Reviewerly/exHarmony huggingface dataset:
Airborne Magnetic Survey and Remote Sensing Applied to Structural Study in Vohilava Area Madagascar

Mananjary district is known for its gold production. To better understand the gold mineralization distribution, a regional structural study was undertaken using airborne geophysics and remote sensing (RS). Magnetometry data, Landsat Operational Land Imager (OLI) 8 satellite-image and DEM (Digital Elevation Model) have been processed and interpreted. Signals associated with deep structures are weak and dominated by strong signals from surficial magnetic sources. To overcome this problem, local-phase based filters were used. A deep knowledge of each filter allows to use it separately or in combination with other filters. RS is a powerful tool for a regional study. Color composite technique and DEM analysis were helpful to bring out faults and structures. The combination of magnetometry and RS allows geologists to efficiently delineate prospective zones. The study has unveiled a dextral strike-slip fault (SSF) over 240km and associated faults, folds, flower structure and pull-apart basins within the Mananjary gold district. Moreover, the SSF structurally controlled the spatial distribution of Vohilava-Nosivolo and Maha groups. R Riedel shear faults appear as North North East – South South West faults. R’ Riedel shear faults occur in the north of the study area with East North East -West South West orientation. Tectonic block rotation occurred between Ampasinambo and Soavina. P’ shear faults striking West North West – East South East occur in Vohilava. Some folds are associated with the SSF as well. Vohilava positive flower structure which used to be a syncline and its antiform equivalent are SSF footprints. The latter was active since Mesoarchean.
Next-generation Education Approach in Medical and Biological Engineering Education Through Online Lessons (オンライン授業による生体医工学分野の次世代教育の取組み)

With the widespread of Covid-19, Ministry of Education, Culture, Sports, Science and Technology of Japan decided to promote online education to compensate for the loss of school closing through ICT. This study suggests the e ff ect of online education through classes on medical endoscopy provided for elementary school students in remote areas. The result shows that online education helps students understand the innovative endoscopic technology and human resource development in the medical and engineering fields. 新型コロナウィルスの感染拡大により、学校現場の一斉休校などの要請にてICTの整備 にともなうオンライン教育が文部科学省により推進された。そこで本研究は、小学生に 遠隔地での医療内視鏡に関する内容のオンライン授業を通して、医工学分野の革新的な 医療内視鏡の技術の理解及び人材育成に関する教育効果を授業前・後でのアンケート調 査から明らかとしたので報告する。
A Review of Board Level IT Governance: A Taxonomy to Inform the Quality of Financial Reports

Financial reports are important documents that stakeholders never ignore, and Information Technology (IT) is utilized throughout firms to increase operation efficiency.However, prior researches studies Information Technology Governance (ITG) that relates to cybersecurity risks, thus, this paper takes step further to reviews board level ITG literature that is relevant to Corporate Governance (CG) and discuss how it impacts on the quality of financial reports.This paper systematically presents a taxonomy of research encompassing the focus areas: performance, cybersecurity losses, more accurate earnings forecasts, lower audit fees based upon prior studies in ITG over the period 2010-2020.The main finding is that results reveal a lack of board level ITG under CG research and a lack of the relation between board level ITG and financial results.Therefore, this paper discusses research perspectives and identify avenues for future research in last part, research perspectives and future research could focus on how to improve CG based on IT and the financial consequences of CG with IT.
Malaysian Second Language Learners’ Linguistic Accuracy in Writing

Second language production, and proficiency are measured based on the Complexity, Accuracy, and Fluency dimensions.During their learning process, learners master the multicomponent dimensions also known as the CAF construct.Second language learners often master one component at a time and prior research on the present demography has been minimal.Hence, the present research looks at language accuracy among Malaysian English as a second language (ESL) learners' written language.The population chosen for this study is tertiary level students who have undergone formal English language classes in schools for 11 years.In the present setting, they are undergoing prerequisite English classes in the local university that they attend.Following six weeks of classes where learners revise the grammatical and writing aspects of the lesson, they then complete a writing task which is then analyzed for errors and language accuracy.Six major areas are identified as inaccuracy in learners' language production.20 writing tasks are analyzed through error analysis, and this process reveals that learners' accuracies are apparent in their vocabulary, first language/mother tongue interference, sentence structure, subject-verb agreement, tenses and word form.Ten out the total 20 documents contained severe accuracy errors that affect coherence, comprehension and maturity in the written product.Learners who struggle with accuracy often have unsuitable, long-winded and/or incorrect products be it in listening, speaking, reading, or in the present study's scenario, writing.Further research in the CAF construct is encouraged especially among the same demography to address the issue of poor language mastery and proficiency.
Real World Offline Reinforcement Learning with Realistic Data Source

Offline reinforcement learning (ORL) holds great promise for robot learning due to its ability to learn from arbitrary pre-generated experience. However, current ORL benchmarks are almost entirely in simulation and utilize contrived datasets like replay buffers of online RL agents or sub-optimal trajectories, and thus hold limited relevance for real-world robotics. In this work (Real-ORL), we posit that data collected from safe operations of closely related tasks are more practical data sources for real-world robot learning. Under these settings, we perform an extensive (6500+ trajectories collected over 800+ robot hours and 270+ human labor hour) empirical study evaluating generalization and transfer capabilities of representative ORL methods on four real-world tabletop manipulation tasks. Our study finds that ORL and imitation learning prefer different action spaces, and that ORL algorithms can generalize from leveraging offline heterogeneous data sources and outperform imitation learning. We release our dataset and implementations at URL: https://sites.google.com/view/real-orl
Relational Graph Convolutional Neural Networks for Multihop Reasoning: A Comparative Study

Multihop Question Answering is a complex Natural Language Processing task that requires multiple steps of reasoning to find the correct answer to a given question. Previous research has explored the use of models based on Graph Neural Networks for tackling this task. Various architectures have been proposed, including Relational Graph Convolutional Networks (RGCN). For these many node types and relations between them have been introduced, such as simple entity co-occurrences, modelling coreferences, or "reasoning paths" from questions to answers via intermediary entities. Nevertheless, a thoughtful analysis on which relations, node types, embeddings and architecture are the most beneficial for this task is still missing. In this paper we explore a number of RGCN-based Multihop QA models, graph relations, and node embeddings, and empirically explore the influence of each on Multihop QA performance on the WikiHop dataset.
Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models

We introduce equi-tuning, a novel fine-tuning method that transforms (potentially non-equivariant) pretrained models into group equivariant models while incurring minimum $L_2$ loss between the feature representations of the pretrained and the equivariant models. Large pretrained models can be equi-tuned for different groups to satisfy the needs of various downstream tasks. Equi-tuned models benefit from both group equivariance as an inductive bias and semantic priors from pretrained models. We provide applications of equi-tuning on three different tasks: image classification, compositional generalization in language, and fairness in natural language generation (NLG). We also provide a novel group-theoretic definition for fairness in NLG. The effectiveness of this definition is shown by testing it against a standard empirical method of fairness in NLG. We provide experimental results for equi-tuning using a variety of pretrained models: Alexnet, Resnet, VGG, and Densenet for image classification; RNNs, GRUs, and LSTMs for compositional generalization; and GPT2 for fairness in NLG. We test these models on benchmark datasets across all considered tasks to show the generality and effectiveness of the proposed method.
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories

Natural agents can effectively learn from multiple data sources that differ in size, quality, and types of measurements. We study this heterogeneity in the context of offline reinforcement learning (RL) by introducing a new, practically motivated semi-supervised setting. Here, an agent has access to two sets of trajectories: labelled trajectories containing state, action and reward triplets at every timestep, along with unlabelled trajectories that contain only state and reward information. For this setting, we develop and study a simple meta-algorithmic pipeline that learns an inverse dynamics model on the labelled data to obtain proxy-labels for the unlabelled data, followed by the use of any offline RL algorithm on the true and proxy-labelled trajectories. Empirically, we find this simple pipeline to be highly successful -- on several D4RL benchmarks~\cite{fu2020d4rl}, certain offline RL algorithms can match the performance of variants trained on a fully labelled dataset even when we label only 10\% of trajectories which are highly suboptimal. To strengthen our understanding, we perform a large-scale controlled empirical study investigating the interplay of data-centric properties of the labelled and unlabelled datasets, with algorithmic design choices (e.g., choice of inverse dynamics, offline RL algorithm) to identify general trends and best practices for training RL agents on semi-supervised offline datasets.
Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles

Heterogeneous Face Recognition (HFR) refers to matching face images captured in different domains, such as thermal to visible images (VIS), sketches to visible images, near-infrared to visible, and so on. This is particularly useful in matching visible spectrum images to images captured from other modalities. Though highly useful, HFR is challenging because of the domain gap between the source and target domain. Often, large-scale paired heterogeneous face image datasets are absent, preventing training models specifically for the heterogeneous task. In this work, we propose a surprisingly simple, yet, very effective method for matching face images across different sensing modalities. The core idea of the proposed approach is to add a novel neural network block called Prepended Domain Transformer (PDT) in front of a pre-trained face recognition (FR) model to address the domain gap. Retraining this new block with few paired samples in a contrastive learning setup was enough to achieve state-of-the-art performance in many HFR benchmarks. The PDT blocks can be retrained for several source-target combinations using the proposed general framework. The proposed approach is architecture agnostic, meaning they can be added to any pre-trained FR models. Further, the approach is modular and the new block can be trained with a minimal set of paired samples, making it much easier for practical deployment. The source code and protocols will be made available publicly.
GULP: a prediction-based metric between representations

Comparing the representations learned by different neural networks has recently emerged as a key tool to understand various architectures and ultimately optimize them. In this work, we introduce GULP, a family of distance measures between representations that is explicitly motivated by downstream predictive tasks. By construction, GULP provides uniform control over the difference in prediction performance between two representations, with respect to regularized linear prediction tasks. Moreover, it satisfies several desirable structural properties, such as the triangle inequality and invariance under orthogonal transformations, and thus lends itself to data embedding and visualization. We extensively evaluate GULP relative to other methods, and demonstrate that it correctly differentiates between architecture families, converges over the course of training, and captures generalization performance on downstream linear tasks.
Blockchain for Unmanned Underwater Drones: Research Issues, Challenges, Trends and Future Directions

Underwater drones have found a place in oceanography, oceanic research, bathymetric surveys, military, surveillance, monitoring, undersea exploration, mining, commercial diving, photography and several other activities. Drones housed with several sensors and complex propulsion systems help oceanographic scientists and undersea explorers to map the seabed, study waves, view dead zones, analyze fish counts, predict tidal wave behaviors, aid in finding shipwrecks, building windfarms, examine oil platforms located in deep seas and inspect nuclear reactors in the ship vessels. While drones can be explicitly programmed for specific missions, data security and privacy are crucial issues of serious concern. Blockchain has emerged as a key enabling technology, amongst other disruptive technological enablers, to address security, data sharing, storage, process tracking, collaboration and resource management. This study presents a comprehensive review on the utilization of Blockchain in different underwater applications, discussing use cases and detailing benefits. Potential challenges of underwater applications addressed by Blockchain have been detailed. This work identifies knowledge gaps between theoretical research and real-time Blockchain integration in realistic underwater drone applications. The key limitations for effective integration of Blockchain in real-time integration in UUD applications, along with directions for future research have been presented.
SUMBot: Summarizing Context in Open-Domain Dialogue Systems

In this paper, we investigate the problem of including relevant information as context in open-domain dialogue systems. Most models struggle to identify and incorporate important knowledge from dialogues and simply use the entire turns as context, which increases the size of the input fed to the model with unnecessary information. Additionally, due to the input size limitation of a few hundred tokens of large pre-trained models, regions of the history are not included and informative parts from the dialogue may be omitted. In order to surpass this problem, we introduce a simple method that substitutes part of the context with a summary instead of the whole history, which increases the ability of models to keep track of all the previous relevant information. We show that the inclusion of a summary may improve the answer generation task and discuss some examples to further understand the system's weaknesses.
Auto-Encoding Goodness of Fit

For generative autoencoders to learn a meaningful latent representation for data generation, a careful balance must be achieved between reconstruction error and how close the distribution in the latent space is to the prior. However, this balance is challenging to achieve due to a lack of criteria that work both at the mini-batch (local) and aggregated posterior (global) level. Goodness of fit (GoF) hypothesis tests provide a measure of statistical indistinguishability between the latent distribution and a target distribution class. In this work, we develop the Goodness of Fit Autoencoder (GoFAE), which incorporates hypothesis tests at two levels. At the mini-batch level, it uses GoF test statistics as regularization objectives. At a more global level, it selects a regularization coefficient based on higher criticism, i.e., a test on the uniformity of the local GoF p-values. We justify the use of GoF tests by providing a relaxed $L_2$-Wasserstein bound on the distance between the latent distribution and target prior. We propose to use GoF tests and prove that optimization based on these tests can be done with stochastic gradient (SGD) descent on a compact Riemannian manifold. Empirically, we show that our higher criticism parameter selection procedure balances reconstruction and generation using mutual information and uniformity of p-values respectively. Finally, we show that GoFAE achieves comparable FID scores and mean squared errors with competing deep generative models while retaining statistical indistinguishability from Gaussian in the latent space based on a variety of hypothesis tests.
BLADERUNNER: Rapid Countermeasure for Synthetic (AI-Generated) StyleGAN Faces

StyleGAN is the open-sourced TensorFlow implementation made by NVIDIA. It has revolutionized high quality facial image generation. However, this democratization of Artificial Intelligence / Machine Learning (AI/ML) algorithms has enabled hostile threat actors to establish cyber personas or sock-puppet accounts in social media platforms. These ultra-realistic synthetic faces. This report surveys the relevance of AI/ML with respect to Cyber & Information Operations. The proliferation of AI/ML algorithms has led to a rise in DeepFakes and inauthentic social media accounts. Threats are analyzed within the Strategic and Operational Environments. Existing methods of identifying synthetic faces exists, but they rely on human beings to visually scrutinize each photo for inconsistencies. However, through use of the DLIB 68-landmark pre-trained file, it is possible to analyze and detect synthetic faces by exploiting repetitive behaviors in StyleGAN images. Project Blade Runner encompasses two scripts necessary to counter StyleGAN images. Through PapersPlease acting as the analyzer, it is possible to derive indicators-of-attack (IOA) from scraped image samples. These IOAs can be fed back into AmongUs acting as the detector to identify synthetic faces from live operational samples. The opensource copy of Blade Runner may lack additional unit tests and some functionality, but the open-source copy is a redacted version, far leaner, better optimized, and a proof-of-concept for the information security community. The desired end-state will be to incrementally add automation to stay on-par with its closed-source predecessor.
Improving Question Answering with Generation of NQ-like Questions

Question Answering (QA) systems require a large amount of annotated data which is costly and time-consuming to gather. Converting datasets of existing QA benchmarks are challenging due to different formats and complexities. To address these issues, we propose an algorithm to automatically generate shorter questions resembling day-to-day human communication in the Natural Questions (NQ) dataset from longer trivia questions in Quizbowl (QB) dataset by leveraging conversion in style among the datasets. This provides an automated way to generate more data for our QA systems. To ensure quality as well as quantity of data, we detect and remove ill-formed questions using a neural classifier. We demonstrate that in a low resource setting, using the generated data improves the QA performance over the baseline system on both NQ and QB data. Our algorithm improves the scalability of training data while maintaining quality of data for QA systems.
Automatic Real-time Vehicle Classification by Image Colour Component Based Template Matching

Selection of appropriate template matching algorithms to run effectively on real-time low-cost systems is always major issue. This is due to unpredictable changes in image scene which often necessitate more sophisticated real-time algorithms to retain image consistency. Inefficiency of low cost auxiliary hardware and time limitations are the major constraints in using these sorts of algorithms. The real-time system introduced here copes with these problems utilising a fast running template matching algorithm, which makes use of best colour band selection. The system uses fast running real-time algorithms to achieve template matching and vehicle classification at about 4 frames /sec. on low-cost hardware. The colour image sequences have been taken by a fixed CCTV camera overlooking a busy multi-lane road
A Game Theoretical vulnerability analysis of Adversarial Attack

In recent times deep learning has been widely used for automating various security tasks in Cyber Domains. However, adversaries manipulate data in many situations and diminish the deployed deep learning model's accuracy. One notable example is fooling CAPTCHA data to access the CAPTCHA-based Classifier leading to the critical system being vulnerable to cybersecurity attacks. To alleviate this, we propose a computational framework of game theory to analyze the CAPTCHA-based Classifier's vulnerability, strategy, and outcomes by forming a simultaneous two-player game. We apply the Fast Gradient Symbol Method (FGSM) and One Pixel Attack on CAPTCHA Data to imitate real-life scenarios of possible cyber-attack. Subsequently, to interpret this scenario from a Game theoretical perspective, we represent the interaction in the Stackelberg Game in Kuhn tree to study players' possible behaviors and actions by applying our Classifier's actual predicted values. Thus, we interpret potential attacks in deep learning applications while representing viable defense strategies in the game theory prospect.
Application-Driven AI Paradigm for Hand-Held Action Detection

In practical applications especially with safety requirement, some hand-held actions need to be monitored closely, including smoking cigarettes, dialing, eating, etc. Taking smoking cigarettes as example, existing smoke detection algorithms usually detect the cigarette or cigarette with hand as the target object only, which leads to low accuracy. In this paper, we propose an application-driven AI paradigm for hand-held action detection based on hierarchical object detection. It is a coarse-to-fine hierarchical detection framework composed of two modules. The first one is a coarse detection module with the human pose consisting of the whole hand, cigarette and head as target object. The followed second one is a fine detection module with the fingers holding cigarette, mouth area and the whole cigarette as target. Some experiments are done with the dataset collected from real-world scenarios, and the results show that the proposed framework achieve higher detection rate with good adaptation and robustness in complex environments.
Interpreting Neural Policies with Disentangled Tree Representations

The advancement of robots, particularly those functioning in complex human-centric environments, relies on control solutions that are driven by machine learning. Understanding how learning-based controllers make decisions is crucial since robots are often safety-critical systems. This urges a formal and quantitative understanding of the explanatory factors in the interpretability of robot learning. In this paper, we aim to study interpretability of compact neural policies through the lens of disentangled representation. We leverage decision trees to obtain factors of variation [1] for disentanglement in robot learning; these encapsulate skills, behaviors, or strategies toward solving tasks. To assess how well networks uncover the underlying task dynamics, we introduce interpretability metrics that measure disentanglement of learned neural dynamics from a concentration of decisions, mutual information and modularity perspective. We showcase the effectiveness of the connection between interpretability and disentanglement consistently across extensive experimental analysis.