Reviewerly/exHarmony huggingface dataset:
Hierarchical Object Detection and Classification Using SSD Multi-Loss

When merging existing similar datasets, it would be attractive to benefit from a higher detection rate of objects and the additional partial ground-truth samples for improving object classification. To this end, a novel CNN detector with a hierarchical binary classification system is proposed. The detector is based on the Single-Shot multibox Detector (SSD) and inspired by the hierarchical classification used in the YOLO9000 detector. Localization and classification are separated during training, by introducing a novel loss term that handles hierarchical classification in the loss function (SSD-ML). We experiment with the proposed SSD-ML detector on the generic PASCAL VOC dataset and show that additional super-categories can be learned with minimal impact on the overall accuracy. Furthermore, we find that not all objects are required to have classification label information as classification performance only drops from $$73.3\%$$ to $$70.6\%$$ while $$60\%$$ of the label information is removed. The flexibility of the detector with respect to the different levels of details in label definitions is investigated for a traffic surveillance application, involving public and proprietary datasets with non-overlapping class definitions. Including classification label information from our dataset raises the performance significantly from $$70.7\%$$ to $$82.2\%$$ . The experiments show that the desired hierarchical labels can be learned from the public datasets, while only using box information from our dataset. In general, this shows that it is possible to combine existing datasets with similar object classes and partial annotations and benefit in terms of growth of detection rate and improved class categorization performance.
Implementation and Error Analysis of MNIST Handwritten Dataset Classification Based on Bayesian Decision Classifier

Abstract In recent years, with the continuous development of computer technology, pattern recognition technology has gradually entered people’s life and learning, and people’s demand for pattern recognition technology is also growing.In order to adapt to people’s life and study, the application of pattern recognition theory is more and more, such as speech recognition, character recognition, face recognition and so on.The main methods of pattern recognition are statistics, clustering,neural network and artificial intelligence.Statistical method is one of the most classic methods, and Bayesian classification is widely used in statistical method because of its convenience and good classification effect.
Shrinking Sample Search Algorithm for Automatic Tuning of GPU Kernels

Autotuning has been widely studied in high performance computing as a very effective mechanism for improving application performance. Such an approach has become particularly crucial for architectures like the modern GPUs, where obtaining the best performance involves a complex interaction between the architecture and the applications. Autotuning methods rely upon a search strategy, which is designed to search through the (potentially very large) space. A large number of search methods have been proposed in the past, and include both local and global strategies. We observe that on GPU applications, high performing configurations are likely to be spatially clustered. Based on this observation, we propose to apply a strategy we refer to as shrinking sample. This method searches in all areas of the entire space, looking for combinations of different parameter values, and without relying on random (initial) choices that may miss a part of the space. The efficacy and efficiency of this method has been tested against state-of-the-art local and global search algorithms on seven benchmark GPU kernels. Our experiments show that the shrinking-sample method can achieve around 99% percent of the performance from exhaustive search (on average) with orders of magnitude much less tuning time.
Math Word Problem Solver Based on Text-to-Text Transformer Model

In recent years, automatic problem solving for math-ematical words has attracted increasing attention. Therefore, algorithms developed for solving mathematical word problems like humans are a key technology to facilitate the development of digital education. In this paper, a deep learning model based on a text-to-text conversion model is proposed to solve mathematical word problems. The deep learning model treats each mathematical word problem as a “text-to-text” problem, i.e. taking a text as input and producing a new text as output. However, the output text appears in the form of a mathematical expression. In our experiments, this paper evaluates a deep learning model of Ape210K, which consists of 210K Chinese primary school level maths problems. The experimental results show that our deep learning model presented in this paper can solve 78.61% of the mathematical word problems as a whole. In addition, the problems were classified into six categories based on the knowledge points involved, and this paper explores the model's performance on different types of problems.
NUPro: A Mobile and Web Framework for Athlete Profiling and Training Load Monitoring

It is known that the pandemic has brought about changes to various industries, including the sports industry. Face-to-face training and workouts were put on hold, affecting all athletes, coaches, and sports personnel all around the globe. Athletes had to overcome maintaining physical fitness and stabilizing their mental health these past few years. As for the coaches and other sport staff, it is tough to keep their athletes under control. With these being said, an application - NUPro: A Mobile and Web Framework for Athlete Profiling and Training Load Monitoring- is developed to aid the circumstances brought by the pandemic. The application works as a guide for athletes to perform the training load that their coaches and staff entered in the system. It also helps athletes balance their physical and mental health to adapt to the new normal and keep track of their performances and improvement. With these, coaches and staff can examine and evaluate their athletes' condition with the data being entered by the athletes, which maximizes the development of the athlete training load. Additionally, aims to lower the risk of injuries for athletes during their at-home training in adherence to the coaches' training loads.
Ensemble2N et: Learning from Ensemble Teacher Networks via Knowledge Transfer

In machine learning, deep neural networks (DNNs) are becoming mainstream because they can learn higher-level features and thus form deep representations. However, DNNs require a lot of memory and training time. Improving the efficiency and effectiveness of D NN training has been an increasingly important focus of research in recent years. In this paper, we propose a training method, Ensemble2Net, that can accelerate the training of deep convolutional neural networks (DCNNs), and help student networks learn knowledge from DCNN-based teacher networks. We use a novel algorithm, Ensemble2Net, to accelerate the transfer of learning in VGGnet (13/16/19), and ResNet. The results show that the Ensemble2Net technique can help VGGnet and ResNet achieve the best accuracy at lower cost than current approaches. In particular, ResNet using Ensemble2Net with 20 epochs achieves better accuracy than the original Res Net trained with more than 170 epochs, with a 1.503x speedup in performance.
DTLS with Post-quantum Secure Source Authentication and Message Integrity

Recent advances in quantum computing and Shor’s discovery of quantum algorithms that efficiently solve computational problems (integer factorization and discrete logarithm problems) form the basis of the Internet cryptographic infrastructure. It has fueled intensive research into the design and development of post-quantum (PQ) cryptographic systems that maintain their security when a quantum computer is built.We consider the PQ security of DTLS (Datagram Transport Layer Security), a communication security protocol that protects against eavesdropping, tampering, and packet forgery for datagrams. DTLS is the UDP counterpart of TLS (Transport Layer Security) and proposed to secure control messages in 5G networks. We offer a novel and efficient approach for providing secure source authentication and message integrity for DTLS that removes the need for a computationally expensive key exchange for message integrity by using PQ TESLA (Timed Efficient Stream Loss-tolerant Authentication) that relies on delayed key release mechanism to provide integrity and use a PQ hash-based signature for providing PQ source authentication. We analyze the security of our design, implement it in a widely used library called TinyDTLS, report the result of our experiments, and propose direction for future research.
Few Shot POP Chinese Font Style Transfer using CycleGAN

Abstract The new style design of Chinese fonts is an arduous task, because there are many types of commonly used Chinese characters and the composition of Chinese characters is complicated. Therefore, the style transfer of Chinese characters based on GAN has become a research hotspot in the past two years. This line of re-search is dedicated to using a small number of artificially designed new style fonts and learning the map-ping from the source font style domain to the target style domain. However, such methods have two problems: 1. The performance on pop (point of purchase) fonts with exaggerated and random style is not satisfying. 2. Plentiful manually designed fonts are still required. In order to solve the above problems, we propose a few-shot font style transfer model based on CycleGAN. It uses meta-knowledge to reduce the use of manually designed fonts and enables each character to fully learn the knowledge contained in all new style fonts to achieve satisfying pop font style transfer effect. We also construct a dataset based on commonly used 3500 Chinese characters and verify the effectiveness of our model.
An effective lightweight attention mechanism

Aiming at the problem of large parameters and poor portability of attention mechanism modules, an extremely lightweight attention mechanism is designed, which uses a combination of spatial attention and channel attention to enhance the model's attention on important information. From the experimental results, the lightweight attention mechanism can add to the deep convolutional neural network which introduces with negligible parameters and calculations, allows the model to focus more on useful information and improve the accuracy of the object detection task. It surpasses SENet(Squeeze and Excitation Networks) in performance and is easily transplanted to mainstream deep convolutional neural networks such as ResNet.
A Cooperative Game Theory and Blockchain based Elastic Bilateral Task Allocation Algorithm for In-network Computing

The compute first networking is a new network architecture, which aims at combining the computing resources with the network, integrating the ubiquitous and dynamically distributed computing resources, network, storage and other computing resources in the network, and making them unified and intelligently scheduled. This study focuses on realizing the effective assignments of computing power related tasks and interactions between multi-belonging service providers in the service transaction process while taking into consideration of the complex network cooperation scenario, and meeting the needs of the service providers and users. In this paper, a formal abstract model is established for the compute first networking architecture, which highlights the characteristics of the multi-party complex cooperation situation and includes the solution of the task allocation problem in detail. Another main work of this paper is to propose a service trading system based on blockchain. Finally, the researchers conducted simulation experiments on Matlab platform and Ethereum Truffle framework, thereby proving the advantages of the proposed method in having comparatively high efficiency and performance.
Qubit representation of a binary tree and its operations in quantum computation

Based on quantum mechanics, Quantum computing allows states of superposition and entanglement on qubits. Using parallelism, quantum computing performed better in various well-known algorithms. Quantum computing deals with a different data representation called qubit, a challenging format to transfer classical computing into quantum computing. This chapter proposed an architecture for representing Binary trees in quantum computing. Two main tree operations, insertion and deletion are explained in this architecture. Various merits and computation complexity of these quantum circuits are measured and analysed.
Mobile Digital Forensics Framework for Smartphone User Analysis

Blockchain-based digital forensics technology is an efficient way to prevent forgery/modulation of evidence including collecting and analyzing evidential data using the technology in compliance with smartphone forensics procedures after a smartphone is seized. Moreover, the use of large-capacity storage devices and various digital devices have become a realistic solution for its development of IT in situations where the existing digital forensics analysis methods are regarded as limitations. This paper analyzed user’s status on smartphone application and implemented a smartphone user analysis framework that may extract significant digital evidence in a digital forensic way based on a blockchain perspective. In this paper researched a system that may provide important information to digital forensic analysts through these frameworks. It is expected that the proposed system will be expanded by much more structured data and online unstructured data such as SNS reports.
AdaBoosting Clusters on Graph Neural Networks

Graph Neural Networks (GNNs), combining node features and structure information flexibly, have been widely studied and applied in many fields. The growth of graph size and rich features generates a considerable demand for achieving scalability while maintaining good classification performance in the research of GNNs. Graph partition technique, as used in a recent work ClusterGCN, which divides the graph into several sub-graphs, has become an important strategy to achieve the scalability, but the loss of information still affects the results. In this paper, AdClusterGCN is proposed to establish the interaction between graph partition and node classification, in which they can promote each other, and the effectiveness and efficiency of the model can be ensured at the same time. AdClusterGCN combines GNN models trained on a sequence of graph partitions to capture different features, where the current partition is affected using adjusted node/edge weights computed from the results of GNN models on previous partitions. The PageRank and resampling techniques are adopted to keep sufficient attention on important nodes in different models. We implement our method with TensorFlow and experimental studies show that AdClusterGCN achieves state-of-the-art performance on several public benchmarks.
UniformLIME: A Uniformly Perturbed Local Interpretable Model-Agnostic Explanations Approach for Aerodynamics

Abstract Machine learning and deep learning are widely used in the field of aerodynamics. But most models are often seen as black boxes due to lack of interpretability. Local Interpretable Model-agnostic Explanations (LIME) is a popular method that uses a local surrogate model to explain a single instance of machine learning. Its main disadvantages are the instability of the explanations and low local fidelity. In this paper, we propose an original modification to LIME by employing a new perturbed sample generation method for aerodynamic tabular data in regression model, which makes the differences between perturbed samples and the input instance vary in a larger range. We make several comparisons with three subtasks and show that our proposed method results in better metrics.
Data Quality Test Method for Factory Energy Management System

Because data is an important factor in the software industry, how to reliably test data is important. This is even more essential for building Industry 4.0 and smart industrial complexes. This study prepares ISO/IEC 25024-based test methods and guidelines and uses them for energy management at the industrial complex level. In order to provide services by collecting energy data from industrial complexes, it is necessary to verify data quality based on data reliability and compatibility of each plant. Data quality technology needs to conform to ISO TC184/SC4/WG13 (industrial data quality standard) based technology. The study defines the data quality evaluation matrix for the energy management system of industrial parks and factories. It defines five categories and maps detailed indicators to each. The category has three detailed items, which are evaluation items for core requirements, interoperability, and conformity to standards. Each data requirement category covers functionality and reliability, usability and efficiency, and portability as data requirements in the system. Core requirements for system operation such as data consistency are basic evaluation items, and interoperability, which is the semantic compatibility of data for integrated operation of multiple sites, is verified. In addition, data quality is evaluated by verifying standard conformance. Through this evaluation system, the requirements for linking the factory energy management system data with the industrial complex energy management system can be evaluated. This can be used to monitor data quality and develop improvement technologies by developing a master data quality management technology standard suitable for industrial sites.
Doing the Right Thing: It Is in Our Power to Act and Not to Act

This chapter explores the concept of Leadership and Business Ethics through the evolution of privacy laws in the digital age. Business leaders are faced with an increasing volatile, uncertain, complex, and ambiguous (VUCA) regulatory environment to operate in, particularly in the business activity of processing personal data. With the development of privacy laws and regulations aimed at enhancing the privacy rights of individuals, business leaders face ethical challenges in striking a balance between a citizen’s right to privacy and providing assistance to law enforcement agencies in combating serious crime (including but not limited to, organised crime, terrorism and murder) and maintaining public safety (including saving of human lives). This is particularly complex in the environment that telecommunications service providers operate in and also presents significant challenges for global technology companies. It is likely that in the short to medium term, organisations will be subjected to enhanced regulation and businesses will be required to lead in a new way. This chapter outlines the evolution of privacy laws in Ireland specific to the retention of and access to telecommunications data, it aims to demystify the General Data Protection Regulation (GDPR), examines a landmark privacy case - Graham Dwyer v The Commissioner of An Garda Síochána, highlights the importance of balancing competing rights, the rise in the role of Chief Ethics Officers and proposes a new social contract for the digital age.
Review Of Video Captioning Methods

Video captioning is the process of creating a natural language sentence that summarises the video's contents automatically. Modeling the video's effective temporal composition and effectively integrating that information into a plain language description are both required. It has a variety of applications, including assisting the visually impaired, video subtitling, and video surveillance, among others. Due to the advancement of deep learning in computer vision and natural language processing, there has been a surge in study in this area in recent years. Video captioning is the result of combining these two worlds of computer vision and natural language processing. In this study, we examine and analyse various strategies for addressing this issue, as well as benchmark datasets in terms of domains, repository size, and number of classes; and identify the benefits and drawbacks of various evaluation metrics such as BLEU, METEOR, CIDEr, SPICE, and ROUGE.
An Examination Protocol for Handling Programmable Answers Using a Public Blockchain

Examinations that involve programming, such as programming contests and certification exams, are common. However, current examination protocols require full trust in the operating organizations, which allows malicious organizations to cheat. As a countermeasure, efforts are being made to develop examination protocols that do not need a trusted authority. One way to build such a decentralized protocol is to apply blockchain. Several blockchain-based examination protocols have been proposed, but they can only handle a finite set of correct answers. We propose an examination protocol that can handle a set of correct answers defined by a program and mathematically guarantee the validity of the protocol. We also implement a programming contest platform based on the proposed method on Ethereum and show the feasibility of the method. Furthermore, we measure the economic cost of the method using the implementation and confirm that the method is feasible for approximately 30–40 USD.
Development and research of algorithms for recognition of breast diseases by mammographic images using neural networks

Diseases of the mammary glands rank first among the diseases of the female population. This work is devoted to the development of an algorithm for the diagnosis of breast diseases by mammographic images using neural networks. The article presents the result of choosing the optimal parameters of an artificial neural network trained to solve the problem of classifying breast diseases by mammograms based on Haralic's textural features.
Graph-based Adversarial Online Kernel Learning with Adaptive Embedding

Traditional online learning for vertex classification adapts graph Laplacian regularization into ridge regression, which hardly resolve robustness issue against adversarial examples. To tackle the problem, we propose a more general min-max optimization framework for adversarial online kernel learning (OKL). The derived online algorithm can achieve a min-max regret compared with the optimal model found offline. Nonetheless, optimizing in the reproducing kernel Hilbert space suffers from expensive computational costs. While first-order methods accumulate an optimal $\mathcal{O}(\sqrt{T})$ regret, they only require $\mathcal{O}(t)$ time and space per trial. Second-order methods converge to an optimum much faster with $\mathcal{O}(\log T)$, but suffer an expensive $\mathcal{O}(t^{2})$ per-trial cost. This paper adopts selective sampling to make the OKL scaling to large datasets through constructing a small and accurate embedded space for vertex representation, so that OKL could be performed more efficiently on this sketched kernel space. To achieve this goal, we introduce a novel confidence-aware dictionary selection strategy and a model-based update routine, where for an expected sampling probability p, the computational cost can be reduced by a factor of $p^{2}$ to $\mathcal{O}(p^{2}t^{2})$ space and time per trial, while the regret remains comparable. Numerical experiments are conducted on real-world benchmark datasets to illustrate the efficacy of our method.