doppler frequency measurements. The performance of the proposed method is comparable to the adaptive deterministic support point filters, with the advantage of a considerably reduced flops requirement.
Nationwide Adoption of a Digital Contact Tracing App: Examining the Role of Privacy Concern, Political Trust, and Technology Literacy

ABSTRACTABSTRACTDigital contact tracing (DCT) has become an efficient method for authorities to curb COVID-19. However, the current public opinion studies on the adoption of DCT primarily focus on Western countries and rarely explore citizen attitudes in Asian contexts where DCT is often mandatory. Moreover, the existing literature extensively explores how privacy concern negates the adoption of DCT without illuminating the solutions to such barriers. To unveil the mechanisms of DCT adoption, this study explores how Singaporeans' privacy concern, political trust, and technology literacy can influence their attitudes toward TraceTogether (a local DCT program). An analysis of survey data suggests that privacy concern is positively associated with negative attitudes toward DCT. Those with high levels of political distrust also hold negative attitudes toward DCT. Political distrust is also found to positively mediate the relationship between privacy concerns and negative attitudes toward DCT. Finally, moderated mediation findings suggest that the indirect effects of privacy concern on negative attitudes toward DCT via political distrust increase with an increase in technological literacy. Simply put, the indirect effects are stronger for those with higher-tech literacy. We discuss the tension between privacy concerns and public health during the pandemic and propose policy suggestions.KEYWORDS: Digital contact tracingCOVID-19privacy concernpolitical trusttechnology literacy Disclosure StatementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsYifei WangYifei Wang (B.S., Cornell University) is a graduate student at the Department of Communications and New Media at the National University of Singapore. Address: Department of Communications and New Media, Faculty of Arts & Social Sciences, National University of Singapore, Blk AS6, #03-41, 11 Computing Drive, 117416; Email: yifeiw@u.nus.edu.Annabel NgienAnnabel Ngien (M.Sc., Oxford University; B.A., National University of Singapore) is a graduate student at the Department of Communications and New Media at the National University of Singapore. Address: Department of Communications and New Media, Faculty of Arts & Social Sciences, National University of Singapore, Blk AS6, #03-41, 11 Computing Drive, 117416; Email: annabel.ngien@u.nus.edu.Saifuddin AhmedSaifuddin Ahmed (Ph.D., University of California, Davis) is an Assistant Professor at the Wee Kim Wee School of Communication and Information, Nanyang Technological University. Address: Wee Kim Wee School of Communication and Information, Nanyang Technological University, 31 Nanyang Link, Singapore 637718; Email: sahmed@ntu.edu.sg.
Blockchain-Based Automatic Tracking and Extracting Construction Document for Claim and Dispute Support

During a long-term construction project, numerous stakeholders participate and collaborate with each other. In such scenario, the processes of creating, storing, and managing project documents are highly complicated. Due to the nature of the construction projects, problems arise in document management such as loss of documents and difficulties in tracking, which can result in claim failure and huge damage. This study proposes a method to efficiently record, search, and manage construction documents that can protect legal rights in preparation for claims/disputes. This study designed a system that can generate, transfer, and synchronize blocks based on email communication whenever an event occurs. It also provides functions such as document search, history tracking, automatic extraction of related document, and authenticity verification for document management. The findings of the study have established that reliability of documents is secured during the recording, storing, and managing processes, and thus claim- and dispute-supporting tasks are supported.
Design of an integrated learning approach to assist real-time deaf application using voice recognition system

Modern voice recognition is the process of predicting the human voice using emergent artificial intelligence techniques . It is extensively adopted in real-time applications for identity verification , helping deaf/dumb people, electronic voice eavesdropping, and hearing-impairment purposes. Predicting salient and discriminative process flow in the voice recognition process is the most challenging task in identifying the voice. Some existing research works find it complex to provide better prediction results. However, this work concentrates on modelling an efficient approach to predict the voice using deep learning approaches and it includes four essential phases: data acquisition phase, input voice pre-processing phase, word segmentation phase and classification . For experimentation purpose, an available online dataset was initially acquired and fed as an input to the proposed model. Subsequently, the provided input is pre-processed using an improved Mel-DCT filter for voice activity detection and to improve the classifier performance. The pre-processed image is then supplied into the word segmentation phase where the words from the input data are segmented using the Grab Cut segmentation. Finally, the classification was done with the integrated Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). The integration is performed to handle the drawbacks of the single classifier model and to boost the prediction accuracy.
A preference elicitation framework for automated planning

Many automated planning systems allow users to specify their preferences in order to generate plans that are of high quality according to specified preferences. However, specifying preferences upfront can be time-consuming and difficult for users as their preferences can be complex, not be known in advance, or largely incomplete, especially in complex and large applications. Although many preference representation models and elicitation methods have been proposed in the literature, their adaptation to planning is non-trivial and work in this area has been somewhat limited. In this work, we present a preference elicitation framework for automated planning. This framework allows for interaction with a user through a limited number of low burden comparison type queries and for subsequent learning of a preference relation predictor based on the user’s responses. To help the user explore possible planning solutions and to assist them to easily discover and articulate their preferences, the framework prompts the user with planning solutions for small problems and attempts to learn the user’s preference through a variety of small planning problems from a particular domain. The learned preferences can then be generalised to infer preferences for planning solutions to larger planning problems. We evaluate the framework using a variety of different benchmark planning domains. The results suggest that our framework can efficiently learn user preferences when provided with a limited number of preference queries in small problems, and generalise the user preferences with high accuracy to larger planning problems.
Time Interval Aware Self-Attention approach for Knowledge Tracing

Knowledge Tracing aims to discover the students’ states of mastery for different knowledge or concepts on interaction sequences. It has been widely applied to intelligent educational systems. In recent years, deep neural network-based methods have achieved sound performance on this task because they have a powerful feature representation learning ability. However, two main problems hinder the further development of the existing deep learning-based Knowledge Tracing methods. The first problem is that the interaction history is treated as an ordered sequence, which ignores the individual time intervals between each interaction. The second problem is that higher-order temporal features for knowledge tracing have not been extensively explored. In this paper, to address these problems, we propose a method called the Time Interval Aware Self-Attention approach for Knowledge Tracing (TISAKT), which takes the relationship between exercises into consideration. More specifically, firstly, the relationship between exercises is modeled from the interactions introduced into the absolute positions and the time intervals of answered exercises at the same time. Then, temporal features are exploited implicitly by a temporal convolutional network . Extensive experiments on the two real-world datasets show that our model consistently outperforms state-of-the-art knowledge tracing methods.
Can Blockchain Be a Basis to Ensure Transparency in an Agricultural Supply Chain?

Many supply chains within developing countries lack transparency and are fraught with fraud, corruption, and a substantial number of intermediaries. For several decades, the cocoa sector has faced multiple social, economic, and environmental challenges, some of which include the issue of child labor and very low incomes for farmers, leading to poor living conditions. Blockchain technology has a high potential to reduce—or completely eradicate—some of these hurdles. In this article, we present a blockchain-based solution based on the open-source framework Hyperledger Fabric for the cocoa supply chain to promote transparency and reduce fraud. In doing so, we explicitly describe how farmers can be directly integrated into the whole blockchain solution considering the limited infrastructure, knowledge, and technologies available to them. Since about 70% of all cocoa worldwide is produced in West Africa, this case study uses the cocoa sector in Ghana as an example.
A direct construction of continuous leakage-resilient (H)IBE scheme with CCA security from dual system encryption

Continuous leakage resilient semantically secure Identity-based Encryption (IBE) schemes created with dual system encryption technique were presented by Lewko et al. at TCC 2011 and Yuen et al. at Eurocrypt 2012, respectively. It has been a research challenge to achieve the security against chosen-ciphertext attacks (CCA). The previous attempts toward the CCA security rely on q-type assumptions, which are referred as non-static assumptions causing loose reduction in security proofs. In this paper, we provide a complete solution to the problem and show how to construct the continuous leakage-resilient IBE with weaker CCA security based on the dual system encryption technique, where the adversary is allowed to make continuous leakage queries. We are able to prove its security with static assumptions in the standard model. As a feature of our scheme, we allow leakage of multiple keys, i.e., leakage of the master secret key and the private key of user. Our scheme also enjoys better computational performance, since the round leakage parameter is independent of the plaintext space and has a constant size. Furthermore, our scheme can be easily extended to give the weaker CCA secure continuous leakage-resilient hierarchical identity-based encryption scheme with a short ciphertext length.
InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation

Nuclei Segmentation from histology images is a fundamental task in digital pathology analysis. However, deep-learning-based nuclei segmentation methods often suffer from limited annotations. This paper proposes a realistic data augmentation method for nuclei segmentation, named InsMix, that follows a Copy-Paste-Smooth principle and performs morphology-constrained generative instance augmentation. Specifically, we propose morphology constraints that enable the augmented images to acquire luxuriant information about nuclei while maintaining their morphology characteristics (e.g., geometry and location). To fully exploit the pixel redundancy of the background and improve the model’s robustness, we further propose a background perturbation method, which randomly shuffles the background patches without disordering the original nuclei distribution. To achieve contextual consistency between original and template instances, a smooth-GAN is designed with a foreground similarity encoder (FSE) and a triplet loss. We validated the proposed method on two datasets, i.e., Kumar and CPS datasets. Experimental results demonstrate the effectiveness of each component and the superior performance achieved by our method to the state-of-the-art methods.The source code is available at https://github.com/hust-linyi/insmix .
Walking with PACE - Personalized and Automated Coaching Engine

We design and implement a personalized and automated physical activity coaching engine, PACE, which uses the Fogg's behavioral model (FBM) to engage users in mini-conversation based coaching sessions. It is a chat-based nudge assistant that can boost (encourage) and sense (ask) the motivation, ability and propensity of users to walk and help them in achieving their step count targets, similar to a human coach. We demonstrate the feasibility, effectiveness and acceptability of PACE by directly comparing to human coaches in a Wizard-of-Oz deployment study with 33 participants over 21 days. We tracked coach-participant conversations, step counts and qualitative survey feedback. Our findings indicate that the PACE framework strongly emulated human coaching with no significant differences in the overall number of active days, step count and engagement patterns. The qualitative user feedback suggests that PACE cultivated a coach-like experience, offering barrier resolution via motivational and educational support. We use traditional human-computer interaction approaches, to interrogate the conversational data and report positive PACE-participant interaction patterns with respect to addressal, disclosure, collaborative target settings, and reflexivity. As a post-hoc analysis, we annotated the conversation logs from the human coaching arm and trained machine learning (ML) models on these data sets to predict the next boost (AUC 0.73 ± 0.02) and sense (AUC 0.83 ± 0.01) action. In future, such ML-based models could be made increasingly personalized and adaptive based on user behaviors.