Reviewerly/exHarmony huggingface dataset:
Bangla News Classification using Graph Convolutional Networks

Online Bangla news has rapidly increased in the era of the information age. Each news site has its different categorization for grouping their news. The Layout and categorization of the online Bangla news articles cannot perpetually meet the individual's needs due to the heterogeneity. So, overcoming this issue and classifying the online Bangla news articles according to the preference of the user is an arduous task. So, it is essential to provide state-of-the-art solutions as well as the best way to solve this problem. The paper aims to build an automated system to classify the Bangla news contents and also find out the state-of-the-art solutions for the small size dataset. It is known that most of the machine learning models need huge amounts of data for the proper training and testing of the models. But due to the scarcity of the dataset, it is not always possible to provide the state-of-the-art solutions. But, in this research work, we have found that Text-GCN performed better than the BiLSTM, GRU-LSTM, LSTM, Char-CNN, and BERT to classify the online Bangla news in spite of the small size of the dataset. The obtained experimental result shows the efficiency of the Text-GCN over the other models in terms of accuracy, precision recall, and F1-score.
Introduction to the special issue: value sensitive design: charting the next decade

Abstract In this article, we introduce the Special Issue, Value Sensitive Design: Charting the Next Decade , which arose from a week-long workshop hosted by Lorentz Center, Leiden, The Netherlands, November 14–18, 2016. Forty-one researchers and designers, ranging in seniority from doctoral students to full professors, from Australia, Europe, and North America, and representing a wide range of academic fields participated in the workshop . The first article in the special issue puts forward eight grand challenges for value sensitive design to help guide and shape the field. It is followed by 16 articles consisting of value sensitive design nuggets—short pieces of writing on a new idea, method, challenge, application, or other concept that engages some aspect of value sensitive design. The nuggets are grouped into three clusters: theory, method, and applications. Taken together the grand challenges and nuggets point the way forward for value sensitive design into the next decade and beyond.
Augmented reality in oral and maxillofacial surgery

Augmented reality (AR) is a technology which extends the user’s reality by including computer-generated, virtual information within their view. As such, AR technology is a valuable asset in the medical domain, where it has the potential to grant “X-ray vision” to physicians, allowing them to view medical data in situ, in the same physical space as the patient, in the most intuitive way. Consequently, medical applications have been the focus of considerable research efforts since the early days of AR, be it in the context of medical education and training, patient rehabilitation and therapy, or in clinical applications such as computer-aided interventions. In this chapter, we will review the most important technical concepts of AR in general, discussing display technology, tracking paradigms and visualization techniques, among other things. Furthermore, we provide an extensive overview over current applications, practices, and challenges of AR, specific to the medical domain. Finally, AR in oral and cranio-maxillofacial surgery will be discussed, starting with a discussion of shortcomings of current computer-assisted procedures and how AR can aid to overcome them. We then provide a historical perspective on AR in oral and maxillofacial surgery, ending with a review of the current state-of-the-art.
Controllable Image Generation with Semi-supervised Deep Learning and Deformable-Mean-Template Based Geometry-Appearance Disentanglement

Typical deep-neural-network (DNN) based generative image models often (i) show limited ability to learn a disentangled latent representation, (ii) show limited controllability leading to undesirable side effects when manipulating selected attributes during image generation, and (iii) require large attribute-annotated training sets. We propose a generative DNN model for face images by explicitly disentangling geometry and appearance modeling to achieve selective controllability of the desired attributes with less side effects. To learn geometric variability, we leverage grayscale sketch representations to learn (i) a deformable mean template representing the population-mean face geometry and (ii) a generative model of deformations to model individual face-geometry variations, using dense image registration. We learn the appearance variability in a (color-image) space that we explicitly design by factoring out the geometric variability. We propose a variational formulation to enable semi-supervised learning when manually-annotated attributes are severely limited in the training set. Results on large datasets show that, compared to schemes using deformation models or variational learning, our method significantly improves face-image model fits and facial-feature controllability even with semi-supervised learning.
3D geostatistical modeling of metal contents and lithofacies for mineralization mechanism determination of a seafloor hydrothermal deposit in the middle Okinawa Trough, Izena Hole

Seafloor hydrothermal deposits in the Okinawa Trough have been regarded as a modern analog of kuroko-type volcanogenic massive sulfide (VMS) deposits on land. VMS deposit is one of the primary producers of base metals (e.g. Cu, Pb, Zn) and precious metals (e.g. Au, Ag). However, owing to difficulties in accessing subseafloor samples/data without costly drilling operations, the spatial distribution of metal contents below the seafloor remains poorly constrained. We apply a combination of four spatial modeling methods: (1) principal component analysis; (2) k-means clustering; and (3, 4) conditional geostatistical simulations of turning bands and pluri-Gaussian. These modeling methods are based on the whole-rock geochemical data using inductively coupled plasma-quadruple mass spectrometry, together with lithologic log data obtained from onboard visual core descriptions, and X-ray diffraction analyses from the middle Okinawa Trough, Izena Hole, during the cruise CK16-05 (Expedition 909) in 2016 by D/V Chikyu. The primary goal is to construct plausible 3D models for the contents of base metals and silver as well as lithotypes. The constructed models successfully map the configuration and zonation of subseafloor metal deposits with hydrothermal flow paths, which sheds light on hydrothermal circulation systems and metal accumulation mechanisms. This approach is shown to be effective for geologic and mineralization modeling and exploration of (sub)seafloor hydrothermal deposits.
Upslices, Downslices, and Secret-Sharing with Complexity of $$1.5^n$$

A secret-sharing scheme allows to distribute a secret s among n parties such that only some predefined “authorized” sets of parties can reconstruct the secret, and all other “unauthorized” sets learn nothing about s. The collection of authorized/unauthorized sets can be captured by a monotone function $$f:\{0,1\}^n\rightarrow \{0,1\}$$ . In this paper, we focus on monotone functions that all their min-terms are sets of size a, and on their duals – monotone functions whose max-terms are of size b. We refer to these classes as (a, n)-upslices and (b, n)-downslices, and note that these natural families correspond to monotone a-regular DNFs and monotone $$(n-b)$$ -regular CNFs. We derive the following results. We use our results to explore connections between several seemingly unrelated questions about the complexity of secret-sharing schemes such as worst-case vs. average-case, linear vs. non-linear and primal vs. dual access structures. We prove that, in at least one of these settings, there is a significant gap in secret-sharing complexity.
Quantifying reputation risk using a fuzzy cognitive map: a case of a pharmaceutical supply chain

Purpose Reputation risk onsets in focal firm whenever any entity of its supply chain (SC) faces risk-crisis event. A framework for modeling and predicting holistic SC reputation risk is proposed by integrating operational risk (OR) drivers originating from upstream and downstream partners and focal firm. A fuzzy cognitive map (FCM) is then developed to predict and quantify Pharmaceutical SC reputation risk. Design/methodology/approach Using event study methodology, SC reputation risk framework with 13 input OR drivers was developed. Based on pharmaceutical supply chain experts’ opinion, the correlation between reputation risk and its input drivers was estimated. The developed FCM tool was validated using nine real-life instances. A series of “what-if” scenario analyses were performed to demonstrate effectiveness of proactive and reactive mitigation strategies against reputation risk. Findings Quality and unethical governance risks significantly impacted reputation in Pharmaceutical SC and a firm should prefer “risk avoidance” against these risks. The upstream risks significantly affect reputation in a Pharmaceutical SC as compared to the downstream risks. Proactive mitigation strategies and assertive crisis communication are suggested for upstream risks while diminishment/ bolstering/rebuilding reactive crisis communication is recommended for downstream risks. Originality/value Reputation risk is often overlooked in SC literature. This work develops a model to quantify the reputation risk considering the indirect consequences of the ORs that originates at any point in a SC. The proposed FCM tool aids SC manager to focus on higher attribution risk events and devise an optimal combination of proactive and reactive mitigation strategies to avoid/minimize the economic loss due to reputation crisis.
Let's Talk It Out

Research has shown study habits and skills to be correlated with academic success, calling for a deeper comprehension of these behaviors and processes to design effective interventions for struggling students. Chatbots have recently been used as a persuasive technology to help support behavioral change, making them an intriguing design space for students' study habits and skills. This paper investigated the feasibility of using chatbots for promoting behavioral change of college students majoring in Computer Science (CS). We conducted semi-structured interviews with CS peer-tutors and surveyed university freshmen to understand students' study habits and identify technical intervention opportunities. Inspired by the findings, we designed StudyBuddy, a chatbot prototype deployed in Slack that periodically sends tips, provides assessments of students' study habits via surveys, helps the students break down assignments, recommends academic resources, and sends reminders. We evaluated the usability of the prototype in-depth with 8 students (both first-year and senior students) and 5 course instructors followed by a large scale evaluative survey (n=117) using video of the prototype. Our research identified important design challenges such as building trust and preserving privacy, limiting interaction costs, and supporting both immediate and long-term sustainable support. Likewise, we proposed design recommendations that demonstrate context awareness, personalize the experience based on user preferences, and adapt over time as students mature and grow.
A development of a low-cost pyranometer for measuring broadband solar radiation

Abstract In this work, the design of an inexpensive and reliable pyranometer used for measuring broadband solar irradiance is proposed. A BPX43 – 4 phototransistor is used as a light detector, which has the spectral response in the range approximately 450 to 1100 nm. This system is controlled by an Arduino pro mini ATmega328P (5 V, 16 MHz) microcontroller and all components are located inside a FB05 black box. Due to a high level of solar radiation in the different environmental conditions, the pyranometer encountered the voltage saturation. Therefore, a 0.25 mm thick of Teflon is selected for attenuating the sunlight. After investigating against a reference sensor, the transmittance of this attenuator is found to be about 12.5%. Finally, the experimental results from the system are compared with a standard pyranometer performed at Physikalisch-Meteorologisches Observatorium Davos/World Radiation Center (PMOD/WRC, 46.80 °N, 9.83 °E and 1, 610 meters above sea level), Switzerland. The results demonstrate a satisfactory agreement between the global irradiance obtained from our proposed pyranometer and the standard pyranometer.
Research on Real-Time Anomaly Detection of Fishing Vessels in a Marine Edge Computing Environment

Fishing vessel monitoring systems (VMSs) play an important role in ensuring the safety of fishing vessel operations. Traditional VMSs use a cloud centralized computing model, and the storage, processing, and visualization of all fishing vessel data are completed in the monitoring center. Due to the limitation of maritime communications, the data generated by fishing vessels cannot be fully utilized, and communication delays lead to inadequate warnings in cases of fishing vessel abnormalities. In this paper, we present a real-time anomaly detection model (RADM) for fishing vessels based on edge computing. The model runs in the edge layer, making full use of the information of moving edge nodes and nearby nodes, and combines a historical trajectory extraction detection model with an online anomaly detection model to detect anomalies. The detection model of historical trajectory extraction mines frequent patterns in historical trajectories through multifeature clustering and identifies trajectories that are different from the frequent patterns as anomalies. Online anomaly detection algorithms detect anomalous behavior in specific scenarios based on the spatiotemporal neighborhood similarity and reduce the impact of anomaly evolution. Experiments show that RADM was more effective than traditional methods in real-time anomaly detection of fishing vessels, which provides a new method for upgrading the technology of traditional VMS.
Artificial neural networks as clinical decision support systems

Summary In the last decade, artificial intelligent systems based on neural networks have gradually become primary source for clinical decision support systems (CDSS) and are being used in diverse areas of medical diagnosis, classification, and prediction. An artificial neural network (ANN) consists of a large number of processing units which performs the computation in a parallel and distributed environment. They learn the pattern from the examples provided to it and then generalize based on the concepts they have learned while training. This paper presents a review of the current status of ANN and its variants as CDSS in various medical disciplines. The work focuses and describes the methods making use of simple ANN and use of real‐time approaches based on big data using ANN in cloud computing environment for various medical applications. Critical analysis of various methods based on smart approaches indicates that feed‐forward back propagation ANN performs sufficiently better in the domain of medicines with a high degree of accuracy.
Deep Superpixel Convolutional Network for Image Recognition

Due to the high representational efficiency, superpixel largely reduces the number of image primitives for subsequent processing. However, superpixel is scarcely utilized in recent methods since its irregular shape is intractable for standard convolutional layer. In this paper, we propose an end-to-end trainable superpixel convolutional network, named SPNet, to learn high-level representation on image superpixel primitives. We start by treating irregular superpixel lattices as a 2D point cloud, where the low-level features inside one superpixel are aggregated to one feature vector. We replace the standard convolutional layer with the PointConv layer to handle the irregular and unordered point cloud. Besides, we propose grid based downsampling strategies to output uniform 2D sampling result. The resulting network largely utilizes the efficiency of superpixel and provides a novel view for image recognition task. Experiments on image recognition task show promising results compared with prominent image classification methods. The visualization of class activation mapping shows great accuracy at object localization and boundary segmentation.
Facilitated Deep Learning Models for Image Captioning

This paper focuses on developing semantic image caption generation techniques that leverage image and scene understanding. More particularly, we are interested in addressing image captioning by developing a mixture of object detection and attention-enriched deep learning models. To extract the image features, a Convolutional Neural Network (CNN) is used, and then an extended version of Recurrent Neural Networks (LSTM) with attention-enrichment is adopted to generate the caption. We implement image captioning by considering detected objects from the image scene, and then by integrating an attention mechanism for caption generation. This can have multiple advantages from accuracy and semantics perspectives. The objective of this paper is to introduce a combined pipeline that employs several variant models for semantic caption generation. Four variant models are proposed, all of them are implemented and trained on COCO and Flickr30k datasets, and then tested on a subset of COCO dataset. Results of the different models were evaluated using a semantic similarity analysis between the generated captions and the actual ground truth captions. Our framework helps in a deeper understanding of images and decision making in diverse use-cases such as innovative and distinctive responses from multimodal data, and in analyzing and monitoring crowdsourced images from social media and other sources.
Tags, Borders, and Catalogs

Through a computational reading of the online book reviewing community LibraryThing, we examine the dynamics of a collaborative tagging system and learn how its users refine and redefine literary genres. LibraryThing tags are overlapping and multi-dimensional, created in a shared space by thousands of users, including readers, bookstore owners, and librarians. A common understanding of genre is that it relates to the content of books, but this resource allows us to view genre as an intersection of user communities and reader values and interests. We explore different methods of computational genre measurement within the open space of user-created tags. We measure overlap between books, tags, and users, and we also measure the homogeneity of communities associated with genre tags and correlate this homogeneity with reviewing behavior.Finally, by analyzing the text of reviews, we identify the thematic signatures of genres on LibraryThing, revealing similarities and differences between them. These measurements are intended to elucidate the genre conceptions of the users, not, as in prior work, to normalize the tags or enforce a hierarchy. We find that LibraryThing users make sense of genre through a variety of values and expectations, many of which fall outside common definitions and understandings of genre.
From Human Pose to On-Body Devices for Human-Activity Recognition

Human Activity Recognition (HAR), using inertial measurements from on-body devices, has not seen a great advantage from deep architectures. This drawback is mainly due to the lack of annotated data, diversity of on-body device configurations, the class-unbalance problem, and non-standard human activity definitions. Approaches for improving the performance of such architectures, e.g., transfer learning, are therefore difficult to apply. This paper introduces a method for transfer learning from human-pose estimations as a source for improving HAR using inertial measurements obtained from on-body devices. We propose to fine-tune deep architectures, trained using sequences of human poses from a large dataset and their derivatives, for solving HAR on inertial measurements from on-body devices. Derivatives of human poses will be considered as a sort of synthetic data for HAR. We deploy two different temporal-convolutional architectures as classifiers. An evaluation of the method is carried out on three benchmark datasets improving the classification performance.
Motorcycle Rider Helmet Detection for Riding Safety and Compliance Using Convolutional Neural Networks

Traffic violation apprehension is one of the traffic problems here in the Philippines. One example is the No Helmet No Ride Law that is implemented but many motorists still choose to ignore. To alleviate the problem the government has offered many solutions, one of which is the No Contact Traffic Apprehension Policy that uses CCTV Monitoring. To further enhance this solution the government has partnered with the De La Salle University to use artificial intelligence in the system. Computer Vision tasks like image classification and object detection can help automate the traffic apprehension system. Image classification and object detection are technologies which are used in computer vision in defining an image or coordinates of an object in an image. In this work, a novel approach to classifying motorcycle riders between wearing a helmet or not will be developed. It will be demonstrated using deep machine learning, specifically convolutional neural network and by utilizing different pre-trained models to a gathered dataset.
NullaNet Tiny: Ultra-low-latency DNN Inference Through Fixed-function Combinational Logic

While there is a large body of research on efficient processing of deep neural networks (DNNs) [1]-[31], ultra-low-latency realization of these models for applications with stringent, sub-microsecond latency requirements continues to be an unresolved, challenging problem. Field-programmable gate array (FPGA)-based DNN accelerators are gaining traction as a serious contender to replace graphics processing unit/central processing unit-based platforms considering their performance, flexibility, and energy efficiency. NullaNet (2018) [32], LUTNet (2019) [33], and LogicNets (2020) [34] are among accelerators specifically designed to benefit from FPGAs' capabilities.
Performance Analysis of Graph Neural Network Frameworks

Graph neural networks (GNNs) are effective models to address learning problems on graphs and have been successfully applied to numerous domains. To improve the productivity of implementing GNNs, various GNN programming frameworks have been developed. Both the effectiveness (accuracy, loss, etc) and the performance (latency, bandwidth, etc) are essential metrics to evaluate the implementation of GNNs. There are many comparative studies related to the effectiveness of different GNN models on domain tasks. However, the performance characteristics of different GNN frameworks are still lacking. In this study, we evaluate the effectiveness and performance of six popular GNN models, GCN, GIN, GAT, GraphSAGE, MoNet, and GatedGCN, across several common benchmarks under two popular GNN frameworks, PyTorch Geometric and Deep Graph Library. We analyze the training time, GPU utilization, and memory usage of different evaluation settings and the performance of models across different hardware configurations under the two frameworks. Our evaluation provides in-depth observations of performance bottlenecks of GNNs and the performance differences between the two popular GNN frameworks. Our work helps GNN researchers understand the performance differences of the popular GNN frameworks, and gives guidelines for developers to find potential performance bugs of frameworks and optimization possibilities of GNNs.