Reviewerly/exHarmony huggingface dataset:
COMPARISON OF FORECASTING ACCURACY RATE OF EXPONENTIAL SMOOTHING METHOD ON ADMISSION OF NEW STUDENTS

Forecasting is a tool or technique used to predict or predict a value in the future by paying attention to relevant data or information, both past data or information as well as current data or information. There are several forecasting methods, one of which is the exponential smoothing method. In this study a comparison of forecasting accuracy to new student admission data in a study program at a university using the single exponential smoothing, double exponential smoothing and triple exponential smoothing methods. The best forecasting using the single exponential smoothing method is obtained when the parameter value α = 0.9 with the mean percentage error (MPE) = 0.0239, while the best forecasting using the double exponential smoothing method is obtained when the parameter value α = 0.8 and β = 1, with an MPE value of 0.1172. The best forecasting using the triple exponential smoothing method is obtained when the values of α = 0.6 and β = 0.9, with an MPE value of 0.0161. Based on the forecasting results of the three methods, it was concluded that the best forecasting obtained in this study was to use the triple exponential smoothing method with an MPE value of 0.0161.
DATA IMBALANCE IN LANDSLIDE SUSCEPTIBILITY ZONATION: UNDER-SAMPLING FOR CLASS-IMBALANCE LEARNING

Abstract. Machine learning methods such as artificial neural network, support vector machine etc. require a large amount of training data, however, the number of landslide occurrences are limited in a study area. The limited number of landslides leads to a small number of positive class pixels in the training data. On contrary, the number of non-landslide pixels (negative class pixels) are enormous in numbers. This under-represented data and severe class distribution skew create a data imbalance for learning algorithms and suboptimal models, which are biased towards the majority class (non-landslide pixels) and have low performance on the minority class (landslide pixels).In this work, we have used two algorithms namely EasyEnsemble and BalanceCascade for balancing the data. This balanced data is used with feature selection methods such as fisher discriminant analysis (FDA), logistic regression (LR) and artificial neural network (ANN) to generate LSZ maps The results of the study show that ANN with balanced data has major improvements in preparation of susceptibility maps over imbalanced data, where as the LR method is ill-effected by data balancing algorithms. The FDA does not show significant changes between balanced and imbalanced data.
Shadow Detection in Still Road Images Using Chrominance Properties of Shadows and Spectral Power Distribution of the Illumination

A well-known challenge in vision-based driver assistance systems is cast shadows on the road, which makes fundamental tasks such as road and lane detections difficult. In as much as shadow detection relies on shadow features, in this paper, we propose a set of new chrominance properties of shadows based on the skylight and sunlight contributions to the road surface chromaticity. Six constraints on shadow and non-shadowed regions are derived from these properties. The chrominance properties and the associated constraints are used as shadow features in an effective shadow detection method intended to be integrated on an onboard road detection system where the identification of cast shadows on the road is a determinant stage. Onboard systems deal with still outdoor images; thus, the approach focuses on distinguishing shadow boundaries from material changes by considering two illumination sources: sky and sun. A non-shadowed road region is illuminated by both skylight and sunlight, whereas a shadowed one is illuminated by skylight only; thus, their chromaticity varies. The shadow edge detection strategy consists of the identification of image edges separating shadowed and non-shadowed road regions. The classification is achieved by verifying whether the pixel chrominance values of regions on both sides of the image edges satisfy the six constraints. Experiments on real traffic scenes demonstrated the effectiveness of our shadow detection system in detecting shadow edges on the road and material-change edges, outperforming previous shadow detection methods based on physical features, and showing the high potential of the new chrominance properties.
Generalizing Long Short-Term Memory Network for Deep Learning from Generic Data

Long Short-Term Memory (LSTM) network, a popular deep-learning model, is particularly useful for data with temporal correlation, such as texts, sequences, or time series data, thanks to its well-sought after recurrent network structures designed to capture temporal correlation. In this article, we propose to generalize LSTM to generic machine-learning tasks where data used for training do not have explicit temporal or sequential correlation. Our theme is to explore feature correlation in the original data and convert each instance into a synthetic sentence format by using a two-gram probabilistic language model. More specifically, for each instance represented in the original feature space, our conversion first seeks to horizontally align original features into a sequentially correlated feature vector, resembling to the letter coherence within a word. In addition, a vertical alignment is also carried out to create multiple time points and simulate word sequential order in a sentence ( i.e., word correlation). The two dimensional horizontal-and-vertical alignments not only ensure feature correlations are maximally utilized, but also preserve the original feature values in the new representation. As a result, LSTM model can be utilized to achieve good classification accuracy, even if the underlying data do not have temporal or sequential dependency. Experiments on 20 generic datasets show that applying LSTM to generic data can improve the classification accuracy, compared to conventional machine-learning methods. This research opens a new opportunity for LSTM deep learning to be broadly applied to generic machine-learning tasks.
Optical Character Recognition using Convolutional Neural Network

Optical Character Recognition is the process of translating images of handwritten, typewritten, or printed text into a format understood by machines. The purposes of Optical Character Recognition are editing, indexing/searching, and reduction in storage size. This is achieved by first scanning the photo of the text character-by-character, then it is followed by analysis of the scanned image, and finally the translation of the character image into character codes, such as ASCII. In this paper, we have used segmentation algorithm to divide the image into lines, words and then characters. The characters are recognized using Convolutional Neural Network The results obtained by Convolutional Neural Networks seems to be promising in recognizing the characters in comparison to results obtained through other machine learning algorithms such as Support Vector Machines and Artificial Neural Networks.
IEEE Access Special Section Editorial: Trusted Computing

Trusted Computing (TC) is a technology developed and promoted by the Trusted Computing Group (TCG), and aims to enhance the overall security, privacy and trustworthiness of a variety of computing devices. The core of the trusted computing technologies of TCG is the Trusted Platform Module (TPM), a tamper-resistant module embedded in a platform, which is implemented by a TPM chip and designed to resist software attacks and moderate hardware attacks. TPM includes non-volatile storage, a set of Platform Configuration Registers (PCRs) and an engine for cryptographic operations. Based on TPM and TCG Software Stack (TSS), a suite of mechanisms can be realized, including memory curtaining, protected execution, secure I/O, sealed storage, platform measurement and remote attestation. As a fundamental secure computing module, TPM is the root trust module to establish security and trust in digital computing, communication and networking. Basically, it can support secure booting and trustworthy software installation, private data protection and Digital Rights Management (DRM), as well as remote attestation- based trusted communications and collaboration. There is significant research currently in the field of trusted computing, in both academia and industry. Recent efforts focus on TC module development and extension, trustworthy software assurance, Trusted Execution Environment (TEE), trusted collaboration and trusted storage. The recent advance of Intel Software Guard Extensions (Intel® SGX) offers hardware-assistant TEE, which allows user code execution in a private region of memory, called enclave, in an untrusted third party. This technology attracts special attention and motivates innovations of many applications.
A Face Detection Algorithm Based on Two Information Flow Block and Retinal Receptive Field Block

Recently, significant progress has been made in the field of face detection. However, despite the demand for its high accuracy and recall rate, the efficiency of face detection algorithm is another key factor in evaluating its performance, which puts forward serious challenges to current models. To boost up the efficiency of face recognition, in this paper, we propose a lightweight rapid framework, called LRNet, which has fewer convolutional layers and a higher efficiency. In particular, our framework is consist of two major modules. One is the Feature Map Fast Shrink Module (FMFSM), which leads to a fast reduction in the size of feature maps and time consumed for detection by using Two Information Flow Block (TIFB). Another module, namely Variable Scale Face Detection Module (VSFDM), is consist of the Retinal Receptive Field Block (RRFB) and designed to prevent a single or composite feature map from undertaking too much tasks. In addition, we propose a new anchor strategy that considers not only the density of anchors with different scales but also the position and central symmetry of the features. Our proposed LRNet achieves high accuracy and efficiency on the challenging FDDB dataset for face detection. When the number of false positives is 2000, its True Positive Rate (TPR) under discrete and continuous scores can achieve 0.951 and 0.725 respectively. When running on GTX 1080Ti, given images with a resolution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1024\times 1024$ </tex-math></inline-formula> , the average time consumed for detection is merely 8.88 ms.
Structure tuning method on deep convolutional generative adversarial network with nondominated sorting genetic algorithm II

Abstract Currently the generative adversarial networks (GANs) have rapidly become a popular research hotspot that people concerned and have been applied to various fields. Lots of meaningful work have been proposed and various variants of GANs sprung up in last few years. The scholars usually design GAN structure like the layers and hyperparameters setting according to the experience and constantly attempts. For the propose of finding the appropriate structure more conveniently and efficiently. A method with multiobjective algorithm is proposed to obtain the optimal structure for the GANs. In the proposed method, the nondominated sorting genetic algorithm II (NSGA‐II) is utilized to optimize the hyperparameters and structure of deep convolutional generative adversarial network (DCGAN). The experiments are conducted on MNIST and Malware datasets demonstrate the efficiency and high performance of proposed method.
A Dynamic Financial Knowledge Graph Based on Reinforcement Learning and Transfer Learning

The knowledge graph is a means of visualizing data to aid information analysis and understanding. In this paper, we construct a novel dynamic financial knowledge graph, which utilizes time information to capture data changes and trends over time. Firstly, the basic dynamic financial knowledge graph is constructed through structured and semi-structured data related to A-share. Then, using the transfer learning algorithms, we train the financial entity recognition models based on BERT, BiLSTM, and CRF. Next, we train the financial entity linking models based on similarity features and prior knowledge. After that, to alleviate the noise brought by distant supervision, we explore to train the financial relation classification models with the help of reinforcement learning. Finally, we implement the dynamic knowledge graph based on these models and their predictions. Additionally, a display website is designed and implemented to dynamically display the structural changes of the knowledge graph over time. The financial knowledge graph constructed in this paper is practical and the construction pipeline provides insights for a professional dynamic knowledge graph as well.
Blockchain Research a Scientometric Assessment of Global Literature during 2010 to 2018

The paper analyzes global publications in blockchain research on a series of quantitative and qualitative indicators, using Scopus database. Blockchain research published a total of 4629 research publications in 9-year period during 2010-18. The publications registered a fast growth of 150.24 per cent and registered average citation impact per paper of 5.04. The paper profiles most productive countries, organisation, authors and journals on select bibliometric indicators. The top 29 highly-cited papers in this area are also analysed.
Exploration of excitation source information for shouted and normal speech classification

Discrimination between shouted and normal speech is an essential prerequisite for many speech processing applications. Existing works have established that excitation source information plays a significant role in shouted speech production. In speech processing literature, various features have been proposed to model different aspects of the excitation source. The principal contribution of this work is to explore three such features, Discrete Cosine Transform of Integrated Linear Prediction Residual (DCT-ILPR), Mel-Power Difference of Spectrum in Sub-bands (MPDSS), and Residual Mel-Frequency Cepstral Coefficient (RMFCC), for shouted and normal speech classification. The DCT-ILPR feature represents the shape of the glottal cycle, MPDSS estimates the periodicity of the excitation source spectrum, and RMFCC characterizes smoothed spectral information of the excitation source. The authors have also contributed a dataset containing shouted and normal speech. This work is evaluated on three datasets and benchmarked against three baseline methods. Deep neural networks are used to study the classification performance of individual features and their combinations. The generalization performance of features (and combinations) is also investigated. Fusion of excitation source features with Mel-Frequency Cepstral Coefficients (MFCC) provides the best performance compared to other combinations. Noise analysis shows that adding excitation features with MFCC+ΔΔ provides a more robust classification system.
The Architecture of Project-Based Learning in the Supplementary Vocational Education System in a Higher Education

The article deals with the organization of project-based education applied in the supplementary higher vocational education system and considers the needs of modern society in the context of creating a Priority Development Area. (PDA). In the article, the authors present the architecture of the integrated model of traditional and project-based learning in the supplementary vocational education system, offer practical recommendations on the organization and implementation of project activities in the staff training demanded in PDA. The article reflects the results of introducing the integrated model of traditional and project-based learning in the supplementary vocational education system.
Surprise Bug Report Prediction Utilizing Optimized Integration with Imbalanced Learning Strategy

In software projects, a large number of bugs are usually reported to bug repositories. Due to the limited budge and work force, the developers often may not have enough time and ability to inspect all the reported bugs, and thus they often focus on inspecting and repairing the highly impacting bugs. Among the high-impact bugs, surprise bugs are reported to be a fatal threat to the software systems, though they only account for a small proportion. Therefore, the identification of surprise bugs becomes an important work in practices. In recent years, some methods have been proposed by the researchers to identify surprise bugs. Unfortunately, the performance of these methods in identifying surprise bugs is still not satisfied for the software projects. The main reason is that surprise bugs only occupy a small percentage of all the bugs, and it is difficult to identify these surprise bugs from the imbalanced distribution. In order to overcome the imbalanced category distribution of the bugs, a method based on machine learning to predict surprise bugs is presented in this paper. This method takes into account the textual features of the bug reports and employs an imbalanced learning strategy to balance the datasets of the bug reports. Then these datasets after balancing are used to train three selected classifiers which are built by three different classification algorithms and predict the datasets with unknown type. In particular, an ensemble method named optimization integration is proposed to generate a unique and best result, according to the results produced by the three classifiers. This ensemble method is able to adjust the ability of the classifier to detect different categories based on the characteristics of different projects and integrate the advantages of three classifiers. The experiments performed on the datasets from 4 software projects show that this method performs better than the previous methods in terms of detecting surprise bugs.
A New Local Descriptor Based on Strings for Face Recognition

This paper proposes the use of strings as a new local descriptor for face recognition. The face image is first divided into nonoverlapping subregions from which the strings (words) are extracted using the principle of chain code algorithm and assigned into the nearest words in a dictionary of visual words (DoVW) with the Levenshtein distance (LD) by applying the bag of visual words (BoVW) paradigm. As a result, each region is represented by a histogram of dictionary words. The histograms are then assembled as a face descriptor. Our methodology depends on the path pursued from a starting pixel and do not require a model as the other approaches from the literature. Therefore, the information of the local and global properties of an object is obtained. The recognition is performed by using the nearest neighbor classifier with the Hellinger distance (HD) as a comparison between feature vectors. The experimental results on the ORL and Yale databases demonstrate the efficiency of the proposed approach in terms of preserving information and recognition rate compared to the existing face recognition methods.
Unpaired Domain Transfer for Data Augment in Face Recognition

Face recognition is one of the hottest issues in the field of computer vision and pattern recognition. Deep learning-based recognition models already have more exceptional recognition ability than the human being on open datasets, but still cannot fully undertake the identity recognition task in real scenarios without human assistance. In this paper, we mainly analyze two obstacles, i.e., domain gap and training data shortage. We propose the unpaired Domain Transfer Generative Adversarial Network (DT-GAN) to relieve these two obstacles. We improve the GAN baseline to bridge the domain gap among datasets by generating images conforming to the style of a target domain by learning the mapping between the source domain and target domain. The generator could synthesize face with an arbitrary viewpoint at the same time. The model is trained with a combination of style transfer loss, identity loss, and pose loss, which ensures the successive domain transfer and data augment. We conduct experiments to testify the effectiveness and reasonability of DT-GAN. Experimental results demonstrate the recognition performance is dramatically boosted after domain transfer and data augment.
Investigating Security Vulnerabilities in a Hot Data Center with Reduced Cooling Redundancy

Data centers have been growing rapidly in recent years to meet the surging demand of cloud services. However, the expanding scale and powerful servers generate a great amount of heat, resulting in significant cooling costs. A trend in modern data centers is to raise the temperature and maintain all servers in a relatively hot environment. While this can save on cooling costs given benign workloads running in servers, the hot environment increases the risk of a cooling failure. In this article, we unveil a new vulnerability of existing data centers with aggressive cooling energy saving policies. Such a vulnerability might be exploited to launch thermal attacks that could severely worsen the thermal conditions in a data center. Specifically, we conduct thermal measurements and uncover effective thermal attack vectors at the server, rack, and data center levels. We also present damage assessments of thermal attacks. Our results demonstrate that thermal attacks can (1) largely increase the temperature of victim servers degrading their performance and reliability, (2) negatively impact on thermal conditions of neighboring servers causing local hotspots, (3) raise the cooling cost, and (4) even lead to cooling failures. Finally, we propose and evaluate effective server and data center level defenses to enhance thermal stabilities.
Educational Philosophy of Swami Vivekananda

Swami Vivekananda has said that education is the manifestation of the perfection already present in man. Through Vivekananda’s life experiences, he learned many truths and shared them with others. These truths addressed the subject of education. To him education played and plays a vital role in curing the evils in society, and it is critical in shaping the future of humanity. He talked about “man-making” education. In Vivekananda’s educational scheme for India, the uplift of women and the masses received the highest priority. In this paper, a systematic attempt has been to explain the educational philosophy of Swami Vivekananda.
Entanglement of Microwave and Optical Fields Using Electrical Capacitor Loaded With Plasmonic Graphene Waveguide

We propose a novel approach for microwave and optical fields entanglement using an electrical capacitor loaded with graphene plasmonic waveguide. In the proposed scheme, a quantum microwave signal of frequency f_m drives the electrical capacitor, while an intensive optical field (optical pump) of frequency f_1 is launched to the graphene waveguide as surface plasmon polariton (i.e., SPP) mode. The two fields interact by the means of electrically modulating the graphene optical conductivity. It then follows that an upper and lower SPP sideband modes (of f_2 = f_1 + f_m and f_3 = f_1 -f_m frequencies, respectively) are generated. We have shown that the microwave signal and the lower sideband SPP mode are entangled, given a proper optical pump intensity is provided. A quantum mechanics model is developed to describe the fields evolution. The entanglement of the two fields is evaluated versus many parameters including the waveguide length, the pump intensity, and the microwave frequency. We found that the two fields are entangled over a vast microwave frequency range. Furthermore, our calculations show that a significant number of entangled photons are generated at the lower SPP sideband. The proposed scheme attains tunable mechanism for microwave-optical entanglement which paves the way for efficient quantum systems.