Reviewerly/exHarmony huggingface dataset:
Applications of Differential Privacy to Healthcare

This paper examines the potential applications of differential privacy to healthcare data. Differential privacy is a concept that enables the provision of privacy to individuals in a dataset when such data is shared publicly. As differential privacy changes the process for accessing data, rather than the database itself, it can enable patients’ privacy without compromising on the usability of the data. After discussing differential privacy as a concept, we examine the methods for satisfying it effectively in healthcare data. Using the types and characteristics of data in healthcare—including categorical, numeric, integral, and correlated fields—we devise specific techniques for ensuring differential privacy in each data type. We report the following findings. First, differential privacy is satisfied in aggregate categorical data when noise is added from a Laplace distribution with b = 1/ϵ. Second, differential privacy is satisfied in aggregate numeric data when noise is added from a Laplace distribution with b = n/ϵ, where n is the maximum value of the numerical variable. Third, for data fields containing integral values, rounding values drawn from a Laplace distribution provides differential privacy, but drawing only integer values from the Laplace distribution does not. Fourth, when two data fields correlate with each other, noise must be added to only one of them and that field should not be fractional. Fifth, the slope parameter, which is used for health analytics, is lower in magnitude following the addition of noise when compared to the slope parameter in the original dataset. Sixth, the accuracy of analytics following noise addition increases with increases in both the value of ϵ and in the variance of the independent variable. Using actual healthcare data, we confirm this prediction. Finally, the accuracy of analytics following noise addition increases disproportionately with an increase in the value of ϵ when the variance of the independent variable is greater. We again confirm this prediction using actual healthcare data.
Rigorous Predictive Noise Modeling Approach for Model-Based Onset Detection and Enhanced Picking of P-Waves in Seismic Signals

Detecting and picking the onset of P-waves in seismic signals has a fairly rich literature, among which model-based (predictive) approaches hold immense promise. A majority of these models are usually built on certain critical assumptions, namely, stationarity, linearity, and Gaussianity. Despite their criticality, very little reported literature exists on validating these assumptions on real seismic data. Furthermore, the predictive capabilities of these models are not utilized to their best potential in detection. Therefore, there exists a strong need for a rigorous approach to model seismic noise and develop a method that builds on the statistical properties of residuals resulting from the noise models. The objectives of this work are (i) to critically study certain long-held assumptions in seismic noise modeling, (ii) to develop rigorous time-series models for background noise that are commensurate with the noise properties, so as to (iii) devise a residual-based method for detection and enhanced picking of P-waves. An important finding of this work, arising from our study on 185 historical data sets, is that these standard assumptions do not hold for most of the data sets under study; rather, they exhibit additional special features such as heteroskedasticity and integrating effects. Consequent to these novel discoveries, we develop auto-regressive integrated moving average- generalized auto-regressive conditionally heteroskedastic (ARIMA-GARCH) models for seismic noise. The proposed residual-based detector and picker is found to be highly effective with a 90&#x0025; detection rate while picking 91&#x0025; of the events with an accuracy of &#x2264; 0.625 seconds based on tests with 100 historical data sets. Further, when the noise model is used in combination with the existing AIC-based pickers, the number of events picked with an accuracy of &#x2264; 0.625 seconds is 50&#x0025; more than the existing AR-AIC picker. Therefore, the proposed method can be considered highly competitive and effective in P-wave detection and picking.
Application of Computer Technology in the Design of Online and Offline Teaching Mode

With the breakthrough and development of computer technology (CT), online teaching has been developed rapidly, and it has also impacted the existing traditional teaching mode, and has caused great changes in the learning methods of students and teachers. Previously, domestic and foreign scholars' research on online and offline teaching (OFT) models only stayed at the theoretical basis, but now more and more groups are paying attention to the field of OFT, and more people are focusing on the combined application of information technology and teaching models. Combining CT with OFT modes to research and carry out corresponding application practices will help improve the quality of education and give play to the advantages of information technology, so as to keep up with the pace of development of the times. This article mainly adopts the questionnaire survey method to explore the application of CT in the design of OFT models. It investigates and analyzes the attitudes of teachers of different ages on the application of CT to OFT models. According to the survey results, most of the teachers interviewed expressed their preference for the application of CT to OFT models, especially among young teachers.
Named Entity Recognition using Knowledge Graph Embeddings and DistilBERT

Named Entity Recognition (NER) is a Natural Language Processing (NLP) task of identifying entities from a natural language text and classifies them into categories like Person, Location, Organization etc. Pre-trained neural language models (PNLM) based on transformers are state-of-the-art in many NLP task including NER. Analysis of output of DistilBERT, a popular PNLM, reveals that mis-classifications occur when a non-entity word is at a place contextually suitable for an entity. The paper is based on the hypothesis that the performance of a PNLM can be improved by combining it with Knowledge Graph Embeddings (KGE). We show that fine-tuning of DistilBERT along with NumberBatch KGE gives performance improvement over various Open-domain as well as Biomedical-domain datasets.
The Cryptocurrency “Pump-and-Dump”

This chapter describes the author's personal experience as a member of a crypto-trading “pump-and-dump” group – groups organized on Reddit and Discord channels that use social media to spread positive misinformation about a cryptocurrency in order to temporarily inflate its value and collect huge profits. It discusses the nature of cryptocurrency marketplaces, social networking related to crypto, the pump-and-dump phenomenon, its social and economic impacts, and ethical concerns. Following the rise in the value of Bitcoin and the WallStreetBets/GameStop saga in December 2020 and January 2021, these pump-and-dump groups used the frenzy surrounding “get rich quick” investing to generate inordinate profits off of these ambitious individuals' losses. Rallying around a shared philosophy and profit motive, these groups utilized social media disinformation campaigns to fool new crypto investors in squandering their funds, often while failing to acknowledge the legal and ethical conundrum of stealing from the poor and ambitious.
Improving Topic Modeling Performance through N-gram Removal

In recent years, topic modeling has been increasingly adopted for finding conceptual patterns in large corpora of digital documents to organize them accordingly. In order to enhance the performance of topic modeling algorithms, such as Latent Dirichlet Allocation (LDA), multiple preprocessing steps have been proposed. In this paper, we introduce N-gram Removal, a novel preprocessing procedure based on the systematic elimination of a dynamic number of repeated words in text documents. We have evaluated the effects of the utilization of N-gram Removal through four different performance metrics: we concluded that its application is effective at improving the performance of LDA and enhances the human interpretation of topics models.
A Case Study on Cryptocurrencies a Good Investment:The Predictive and Perspective Analysis by Using R-Language

Cryptocurrency which evolved a few years back, is built around facilitating electronic exchange of digital encrypted currency using a peer-to-peer network. Bitcoin being the most popular cryptocurrency, is paving the way and creating disruptions to the existing financial payment systems that have been there for over several decades.While such contemporary types would never replace the traditional, they could pave the way for the global markets to operate seamlessly by clearing away the global barriers specifically in the segment of the exchange rates.There has been considerable advancement in technology but the success of incorporating a given technology is solely governed by the market demands. Cryptocurrencies may in due course revolutionize trading practices with practically minimal to no trading fees in particular.This specific working paper will help investigate the metrics as growth in price and volume of trade for three popular coins namely Bitcoin (BTC), Ethereum (ETH), and Dogecoin (DOGE) and help establish insights for researchers and users to further their study.
Opportunities and Challenges of Cybersecurity for Undergraduate Information Systems Programs

This article investigates the opportunities and challenges of cybersecurity for information systems (IS) programs and proposes a curriculum structure of cybersecurity track for IS programs. The study has collected data from eighty-two course websites of thirteen institutions at the graduate level and sixteen institutions at the undergraduate level as well as twenty descriptions of cybersecurity jobs posted on the internet. The collected qualitative data has been analyzed from the from the perspective of IS education. The findings indicate that the topics of cybersecurity management and essential cybersecurity technology are relevant to the IS discipline. The article suggests that these topics can be the components of two cybersecurity courses offered by IS programs to meet the demands and challenges of cybersecurity.
Feature Descriptor Learning Based on Sparse Feature Matching

The 3D structure reconstruction of endoscopic images is critical for endoscopic-guided surgical navigation systems. Besides, point correspondence estimation of endoscopic images is a critical step to realize 3D structure reconstruction. However, stable and dense matching points are difficult to obtain. We propose a feature descriptor learning method based on sparse feature matching to overcome this limitation. A few matching points were produced for supervised network training by adopting a classical feature matching method, where weight adaptive technique was utilized to mitigate the influence of mismatched points. An end-to-end network architecture was constructed to map endoscopic images to feature descriptor maps and avoid checkerboard artifacts. The proposed method was evaluated on the Stereo Correspondence and Reconstruction of Endoscopic Data and Endoscopic Simultaneous Localization and Mapping datasets. Results showed that our method was able to extract feature descriptors from endoscopic images effectively and simultaneously obtained denser and more accurate matching points.
Energy-Aware Privacy Controls for Clouds

Cloud computing has radically changed the land-scape of computing, storage, and communication infrastructures and services. Cloud computing's benefits encompass on-demand capacity, low cost of ownership, and flexible pricing. While moving towards the concept of on-demand services and resource pooling in distributed computing environments, privacy protection becomes a major concern due to the sharing and consolidation features of clouds. At the same time, blockchain is an ideal privacy protection technology characterized by decentralization, transparency, data security, and system autonomy. In this paper, we navigate leading-edge energy saving and privacy protection techniques for clouds. Next, we investigate privacy controls in blockchain systems. Inspired by modern blockchain and cloud computing techniques, we articulate a research roadmap towards future energy-aware privacy protection mechanisms in clouds. As a case study, we propose a blockchain-based VM consolidation framework accompanied by the DVFS (Dynamic Voltage and Frequency Scaling) technique to offer energy savings and privacy controls in clouds. We expect that the roadmap will open up the potential to develop energy-efficient blockchain-based cloud computing platforms.
Design of MSSTP-SA Net Hardware Accelerator Based on FPGA

Recently, neural network-based methods have achieved great success in many practical applications. However, applications based on these widely used neural networks, such as Convolutional Neural Network (CNN), have a great demand for computing resources. It is difficult to apply to embedded devices. As Field Programmable Gate Array (FPGA) is widely used as an acceleration platform for CNN, more and more network models begin to use FPGA for model deployment. This article uses a Multi-Step Spatial-Temporal Situational-Awareness Network (MSSTP-SA Net) used in the security field as a reference. Firstly, the model is analyzed in depth, and the inference part of the model is optimized according to the demand of inference calculation. The layer fusion method is used to fuse the inference calculation of the batch normalization layer and the convolutional layer of the model, which solves the problem that the batch normalization layer needs to consume extra multiplier resources in FPGA. Then, the convolutional layer of the model is analyzed, and a reasonable parallel scheme of the convolutional layer is designed. And use the method of pipeline to solve the problem of parallelism between layers. Based on the proposed parallel scheme, a dedicated AI accelerator for inference acceleration of the model was designed, and it was deployed on a programmable SOC hardware platform for testing. The results show that the accelerator designed by us only uses 200 multipliers, and it only takes 1.694ms to complete the spatial and time domain decoder inference calculations of the model, which takes less time than the Central Processing Unit(CPU) and Graphics Processing Unit (GPU) to complete the same calculation. And it is far superior to them in terms of performance-to-power ratio.
Intelligent Learning of Digital Finance and Accounting Based on Neural Network Technology

With the rapid economic growth, financial and accounting conditions have become more and more important. This is a summary of the historical process of accounting career development. Practice shows that economic development is inseparable from the update and reform of accounting work, and the content of accounting also needs to keep pace with the times and constantly improve, which requires more knowledge in the classroom. Neural network technology is mainly applied to computer networks to simulate human thinking to solve problems. When digital and intelligent accounting learning combined with neural network, the efficiency of financial learning can be greatly improved. This article uses a combination of theoretical research and empirical research, based on the views and research content of some scholars over the past few years, introduces the basic framework and research content of this article, and then systematically analyzes the data collected through the research. Analyze the data of the intelligent learning of digital accounting, and summarize the relevant characteristics of the neural network plan. This article focuses on exploring the personalized learning mode in the environment of smart learning in finance and accounting to give full play to the effects of smart learning. The final result of the research shows that compared with the traditional teaching model, the test scores of the experimental class of network intelligent learning are 76 points higher than the 70 points of the control class. The intelligent learning environment improves the professional knowledge and knowledge of accounting students. Practical ability is very helpful.
Fast translation method of complex English sentences based on artificial intelligence technology

In order to optimize and improve the fast translation of complex English sentences, this paper proposes a fast translation method of complex English sentences based on artificial intelligence semantic text analysis model. A semantic structure parameter analysis model for rapid translation of English complex sentences is established, and quantitative feature analysis is carried out for rapid translation of English-Chinese complex sentences to obtain a quality analysis scale for rapid translation of English-Chinese complex sentences. The fuzzy decision-making method is adopted to comprehensively investigate the semantic features of sentences and the cognitive features of subjects, and a language cognitive perception model for rapid translation of complex English sentences is established. Through pattern analysis of language syntactic structure, semantic parameters are configured in the semantic text analysis model of artificial intelligence, which forms a text parameter analysis model for rapid translation of complex English sentences. The test results show that the scale analysis ability of this method for rapid translation of English-Chinese complex sentences is strong, the evaluation results are accurate and reliable, and the accuracy of rapid translation of English complex sentences is improved.
System for maintaining effective axial load on the bit when drilling wells

The destruction of rock at the bottom by mechanical means is impossible without creating an axial load on the chisel. The efficiency of the entire process depends on the correct choice of the value and control of this parameter. Control of the axial load on the rock-breaking tool is one of the main methods of controlling the process of rock destruction in any drilling method. In this regard, a device for automating the search for an effective axial load on the bit is proposed, which allows reducing the time to find the optimal values. The operation of the device is based on the method of a braked drum. Its essence is to find the values of the tool weight corresponding to the maximum of the dPoс/dt function, i.e. the maximum of the mechanical drilling speed. The block diagram of the device and the functional diagram of the computing unit are presented. The time diagrams of fluctuations of the axial load on the chisel and the appearance of signals at the outputs of the device blocks are given.
Work in Progress: Immersive Web Environments to Support Pedagogical Activities in Formal Contexts

The present research is in its initial phase and has originated three studies. This meta-study aims to investigate the use of Immersive Web Environments to support pedagogical activities in formal contexts. The general objectives are related to the conceptualization regarding the use and creation of environments suitable for learning contexts, to promote and integrate them and, finally, to search for the most appropriate pedagogical practices for their use. In this article, the conceptualization proposals of the three investigations are described, addressing the ideas and motivations that led to their realization. One of the investigations aims to evaluate the educational potential of immersive web environments in the educational practices of primary and secondary school teachers. In the other, we intend to analyze the interactions between teachers, students, and epistemic objects in an immersive web environment. In the third one, it is intended to analyze the impact and appropriation that students make of immersive web environments, given the fact that they are participating in activities created by them.
Teachers’ perceptions of VR technologies for use in primary school education in the Czech Republic

This study examines the possibilities of using virtual reality (VR) technology in education at the second level of primary schools in the Czech Republic. Due to the relatively new and rapidly developing technology, there is a lack of similarly oriented studies in the Czech Republic. The study uses a focus group method to understand teachers’ perceptions of VR and the possibilities of its use in the educational process. In this regard, the study provides innovative and yet unpublished findings.
Towards Heap-Based Memory Corruption Discovery

Heap-based memory corruption could cause serious hazards such as system crash, denial of service, arbitrary code execution and data leakage. In most cases, these wrong and dangerous behaviors do not immediately lead program to crash. So finding such vulnerabilities in applications is critical for security. However, some existing dynamic analysis tools tend to be specialized for specific classes of heap-based memory vulnerability rather than comprehensive detection of heap-based memory corruption. Some tools do not actively traverses different execation paths and automatically generate test inputs. In this paper, we propose a new method called concolic testing for heapbased memory corruption (CTHM) to discover comprehensive vulnerabilities. We present different heuristics to select initial inputs based on types and numbers of input paramter, which effectively increase the coverage and find the targets to be analyzed as soon as possible. We propose a custom memory model of dynamic symbolic execution, which minimizes the system performance overhead and is strong consistency with the real program running environment. We provide a comprehensive analysis engine, which could detect different types of heap-based memory vulnerabilities and correctly locate their locations. We have implemented a prototype system of CTHM. Through the analysis and comparison of its experimental data, the result shows that CTHM can find nearly 70% more bugs than S2E with only increasing the overhead by 10%.
A Deep Learning Framework for Visual to Caption Translation

In reality videography possesses complicated dynamics; and open-domain interpretation methods should be time-sensitive and enable both variable-size input (frame sequence) and output (word sequence). To address the issue, here we suggest a pioneering head-to-head or sequence-to-sequence model for creating video captions or descriptions. We use recurrent neural networks, particularly Long Short-Term Memory (LSTM) networks, to do this, since they have shown avant-garde success in the production of picture captions. The training of the LSTM model is based on video-phrase pairs and it acquires its knowledge by equating a series of visual frames with a succession of words to produce a summary of the snippet's occurrence. The proposed model V2CT has shown signs to grasp both the temporal structure of a series of frames and the series model of the formed phrases, i.e., a dialect model. The model is trained and tested on a common collection of YouTube videos (MSVD dataset).