Reviewerly/exHarmony huggingface dataset:
CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee

In safe reinforcement learning (SRL) problems, an agent explores the environment to maximize an expected total reward and meanwhile avoids violation of certain constraints on a number of expected total costs. In general, such SRL problems have nonconvex objective functions subject to multiple nonconvex constraints, and hence are very challenging to solve, particularly to provide a globally optimal policy. Many popular SRL algorithms adopt a primal-dual structure which utilizes the updating of dual variables for satisfying the constraints. In contrast, we propose a primal approach, called constraint-rectified policy optimization (CRPO), which updates the policy alternatingly between objective improvement and constraint satisfaction. CRPO provides a primal-type algorithmic framework to solve SRL problems, where each policy update can take any variant of policy optimization step. To demonstrate the theoretical performance of CRPO, we adopt natural policy gradient (NPG) for each policy update step and show that CRPO achieves an $\mathcal{O}(1/\sqrt{T})$ convergence rate to the global optimal policy in the constrained policy set and an $\mathcal{O}(1/\sqrt{T})$ error bound on constraint satisfaction. This is the first finite-time analysis of primal SRL algorithms with global optimality guarantee. Our empirical results demonstrate that CRPO can outperform the existing primal-dual baseline algorithms significantly.
Video Question Answering with Phrases via Semantic Roles

Video Question Answering (VidQA) evaluation metrics have been limited to a single-word answer or selecting a phrase from a fixed set of phrases.These metrics limit the VidQA models' application scenario.In this work, we leverage semantic roles derived from video descriptions to mask out certain phrases, to introduce VidQAP which poses VidQA as a fillin-the-phrase task.To enable evaluation of answer phrases, we compute the relative improvement of the predicted answer compared to an empty string.To reduce the influence of language-bias in VidQA datasets, we retrieve a video having a different answer for the same question.To facilitate research, we construct ActivityNet-SRL-QA and Charades-SRL-QA and benchmark them by extending three vision-language models.We perform extensive analysis and ablative studies to guide future work.Code and data are public.
Optimization of Underwater Marker Detection Based on YOLOv3

The research on the detection and recognition technology of marker is of great significance for some underwater operations, such as marine resource exploration, underwater robot operation and so on. The existing image processing methods can effectively detect and recognize the markers in the air. Nevertheless, in the underwater environment, the complex imaging environment of the ocean leads to serious degradation of underwater images obtained by the optical vision system. Due to the lack of effective information for object recognition, the severely degraded underwater images increases the difficulty of detection and recognition of underwater objects. With the development of high-tech underwater imaging equipment, the quality of underwater images has been improved to a certain extent, but there are still some phenomena such as color fading, low contrast and blurred details. Solutions to overcome these problems are important for the exploration of the ocean. In this paper, we introduce a deep learning model to optimize the performance of detection, and make a unique marker dataset for the application scene of our experiment. We first use the deep learning network to pre-train the marker images in the air. Next, we use the underwater marker images for fine-tuning. Finally, after the target marker is detected, the traditional image processing method is used to recognize the marker. Experimental results show that the optimization method we proposed achieves better performance on the dataset.
A Novel Biometric Approach for Facial Image Recognition using Deep Learning Techniques

In this paper, age and gender specific user identity recognition is implemented using facial features for biometric application.A novel approach has been developed by extracting Local Binary Pattern (LBP) and Gray Level Co-Occurrence Matrix (GLCM) image features which effectively represents facial and skin regions of user to classify facial images based on various age groups and gender.These extracted features are classified by Convolution Neural Network (CNN), Region-based CNN (RCNN) and Fast RCNN, namely three popular Deep Learning Classification techniques using IMDB wikicrop facial dataset.Experimentations using CNN classifier achieved best result of 96.4% accuracy in contrast to other two classifier results.
Invertible Color-to-Grayscale Conversion by Using Clustering and Reversible Watermarking

Invertible color-to-grayscale conversion is a method that embeds the color information into the corresponding grayscale image and extracts the color information to reconstruct the color image when necessary. In this paper, we propose an efficient method by using K-means clustering to generate a color palette and its corresponding grayscale image, and further making use of a reversible watermarking technique to embed the color palette into the grayscale image. For purpose of integrality authentication, the grayscale image is hashed as part of the embedded information before the embedding. In the process of reconstructing the color image, the color palette can be extracted correctly and the grayscale image can be recovered without any loss. Experimental results have shown that the proposed method can provide satisfactory performance.
Research on English Teaching Process Supported by Network Multimedia Technology in Higher Vocational Colleges

Abstract The utilization of network multimedia tech in the process of vocational-academy English teaching can build an interactive English teaching form, enhance students’ English learning initiative and release their motivation of autonomous learning, so it has important research value. Based on this, this paper first analyses the current situation and problems of English teaching in vocational-academy colleges, then studies the vocational-academy English teaching mode supported by network multimedia tech, and finally gives the utilization strategies of network multimedia tech in vocational-academy English teaching.
Multi-category Bangla News Classification using Machine Learning Classifiers and Multi-layer Dense Neural Network

Online and offline newspaper articles have become an integral phenomenon to our society.News articles have a significant impact on our personal and social activities but picking a piece of an appropriate news article is a challenging task for users from the ocean of sources.Recommending the appropriate news category helps find desired articles for the readers but categorizing news article manually is laborious, sluggish and expensive.Moreover, it gets more difficult when considering a resource-insufficient language like Bengali which is the fourth most spoken language of the world.However, very few approaches have been proposed for categorizing Bangla news articles where few machine learning algorithms were applied with limited resources.In this paper, we accentuate multiple machine learning approaches including a neural network to categorize Bangla news articles for two different datasets.News articles have been collected from the popular Bengali newspaper Prothom Alo to build Dataset I and dataset II has been gathered from the famous machine learning competition platform Kaggle.We develop a modified stop-word set and apply it in the preprocessing stage which leads to significant improvement in the performance.Our result shows that the Multi-layer Neural network, Naïve Bayes and support vector machine provide better performance.Accuracy of 94.99%, 94.60%, 95.50% has been achieved for SVM, Logistic regression and Multi-layer dense neural network, respectively.
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message

The shuffle model of differential privacy has attracted attention in the literature due to it being a middle ground between the well-studied central and local models. In this work, we study the problem of summing (aggregating) real numbers or integers, a basic primitive in numerous machine learning tasks, in the shuffle model. We give a protocol achieving error arbitrarily close to that of the (Discrete) Laplace mechanism in the central model, while each user only sends $1 + o(1)$ short messages in expectation.
Information security cultural differences among health care facilities in Indonesia

Health information security (IS) breaches are increasing with the use of information technology for health care services, and a strong security culture is important for driving employees' information asset protection behavior.This study aimed to analyze differences in information security cultures (ISCs) across health care providers based on factors drawn from the ISC model.We used twelve factors to measure the ISCs of health care providers. This research applied a survey method with the Kruskal-Wallis H Test and the Mann-Whitney U Test as data analysis techniques. We collected the data through a questionnaire distributed to 470 employees of health care facilities (i.e. hospitals, community health centers, and primary care clinics) in Indonesia.The results revealed the differences between health care provider types for 9 of the 12 security culture factors. Top management support, change management, and knowledge were the differentiating factors between all types of health care providers. Organizational culture and security compliance only differed in primary care clinics. Meanwhile, security behavior, soft issues and workplace independence, information security policies, training, and awareness only differed in hospitals.The results indicated that each type of health care provider required different approaches to develop an ISC considering the above factors. They provided insight for top management to design suitable programs for cultivating ISCs in their institutions.
Lexical Semantic Change Discovery

While there is a large amount of research in the field of Lexical Semantic Change Detection, only few approaches go beyond a standard benchmark evaluation of existing models.In this paper, we propose a shift of focus from change detection to change discovery, i.e., discovering novel word senses over time from the full corpus vocabulary.By heavily fine-tuning a type-based and a token-based approach on recently published German data, we demonstrate that both models can successfully be applied to discover new words undergoing meaning change.Furthermore, we provide an almost fully automated framework for both evaluation and discovery.
Supervised Learning in Neural Networks: Feedback-Network-Free Implementation and Biological Plausibility

The well-known backpropagation learning algorithm is probably the most popular learning algorithm in artificial neural networks. It has been widely used in various applications of deep learning. The backpropagation algorithm requires a separate feedback network to back propagate errors. This feedback network must have the same topology and connection strengths (weights) as the feed-forward network. In this article, we propose a new learning algorithm that is mathematically equivalent to the backpropagation algorithm but does not require a feedback network. The elimination of the feedback network makes the implementation of the new algorithm much simpler. The elimination of the feedback network also significantly increases biological plausibility for biological neural networks to learn using the new algorithm by means of some retrograde regulatory mechanisms that may exist in neurons. This new algorithm also eliminates the need for two-phase adaptation (feed-forward phase and feedback phase). Hence, neurons can adapt asynchronously and concurrently in a way analogous to that of biological neurons.
DSXplore: Optimizing Convolutional Neural Networks via Sliding-Channel Convolutions

As the key advancement of the convolutional neural networks (CNNs), depthwise separable convolutions (DSCs) are becoming one of the most popular techniques to reduce the computations and parameters size of CNNs meanwhile maintaining the model accuracy. It also brings profound impact to improve the applicability of the compute- and memory-intensive CNNs to a broad range of applications, such as mobile devices, which are generally short of computation power and memory. However, previous research in DSCs are largely focusing on compositing the limited existing DSC designs, thus, missing the opportunities to explore more potential designs that can achieve better accuracy and higher computation/parameter reduction. Besides, the off-the-shelf convolution implementations offer limited computing schemes, therefore, lacking support for DSCs with different convolution patterns.To this end, we introduce, DSXplore, the first optimized design for exploring DSCs on CNNs. Specifically, at the algorithm level, DSXplore incorporates a novel factorized kernel-sliding-channel convolution (SCC), featured with input-channel overlapping to balance the accuracy performance and the reduction of computation and memory cost. SCC also offers enormous space for design exploration by introducing adjustable kernel parameters. Further, at the implementation level, we carry out an optimized GPU-implementation tailored for SCC by leveraging several key techniques, such as the input-centric backward design and the channel-cyclic optimization. Intensive experiments on different datasets across mainstream CNNs show the advantages of DSXplore in balancing accuracy and computation/parameter reduction over the standard convolution and the existing DSCs.
In-House Developed Surface-Guided Repositioning and Monitoring System to Complement In-Room Patient Positioning System for Spine Radiosurgery

Purpose: This study aimed to develop a surface-guided radiosurgery system customized for a neurosurgery clinic that could be used as an auxiliary system for improving the accuracy, monitoring the movements of patients while performing hypofractionated radiosurgery, and minimizing the geometric misses.Methods: RGB-D cameras were installed in the treatment room and a monitoring system was constructed to perform a three-dimensional (3D) scan of the body surface of the patient and to express it as a point cloud.This could be used to confirm the exact position of the body of the patient and monitor their movements during radiosurgery.The image from the system was matched with the computed tomography (CT) image, and the positional accuracy was compared and analyzed in relation to the existing system to evaluate the accuracy of the setup. Results:The user interface was configured to register the patient and display the setup image to position the setup location by matching the 3D points on the body of the patient with the CT image.The error rate for the position difference was within 1-mm distance (min, 一0.21 mm; max, 0.63 mm).Compared with the existing system, the differences were found to be as follows: x=0.08 mm, y=0.13 mm, and z=0.26 mm. Conclusions:We developed a surface-guided repositioning and monitoring system that can be customized and applied in a radiation surgery environment with an existing linear accelerator.It was confirmed that this system could be easily applied for accurate patient repositioning and intertreatment motion monitoring.
SMS Spam Detection Through Skip-gram Embeddings and Shallow Networks

The drastic decrease in mobile SMS costs turned phone users more prone to spam messages, usually with unwanted marketing or questionable content.As such, researchers have proposed different methods for detecting SMS spam messages.This paper presents a technique for embedding SMS messages into vector spaces that is suitable for spam detection.The proposed approach relies on mining patterns that are relevant for distinguishing spam from legitimate messages.A subset of those patterns is used to construct a function that maps text messages into a multidimensional vector space.The extracted patterns are represented as skip-grams of token attributes, where a skip-gram can be seen as a generalization of the n-gram model that allows a distance greater than one between matched tokens in the text.We evaluate the proposed approach using the generated vectors for spam classification on the UCI Spam Collection dataset.The experiments showed that our method combined with shallow networks reached accuracy that is competitive with state-of-the-art approaches.
An Ontology-Based Concept for Meta AutoML

Automated machine learning (AutoML) supports ML engineers and data scientists by automating tasks like model selection and hyperparameter optimization. A number of AutoML solutions have been developed, open-source and commercial. We propose a concept called OMA-ML (Ontology-based Meta AutoML) that combines the strengths of existing AutoML solutions by integrating them (meta AutoML). OMA-ML is based on a ML ontology that guides the meta AutoML process. It supports multiple user groups, with and without programming skills. By combining the strengths of AutoML solutions, it supports any number of ML tasks and ML libraries.
A Decade of Reoccurring Software Weaknesses

The Common Weakness Enumeration community publishes an aggregate metric to calculate the ”most dangerous software errors.” However, the equation highly biases frequency over exploitability and impact. We provide a metric to mitigate this bias and discuss the most significant weaknesses.
Real-time UVMS torque distribution algorithm based on weighting matrix

This study presents a real-time algorithm for even distributing the torque burden on the parallel manipulator with an autonomous underwater vehicle (AUV) through the cooperation of the AUV and manipulator. For the redundant resolution of the underwater vehicle manipulator system (UVMS), we used the weighting matrix of the weighted pseudo inverse for kinematic and dynamic modeling. We made dynamic and kinematic modeling using the force distribution characteristics of parallel manipulators. Using the parallel manipulator's model, the weighting matrix was changed every second to share the manipulator torque with the AUV. The Taguchi method was used to reduce the calculation time for real-time calculation and to perform valve rotation operations with as little torque as possible even in an underwater environment where it is difficult to determine any cause of errors. To demonstrate the effectiveness of this algorithm, we experimented with valve rotation in water using the UVMS. Analysis of the experimental results revealed that the manipulator torque load was greatly reduced due to the AUV load distribution.
Factors affecting organizational adoption and acceptance of computer-based security awareness training tools

Purpose As insiders remain to be a main reason behind security breaches, effective information security awareness campaigns become critical in protecting organizations from security incidents. The purpose of this paper is to identify factors that influence organizational adoption and acceptance of computer-based security awareness training tools. Design/methodology/approach The paper uses content analysis of online reviews of the top ten computer-based security awareness training tools that received Gartner peer insights Customers’ Choice 2019 award. Findings This study identifies nine critical adoption and success factors. These are synthesized into a conceptual framework based on the technology–organization–environment framework. The findings reveal that technological, organizational and environmental factors come into play in adoption decisions but with varying degrees of importance. Practical implications This study highlights key factors that technology vendors should take into consideration when designing computer-based security awareness training tools to increase adoption rates. Originality/value This research offers a novel contribution to the literature on information security awareness delivery methods by identifying key factors that influence organizational adoption and acceptance of computer-based security awareness training tools. Those factors were identified using content analysis of online reviews, which is a new methodological approach to the information security awareness literature.
A Survey of Methods for Detection and Correction of Noisy Labels in Time Series Data

Mislabeled data in large datasets can quickly degrade the performance of machine learning models. There is a substantial base of work on how to identify and correct instances in data with incorrect annotations. However, time series data pose unique challenges that often are not accounted for in label noise detecting platforms. This paper reviews the body of literature concerning label noise and methods of dealing with it, with a focus on applicability to time series data. Time series data visualization and feature extraction techniques used in the denoising process are also discussed.
A real-time multi-vehicle tracking framework in intelligent vehicular networks

In this paper, we provide a new approach for intelligent traffic transportation in the intelligent vehicular networks, which aims at collecting the vehicles' locations, trajectories and other key driving parameters for the time-critical autonomous driving's requirement. The key of our method is a multi-vehicle tracking framework in the traffic monitoring scenario. Our proposed framework is composed of three modules: multi-vehicle detection, multi-vehicle association and miss-detected vehicle tracking. For the first module, we integrate self-attention mechanism into detector of using key point estimation for better detection effect. For the second module, we apply the multi-dimensional information for robustness promotion, including vehicle re-identification (Re-ID) features, historical trajectory information, and spatial position information For the third module, we re-track the miss-detected vehicles with occlusions in the first detection module. Besides, we utilize the asymmetric convolution and depth-wise separable convolution to reduce the model's parameters for speed-up. Extensive experimental results show the effectiveness of our proposed multi-vehicle tracking framework.
Operationalizing a National Digital Library: The Case for a Norwegian Transformer Model.

In this work, we show the process of building a large-scale training set from digital and digitized collections at a national library. The resulting Bidirectional Encoder Representations from Transformers (BERT)-based language model for Norwegian outperforms multilingual BERT (mBERT) models in several token and sequence classification tasks for both Norwegian Bokm{\aa}l and Norwegian Nynorsk. Our model also improves the mBERT performance for other languages present in the corpus such as English, Swedish, and Danish. For languages not included in the corpus, the weights degrade moderately while keeping strong multilingual properties. Therefore, we show that building high-quality models within a memory institution using somewhat noisy optical character recognition (OCR) content is feasible, and we hope to pave the way for other memory institutions to follow.