Reviewerly/exHarmony huggingface dataset:
Training and Serving Machine Learning Models at Scale

In recent years, Web services are becoming more and more intelligent (e.g., in understanding user preferences) thanks to the integration of components that rely on Machine Learning (ML). Before users can interact (inference phase) with an ML-based service (ML-Service), the underlying ML model must learn (training phase) from existing data, a process that requires long-lasting batch computations. The management of these two, diverse phases is complex and meeting time and quality requirements can hardly be done with manual approaches. This paper highlights some of the major issues in managing ML-services in both training and inference modes and presents some initial solutions that are able to meet set requirements with minimum user inputs. A preliminary evaluation demonstrates that our solutions allow these systems to become more efficient and predictable with respect to their response time and accuracy.
Watermarking Techniques Performance Analysis in Medical Images for Secure Communication

Over the past few years, most medical diagnostics and treatments have shifted to digital content. COVID-19 is a viral disease first identified in Wuhan, China 2019. Its pandemic caused a dramatic loss in human health, work, and food systems worldwide. WHO recommended social distancing as a preventive measure to protect ourselves from corona viral infections. Hence, now many avail hospitals facilities are online. It enables telemedicine where patients, doctors, and medical research units can easily share their digital medical information through various communication channels. At the receiver’s end, the patient’s record must not be lost or altered during transmission. As medical imaging contains many fine features, even small changes cause confusion among medical staff for diagnosis. One of the best techniques for image authentication is digital image watermarking. When developing an effective watermark method, it’s necessary to have a balanced trade-off among imperceptibility, capacity, and robustness. The work gives a comprehensive survey of cryptography, biometrics, and blockchain-based on various watermarking schemes in medical images that gives new ideas to improve the already existing techniques.
The performance of Faster R-CNN algorithm on a dataset with poor capturing conditions

In the past few years, computer vision algorithms have made a breakthrough in the field of object discovery, taking advantage of the significant development in computing capabilities and the availability of huge amounts of data with the emergence of many methods and techniques that have been used to achieve efficient results. In this research paper, we examine one of the most important object-detection algorithms, the Faster RCNN algorithm, and explore its ability and efficiency using low-quality or low-light image datasets captured in harsh conditions such as darkness or fog. Many research papers do not contain this kind of nature of images in their data, and their percentage is small in research, so this is a gap that we are trying to cover in this paper, at which we got a detection accuracy of mAP(50)=70.7%. The data contained ten capturing conditions (shadow, twilight, etc.) and 12 categories between human, animal, vehicle or furniture.
Utilizing Augmented Reality for Performance and Decision-Making in Collaborative Time-Critical Environments

The world has seen the introduction of new technology such as smart-phones and more readily accessible and portable AR HMDs. These technological advancements have paved the way for advances in the space of AR resulting in a boom of research interested in this field. The use of AR for supporting task performance, decision-making, and collaboration are some of the areas that are being explored within this space. Research conducted within these areas has managed to produce promising results showing that AR if used and designed correctly can be used as an effective tool to increase performance and support decision-making and collaboration. However, there is a lack of research that brings together all these elements. There is also a lack of research that has been conducted using AR with a focus on time-critical tasks. This work aims to explore whether augmented reality can improve performance and decision-making in collaborative time-critical tasks and how this can be achieved.
Toward In-Situ Authoring of Situated Visualization with Chorded Keyboards

Authoring situated visualizations in-situ is challenging due to the need of writing code in a mobile and highly dynamic fashion. To provide better support for that, we define requirements for text input methods that target situated visualization authoring. We identify wearable chorded keyboards as a potentially suitable method that fulfills some of these requirements. To further investigate this approach, we tailored a chorded keyboard device to visualization authoring, developed a learning application, and conducted a pilot user study. Our results confirm that learning a high number of chords is the main barrier for adoption, as in other application areas. Based on that, we discuss ideas on how chorded keyboards with a strongly reduced alphabet, hand gestures, and voice recognition might be used as a viable, multi-modal support for authoring situated visualizations in-situ.
Joint Handwritten Text Recognition and Word Classification for Tabular Information Extraction

In this paper, we present a system for extracting tabular information from loosely structured handwritten documents. The system consists of three parts, (i) a u-net like CNN-based method for text detection and segmentation, (ii) a new attention-based method for simultaneous text recognition and classification of word-parts, and (iii) a method for matching the word parts into a tabular structure for each entry. A key contribution is the observation that the new attention-based recognition and classification module makes it possible for improved spatial analysis of the tabular information. The method is evaluated on a unique historical document: The Swedish Wealth Tax of 1571, consisting of 11,453 pages of hand-written tax records. The evaluation shows that the system provides a significant improvement to the state-of-the-art to the problem of tabular extraction from loosely structured historical documents.
Efficient Scale Divide and Conquer Network for Object Detection

Multi-scale methods are often used to improve the accuracy of detection models. However, this approach is usually computationally expensive. In this paper, we introduce an efficient Scale Divide and Conquer Network (ScaleDCNet) based on the keypoint object detection framework, which accomplishes independent detection at each scale with minimal cost. To achieve this goal, we propose a new one-stage detection network, which consists of a scale feature selection module and a scale-aware keypoint matching approach. The scale feature selection module obtains features of various sizes through an attention mechanism to divide and conquer the scale problem. The scale-aware keypoint matching method is used to make independent predictions for the positions of objects of different sizes. With the above approaches, our ScaleNet improves the detection ability of objects of different sizes, reduces the probability of missing and false detection, and performs better in scale deformation and occlusion. We evaluate on the MS-COCO dataset and our model achieves high accuracy with low computational resources compared to baseline and other algorithms.
Competence Model Development at Astana IT University

This article is devoted to analysis of existing competencies that are relevant for educational system. Global competition and rapid development of technologies create the need for Higher Education Institutions (HIE) to teach future graduates competencies required by employers. Authors propose competence-based model of education for Astana IT University. This model will be developed as a student digital profile with access by potential employers. Such model would be beneficial for all stakeholders and need further development.
Introspecting diverse IoT-traffic analysis methods in Smart Environments and Prospects

The intelligent Universally Interacting IoT Objects (UIO) ecosystem is deployed at the perception layer in smart systems. Furthermore, application of IoT devices in virtually all domains has outfitted the path for implementing efficient and sustainable intelligent systems. The digital wave heralded the IoT as the globe’s most technical revolution. Cyber-physical systems, Smart ecosystems, digital technologies, and organizations constantly redesign and accept IoT devices. However, the advent of IoT devices has incubated cyber security issues. Researchers have invested efforts in understanding IoT-traffic behavior to defend against such attacks. This paper outlines the attributes of IoT traffic, performs a comparative analysis of existing traffic classification solutions, and recommends future cyber defense solutions based on IoT-traffic characteristics in Smart Environments. The paper also discusses the characteristics of a resilient classification and defense framework. It emphasizes the vital performance metrics and proposes a distributed, resilient, and scalable framework based on intelligent learning approaches.
Behavioral Analysis of ROS motion planners integrated with Robotics Middleware Framework (RMF)

Robot operating system (ROS) is one of the most popular middleware for robot applications, being integrated with the majority of projects in robotics. Since the rollout of ROS2 having an improved communication stack than ROS1 with its real-time data distribution service (DDS) protocol, the problem of robot interoperability has been one of the most sought problems. Although many different multi-robot management frameworks have been developed, only a few of them are open-sourced. In this paper, we present a study that evaluates one such interoperability framework called “Robotics Middleware Framework (RMF)”. Herein, RMF is integrated with “Free Fleet” an open-source robot fleet management system and tested with various path planning algorithms on different possible real-world test scenarios. The outcome of this study gives an insight into the performance of path planners in complex scenarios.
In-Place Gestures Classification via Long-term Memory Augmented Network

In-place gesture-based virtual locomotion techniques enable users to control their viewpoint and intuitively move in the 3D virtual environment. A key research problem is to accurately and quickly recognize in-place gestures, since they can trigger specific movements of virtual viewpoints and enhance user experience. However, to achieve real-time experience, only short-term sensor sequence data (up to about 300ms, 6 to 10 frames) can be taken as input, which actually affects the classification performance due to limited spatiotemporal information. In this paper, we propose a novel long-term memory augmented network for in-place gestures classification. It takes as input both short-term gesture sequence samples and their corresponding long-term sequence samples that provide extra relevant spatio-temporal information in the training phase. We store long-term sequence features with an external memory queue. In addition, we design a memory augmented loss to help cluster features of the same class and push apart features from different classes, thus enabling our memory queue to memorize more relevant long-term sequence features. In the inference phase, we input only short-term sequence samples to recall the stored features accordingly, and fuse them together to predict the gesture class. We create a large-scale in-place gestures dataset from 25 participants with 11 gestures. Our method achieves a promising accuracy of 95.1% with a latency of 192ms, and an accuracy of 97.3% with a latency of 312ms, and is demonstrated to be superior to recent in-place gesture classification techniques. User study also validates our approach. Our source code and dataset will be made available to the community.
A Penalized Neural Network Model for Predicting Unobserved Scores of Construct Indicators and Reproducing Latent Scores of the Theoretical Constructs by Using Text Information

This paper discusses a novel application of construct measurement with a penalized neural network in a text analysis. Conducting a survey with the measures of constructs is a traditional approach to scoring customers’ mental states, such as feelings, perceptions, and attitudes. Numerous types of scales are continuously developed for marketing and consumer behavior research. To connect this traditional methodology with machine learning and big data analysis, our proposed neural network model uses two penalty functions, which specify the confirmatory factor model as a measurement equation. In the empirical analysis, the proposed model shows stable parameter estimates compared with a standard unconstrained neural network and supports theoretical interpretations. By applying the learned proposed model, predictive item scores of each questionnaire and alternative scores of theoretical constructs can be obtained through text information. The proposed methodology helps marketers and researchers visualize consumers’ representations of perceived theoretical constructs without the survey. To enhance the proposed model’s accuracy and validity, limitations and potential works are also discussed for future study.
Firebase Cloud: Web-Based Streaming Platform

Video streaming technology has swiftly emerged in the field of information and technology. In comparison to traditional cable broadcasters whose content could only be watched on TVs, video streaming is ubiquitous and viewers can flexibly watch the video contents on various devices, ranging from smartphones to laptops, and large TV screens. In the Applications nowadays, content on the platform exists far in advance even before a user thinks to watch it. It can be preprocessed easily. Heavy compression techniques can be used and videos can be prepared with different qualities and transmission rates. In past some decades devices on which streaming is done have been transformed totally from televisions to OTTs which stream a large amount of content. Also, various electronic gadgets like laptops, tablets etc. provide ease to users to watch the content with portable facility. It has been noted that among all the sites browsed, streaming websites/services holds around 60% of all. The basic idea behind implementing the modules is that some content is widely available over the internet and is accessible to everyone irrespective of their age. Also, people are using these platforms as a source of entertainment for most of the time during the day which eventually leads to eye problems. Be it phone or laptop or any other device watching screen for so long in a day is not good for health.
On the Possibility of Ontology Map Constructing on the Cerebral Cortex

A hypothesis has been put forward about the possibility of setting up an experiment to test the correlation between the factorial mathematical model of the subject area, and the "thinking model of the subject area" on the cerebral cortex. By analogy with the semantic map of the cerebral cortex, it is proposed to build an ontological map of the cerebral cortex, i.e. to localize denotations of the phenomena. A number of arguments are given in favor of such a hypothesis and the possibilities of setting up an experiment are discussed.
Jointly-Learnt Networks for Future Action Anticipation via Self-Knowledge Distillation and Cycle Consistency

Future action anticipation aims to infer future actions from the observation of a small set of past video frames. In this paper, we propose a novel Jointly-learnt Action Anticipation Network (J-AAN) via Self-Knowledge Distillation (Self-KD) and cycle consistency for future action anticipation. In contrast to the current state-of-the-art methods which anticipate the future actions either directly or recursively, our proposed J-AAN anticipates the future actions jointly in both direct and recursive ways. However, when dealing with future action anticipation, one important challenge to address is the future’s uncertainty since multiple action sequences may come from or be followed by the same action. Training an action anticipation model with one-hot-encoded hard labels that assign zero probabilities to incorrect yet semantically similar actions may not handle the uncertain future. To address this challenge, we design a Self-KD mechanism to train our J-AAN, where the J-AAN gradually distills its own knowledge during the training to soften the hard labels to model the uncertainty on future action anticipation. Furthermore, we design a forward and backward action anticipation framework with our proposed J-AAN based on a cyclic consistency constraint. The forward J-AAN anticipates the future actions from the observed past actions, and the backward J-AAN verifies the anticipation of the forward J-AAN by anticipating the past actions from the anticipated future actions. The proposed method outperforms all the latest state-of-the-art action anticipation methods on the Breakfast, 50Salads, and EPIC-Kitchens-55 datasets. This project will be publicly available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/MoniruzzamanMd/J-AAN</uri> .
DependencyMerge Guided Latent Tree Structure for Aspect-Based Sentiment Analysis

Recent studies on aspect-based sentiment analysis (ABSA) demonstrated the efficiency of using task-specific latent trees to model the interaction between aspects and opinion words. However, as information propagates mostly between neighbors, a tree inducer module often produces a random structure at early iterations, which makes the performance unstable. Therefore, this paper uses multiple dependency trees from off-the-shelf parsers as the structured guidance for a latent tree to conquer the unstable problem. A simple yet effective dependency ensemble technique, DependencyMerge, is proposed to facilitate different parsers. With the rich prior knowledge, the latent tree-inducing process is stable and the latent tree is more task-specific. Experiments on five ABSA datasets show that the proposed model has better performance compared to the recent strong baselines.
Multisource Image Matching Method Using Hierarchical Structure Constraint and Phase Congruency

Multisource image matching is still a challenging task due to the significant nonlinear radiometric differences and scale variations. To address the problem, we present a novel phase congruency approach and hierarchical structure constraint strategy for multisource image matching. Specifically, we employ the phase congruency of image frequency domain in Gaussian scale space, and KAZE operator was used to detect feature point in the maximum moment space, which was obtained by the Fourier transform of the Log-Gabor even-symmetric filter. And then, extended phase features within the neighborhood region were generated, next feature description in the framework of polar coordinates was obtained. Finally, in the stage of image matching, we use the hierarchical structure constraint strategy for random sampling and verification. The experiments show that the proposed algorithm is superior to d2-NET, LGHD, RIFT, and other mainstream multisource image matching methods. Especially for scale change and rotation, the proposed algorithm provides a better way to describe the common features of multisource images, and results in a reliable and accurate matching for multisource image with obvious intensity and radiation differences.
Leveraging Indigenous Knowledge for Effective Environmental Governance in West Africa

Effective environmental governance is key to achieving Sustainable Development Goals.Environmental sustainability programs in developing countries across West Africa are anchored on western scientific knowledge.This has led to a tendency to marginalize the application of indigenous knowledge in environmental protection in West Africa.Yet, no knowledge system develops in isolation as they cross-fertilize and benefit from each other.Indigenous knowledge systems are as old as the traditional societies having been passed from one generation to another.They are those voluminous, diverse, and highly localized sources of wisdom that are neither specific nor universal.Indigenous knowledge systems such as using animal dung as manure, sighting of the new moon before planting, rain-making rituals, and planting of trees to serve as shades have helped to conserve the wetlands and the forests of most societies in West Africa and are effective in promoting environmental sustainability.Hence, the reason they are considered as important as scientific knowledge which must be integrated through multiple evidence base approaches for effective environmental governance.This article using doctrinal research methodology aims at examining how traditional societies in West Africa can leverage indigenous knowledge to achieve effective environmental governance, mitigate climate change, and promote sustainable development.The article recommends that mainstreaming indigenous knowledge systems into environmental legislation, policy and institutional frameworks will not only help in mitigating environmental issues such as climate change but will help developing countries in West Africa meet the SDG goals and their commitments in the Paris Agreement.The article concludes that the integration of such unique knowledge systems into other evidence or scientific based knowledge systems could be one of the best ways to ensure effective and participatory environmental governance in West Africa.
An ETL strategy for integrating the LA Referencia platform and VIVO for the Brazilian CRIS

The BrCris project has as its main goal to provide a unified view of the Brazilian research ecosystem. The LA Referencia platform is the main resource supporting the project, allowing us to treat heterogeneous data gathered from distinct sources and export the resulting datasets to different tools for data visualization, search, and analysis. We also use the VIVO platform for visualizing the data as a knowledge graph, which builds upon a semantic model based on the VIVO ontology. To make the data flow from the LA Referencia relational database to VIVO, we developed an Extraction, Transformation, and Load tool, which integrates both platforms ensuring data synchronization and making the resulting graph available for easy navigation and search
Debugging with stack overflow

Debugging can be challenging for novice and expert programmers alike. Programmers routinely turn to online resources such as Stack Overflow for help, but understanding of debugging search practices, as well as tool support to find debugging resources, remains limited. Existing tools that mine online help forums are generally not aimed at novices, and programmers face varying levels of success when looking for online resources. Furthermore, training online code search skills is pedagogically challenging, as we have little understanding of how expertise impacts programmers' web search behavior while debugging code.