Reviewerly/exHarmony huggingface dataset:
Two Distillation Perspectives Based on Tanimoto Coefficient

Knowledge distillation is a process which uses a complex teacher model to guide the training of a smaller student model. The output from the teacher model’s last hidden layer is commonly used as knowledge. This paper proposes a novel method on how to use this knowledge to guide the student model. Tanimoto coefficient is used to measure the length and angle information of sample pair. Knowledge distillation is conducted from two perspectives. The first perspective is to calculate a Tanimoto similarity matrix for every training sample pair within a batch for the teacher model, and then use this matrix to guide the student model. The second perspective is to calculate a Tanimoto diversity between the teacher model and the student model for every training sample and minimize the diversity. On FOOD101 and VOC2007 datasets, the top1-accuracy and mAP obtained by our method is higher than that of existing distillation methods.
Logout

Logout is probably not something that you think about very often, if ever. It might not even be on any of your project’s planned sprints, but it should be. Implementing logout can be more complex to design and test in some cases than login. In this chapter we’ll explain the complexities of logout and considerations for implementing it in your application.
3D Hand Pose Recognition Over a Wide Area Using Two Omnidirectional Cameras with Field-of-view Division

In this paper, we propose a method for 3D hand pose recognition using two omnidirectional cameras that enables users to perform proximity gesture operations in the entire range in front of a display. In this method, we use a technique of FOV division, which transforms an input omnidirectional camera image into multiple perspective projection images with virtually rotating the camera, in order to avoid distortion in the peripheral area of a perspective projection image. We also introduce two-stage skeleton detection, which uses the results of whole-body skeleton detection for determining the range of hand skeleton detection to reduce false detections. We evaluated the detection rate with and without FOV division. The detection rate with FOV division is higher than that without FOV division, and complex poses can be detected. In addition, the effectiveness of the two-stage skeleton detection was confirmed by comparing the results with and without the two-stage detection.
Systems Leadership in the Transformation of Higher Education

The importance of systems leadership in transforming worldwide complex systems has never been greater than that experienced throughout the COVID-19 pandemic. Higher education – defined as education beyond high school typically resulting in an earned post-secondary degree – is one example of a global reaching complex system directly impacted by the pandemic. The 2020–2021 higher education transformation during the pandemic coincided with a period of renewed focus on ongoing social challenges, external pressures to address a widening skills gap, increasing uncertainties in students’ enrollments and funding, and a slow but continuing decade-long digital transformation. This chapter starts by examining examples of emerging higher education systems leadership with respect to three decision areas: (1) closing physical access to higher education globally, (2) removing operations or offerings or moving services to remote mediums, and (3) changing near-term and strategic growth priorities. Higher education leadership guidance is provided through the lenses of 14 areas of systems thinking skills (ASTS) framework: multiple perspectives; different scales of abstraction; interconnections, intrarelationships, and dependencies; dynamic behavior; stock, flow, and delay; feedback; non-linear relationships; mental and formal models; system structure and boundary; conceptual modeling; prospection and prediction; hypothetical and inferential consideration; paradoxical and ambiguity tolerance; and creativity. The chapter reviews the impact of leadership decision-making on higher education’s main stakeholders – students, administrators/administration, faculty, and employers/community (SAFE) – using examples from around the world. The chapter concludes with a discussion on a systems leadership-focused path forward to shift higher education from a reactive to a proactive systematic transformation.
A Fusion-Based Approach to Generate and Classify Synthetic Cancer Cell Image Using DCGAN and CNN Architecture

The most talked about disease of our era, cancer, has taken many lives, and most of them are due to late prognosis. Statistical data shows around 10 million people lose their lives per year due to cancer globally. With every passing year, the malignant cancer cells are evolving at a rapid pace. The cancer cells are mutating with time, and it's becoming much more dangerous than before. In the chapter, the authors propose a DCGAN-based neural net architecture that will generate synthetic blood cancer cell images from fed data. The images, which will be generated, don't exist but can be formed in the near future due to constant mutation of the virus. Afterwards, the synthetic image is passes through a CNN net architecture which will predict the output class of the synthetic image. The novelty in this chapter is that it will generate some cancer cell images that can be generated after mutation, and it will predict the class of the image, whether it's malignant or benign through the proposed CNN architecture.
《燕行錄》的成書過程

This study examines the creation process of the Yeonhaengrok. The Yeonhaengrok or a collection of diplomatic mission records consists of observation and records. Observation can be further divided into direct observation and indirect observation. A diplomatic envoy and his entourage as well as individuals they interacted with function as a knowledge-information provider or conveyor. Records includes those written by Bandang (messenger), Jajegungwan (informal attendants made up of family members of an envoy), and fellow intellectuals, and previous diplomatic mission records and various literatures. It shows the subjects of recording the Yeonhaengrok. The Yeonhaengrok serve to collect and organize knowledge and information obtained during the missions while expanding and reproducing new knowledge and information.
A multi branch feature learning approach for fine-grained visual recognition

Fine-grained visual classification (FGVC) has always been subjecting to large intra-class variances and fine inter-class variances. How to seek discriminative features as many as possible is key factor for FGVC. Traditional FGVC methods tended to adopt strongly supervised learning with part/bounding box annotations to locate objects region, which design is relatively complicated and training process is not end-to-end. Most existing weakly supervised approaches, based on attention mechanism, usually focus on the most salient feature so that overlook other potential distinguishing features and do not fully utilize multi dimensions information. In this paper, we introduce a three branches framework made up by global branch, which is responsible for extracting whole image's features, cropping branch that focuses on the object region and hidden branch aiming at hiding high response and mining potential distinct features for classification. In addition, we adopt a multi-branch feature learning strategy to reduce the intra-class loss caused by cross-entropy function and further improve the performance of the proposed network. Furthermore, our method only uses image level labels and can be trained in an end-to-end manner. Experiment results on three datasets show that our method can get competitive performance compared with some state-of-the-art methods.
Monocular Camera Video Based Reconstruction of 3D human model

This paper addresses a method to obtain an accurate 3D human body model and a photorealistic free-view image of an arbitrary person from a monocular camera video. Recent works has shown that it is possible to reconstruct a human model at a level of detail from a single image. However, inferring a complete 3D human model from a network model will be ill-posed if rely on a single photograph of a person. In order to reasonably infer the 3D human model, we propose method based on implicit field representation to integrate the information of video frames by a set of structured latent code. The core of our method is to construct the implicit field by relatively sparse structured latent code. Meanwhile, align the vertices of the parametric human model and structured latent code to the same coordinate system. Extensive experimental results on monocular datasets demonstrate the effectiveness of our approach in generating accurate 3D human models. Our method utilizes a monocular camera to obtain a 3D model which enables consumers create their personality digital model.
‘I am very <i>emo</i>’

The contemporary expansion of English is becoming remarkably rapid and exceptionally global (Ostler, 2011). In present-day China, English has gained unprecedented popularity, fueled by the nation's current political and social development (Bolton &amp; Graddol, 2012). There is a notable trend of bilingual education using English as a medium of instruction in Chinese schools (Wei, 2013). Therefore, an increasing number of Chinese are enthusiastic about learning and using English in communication. With the active participation of youths, ‘Internet English’ has been used widely in social networking spaces. The practice of ‘English mixing’ in various Chinese homegrown social networking sites has become the most remarkable intranational use of English in today's mainland China (Zhang, 2012). Interestingly, youngsters 1 often create novel meanings when using languages on the Internet as ‘teenagers are well-known for introducing innovations into language, and indeed are generally regarded as prime agents of language change’ (Palacios Martínez, 2018: 363). Many linguistic studies have dealt with the mechanisms of the evolution of word meanings in past decades (Kachru, 1983; Qin &amp; Guo, 2020; Tan, 2009; Yang, 2005). Much evidence indicates that meanings and usages of words are variable and composite, and may turn out differently depending on how words are used in contexts.
Handwritten Signature Identification and Fraud Detection using Deep Learning and Computer Vision

Secure authentication plays a major role in ensuring the identity of a person. Everyone has an individual and unique signature, which is used as their personal identification in all their legal transactions. Even though a lot of things got digitalized, traditional signatures are still used in a lot of places, such as check payments and government offices, and they still rely on a human manually verifying them. As the customer base keeps increasing, this will become a greater and greater problem in the near future. So, forgery detection plays a key role in reducing these kinds of overhead. Manual verification is not only difficult to check if two signatures are the same but also very time-consuming. Pandemic further made people do tasks digitally, which also included uploading their own signatures digitally. This increases the urgency of implementing a system to identify and verify the user’s signature. A lot of existing techniques are patented now, and few are less effective. This paper proposes a method to pre-process the signature to make verification simple as well as use methods like the Convolution Neural Network (CNN), Scale Invariant Feature Transform (SIFT), Oriented FAST and Robust BRIEF (ORB), and Mean Square Error (MSE) to identify forged signatures and compare the results obtained with various parameters.
Dual-Head Fusion Network for Image Enhancement

Image enhancement algorithms have made great progress recently. However, most existing methods tend to construct a uniform enhancer for the color transformation of all pixels and ignore the local context information which is significant for photographs, causing unsatisfactory results. To solve these issues, we propose a novel dual-head fusion network for image enhancement, which synthetically considers both global scenario and local content information. Our network consists of four lightweight modules. We first develop a dual-head feature extraction module to extract the global condition vector and spatial context map. After that, we propose a context-aware retouching module and a global color rendering module to generate latent results. Finally, we employ the spatial attention based fusion module to adaptively aggregate the latent results. Experiments on public datasets show that our method consistently achieves the best results compared with SOTA methods both quantitatively and qualitatively.
Variational Approximation for Adaptive Extended Target Tracking in Clutter With Random Matrix

As a computationally efficient framework, the random matrix approach can simultaneously estimate the kinematic state and extent of the extended target. For the extended target tracking in clutter, the measurement origin uncertainty, the unknown detection probability and measurement rate challenge the existing methods. In this article, we propose the Beta Gamma Gaussian inverse Wishart filter based on the variational approximation. The proposed method takes the association event as an unknown parameter with a prior distribution. Following a more rigorous path, we derive an approximate posterior distribution of the unknowns using the analytical techniques of variational Bayesian inference. The joint estimations of the kinematic state, extent, detection probability, measurement rate and association events are obtained in this work. The simulation results illustrate the effectiveness and robustness of the proposed approach.
Can Language Models Be Used in Multistep Commonsense Planning Domains?

Transformer-based language models have recently been the focus of much attention, due to their impressive performance on myriad natural language processing (NLP) tasks. One criticism when evaluating such models on problems such as commonsense reasoning is that the benchmarking datasets may not be challenging or global enough. In response, task environments involving some kind of multistep planning, have emerged as a more stringent, and useful, evaluation paradigm. ScienceWorld is one such environment that has weaker dependence on language itself (compared to core commonsense reasoning). In the original publication, ScienceWorld problems proved difficult to solve even for a reasonably advanced language model. This paper demonstrates that, while true for the hardest version of the problem, even first-generation models like BERT can achieve good performance on many interesting intermediate problems within ScienceWorld. Our results, in addition to proposing a more practical methodology and metrics for evaluating language models on multistep planning domains involving commonsense reasoning, also suggest that language models are still likely to be an essential component of (rather than completely orthogonal to) a more comprehensive approach.
Multi-target Detection and Classification for Intelligent Vehicle Based on Deep Learning

A multi-target detection and classification method was presented for intelligent vehicle. This method is based on deep learning. Multi-target detection and classification under the traffic road scene is hard to realize since the complexity of road traffic conditions and the diversity of demand. By finding a suitable deep learning algorithm, each detection and recognition task is completed. Based on You Only Look Once (YOLO) v5 algorithm, the task of road multi-target detection is completed. Based on Deep Simple Online and Real Time Tracking (Deep SORT) algorithm, the target tracking of YOLOv5 detection results is realized. Based on Multi-Task Convolutional neural network (MTCNN), the license plate recognition is realized. The built system is adopted to guarantee target detection and tracking of input video. The experiment results are presented and the results show the effectiveness and high accuracy of the target detection and tracking.
Epilogue: Generic, Inattentive, Asocial

In this epilogue, I discuss central issues related to the pervasive dissemination and immense cultural impact of ambient sound on most parts of modern auditory culture. In particular, I identify and explore, in a critical perspective, some of the key aesthetico-political implications of ambient sound for the general production of subjectivity and sociality in contemporary culture. This involves, among other things, how ambient sound imbues the sonic environment with a generic quality that dissolves the sense of spatiotemporal particularity; how it entails and promotes a basic aesthetics of inattention; and how it involves the production of a detached, encapsulated and in principle asocial subjectivity.
A Hybrid Post-Quantum Cryptography Driven Key Exchange Scheme for Cloud Computing Environments

In connection with Post-Quantum Cryptography (PQC), it is indispensable to upgrade cloud data security schemes and also key exchange schemes. The former is for data security while the latter is for secure key exchange in untrusted channels. This paper focuses on proposing a hybrid key exchange scheme for secure key exchange to withstand PQC threats. The hybrid key exchange scheme is known as Elliptic Curve Supersingular Isogeny Diffie–Hellman (ECSIDH) which is made up of two existing key agreement schemes known as Elliptic-Curve Diffie–Hellman (ECDH) and Supersingular Isogeny Diffie–Hellman (SIDH). By combining these two schemes, the proposed scheme becomes highly secure as it strengthens PQC candidate SIDH with classical primitive ECDH. SIDH is a key exchange scheme that could establish secret key, over untrusted channel, between two parties. It was designed to be one of the PQC candidates but failed to withstand PQC attack scenarios. Elliptic Curve Supersingular Isogeny Diffie–Hellman (ECSIDH) is designed to overcome this problem by combining SIDH and ECDH.
Transforming Geospatial Data into RDF

chapter Share on Transforming Geospatial Data into RDF Author: George Mandilaras View Profile Authors Info & Claims Geospatial Data Science: A Hands-on Approach for Building Geospatial Applications Using Linked Data TechnologiesJune 2023Pages 151–160https://doi.org/10.1145/3581906.3581916Published:14 June 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Fusion-Based Activity Recognition

In the previous chapter, we have discussed that SEH can be employed as source of context information and energy simultaneously. However, it may face problems during low light conditions such as at night to harvest sufficient energy to power a sensor node. Therefore, in order to enhance the harvested energy and context recognition performance, a fused signal which employs both solar and kinetic energy harvesting signals can be explored.
Corpus Development for Pronoun Substitute and Address Term Study

The development of a corpus of pronoun substitute and address term annotations is aimed at exploring the actual use of these language elements in dialogue sentences. To create the corpus, dialogue sentences were extracted from a collection of popular TV drama scripts and novels, representing both genres in order to provide a comprehensive overview of conversation settings. One key feature of the corpus is the inclusion of a detailed definition of kinship relations. This was done to gain a better understanding of the usage and meaning of kinship terms in dialogue. To ensure the accuracy of the corpus annotations, each sentence is annotated by two annotators, and the results are compared. This approach helps to minimize errors and personal biases, leading to a high-quality corpus that accurately reflects the usage of pronoun substitutes and address terms in natural language conversations. The comparison of annotations can also highlight any discrepancies, which can then be resolved through further discussion and collaboration between the annotators. This rigorous process helps to ensure that the corpus is a reliable and trustworthy resource. The resulting corpus will provide a wealth of information about these language elements and the way they are used in real-life dialogues, making it a useful resource for researchers in this field.
Data Visualization Using R Language

Data visualization is the graphical and pictorial display of data. Data visualization and statistical graphics are often conceived of as latest advancement in statistics and relatively moves hand in hand. Process of creating a research objective, acquiring data, cleaning the data, picking a chart type and tool, evaluating information, and ultimately building a chart are the most basic steps in visual analytics. It enables viewers to view analytics in a visual format. In fact, the pictorial representation makes it easier for viewers to grasp difficult concepts or spot new patterns. Data visualization tools make it simple to study and evaluate themes, outliers, and relationships in data while using graphic elements like charts, graphs, and maps. This is a self-explanatory paper which provides an overview of the data visualization techniques. This paper will first make you understand what basically we mean about the data representation. This will be followed by, illustrating some techniques like heatmap, bar chart, line plot, etc., to precise data in a smart and intelligible way. This paper contains strategies for visualization of data using the statistical software program R.
Recover Fractional Lévy Flight Bat Algorithm for Optimization in Artificial Neural Networks Model

The bat algorithm (BA) is a heuristic optimization method based on the loudness and the rate of ultrasonic bursts. It has been simulated bats use echolocation for foraging. It has been proven that this algorithm has a good ability to search for the global optimum, but it suffers from slow searching speed in the last iterations. This paper proposes a recover fractional Lévy flight bat algorithm (rFLFBA) to improve BA by adjusting the inertia weight of velocity update instead of fractional Lévy flight from fractional Lévy flight bat algorithm (FLFBA). rFLFBA is deployed as learning method for artificial neural networks (ANNs) to increase the efficiencies in avoiding the local minima problem and the slow convergence rate of bat algorithms. The results are compared with a Particle Swarm optimization and Gravitational Search Algorithm (PSOGSA), Grey Wolf optimizer (GWO), and FLFBA learning methods for ANNs. The resulting classification rate of ANNs trained with GWO, PSOGSA, and FLFBA is also examined. The simulation results show that rFLFBA better than GWO and PSOGSA for training ANNs in terms of convergence curve. It is also proven that an ANN trained with rFLFBA has better accuracy than one trained with FLFBA.