Reviewerly/exHarmony huggingface dataset:
ArDO

Cultural heritage institutions store and digitize large amounts of multimedia data inside archives to make archival records findable by archivists, scientists, and general public. Cataloging standards vary from archive to archive and, therefore, the sharing and use of this data are limited. To solve this issue, linked open data (LOD) is rising as an essential paradigm to open and provide access to the archival resources. Archives which are opened to the world knowledge benefit from external connections by enabling the application of automated approaches to process archival records, helping all stakeholders to gain valuable insights. In this paper, we present the Archive Dynamics Ontology (ArDO) - an ontology designed for describing the hierarchical nature of archival multimedia data, as well as its application on the example of archival resources about the Weimar Republic. Furthermore, ArDO semantically organizes multimedia archival resources in form of texts, images, audios, and videos by representing the dynamics related to their classification over time. ArDO tracks the changes of a specific hierarchical classification schema referred to as systematics adopted to organize archival resources under semantically defined keywords.
Examining Bitcoin and Economic Determinants: An Evolutionary Perspective

After Nakamoto introduced Bitcoin in 2008 as an alternative online payment system, it became an appealing investment vehicle and an attractive area of study for many researchers. Although much research has been done on the valuation of Bitcoin, comparatively little treatment has been spent on Bitcoin's relationship to established economic indicators, at least in IS research. This study examines Bitcoin across its history using a time-series structural break analysis to differentiate five distinct periods within Bitcoin's evolution. Data obtained includes the open-high-low-close and volume Bitcoin trading data by the minute from October 15, 2010 until January 1, 2020. Within each period, we examine Bitcoin's closing price in relation to an established set of economic indicators that encompass economic health, long-term economic stability, monetary policy, and investor sentiment. We find that Bitcoin has matured from a speculative trading mechanism to an independent investment instrument that is responsive to underlying macroeconomic factors.
An examination of mobile phone pointing in surface mapped spatial augmented reality

We investigate mobile phone pointing in Spatial Augmented Reality (SAR), where digital content is mapped onto the surfaces of a real physical environment. Three pointing techniques are compared: raycast, viewport, and direct. A first experiment examines these techniques in a realistic five-projector SAR environment with representative targets distributed across different surfaces. Participants were permitted free movement, so variations in target occlusion and target view angle occurred naturally. A second experiment validates and further generalizes findings by strictly controlling target occlusion and view angle in a simulated SAR pointing task using an AR HMD. Overall, results show raycast is fastest for non-occluded targets, direct is most accurate, and fastest for occluded targets in close proximity, and viewport falls in between. Using the experiment data, we formulate and evaluate a new Fitts’ model combining two spatial configurations in a SAR pointing task to capture key characteristics, initial target occlusion, target view angle, and user movement. Analysis shows it is a better predictor than previous models.
A robust multi-response VNS-aiNet approach for solving scheduling problems under unrelated parallel machines environments

In the last few years, a lot of researchers addresses scheduling problems considering unrelated parallel machines to bridge theoretical problems and real-world manufacture environments. Most of these works consider objective function’s characteristics to assist the algorithms in exploiting the search space. This assistance usually leads to a better convergence of the algorithms, but it tends to leave these algorithms restricted only to the own optimization criterion studied. This feature hinders to extend these works to optimization criteria with less theoretical visibility. This work proposes an immuno-inspired algorithm for the resolution of scheduling problems under unrelated parallel environments, evaluating the hypothesis that it is possible to construct a robust approach that is extensible to different optimization criteria, achieving results similar to the state-of-art approaches in the literature for specific criteria. Four case studies are proposed, each one addressing an optimization criterion, which is a benchmark for sequencing problems. The criteria are Total Weighted Tardiness, Makespan, Total Tardiness, and Total Weighted Completion Time. The experiments indicate that the results achieved by the proposed approach are compatible and, in many cases, superior to the state-of-art approaches for the optimization criteria studied, using computational resources compatible with those presented by these approaches.
Testing the Local Martingale Theory of Bubbles using Cryptocurrencies

Cryptocurrencies provide the ideal and natural experimental setting to test the local martingale theory of bubbles, because they have no cash flows. Using this theory, we test for the existence of price bubbles in eight cryptocurrencies from January 1, 2019 to July 17, 2019. The cryptocurrencies are Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Ripple (XRP), Bitcoin Cash (BCH), EOS (EOS), Monero (XMR), and Zcash (ZEC). A novel, simple, and robust testing methodology is created to facilitate this estimation. During this time frame, five of the eight currencies (BTC, BCH, EOS, XMR, ZEC) exhibit price bubbles, Litecoin does not, and the evidence for Ethereum and Ripple is inconclusive. The paper provides strong evidence for the prevalence of bubbles in cryptocurrencies and supports the feasibility of applying the local martingale theory of bubbles to various asset classes.
Deep Field-Aware Interaction Machine for Click-Through Rate Prediction

Modeling feature interactions is of crucial importance to predict click-through rate (CTR) in industrial recommender systems. Because of great performance and efficiency, the factorization machine (FM) has been a popular approach to learn feature interaction. Recently, several variants of FM are proposed to improve its performance, and they have proven the field information to play an important role. However, feature-length in a field is usually small; we observe that when there are multiple nonzero features within a field, the interaction between fields is not enough to represent the feature interaction between different fields due to the problem of short feature-length. In this work, we propose a novel neural CTR model named DeepFIM by introducing Field-aware Interaction Machine (FIM), which provides a layered structure form to describe intrafield and interfield feature interaction, to solve the short-expression problem caused by the short feature-length in the field. Experiments show that our model achieves comparable and even materially better results than the state-of-the-art methods.
Improving Text Classification Using Knowledge in Labels

Various algorithms and models have been proposed to address text classification tasks; however, they rarely consider incorporating the additional knowledge hidden in class labels. We argue that hidden information in class labels leads to better classification accuracy. In this study, instead of encoding the labels into numerical values, we incorporated the knowledge in the labels into the original model without changing the model architecture. We combined the output of an original classification model with the relatedness calculated based on the embeddings of a sequence and a keyword set. A keyword set is a word set to represent knowledge in the labels. Usually, it is generated from the classes while it could also be customized by the users. The experimental results show that our proposed method achieved statistically significant improvements in text classification tasks. The source code and experimental details of this study can be found on Github <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> https://github.com/HeroadZ/KiL.
On a hybrid lossless compression technique for three‐dimensional medical images

Abstract In the last two decades, incredible progress in various medical imaging modalities and sensing techniques have been made, leading to the proliferation of three‐dimensional (3D) imagery. Byproduct of such great progress is the production of huge volume of medical images and this big data place a burden on automatic image processing methods for diagnostic assistance processes. Moreover, large amount of medical imaging data needs to be transmitted with no loss of information for the purpose of telemedicine, remote diagnosis etc. In this work, we consider a hybrid lossless compression technique with object‐based features for three‐dimensional (3D) medical images. Our approach utilizes two phases as follows: first we determine the volume of interest (VOI) for a given 3D medical imagery using selective bounding volume (SBV) method, and second the obtained VOI is encoded using a hybrid lossless algorithm using Lembel‐Ziv‐Welch Coding (LZW) followed by arithmetic coding (L to A). Experimental results show that our proposed 3D medical image compression method is comparable with other existing standard lossless encoding methods such as Huffman Coding, Run Length Coding, LZW, and Arithmetic Coding and obtains superior results overall.
Adaptive Ensemble Learning with Concept Drift Detection for Intrusion Detection

Intrusion Detection Systems (IDS) operate in a dynamic real-time environment and are subject to evolving data streams. Recent IDS use machine learning techniques to rapidly identify new attacks and update themselves. However, proposed techniques in literature do not consider the problem of Concept Drift in which data come from different distributions. This paper proposes a technique of adaptation to Concept Drift using ensemble learning for intrusion detection in a network system. The primary objective is minimizing the number of ‘malicious attacks which are marked as genuine’ thereby increasing the ‘Recall’ metric. The technique firstly identifies the change in data distributions and then applies ensemble learning to detect if it is an attack. It is incremental in adapting to the changing data distributions on the go. The experimental dataset was converted into streaming data and input to the model. The proposed technique performed better when compared with logistic regression.
House Price Prediction Using Machine Learning

Machine learning plays an important role in image detection, voice recognition, speech to text conversion, weather forecasting, etc. Safe automobile systems and fine customer services are also provided by machine learning systems. It is a part of artificial intelligence which means making the computers think like humans and solve the problems in the way that we do. In this paper, the prediction of housing prices that are generated by machine learning algorithms are discussed. We compare various prediction methods for the good results. Linear regression is chosen as our model because of the linearity. It makes the prediction procedure simple. We use the model and train the data and then do the testing. Finally, a supervised data is produced which is very essential to estimate the housing price in the real estate business. Our approach of this issue should be successful, and it must predict correctly. In this way, the buyers will get an idea whether to buy a house or not according to their budget.
A Face-off - Classical and Heuristic-based Path Planning Approaches

Robot path planning is a computational problem to find a valid sequence of configurations to move a robot from an initial to a final destination. Several classical and heuristic-based methods exist that can be used to solve the problem. This paper compares the performance of a classical method based on potential field, Lyapunov-based Control Scheme, with those of the standard and stepping ahead Firefly Algorithms. The performance comparison is based on the optimal path distance and time. The results show that the stepping ahead Firefly algorithm finds a shorter path in lesser duration when compared with the Lyapunov-based method. The LbCS also inherently faces the local minima problem when the start, target, and obstacle's center coordinates are collinear. This problem is solved using the firefly algorithm where the diversification of the fireflies helps escape local minima.
Real Time Handwritten Digits Recognition Using Convolutional Neural Network

Reading handwritten information like examination answer sheets is still a difficult task for many of us, because each one of us is having a different interpretation style. As the world is moving towards digitization, converting the handwritten information to a readable digital format reduces the difficulty. This approach will be beneficial for the readers as it gives a better understanding of the information. With the help of machine learning and deep learning algorithms, the handwritten patterns can be recognized and classify them accordingly to a digital format with human level accuracy. This research paper deals with predicting the real time handwritten digits only. To classify the handwritten digits MNIST data set is used for training the model. OpenCV python library is used for detecting the patterns in the real time handwritten digits. These detected patterns are predicted to human level accuracy with the help of a Convolutional Neural Network model.
Deep Learning Based Channel Estimation with Flexible Delay and Doppler Networks for 5G NR

In this paper, we propose a deep learning based algorithm for downlink channel estimation for 5G new radio. The channel estimation block in the downlink plays an important role in mobile device performance. Here, we explore the usage of convolutional neural network (CNN) and compare its performance with conventional and other known CNN based channel estimation methods. The novelty of this method is that, it separates the channel estimation method into three logical blocks. First, obtaining channel coefficients from pilots and filtering for noise reduction. Second, interpolating the channel in frequency domain using the noise filtered channel estimate on pilots. Third, interpolating the channel in time domain across orthogonal frequency-division multiplexing (OFDM) symbols. This architectures allows considerable flexibility in handling various combinations of delay spread (DS) and Doppler spread (DoS) that are possible in practical scenarios. This in turn helps in reducing the complexity and storage requirements. The proposed channel estimation method showed a performance gain of 4 dB in NMSE compared to linear minimum mean square error for clustered delay line channel model at signal to noise ratio of 30 dB, and is robust to mismatches in parameters like DS and DoS because of estimation errors.
A Preliminary Study on Data Security Technology in Big Data Cloud Computing Environment

In the rapid development of Internet technology, the application of various new technologies has become more and more extensive. Among them, the development of big data technology and cloud computing technology is very rapid, and these technologies are of great help to improving the efficiency of data storage and management. However, the data system itself has certain data security problems in the big data cloud computing environment. In order to improve people's information security in the process of data processing and integration, we need to study data security protection technology in the big data cloud computing environment. Only in this way can we solve data and information security issues based on specific conditions and improve the reliability and security of data transmission.
Big Data Hadoop: Security and Privacy

Data is growing in the world due to fame of gregarious media and computer-predicated communication. This plethora of data accumulated from different sources including Emails, IP address, log files, the Internet, by utilizing the hand-held contrivances, convivial media sites and many more. Most popular organization use immensely colossal data for analytics purpose to identify the threat, anomalies and to surmount the chance of cyber-attack. This paper mainly discusses security and privacy issue in immensely colossal data, reviews the present research challenges, traditional privacy methods (De-identification, K-anonymity, L-diversity, T-propinquity) and oversight concerning data confidentiality and security issues. How to reconcile security and privacy, privacy and security concern, different stage privacy-preserving mechanism, challenges present in subsisting security and privacy mechanism, study of an advanced technique (Differential privacy, Identity-predicated anonymization) for immensely colossal data privacy in Sizably voluminous data.
Towards a new image archive for the built environment

The ever-growing online corpus of images of the built environment, on social media and mapping platforms, offers a new kind of archive of the built environment. Recent advances in computer vision, specifically convolutional neural networks, offer new ways of querying and analyzing large image corpuses. In this paper, we propose a new method by which historians of the built environment can use these vast image corpuses in their study, enabling new research questions. To demonstrate proof of need, we report on an ongoing case study in Tel Aviv that attempts to show the feasibility of our proposed method for enabling a Historic Urban Landscapes (HUL)-based approach to the study of the built environment. In so doing, we show how such image corpuses could potentially form a new type of archive for architectural and urban history.
Lossless text compression using GPT-2 language model and Huffman coding

Modern daily life activities produced lots of information for the advancement of telecommunication. It is a challenging issue to store them on a digital device or transmit it over the Internet, leading to the necessity for data compression. Thus, research on data compression to solve the issue has become a topic of great interest to researchers. Moreover, the size of compressed data is generally smaller than its original. As a result, data compression saves storage and increases transmission speed. In this article, we propose a text compression technique using GPT-2 language model and Huffman coding. In this proposed method, Burrows-Wheeler transform and a list of keys are used to reduce the original text file’s length. Finally, we apply GPT-2 language mode and then Huffman coding for encoding. This proposed method is compared with the state-of-the-art techniques used for text compression. Finally, we show that the proposed method demonstrates a gain in compression ratio compared to the other state-of-the-art methods.
Exploring Masculinities and Perceptions of Gender in Online Cybercrime Subcultures

While there is now a substantial literature on the role played by online forums in cybercrime economies, there has been little research which accounts for the role played by gender in these communities. We study the role of gender in cybercrime communities, using an innovative research methodology which makes use of both qualitative and data science elements approaches to analyse a very large sample of posts on a cybercrime forum. Our findings suggest that a substantial subsection of cybercriminal activity associated with these forums is deeply tied up with ideas about gender. A significant number of the actors we studied first became involved in these forums in an attempt to hack, stalk or blackmail an intimate partner (usually a woman). Additionally, once involved in these communities, the performance and commodification of femininity was a key part of many of the 'less-technical' or 'entry-level' forms of cybercrime which we observed. Finally, despite the low technical skill of most of these actors, we found that they still had a deep connection to the 'hacker' identity, using misogyny to legitimise their position within this subculture and construct hacking as intrinsically masculine. We conclude by reflecting on the potential relevance for these findings for policy and intervention approaches in low-level cybercrime communities.
Evolving Character-Level DenseNet Architectures Using Genetic Programming

Densely Connected Convolutional Networks (DenseNet) have demonstrated impressive performance on image classification tasks, but limited research has been conducted on using character-level DenseNet (char-DenseNet) architectures for text classification tasks. It is not clear what DenseNet architectures are optimal for text classification tasks. The iterative task of designing, training and testing of char-DenseNets is a time consuming task that requires expert domain knowledge. Evolutionary deep learning (EDL) has been used to automatically design CNN architectures for the image classification domain, thereby mitigating the need for expert domain knowledge. This study demonstrates the first work on using EDL to evolve char-DenseNet architectures for text classification tasks. A novel genetic programming-based algorithm (GP-Dense) coupled with an indirect-encoding scheme, facilitates the evolution of performant char-DenseNet architectures. The algorithm is evaluated on two popular text datasets, and the best-evolved models are benchmarked against four current state-of-the-art character-level CNN and DenseNet models. Results indicate that the algorithm evolves performant models for both datasets that outperform two of the state-of-the-art models in terms of model accuracy and three of the state-of-the-art models in terms of parameter size.
A Comparative Analysis of Sentiment Analysis Using RNN-LSTM and Logistic Regression

Social media analytics makes a big difference in the success or failure of an organization. The data gathered from social media can be used to get a hit type product by analyzing the data and getting important information about the need of the people. This can be done by implementing sentiment analysis on the available data and then accessing the feelings of the customers about the product or service and knowing if it is actually being liked by them or not. Tracking data of the customers helps the organization in many ways. This study was done to get familiarized with the concept of data analytics and how social media plays an important role in it. Furthermore, Web scraping of Twitter and YouTube data was done following which a standard dataset was selected to do the other analytics. The field of sentiment analysis was used to get the emotions of the people. Logistic regression and RNN-LSTM models were used to perform the same, and then, the results were compared.
Adaptive Matching of Kernel Means

As a promising step, the performance of data analysis and feature learning are able to be improved if certain pattern matching mechanism is available. One of the feasible solutions can refer to the importance estimation of instances, and consequently, kernel mean matching (KMM) has become an important method for knowledge discovery and novelty detection in kernel machines. Furthermore, the existing KMM methods have focused on concrete learning frameworks. In this work, a novel approach to adaptive matching of kernel means is proposed, and selected data with high importance are adopted to achieve calculation efficiency with optimization. In addition, scalable learning can be conducted in proposed method as a generalized solution to matching of appended data. The experimental results on a wide variety of real-world data sets demonstrate the proposed method is able to give outstanding performance compared with several state-of-the-art methods, while calculation efficiency can be preserved.