Reviewerly/exHarmony huggingface dataset:
On the Effectiveness of Generative Adversarial Networks as HEp-2 Image Augmentation Tool

One of the big challenges in the recognition of biomedical samples is the lack of large annotated datasets. Their relatively small size, when compared to datasets like ImageNet, typically leads to problems with efficient training of current machine learning algorithms. However, the recent development of generative adversarial networks (GANs) appears to be a step towards addressing this issue. In this study, we focus on one instance of GANs, which is known as deep convolutio nal generative adversarial network (DCGAN). It gained a lot of attention recently because of its stability in generating realistic artificial images. Our article explores the possibilities of using DCGANs for generating HEp-2 images. We trained multiple DCGANs and generated several datasets of HEp-2 images. Subsequently, we combined them with traditional augmentation and evaluated over three different deep learning configurations. Our article demonstrates high visual quality of generated images, which is also supported by state-of-the-art classification results.
A Comparison and Strategy of Semantic Segmentation on Remote Sensing Images

In recent years, with the development of aerospace technology, we use more and more images captured by satellites to obtain information. But a large number of useless raw images, limited data storage resource and poor transmission capability on satellites hinder our use of valuable images. Therefore, it is necessary to deploy an on-orbit semantic segmentation model to filter out useless images before data transmission. In this paper, we present a detailed comparison on the recent deep learning models. Considering the computing environment of satellites, we compare methods from accuracy, parameters and resource consumption on the same public dataset. And we also analyze the relation between them. Based on experimental results, we further propose a viable on-orbit semantic segmentation strategy. It will be deployed on the TianZhi-2 satellite which supports deep learning methods and will be lunched soon.
Ethical Implementation of the Learning Healthcare System with Blockchain Technology

We propose that blockchain technology complemented by secure computation methods can foster implementation of a learning healthcare system (LHCS) by minimizing upfront patient-facing compromises with unsurpassed data security and privacy, and by optimizing the system’s fulfillment of its obligations to respect patients through transparency, engagement, and accountability. We demonstrate how a blockchain-enabled LHCS could foster patient willingness to contribute to learning by providing desired security and control over health data. In addition, secure computation methods could enable meta-analysis without exposing individual-level data, thus allowing the system to protect patients’ privacy while simultaneously learning from their data. The transparency and immutability of blockchain ledgers would also support the public’s trust in the system by allowing patients to audit and oversee which of their data are used, how they are used, and by whom. Furthermore, blockchain communities are community-governed peer-to-peer networks in which sharing builds mutually beneficial value, offering a model for engaging patients as LHCS stakeholders. Smart contracts could be used to ensure accountability of the system by embedding feedback mechanisms by which patients directly and automatically realize benefits of sharing their data.
User click fraud detection method based on Top-Rank-<i>k</i> frequent pattern mining

A user click fraud detection method based on Top-Rank-k frequent pattern mining algorithm is presented to solve the click fraud problem appearing in current online advertising. Firstly, this method combines the click frequency of event samples, calculates the real evaluation score of click stream, and the click stream density function and evaluation score expression under multi-dimensional variables, and further obtains the time complexity of the next user’s click fraud process. Secondly, according to the Top-Rank-k frequent pattern, the process of click fraud detection algorithm is designed, and the click fraud user is analyzed and obtained. The results show that this method has good efficiency and correctness, and is superior to other similar algorithms.
Utterance-level Aggregation For Speaker Recognition In The Wild

The objective of this paper is speaker recognition "in the wild"-where utterances may be of variable length and also contain irrelevant signals. Crucial elements in the design of deep networks for this task are the type of trunk (frame level) network, and the method of temporal aggregation. We propose a powerful speaker recognition deep network, using a "thin-ResNet" trunk architecture, and a dictionary-based NetVLAD or GhostVLAD layer to aggregate features across time, that can be trained end-to-end. We show that our network achieves state of the art performance by a significant margin on the VoxCeleb1 test set for speaker recognition, whilst requiring fewer parameters than previous methods. We also investigate the effect of utterance length on performance, and conclude that for "in the wild" data, a longer length is beneficial.
Practical Attacks on Reduced-Round AES

In this paper we investigate the security of 5-round AES against two different attacks in an adaptive setting. We present a practical key-recovery attack on 5-round AES with a secret s-box that requires $$2^{32}$$ adaptively chosen ciphertexts, which is as far as we know a new record. In addition, we present a new and practical key-independent distinguisher for 5-round AES which requires $$2^{27.2}$$ adaptively chosen ciphertexts. While the data complexity of this distinguisher is in the same range as the current best 5-round distinguisher [14], it exploits new structural properties of 5-round AES.
The Process of Self-Realization—From the Humanist Psychology Perspective

Humanistic psychology has provided a series of fundamental theories about human personality and its development.Prominent representatives such as A. Maslow, C. Rogers or R. Assagiolli, along with the psychoanalyst C. G. Jung, have defined the basic concepts that help us today to better understand the individual evolutionary path from intuitive thinking structures and primary group integration, to elements of metacognition, creativity and integration into society through high moral values.Self-realization is a complex process that needs to be addressed from a number of perspectives, to provide a more complex and true picture of how individual development takes place.The paper aims to identify the role of family, interpersonal relationships, to understand which are the functions of knowledge and emotional experiences during development and actualization of individual's own potential.Achieving self-realization involves going through some stages, overcoming various difficulties and, above all, practicing self-regulation over individual emotions and behaviors.Education also provides the logical-scientific basis of going beyond the stages of self-realization, it provides insight and understanding, but it also means overcoming the theoretical boundaries, through personal involvement in actions that reflect moral and humanistic values.Positive and active approaches are ways that lead to self-realization.
On the specification and verification of atomic swap smart contracts (extended abstract)

This paper investigates the specification and verification of atomic swap multi-party transactions. It is argued that logics with the ability to express properties of strategies of players in a multi-agent setting are conceptually useful for this purpose, although ultimately, the less expressive setting of temporal logic suffices for verification of concrete implementations. This is illustrated through the use of a model checker to verify atomic swap smart contracts in on-chain and cross-chain settings.
Pseudo-contractions as Gentle Repairs

Updating a knowledge base to remove an unwanted consequence is a challenging task. Some of the original sentences must be either deleted or weakened in such a way that the sentence to be removed is no longer entailed by the resulting set. On the other hand, it is desirable that the existing knowledge be preserved as much as possible, minimising the loss of information. Several approaches to this problem can be found in the literature. In particular, when the knowledge is represented by an ontology, two different families of frameworks have been developed in the literature in the past decades with numerous ideas in common but with little interaction between the communities: applications of AGM-like Belief Change and justification-based Ontology Repair. In this paper, we investigate the relationship between pseudo-contraction operations and gentle repairs. Both aim to avoid the complete deletion of sentences when replacing them with weaker versions is enough to prevent the entailment of the unwanted formula. We show the correspondence between concepts on both sides and investigate under which conditions they are equivalent. Furthermore, we propose a unified notation for the two approaches, which might contribute to the integration of the two areas.
The adaptive particle swarm optimization technique for solving microseismic source location parameters

Abstract. An intelligent method is presented for locating a microseismic source based on the particle swarm optimization (PSO) concept. It eliminates microseismic source locating errors caused by the inaccurate velocity model of the earth medium. The method uses, as the target of PSO, a global minimum of the sum of squared discrepancies between differences of modeled arrival times and differences of measured arrival times. The discrepancies are calculated for all pairs of detectors of a seismic monitoring system. Then, the adaptive PSO algorithm is applied to locate the microseismic source and obtain optimal value of the P-wave velocity. The PSO algorithm adjusts inertia weight, accelerating constants, the maximum flight velocity of particles, and other parameters to avoid the PSO algorithm trapping by local optima during the solution process. The origin time of the microseismic event is estimated by minimizing the sum of squared discrepancies between the modeled arrival times and the measured arrival times. This sum is calculated using the obtained estimates of the microseismic source coordinates and P-wave velocity. The effectiveness of the PSO algorithm was verified through inversion of a theoretical model and two analyses of actual data from mine blasts in different locations. Compared with the classic least squares method (LSM), the PSO algorithm displays faster convergence and higher accuracy of microseismic source location. Moreover, there is no need to measure the microseismic wave velocity in advance: the PSO algorithm eliminates the adverse effects caused by error in the P-wave velocity when locating a microseismic source using traditional methods.
Research on Radar Emitter Signal Feature Extraction Method Based on Fuzzy Entropy

With the rapid application of the new radar system, radar signal sorting based on conventional parameters has not achieved the desired results. Therefore, new features need to be extracted for signal sorting. In response to this problem, the paper proposes to use the entropy theory to extract the fuzzy entropy characteristics of the radar signal to determine the uncertainty of the radar source signal. Experiments with test signals show that the fuzzy entropy characteristics are less affected by noise over a wide range of SNR.
GPU acceleration of edge detection algorithm based on local variance and integral image: application to air bubbles boundaries extraction

Accurate detection of air bubbles boundaries is of crucial importance in determining the performance and in the study of various gas/liquid two-phase flow systems. The main goal of this work is edge extraction of air bubbles rising in two-phase flow in real-time. To accomplish this, a fast algorithm based on local variance is improved and accelerated on the GPU to detect bubble contour. The proposed method is robust against changes of intensity contrast of edges and capable of giving high detection responses on low contrast edges. This algorithm is performed in two steps: in the first step, the local variance of each pixel is computed based on integral image, and then the resulting contours are thinned to generate the final edge map. We have implemented our algorithm on an NVIDIA GTX 780 GPU. The parallel implementation of our algorithm gives a speedup factor equal to 17x for high resolution images (1024×1024 pixels) compared to the serial implementation. Also, quantitative and qualitative assessments of our algorithm versus the most common edge detection algorithms from the literature were performed. A remarkable performance in terms of results accuracy and computation time is achieved with our algorithm.
#greysanatomy vs. #yankees: Demographics and Hashtag Use on Twitter

Demographics, in particular, gender, age, and race, are a key predictor of human behavior. Despite the significant effect that demographics plays, most scientific studies using online social media do not consider this factor, mainly due to the lack of such information. In this work, we use state-of-the-art face analysis software to infer gender, age, and race from profile images of 350K Twitter users from New York. For the period from November 1, 2014 to October 31, 2015, we study which hashtags are used by different demographic groups. Though we find considerable overlap for the most popular hashtags, there are also many group-specific hashtags.
Refining Word Representations by Manifold Learning

Pre-trained distributed word representations have been proven useful in various natural language processing (NLP) tasks. However, the effect of words’ geometric structure on word representations has not been carefully studied yet. The existing word representations methods underestimate the words whose distances are close in the Euclidean space, while overestimating words with a much greater distance. In this paper, we propose a word vector refinement model to correct the pre-trained word embedding, which brings the similarity of words in Euclidean space closer to word semantics by using manifold learning. This approach is theoretically founded in the metric recovery paradigm. Our word representations have been evaluated on a variety of lexical-level intrinsic tasks (semantic relatedness, semantic similarity) and the experimental results show that the proposed model outperforms several popular word representations approaches.
Learning-Aided Physical Layer Attacks Against Multicarrier Communications in IoT

Internet-of-Things (IoT) devices that are limited in power and processing are susceptible to physical layer (PHY) spoofing (signal exploitation) attacks owing to their inability to implement a full-blown protocol stack for security. The overwhelming adoption of multicarrier techniques such as orthogonal frequency division multiplexing (OFDM) for the PHY layer makes IoT devices further vulnerable to PHY spoofing attacks. These attacks which aim at injecting bogus/spurious data into the receiver, involve inferring transmission parameters and finding PHY characteristics of the transmitted signals so as to spoof the received signal. Non-contiguous (NC) OFDM systems have been argued to have low probability of exploitation (LPE) characteristics against classic attacks based on cyclostationary analysis, and the corresponding PHY has been deemed to be secure. However, with the advent of machine learning (ML) algorithms, adversaries can devise data-driven attacks to compromise such systems. It is in this vein that PHY spoofing performance of adversaries equipped with supervised and unsupervised ML tools are investigated in this paper. The supervised ML approach is based on deep neural networks (DNN) while the unsupervised one employs variational autoencoders (VAEs). In particular, VAEs are shown to be capable of learning representations from NC-OFDM signals related to their PHY characteristics such as frequency pattern and modulation scheme, which are useful for PHY spoofing. In addition, a new metric based on the disentanglement principle is proposed to measure the quality of such learned representations. Simulation results demonstrate that the performance of the spoofing adversaries highly depends on the subcarriers' allocation patterns. Particularly, it is shown that utilizing a random subcarrier occupancy pattern secures NC-OFDM systems against ML-based attacks.
Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework

Distant supervision for relation extraction enables one to effectively acquire structured relations out of very large text corpora with less human efforts.Nevertheless, most of the priorart models for such tasks assume that the given text can be noisy, but their corresponding labels are clean.Such unrealistic assumption is contradictory with the fact that the given labels are often noisy as well, thus leading to significant performance degradation of those models on real-world data.To cope with this challenge, we propose a novel label-denoising framework that combines neural network with probabilistic modelling, which naturally takes into account the noisy labels during learning.We empirically demonstrate that our approach significantly improves the current art in uncovering the ground-truth relation labels.
Arabic Named Entity Recognition: What Works and What’s Next

This paper presents the winning solution to the Arabic Named Entity Recognition challenge run by Topcoder.com.The proposed model integrates various tailored techniques together, including representation learning, feature engineering, sequence labeling, and ensemble learning.The final model achieves a test F 1 score of 75.82% on the AQMAR dataset and outperforms baselines by a large margin.Detailed analyses are conducted to reveal both its strengths and limitations.Specifically, we observe that (1) representation learning modules can significantly boost the performance but requires a proper pre-processing and (2) the resulting embedding can be further enhanced with feature engineering due to the limited size of the training data.All implementations and pre-trained models are made public 1 .
Quantum Embedding of Knowledge for Reasoning

Statistical Relational Learning (SRL) methods are the most widely used techniques to generate distributional representations of the symbolic Knowledge Bases (KBs). These methods embed any given KB into a vector space by exploiting statistical similarities among its entities and predicates but without any guarantee of preserving the underlying logical structure of the KB. This, in turn, results in poor performance of logical reasoning tasks that are solved using such distributional representations. We present a novel approach called Embed2Reason (E2R) that embeds a symbolic KB into a vector space in a logical structure preserving manner. This approach is inspired by the theory of Quantum Logic. Such an embedding allows answering membership based complex logical reasoning queries with impressive accuracy improvements over popular SRL baselines.
Semantically Constrained Multilayer Annotation: The Case of Coreference

We propose a coreference annotation scheme as a layer on top of the Universal Conceptual Cognitive Annotation foundational layer, treating units in predicate-argument structure as a basis for entity and event mentions.We argue that this allows coreference annotators to sidestep some of the challenges faced in other schemes, which do not enforce consistency with predicate-argument structure and vary widely in what kinds of mentions they annotate and how.The proposed approach is examined with a pilot annotation study and compared with annotations from other schemes.
Reinforcement Learning and Neuroevolution in Flappy Bird Game

Games have been used as an effective way to measure the advancement of artificial intelligence. Chess, Atari2600 and Go, are some of the most mediatic demonstrations where AI computer programs defeated human players. In this paper we add the popular Flappy Bird game in the list of games to quantify the performance of an AI player. Based on Q-Reinforcement Learning and Neuroevolution (neural network fitted by genetic algorithm), artificial agents were trained to take the most favorable action at each game instant. The Neuroevolution agent outperformed by far the Reinforcement Learning agent (111 points average result) and achieved on average super-human performance of impressive score of 28700 points.
SEAKER: A mobile digital forensics triage device

As our world of digital devices continues to expand, the amount of digital evidence encountered by law enforcement during case investigation is ever increasing. Faced with a preponderance of high capacity digital media devices, forensic investigators must be able to review them quickly, and establish which devices merit further attention. Utilizing a small single-board computer called Raspberry Pi, this mobile digital forensics triage device provides fast, targeted feedback for immediate evidence assessment. A digital forensic device is presented in this paper. It is named SEAKER (Storage Evaluator and Knowledge Extraction Reader)[1] and it enables forensic investigators to perform triage on many digital devices very quickly. Developed for on-scene, time-sensitive investigations, its utility extends to preventing over-collection and large backlogs at digital forensics labs worldwide.
Image steganography based on color palette transformation in color space

Abstract In this paper, we present a novel image steganography method which is based on color palette transformation in color space. Most of the existing image steganography methods modify separate image pixels, and random noise appears in the image. By proposing a method, which changes the color palette of the image (all pixels of the same color will be changed to the same color), we achieve a higher user perception. Presented comparison of stegoimage quality metrics with other image steganography methods proved the new method is one of the best according to Structural Similarity Index (SSIM) and Peak Signal Noise Ratio (PSNR) values. The capability is average among other methods, but our method has a bigger capacity among methods with better SSIM and PSNR values. The color and pixel capability can be increased by using standard or adaptive color palette images with smoothing, but it will increase the embedding identification possibility.
Cloud query processing analysis: encryption and decryption

The usage of clouds to provide data query services is becoming an attractive solution to services that demand scalability and cost minimization.Despite the huge advantages, cloud consumers require their confidential or delicate data to be safe and secured, because violating their private data would be a great concern that is not tolerable.Therefore, corporations who use the cloud services especially the databaseas-a-service have tended to encrypt sensitive data for security and confidentiality purpose.Encrypting data would facilitate protecting private information from any violation by the service provider.Several studies have proposed AES and RSA encryption methods.Yet, each encryption method provides a specific level of security which comes with an opposite level of efficiency.Therefore, this work will examine the performance for both encryption methods.The environment setup consists of Microsoft SQL Server as cloud database simulation, and a Visual Studio platform to simulate the local processing of queries.The performance evaluation parameters are time consumption for encryption and decryption.Overall, the results appeared are not significant as each method has its own benefits.