Reviewerly/exHarmony huggingface dataset:
Classification for high-dimension low-sample size data

Huge amount of applications in various fields, such as gene expression analysis or computer vision, undergo data sets with high-dimensional low-sample-size (HDLSS), which has putted forward great challenges for standard statistical and modern machine learning methods. In this paper, we propose a novel classification criterion on HDLSS, tolerance similarity, which emphasizes the maximization of within-class variance on the premise of class separability. According to this criterion, a novel linear binary classifier is designed, denoted by No-separated Data Maximum Dispersion classifier (NPDMD). The objective of NPDMD is to find a projecting direction w in which all of training samples scatter in as large an interval as possible. NPDMD has several characteristics compared to the state-of-the-art classification methods. First, it works well on HDLSS. Second, it combines the sample statistical information and local structural information (supporting vectors) into the objective function to find the solution of projecting direction in the whole feature spaces. Third, it solves the inverse of high dimensional matrix in low dimensional space. Fourth, it is relatively simple to be implemented based on Quadratic Programming. Fifth, it is robust to the model specification for various real applications. The theoretical properties of NPDMD are deduced. We conduct a series of evaluations on one simulated and six real-world benchmark data sets, including face classification and mRNA classification. NPDMD outperforms those widely used approaches in most cases, or at least obtains comparable results.
Innovative Application of Artificial Intelligence (AI) in the Management of Higher Education and Teaching

Abstract Internet and large data technology provide us with abundant information resources, which has caused great changes in society and economy. Therefore, their application in education and teaching management in colleges and universities has attracted much attention. Artificial intelligence, with its more intelligent, personalized, accurate and diversified characteristics, brings a new idea for the innovation of teaching management methods in colleges and universities. At present, in China, the application of artificial intelligence in the education and teaching management of colleges and universities has got some foundation and achievements, but compared with other industries, it is still at the primary level. It is the direction that AI can be applied to the management of education and teaching in colleges and universities to actively construct the AI ecosystem of higher education and innovate the application mode from the aspects of student management, faculty construction and teaching management. The application of AI will make the management level of education and teaching step on a new level.
Performance Analysis of Attribute based Encryption and Cloud Health data Security

Cloud computing has been more trending technology. It emerging most of the industries. One of the most widely used in industries are cloud health care. By using cloud- based health care reduces the cost, making easy sharing, and processing health data. Cloud health care makes an easy and time-efficient diagnosis for an efficient situation in cloud health care. The patient information has been stored under a centralized repository to make easily available data in all situations. One of the main disadvantages of using cloud health care data is security and privacy issue. This paper analysis the various attribute-based encryption.
Energy Efficient Scheduling Based on Marginal Cost and Task Grouping in Data Centers

High energy consumption of data centers has been a widely concerned issue and has become a research hotspot. Recent studies show that both servers and cooling systems are main energy consuming factors of data centers. However, most of these studies only consider energy optimization either in servers or the cooling system, separately. Thus, it is essential to develop a scheme to jointly optimize the energy consumption of servers and cooling systems in the data center. In this paper, a joint energy optimization scheme is proposed to coordinately optimize the energy consumption of servers and the cooling system. Firstly, in the cooling system, different cooling modes, including the outside air cooling and liquid cooling are jointly considered. An energy consumption model for the cooling system is designed, where a segmented function is used to describe different energy consumption rate at different stages of the cooling system. A strategy is designed to use different proportion of the outside air cooling and liquid cooling according to real-time workload characteristics. Then, combining the energy consumption of the cooling system and servers, a joint energy optimization problem for data centers is formulated. Meanwhile, an energy-efficient task scheduling strategy based on marginal cost and task grouping is developed for solving the problem. Simulations have been conducted based on real-world workload traces, and simulation results demonstrate that the proposed approach outperforms previous techniques in energy savings.
A post-quantum hybrid encryption based on QC-LDPC codes in the multi-user setting

The encryption schemes based on coding theory are one of the most accredited choices in post-quantum scenario, where QC-LDPC codes are usually employed to construct concrete schemes due to the well security and good efficiency. In this work, we introduce a new IND-CCA secure multi-instance framework for code-based hybrid encryption primitive in the random oracle model, which is derived from our new multi-instance KEM and DEM building modules. We note that previous multi-instance KEM and DEM are usually derived from single-instance KEM and DEM, and hence suffers from large parameter sizes and security loss. Nevertheless, our multi-instance KEM is a direct construction based on a key generation function and a one-way trapdoor function, and our multi-instance DEM is constructed from a standard DEM and MAC with a tag in the input to achieve a tighter security loss. Finally, we present a IND-CCA secure multi-instance hybrid encryption scheme based on QC-LDPC codes in the random oracle model, where the scheme achieves small private key size and only consumes addition and multiplication operations over F2[x].
Intelligence in Digital Forensics Process

Digital forensics is the digital equivalence of traditional crime investigations that leverages digital technologies to facilitate criminal investigations. The expertise and previous experience of an investigator or any security agent in traditional forensic investigations play an important role in the success, efficiency, and effectiveness of the investigation. Similarly employing the power of intelligence in the current computational resources in the digital investigation process will lead to more efficient and effective digital investigation results. This study builds on existing works in the literature to propose the use of clustering and classification machine learning algorithms on evolving database files to achieve intelligence in the digital investigation process and finally come up with a comprehensive block diagram as a general-purpose framework for intelligent forensic investigation.
Fragmentation in the Bitcoin market: Evidence from multiple coexisting order books

We explore the consolidated order book of five major exchanges to investigate how fragmentation affects the Bitcoin market. Using intraday snapshots of the order book data, we find that the Bitcoin market is quite fragmented, wherein the exchange liquidity measure is significantly negative, even for small transactions. Our results suggest that consolidation tools, such as smart order routers, can be effective in reducing the cost of trading, and that further development of the Bitcoin marketâs trading and regulation is needed.
Multi-Attention Network for Stereo Matching

In recent years, convolutional neural network (CNN) algorithms promote the development of stereo matching and make great progress, but some mismatches still occur in textureless, occluded and reflective regions.In feature extraction and cost aggregation, CNNs will greatly improve the accuracy of stereo matching by utilizing global context information and high-quality feature representations.In this paper, we design a novel end-to-end stereo matching algorithm named Multi-Attention Network (MAN).To obtain the global context information in detail at the pixel-level, we propose a Multi-Scale Attention Module (MSAM), combining a spatial pyramid module with an attention mechanism, when we extract the image features.In addition, we introduce a feature refinement module (FRM) and a 3D attention aggregation module (3D AAM) during cost aggregation so that the network can extract informative features with high representational ability and high-quality channel attention vectors.Finally, we obtain the final disparity through bilinear interpolation and disparity regression.We evaluate our method on the Scene Flow, KITTI 2012 and KITTI 2015 stereo datasets.The experimental results show that our method achieves state-ofthe-art performance and that every component of our network is effective.
SOFEA: A Non-iterative and Robust Optical Flow Estimation Algorithm for Dynamic Vision Sensors

We introduce the single-shot optical flow estimation algorithm (SOFEA) to non-iteratively compute the continuous-time flow information of events produced from bio-inspired cameras such as the dynamic vision sensor (DVS). The output of a DVS is a stream of asynchronous spikes ("events"), transmitted at very minimal latency (1-10 μs), caused by local brightness changes. Due to this unconventional output, a continuous representation of events over time is invaluable to most applications using the DVS. To this end, SOFEA consolidates the spatio-temporal information on the surface of active events for flow estimation in a single-shot manner, as opposed to iterative methods in the literature. In contrast to previous works, this is also the first principled method towards finding locally optimal set of neighboring events for plane fitting using an adaptation of Prim's algorithm. Consequently, SOFEA produces flow estimates that are more accurate across a wide variety of scenes compared to state-of-the-art methods. A direct application of such flow estimation is rendering sharp event images using the set of active events at a given time, which is further demonstrated and compared to existing works (source code will be made available at our homepage after the review process).
Let’s Get in Touch! Adding Haptics to Social VR

Social VR shall allow natural communication between users with high social presence, as if users are in the same room. One way to increase social presence is to add haptic interaction to allow, for example, users to give each other a "high-five" or to pass documents among them. In this paper, we present our web-based VR communication framework with an added haptic component to simulate touch. The goal of this framework is to enhance the VR communication experience and the social cues exchange between users in VR. We describe our method for rendering haptic feedback within the web-based framework and evaluate the perceived quality of our system with a user survey (with 119 participants). Our proof-of-concept system was rated positively, with the haptic component offering an enhanced quality of the VR experience for 78% of the participants.
Vanishing Flats: A Combinatorial Viewpoint on the Planarity of Functions and Their Application

For a function $f$ from $\mathbb {F}_{2}^{n}$ to $\mathbb {F}_{2}^{n}$ , the planarity of $f$ is usually measured by its differential uniformity and differential spectrum. In this paper, we propose the concept of vanishing flats, which supplies a combinatorial viewpoint on the planarity. First, the number of vanishing flats of $f$ can be regarded as a measure of the distance between $f$ and the set of almost perfect nonlinear functions. In some cases, the number of vanishing flats serves as an “intermediate” concept between differential uniformity and differential spectrum, which contains more information than differential uniformity, however less than the differential spectrum. Secondly, the set of vanishing flats forms a combinatorial configuration called partial quadruple system, since it conveys a detailed structural information about $f$ . We initiate this study by considering the number of vanishing flats and the partial quadruple systems associated with monomials and Dembowski-Ostrom polynomials. In addition, we present an application of vanishing flats to the partition of a vector space into disjoint equidimensional affine spaces. We conclude the paper with several further questions and challenges.
Real-time Pedestrian Tracking based on Deep Features

Multi-object tracking is a significant domain in Computer vision applications, which involves giving unique identities to different objects, and maintaining the association between them, for real-time applications. However, most of the trackers fail to achieve decent levels of accuracy as well as speed. In this paper, we propose a tracking technique, which utilizes both high-speed detections from Yolo, as well as deep feature extraction, from a convolutional neural network. The extracted features, along with position vectors and color histograms, are matched between corresponding frames, to develop an association between pedestrians. It can face issues like slight changes in object appearance in continuous frames, such as shape, size or illumination changes, partial occlusions, or re-identification of pedestrians, on re-entering the view, or after being occluded, for a certain length of frames. We have used the Yolo framework for fast object detection, a MobileNet architecture based custom CNN for feature extraction, and a set of algorithms to generate associations between frames. On the publically available Towncentre dataset, our framework can reach a MOTA of 93.2%.
Ontology based Concept Extraction and Classification of Ayurvedic Documents

India is rich with its culture and heritage. It is known for its traditional medicinal system and it is mentioned even in the ancient Vedas and other scriptures also. In India, Traditional Medical system includes Ayurveda, yoga, siddha, Unani, and homeopathy. Biomedical Text Mining (BioTM) is aiming at the extraction of novel, non-trivial information from the large amounts of biomedical related documents. This unstructured bio medical document holds greater knowledge about medical diagnostics, treatment, and prevention. Ontology plays a vital role in deep understanding of information. It is the building block of the Semantic Web and considers it's important on the semantic clarity of concepts and entities. The main objective is to search the most relevant content from this huge set of text by understanding the meaning of conceptual terms. We proposed Ontology based Concept Extraction and Classification in which the domain ontology and semantic document description was used to improve classification accuracy. The results show that the classification accuracy of proposed algorithm outperforms than the other existing supervised machine learning algorithm. To further prove the efficiency of the model, experiments were conducted by giving different queries and the results are compared with other existing methods. The results show that the content retrieved by the proposed model was most relevant when compared with existing system.
A Novel Broad Learning Model-Based Semi-Supervised Image Classification Method

Broad learning system (BLS) is an effective and efficient incremental learning system without the deep architecture. It has strong feature extraction ability and high computational efficiency. However, it is greatly limited in the applicability of supervised learning. For the collected actual data, more data are unlabeled data and less data are labeled data. To overcome these problems, Fick's law assisted propagation (FLAP) is introduced into the BLS to propose a new semi-supervised classification algorithm, namely FLAP-BLS in this paper. In the FLAP-BLS, the FLAP has the labeled ability from the labeled examples to unlabeled examples, it is used to mark plenty of unlabeled samples by few labeled samples in order to obtain a large number of labeled samples and build the sample data matrix. Then an efficient incremental BLS without deep structure can effectively extract features from large-scale data, it is used to effectively classify the sample matrix. Finally, USPS, MNIST and NORB datasets are selected to validate the effectiveness of the FLAP-BLS. The experiment results show that the FLAP-BLS can effectively classify the few labeled samples and a large of unlabeled samples and obtain classification results with high accuracy, and it has faster classification speed, stronger generalization ability and better stability. The proposed method provides a new method for image classification.
Robust One Shot Audio to Video Generation

Audio to Video generation is an interesting problem that has numerous applications across industry verticals including film making, multi-media, marketing, education and others. High-quality video generation with expressive facial movements is a challenging problem that involves complex learning steps for generative adversarial networks. Further, enabling one-shot learning for an unseen single image increases the complexity of the problem while simultaneously making it more applicable to practical scenarios.In the paper, we propose a novel approach OneShotA2V to synthesize a talking person video of arbitrary length using as input: an audio signal and a single unseen image of a person. OneShotA2V leverages curriculum learning to learn movements of expressive facial components and hence generates a high-quality talking head video of the given person.Further, it feeds the features generated from the audio input directly into a generative adversarial network and it adapts to any given unseen selfie by applying few-shot learning with only a few output updation epochs. OneShotA2V leverages spatially adaptive normalization based multi-level generator and multiple multi-level discriminators based architecture. The input audio clip is not restricted to any specific language, which gives the method multilingual applicability. Experimental evaluation demonstrates superior performance of OneShotA2V as compared to Realistic Speech-Driven Facial Animation with GANs(RSDGAN) [43], Speech2Vid [8], and other approaches, on multiple quantitative metrics including: SSIM (structural similarity index), PSNR (peak signal to noise ratio) and CPBD (image sharpness). Further, qualitative evaluation and Online Turing tests demonstrate the efficacy of our approach.
Deep-Learning for Volcanic Seismic Events Classification

In this work, we proposed a new method to classify long-period and volcano-tectonic spectrogram images using six different deep learning architectures. The developed method used three deep convolutional neural networks named: DCNN1, DCNN2, and DCNN3. Also, three deep convolutional neural networks combined with deep recurrent neural networks named DCNN-RNN1, DCNN-RNN2, and DCNN-RNN3 to maximize the area under the curve of the receiver operating characteristic scores on a dataset of volcano seismic spectrogram images. The three deep recurrent neural network-based models reached the worst results due to the overfitting produced by the small number of samples in the training sets. The DCNN1 overcame the remaining models by touching an area under the curve of the receiver operating characteristic and accuracy scores of 0.98 and 95%, respectively. Although these values were not the highest values per metric, they did not represent statistical differences against other results obtained by more algorithmically complex models. The proposed DCNN1 model showed similar or superior performance when compared to the majority of the state of the art methods in terms of the accuracy metric. Therefore it can be considered a successful scheme to classify LP and VT seismic events based on their spectrogram images.
Application of Web 3D GIS to Display Urban Model and Solar Energy Analysis using The Unmanned Aerial Vehicle (UAV) Data (Case Study: National Cheng Kung University Buildings)

Abstract Nowadays, the era encompasses everything smart, whether they are smartphones, smart people, or smart cities. The term ‘Smart Cities’ refers to an advanced urban town that has well-connected infrastructure and communication through data centers and automated networks, unless for the energy system. Solar energy is one kind of renewable energy. It is a clean, sustainable, and free energy source both for the public and governments. With the rapidly growing technology, the yield of solar energy increases, while its cost reduces. Thus, it gains importance and becomes widespread. Presently, the increasing importance of solar power makes a hot topic. We are currently facing a global warming issue where one of the leading causes is the use of fossil energies. Non-fossil energy, or better known as renewable energy, is an option to reduce the use of fossil energy as an effort to minimize the impacts of global warming. In this study, we collected the data from The Unmanned Aerial Vehicle (UAV) that is combined with GPS and IMU technology to get the 3D campus model in National Cheng Kung University (NCKU), Taiwan. The output of the data is true ortho-image, digital surface model (DSM), and building footprint. The building footprint was processed using the Autodesk Insight 360 and Autodesk Revit to get better 3d campus model. All three data were processed using ArcGIS Pro using Calculate Solar Radiation plugin. The result of this step is reaching the solar energy potential of NCKU. The final step is to show it into the Web 3D GIS to display the result. From the Web 3D GIS, everyone can access the information of NCKU solar energy potential using computer and also smartphones.
Analysis of Encrypted Traffic with time-based features and time frequency analysis

The classification of encrypted traffic has received increased attention by the research community in the cyber-security domains and network management domains. Classification of encrypted traffic can also expose privacy threats as the activities of an user can be detected and identified. This paper investigates the novel application of Time Frequency analysis to encrypted traffic classification. Features extracted from encrypted traffic are normalized and transformed to time series on which different time frequency transforms are applied. In particular, the constant-Q transform (CQT), the Continuous Wavelet Transform and the Wigner-Ville distribution are used. Then, different machine learning algorithms are applied to identify the different types of traffic. This approach is validated with the public ISCX VPN-nonVPN traffic dataset with time-based features extracted from the encrypted traffic. The results show the superior classification performance (evaluated using identification, precision and recall metrics) of the time frequency approach across different machine learning algorithms. Because analysis of encrypted traffic can also generate privacy threats, a technique to obfuscate the time based features and reduce the classification performance is also applied and successfully validated.
Competencies Ontology for the Analysis of Educational Programs

The following topics are dealt with: diseases; medical signal processing; learning (artificial intelligence); security of data; blood; patient treatment; patient monitoring; bioelectric phenomena; biomedical electrodes; biological tissues.