Reviewerly/exHarmony huggingface dataset:
Wavelet-Denoised ARIMA and LSTM Method to Predict Cryptocurrency Price

Cryptocurrencies, which occupy a risky position among investment instruments, continue their technological developments day by day with the speed of money transfers and the confidence in the decentralization of production. In this paper, we propose a denoised Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) method for predicting cryptocurrency prices. The daily cryptocurrency price data of Bitcoin (BTC) is collected from a freely available website (cryptocompare.com). For the prediction of BTC/USD price, we considered the average values of daily opening, high, low and closing price as OHLC value. The data set is denoised from white noise using the discrete wavelet transform method (DWT) by VisuShrink thresholding on Daubechies (db4) wavelet at level=5. OHLC and denoised OHLC (DOHLC) values are predicted using ARIMA(p,d,q) and LSTM methods. LSTM hyperparameters are evaluated using 28 different combinations. A pair of Adam-linear optimization and activation function is the best hyperparameter with the lowest mean loss value of 1.42e-03. Finally, DLSTM was found to be the best prediction method according to the Root Mean Squared Error (RMSE) prediction metric: 556.85.
Detection Method of Abnormal Data in Network Transmission Based on Improved Genetic Algorithm

Based on the improvement of genetic algorithm, this paper designs a method for detecting abnormal data in network transmission. Firstly, establish a network transmission abnormal data detection framework based on improved genetic algorithm, analyze the characteristics of network transmission data, then design the network host transmission communication mode, determine the communication direction of one-way flow and two-way flow, and finally design the network transmission abnormality based on improved genetic algorithm The data detection process obtains the update process of the abnormal data detection model. The experimental results show that, compared with the two traditional methods, the nonlinear data classification of the design method is more obvious and disjoint, the nonlinear data classification effect is better, and the recall and precision rates are higher than the common detection methods.
Statistical Analysis of Pollard's Rho Attack on Elliptic Curve Cryptography

Elliptic curve cryptography is a relatively new public-key cryptography mechanism which has been becoming more popular in recent years. Its usage has been increasing in various fields using different cryptographic protocols, such as Elliptic Curve Diffie-Hellman (ECDH), Elliptic Curve Digital Signature Algorithm (ECDSA) and the ElGamal encryption scheme. Several attacks have been developed to solve the elliptic curve discrete logarithm problem. One of the most practical generic attacks is the Pollard's Rho algorithm, which originates as an algorithm for prime factorisation. This paper will attempt to provide a statistical insight on how various factors, namely the private-public key pairs and parameters within the sequence used in Pollard's Rho algorithm, may affect the performance speed of the algorithm. This will be done using the ANOVA statistical test. which shows that from the perspective of the initial values and addends of the sequences, the Pollard's Rho algorithm is “random” enough.
Big Data-based Solutions for Sustainable Digital Services: Evaluation of Research Methods

Modern information technology infrastructure is highly complex, and its monitoring requires the integration of different monitoring tools and management systems. This is especially important for businesses that must be able to provide their digital services in crisis situations, such as the COVID-19 pandemic. This paper identifies research methods suitable to evaluate algorithms for integrated processing of graphs and vertex metrics in data streams. They are identified by means of a literature review. The research finding will serve as an input for further research activities on methods and technological solutions that enable the creation of resilient digital services that are able to adapt to changing contexts and crises, combining big data analysis, knowledge management, business data, and knowledge ecosystems.
Framework Design for Similar Video Detection: A Graph Based Video Clustering Approach

Partial duplication of existing videos is a widespread phenomenon afflicting the world. Old videos are re-circulated to fit an agenda or narrative. Such videos cause misunderstandings, often leading to clashes and violence. It is challenging for media companies to keep track of proprietary videos being trimmed and shared across various content-sharing platforms. Thus, the duplication of videos has social and economic repercussions and is a growing menace in the digital age. In this work, we propose a strategy for efficiently identifying duplicated videos. We create a system that first scans videos for important frames (called keyframes), collects and stores their hash values, and finally matches the hash of the keyframes of a new video with the existing ones in the archive to find a match. We have improved the existing methods of keyframe detection with an additional step of window selection to maintain a smooth transition between keyframes. We have used various hashing algorithms like perceptual hashing, differential hashing, and average hashing to find similarities between keyframes. Also, we have tested our pipeline on a recently published Partial Video Copy Detection (PVCD) dataset, which contains highly perturbed versions of the same video. Our method achieved an accuracy of 94% for a subset of the dataset. We finally propose a graph-based architecture to arrange the videos with similar ones clustered together. For any new video, we follow an innovative search strategy, comparing it with a representative video from each cluster and then iterating through selected clusters with high similarity scores.
Physicochemical and Heavy Metal Properties of Soil Samples in Waste Disposal Site, Suq Al-Shyokh, Iraq

In a number of third-world nations, municipal solid waste (MSW) is deposited on the ground without treatment. The accumulation of heavy metals in the surrounding environment is one of the greatest difficulties, and there is evidence that the toxic substances included in MSW can produce a variety of adverse consequences in humans. At the landfill in Suq al-shyokh City, Iraq, research was done to see if there was a seasonal change in the impact of solid waste discharge on the soil. In the investigation, electrical conductivity of the soil, pH, and the presence of heavy metals in organic matter were all considered. Transect sampling was utilized to gather soil samples from the surface to a depth of 20 centimeters at distances of 10, 30, and 60 meters from the perimeter of the landfills. Soil samples from the Suq al-shyokh dump sites have the nature of silty loam, have little organic matter, and are severely salinized. Increases in soil temperature decreased the soil's capilliary force, causing seasonal variations in the concentrations of specific chemical characteristics. During the two seasons, the average soil pH was 7.26, which is slightly alkaline, and 7.18, which is slightly acidic. Average EC measurements for both seasons revealed an increase in salt concentration (4.80 and 5.4). At the Al-Nasiriyah disposal site, soil physiochemical studies found an alarming upward trend in the vertical distribution of Pb, Cr, Cu, and Cd, which is indicative of the region's high pollution levels. The research revealed that heavy metal contamination of soil endangers the ecology and human health by contaminating groundwater supplies through the food chain.
MDA: Multiple Decentralized Anchors for Hiding Communication Information

This paper presents a novel network architecture based on The Onion Routing (TOR) network and BitTorrent concepts to advance privacy and anonymity in communication networks. In the traditional TOR network, when the transmitter sends a message, any external intruder or the first proxy knows that a message has been sent from a specific location or IP address. The same issue is on the receiver side. This can reveal some privacy about who using the network, the time of activity, and the density of communications. Another issue with the services that provide end-to-end encryption is that the powerful entities can force the service's owner to deliver their encryption keys and other related data that can reveal information about the communications within the network. Therefore, the new architecture decentralizes the network entirely in which nodes are the only fundamental component of the network. The nodes themselves act as hops and control the rules of the network by using a distributed hash table (DHT). There are no proxy servers working as hops in TOR networks or servers to hold DHTs as trackers in the BitTorrent network. The nodes act as an end client and a hop server for other nodes that want to join, leave, and communicate. The actual implementation of the MDA network is under development for peer-to-peer (P2P) messaging. Further development can be made for web browsing, file sharing and video streaming.
A Numerical Fact Extraction Method for Chinese Text

The understanding of text with numerical values is now involved in many application areas and the extraction of numerically relevant and important information from unstructured data is a hot topic of research. The main work in this paper is divided into three parts. The first part is a specification of Chinese numerical fact extraction and annotation, using numerical values as the core of the annotation to find other important information, where numerically measured entities and attributes are the main targets. The second part is the design of extraction methods, using two methods based on deep learning of different task forms as extraction models, namely the NER Combine and Quantity MRC methods. The former uses the sequence annotation task to extract fields, and the combination algorithm based on field distance connects values with other information; The latter uses machine reading comprehension to find its counterpart in other information by introducing numerical information as interrogative sentences. The aim of designing a supervised algorithm based on deep learning is to find the desired target more accurately than an unsupervised algorithm, to avoid the problem of having to exhaust a large number of rules to deal with trivial situations in an unsupervised algorithm, and to benefit from the a priori knowledge and strong representational power of the pre-trained language model to improve the robustness and usability of the extraction results. The third part is experimental verification, which shows the advantages and disadvantages of the two extraction methods in different contexts.
Idea Mining From Online Reviews Using Transformation-Based Natural Language Processing Tasks

Product reviews from online communities are good sources to innovative ideas. Making use of this unstructured data from these reviews is a complicated process. The major task of identifying good ideas is ensuring the correct reviews are picked up for consideration. We propose a method of mining ideas by exploring online reviews data with a particular interest in discovering helpful suggestions in innovation. We use a classifier model to categorize whether the reviews from an online community provide a suggestion. The non-suggestive texts were discarded from the further analysis. The texts are then processed further by a sentence similarity comparison module that uses the Bidirectional Encoder Representations from Transformers (BERT) model. We use the sentence similarity techniques to filter common suggestions that are attractive to many reviewers. We describe how to use BERT based model for Natural Language Tasks. We conclude that mining reviews can help extract ideas from online communities. This is a generic implementation and can be adopted in any domain. We also suggest a human intervention to assess the context of the generated ideas. This method can help researchers and professionals discover ideas buried in unstructured online reviews.
Trustworthy Machine Learning for Securing IoT Systems

This paper first describes the security and privacy challenges for the Internet of Things IoT) systems and then discusses some of the solutions that have been proposed. It also describes aspects of Trustworthy Machine Learning (TML) and then discusses how TML may be applied to handle some of the security and privacy challenges for IoT systems.
Chinese Named Entity Recognition Based on Location Information and Word Frequency

In view of the problem of error propagation caused by the traditional word segmentation error, and the insufficient application of the word vector in the Chinese named entity recognition, this paper proposes an improvement method based on location information and word frequency. Enter it in terms of characters, match the single character and the vocabulary in the vocabulary bank, classify the matched vocabulary according to the position of the characters in the vocabulary, calculate the weight according to the vocabulary of different positions, fuse it with each word vector, and finally join it with the word vector. The splicing results were taken as input to the bidirectional long and short-term network and finally decoded through a conditional random field. Simulations performed on the People’s Daily dataset achieved 95.80% F1 values, better than BiLSTM-CRF, BiLSTM-CNN, etc.
Research on 3D Virtual Image Robust Reconstruction Algorithm Based on Visual Communication

Three-dimensional virtual image reconstruction is a research hotspot in the field of image processing. Aiming at improving the effect of three-dimensional image reconstruction, this paper proposes a three-dimensional virtual image reconstruction algorithm based on visual communication. The proposed algorithm firstly constructs a three-dimensional virtual image visual communication model, and uses the calibrated camera equipment to collect the initial images from multiple dimensions. Secondly, through the steps of image degradation, distortion correction, denoising and segmentation, the preprocessing of the initial image is completed. Finally, the edge information of 3D virtual image is extracted, and the 3D virtual image is reconstructed by feature reconstruction. The experimental results show that the signal-to-noise ratio of the three-dimensional virtual image reconstructed by the proposed algorithm is improved by 1.466, and the matching coefficient with the actual scene is significantly improved, that is, the quality of the three-dimensional virtual image reconstructed by the optimized algorithm is higher.
Research and design of web-based capital transaction data dynamic multi-mode visual analysis tool

For multi-source heterogeneous complex data types of data cleaning and visual display, we proposed to build dynamic multimode visualization analysis tool, according to the different types of data designed by the user in accordance with the data model, and use visualization technology tools to build and use CQRS technology to design, external interface using a RESTFul architecture, The domain model and data query are completely separated, and the underlying data store adopts Hbase, ES and relational database. Drools is adopted in the data flow engine. According to the internal algorithm, three kinds of graphs can be output, namely, transaction relationship network analysis graph, capital flow analysis graph and transaction timing analysis graph, which can reduce the difficulty of analysis and help users to analyze data in a more friendly way
Comprehensive Evaluation Method of Moral Education in Colleges And Universities Based on Viterbi Algorithm

In view of the relatively poor index selection and sorting ability of the current comprehensive evaluation method of moral education in colleges and universities, which leads to serious distortion of the evaluation results, a comprehensive evaluation method of moral education in colleges and universities is proposed. Using the Viterbi method to analyze the factors that affect the quality of moral education of college students, an index system for the evaluation of moral education in colleges is established. Using the analytic hierarchy process, on this basis, the weight of the comprehensive evaluation index of university moral education is calculated. The fuzzy comprehensive evaluation method is used to construct a comprehensive evaluation model of moral education in colleges and universities, and obtain the evaluation results. So far, the design of the comprehensive evaluation method of moral education in colleges and universities based on the Viterbi algorithm is completed. The experimental link is constructed, and the experimental results confirm that the evaluation results of this method have high reliability and are better than the evaluation effect of the current method.
Research on The Integration Algorithm of Online and Offline Hybrid Teaching Resources in Undergraduate Colleges

Aiming at the problems of scattered resources and difficult interaction in the integration of teaching resources in Colleges and universities, this paper puts forward the research of teaching resources integration algorithm. This paper analyzes the research and application background of teaching resource integration, combines data mining technology to mine and identity the characteristics of teaching resources, constructs an evaluation algorithm for teaching resource integration, and constructs a management model for teaching resource integration. Finally, experiments prove that the online and offline hybrid teaching resource integration algorithm of undergraduate colleges has high practicality and accuracy, which fully meets the research requirements.
Closing the Quality Assurance Feedback Loop: Independent Recommender System VALidator

Recommender systems are endemic, increasingly used for social decision-making processes, reducing a complex human to a single data item, and are unfortunately known for bias particularly when used in the context of criminal justice. Administrative demands, ‘doing more with less’, ensure that recommender systems will remain in use to varying degrees in different jurisdictions. Consider the potential circumstances of a judicial officer using a false positive bail recommendation to release a serial rapist into the community, or a false negative bail recommendation to remand a defendant where they have carer's responsibility for elderly parents or children. Quality assurance including quality control activities are standard in other fields, namely medicine, however software quality assurance for recommender systems supporting social decision-making processes has not yet matured, focusing on technical internal system improvements. When the outcome of a data-driven recommender system can erroneously drastically impose on freedom, we need to have reliable quality assurance alongside the ability to invoke our “right to be an exception” [1]. This paper briefly outlines the co-design and testing of a novel quality control validation tool (VAL *©) that highlights such exceptions, independent of recommender systems, with case studies. VAL re-humanizes the defendant for the judicial officer or prosecutor, providing key specific circumstances alongside the recommendation, enabling better informed outcomes.
Research on Entity Update Technology for Fault Diagnosis Knowledge Graph of Cloud Data Center

The Fault Diagnosis Knowledge Graph (FDKG) of Cloud Data Center (CDC), in a broad sense, is the knowledge Digital Twin of the fault phenomenon, reasoning, and maintenance process of Cloud Data Center in the physical world. The key to the Digital Twin is to establish the information interface between the physical space and the virtual space, and the key to the construction of the fault diagnosis KG is also here. FDKG of The State Grid Cloud Data Center needs to integrate multi-source knowledge to establish an information interface with the CDCs of subsidiaries in each province. However, in the process of updating the FDKG, the entity name attribute represented by long sentences reduces the accuracy of entity alignment, and it is difficult to efficiently integrate knowledge into the FDKG without increasing knowledge redundancy. This paper proposes an entity alignment method based on the fusion of attribute and relationship similarity, which will use the clearly defined relationship information in the FDKG to effectively improve the accuracy of entity alignment. The knowledge update tool developed based on this, effectively improves the entity alignment accuracy of the FDKG, and improves the information interface connection efficiency of the FDKG of the CDC.
Low Error Recommendation Method of Mobile Teaching Video Resources For Mental Health In Colleges and Universities Based on Topic Clustering

In order to better improve the management effect of mobile teaching video resources and ensure the mental health of students, this paper proposes a recommendation method of mobile teaching video resources for mental health in colleges and universities based on topic clustering. Constructing a management platform for college mental health mobile teaching video resources, and combining the topic clustering method to construct an interest recognition model for college mental health mobile teaching video resources. We standardize the recommended steps for mobile teaching video resources. Finally, it is confirmed by experiments that the recommendation method of mobile teaching video resources for mental health in colleges and universities based on topic clustering has high practicability and fully meets the research requirements.
Night Fatigue Driving Detection Algorithm based on Lightweight Zero-DCE

Aiming at the problem of low image exposure in low-light scenes at night, resulting in low accuracy of fatigue driving detection, a lightweight Zero-DCE night fatigue driving detection algorithm was proposed The depthwise separable convolution is used in the backbone feature extraction nebv0rk of the Zero-DCE model to improve the speed of the detection nebv0rk and reduce the amount of nebv0rk parameters; the down-sampled input is used as the input of the enhanced nebv0rk, and the output is mapped back to the original resolution by up-sampling. Perf0rm image enhancement, effectively balancing enhancement performance and significantly reducing computational cost. The facial eye and mouth features are detected by the target detection algorithm and the open and closed states are identified and the detection results are calculated and output according to the eye and mouth fatigue parameters combined with the threshold The experimental results show that in the low-light environment at night, the detection algorithm proposed in this paper improves the detection accuracy by 17.07% compared with the existing algorithm, and the detection time after algorithm fusion is 0.012s, which is more in line with the application requirements of fatigue driving detection scenarios.
GAN-based Abnormal Transaction Detection in Bitcoin

Since its inception, blockchain technology attracts great attention from the industry and academia. With its development, cryptocurrencies such as bitcoin based on blockchain technology gradually emerge and enter the financial field. Meanwhile, malicious behaviors aimed at bitcoin become more and more common and cause huge damage to cryptocurrency users and the evolution of blockchain technology, which prompt researchers to establish various models to deal with this problem. In this paper, we collected the historical bitcoin transaction dataset and extracted features from it. After standardizing features, we used an unsupervised learning model based on Generative Adversarial Networks (GAN) to detect dataset containing more than 30 million normal and 108 malicious samples and reached a precision of 23% and recall value close to 100%.