Reviewerly/exHarmony huggingface dataset:
An Assessment of Machine Learning and Deep Learning Techniques with Applications

Nowadays, sheer amount of data is being generated by plethora of sources, like science, business, medicine, sports, geography, environment, etc. This produced data may be formless, bigger sized, and in the raw format and has no importance at all, until analyzed. Conventional techniques of data analysis may be inappropriate due to vast data diverse nature, high dimensionality of data, and much of the data is never explored. So, in order to get relevant data, some techniques need to be incorporated on the existing data, which would be effective for the real-world applications. Artificial intelligence, machine learning, and deep learning are the extensively used technologies with the utmost buzz. Machine learning is a subfield of AI that designs the intelligent model based on past and current trends. The only concentration of this ground is pre-programmed learning techniques without any human interference/intervention. In addition to this, deep learning processes the data and creates pattern for decision use after imitating the working of human brain. So, the objective of this paper is to explore the research application areas and the widely used approaches/techniques in the domain of machine learning and deep learning.
Image Encryption Based on Arnod Transform and Fractional Chaotic

An image encryption and decryption algorithm based on Arnod transform and fractional chaos is proposed in this work for solving the problem that the encrypted image is easily cracked and the content of the decrypted image is distorted. To begin with, the Arnold transform is used to encrypt, so that the spatial confidence of the original image has been comprehensively disturbed. Secondly, the XOR involving the fractional order chaotic sequence is used to encrypt. The key sequence is dynamically generated to ensure the randomness and difference of key generation. When decryption is required, the first decryption is performed using the key and XOR. Then, the second decryption is carried out by using the inverse Arnold transform, and finally the decrypted image is obtained. Experimental results show that the improved algorithm has achieved a better performance in encryption and decryption.
A New Simple, Cost-Effective Navigation System (EASY Navigator) for Neurosurgical Interventions

Navigation technique facilitates reaching deeply seated masses more precisely with the least sacrifice of normal tissues. There are 2 primary navigation systems: electromagnetic and infrared (optical). The technique is based on recording the head position intraoperatively and merging this image with preoperative magnetic resonance imaging. We tried to replace these conventional navigating systems with a simple technique using free software (3-dimensional slicer) and a computer air mouse. The study was designed into 3 stages. In stage 1, we used this method on 3-dimensional printed models of patients' magnetic resonance images with deeply seated brain tumors. In stage 2, we performed the surgeries on patients. In the last stage, we used both the conventional navigation system with our method simultaneously on the same patients and compared the accuracy of both methods. This new system was as accurate as conventional navigating systems, ranging from ±2 mm. This new technique, if done properly, can replace conventional methods with less time of preparation, and its cost is almost free.
CUP: Curriculum Learning based Prompt Tuning for Implicit Event Argument Extraction

Implicit event argument extraction (EAE) aims to identify arguments that could scatter over the document. Most previous work focuses on learning the direct relations between arguments and the given trigger, while the implicit relations with long-range dependency are not well studied. Moreover, recent neural network based approaches rely on a large amount of labeled data for training, which is unavailable due to the high labelling cost. In this paper, we propose a Curriculum learning based Prompt tuning (CUP) approach, which resolves implicit EAE by four learning stages. The stages are defined according to the relations with the trigger node in a semantic graph, which well captures the long-range dependency between arguments and the trigger. In addition, we integrate a prompt-based encoder-decoder model to elicit related knowledge from pre-trained language models (PLMs) in each stage, where the prompt templates are adapted with the learning progress to enhance the reasoning for arguments. Experimental results on two well-known benchmark datasets show the great advantages of our proposed approach. In particular, we outperform the state-of-the-art models in both fully-supervised and low-data scenarios.
Career Recommendation for College Students Based on Deep Learning and Machine Learning

With the popularization of higher education, China’s higher education has moved from the stage of elite education to the stage of universal education, and the number of graduates is also increasing. At present, college students are facing tremendous pressure on employment. One is the huge number of jobs, the other is the difference in professional needs, and the third is the spread of job information, which makes it difficult for college students to find a job that suits them. In order to solve this dilemma, this paper analyzes the related technologies and in-depth basic theories of data mining. After introducing several traditional recommendation algorithms, the traditional convolutional network method is improved from three aspects: activation function, pooling strategy, and loss function. Finally, using the hybrid convolutional neural network, a career recommendation model for college students based on deep learning and machine learning is proposed, and simulation experiments are carried out on it. The main research work is as follows: (1) a hybrid convolutional neural network is proposed, which uses convolution operation to learn high-level features to achieve personalized employment recommendation; (2) the training optimization strategy of the hybrid convolutional neural network is studied, aiming at the activation function, pooling processing, and loss function, and the feasibility of the optimization method is verified through simulation experiments; (3) finally, according to the evaluation index of the recommendation algorithm (recall rate and F1-Score), the recall rate of the algorithm in this paper is nearly 15% higher than that of the DNN model. The experiment is compared with the traditional commonly used recommendation algorithm, and the comparative analysis of the experimental results proves the effectiveness of the algorithm for the employment recommendation of college students.
Research on energy supply chain risk prediction based on the fuzzy C-means clustering algorithm

In order to improve the ability of risk prediction, a risk prediction method of energy supply chain based on fuzzy C-means clustering algorithm is proposed.Based on the regression analysis results of risk data samples, panel data fusion is carried out to extract the correlation feature of risk panel data of energy supply chain.Using the prior information distributed detection method to construct the statistical characteristic quantity of energy supply chain risk prediction.According to the prior sample regression analysis results of risk prediction of energy supply chain, the risk characteristics of energy supply chain are extracted, and the fuzzy C-means clustering method is used to cluster the risk characteristics, and the risk prediction of energy supply chain is carried out.The simulation results show that this method has high accuracy and credibility for energy supply chain risk prediction, and improves the risk management ability of energy supply chain.
Demystifying Blockchain in 5G and Beyond Technologies

5G and beyond systems are being built to be prospective by responding to the different needs of a diverse range of applications, which represents a significant departure from this approach. Owing to its openness, encryption techniques, verifiability, integrity, and decentralized design, blockchain has evolved as an important approach. 5G and Blockchain might transform emerging technology. 5G offers consumers high speeds and QoS, while blockchain ensures peer-to-peer trust and privacy. Blockchain is a revolutionary technology having wide range of applications in various domains like Artificial Intelligence, Internet of Things (IoT), Cybersecurity, Wireless networks, 5G and beyond networks, etc. In this paper, the role of Blockchain technology in 5G and beyond networks is discussed. This paper also highlights the various challenges that acts as a hindrance while implementing the Blockchain in 5G and beyond networks. Moreover, the paper also discusses the future aspect of Blockchain in 5G networks.
Smart Anomaly Detection Using Data-Driven Techniques in IoT Edge: A Survey

In this article, we survey the different data-driven techniques for smart anomaly detection at the Internet of Things (IoT) edge. Anomaly detection is a significant study issue because of its wide arrangement of use case, from data analytics to network protection, national security, low-cost solutions, and industrial automation. Since IoT sensors are dynamic in nature, they produce huge volume and multi-dimensional data, it is a complex process to detect anomaly. Machine learning-based edge computing can rectify the difficulties in IoT like network traffic, latency, and security, by automated response and shifting calculation physically nearer to the device edge where the information are generated. Our survey discovered several problems in research and conflicts using data-driven-based abnormality detection techniques for limited gadgets in genuine problems of IoT. Based on our findings, researchers may acquaint themselves with current methods, apply them to real-world issues, and grant to the growth of smart anomaly detection techniques.
Visual Tracking With Siamese Network Based on Fast Attention Network

Visual tracking remains an open challenge, as it requires real-time and long-term accurate target prediction. Siamese network has been widely studied due to its excellent accuracy and speed. Since long-term tracking may lead to model degradation and drift, most existing algorithms cannot well solve this problem. This article proposes a new Siamese Network based on Fast Attention Network named SiamFA. This method designs an attention model, which can enhance the key and global information of the target, to obtain a more robust target model and achieve long-term tracking. At the same time, the attention model is used to obtain the potential position information of the target when calculating the similarity between the template and the search area. In addition, the attention network we design reduces many redundant operations and effectively improves computational efficiency. We utilize a multi-layer perceptron to forecast the bounding box to avoid excessive hyper-parameters. In order to verify the effectiveness of our network, we conduct tests on many commonly used datasets, such as OTB100, GOT-10k, LaSOT, TrackingNet, UAV123. Our method can achieve a success rate of 62.7% and the precision rate of 64.3% on LaSOT. At the same time, it can run at about 100fps, which exceeds the comparison network, proving that our network can run in real-time.
SAODFT: Socially Aware Ontology Driven Approach for Query Facet Generation in Text Classification

Query facet is a collection of topics or information that provides an overall summary of the user query. Only a few words or phrases from the relevant query facet can describe the user query better than the long sentences and this can be helpful during searches made on public search engines. In this chapter, a socially aware and ontology driven query facet generation framework is proposed. The proposed framework takes user query as input which is preprocessed and the obtained individual query words are integrated with domain ontologies. Furthermore, terms and events are integrated from the Twitter API which makes the model socially aware and relatable to real-world social events. The metadata is generated using OpenCalais and RDF Distiller, and is classified using Random Forest, taking individual query words as input and only the top 20% of the classified instances are taken. Similarly, the RandQ dataset is classified by taking individual query words as input using Random Forest. Finally, SemantoSim measure and Gamma Diversity measure is computed between the classified instances from the RandQ dataset and the instances coming from top 20% of the classified metadata and the entities passing through threshold value is formulated into a facet set by grouping them on decreasing value of the semantic similarity. The proposed approach achieves the highest average accuracy with the Precision of 90.61%, Recall of 93.18%, Accuracy of 91.89%, Purity of 0.95, F-Measure of 91.88%, nDCG value of 0.90, and low False Discovery Rate of 0.094 making it better approach than the baseline models.
Multi-modal Masked Pre-training for Monocular Panoramic Depth Completion

In this paper, we formulate a potentially valuable panoramic depth completion (PDC) task as panoramic 3D cameras often produce 360 $$^\circ $$ depth with missing data in complex scenes. Its goal is to recover dense panoramic depths from raw sparse ones and panoramic RGB images. To deal with the PDC task, we train a deep network that takes both depth and image as inputs for the dense panoramic depth recovery. However, it needs to face a challenging optimization problem of the network parameters due to its non-convex objective function. To address this problem, we propose a simple yet effective approach termed M $$^{3}$$ PT: multi-modal masked pre-training. Specifically, during pre-training, we simultaneously cover up patches of the panoramic RGB image and sparse depth by shared random mask, then reconstruct the sparse depth in the masked regions. To our best knowledge, it is the first time that we show the effectiveness of masked pre-training in a multi-modal vision task, instead of the single-modal task resolved by masked autoencoders (MAE). Different from MAE where fine-tuning completely discards the decoder part of pre-training, there is no architectural difference between the pre-training and fine-tuning stages in our M $$^{3}$$ PT as they only differ in the prediction density, which potentially makes the transfer learning more convenient and effective. Extensive experiments verify the effectiveness of M $$^{3}$$ PT on three panoramic datasets. Notably, we improve the state-of-the-art baselines by averagely 29.2% in RMSE, 51.7% in MRE, 49.7% in MAE, and 37.5% in RMSElog on three benchmark datasets.
Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Data

Corrupted labels and class imbalance are commonly encountered in practically collected training data, which easily leads to over-fitting of deep neural networks (DNNs). Existing approaches alleviate these issues by adopting a sample re-weighting strategy, which is to re-weight sample by designing weighting function. However, it is only applicable for training data containing only either one type of data biases. In practice, however, biased samples with corrupted labels and of tailed classes commonly co-exist in training data. How to handle them simultaneously is a key but under-explored problem. In this paper, we find that these two types of biased samples, though have similar transient loss, have distinguishable trend and characteristics in loss curves, which could provide valuable priors for sample weight assignment. Motivated by this, we delve into the loss curves and propose a novel probe-and-allocate training strategy: In the probing stage, we train the network on the whole biased training data without intervention, and record the loss curve of each sample as an additional attribute; In the allocating stage, we feed the resulting attribute to a newly designed curve-perception network, named CurveNet, to learn to identify the bias type of each sample and assign proper weights through meta-learning adaptively. The training speed of meta learning also blocks its application. To solve it, we propose a method named skip layer meta optimization (SLMO) to accelerate training speed by skipping the bottom layers. Extensive synthetic and real experiments well validate the proposed method, which achieves state-of-the-art performance on multiple challenging benchmarks.
Directed Graph Auto-Encoders

We introduce a new class of auto-encoders for directed graphs, motivated by a direct extension of the Weisfeiler-Leman algorithm to pairs of node labels. The proposed model learns pairs of interpretable latent representations for the nodes of directed graphs, and uses parameterized graph convolutional network (GCN) layers for its encoder and an asymmetric inner product decoder. Parameters in the encoder control the weighting of representations exchanged between neighboring nodes. We demonstrate the ability of the proposed model to learn meaningful latent embeddings and achieve superior performance on the directed link prediction task on several popular network datasets.
Performance Analysis of an Adaptive Rate Scheme for QoE-Assured Mobile VR Video Streaming

The emerging 5G mobile networks are essential enablers for mobile virtual reality (VR) video streaming applications assuring high quality of experience (QoE) at the end-user. In addition, mobile edge computing brings computational resources closer to the user equipment (UE), which allows offloading computationally intensive processing. In this paper, we consider a network architecture for mobile VR video streaming applications consisting of a server that holds the VR video content, a mobile edge virtualization with prefetching (MVP) unit that handles the VR video packets, and a head-mounted display along with a buffer, which together serve as the UE. Several modulation and coding schemes with different rates are provided by the MVP unit to adaptively cope with the varying wireless link conditions to the UE and the state of the UE buffer. The UE buffer caches VR video packets as needed to compensate for the adaptive rates. A performance analysis is conducted in terms of blocking probability, throughput, queueing delay, and average packet error rate. To capture the effect of fading severity, the analytical expressions for these performance metrics are derived for Nakagami-m fading on the wireless link from the MVP unit to the UE. Numerical results show that the proposed system meets the network requirements needed to assure the QoE levels of different mobile VR video streaming applications.
NTTGen

Homomorphic encryption (HE) is a promising technique to ensure the security and privacy of applications in the cloud. Number Theoretic Transform (NTT) is a key operation in HE-based applications. HE requires vastly different NTT parameters to meet the performance and security requirements of applications. The increasing compute capabilities and flexibility of FPGAs make them attractive to accelerate NTT. However, programming FPGA still involves hardware design expertise and significant development effort. To close the gap, we propose NTTGen, a framework to automatically generate low latency NTT designs targeting HE-based applications. NTTGen takes application parameters, latency and hardware resource constraints as input, determines the design parameters, and produces synthesizable Verilog code as output. Low latency NTT implementations are obtained by varying the data, pipeline and batch parallelism. NTTGen utilizes streaming permutation network to reduce the interconnect complexity between stages in the NTT computation. The framework supports two types of NTT cores to perform modular arithmetic, the key computation in NTT: a low latency and resource efficient NTT core for a specific class of prime moduli and a general purpose NTT core for other primes. We further develop a design space exploration flow to identify the hardware design parameters of an optimal design. We evaluate NTTGen by generating designs for various NTT parameters. The designs result in up to 2.9X improvement in latency over the state-of-the-art FPGA implementations.
XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression Extraction

Temporal Expression Extraction (TEE) is essential for understanding time in natural language.It has applications in Natural Language Processing (NLP) tasks such as question answering, information retrieval, and causal inference.To date, work in this area has mostly focused on English as there is a scarcity of labeled data for other languages.We propose XLTime, a novel framework for multilingual TEE.XLTime works on top of pre-trained language models and leverages multi-task learning to prompt cross-language knowledge transfer both from English and within the non-English languages.XLTime alleviates problems caused by a shortage of data in the target language.We apply XLTime with different language models and show that it outperforms the previous automatic SOTA methods on French, Spanish, Portuguese, and Basque, by large margins.XL-Time also closes the gap considerably on the handcrafted HeidelTime method.
Zero-shot Sonnet Generation with Discourse-level Planning and Aesthetics Features

Poetry generation, and creative language generation in general, usually suffers from the lack of large training data.In this paper, we present a novel framework to generate sonnets that does not require training on poems.We design a hierarchical framework which plans the poem sketch before decoding.Specifically, a content planning module is trained on non-poetic texts to obtain discourse-level coherence; then a rhyme module generates rhyme words and a polishing module introduces imagery and similes for aesthetics purposes.Finally, we design a constrained decoding algorithm to impose the meter-and-rhyme constraint of the generated sonnets.Automatic and human evaluation show that our multi-stage approach without training on poem corpora generates more coherent, poetic, and creative sonnets than several strong baselines.
Linking entrepreneurship, innovation and economic growth: evidence from GEM countries

Using a simple least squares regression model though analysis of cross-sectional data of the 65 world economies participating in global entrepreneurship monitor (GEM) 2016, this paper explores the role of entrepreneurship, technological innovation, entrepreneurship finance and physical infrastructure as indicators of economic growth. It was empirically found that total entrepreneurial activity (TEA), technological innovation, entrepreneurial finance and physical infrastructure are statistically significant and positively correlated with the economic growth rate of the selected GEM countries in 2016.
An improved genetic clustering architecture for real-time satellite image segmentation

In the last decade, researchers have focused on the development of hardware architectures for several real-life applications including image segmentation. Accurate analysis of segmented high-resolution satellite image help in identifying flood, fire, cloud, snow, and other natural phenomenon. In this paper, an improved genetic clustering architecture is proposed by introducing innovative architectures for crossover and mutation modules. In this architecture, complexity is low due to the use of Manhattan distance instead of traditional Euclidean distance. Testing of the proposed architecture has been carried out on two satellite captured flood images of Myanmar, Burma 2015, and Chennai, India 2015. Both the satellite images have been successfully segmented and obtained satisfactory PSNR and SSIM values, with an improved power consumption of 31 mW and 191 MHz clock frequency. In comparison with state-of-art architectures, the proposed work delivers satisfactory results in terms of power reduction, clock period, design complexity and resource utilization.