Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Virtual Reality and Computer-Aided Design to Evaluate Parts for Additive Manufacturing

Abstract Virtual Reality (VR) has been shown to be an effective assistive tool in the engineering design process, aiding designers in ergonomics studies, data visualization, and manufacturing simulation. Yet there is little research exploring the advantages of VR to assist in the design for the additive manufacturing (DfAM) process. VR may present advantages over traditional computer-aided design (CAD) tools, and these advantages may be more evident as designs become more complex. The following study investigates two types of environments: 1) Immersive Virtual Reality (VR) and 2) Non-Immersive Virtual Reality (CAD) and the advantages that each environment gives to designers to assess parts for additive manufacturing. The two environments are compared to assess potential differences in DfAM decision-making. Participants familiar with DfAM are tasked with evaluating five designs of varying complexity using the Design for Additive Manufacturing Worksheet. Participant scores, evaluation times, and self-reported metrics are recorded and analyzed. Our findings indicate that as part complexity increases, DfAM scores and evaluation times increasingly differ between VR and CAD groups. We found that the VR group evaluates more complex parts at a faster rate, but with a lower accuracy when compared to the CAD group. In evaluating self-reported metrics, both groups were relatively similar; however, the CAD group reported improved confidence in identifying stress concentrations in DfAM parts. Our findings in this research identify VR as a design evaluation tool that enhances evaluation speed which speaks to its efficiency and usability; however, VR in its current form may not present the resolution necessary to identify smaller details when compared to CAD, the more accurate evaluation tool.
Nearest labelset using double distances for multi-label classification

Multi-label classification is a type of supervised learning where an instance may belong to multiple labels simultaneously. Predicting each label independently has been criticized for not exploiting any correlation between labels. In this article we propose a novel approach, Nearest Labelset using Double Distances ( NLDD ), that predicts the labelset observed in the training data that minimizes a weighted sum of the distances in both the feature space and the label space to the new instance. The weights specify the relative tradeoff between the two distances. The weights are estimated from a binomial regression of the number of misclassified labels as a function of the two distances. Model parameters are estimated by maximum likelihood. NLDD only considers labelsets observed in the training data, thus implicitly taking into account label dependencies. Experiments on benchmark multi-label data sets show that the proposed method on average outperforms other well-known approaches in terms of 0/1 loss , and multi-label accuracy and ranks second on the F-measure (after a method called ECC) and on Hamming loss (after a method called RF-PCT ).
Augmented Reality Underpinned Instructional Design (ARUIDs) for Cogno-Orchestrative Load

In modern times the research and technology in the education sector have revolutionized the teaching-learning environment. Traditional methodologies are being supported by tech-savvy practices. These practices demand young learners to be more advanced, and modern teachers to be more efficient. Under k-12 education program, Science, Technology, Engineering and Mathematics (STEM) are the desired domains to be learned and pursued as future professions today. It is, therefore, apparent to prepare young learners in these domains at an early stage. This situation requires an increased mental load on learners as well as on teachers. Modern technology comes to resolve this situation to some extent where computers, Augmented Reality (AR), Virtual reality (VR) gadgets, etc. help both the learners and the teachers in decreasing their mental load. The orchestration load of the teachers and cognitive load of the learners can further be decreased using AR applications. AR can overlay virtual 3D images on real objects and enhance learning in a fun way. This paper introduces the development of Augmented Reality Learning System (ARLS) followed by an Electronics Kit to make young learners understand the basic fundamentals of science such as electricity, current, electric circuits, working of light switch, thermister, light-dependent register (LDR) and capacitor-based circuits. This ARLS makes use of the markers overlaid on Tangible User Interface (TUI) and shows virtual functioning of these electrical/electronic circuits. The combination of TUI and ARLS results the formation of Augmented Reality Underpinned Design Kit (ARUID Kit). Students work individually on this ARUID kit to gain new learning experience of science fundamentals.
Integrating Landsat 7 and 8 data to improve basalt formation classification: A case study at Buon Ma Thuot region, Central Highland, Vietnam

Abstract Cenozoic basalt regions contain various natural resources that can be used for socio-economic development. Different quantitative and qualitative methods have been applied to understand the geological and geomorphological characteristics of basalt formations. Nowadays the integration of remote sensing and geographic information systems (GIS) has become a powerful method to distinguish geological formations. In this paper, authors combined satellite and fieldwork data to analyze the structure and morphology of highland geological formations in order to distinguish two main volcanic eruption episodes. Based on remote sensing analysis in this study, different spectral band ratios were generated to select the best one for basalt classification. Lastly, two spectral combinations (including band ratios 4/3, 6/2, 7/4 in Landsat 8 and 3/2, 5/1, 7/3 in Landsat 7) were chosen for the Maximum Likelihood classification. The final geological map based on the integration of Landsat 7 and 8 outcomes shows precisely the boundary of the basalt formations with the accuracy up to 93.7%. This outcome contributed significantly to the correction of geological maps. In further studies, authors suggest the integration of Landsat 7 and 8 data in geological studies and natural resource and environmental management at both local and regional scales.
At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks?

Background: Recent developments have made it possible to accelerate neural networks training significantly using large batch sizes and data parallelism. Training in an asynchronous fashion, where delay occurs, can make training even more scalable. However, asynchronous training has its pitfalls, mainly a degradation in generalization, even after convergence of the algorithm. This gap remains not well understood, as theoretical analysis so far mainly focused on the convergence rate of asynchronous methods. Contributions: We examine asynchronous training from the perspective of dynamical stability. We find that the degree of delay interacts with the learning rate, to change the set of minima accessible by an asynchronous stochastic gradient descent algorithm. We derive closed-form rules on how the learning rate could be changed, while keeping the accessible set the same. Specifically, for high delay values, we find that the learning rate should be kept inversely proportional to the delay. We then extend this analysis to include momentum. We find momentum should be either turned off, or modified to improve training stability. We provide empirical experiments to validate our theoretical findings.
Ranking Policy Gradient

Sample inefficiency is a long-lasting problem in reinforcement learning (RL). The state-of-the-art uses action value function to derive policy while it usually involves an extensive search over the state-action space and unstable optimization. Towards the sample-efficient RL, we propose ranking policy gradient (RPG), a policy gradient method that learns the optimal rank of a set of discrete actions. To accelerate the learning of policy gradient methods, we establish the equivalence between maximizing the lower bound of return and imitating a near-optimal policy without accessing any oracles. These results lead to a general off-policy learning framework, which preserves the optimality, reduces variance, and improves the sample-efficiency. We conduct extensive experiments showing that when consolidating with the off-policy learning framework, RPG substantially reduces the sample complexity, comparing to the state-of-the-art.
Decidable subtyping for path dependent types

Path dependent types have long served as an expressive component of the Scala programming language. They allow for the modelling of both bounded polymorphism and a degree of nominal subtyping. Nominality in turn provides the ability to capture first class modules. Thus a single language feature gives rise to a rich array of expressiveness. Recent work has proven path dependent types sound in the presence of both intersection and recursive types, but unfortunately typing remains undecidable, posing problems for programmers who rely on the results of type checkers. The Wyvern programming language is an object oriented language with path dependent types, recursive types and first class modules. In this paper we define two variants of Wyvern that feature decidable typing, along with machine checked proofs of decidability. Despite the restrictions, our approaches retain the ability to encode the parameteric polymorphism of Java generics along with many idioms of the Scala module system.
A Perception-based Quality Metric for Omnidirectional Images

A key component in VR systems is omnidirectional content, which provides 360-degree views of scenes. At a given time, only a portion of a content, called viewport, is displayed using head-mounted displays. In this work, we develop an objective quality metric, called Weighted Viewport PSNR (W-VPSNR), for omnidirectional content of spatially-varying quality. Through experiment results, it is found that the W-VPSNR metric well correlates with the subjective quality scores.
Sentence Similarity Measurement for Bengali Abstractive Text Summarization

Text summarization is a massive research area in natural language processing. It reduces the larger text and provided the prime meaning of a text document. Find the meaning of the larger text needed of a proper text analysis which gives a better text summarizer. Abstractive text summarizer gives a summary which can present or not present in the text document. The machine produces a text summary after learning from the human given summary. Sentence similarity is a way to judge a better text summarizer. It is exploring the similarity between sentences or words. This paper we discuss several methods of sentence similarity and proposed a method for identifying a better Bengali abstractive text summarizer. We used human given summary and machine response summary sentences for similarity measurement where both sentences contain a Bengali short text. There are several approaches to English sentences similarity measurement, and we applied some of the approaches for similarity measure for our Bengali text which give a satisfying result. For our given methods we collect data from online and social media and create a summary of those texts. After creating a summary pre-processing this text and generate a summary from our abstractive text summarization model. All summary sentence similarity measurement cases using the method provided an effective value and optimal result.
PURE: Using Verified Remote Attestation to Obtain Proofs of Update, Reset and Erasure in low-End Embedded Systems

Remote Attestation ( RA) is a security service that enables a trusted verifier ( Vrf) to measure current memory state of an untrusted remote prover ( Prv). If correctly implemented, RA allows Vrf to remotely detect if Prv's memory reflects a compromised state. However, RA by itself offers no means of remedying the situation once P rv is determined to be compromised. In this work we show how a secure RA architecture can be extended to enable important and useful security services for low-end embedded devices. In particular, we extend the formally verified RA architecture, VRASED, to implement provably secure software update, erasure, and system-wide resets. When (serially) composed, these features guarantee to Vrf that a remote Prv has been updated to a functional and malware-free state, and was properly initialized after such process. These services are provably secure against an adversary (represented by malware) that compromises Prv and exerts full control of its software state. Our results demonstrate that such services incur minimal additional overhead (0.4% extra hardware footprint, and 100-s milliseconds to generate combined proofs of update, erasure, and reset), making them practical even for the lowest-end embedded devices, e.g., those based on MSP430 or AVR ATMega micro-controller units (MCUs). All changes introduced by our new services to VRASED trusted components are also formally verified.
Design and Research of Intelligent Tutor System Based on Natural Language Processing

Through the investigation and study of on-line learning platform in China, it was found that the on-line question answering system between learners and teachers on the on-line learning platform which was almost paralyzed, because it cannot give timely feedback to the questions raised by learners. In this paper, we summarize and classify some common the system of service, explain their principles, differentiate their advantages and disadvantages, summarize their need and the framework. On this basis, we design an intelligent assistant teaching system which uses natural language processing technology as developing-designing tool. Based on deep learning and natural language processing (NLP) technology, this intelligent assistant teaching system is built for on-line learning platforms, which is the first application of intelligent customer service on on-line learning platforms. It can provide timely feedback for learners and improve their enthusiasm for learning.
Design and Implementation of a Lightweight Authentication Framework for the Internet of Things (IoT)

Authentication is a critical issue in the rapidly growing IoT environment, considering that the network security is dependent on how properly the authentication process is done. Cyber attackers are turning their attention from traditional computers to IoT devices in smart homes for malicious activities, such as compromising smart home owner privacy and using smart home devices as stepping stones for large scale attacks through botnets. Many IoT devices run with weak or manufacturer-supplied default passwords, which make them highly vulnerable to various forms of credential theft attacks. Furthermore, the hackers may use stolen identities or counterfeit identities of rogue devices to connect to the IoT network. In this paper, we provide a lightweight authentication framework that uses dynamic identities and temporary keys for IoT nodes in smart homes. We implement the framework using the Objective Modular Network Testbed in C++ (OMNeT++). The implementation and evaluation show that the framework is efficient and can protect smart devices against well-known security attacks.
A New Method for Improving Content-Based Image Retrieval using Deep Learning

In this work we will show a novel method for improving the performance of content-based image retrieval using a deep neural network. The main focus of the method is training the distance function using the deep neural network and transfer learning. An existing pretrained network for image classification is used as a basis. One of the method's benefits is the fact that the existing network is not retrained and features for the distance function are the neural network's layers which were trained and stored before. The method is tested with the publicly available VGG19 deep neural network. Obtained results are comparable or in some cases better than the state of the art methods with a similar approach.
YOLO and K-Means Based 3D Object Detection Method on Image and Point Cloud

LIDAR based 3D object detection and classication tasks are essential for automated driving(AD). A LIDAR sensor can provide the 3D point cloud data reconstruction of the the environment surrounding a vehicle. However, the detection in a 3D point cloud still needs a strong algorithmic challenge to bring the AD to real life. This paper consists of three parts focuses on the realize the 3D object detection function. (1)LIDAR-camera calibration. (2)YOLO-based detection and Point Cloud extraction, and (3) k-means-based point cloud segmentation. In our research, we used a camera that can capture an image to achieve real-time 2D object detection by using YOLO to transfer a bounding box to a node whose function was to make 3D object detection on LIDAR-based point cloud data. A high-speed 3D object recognition function in GPU can be achieved by comparing whether the 2D coordinates transferred from the 3D points are in the object bounding box or not and by performing a k-means clustering
An Efficient Hierarchical Near-Duplicate Video Detection Algorithm Based on Deep Semantic Features

With the rapid development of the Internet and multimedia technology, the amount of multimedia data on the Internet is escalating exponentially, which has attracted much research attention in the field of Near-Duplicate Video Detection (NDVD). Motivated by the excellent performance of Convolutional Neural Networks (CNNs) in image classification, we bring the powerful discrimination ability of the CNN model to the NDVD system and propose a hierarchical detection method based on the derived deep semantic features from the CNN models. The original CNN features are firstly extracted from the video frames, and then a semantic descriptor and a labels descriptor are obtained respectively based on the basic content unit. Finally, a hierarchical matching scheme is proposed to promote fast near-duplicate video detection. The proposed approach has been tested on the widely used CC_WEB_VIDEO dataset, and has achieved state-of-the-art results with the mean Average Precision (mAP) of 0.977.
Best Parameter Selection Of Rabin-Karp Algorithm In Detecting Document Similarity

Text mining is usually used to detect document similarities and plagiarism. The field of education is one area that is prone to plagiarism. Plagiarism can kill someone's creativity because this action does not require energy and does not have to think hard. Therefore, the act of plagiarism must be prevented from causing harm to various parties. By using matching strings on documents, it can be used to detect plagiarism. One method that can be used is Rabin-Karp Algorithm, but in several studies that have been done the researchers did not test the k-gram value and database value, in theory, this would affect the performance of the Rabin-Karp Algorithm. Therefore in this study, the selection of k-gram values and prime bases was conducted to determine the effect on the performance of the Rabin-Karp Algorithm. The results showed that the selection of gram values and prime bases affected the processing time in testing the data and the similarity values of the documents being tested. In this study the value of k = 5 on k-gram has the fastest time for the testing process, both testing with multiple data 25 and testing the data for all amounts of data the number is 300.
An Application of Virtual Reality with Android Device for Tourism Assistant

The rapid development of science and technology has been influential and has significance for human life. It can be seen from the various facilities offered and provided. With the advancement of technology and information, it also makes people connect with other parties as if they are no longer limited by time and place. In general, applications that implement Virtual Reality technology aim to provide an overview of an environment and information to users. The use of virtual reality technology in the field of tourism has not been widely used. This research was made to build interactive applications that can display information and tourist objects in the form of 360° images visually to facilitate users in viewing tourist attractions in the Majalengka area. The methodology used in this study is the MDLC methodology (Multimedia Development Life Circle). This application was built using C # programming language with Unity 3D software. The final result of this research is the making of a Virtual Reality application for the introduction of a tourist attraction in the Majalengka Regency West Java province that can be used by users to view 360-degree information and attractions using smartphones that already support virtual reality technology and are paired with virtual reality glasses.
Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations.

Deep neural networks have shown excellent performances in many real-world applications. Unfortunately, they may show Hans-like behavior---making use of confounding factors within datasets---to achieve high performance. In this work we introduce the novel setting of explanatory interactive learning (XIL) and illustrate its benefits on a plant phenotyping research task. XIL adds the scientist into the training loop such that she interactively revises the original model via providing feedback on its explanations. Our experimental results demonstrate that XIL can help avoiding Clever Hans moments in machine and encourages (or discourages, if appropriate) trust into the underlying model.
Estimating Lossy Compressibility of Scientific Data Using Deep Neural Networks

Simulation based scientific applications generate increasingly large amounts of data on high-performance computing (HPC) systems. To allow data to be stored and analyzed efficiently, data compression is often utilized to reduce the volume and velocity of data. However, a question often raised by domain scientists is the level of compression that can be expected so that they can make more informed decisions, balancing between accuracy and performance. In this letter, we propose a deep neural network based approach for estimating the compressibility of scientific data. To train the neural network, we build both general features as well as compressor-specific features so that the characteristics of both data and lossy compressors are captured in training. Our approach is demonstrated to outperform a prior analytical model as well as a sampling based approach in the case of a biased estimation, i.e., for SZ. However, for the unbiased estimation (i.e., ZFP), the sampling based approach yields the best accuracy, despite the high overhead involved in sampling the target dataset.
Detection of compromised accounts for online social networks based on a supervised analytical hierarchy process

In recent years, the security of online social networks (OSNs) has become an issue of widespread concern. Searching and detecting compromised accounts in OSNs is crucial for ensuring the security of OSN platforms. In this study, the authors proposed a new method of detecting compromised accounts based on a supervised analytical hierarchy process (SAHP). First, they considered the expression habits of a user to present the profile features of a user more comprehensively than previous research. Next, the information gain ratio was combined with the analytical hierarchy process algorithm to calculate the weight of each feature. Finally, a detection decision was taken, and varying thresholds were used to obtain different detection results. The experimental results showed that the accuracy and precision of the SAHP were 81.7 and 96.4%, respectively. The results indicated that the new method improved upon the previously established COMPA (detecting compromised accounts on social networks) methods for detecting compromised accounts.