Reviewerly/exHarmony huggingface dataset:
A Piloting Study of Measuring Effectiveness of Virtual Reality in Understanding a New Concept in Educational Support Systems

In this paper, we report a piloting study before to confirm the effectiveness of Virtual Reality (VR) on education, especially grasping a new concept. Quantum programming is a new field of programming which is basically unknown to conventional programmers. Therefore, we select quantum programming as a topic in which we check the effectiveness of VR. We develop two education support systems on quantum programming, one with VR and the other without VR. Subjects use both systems and we compare the results. There are two limitaions of this study. One is that we cannot say the experiment condition is carefully controlled because the two systems are different at many parts, not only the part we want to test. The other is the number of subjects for the experiment is very small. So we need an additional study with improvement the limitations. Even with the liitations, the results as a pilot study show that VR can be effective in helping people grasp new concepts.
Development of Broadband Criterion for Spatially Distributed Radio Systems Synthesis

An example for designing radio systems with aperture synthesis is given. Considerable attention was paid to the study of the ambiguity functions in such systems and the region of their spatial spectral sensitivity. A new criterion for the broadband radars with spatially distributed antenna systems is proposed. This criterion, in contrast to the existing ones, takes into account the specificities of the antenna system geometry and the operating frequency range of the radar.
Object Detection Via Flexible Anchor Generation

This paper designs a method that can generate anchors of various shapes for the object detection framework. This method has the characteristics of novelty and flexibility. Different from the previous anchors generated by a pre-defined manner, our anchors are generated dynamically by an anchor generator. Specially, the anchor generator is not fixed but learned from the hand-designed anchors, which means that our anchor generator is able to work well in various scenes. In the inference time, the weights of anchor generator are estimated by a simple network where the input is some hand-designed anchor. In addition, in order to make the difference between the number of positive and negative samples smaller, we use an adaptive IOU threshold related to the object size to solve this problem. At the same time, we proved that our proposed method is effective and conducted a lot of experiments on the COCO dataset. Experimental results show that after replacing the anchor generation method in the previous object detectors (such as SSD, mask RCNN, and Retinanet) with our proposed method, the detection performance of the model has been greatly improved compared to before the replacement, which proves our method is effective.
Hybrid Cryptography for Data Security in Wireless Sensor Network

Nowadays, Wireless Sensor Network (WSN) is rapidly growing because it can be applied in various fields such as agriculture, transportation, and mining. Security is one of the main focuses in the development of wireless sensor networks. In this paper, a Hybrid Cryptography security mechanism was implemented using an Advanced Encryption Standard (AES) encryption algorithm to carry out a data security mechanism and a Hash-based Message Authentication Code (HMAC) to ensure data integrity. Security testing is done by performing brute force and man-in-the-middle attacks. The results show that the system can determine the attacker's data changes and overcome the brute force attack carried out for 50 hours. The average time needed to perform the encryption process is 409 μs, and it takes an average of 7304 μs to do the decryption process.
Hindi Text to Speech Conversion

Recognition of handwritten text and its conversion to speech is one of the most challenging areas of research because the most crucial step involved in this process is to identify the text written by different individuals which vary greatly in its appearance. In this paper, Hindi text to speech conversion has been proposed. Here an image of the handwritten Hindi text is preprocessed and segmented. Followed by that the segmented character is recognized and converted to speech. Support Vector Machine and projection method are the two main techniques used in this process.
Safety-Centric and Smart Outdoor Workplace: A New Research Direction and Its Technical Challenges

Li, Zheng Pradena Miquel, Mauricio Pinacho-Davidson, PedroDespite the fact that outside is becoming the frontier of indoor workplaces, a large amount of real-world work like road construction has to be done by outdoor human activities in open areas. Given the promise of the smart workplace in various aspects including productivity and safety, we decided to employ smart workplace technologies for a collaborative outdoor project both to improve the work efficiency and to reduce the worker injuries. Nevertheless, our trials on smart workplace implementation have encountered a few problems ranging from the theoretical confusion among different stakeholders, to the technical difficulties in extending underground devices’ lifespan. This triggers our rethinking of and discussions about “smart workplace.” Eventually, considering the unique characteristics of outdoor work (e.g., more sophisticated workflows and more safety-related situations than office work), we argue that “safety-centric and smart outdoor workplace” deserves dedicated research attentions and efforts under the umbrella discipline of smart environment. In addition, the identified technical challenges can in turn drive different research dimensions of such a distinguishing topic.
Bridging from Crisis to Everyday Life – An Analysis of User Reviews of the Warning App NINA and the COVID-19 Regulation Apps CoroBuddy and DarfIchDas

During a dynamic and protracted crisis such as the COVID-19 pandemic, citizens are continuously challenged with making decisions under uncertainty. In addition to evaluating the risk of their behaviors to themselves and others, citizens also have to consider the most current regulation, which often varies federally and locally and by incidence numbers. Few tools help to stay informed about the current rules. The state-run German multi-hazard warning app NINA incorporated a feature for COVID-19, while two apps, DarfIchDas and CoroBuddy, focus only on COVID-19 regulation and are privately run. To investigate users' expectations, perceived advantages, and gaps as well as the developers' challenges, we analyze recent app store reviews of the apps and developers' replies. We show that the warning app and the COVID-19 regulation apps are evaluated on different terms, that the correctness and portrayal of complex rules are the main challenges and that developers and editors are underusing users' potential for crowdsourcing.
Restoration of Adversarial Examples Using Image Arithmetic Operations

The current development of artificial intelligence is largely based on deep Neural Networks (DNNs). Especially in the computer vision field, DNNs now occur in everything from autonomous vehicles to safety control systems. Convolutional Neural Network (CNN) is based on DNNs mostly used in different computer vision applications, especially for image classification and object detection. The CNN model takes the photos as input and, after training, assigns it a suitable class after setting traceable parameters like weights and biases. CNN is derived from Human Brain's Part Visual Cortex and sometimes performs even better than Haman visual system. However, recent research shows that CNN Models are much vulnerable against adversarial examples. Adversarial examples are input image huts that are deliberately modified, which are imperceptible to humans, but a CNN model strongly misrepresents them. This means that adversarial attacks or examples are a serious threat to deep learning models, especially for CNNs in the computer vision field. The methods which are used to create adversarial examples are called adversarial attacks. We have proposed an easy method that restores adversarial examples, which are created due to different adversarial attacks and misclassified by a CNN model. Our reconstructed adversarial examples are correctly classified by a model again with high probability and restore the prediction of a CNN model. We will also prove that our method is based on image arithmetic operations, simple, single-step, and has low computational complexity. Our method is to reconstruct all types of adversarial examples for correct classification. Therefore, we can say that our proposed method is universal or transferable. The datasets used for experimental evidence are MNIST, FASHION-MNIST, CIFAR10, and CALTECH-101. In the end, we have presented a comparative analysis with other state-of-the methods and proved that our results are better.
Overview of R and RStudio

Abstract Computational statistics is now an increasingly popular method of analysis for researchers that combines a vast array of algorithms, statistical methods, and the power of functional coding. The R programming language, in particular, has benefitted from this development alongside of traditional graphical user interface (GUI) software. Today, it has become the language of choice for empirical researchers. In this chapter, we introduce the R programming language as well as its popular development environment in the form of RStudio. We walk the reader through downloading both the R language and the RStudio integrated development environment (IDE). Then, we discuss the software layout and demonstrate how to interact with the software. Finally, we address creating and managing R projects and scripts, gaining access to documentation and help via various sources. This chapter is not intended as a tutorial on the writing of code in the R programming language. We do, however, provide useful open-source resources for learning R, which can be accessed from the R console RStudio environment.
Nonlinear wireless time synchronization based on variational Bayesian adaptive filtering

Traditional time synchronization optimization method uses the linearized Kalman filter method based on time domain processing to predict and estimate the clock offset, which can solve the problem of time-frequency offset problem to a certain extent. However, in a wireless synchronization system, hardware forwarding delay and wireless channel transmission have a nonlinear effect on the time synchronization system, and it is difficult to achieve high-precision synchronization with the linearized Kalman filter method. This paper uses the UKF (Unscented Kalman Filter)method to construct a synchronization model under non-linear conditions, and analyzes the optimization performance of the synchronization algorithm. For the adaptive process, the variational Bayesian method is used to approximate the joint posterior distribution of the clock synchronization state and the measurement noise, and the synchronization estimation of the clock synchronization state and the measurement noise variance is realized. Combined with the UKF algorithm, the VB-AUKF (Variational Bayesian-Adaptive Unscented Kalman Filter) synchronization optimization algorithm adapted to the nonlinear synchronization model is derived. The experimental results show that the VB-AUKF algorithm effectively reduces the influence of the uncertainty of measurement noise statistics in nonlinear synchronization on the synchronization performance, and improves the synchronization accuracy and robustness of the synchronization system.
Local Belief Dynamics in Network Knowledge Bases

People are becoming increasingly more connected to each other as social networks continue to grow both in number and variety, and this is true for autonomous software agents as well. Taking them as a collection, such social platforms can be seen as one complex network with many different types of relations, different degrees of strength for each relation, and a wide range of information on each node. In this context, social media posts made by users are reflections of the content of their own individual (or local) knowledge bases; modeling how knowledge flows over the network—or how this can possibly occur—is therefore of great interest from a knowledge representation and reasoning perspective. In this article, we provide a formal introduction to the network knowledge base model, and then focus on the problem of how a single agent’s knowledge base changes when exposed to a stream of news items coming from other members of the network. We do so by taking the classical belief revision approach of first proposing desirable properties for how such a local operation should be carried out (theoretical characterization), arriving at three different families of local operators, exploring concrete algorithms (algorithmic characterization) for two of the families, and proving properties about the relationship between the two characterizations (representation theorem). One of the most important differences between our approach and the classical models of belief revision is that in our case the input is more complex, containing additional information about each piece of information.
Analytical and Sentiment based text generative chatbot

Chatbots have existed for a decade and they have been used for many objective specific tasks in the industrial area extensively. The rise of deep neural nets has enhanced further growth of these chatbots. The chatbots are used to understand the sentences and decipher the meaning and continue the conversations depending on the need but they don't capture the emotion of the users. While conversing with a user, especially for the industries which implement these chatbots, maintaining a good interpersonal relationship is everything. With a chatbot which not only provides answers to the user of it but also understands it on a sentimental level, this can be achieved. Through our paper we focus on building a chatbot which generates responses on the basis of the emotions of the users in order to recreate a more sympathetic and human-like aspect towards the user. We have used the feed forward neural network architecture that fits in perfectly for a fast and accurate response. We have used the knowledge on Natural Language Processing to pre-process the data in a suitable format. The model is trained to understand the emotion based on the sentences and understand the meaning with a dataset specifically curated by the authors of the paper. The accuracy achieved on the model was 93.45% with a response generation time of 50 milliseconds.
Optimizing a medical image registration algorithm based on profiling data for real-time performance

Image registration is a commonly task in medical image analysis. Therefore, a significant number of algorithms have been developed to perform rigid and non-rigid image registration. Particularly, the free-form deformation algorithm is frequently used to carry out non-rigid registration task; however, it is a computationally very intensive algorithm. In this work, we describe an approach based on profiling data to identify potential parts of this algorithm for which parallel implementations can be developed. The proposed approach assesses the efficient of the algorithm by applying performance analysis techniques commonly available in traditional computer operating systems. Hence, this article provides guidelines to support researchers working on medical image processing and analysis to achieve real-time non-rigid image registration applications using common computing systems. According to our experimental findings, significant speedups can be accomplished by parallelizing sequential snippets, i.e., code regions that are executed more than once. For the selected costly functions previously identified in the studied free-form deformation algorithm, the developed parallelization decreased the runtime by up to seven times relatively to the related single thread based implementation. The implementations were developed based on the Open Multi-Processing application programming interface. In conclusion, this study confirms that based on the call graph visualization and detected performance bottlenecks, one can easily find and evaluate snippets which are potential optimization targets in addition to throughput in memory accesses.
A Defense Strategy Selection Method Based on the Cyberspace Wargame Model

Network defenders always face the problem of how to use limited resources to make the most reasonable decision. The network attack-defense game model is an effective means to solve this problem. However, existing network attack-defense game models usually assume that defenders will no longer change defense strategies after deploying them. However, in an advanced network attack-defense confrontation, defenders usually redeploy defense strategies for different attack situations. Therefore, the existing network attack-defense game models are challenging to accurately describe the advanced network attack-defense process. To address the above challenges, this paper proposes a defense strategy selection method based on the network attack-defense wargame model. We model the advanced network attack-defense confrontation process as a turn-based wargame in which both attackers and defenders can continuously adjust their strategies in response to the attack-defense posture and use the Monte Carlo tree search method to solve the optimal defense strategy. Finally, a network example is used to illustrate the effectiveness of the model and method in selecting the optimal defense strategy.
Modernn: Towards Fine-Grained Motion Details for Spatiotemporal Predictive Learning

Spatiotemporal predictive learning (ST-PL) aims at predicting the subsequent frames via limited observed sequences, and it has broad applications in the real world. However, learning representative spatiotemporal features for prediction is challenging. Moreover, chaotic uncertainty among consecutive frames exacerbates the difficulty in long-term prediction. This paper concentrates on improving prediction quality by enhancing the correspondence between the previous context and the current state. We carefully design Detail Context Block (DCB) to extract fine-grained details and improve the isolated correlation between upper context state and current input state. We integrate DCB with standard ConvLSTM and introduce Motion Details RNN (MoDeRNN) to capture fine-grained spatiotemporal features and improve the expression of latent states of RNNs to achieve significant quality. Experiments on Moving MNIST and Typhoon datasets demonstrate the effectiveness of the proposed method. MoDeRNN outperforms existing state-of-the-art techniques qualitatively and quantitatively with lower computation loads.
Pyramid Self-attention for Semantic Segmentation

Self-attention is vital in computer vision since it is the building block of Transformer and can model long-range context for visual recognition. However, computing pairwise self-attention between all pixels for dense prediction tasks (e.g., semantic segmentation) costs high computation. In this paper, we propose a novel pyramid self-attention (PySA) mechanism which can collect global context information far more efficiently. Concretely, the basic module of PySA first divides the whole image into \(R \times R\) regions, and then further divides every region into \(G \times G\) grids. One feature is extracted for each grid and then self-attention is applied to the grid features within the same region. PySA keeps increasing R (e.g., from 1 to 8) to harvest more local context information and propagate global context to local regions in a parallel/series manner. Since G can be kept as a small value, the computation complexity is low. Experiment results suggest that as compared with the traditional global attention method, PySA can reduce the computational cost greatly while achieving comparable or even better performance on popular semantic segmentation benchmarks (e.g., Cityscapes, ADE20k). The project code is released at https://github.com/hustvl/PySA.
Question Answering using Web Lists

There are many natural questions that are best answered with a list. We address the problem of answering such questions using lists that occur on the Web, i.e. List Question Answering (ListQA). The diverse formats of lists on the Web makes this task challenging. We describe state-of-the-art methods for list extraction and ranking, that also consider the text surrounding the lists as context. Due to the lack of realistic public datasets for ListQA, we present three novel datasets that together are realistic, reproducible and test out-of-domain generalization. We benchmark the above steps on these datasets, with and without context. On the hardest setting (realistic and out-of-domain), we achieve an end-to-end [email protected] of 51.28% and [email protected] of 79.38%, effectively demonstrating the difficulty of the task and quantifying the immediate opportunity for improvement. We highlight some future directions through error analysis and release the datasets for further research.
Movement Technology: From Kinetic Art to Digital Art

Movement has attracted artists for a very long time, however it became possible to apply movement in art only in the 20th century, when rapid development of technologies coincided with destructing the limits of artistic means to express creative thought. The authors consider kinetic art in terms of using various technologies, applied in artworks, such as the wind force, motors and electricity. Movement in Kinetic Art can be treated not only in a broad philosophical understanding as any change, but also in a phenomenological way as a fact sensually perceived, showing static kinetic sculptures paradoxically. Digital technologies have enormously expanded possibilities to create changing objects, but, at the same time, increased expectations from artwork. Demanding some knowledge of certain scientific laws and their technological use for creation, technological artworks are becoming a technology language. Having refused the role of dumb servants, the technologies can appear as a miracle or curse.
Reflectance Transformation Imaging (RTI) Investigation of Engraved Chalk Plaques from the Stonehenge Region

Newly discovered and previously documented Late Neolithic chalk plaques from the Stonehenge locality have been subjected to new, non-invasive techniques which allow access to previously unseen elements of archaeological evidence. The application of these methods – involving Reflectance Transformation Imaging (RTI) and Polynomial Texture Mapping (PTM) – has revealed detail of the surface preparation and allowed methods and sequence of the compositions to be unpicked, clarifying their complexities. The results reveal a range of approaches to the compositions, some of which demonstrate planning, order, and intention while others include less systematic, rapidly executed sketches. Investigations of lines and surfaces have been made, supplemented by preliminary studies of replicated test pieces, to examine potential implements used in their creation and remark on plaque biographies and surface attrition following manufacture. Furthermore, detail revealed by RTI provides indications of the orientations in which some of the plaques should be viewed and – in one instance – suggests a ‘reflected’ element that may not be entirely abstract. Results from improved radiocarbon determinations place the plaques in the early part of the 3rd millennium bc which, together with identification of individual motifs, allows the plaques and the designs to be reconsidered within the corpus of Neolithic art in the British Isles.