Reviewerly/exHarmony huggingface dataset:
An Empirical Analysis of Generative Adversarial Network Training Times with Varying Batch Sizes

Increasing the performance of a Generative Adversarial Network (GAN) requires experimentation in choosing the suitable training hyper-parameters of learning rate and batch size. There is no consensus on learning rates or batch sizes in GANs, which makes it a "trial-and-error" process to get acceptable output. Researchers have differing views regarding the effect of batch sizes on run time. This paper investigates the impact of these training parameters of GANs with respect to actual elapsed training time. In our initial experiments, we study the effects of batch sizes, learning rates, loss function, and optimization algorithm on training using the MNIST dataset over 30,000 epochs. The simplicity of the MNIST dataset allows for a starting point in initial studies to understand if the parameter changes have any significant impact on the training times. The goal is to analyze and understand the results of varying loss functions, batch sizes, optimizer algorithms, and learning rates on GANs and address the key issue of batch size and learning rate selection.
The Challenge of Building a Scalable Postcapitalist Commons: The Limits of FairCoin as a Commons‐Based Cryptocurrency

Abstract Postcapitalist commons are a growing area of interest in the efforts to generate alternatives to capitalism in the present. Commons are understood as self‐organised collectives based around shared resources; yet postcapitalist commons have an additional element, in operating within while projecting an “after” capitalism. This can give rise to tensions since commons striving for postcapitalism also require a certain amount of capital to survive and function within capitalism. FairCoop is a radical postcapitalist commons that adopted the cryptocurrency FairCoin in 2014. FairCoop, through FairCoin, was able to generate some trans‐local connections through its use of peer2peer technologies and was thus able to scale‐up. Its design, however, was ultimately unsustainable due to insufficiently clear boundaries from capital. After highlighting the lack of commons boundaries around FairCoop, we identify some additional commons‐capital boundary design principles which could contribute to the sustainability of future postcapitalist commons experiments that are seeking to scale.
Knowledge Discovery Web Service for Spatial Data Infrastructures

The size, volume, variety, and velocity of geospatial data collected by geo-sensors, people, and organizations are increasing rapidly. Spatial Data Infrastructures (SDIs) are ongoing to facilitate the sharing of stored data in a distributed and homogeneous environment. Extracting high-level information and knowledge from such datasets to support decision making undoubtedly requires a relatively sophisticated methodology to achieve the desired results. A variety of spatial data mining techniques have been developed to extract knowledge from spatial data, which work well on centralized systems. However, applying them to distributed data in SDI to extract knowledge has remained a challenge. This paper proposes a creative solution, based on distributed computing and geospatial web service technologies for knowledge extraction in an SDI environment. The proposed approach is called Knowledge Discovery Web Service (KDWS), which can be used as a layer on top of SDIs to provide spatial data users and decision makers with the possibility of extracting knowledge from massive heterogeneous spatial data in SDIs. By proposing and testing a system architecture for KDWS, this study contributes to perform spatial data mining techniques as a service-oriented framework on top of SDIs for knowledge discovery. We implemented and tested spatial clustering, classification, and association rule mining in an interoperable environment. In addition to interface implementation, a prototype web-based system was designed for extracting knowledge from real geodemographic data in the city of Tehran. The proposed solution allows a dynamic, easier, and much faster procedure to extract knowledge from spatial data.
Receptive Field Fusion RetinaNet for Object Detection

In modern convolutional neural network (CNN)-based object detector, the extracted features are not suitable for multi-scale detection and all the bounding boxes are simply ranked according to their classification scores in nonmaximum suppression (NMS). To address the above problems, we propose a novel one-stage detector named receptive field fusion RetinaNet. First, receptive field fusion module is proposed to extract richer multi-scale features by fusing feature maps of various receptive fields. Second, joint confidence guided NMS is proposed to optimize the post-processing process of object detection, which introduce location confidence in NMS and take joint confidence as the NMS rank basis. According to our experimental results, significant improvement in terms of mean of average precision (mAP) can be achieved on average compared with the state-of-the-art algorithm.
Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism

This research paper evaluates the feasibility of cold boot attacks on the Supersingular Isogeny Key Encapsulation (SIKE) mechanism. This key encapsulation mechanism has been included in the list of alternate candidates of the third round of the National Institute of Standards and Technology (NIST) Post-Quantum Cryptography Standardization Process. To the best of our knowledge, this is the first time this scheme is assessed in the cold boot attacks setting. In particular, our evaluation is focused on the reference implementation of this scheme. Furthermore, we present a dedicated key-recovery algorithm for SIKE in this setting and show that the key recovery algorithm works for all the parameter sets recommended for this scheme. Moreover, we compute the success rates of our key recovery algorithm through simulations and show the key recovery algorithm may reconstruct the SIKE secret key for any SIKE parameters for a fixed and small α=0.001 (the probability of a 0 to 1 bit-flipping) and varying values for β (the probability of a 1 to 0 bit-flipping) in the set {0.001,0.01,…,0.1}. Additionally, we show how to integrate a quantum key enumeration algorithm with our key-recovery algorithm to improve its overall performance.
A Schema-Driven Synthetic Knowledge Graph Generation Approach With Extended Graph Differential Dependencies (GDD<sup>x</sup>s)

Knowledge Graphs (KGs), as one of the key trends which are driving the next wave of technologies, have now become a new form of knowledge representation, and a cornerstone for several applications from generic to specific industrial use cases. However, in some specific domains such as law enforcement, a real and large domain-oriented KG is often unavailable due to data privacy concerns. In such domains it is necessary to generate a synthetic KG which mimics the properties of a real KG in the domain. Although during the last two decades, a variety of graph data generators has been proposed to achieve the generation of different kinds of networks, the state-of-the-art synthetic graph data generators are not feasible to generate a realistic and synthetic KGs because KGs always contain data characteristics with specified semantics. In this work, we propose a schema-driven synthetic KG generation approach with extended graph differential dependencies (GDD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sup> ), which is an extension of the recently developed graph entity/differential dependencies that represent formal constraints for graph data to enable the generation of desired graph patterns in synthetic KG. Next, we develop an effective KG generation algorithm that employs the schema and the pre-defined GDD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sup> s. Finally, we evaluate our synthetic KG generator and compare with several state-of-the-art synthetic graph generators. The results from the experiments show that our KG generation method can generate KGs that exhibit the desired graph patterns, node attributes and degree distributions associated with each entity type in the graph's schema.
Longest common subsequence in sublinear space

We present the first o(n)-space polynomial-time algorithm for computing the length of a longest common subsequence. Given two strings of length n, the algorithm runs in O(n3) time with O(nlog1.5⁡n2log⁡n) bits of space.
Automatic Prediction and Insertion of Multiple Emojis in Social Media Text

With the development of social media, many users are attracted by social platforms such as Twitter, Youtube, and TikTok. Emojis can be seen as a visual language inserted in texts to express emotions, attitudes, and situations. It is also widely used in social media communication, e.g., chit-chat and status sharing. The emojis can express more detailed and lively information beyond text information and can help chatbot become more like human beings. Current studies have explored how to predict single emoji according to a set of texts and context information. Some studies stated that people often add multiple emojis in texts and the different inserted positions corresponding to emojis' different functions. However, there is no study on inserting multiple emojis in texts. The latest advances in natural language processing and neural approaches have made it possible for chatbots to automatically add multiple emojis in chatbot's dialogue. In this article, we first construct a benchmark dataset contains more than 3.9 million comments in the video-sharing website Bilibili, and then analyze the features of emoji's usage and the relationships between emojis. Finally, a neural-based model is proposed to predict and insert multiple emojis in social media texts. The experiments and evaluation show our model got significant performance on predict and insert multiple emojis according to given sentences.
Online Multiple Athlete Tracking with Pose-Based Long-Term Temporal Dependencies

This paper addresses the Multi-Athlete Tracking (MAT) problem, which plays a crucial role in sports video analysis. There exist specific challenges in MAT, e.g., athletes share a high similarity in appearance and frequently occlude with each other, making existing approaches not applicable for this task. To address this problem, we propose a novel online multiple athlete tracking approach which make use of long-term temporal pose dynamics for better distinguishing different athletes. Firstly, we design a Pose-based Triple Stream Network (PTSN) based on Long Short-Term Memory (LSTM) networks, capable of modeling long-term temporal pose dynamics of athletes, including pose-based appearance, motion and athletes’ interaction clues. Secondly, we propose a multi-state online matching algorithm based on bipartite graph matching and similarity scores produced by PTSN. It is robust to noisy detections and occlusions due to the reliable transitions of multiple detection states. We evaluate our method on the APIDIS, NCAA Basketball and VolleyTrack databases, and the experiment results demonstrate its effectiveness.
A Framework of Abnormal Behavior Detection and Classification Based on Big Trajectory Data for Mobile Networks

Big trajectory data feature analysis for mobile networks is a popular big data analysis task. Due to the large coverage and complexity of the mobile networks, it is difficult to define and detect anomalies in urban motion behavior. Some existing methods are not suitable for the detection of abnormal urban vehicle trajectories because they use the limited single detection techniques, such as determining the common patterns. In this study, we propose a framework for urban trajectory modeling and anomaly detection. Our framework takes into account the fact that anomalous behavior manifests the overall shape of unusual locations and trajectories in the spatial domain as well as the way these locations appear. Therefore, this study determines the peripheral features required for anomaly detection, including spatial location, sequence, and behavioral features. Then, we explore sports behaviors from the three types of features and build a taxi trajectory model for anomaly detection. Anomaly detection, including sports behaviors, are (i) detour behavior detection using an algorithm for global router anomaly detection of trajectories having a pair of same starting and ending points; this method is based on the isolation forest algorithm; (ii) local speed anomaly detection based on the DBSCAN algorithm; and (iii) local shape anomaly detection based on the local outlier factor algorithm. Using a real-life dataset, we demonstrate the effectiveness of our methods in detecting outliers. Furthermore, experiments show that the proposed algorithms perform better than the classical algorithm in terms of high accuracy and recall rate; thus, the proposed methods can accurately detect drivers’ abnormal behavior.
Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training

Unsupervised neural machine translation (UNMT) has recently attracted great interest in the machine translation community.The main advantage of the UNMT lies in its easy collection of required large training text sentences while with only a slightly worse performance than supervised neural machine translation which requires expensive annotated translation pairs on some translation tasks.In most studies, the UMNT is trained with clean data without considering its robustness to the noisy data.However, in real-world scenarios, there usually exists noise in the collected input sentences which degrades the performance of the translation system since the UNMT is sensitive to the small perturbations of the input sentences.In this paper, we first time explicitly take the noisy data into consideration to improve the robustness of the UNMT based systems.First of all, we clearly defined two types of noises in training sentences, i.e., word noise and word order noise, and empirically investigate its effect in the UNMT, then we propose adversarial training methods with denoising process in the UNMT.Experimental results on several language pairs show that our proposed methods substantially improved the robustness of the conventional UNMT systems in noisy scenarios.
Comparing heuristic evaluation and MALTU model in interaction evaluation of ubiquitous systems

Ubiquitous systems are made up of sensors and smart devices that communicate with each other to achieve certain goals. They have specific characteristics, such as transparency, attention, calmness, mobility, and context-awareness. Given that the Human-Computer Interaction (HCI) evaluation methods have been proposed for traditional systems, these characteristics present challenges that may require their adaptation. In this paper, we conducted an exploratory study aimed to compare the suitability of Heuristic Evaluation and the MALTU Model to evaluate the Usability and User eXperience (UX) of ubiquitous systems. For this, we apply both methods for evaluating a healthcare system and perform a comparative analysis. From our findings, MALTU is indicated for UX evaluation, being a low-cost approach when compared to Heuristic Evaluation, which is indicated for usability evaluation. Both methods were able to identify the problems that emerge from ubiquity and can provide better results when applied in aggregate. Furthermore, we provide recommendations to apply both methods for the Usability and UX evaluation of ubiquitous systems.
Online Monitoring for Neural Network Based Monocular Pedestrian Pose Estimation

Several autonomy pipelines now have core components that rely on deep learning approaches. While these approaches work well in nominal conditions, they tend to have unexpected and severe failure modes that create concerns when used in safety-critical applications, including self-driving cars. There are several works that aim to characterize the robustness of networks offline, but currently there is a lack of tools to monitor the correctness of network outputs online during operation. We investigate the problem of online output monitoring for neural networks that estimate 3D human shapes and poses from images. Our first contribution is to present and evaluate model-based and learning-based monitors for a human-pose-and-shape reconstruction network, and assess their ability to predict the output loss for a given test input. As a second contribution, we introduce an Adversarially-Trained Online Monitor (ATOM) that learns how to effectively predict losses from data. ATOM dominates model-based baselines and can detect bad outputs, leading to substantial improvements in human pose output quality. Our final contribution is an extensive experimental evaluation that shows that discarding outputs flagged as incorrect by ATOM improves the average error by 12.5%, and the worst-case error by 126.5%.
Individual Identification Technology of Communication Radiation Sources Based on Deep Learning

With the rapid development of wireless communication, the types of radiation sources are numerous and complex, and the signals are diversified. Traditional methods of identifying individual radiation sources may no longer meet the needs of society. The identification of individual wireless radiation sources is of great significance for ensuring the security of communication systems and improving the ability of military communication reconnaissance and countermeasures, but most of them use traditional identification methods. This article introduces deep learning as a classification method. Since there is no suitable public data set, we use 6 USRP devices of the same model, combined with LabVIEW software, in a laboratory environment to collect the IQ signals emitted by 5 individual radiation sources, perform preprocessing, and then invest in neural network training. The collected data can be used as a public data set for individual identification in the future. Using a variety of neural network structures and adjusting the parameters, we have obtained a more satisfactory classification effect.
Digital Image Forensic Approach to Counter the JPEG Anti-Forensic Attacks

Rapid advancement in digital image processing tools and software's has made it extremely simple to manipulate the digital images without leaving any footprints. It becomes a hot issue about the security and threat to society with increasing growth of social media. JPEG compression format has been widely used in most of the digital cameras. The investigation of JPEG compression footprints can play an important role in image tampering detection. In this paper, a novel method is proposed to detect the JPEG compression. The proposed forensic scheme comprises of two steps i.e. selection of target difference image and generation of second-order statistical features by evaluating the Markov Transition Probability Matrices (MTPMs) for both intra and inter-block DCT domain. Finally, the resultant feature is used to train the SVM classifier for classification purposes. The experiment results on UCID and BOSSBase datasets show that the proposed forensic technique based on MTPM is capable of detecting the JPEG compression traces even in the presence of anti-forensic attacks.
Deep Near Infrared Colorization with Semantic Segmentation and Transfer Learning

Although near infrared (NIR) images contain no color, they have abundant and clear textures. In this paper, we propose deep NIR colorization with semantic segmentation and transfer learning. NIR images are capable of capturing invisible spectrum (700-1000 nm) that is quite different from visible spectrum images. We employ convolutional layers to build relationship between single NIR images and three-channel color images, instead of mapping to Lab or YCbCr color space. Moreover, we use semantic segmentation as global prior information to refine colorization of smooth regions for objects. We use color divergence loss to further optimize NIR colorization results with good structures and edges. Since the training dataset is not enough to capture rich color information, we adopt transfer learning to get color and semantic information. Experimental results verify that the proposed method produces a natural color image from single NIR image and outperforms state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).
Towards Learning-Based, Content-Agnostic Detection of Social Bot Traffic

With the fast-growing popularity of online social networks (OSNs), the security and privacy of OSN ecosystems becomes essential for the public. Among threats OSNs face, malicious social bots have become the most common and detrimental. They are often employed to violate users’ privacy, distribute spam, and disturb the financial market, posing a compelling need for effective social bot detection solutions. Unlike traditional social bot detection approaches that have strict requirements on data sources (e.g., private payload information, social relationships, or activity histories), this article proposes a method called BotFlowMon that relies only on content-agnostic flow-level data as input to identify OSN bot traffic. BotFlowMon introduces several new algorithms and techniques to classify social bot traffic from real OSN user traffic, including aggregating network flow records to obtain OSN transaction data, fusing transaction data to extract features and visualize flows, and an innovative density-valley-based clustering algorithm to subdivide each transaction into individual actions. The evaluation shows BotFlowMon can identify the traffic from social bots with a 96.1 percent accuracy, which, based on the worst case study on a testing machine, only takes no more than 0.71 seconds on average after it sees the traffic.
Instantaneous angular velocity of quantum evolution

We introduce a metric in a geometric way for measuring the distance between two quantum states and derive the instantaneous angular velocity of a quantum state's dynamical evolution, which has two components: $\mathrm{\ensuremath{\Delta}}{E}_{t}/\ensuremath{\hbar}$ in the longitudinal direction and ${\overline{E}}_{t}/\ensuremath{\hbar}$ in the latitudinal direction. By applying the instantaneous angular velocities in the dynamical evolution governed by a time-independent Hamiltonian, the fastest way for a pure state to evolve to a target state is discussed. As a consequence, we generalize the Mandelstam-Tamm and Margolus-Levitin bounds without using the Heisenberg uncertainty relation and point out the conditions for saturating the two bounds. The fastest dynamical evolution of a quantum system with a particular time-dependent Hamiltonian is also briefly discussed. The quantum features in the dynamical evolution are revealed through phase change and lifetime of the initial state.
Review of different robust x-vector extractors for speaker verification

Recently, the x-vector framework, extracted with deep neural network architectures, became the state-of-the-art method for speaker verification. Although another level of performance has been overcome with this approach, fine-tuning and optimizing the hyper-parameters of a deep neural network to obtain a robust x-vector extractor is cost- and time-consuming. Several approaches have been proposed to train robust x-vector extractors. In this paper, we propose to review and analyse the impact of the most significant x-vector related approaches, including variations in terms of data augmentation, number of epochs, size of mini-batch, acoustic features and frames per iteration. By applying these approaches to the default recipe provided in the Kaldi toolkit, we observed a significant relative gain of more than 50% in terms of EER on Speaker in the Wild and Voxceleb1-E datasets.