Reviewerly/exHarmony huggingface dataset:
Characterizing Orphan Transactions in the Bitcoin Network

Orphan transactions are those whose parental income sources are missing at the time that they are processed. These transactions typically languish in a local buffer until evicted or all their parents are discovered, at which point they may be propagated further. So far, there has been little work in the literature on characterizing the nature and impact of such orphans. Yet, it is intuitive that they should affect performance of the Bitcoin network. This work thus seeks to methodically research such effects through a measurement campaign of orphan transactions on live Bitcoin nodes. Our data show that, surprisingly, orphan transactions tend to have fewer parents on average than non-orphan transactions. The salient features of their missing parents are a lower fee, a larger size, and a lower transaction fee per byte than all other received transactions. We also find out that the network overhead incurred by these orphan transactions can be significant, exceeding 17% when using the default orphan memory pool size (i.e.,100 transactions). However, this overhead can be made negligible, without significant computational or memory demands, if the pool size is simply increased to 1000 transactions.
Classification of pyrite types using fractal and stepwise factor analyses in the Chah Zard gold-silver epithermal deposit, Central Iran

The major target of this research is the classification of pyrite types using fractal and stepwise factor analyses in the Chah Zard ore deposit, Central Iran. The ore occurs within a breccia/vein type and the major ore mineral hosting gold mineralization is pyrite. In this study, data were selected using optical, scanning electron microscopy (SEM) and backscattered electron observations as well as laser ablation-inductively coupled plasma–mass spectrometry (LA-ICP–MS) analysis. Conventional interpretations represent four gold-bearing pyrite types of various textures including fractured and porous Py1, oscillatory-rimmed and simple-zoned Py2, colloform Py3 and inclusion-rich Py4. The stepwise factor process was performed on the centred log ratio (clr) transformed data in two phases and Au was grouped with As, Te, Ni and Co in the second factor from the second stage (F2-2). Also, C-N fractal modelling was performed on the As, Au, Te and F2-2 values, all of which demonstrate multifractal nature. Four populations were separated based on F2-2 values and the C-N log–log plot. The main gold mineralization starts from 32 ppm, 2.8%, 7.94 ppm and 1.26 for Au, As, Te concentrations and F2-2, respectively, based on the C-N fractal modelling. These values are correlated with inclusion-rich Py4 and simple-zoned and oscillatory-rimmed Py2. The results obtained in this study show that fractal interpretation of LA-ICP–MS data by stepwise factor analysis may provide a suitable tool for the recognition of ore mineralization in epithermal gold deposits.
Understanding the MeToo Movement Through the Lens of the Twitter

In recent years, social media has provided platforms for raising the voice against sexual harassment (SH). The MeToo movement is one such online movement that aims to show the magnitude of this stigmatized issue in society. In particular, on Twitter, which is the focus of this study, has attracted a large number of tweets from all over the world regarding the MeToo movement. The studies of the MeToo movement focus on the SH and sexual assault (SA) incidents but fails to analyze its other hidden facets. In this work, we perform micro-analysis of the MeToo movement using tweets and present a descriptive analysis coupled with macro level tweets analysis in order to reveal and understand the diverse subtopics of the MeToo movement. In addition, we also identify and characterize varied user-groups derived through social network analysis. We find that users discussing a similar facet forms a strong community. Some of the facets out of many being discovered are as follows (1) SH incidents reporting is high for people of color (color means other than white); (2) discussion over color often leads to the use of hate and offensive vocabulary; and (3) along with workplaces, domestic SH cases are higher.
Self-organization of Network Communities via Blockchain Technology: Reputation Systems and Limits of Digital Democracy

The article is devoted to the analysis of the use of blockchain technology for self-organization of network communities. Network communities are characterized by the key role of trust in personal interactions, the need for repeated interactions, strong and weak ties within the network, social learning as the mechanism of self-organization. Therefore, in network communities reputation is the central component of social action, assessment of the situation, and formation of the expectations. The current proliferation of virtual network communities requires the development of appropriate technical infrastructure in the form of reputation systems - programs that provide calculation of network members reputation and organization of their cooperation and interaction. Traditional reputation systems have vulnerabilities in the field of information security and prevention of abusive behavior of agents. Overcoming these restrictions is possible through integration of reputation systems and blockchain technology that allows to increase transparency of reputation assessment system and prevent attempts of manipulation the system and social engineering. At the same time, the most promising is the use of blockchain-oracles to ensure communication between the algorithms of blockchain-based reputation system and the external information environment. The popularization of blockchain technology and its implementation in various spheres of social management, production control, economic exchange actualizes the problems of using digital technologies in political processes and their impact on the formation of digital authoritarianism, digital democracy and digital anarchism. The paper emphasizes that blockchain technology and reputation systems can equally benefit both the resources of government control and tools of democratization and public accountability to civil society or even practices of avoiding government. Therefore, it is important to take into account the problems of political institutionalization, path dependence and the creation of differentiated incentives as well as the technological aspects.
A system for automatically extracting clinical events with temporal information

Abstract Background The popularization of health and medical informatics yields huge amounts of data. Extracting clinical events on a temporal course is the foundation of enabling advanced applications and research. It is a structure of presenting information in chronological order. Manual extraction would be extremely challenging due to the quantity and complexity of the records. Methods We present an recurrent neural network- based architecture, which is able to automatically extract clinical event expressions along with each event’s temporal information. The system is built upon the attention-based and recursive neural networks and introduce a piecewise representation (we divide the input sentences into three pieces to better utilize the information in the sentences), incorporates semantic information by utilizing word representations obtained from BioASQ and Wikipedia. Results The system is evaluated on the THYME corpus, a set of manually annotated clinical records from Mayo Clinic. In order to further verify the effectiveness of the system, the system is also evaluated on the TimeBank _Dense corpus. The experiments demonstrate that the system outperforms the current state-of-the-art models. The system also supports domain adaptation, i.e., the system may be used in brain cancer data while its model is trained in colon cancer data. Conclusion Our system extracts temporal expressions, event expressions and link them according to actually occurring sequence, which may structure the key information from complicated unstructured clinical records. Furthermore, we demonstrate that combining the piecewise representation method with attention mechanism can capture more complete features. The system is flexible and can be extended to handle other document types.
Direct Strong Measurement of a High-Dimensional Quantum State*

It is of great importance to determine an unknown quantum state for fundamental studies of quantum mechanics, yet it is still difficult to characterize systems of large dimensions in practice. Although the scan-free direct measurement approach based on a weak measurement scheme was proposed to measure a high-dimensional photonic state, how weak the interaction should be to give a correct estimation remains unclear. Here we propose and experimentally demonstrate a technique that measures a high-dimensional quantum state with the combination of scan-free measurement and direct strong measurement. The procedure involves sequential strong measurement, in which case no approximation is made similarly to the conventional direct weak measurement. We use this method to measure a transverse state of a photon with effective dimensionality of 65000 without the time-consumed scanning process. Furthermore, the high fidelity of the result and the simplicity of the experimental apparatus show that our approach can be readily used to measure the complex field of a beam in diverse applications such as wavefront sensing and quantitative phase imaging.
From Legal Agreements to Blockchain Smart Contracts

Complex legal agreements enable many real-world applications, from data sharing systems to financial transactions. However, legal expenses scale with complexity because of the manual processes to draft, revise, and enforce agreements. To reduce such costs, we propose a new framework for lawyers to develop machine readable legal agreements, which are automatically verified and deployed on the Ethereum blockchain. Specifically, our framework introduces domain specific repositories to store human and machine readable legal language, a web interface and Python API to draft legal agreements, correctness checking via formal verification, and a voting system for blockchain based adjudication. Experimental evaluation found that our proposed framework offers an efficient verification system, incurs linear scaling of Ethereum blockchain gas consumption in terms of agreement size, and correctly models 81% of conditions in real-world agreements through the domain specific repositories. These results suggest a practical approach for developing verifiable and blockchain compatible legal agreements.
ERDNN: Error-Resilient Deep Neural Networks With a New Error Correction Layer and Piece-Wise Rectified Linear Unit

Deep Learning techniques have been successfully used to solve a wide range of computer vision problems. Due to their high computation complexity, specialized hardware accelerators are being proposed to achieve high performance and efficiency for deep learning-based algorithms. However, soft errors, i.e., bit flipping errors in the layer output, are often caused due to process variation and high energy particles in these hardware systems. These can significantly reduce model accuracy. To remedy this problem, we propose new algorithms that effectively reduce the impact of errors, thus keeping high accuracy. We firstly propose to incorporate an Error Correction Layer (ECL) into neural networks where convolution is performed multiple times in each layer and majority reporting is conducted for the outputs at bit level. We found that ECL can eliminate most errors while bypassing the bit-error when the bits at the same position are corrupted multiple times under the simulated condition. In order to solve this problem, we analyze the impact of errors depending on the position of bits, thus observing that errors in most significant bit (MSB) positions tend to severely corrupt the output of the network compared to the errors in the least significant bit (LSB) positions. According to this observation, we propose a new specialized activation function, called Piece-wise Rectified Linear Unit (PwReLU), which selectively suppresses errors depending on the bit positions, resulting in an increased model resistance against the errors. Compared to existing activation functions, the proposed PwReLU outperforms with large accuracy margins of up-to 20% even with very high bit error rates (BERs). Our extensive experiments show that the proposed ECL and PwReLU work in a complementary manner, achieving comparable accuracy to the error-free networks even at a severe BER of 0.1% on CIFAR10, CIFAR100, and ImageNet.
GIFSL - grafting based improved few-shot learning

A few-shot learning model generally consists of a feature extraction network and a classification module. In this paper, we propose an approach to improve few-shot image classification performance by increasing the representational capacity of the feature extraction network and improving the quality of the features extracted by it. The ability of the feature extraction network to extract highly discriminative features from images is essential to few-shot learning. Such features are generally class agnostic and contain information about the general content of the image. Our approach improves the training of the feature extraction network in order to enable them to produce such features. We train the network using filter-grafting along with an auxiliary self-supervision task and a knowledge distillation procedure. Particularly, filter-grafting rejuvenates unimportant (invalid) filters in the feature extraction network to make them useful and thereby, increases the number of important filters that can be further improved by using self-supervision and knowledge distillation techniques. This combined approach helps in significantly improving the few-shot learning performance of the model. We perform experiments on several few-shot learning benchmark datasets such as mini-ImageNet, tiered-ImageNet, CIFAR-FS, and FC100 using our approach. We also present various ablation studies to validate the proposed approach. We empirically show that our approach performs better than other state-of-the-art few-shot learning methods.
Does Removing Pooling Layers from Convolutional Neural Networks Improve Results?

Due to their number of parameters, convolutional neural networks are known to take long training periods and extended inference time. Learning may take so much computational power that it requires a costly machine and, sometimes, weeks for training. In this context, there is a trend already in motion to replace convolutional pooling layers for a stride operation in the previous layer to save time. In this work, we evaluate the speedup of such an approach and how it trades off with accuracy loss in multiple computer vision domains, deep neural architectures, and datasets. The results showed significant acceleration with an almost negligible loss in accuracy, when any, which is a further indication that convolutional pooling on deep learning performs redundant calculations.
A Simulation-Based Analysis of Multiplayer Selfish Mining

Proof-of-Work (PoW) is the core of the most popular consensus protocol among current mainstream blockchain systems including Bitcoin. Selfish mining attacks are a possible threat towards PoW-based systems and recent works have shown that a miner can profit using selfish mining if it has more than 25% of the overall mining power. In this paper, we present a simulator based on a Markov Process model that can analyze scenarios where there are multiple, independently working selfish miners. In our evaluations, we present detailed results with two attackers showing that the threshold for profitable selfish mining decreases to 21% in this case.
Syllable-Based Indonesian Lip Reading Model

Lip reading is a communication method by reading the lips movement of a speaker. It is also called visual speech recognition, which converts a video into a text. The text is consisting of some words or even sentences spoken by the speakers. One of the challenges often encountered in a lip reading is the high variances of inputs. The variances, like facial features and different speed of speech, can decrease the accuracy. Nowadays, deep learning provides promising results in extracting visual features. In order to be able to use a video as the input, a 3D Deep Learning architecture is exploited. Besides, the out-of-vocabulary (OOV) problem also makes the visual speech recognition system harder to apply in the real world. It can only predict the words appear in the dictionary. However, the vocabulary continues to grow each year, especially in the Indonesian language. It is hard to fit all possible words into the system. Hence, a syllable-based model is proposed in this research to handle such a problem. The syllable-based model gives a chance to build a new word that does not appear in the dictionary. The combination of the existing syllable is used to construct a new word. Since the data obtained too small for deep learning, the augmentation process is performed 40 times. Evaluation using the augmented data, the proposed model reaches a high accuracy of 100% for the testing set. An examination using ten OOV words informs that the developed model gives a lower accuracy of 80%.
Application and Analysis on Geological Hazard Monitoring and Early Warning System Based on Internet of Things

Abstract On the basis of reviewing the development of technology at home and abroad, this paper proposes the architecture, functions, and characteristics of a geological disaster monitoring and early warning system based on the Internet of Things (sensor network). The geological disaster monitoring and early warning system based on the Internet of Things (sensor network) includes on-site sensors and data acquisition processors, (wireless, wired) communication modules, and background data processing and information release system software. The system has the characteristics of intelligence, scale, standardization and real-time, reliability and security. Finally, combined with China’s national conditions, several key technologies and basic theoretical research directions of the geological disaster monitoring and early warning system based on the Internet of Things (sensor network) in China are proposed.
Virtual Observations: a software tool for contextual observation and assessment of user’s actions in virtual reality

Abstract In this paper, we present ‘Virtual Observation’ (VO) a software tool for contextual observation and assessment of user’s directly from within the virtual reality (VR) simulation framework. Unlike other recording systems, the VO system described in this paper focuses on recording and reconstructing VR user’s positional, rotational and input data to recreate the same experience the user had with a VR simulation. Different from animation-based approaches, VO records user inputs and reconstructs the simulation from them and the user positional data. Moreover, the system allows the broadcast of this information to a remote machine enabling remote live observation of the simulation. Datasets recorded by the system can be shared by exporting them as XML files or, optionally, into a standalone online application, such as browser WebGL, allowing researchers, developers and educators to share and review a VR user simulation through a free-moving camera using a web browser. In this paper, the consistency of the data generated from the software by the client, server and reconstructed datasets acquired during real-time live observations was evaluated. We conclude that this Virtual Observation software offers detailed reconstruction of low-level information and visual information of user actions during simulations for both live and offline observations. We envision that our system will be of benefit for researchers, developers and educators that work with VR applications.
Aladdin’s magic carpet: Navigation by in-air static hand gesture in autonomous vehicles

This paper presents a novel and exploratory investigation of how users might control future autonomous vehicles with user-defined in-air static hand gestures. In the era of autonomous vehicles, how to support “driving” without steering control may become a key issue affecting user experience. As the navigation interface of future autonomous cars will be wholly dependent on GPS, and passengers will be unable to make manual control adjustments, verbal or gestural communication will become a primary means to assist them in vehicle control. We thus focus on gesture as an innovative solution for this “final 100 meters” problem in automated navigation. A study (N = 24) conducted in a full chassis simulator shows that hand gesture control is feasible for autonomous vehicle navigation. It further reveals that hand gestures are influenced by task types, local regional norms, and participant culture. In particular, the spatial region where gestures occur can affect execution time, gender can impact user preference and demand, and culture differences and priorities can affect adoption, ease of learning, and user comfort, influencing longer-term use.
Covert storage caches using the NTP protocol

Recently, new methods were discovered to secretly store information in network protocol caches by exploiting functionalities of ARP and SNMP. Such a covert storage cache is referred to as a "Dead Drop". In our present research, we demonstrate that hidden information can also be stored on systems with an active NTP service. We present one method based upon ephemeral associations and one method based upon the most recently used (MRU) list and measure their storage duration and capacity. Our approach improves over the previous approach with ARP as it allows to transport hidden information across the internet and thus outside of local area networks. The preliminary results for both Dead Drops indicate that more than 100 entries with secret data can persist for several hours. Finally, we discuss the detectability and countermeasures of the proposed methods as well as their limitations.
WebShell Attack Detection Based on a Deep Super Learner

WebShell is a common network backdoor attack that is characterized by high concealment and great harm. However, conventional WebShell detection methods can no longer cope with complex and flexible variations of WebShell attacks. Therefore, this paper proposes a deep super learner for attack detection. First, the collected data are deduplicated to prevent the influence of duplicate data on the result. Second, to detect the results of the algorithm, static and dynamic feature are taken as the feature of the algorithm to construct a comprehensive feature set. We then use the Word2Vec algorithm to vectorize the features. During this period, to prevent the outbreak of the number of features, we use a genetic algorithm to extract the validity of the feature dimension. Finally, we use a deep super learner to detect WebShell. The experimental results show that this algorithm can effectively detect WebShell, and its accuracy and recall are greatly improved.
Framework for Task scheduling in Cloud using Machine Learning Techniques

Task scheduling plays a vital role in the function and performance of the cloud computing system. While there exist many approaches for improving task scheduling in the cloud, it is still an open issue. In this proposed framework we try to optimize the utilization of cloud computing resources by using machine learning techniques. Task scheduling algorithms can be designed for static or dynamic scenarios. The proposed framework is for the dynamic scenario. Task scheduling can consider different parameters for scheduling purposes like Makespan, QoS, energy consumption, execution time, and load balancing. We propose to apply a machine learning technique for the incoming task requests so as to classify the best suitable algorithm for the task request rather than randomly assigning the scheduling algorithm. Supervised machine learning techniques can be used here. The outcome of the proposed work leads to the selection of the best task scheduling algorithm for the input task(request).