Reviewerly/exHarmony huggingface dataset:
Hyperbit: A Financial Temporal Knowledge Graph Data Storage System

In the field of finance, data has the characteristics of temporal multi-frequency and heterogeneous high dimension, and the traditional knowledge graph is not suitable to express the temporal relation of financial data, so the financial temporal knowledge graph has become a research hotspot recently. There are a large number of temporal edges in the financial temporal knowledge graph. The use of general graph database management system to store financial knowledge graph data has problems such as large storage scale and slow update speed, and it cannot realize the traceability of financial data, so it cannot meet the needs of the financial field. In order to solve these problems, HyperBit, a storage system for efficient storage and management of financial data, is designed and implemented. Firstly, a temporal quad storage structure is proposed. Secondly, the Log Structured Cumulative Update (LSCU) is proposed to update financial temporal data efficiently. The update log module is designed in the system. When there is data to update, it is not directly updated in the data storage of the system, but recorded in the update log, and the system updates the content of the update log cumulatively. The system manages update logs by partition and chunk, which makes the log storage compact and independent. Besides, it is convenient for parallel processing and it improves the data update speed. Thirdly, a Bidirectional Backtracking by Final and Optimal Root (BBFOR) method is proposed to meet the requirements of efficient traceability of financial data. Unlike traditional version tracing method, BBFOR adopt both the snapshot and incremental methods which balance the space and time cost of version switching, and BBROF consider the version of the access frequency characteristic and set up the final root and the optimal root. Besides, BBROF adopt bidirectional backtracking technology to improve the efficiency of the version switching. Experimental results show that the storage cost of HyperBit system is 67.1% lower than that of TripleBit. HyperBit is 17 times more efficient in updating financial temporal data than the comparison system RDF-3X. In terms of version switching, the switching efficiency based on BBROF method is improved by 36.0% compared with incremental method.
Fingerprint Voting System (for Afghanistan)

Election is the process of giving opportunity and possibility for a group of people to freely choose of the several or at least two offers or alternatives. The principle of free elections in democratic societies is a mechanism for sharing power and legitimizing government institutions. Most of the democratic countries which are using election for choosing their leader as well as Afghanistan which also a democratic country, has been facing with some problems such as fraud votes. Before most countries are using EVMs for casting votes for elections but sometimes these systems are not working well too. To make the elections clearer and for more accuracy and also to prevent fraud votes like voting more than once or giving vote instead of other person we have designed a fingerprint based system which is made by python language and its libraries will help us to came up with the above problems. Because every human being has his/her own unique fingerprint even twins donâ€™t have the same fingerprint, this method will help us to identify each and every voter by their fingerprint to came up with the above problems regarding to election and voting. Our system will be consisted of two stages, at the first we will save the information of voters include their fingerprints in the database and the second stage will be happening on the day of election which will identify the voters by their fingerprints and system will allow them to vote if they are registered if not then system will do not allow them to cast their vote.
Proposal of Input Screen Design in Eye Glance Input Interface

Text input on smart devices with small screens has problems such as erroneous input due to the small size of each button or a small information presentation screen even if the input screen is large. To solve these problems, a previous study investigated a gaze input interface using the Eye Glance input method as an input method that does not depend on the touch panel area. Input is performed by quickly moving the line of sight back and forth from the center to the four corners of the smartphone. The purpose of this study is to propose an efficient screen design for text input using the Eye Glance input interface. Hereby, the display area is reduced by centering the input screen with a smaller size in accordance with the input operation, and at the same time, it is less likely to cause erroneous input due to unintentional eye movement. In addition, the available input options are arranged to match the input movement of the eye. Further, back taps and winks are considered for switching between input options to achieve a more intuitive input design. In the experiment, data were obtained by asking subjects to enter text using the proposed input screen design. Data analysis results showed that the interface is less prone to error and requires less time to input data, indicating its practicality. In addition, since it is possible to distinguish which eye is performing the winking, it is suggested that this could be used to create even more efficient input screen designs.
Load Balancing Techniques in Cloud Computing

Cloud computing has been drastically changed by the enormous advancement of Internet services. Many centres of Cloud data are known for serving demands of several tenants regarding cloud computing which release huge amount of electricity that leads to high cost of operation and diffusion of carbon dioxide (CO2) in the environment. In order to repair this, preservation is mandatory for the potential use by constructing a fresh structure and to measure its effect in various cloud data centres. As a result, price of processing power is reduced by the usage of pruned electricity. One of the appropriate ways of saving energy is to adjust the balancing of optimal load to meet clod data centres which are energy efficient. Focus is to be maintained upon enhancing the efficacy by dividing the workload equally in order to reduce the enormous use of energy in the cloud data centres. In this paper, assessment of cloud computing in relation to algorithms of balancing load is discussed.
Factors Affecting Successful Implementation of Smart Manufacturing Systems

Although the introduction and utilization of smart manufacturing systems within the industry is accelerating, it is difficult to quantitatively analyze the effects of smart manufacturing systems. It is also difficult to grasp the factors that influence the success of system implementation. In this study, we measured the factors affecting the successful implementation of various types of smart manufacturing systems by confirming the factors affecting user satisfaction of smart manufacturing system. Based on the 282 samples, this study empirically tests a structural model using PLS 3.0 for the explanation of the successful implementation of smart manufacturing system. As a result of this study, it is expected to find that which factors affect to the user satisfaction of the system among the sub-factors representing aspects of information, systemic, organizational, and user background.
An Intelligent Code Smell Detection Technique Using Optimized Rule-Based Architecture for Object-Oriented Programmings

Code smell is a sign that tells something has gone incorrect, somewhere in the code. Such problems are neither bugs nor they are technically wrong. Moreover, it does not prevent the program from its functioning. Code smell indicates the flaws in the design that may be a reason to slow down the development in the future. From software engineer’s perspective, detecting code smell remains a major concern so to enhance maintainability. However, it is a time-consuming task. Current code smell detection tools are not equipped with functionality to assess the parts of code where improvements are required. Hence, they are unable to refactor the actual code. In existing work, they extracted the different instances in the associated method level dataset and experienced tree-based classification methods. In addition, the accuracy of the dataset achieved up to 95 to 98%. In this research work, find the bad smell in the code form like unused empty catch, unused variable, dead code and long method. Besides, the proposed method implements and detects the bad smell using optimization rule-based architecture with Ant Lion Optimization (ALO) algorithms. Bad smell means to write a code in wrong way. It is not a standard form. Bad smell may decreases overall performance of the software system, high time consumption, high space complexity, software maintenance cost and high cost. The performance evaluation of this research is based on detection accuracy and time etc.
Deep Learning-Based Segmentation of Connected Components in Arabic Handwritten Documents

This work proposes a practical and powerful segmentation approach that allows touching or overlapping characters in adjacent text lines or words within Arabic manuscripts to be segmented correctly. It is the first deep learning-based method proposed to solve this problem. It is based on a modified U-Net named AR2U-net: an Attention-based Recurrent Residual U-net model trained to separate touching characters. It is trained on the LTP (Local Touching Patches) database to segment touching characters in a pixel-wise classification. The network labels pixels of the touching characters’ images in four classes: pixels of background, pixels of the first character, pixels of the second character, and those where characters touch. Once the segmentation is done, the separation of touching text lines or words can be done efficiently and speedily. We also propose a post-treatment to segment successive touching text lines in this work. Experimental results on the LTP database show that our proposed method is practical in copes with touching and overlapped characters separation. It achieves higher accuracy of 94.6% than those reported in the state-of-the-art.
Random Forest and AdaBoost-DT: Ensemble Machine Learning Estimators to Model Malayalam Poem Syllable Duration

Text-to-speech synthesis (TTS) is an active area of research to generate synthetic speech from the underlying text. Compared to English and many other European languages, TTS is yet to mature in Malayalam, the principal language of the South Indian state of Kerala. A syllable has to be uttered with proper durational and prosodic characteristics to emulate natural speech. When it comes to poems in Malayalam, many of them have an inherent rhythm attached to them caused by the Vruta in which they have written. This study makes use of nineteen extractible features that determine the duration of utterance from the orthographic representation of poems written in Kakali, Manjari, and Keka Vrutas. Random forest(RF) and AdaBoost-decision tree (AdaBoost-DT)-based estimators are used to model the duration using these features. The hyperparameters of RF are optimized using the RandomizedsearchCV algorithm. The hyperparameters of DT are optimized using AdaBoost regressor and GridsearchCV algorithm. Both the algorithms are imported from the scikit-learn machine learning library. Both the models give superior values for the coefficient of determination (90.55) compared to other major models. Also both the models exhibit lesser values for statistical error indices(SEI) MAE, RMSE, and MAPE.
Based on Big Data the Influence of Chinese Words in Korean on Korean Chinese Teaching

The Chinese characters in Korean system are not only the witness of history, but also the wealth of language and culture exchange. It is a very effective way to make full use of this nearly 60% Chinese characters, whether to teach Korean or Chinese to learn Korean. Based on the historical background of the existence of Chinese words, this paper discusses the differences between Chinese and Korean words, and explores the positive and negative transfer of Chinese words in the process of learning Chinese, and puts forward some strategies for teaching Korean Chinese words. In order to make rational use of Chinese characters to improve the efficiency of Korean vocabulary teaching.
Breast Cancer Mammography Identification with Deep Convolutional Neural Network

Breast cancer is considered as one of the most common invasive diseases in women and a major cause of cancer death. One of the most effective diagnostic methods used for the diagnosis of breast cancer is mammography. However, from last one decade, the use of intelligent-based techniques by the medical expert and radiologists has been suggested by the breast cancer researchers. Especially, the application of deep learning such as convolutional neural network (CNN) in particular is providing effective performance in classifying the mammograms accurately which can assist imaging specialists. For getting an accurate classification of mammograms, the CNN model should be trained with more number of labeled mammograms. But it is not always available to get mammograms more labels. The main purpose of this experiment is to perform a highly accurate classification of mammograms through CNN with dense layers. In our study, we developed two classification models such as the proposed CNN-based models with a single dense layer and CNN with two dense layers. Here, the dense layers act as a backbone for the CNN model for the accurate classification of mammograms. This work is intended to improve the performance of CNN with more dense layers including the preprocessed mammograms having multi-views. The final experiments from our two proposed models show that the first CNN model with a single layer is obtaining 100% accuracy for breast cancer detection in 38.64 s execution time. The second CNN model with two dense layers is also achieving the same result in 42.64 s execution time.
A study of the integration of disciplines and specialties in the context of “double first-class” initiative

From the perspectives of philosophy, planning, mechanism, faculty, curriculum and assessment, this paper mainly deals with the integration of disciplines and specialties within the background of “double first-class” initiative. Overall planning, coordination of related elements, resource allocation optimization, engagement of science and education are essential measures. It is argued that in order to realize the integration of disciplines and specialties successfully, we should carry out the systematic top-level design, advance comprehensive construction with holistic thinking, and promote coordinated development through integration.
Hangul Fonts Dataset: A Hierarchical and Compositional Dataset for Investigating Learned Representations

Hierarchy and compositionality are common latent properties in many natural and scientific image datasets. Determining when a deep network's hidden activations represent hierarchy and compositionality is important both for understanding deep representation learning and for applying deep networks in domains where interpretability is crucial. However, current benchmark machine learning datasets either have little hierarchical or compositional structure, or the structure is not known. This gap impedes precise analysis of a network's representations and thus hinders development of new methods that can learn such properties. To address this gap, we developed a new benchmark dataset with known hierarchical and compositional structure. The Hangul Fonts Dataset (HFD) is comprised of 35 fonts from the Korean writing system (Hangul), each with 11,172 blocks (syllables) composed from the product of initial, medial, and final glyphs. All blocks can be grouped into a few geometric types which induces a hierarchy across blocks. In addition, each block is composed of individual glyphs with rotations, translations, scalings, and naturalistic style variation across fonts. We find that both shallow and deep unsupervised methods show only modest evidence of hierarchy and compositionality in their representations of the HFD compared to supervised deep networks. Thus, HFD enables the identification of shortcomings in existing methods, a critical first step toward developing new machine learning algorithms to extract hierarchical and compositional structure in the context of naturalistic variability.
Improved Wolf Pack Algorithm for UAV Path Planning Problem

In order to obtain the global optimal path of UAV flight path planning in low altitude penetration mode, this paper studies a method of UAV 3d flight path planning. Aiming at the shortcomings of original wolf pack algorithm, such as poor local search ability, invariable step size and slow convergence rate, this paper proposes an improved wolf pack algorithm based on the fluorescein guide mechanism in the artificial glowworm swarm optimization algorithm, which also improves the searching mode in wandering behavior, update the individual position in wolf pack with the teaching and learning optimization algorithms with feedback mechanism. In addition, dynamic mutation strategy was introduced to jump out of the local optimal solution. The results show that the improved algorithm is more effective in solving the problem of UAV 3d trajectory planning.
BPE beyond Word Boundary: How NOT to use Multi Word Expressions in Neural Machine Translation

BPE tokenization merges characters into longer tokens by finding frequently occurring contiguous patterns within the word boundary.An intuitive relaxation would be to extend a BPE vocabulary with multi-word expressions (MWEs): bigrams (in_a), trigrams (out_of _the), and skip-grams (he•his).In the context of Neural Machine Translation (NMT), we replace the least frequent subword/whole-word tokens with the most frequent MWEs.We find that these modifications to BPE end up hurting the model, resulting in a net drop of BLEU and chrF scores across two language pairs.We observe that naively extending BPE beyond word boundaries results in incoherent tokens which are themselves better represented as individual words.Moreover, we find that Pointwise Mutual Information (PMI) instead of frequency finds better MWEs (e.g., N ew_Y ork, Statue_of _Liberty, neither•nor) which consistently improves translation performance.
Human-Algorithm Interactions: Evidence from Zillow.com

Using bi-weekly snapshots of Zillow in three US cities, we document how home sellers and buyers interact with Zillow's Zestimate algorithm during the sales cycle of residential properties. We find that listing and selling outcomes respond significantly to Zestimate, and Zestimate is quickly updated for the focal and comparable houses after a listing or a transaction is completed. The user-Zestimate interactions have mixed implications: on the one hand, listing price depends more on Zestimate if the city does not mandate disclosure of sales information or if the neighborhood is more heterogeneous, suggesting that Zestimate provides valuable information when alternative information is more difficult to obtain; on the other hand, the post-listing update of Zestimate tracks listing price more closely in non-disclosure and heterogeneous neighborhoods, raising the concern that the feedback loop may propagate disturbances in the sales process. However, by leveraging COVID-19 pandemic as a natural experiment, we find no evidence that Zestimate propagates the initial shock from the March-2020 declaration of national emergency, probably because Zestimate has built-in guard rails and users tend to adjust their confidence in Zestimate according to observed market outcomes.
BFLUT Bloom Filter for Private Look Up Tables

Open addressing hash tables, possibly under double hashing policy, are regarded more memory efficient than linked list hashing; as the memory used for pointers can be used for a longer table, and allow better-expected performance as the load factor is smaller and there are fewer expected collisions. We suggest further eliminating the single pointer to the memory location used in each entry of the open addressing, and using a single bit per entry, namely use a Bloom Filter to encode the memory address. Thus, obtain even a better load factor, with the same memory, and less number of wrongly mapped addresses when the load is low. Moreover, we can prove that the content in the lookup table that is based on the bloom filter is pseudo-random (in the level of randomization implied by the hash function), thus, keeping the items and the addresses that the LookUp Table (LUT) encodes private.
Deep Decoding of Strategies

To the best of our knowledge, the application of machine learning and in particular graphical models in the field of quantitative risk management is still a relatively recent and new phenomenon. This paper presents a new and effective methodology for decoding strategies. Given an investment universe, we calculate dynamic weights for a sparseportfolio whose aim is to replicate the strategy with the most stable allocation rules. Naturally, this can be formulated as a reinforcement learning problem whose reward is a weighted sum of tracking error and turnover. We show on stylized examples that we can accurately decode strategies or funds with meaningful factors and allocations.
Remote Sensing Image Captioning via Multilevel Attention-Based Visual Question Answering

Image captioning refers to generating a description for an image. Remote sensing image(RSI) captioning is a cross-field between RSI processing and natural language processing. Most of the RSI suffer from large inter-class similarity. The proposed system uses visual question answering (VQA) to overcome this problem and to aid the caption generation process. VQA model helps the system to identify important words that can be included in the captions. By doing this, the captions include all the objects in the image and the count of those objects, thus making the captions more informative. The process has two phases. The first phase performs VQA using three levels of attention. The model uses the knowledge that it has obtained from the VQA to enhance the captions in the second phase. An overall BLEU score of 78.8% is obtained by the proposed model.
Key Generation

The Vernam cipher of Section 8.5 is the only cryptosystem that has perfect secrecy (Definition 8.5.8 ), because the key is a random sequence of bits of the same length as the message (Proposition 8.5.9 ).
Keystroke-Based Biometric Authentication in Mobile Applications

Keystroke dynamics is the study of how people can be distinguished based on their typing rhythms. It is considered as a real alternative to several authentication mechanisms. In order to improve authentication security in mobile applications, especially mobile chat applications , we propose in this work an approach of user keystroke-based authentication which is based on two important mechanisms, firstly the authentication is done on the basis of a supervised classification model trained on the dynamic keystroke database (android platform) the prediction accuracy has reached high values. When the attacker used the same behavior as the legitimate user we are strengthening security by another authentication mechanism based on three parameters during the opening of a communication session between two users (the number of messages per session, connection time and inter-click time). The proposed system has shown satisfactory results by combining user behaviours and connection parameters during an open session between two users to reinforce the authentication aspect.