Reviewerly/exHarmony huggingface dataset:
Dual Attention Model for Citation Recommendation with Analyses on Explainability of Attention Mechanisms and Qualitative Experiments

Abstract Based on an exponentially increasing number of academic articles, discovering and citing comprehensive and appropriate resources have become non-trivial tasks. Conventional citation recommendation methods suffer from severe information losses. For example, they do not consider the section header of the paper that the author is writing and for which they need to find a citation, the relatedness between the words in the local context (the text span that describes a citation), or the importance of each word from the local context. These shortcomings make such methods insufficient for recommending adequate citations to academic manuscripts. In this study, we propose a novel embedding-based neural network called dual attention model for citation recommendation (DACR) to recommend citations during manuscript preparation. Our method adapts the embedding of three semantic pieces of information: words in the local context, structural contexts,1 and the section on which the author is working. A neural network model is designed to maximize the similarity between the embedding of the three inputs (local context words, section headers, and structural contexts) and the target citation appearing in the context. The core of the neural network model comprises self-attention and additive attention; the former aims to capture the relatedness between the contextual words and structural context, and the latter aims to learn their importance. Recommendation experiments on real-world datasets demonstrate the effectiveness of the proposed approach. To seek explainability on DACR, particularly the two attention mechanisms, the learned weights from them are investigated to determine how the attention mechanisms interpret “relatedness” and “importance” through the learned weights. In addition, qualitative analyses were conducted to testify that DACR could find necessary citations that were not noticed by the authors in the past due to the limitations of the keyword-based searching.
Study of Speech Recognition Using CNN

Artificial Neural Networks is one of the most preferable topics in Machine Learning. Various applications like Google Assistant, Google Translator, Apple Siri, etc are implemented on these Artificial Neural Networks. All these applications are based on Speech Recognition. Nowadays Speech Recognition plays a prominent role in many fields, the machine can recognize words and sentences in spoken language and translate them into machine-readable formats. Different methods of speech recognition include Hidden Markov Model (HMM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), etc. The main objective of this paper is to know how MFCC is helpful in the recognition of speech commands spoken by the speakers using Convolutional Neural Networks. Convolutional neural networks help train the machine to recognize the spoken words by using Speech Recognition.
Toward Blockchain Realization

Since FinTech was stimulated by the invention of blockchain, without the full realization of blockchain technologies in the following years, FinTech has not been fully realized. We discuss some myths and reasons for why blockchain technologies were not fully realized. The lack of distributed synchronization might be the most difficult challenge such that the trust provided by blockchain is not good enough for public use. We propose a mathematical solution with a new consensus mechanism based on general Proof-of-Work mining, called Proof-of-PowerTimestamp, to reach distributed synchronization and reduce power consumption to less than one billionth of Bitcoin. We also discuss related issues toward blockchain realization once the distributed synchronization and energy consumption problems are solved. Since the issues are mostly interdisciplinary or multidisciplinary, researchers are invited to cooperate to help blockchain realization as soon as possible.
A low-power fall detection method based on optimized TBM and RNN

Falls are one of the most common accidental injuries among elderly, which would cause injuries such as bruises and fracture. The fall detection system can reduce injuries by promptly notifying health care providers. Existing methods mainly focus on performance optimization of fall detection algorithm. However, it is unsuitable for the application of wearable devices because it increases the complexity of the algorithm and the power consumption of wearable devices. In this paper, a fall detection technology based on wearable device (placed at the waist) and cloud platform is proposed to minimize the fall-related injuries. Low computational complexity Threshold Based Method is implemented i n wearable device to save power. Furthermore, in the TBM phase, computationally simple acceleration correlation features are selected and the thresholds are optimized to decrease the uploaded data. On the cloud platform, the RNN algorithm with high performance is deployed to recognize suspected fall. Finally, the performance of fall detection system is improved to sensitivity (98.26%) and specificity (99.21%). In conclusion, the algorithm performance and power consumption can be balanced perfectly in this paper.
A New Framework for Quantum Image Processing and Application of Binary Template Matching

Quantum computing is promising for image processing applications with its parallel processing capability. Today, studies are carried out to perform various image processing operations via quantum computing. In this study, a framework for window-based image processing is proposed. After encoding input images, the proposed framework keeps all the values in the relevant window in separate registers, depending on the window size. Window-based operations can be performed in parallel by applying Hadamard gate to the inputs and performing the related operations on the values in the window. The proposed framework is applied for image matching applications, which is an important branch of image processing. By comparing the searched pattern with the values in the window, it is checked whether it matches the searched pattern. Binary values are used to make the application more understandable.
Stream Sediment Geochemical Survey of Rare Earth Elements in and Around Daringbadi, Kandhamal District, Odisha

There are number of Rare Earth Element (REE) bearing minerals, which get into the stream sediment by weathering of host rock. A chemical analysis of the stream sediment samples help to delineate the source rock, as they represent the composition of the drainage basin. So with main objective to identify new prospective areas for possible occurrences of mineral deposits on the basis these stream sediment samples, Geological Survey of India launched a national geochemical mapping programme in 2001. During the FS 2018-19 geochemical mapping was carried out in and around Daringbadi, Kandhamal district, Odisha in parts of toposheet no. 74A01. To study the distribution of REE group of elements in the stream sediment samples of the study area, 364 nos. of stream sediment/slope wash samples were collected on 1km x 1km grid interval constituting 91 nos. of composite samples. The collected samples were processed and analysed by inductively coupled plasma-mass spectrometer (ICP-MS) in GCM Lab, GSI, ER, Kolkata. Analytical results of composite and Soil samples shows that in the study area average value of ΣREE is 666.81 ppm and higher concentration is found in the central part of the study area near Daringbadi village over granite gneiss, khondalite and charnockite rocks. Elements such as Pb and Zr have average concentration 3 to 3.5 times higher than the average UCC value which may be due to the presence of charnockite.
Multimodal sarcasm semantic detection based on inter-modality incongruity

The irony is a complex linguistic phenomenon used to express the opposite of its true meaning and is a more difficult part of sentiment analysis. Multimodal sentiment analysis complements each other with multiple modal data, thus reflecting the user's sentiment tendency more accurately. Sarcasm is often caused by semantic inconsistencies between modalities. In this paper, we focus on multimodal sarcasm semantic detection by analyzing the intra- and inter-modal inconsistencies. Firstly, image features, image attributes are extracted using ResNet, and then text features and label features are extracted using a pre-trained BERT model. The image features with image attribute splicing and text features are input into the self-attentive mechanism, and output inter-modal inconsistency. The text features and label features are input into the common attention mechanism and the output intra-modal inconsistency. After experiments sarcasm, semantic prediction accuracy can reach 86%.
A Parallel Deep Learning Approach for Age Invariant Face Recognition system

Age robust face recognition is a challenging technology to implement in various applications. One of its most critical features is the ability to identify the different facial features due to the varying shapes and textures of the human body. All these variations result in poor recognition performance. Soft computing methodologies have helped it to achieve better recognition results. Deep learning has been regarded as a promising technique in terms of face recognition. However, despite the advancements in this field, many challenges still remain. This paper presents an age-invariant face identification system that uses high-dimensional binary pattern features and a parallel deep convolutional neural network. We performed experiments on two standard datasets, namely the FGNET and the CACD. Our method outperforms the existing techniques and provides a high degree of recognition accuracy.
Fast Constant-Time Modular Inversion over Fp Resistant to Simple Power Analysis Attacks for IoT Applications

The advent of the Internet of Things (IoT) has enabled millions of potential new uses for consumers and businesses. However, with these new uses emerge some of the more pronounced risks in the connected object domain. Finite fields play a crucial role in many public-key cryptographic algorithms (PKCs), which are used extensively for the security and privacy of IoT devices, consumer electronic equipment, and software systems. Given that inversion is the most sensitive and costly finite field arithmetic operation in PKCs, this paper proposes a new, fast, constant-time inverter over prime fields Fp based on the traditional Binary Extended Euclidean (BEE) algorithm. A modified BEE algorithm (MBEEA) resistant to simple power analysis attacks (SPA) is presented, and the design performance area-delay over Fp is explored. Furthermore, the BEE algorithm, modular addition, and subtraction are revisited to optimize and balance the MBEEA signal flow and resource utilization efficiency. The proposed MBEEA architecture was implemented and tested on Xilinx FPGA Virtex #5, #6, and #7 devices. Our implementation over Fp (length of p = 256 bits) with 2035 slices achieved one modular inversion in only 1.12 μs on Virtex-7. Finally, we conducted a thorough comparison and performance analysis to demonstrate that the proposed design outperforms the competing designs, i.e., has a lower area-delay product (ADP) than the reported inverters.
Hypernetwork Representation Learning with the Set Constraint

There are lots of situations that cannot be described by traditional networks but can be described perfectly by the hypernetwork in the real world. Different from the traditional network, the hypernetwork structure is more complex and poses a great challenge to existing network representation learning methods. Therefore, in order to overcome the challenge of the hypernetwork structure faced by network representation learning, this paper proposes a hypernetwork representation learning method with the set constraint abbreviated as HRSC, which incorporates the hyperedge set associated with the nodes into the process of hypernetwork representation learning to obtain node representation vectors including the hypernetwork topology structure and hyperedge information. Our proposed method is extensively evaluated by the machine learning tasks on four hypernetwork datasets. Experimental results demonstrate that HRSC outperforms other best baseline methods by about 1% on the MovieLens and wordnet datasets in terms of node classification, and outperforms the other best baseline methods, respectively, on average by about 29.03%, 1.94%, 26.27% and 6.24% on the GPS, MovieLens, drug, and wordnet datasets in terms of link prediction.
Feature Extraction to Filter Out Low-Quality Answers from Social Question Answering Sites

Social Question Answering sites (SQAs) are online platforms that allow Internet users to ask questions, and obtain answers from others in the community. SQAs have been marred by the problem of low-quality answers. Worryingly, answer quality on SQAs have been reported to be following a downward trajectory in recent years. To this end, existing research has predominantly focused on finding the best answer, or identifying high-quality answers among the available responses. However, such scholarly efforts have not reduced the volume of low-quality answers on SQAs. Therefore, the goal of this research is to extract features in order to weed out low-quality answers as soon as they are posted on SQAs. Data from Stack Exchange was used to carry out the investigation. Informed by the literature, 26 features were extracted. Thereafter, machine learning algorithms were implemented that could correctly identify 85% to 96% of low-quality answers. The key contribution of this research is the development of a system to detect subpar answers on the fly at the time of posting. It is intended to be used as an early warning system that warns users about answer quality at the point of posting.
A Confidence-Aware Cascade Network for Multi-Scale Stereo Matching of Very-High-Resolution Remote Sensing Images

Stereomatching plays an essential role in 3D reconstruction using very-high-resolution (VHR) remote sensing images. However, it still faces unignorable challenges due to the multi-scale objects in large scenes and the multi-modality probability distribution in challenging regions, especially the occluded and textureless areas. Accurate disparity estimation in stereo matching for multi-scale objects has become a hard but crucial task. In this paper, to tackle these problems, we design a novel confidence-aware unimodal cascade and fusion pyramid network for stereo matching. The fused cost volume from the coarsest scale is used to generate the initial disparity map, and then the learnable confidence maps are generated to construct the unimodal cost distributions, which are used to narrow down the next-stage disparity search range. Moreover, we design a cross-scale interaction aggregation module to leverage multi-scale information. Both smooth-L1 loss and stereo focal loss are applied to regularize the disparity map and unimodal cost distribution, respectively. Compared to two state-of-the-art stereo matching networks, extensive experimental results show that our proposed network outperforms them in terms of average endpoint error (EPE) and the fraction of erroneous pixels (D1).
Clustering Merchants and Accurate Marketing of Products Using the Segmentation Tree Vector Space Model

Using social commerce users as the data source, a reasonable and effective interest expression mechanism is used to construct an interest graph of sample users to achieve the purpose of clustering merchants and users as well as realizing accurate marketing of products. By introducing an improved vector space model, the segmentation tree vector space model, to express the interests of the target user group and, on this basis, using the complex network analysis tool Gephi to construct an interest graph, based on the user interest graph, we use Python to implement the K-means algorithm and the users of the sample set according to interest topics for community discovery. The experimental results show that the interests of the sample users are carefully divided, each user is divided into different thematic communities according to different interests, and the constructed interest graph is more satisfactory. The research design of the social commerce user interest mapping scheme is highly feasible, reasonable, and effective and provides new ideas for the research of interest graph, and the boundaries of thematic communities based on interests are clear.
Natural Backdoor Attacks on Deep Neural Networks via Raindrops

Recently, deep learning has made significant inroads into the Internet of Things due to its great potential for processing big data. Backdoor attacks, which try to influence model prediction on specific inputs, have become a serious threat to deep neural network models. However, because the poisoned data used to plant a backdoor into the victim model typically follows a fixed specific pattern, most existing backdoor attacks can be readily prevented by common defense. In this paper, we leverage natural behavior and present a stealthy backdoor attack for image classification tasks: the raindrop backdoor attack (RDBA). We use raindrops as the backdoor trigger, and they are naturally merged with clean instances to synthesize poisoned data that are close to their natural counterparts in the rain. The raindrops dispersed over images are more diversified than the triggers in the literature, which are fixed, confined, and unpleasant patterns to the host content, making the triggers more stealthy. Extensive experiments on ImageNet and GTSRB datasets demonstrate the fidelity, effectiveness, stealthiness, and sustainability of RDBA in attacking models with current popular defense mechanisms.
Entity understanding with hierarchical graph learning for enhanced text classification

Text classification plays an important role in the areas of natural language processing and data mining. In general, a text is usually described around a collection of entities, i.e., the entities are the core part of the text. As a result, a deep understanding of the entities in a text benefits the classification of texts. To understand entities, traditional work tends to introduce concepts or web data for entities. However, we argue that the potential relations between entities are also important for the understanding of entity semantics, thus further supporting the classification of texts. In this paper, we focus on enhancing the performance of the existing text classification models by extracting features from entities with hierarchical graph learning. To this end, we mine the concepts of entities and the relations between them for a given text simultaneously, and further construct the semantic graph of the text. Then a novel hierarchical graph learning model is proposed to learn the graph embedding that well captures the node, relation, and graph structure information. Our experiments show that the proposed method has the ability to effectively improve the performance of the existing text classifiers.
Identification of stream sediment geochemical anomalies in lithologically complex regions: case study of Cu mineralization in Hunan province, SE China

Owing to a strong control the bedrock geology may exert on chemical composition of stream sediments, the determination of stream sediment geochemical anomalies is always affected by lithology background in areas with variable lithologies. In this study, the Expectation-Maximization (EM) algorithm was used to separate lithologies of different chemical compositions in a 1: 200,000 scale regional geochemical data set of stream sediments in a lithologically complex region in Hunan province, SE China. The data set included 1024 minerogenic stream sediment samples which were analysed for Cu, La, Li, Be, Cr, Ni, Sr, V, Th, Ti and Zr. A comparison between Cu anomalies determined with and without taking into account the separation of lithologies was carried out. The result shows that stream sediment geochemical anomalies in lithologically complex regions can be determined in a more reasonable way with application of the EM clustering method. Strong but false or meaningless anomalies could be eliminated, and on the other hand, weak but important or meaningful anomalies were better revealed.
Hybrid Big Bang-Big Crunch based resource scheduling to improve QoS in cloud infrastructure

The service-oriented computing paradigm changes the way of computing. Emerging technologies like grid computing, cloud computing, and smart health care application have changed the way we compute and communicate. Cloud computing has made computing huge data on the fly and uses flexible resources according to the requirement for real-time applications. Cloud computing comes with pay per use model to pay for only those resources that you have used. Inside the cloud there lie many issues related to efficient and cost-effective models to improve cloud performance and complete the client task with the least cost and high performance. E-Health care services are one of the most computational intensive services in the cloud, they require real-time computing which can only be achieved if the computational resources can compute it in the least time. Cloud can accomplish this using an efficient scheduling algorithm. This manuscript focuses on the task scheduling technique which enhances the performance in real-time with the least execution time, network cost, and execution cost. The presented model is inspired by Big Bang-Big Crunch algorithm in astronomy. The presented algorithm enhances the quality of service by reducing the scheduling delay, network delay with the least resource cost to complete the task in the least cost to the user with high quality of service.
Military Information Leak Response Technology through OSINT Information Analysis Using SNSes

Open-source intelligence (OSINT), an information gathering and analysis system that utilizes public information on SNSes, is a necessary information gathering activity to counter terrorism and cyberterrorism. Although it is not possible to patrol cyberspace directly, as in real space, cyberspace can be patrolled by collecting information using OSINT technology. In this study, OSINT information analysis activities related to military information leakage are presented to SNSes. In this study, two or more OSINT collection tools are used to search for military information keywords, for characters’ names, and for personal identification information about the characters. The results of 100,209 cases of military information keyword search and 471 cases of name search are presented. It was also confirmed that personal identification information was not searched because of the strengthening of personal information protection.
Rethinking selfish mining under pooled mining

Bitcoin’s core security requires honest participants to control at least 51% of the total hash power. However, it has been shown that several techniques can exploit the fair mining in the Bitcoin network. This study focuses on selfish mining, which is based on the idea, ”keeping blocks secret.” Herein, we analyze selfish mining regarding competition between mining pools. We emphasize that mining-related information is shared between mining pools and participants. Based on shared information about selfish mining, we have developed an effective and practical counter strategy.