Reviewerly/exHarmony huggingface dataset:
Cryptography using chaotic theory for image encryption and decryption

Nowadays, data transmission plays a vital role because every person sends and receives confidential data every second. The name itself says secret, which should not be ready by others because many use wireless communication to transmit data that is vulnerable but for secure communication. Many introduced several algorithms for encoding and decoding data, but hackers also translated these algorithms. Our idea is to strengthen data transmission security by simultaneously encoding data to the destination using chaotic theory in cryptography. The concept of chaos cannot be easily predictable; like our vision, the data cannot be predictable. We can achieve this by adding weight to data and turn the information on both sides for decoding vice versa. But the innovative part is to repeat this process. The repeating cycle will change the encoding process until the receiver’s end. It will be more challenging to crack the data, so applying chaos with continuous encoding will strengthen the secure transmission of data.
Systematic Literature Review and Qualitative Survey of Blockchain Impact on Social Media Security

In this digital era, everything is done digitally. With the use of digital systems that almost fulfill all aspects of our lives, more and more data exists or is stored. With so much data stored, there is also a lot of data lost or stolen. Blockchain can prevent data loss and increase security and protect our data from third parties. Blockchain allows users to maintain their data in social media using the concept of blockchain, namely, decentralization. In this study, we use a systematic literature review (SLR) for several scientific papers explaining how blockchain algorithms work, such as RSA and SHA-256. Then we compare to choose which one of the algorithms is the best suitable for social media. We also spread some questions to experts and we will ask them to get the solution. This paper concluded the type of algorithm that can be used for social media problems i.e. RSA algorithm and analyzes the answer from experts to find out the impact of blockchain from different aspects.
A Hybrid Approach for Automatic Feedback Generation in Natural Language Programming

Developing a natural language programming (NLPr) tool is challenging due to the complex nature of natural language (NL). NLPr can be brittle and prone to failure when faced with unknown vocabulary or new sentence structures not supported by the system. In the absence of helpful feedback, the time it takes to complete the programming task might increase, and users might be discouraged. Recognizing the necessity of clear and actionable feedback, we propose a novel hybrid automatic feedback generation system in which a formal rule-based method is combined with a data-based multi-label classification (MLC) method to predict the user’s intent and present examples of known-good ways to accomplish the intended or similar tasks. We evaluate the feedback system using two phases of end-user studies, demonstrating its usefulness in helping users troubleshoot their inputs and complete their tasks.
Truncated Cross-entropy: A New Loss Function for Multi-category Classification

Deep learning is one of the hottest research topics which has received a lot of attention in recent years. During the training of deep learning models, the loss function is a critical indicating objective which measures the difference between the predicted value and the distribution of the real data. It is also an important indicator for evaluating the performance of a deep learning model. The most popular loss functions used in deep learning include mean square error (MSE), cross-entropy error, etc. Obviously, the loss function has non-negligible influence to the optimizer. The most common optimizers include stochastic gradient descent method (SGD), mini-batch stochastic gradient descent method (MBGD) and Adaptive moment estimation (ADAM). Among them, the MBGD is widely used for its equilibrium between accuracy and speed. However, how to set the batch size is a big challenge. If the batch size is too large, the cost of computation and memory increases accordingly. On the other hand, the gradient descent process could be more oscillated with small batch size. Therefore, this paper proposes a improved loss function named truncated cross-entropy to stabilize the convergence procedure of the optimizer. Experiments show that the proposed method could speed up the convergence of training and reduce the oscillation. The proposed method can achieve similar performance to large-batch-size training with relatively small batch size.
Data Processing: Neural and Fuzzy Techniques

In the previous chapters, we argued that to make neural techniques more understandable, we need to use fuzzy techniques. In this chapter, we show that this use leads to one more explanation of why rectified linear neurons are so successful.
CRC: Consolidated Rules Construction for Expressive Ensemble Classification

Predictive modelling is one of the most important data mining tasks, where data mining models are trained on data with ground truth information and then applied to previously unseen data to predict the ground truth of a target variable. Ensemble models are often used for predictive modelling, since ensemble models tend to improve accuracy compared with standalone classification models. Although ensemble models are very accurate, they are opaque and predictions derived from these models are difficult to interpret by human analysts. However, explainability of classification models is needed in many critical applications such as stock market analysis, credit risk evaluation, intrusion detection, etc. A recent development of the authors of this paper is ReG-Rules, an ensemble learner that aims to extract a classification (prediction) committee, which comprises the first rule from each base classifier that fired. The rules are interpretable by humans, thus ReG-Rules is a step towards explainable ensemble classification. Since there is a set of matching rules presented to the human analyst for each prediction, there are still numerous rules that need to be considered for explaining the model to the human analyst. This paper introduces an extension of ReG-Rules termed Consolidated Rules Construction (CRC). CRC merges individual base classification models into a single rule set, that is then applied for each prediction. Only one rule is presented to the human analyst per prediction. Therefore, CRC is more explainable than ReG-Rules. Empirical evaluation also shows that CRC is competitive with ReG-Rules with respect to various performance measures.
Single-Stage Real-Time Face Mask Detection

With the battle against COVID-19 entering a more intense stage against the new Omicron variant, the study of face mask detection technologies has become highly regarded in the research community. While there were many works published on this matter, we still noticed three research gaps that our contributions could possibly suffice. Firstly, despite the introduction of various mask detectors over the last two years, most of them were constructed following the two-stage approach and are inappropriate for usage in real-time applications The second gap is how the currently available datasets could not support the detectors in identifying correct, incorrect and no mask-wearing efficiently without the need for data pre-processing. The third and final gap concerns the costly expenses required as the other detector models were embedded into microcomputers such as Arduino and Raspberry Pi. In this paper, we will first propose a modified YOLO-based model that was explicitly designed to resolve the real-time face mask detection problem; during the process, we have updated the collected datasets and thus will also make them publicly available so that other similar experiments could benefit from; lastly, the proposed model is then implemented onto our custom web application for real-time face mask detection. Our resulted model was shown to exceed its baseline on the revised dataset, and its performance when applied to the application was satisfactory with insignificant inference time. Code available at: https://bitbucket.org/indigoYoshimaru/facemask-web
TSHML: Token Shuffling under Haircut Policy Based on Machine Learning

The token shuffling policy determines whether the shuffling parties have shuffling motives. Existing token shuffling policies, such as poisoning policy, haircut policy, and suicide policy, all need to rely on blacklists in the implementation process. Blacklisting itself runs counter to the idea of decentralization. Therefore, how to weaken the blacklist in the token shuffling policy is an urgent problem to be solved. Utilizing the concept of machine learning, this paper proposes a general framework for token shuffle service under incomplete information, mainly Leveraging machine learning to replace the role of nature under incomplete information. Then, according to the haircut policy, the token shuffling service is reduced to an incomplete information game model. Finally, the sequential equilibrium under the incomplete information game based on haircut policy is analyzed by simulation. The simulation results show that in the incomplete information game based on haircut policy, it is an sequential equilibrium that players do not shuffle their tokens.
Artificial Intelligence and Emerging Technologies in Hospitality

Over the past four decades, operations research (OR) has played a key role in solving complex problems in travel. More recently, artificial intelligence (AI) has seen rapid adoption in travel that covers everything from robotic process automation, to cognitive insight, to cognitive engagement. This chapter discusses the role of AI in lodging and its potential to address travel complexity, solve a range of problems, and create new value propositions. This chapter also reviews the role of big data and blockchain technology in travel. Leveraging big data and blockchain is reviewed with a series of examples.
Financial Risk Prediction Method Based on Texting Mining

Based on text mining, this paper prevents and controls financial risks by quantifying the logical relationship of financial risks, and at the same time deals with the problem of unreliable word frequency quantification of financial events. This paper proposes a quantitative analysis method of financial risk logical relationship based on BERT and mutual information combined with domain knowledge and quantifies the relationship in the general data set COPA and the financial domain data set. Based on BERT and mutual information, it can effectively solve the problem of unreliable word frequency quantification. The accuracy rate of this method in the quantification of financial risk logical relationship reaches 80.1%, which is 3.09~37.39% higher than the benchmark model. Only the corpus in the financial field is considered, and the effect on other non-financial corpus is pending inspection. This method can reveal the evolution path of financial risk events and improve the effect of quantifying the logical relationship of financial risk.
ProTAMP: Probabilistic Task and Motion Planning Considering Human Action for Harmonious Collaboration

For the proper functioning of mobile manipulator-type autonomous robot performing complicated tasks in a human-robot coexistence environment, tasks and motions must be planned simultaneously. In such environments, a human and robot should collaborate with each other. Therefore, the robot must act in accordance with the human and avoid useless actions duplicated with those of humans. However, any action undertaken by a human has uncertainty, and thus, predicting them correctly is challenging. This study proposed probabilistic task and motion planning considering both deterministic and probabilistic environment changes caused by robot and human actions temporarily and spatially, respectively. First, the environmental changes were modeled, where the robot is capable of recognizing the possibility of environmental changes. Second, in task planning, the probabilities of each environmental change owing to human actions was minimized. Finally, in motion planning, a movement path connecting each task in a planned order was planned, thereby enabling the robot to perform actions not duplicated with those by a human. Furthermore, the plans generated were compared without considering possibility of human actions and the effectiveness of the proposed method was verified. Consequently, the proposed method was confirmed to reduce the time required for finishing the tasks.
Automatic Dense Tissue Segmentation in Digital Mammography Images Based on Fully Convolutional Network and Intensity-Based Clustering

Mammographic breast percent density (PD) is one of the strongest risk factors associated with the development of breast cancer. As a result, the accurate estimation of PD from screening mammograms is an important problem for breast cancer risk assessment. Nevertheless, automatic segmentation of the dense fibroglandular tissue (FGT) is a difficult task due to the complexity of morphological characteristics and heterogeneity of the breast. In this work, we present a hybrid algorithm based on convolutional neural networks (CNN) and intensity-based clustering used for the fully-automated segmentation of dense tissue in mammograms. We utilize a dataset of 582 mammograms with expert reader’s manually segmented dense tissue areas as a reference. The PD estimates obtained with the proposed method yield a median PD error of 7.7% with no statistically significant differences with respect to the expert. The proposed method is also compared to a clinically validated algorithm
Xeno-autolithic nodules from kimberlites of the “Nadiia” pipe (Eastern Azov region, Ukraine)

Xeno-autolithic nodules from kimberlites of the Nadiia pipe (the Eastern Azov region, Ukraine) have been investigated. Based on petrographic and petrochemical criteria it was concluded that the analyzed nodules were formed according to the principle of the “snowballs” formation in the apical parts of the penetrating magma column at various pressures and temperatures. Nodules originated around xenocrysts, xenoliths and viscous magmatic clots by overgrowing them with orbicular shells under conditions of a mobile medium and flotation gas-liquid differentiation. The nodule shells are represented by porphyric kimberlite, composed of porphyric pseudomorphs of diaphtoritic minerals after olivine, clinopyroxene, phlogopite, biotite and ilmenite, and a groundmass consisted of micron-sized crystallites of calcite, barite, serpentine, perovskite, phlogopite, amphibole, chlorite, formed by crystallization of kimberlite glass of heterogeneous composition. The groundmass also contains newly formed feldspar and quartz. Accessory minerals include zircon, apatite, and rutile. Ore minerals are represented by magnetite, titanomagnetite, ilmenite, nickel and copper sulfides. Due to the assimilation of carbonate rocks by the melt, the composition of the shells, in the direction from the center to the edge of the nodules, changes from serpentine, through chlorite-serpentine to silicate-carbonate. The initial portions of the melt with olivine and phlogopite megacrysts were successively transformed into a homogeneous one. The estimated depth of kimberlite magma generation did not exceed 150 km at the initial crystallization temperatures of the silicate component of 1300-900 °C. Vitrification of the nodules groundmass in the crater zone of diatreme, estimated using monomineral phlogopite geothermometer, occurred at temperatures of 650-550 °C. The absence of diamond paragenesis minerals in nodules, in combination with other mineralogical, petrological and petrochemical criteria indicates that kimberlites of the «Nadiia» pipe are diamond barren.
Recommendation of Clip Templates Based on Cross-Modal Retrieval

Nowadays, the use of video editing software has increased dramatically. However, there is a problem of insufficient intelligence in the recommendation of clip templates in these software. Therefore, this paper addresses this problem and devotes to combining machine learning algorithms and deep learning to achieve optimization of video clip template recommendation, and proposes the design of a clip template recommendation system based on cross-modal retrieval technology. Firstly, the Requests module is used to crawl some data from Baidu images and NetEase cloud music websites and store them persistently as components of user templates to make the templates diverse and meet the needs of more users. Secondly, the algorithm network construction based on PyTorch framework was completed to realize background replacement and music matching, improve the template matching mechanism for users, and generate videos from images; finally, the Android Studio platform was used to develop the APP for Android system, and the Web server was built to realize the data interaction between the client side and the server side, so that users can easily use the APP to get functional experience.
Research on Smart City Platform Based on 3D Video Fusion

At present, It is a pain point that there are much isolated and scattered video data of urban cameras, which cannot provide strong technical support for command and decision-making of managers. This paper studies the smart city scheme with high precision and high real-time display of video data, using video fusion technology to build a digital smart city. The platform architecture includes access layer, fusion rendering layer and presentation layer. The access layer provides access of sensors, cameras as well as models for data processing. The fusion rendering layer realizes the deep integration of multi-channel video into 3D models, which mainly involves camera calibration, image stitching, image projection and rendering optimization. Presentation layer is responsible for interface between a capability set and Web applications. The platform provides abilities of comprehensive and full-space awareness of security and response to emergency, and brings great economic and social benefits. It can be widely used in civil aviation, customs, ports, smart cities and other fields.
INEFFICIENCY OF DATA MINING ALGORITHMS AND ITS ARCHITECTURE: WITH EMPHASIS TO THE SHORTCOMING OF DATA MINING ALGORITHMS ON THE OUTPUT OF THE RESEARCHES

This review paper presents a shortcoming associated to data mining algorithm(s) classification, clustering, association and regression which are highly used as a tool in different research communities. Data mining researches has successfully handling large amounts of dataset to solve the problems. An increase in data sizes was brought a bottleneck on algorithms to retrieve hidden knowledge from a large volume of datasets. On the other hand, data mining algorithm(s) has been unable to analysis the same rate of growth. Data mining algorithm(s) must be efficient and visual architecture in order to effectively extract information from huge amounts of data in many data repositories or in dynamic data streams. The increasing use of information visualization tools (architecture) and data mining algorithm(s) stems from two separate lines of research. Data visualization researchers believe in the importance of giving users an overview and insight into the data distributions. Many powerful visual graphical interfaces are built on top of statistical analysis and data mining algorithms to permit users to leverage their power without a deep understanding of the underlying technology. The combination of the graphical interface is permit to navigate through the complexity of statistical and data mining techniques to create powerful models. Therefore, there is an increasing need to understand the bottlenecks associated with the data mining algorithms in modern architectures and research community. This review paper basically to guide and help the researchers specifically to identify the shortcoming of data mining techniques with domain area in solving a certain problems they will explore. It also shows the research areas particularly a multimedia (where data can be sequential, audio signal, video signal, spatio-temporal, temporal, time series etc) in which data mining algorithms not yet used.
When Left Behind in Multi-User Virtual Reality: Clues to Indicate the Absence of Users

Recently, with the increasing popularity of immersive virtual reality, synchronous communication via avatars in virtual worlds has become more popular. In contrast to asynchronous communication—like social media platforms—people expect others to be present during embodied, virtual interactions. However, for a variety of reasons, this is not always the case.While some communication media provide a good indicator that the other is still present, such as live images of the conversation partner in video conferencing, not all media do provide this information naturally, e.g., when the interlocutor is silent on the phone. In immersive multi-user virtual reality, a wrong and misleading signal may even be provided when an avatar is still shown while its owner is absent. In many situations, an assumed presence of the other who is actually no longer there can lead to confusion. In the presented empirical study, we show that users in shared immersive environments prefer an indicator when a person is absent and that the preferred way of visualization depends on the use case.
Level-Aware Consistent Multilevel Map Translation From Satellite Imagery

With the rapid development of remote sensing technology, the quality of satellite imagery (SI) is getting higher, which contains rich cartographic information that can be translated into maps. However, existing methods either only focus on generating single-level map or do not fully consider the challenges of multilevel translation from satellite imageries, i.e., the large domain gap, level-dependent content differences, and main content consistency. In this article, we propose a novel level-aware fusion network for the SI-based multilevel map generation (MLMG) task. It aims to tackle these three challenges. To deal with the large domain gap, we propose to generate maps in a coarse-to-fine way. To well-handle the level-dependent content differences, we design a level classifier to explore different levels of the map. Besides, we use a map element extractor to extract the major geographic element features from satellite imageries, which is helpful to keep the main content consistency. Next, we design a multilevel fusion generator to generate a consistent multilevel map from the multilevel preliminary map, which further ensures the main content consistency. In addition, we collect a high-quality multilevel dataset for SI-based MLMG. Experimental results show that the proposed method can provide substantial improvements over the state-of-the-art alternatives in terms of both objective metric and visual quality.