Reviewerly/exHarmony huggingface dataset:
Road Segment Re-Identification in Dashcam Videos

Due to the widespread of dashcams, we will have more videos that capture roads/streets in driving. Consequently, a vast amount of the videos will be available and can be utilized for analyzing road safety and similar purposes. For example, suppose different dashcams can take vehicle/pedestrian traffic at a risky intersection at different timings. In that case, the collection of such videos will effectively recognize the cause of dangerous situations without surveillance camera infrastructure. However, identifying a Road Segment of Interest (RSI) in the video, such as near the intersection region, is challenging as the video frames do not usually include location tags. In this paper, we present a unique approach to attack this challenge. Assume that a video segment, called reference video, captures an RSI. We re-identify the RSI taken in another video (called test video) that captures the roads containing that RSI. By this approach, we can automatically extract the video segment corresponding to RSI from a given test video, using the reference video. We introduce AKAZE features to assess frame-level similarity and develop an algorithm to find frame-by-frame matching between reference and test videos. We have evaluated our method using 10 reference videos that correspond to 10 RSIs, each with 5 test videos. The result has shown that the average frame error distance was only 3.03 in daytime and 4.93 in nighttime, which are sufficiently low to re-identify RSI in the newly obtained test videos.
Smart school strategic design for information systems using framework ward &amp; peppard in smk negeri 1 tampaksiring

Abstract Vocational School (SMK) is a formal institution which is aimed at producing graduates who are ready for the 21 st century workplace. In order to do this, SMK Negeri 1 Tampaksiringhas been improving the quality of the human resources through the implementation of technologyin the learning process. It is started by updating the information systems and other supporting facilities like integrating planning, or blueprint, in regard to school information systems. These become a necessity in order to be aligned with the vision, mission and objective of SMK Negeri 1 Tampaksiring. A model design by Ward and Peppard was applied to analyze school internal conditions and activities inside the school. Furthermore, several tools namely SWOT analysis, value chain analysis, five force factor analysis, McFarrland Grid, and Critical Success Factor (CSF) analysis were used to analyze the purpose. The findings revealed that formation of Information and Communication Technology (ICT) and IT strategy required to design Local Area Network (LAN), e-Learning, and AplikasiBuku Point in its preliminary stage for the success of SMK Negeri 1 Tampaksiring. These results implies that SMK Negeri 1 Tampaksiring has pioneered itself to become a smart school.
An innovative Schematic design to Customize Blowfish for Better Security

with the advent of the internet, protection has become a chief worry. And the past of safety permits a superior expertise of the development of safety tools. The net arrangement itself permitted for plenty of safekeeping fears to arise. Many corporations secure themselves from the net with the help of firewalls and encryption mechanisms. The corporations can create an “intranet” remains connected to the internet and secured it from possible threats. To maintain integrity of data healthier encryption techniques are required for improved safety. It is required to consider various issues such as key size, block size and encryption ratio for better encryption. Encryption is required to provide security of documents that are exported to secondary storage devices (such as pen drive, SD card) so that unauthorized access is not possible. It is very easy to decrypt the document if a key is stored along with the document on secondary storage. Instead of storing the key along with document it is better to generate cryptographic keys from user password while encrypting or decrypting the file. A new scheme to customize blowfish variable parameters (Key generation from user defined password) is proposed and evaluated on the hardness of keys for various encryption standards with proposed algorithm.
Modeling of Patterns with Spectral Data and Time-Varying PSO to Identify Concealed Character Strokes of Historical Manuscripts

Lakshmi, T. R. Vijaya Reddy, Ch. Venkata KrishnaDigitization of palm-leaf manuscripts can be carried out either by flat-bed scanning or camera-capturing. In such digitization processes, distortions due to motion blur, uneven background, low illumination, etc., are obvious. Decomposing the articulated parts of camouflaged text regions embedded in an unstructured background is far reaching. The unstructured background from the manuscripts can be eliminated by digitizing them with a special scanner and by adopting Time-varying PSO. The spectral feature captured with profilometer is robust to identify the concealed strokes of the character. By partitioning the character patterns into fixed and fuzzy boundary regions, the most relevant attributes are extracted for classification. The patterns modeled with the proposed model outperformed compared to conventional approach.
Object-Aware Monocular Depth Prediction With Instance Convolutions

With the advent of deep learning, estimating depth from a single RGB image has recently received a lot of attention, being capable of empowering many different applications ranging from path planning for robotics to computational cinematography. Nevertheless,while the depth maps are in their entirety fairly reliable, the estimates around object discontinuities are still far from satisfactory. This can beattributed to the fact that the convolutional operator naturally aggregates features across object discontinuities, resulting in smooth transitions rather than clear boundaries. Therefore, in order to circumvent this issue, we propose a novel convolutional operator which is explicitly tailored to avoid feature aggregation of different object parts. In particular, our method is based on estimating per-part depth values by means of super-pixels. The proposed convolutional operator, which we dub “Instance Convolution,” then only considers each object part individually on the basis of the estimated super-pixels. Our evaluation with respect to the NYUv2, iBims and KITTI datasets demonstrate the advantages of Instance Convolutions over the classical convolution at estimating depth around occlusion boundaries, while producing comparable results elsewhere. Our code is available at github.com/enisimsar/instance-conv.
Improving the Comprehensive Quality of Business Students by Subject Competition under the Blended Learning Mode Based on the Network Platform

The strategy of “Mass entrepreneurship and innovation initiative” has stimulated the new climax of innovation and entrepreneurship. Based on the needs of national development and the development of the times, this paper discusses the shortcomings of the traditional education model and elaborates how to rely on the subject competition to improve the comprehensive quality of business students under the blended learning mode. With the help of the special software of subject competition, the virtual business environment of the company is simulated on the network platform. Many companies continue to carry out entrepreneurship competition and rank according to their business performance. Through the blended learning mode, students' innovation and entrepreneurship ability and team cooperation ability can be improved.
Movement, Cognition and Learning

This chapter discusses the relationship between movement and mental and cognitive function, as well as the potential for it to be utilized by new technologies. The first section of the chapter presents the cerebral mechanisms responsible for associating physical with mental activity, and discusses examples of this influence on cognitive and emotional processes, as well as learning. The second section focuses on physical activity as an element of human interaction with a computer, including, but not limited to, so-called “exergames”. The subject of physical activity and cognitive functions is presented in the final section of the chapter from the perspective of immersive virtual reality technology—a tool which appears to be highly compelling. The potential of virtual reality stems from it being ideally suited to the study of the phenomenon of motion, and to its relationship with mental functioning. Immersive virtual reality is also a potentially effective motivator for increasing individuals’ physical activity, with a view to improving their mental functioning.
Automating the Choice Between Single or Dual Annotation for Classifier Training

Many emerging digital library applications rely on automated classifiers that are trained using manually assigned labels. Accurately labeling training data for text classification requires either highly trained coders or multiple annotations, either of which can be costly. Previous studies have shown that there is a quality-quantity trade-off for this labeling process, and the optimal balance between quality and quantity varies depending on the annotation task. In this paper, we present a method that learns to choose between higher-quality annotation that results from dual annotation and higher-quantity annotation that results from the use of a single annotator per item. We demonstrate the effectiveness of this approach through an experiment in which a binary classifier is constructed for assigning human value categories to sentences in newspaper editorials.
A Deep Learning Approach for Aspect Sentiment Triplet Extraction in Portuguese

Aspect Sentiment Triplet Extraction (ASTE) is an Aspect-Based Sentiment Analysis subtask (ABSA). It aims to extract aspect-opinion pairs from a sentence and identify the sentiment polarity associated with them. For instance, given the sentence “Large rooms and great breakfast”, ASTE outputs the triplet T = {(rooms, large, positive), (breakfast, great, positive)}. Although several approaches to ASBA have recently been proposed, those for Portuguese have been mostly limited to extracting only aspects without addressing ASTE tasks. This work aims to develop a framework based on Deep Learning to perform the Aspect Sentiment Triplet Extraction task in Portuguese. The framework uses BERT as a context-awareness sentence encoder, multiple parallel non-linear layers to get aspect and opinion representations, and a Graph Attention layer along with a Biaffine scorer to determine the sentiment dependency between each aspect-opinion pair. The comparison results show that our proposed framework significantly outperforms the baselines in Portuguese and is competitive with its counterparts in English.
REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions

Deep Neural Networks (DNNs) are often criticized for being susceptible to adversarial attacks. Most successful defense strategies adopt adversarial training or random input transformations that typically require retraining or finetuning the model to achieve reasonable performance. In this work, our investigations of intermediate representations of a pre-trained DNN lead to an interesting discovery pointing to intrinsic robustness to adversarial attacks. We find that we can learn a generative classifier by statistically characterizing the neural response of an intermediate layer to clean training samples. The predictions of multiple such intermediate-layer based classifiers, when aggregated, show unexpected robustness to adversarial attacks. Specifically, we devise an ensemble of these generative classifiers that rank-aggregates their predictions via a Borda count-based consensus. Our proposed approach uses a subset of the clean training data and a pre-trained model, and yet is agnostic to network architectures or the adversarial attack generation method. We show extensive experiments to establish that our defense strategy achieves state-of-the-art performance on the ImageNet validation set.
Clustering, Separation, and Connection: A Tale of Three Characteristics

In large and complex software development ecosystems, developers collaborate in multiple dimensions. How characteristics of such collaboration vary over time can offer insights about the dynamics of large scale software development. In this paper we have constructed networks of developers who co-comment on and co-change units of code, and analysed the patterns of variation of clustering, connection, and separation between such developers over time, using development data from a large open source system. Though clustering, connection, and separation essentially represent facets of the same collaboration activities, we found them exhibiting distinct time-varying characteristics. The variation of clustering indicate that developers congregate more closely towards the beginning of the project when the architecture of the system is not yet stable and they need to reach out to one another to fulfil their collective responsibilities. However, separation between developers shows a quick rise and then a saturation around a particular value. Developer connection continues rise throughout the observation period. Using time-series analysis, our results allow us to derive insights on the evolutionary trends in large scale software development and can inform the tuning of tools and process towards effective team assembly and governance.
Dealing with a Panic Attack: a Virtual Reality Training Module for Postgraduate Psychology Students

In this paper we present a virtual reality training simulator for postgraduate psychology students. This simulator features an interaction between a clinical psychologist (student) and a patient (virtual agent) suffering from Obsessive Compulsive Disorder (OCD). Our simulation focuses on the form of OCD treatment called “Exposure Therapy”. The traditional way of learning how to perform Exposure Therapy (ET) currently involves watching video recordings and discussing those in the class. In our simulation we conduct an immersive exposure therapy session in VR. This session involves a live interaction with a patient that at one stage triggers a panic attack. Our hypothesis is that the immersive nature of the training session will affect the decision making process of the students so that they are more likely to cease the exposure task than those student participating in a less immersive form of learning (watching a video recording). We also hypothesise that participating in an immersive VR training session is more effective than watching videos, as far as information retention goes.
VRAT: A Proposal of Training Method for Auditory Information Processing Using Virtual Space

Auditory training (ATEx) is an important component of rehabilitation for patients with auditory processing disorder (APD). Computerbased auditory training (CBAT) approaches have been frequently used, but a Japanese version of CBAT is not available at present. Therefore, we have developed Virtual Reality ATEx (VRAT) using Japanese language for Japanese APD patients. Virtual reality (VR) technology was introduced into our VRAT to create a stereophonic sound environment and increase adherence to ATEx. Android smartphones with a head-mounted display (HMD) were used. The requirements for the development of the application were extracted from the relevant literature and expert comments. Unity (Unity Technologies Inc., San Francisco, CA) and the C# programming language were used to create the environment for the VR application. Twenty-one healthy adults and three patients with APD participated in this study. The Speech in Noise Test and Speech Perception in Multiple Talkers Test were used as controls. Participants performed these two auditory tests and used the developed VRAT, and then answered the Intrinsic Motivation Inventory (IMI) to measure Interest/Enjoyment and Value/Usefulness. Interest/Enjoyment score was significantly higher than the AT control in APD patients (p < 0.05). The other scores of the developed VRAT were higher than the controls, suggesting that the developed VRAT may be effective for ATEx in APD patients.
FIR Filter Design Method Based on LASSO to Adjust the Number of Non-zero Coefficients

In this document a new FIR filter design algorithm is proposed. The algorithm gives the designer a set of filters which differ in the approximation of the ideal filter characteristic and the number of none-zero elements. The designer can choose a filter based on proposed filter selection criteria which filter is suitable for the tradeoff between approximation as well as processing power and storage capacity.
Review Of The Application Of Deep Learning In Health Management

As the cutting-edge methods of Health Management (HM), deep learning is getting more and more attention. This paper reviews the application of deep learning algorithms to HM, summarizes the advantages and defects of these algorithms, and proposes three problems to be solved as well as possible solutions. Most importantly, instead of depicting different deep learning methods from different perspectives, like statistics, algebra, topology, this paper gives a universal algebra perspective towards deep learning algorithms.
Feasibility research of fiber optical gyroscope based on entangled photons

With the increasing demand of navigation and positioning services, the accuracy and safety of traditional navigation system have been limiting factor of future application. The development of quantum technology brings hope to the research and development of a new generation of navigation system. In this paper, the application of quantum technologies in fiber optic gyroscope is introduced and analyzed.
The Interactive Effect of Computer Aided Teaching Management Reform of Police Colleges

At present, the comprehensive teaching reform in police colleges is in full swing, but there is a problem that "teaching", "learning" and "management" are separated from each other in the existing teaching reform. From the perspective of system theory, combined with computer aided technology, the distinctive characteristics of education and teaching reform in police colleges, this paper organically integrates the basic elements of "teaching", "learning" and "management" education and teaching reform, and puts forward the computer aided "teaching learning management" trinity joint framework and interactive effect mechanism of teaching reform in police colleges, which reflects the integrity, relevance and hierarchical characteristics. Based on the practical work of Nanjing Forest Police College, this paper puts forward the comprehensive teaching reform strategy of police colleges as follows: with computer aided technology, the reform of teachers' "teaching" should pay attention to the construction of teachers' ethics and style and improve teachers' teaching ability, the reform of students' "learning" should pay attention to the external environment and internal training, and the reform of school "management" should pay attention to the combination of humanism and diversification and loose management.
Invariant subspaces of two-qubit quantum gates and their application in the verification of quantum computers

We present a set of techniques, based on the repeated arbitrary application of cp, cnot, and swap${}^{\ensuremath{\alpha}}$ (power-of-swap) quantum gate operations to an $n$-qubit quantum computer that can be used in its verification. We find isomorphisms between the groups generated by these gate operations and known groups and use techniques from representation theory to determine their invariant subspaces. For the cp operation, we find an isomorphism to the direct product of $n(n\ensuremath{-}1)/2$ cyclic groups of order 2, and determine ${2}^{n}$ one-dimensional invariant subspaces corresponding to the computational-basis vectors. For the cnot operation, we find an isomorphism to $GL(n,2)$, and determine two one-dimensional invariant subspaces and one $({2}^{n}\ensuremath{-}2)$-dimensional invariant subspace. For the swap${}^{\ensuremath{\alpha}}$ operation we find a complex structure of invariant subspaces with varying dimensions and occurrences and present a recursive procedure to construct them. Using knowledge of these invariant subspaces, we propose a hardware verification scheme which tests the correct functioning of a quantum computer.
Paper2vec and Cite2vec Methods for Analyzing Collections of Scientific Publications

Visualizations are used to better understand collections of scientific publications. Various methods of analyzing text collections can be used to build these visualizations. This article discusses two methods Paper2vec and Cite2vec that get vector representations of documents using citation information. To demonstrate a work of these techniques and an example of their application, visualizations were developed, which are described in this paper.
Gas Concentration Prediction in Continuous Monitoring Based on Echo State Network

In this paper, we introduce an echo state network (ESN) model approach for gas concentration estimation using Metal Oxide (MOX) sensors in Open Sampling System (OSS). Our approach focuses on the compensation of the slow response of MOX sensors, while concurrently solving the problem of estimating the gas concentration in OSS. By comparing with SVR algorithm model commonly used in the prediction system, it fully reflects high accuracy of the gas prediction model based on ESN algorithm and the well tracking performance with rapidly changing gases. A prediction model based on ESN can overcome these limitations because of its nonlinear and implicit storage characteristics. In this paper, the concentration and variation of gas mixture are predicted successfully.
Problems about Calculation in Calculating Table in the Tsinghua Bamboo Slips

Abstract At the end of 2013, Zhongxi Book Company published Bamboo Slips of the Warring States Period Reserved by Tsinghua University (4) [1]. In it, the content of Calculating Table is about mathematical calculations, including not only multiplication, but also division and square root. From the perspective of time, Calculating Table is not only earlier than the calculating table in Liye Bamboo Slips of Qin and others that have been discovered in China, but also earlier than the calculating tables of other countries. From the perspective of content, it integrates multiplication, division and square root, which is richer than any other calculating tables. Therefore, Calculating Table in The Tsinghua Bamboo Slips is an important discovery on calculation formulas, and it has a milestone significance in the history of mathematics.
Exploration on the Cultivation of Civil Engineering Computer Aided Drawing Ability Driven by Information Modularization

Applied undergraduate colleges should cultivate talents who can solve practical problems in production. Engineering drawing ability is an important requirement for graduates of civil engineering-related enterprises. Pay attention to the integration of descriptive geometry and professional curriculum system, throughout the four years of university, in order to improve students' three-dimensional logical thinking ability and meet the requirements of enterprises for graduates in terms of drawing ability. According to the curriculum setting of the civil engineering talent training program, extract professional coursework modules, integrate manual drawing and Autocad into it, cultivate drawing ability and improve the efficiency of coursework, and innovate the way to complete the coursework. Transform professional coursework modules into engineering practice, and change learning from passive to active to improve the ability to understand pictures.