Reviewerly/exHarmony huggingface dataset:
Parsing with Minimalist Grammars and prosodic trees

Advances in the syntactic parsing of written language have proceeded apace in the last few decades, but much less progress has been made in the syntactic parsing of spoken language. Here, we address one question important for such progress: how can we bring prosody in to inform syntactic parsing of spoken language? We identify and take on two key challenges: (i) explicitly defining aspects of the syntax–prosody interface, including prosodic trees, and (ii) modeling distinct contributions of syntax to conditioning prosodic events, including aspects other than relations between syntactic and prosodic domain edges, such as prosodic reflexes of spell-out. We demonstrate a proof-of-concept implementation of simultaneous (post-)syntactic and prosodic parsing of a Samoan utterance, with syntactic parsing informed by prosody, and prosodic parsing informed by syntax.
Long-distance device-independent quantum key distribution

Abstract Besides being a beautiful idea, device-independent quantum key distribution (DIQKD) is probably the ultimate solution to defeat quantum hacking. Its security is based on a loophole-free violation of a Bell inequality, which results in a very limited maximum achievable distance. To overcome this limitation, DIQKD must be furnished with heralding devices like, for instance, qubit amplifiers, which can signal the arrival of a photon before the measurement settings are actually selected. In this way, one can decouple channel loss from the selection of the measurement settings and, consequently, it is possible to safely post-select the heralded events and discard the rest, which results in a significant enhancement of the achievable distance. In this work, we investigate photonic-based DIQKD assisted by two main types of qubit amplifiers in the finite data block size scenario, and study the resources—particularly, the detection efficiency of the photodetectors and the quality of the entanglement sources—that would be necessary to achieve long-distance DIQKD within a reasonable time frame of signal transmission.
CFDD (CounterFeit Drug Detection) using Blockchain in the Pharmaceutical Industry

Counterfeit drugs is an expanding serious issue associated with the healthcare industry which causes extreme threats to the society.The traceability of the drugs throughout the pharma supply chain is a difficult task.Another serious issue in combating counterfeit medicines in healthcare systems is the maintenance and sharing of health records.The security of Ehealth records is of prime concern as they are prone to confidentiality and integrity threats.The negligence of these threats might have severe consequences to the healthcare systems like death of the patient.Thus the need arises to design a blockchain based system which helps in fighting with the falsified medicines and ensuring the security of e-health records.In the proposed system the data integrity is ensured through the posed block-chain technology and authentication is assured by the Keyless Signature Infrastructure which ensures the privacy of digital signature.The advance features of Blockchain makes it capable of giving a basis for complete traceability of medicines from manufacturer to end patients, and the ability to detect counterfeit medicines or drugs.
Graph Representation Learning via Ladder Gamma Variational Autoencoders

We present a probabilistic framework for community discovery and link prediction for graph-structured data, based on a novel, gamma ladder variational autoencoder (VAE) architecture. We model each node in the graph via a deep hierarchy of gamma-distributed embeddings, and define each link probability via a nonlinear function of the bottom-most layer's embeddings of its associated nodes. In addition to leveraging the representational power of multiple layers of stochastic variables via the ladder VAE architecture, our framework offers the following benefits: (1) Unlike existing ladder VAE architectures based on real-valued latent variables, the gamma-distributed latent variables naturally result in non-negativity and sparsity of the learned embeddings, and facilitate their direct interpretation as membership of nodes into (possibly multiple) communities/topics; (2) A novel recognition model for our gamma ladder VAE architecture allows fast inference of node embeddings; and (3) The framework also extends naturally to incorporate node side information (features and/or labels). Our framework is also fairly modular and can leverage a wide variety of graph neural networks as the VAE encoder. We report both quantitative and qualitative results on several benchmark datasets and compare our model with several state-of-the-art methods.
Examining the Robustness of Learning-Based DDoS Detection in Software Defined Networks

With the rapid development of Software-Defined Networking (SDN) advocating a centralized view of networks, efficient and reliable Distributed Denial of Service (DDoS) defenses are necessary to protect the centralized SDN controller. Recently, an amalgamation of work has realized such defenses using Deep Learning (DL) based algorithms. Although DL-based algorithms are generally prone to adversarial learning attacks, the extent to which those attacks are applicable to DDoS defenses in SDN is unexamined. In this work, we explore the robustness of DL-based DDoS defenses in SDN against adversarial learning attacks. First, we investigate generic off-the-shelf adversarial attacks to test the robustness of DDoS defenses in SDN, and demonstrate that while they lead to misclassification, these attacks do not preserve the characteristics of flows. As a result, we propose Flow-Merge for realistic adversarial flows while achieving a high evasion rate, with both targeted and untargeted misclassification attacks. The proposed Flow-Merge is able to force the DL-based DDoS defenses to misclassify 100% of benign flows as malicious, while preserving original characteristics of flows. Using state-of-the-art defenses, we show that the adversarial flows generated using Flow-Merge are difficult to detect, with only 49.31% detection rate when using adversarial training.
Pengujian Black Box pada Aplikasi Sistem Seleksi Sales Terbaik Menggunakan Teknik Equivalence Partitions

Testing on an application aims to check whether a program is running properly so that the program created will be a program that has good quality. The testing technique consists of Whitebox Testing and Blackbox Testing which are widely used by examiners is appropriate. Study the software that will be examined using Blackbox Testing is a best sales selection system consisting of 1 form that will be filled with sales data, as well as sales performance while in the company. The Best Sales Selection Application will be tested using Blackbox Testing where this test only aims to look the program is the same with the function desired by program without knowing the program code used. Types of testing of the various Blackbox methods one of which is the Equivalence Partitions used by the authors in this study. Equivalence Partitions technique is a test based on entering data on each form in the best sales selection system, each menu input will be tested and grouped according to function whether it is valid or invalid. So it is expected that with this test is the quality of the software produced in accordance with the expected function, and can provide benefits for the company.
HscoreNet: A Deep network for estrogen and progesterone scoring using breast IHC images

Estrogen and progesterone receptors serve as an important predictive and prognostic biomarkers for breast cancer immunohistological analysis. For breast cancer prognosis, pathologists manually compute the score based on the visual expression and the number of immunopositive and immunonegative nuclei. This manual scoring technique is time-consuming, cumbersome, expensive, error-prone, and susceptible to intra- and interobserver ambiguities. To solve these issues, we proposed a deep neural network (i.e., HscoreNet), which consists of three parts, i.e., encoder, decoder, and scoring layer. A total of 600 (300 ER and 300 PR) regions of interest at 40 × magnification from 100 histologically confirmed slides were used in this study. The size of each region of interest was 2048 × 1536 pixels (width × height). The encoder layer has been used to transform input pixels into a lower-dimensional representation, whereas the decoder reconstructs the output of the encoder through minimization of a cost function. The decoder generates an image that only contains immunopositive and immunonegative nuclei. The output of the decoder is fed to the input of the scoring layer. This layer computes the Histo-score or H-score based on the staining intensity, the color expression, and the number of immunopositive and immunonegative nuclei. Pathologists compute this score to subcategorize the cancer grades and to decide proper treatment procedures. Our proposed approach is affordable, accurate, and fast. We achieved excellent performance, with 95.87% precision and 94.53% classification accuracy. Our proposed approach streamlines the human error-prone and time-consuming process. This methodology can also be used for other types of histology and immunohistology image segmentation and scoring.
A Novel High-Capacity Data Hiding in Encrypted Images Based on Compressive Sensing Progressive Recovery

Due to the emergence of visual privacy issues, encrypted image processing has attracted a lot of attention. Data hiding in encrypted images (DHEI) is an effective method to embed secret data in the encrypted domain and protect the image privacy. However, existing methods do not allow a large amount of embedding capacity. In this letter, we propose a novel DHEI scheme utilizing prediction coding based on compressive sensing (CS) progressive recovery. The original image is pre-processed using CS prediction technique to create spare room, and then the image is encrypted and sent to the data hider. Furthermore, the data hider could embed secret data into Most Significant Bits (MSBs) of the corresponding locations. In the decoding phase, the embedded data can be extracted without any distortion and the original image could be reconstructed. Experimental results demonstrate that our scheme can achieve a higher embedding capacity than other state-of-the-art schemes while keeping a considerable recovered image quality.
B2SFinder: Detecting Open-Source Software Reuse in COTS Software

COTS software products are developed extensively on top of OSS projects, resulting in OSS reuse vulnerabilities. To detect such vulnerabilities, finding OSS reuses in COTS software has become imperative. While scalable to tens of thousands of OSS projects, existing binary-to-source matching approaches are severely imprecise in analyzing COTS software products, since they support only a limited number of code features, compute matching scores only approximately in measuring OSS reuses, and neglect the code structures in OSS projects. We introduce a novel binary-to-source matching approach, called B2SFINDER <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , to address these limitations. First of all, B2SFINDER can reason about seven kinds of code features that are traceable in both binary and source code. In order to compute matching scores precisely, B2SFINDER employs a weighted feature matching algorithm that combines three matching methods (for dealing with different code features) with two importance-weighting methods (for computing the weight of an instance of a code feature in a given COTS software application based on its specificity and occurrence frequency). Finally, B2SFINDER identifies different types of code reuses based on matching scores and code structures of OSS projects. We have implemented B2SFINDER using an optimized data structure. We have evaluated B2SFINDER using 21991 binaries from 1000 popular COTS software products and 2189 candidate OSS projects. Our experimental results show that B2SFINDER is not only precise but also scalable. Compared with the state ofthe art, B2SFINDER has successfully found up to 2.15× as many reuse cases in 53.85 seconds per binary file on average. We also discuss how B2SFINDER can be leveraged in detecting OSS reuse vulnerabilities in practice.
Path Following Based on Waypoints and Real-Time Obstacle Avoidance Control of an Autonomous Underwater Vehicle

This paper studies three-dimensional (3D) straight line path following and obstacle avoidance control for an underactuated autonomous underwater vehicle (AUV) without lateral and vertical driving forces. Firstly, the expected angular velocities are designed by using two different methods in the kinematic controller. The first one is a traditional method based on Line-of-sight (LOS) guidance law, and the second one is an improved method based on model predictive control (MPC). At the same time, a penalty item is designed by using the obstacle information detected by onboard sensors, which can realize the real-time obstacle avoidance of the unknown obstacle. Then, in order to overcome the uncertainty of the dynamics model and the saturation of actual control input, the dynamic controller is designed by using sliding mode control (SMC) technology. Finally, in the simulation experiment, the performance of the improved control method is verified by comparison with two traditional control methods based on LOS guidance law. Since the constraint of an AUV’s angular velocities are considered in MPC, simulation results show that the improved control method uses MPC, and SMC not only improves the tracking quality of the AUV when switching paths near the waypoints and realizes real-time obstacle avoidance but also effectively reduces the mean square error (MSE) and saturation rate of the rudder angle. Therefore, this control method is more conducive to the system stability and saves energy.
Design of optimal search engine using text summarization through artificial intelligence techniques

Natural language processing is the trending topic in the latest research areas, which allows the developers to create the human-computer interactions to come into existence. The natural language processing is an integration of artificial intelligence, computer science and computer linguistics. The research towards natural Language Processing is focused on creating innovations towards creating the devices or machines which operates basing on the single command of a human. It allows various Bot creations to innovate the instructions from the mobile devices to control the physical devices by allowing the speech-tagging. In our paper, we design a search engine which not only displays the data according to user query but also performs the detailed display of the content or topic user is interested for using the summarization concept. We find the designed search engine is having optimal response time for the user queries by analyzing with number of transactions as inputs. Also, the result findings in the performance analysis show that the text summarization method has been an efficient way for improving the response time in the search engine optimizations.
Reducing the Dynamical Degradation of Digital Chaotic Maps with Time-Delay Linear Feedback and Parameter Perturbation

Digital chaotic maps are not secure enough for cryptographic applications due to their dynamical degradation. In order to improve their dynamics, in this paper, a novel method with time-delay linear feedback and parameter perturbation is proposed. The delayed state variable is used to construct the linear feedback function and parameter perturbation function. This method is universal for all different digital chaotic maps. Here, two examples are presented: one is 1D logistic map and the other is 2D Baker map. To show the effectiveness of this method, we take some numerical experiments, including trajectory and phase space analysis, correlation analysis, period analysis, and complexity analysis. All the numerical results prove that the method can greatly improve the dynamics of digital chaotic maps and is quite competitive with other proposed methods. Furthermore, a simple pseudorandom bit generator (PRBG) based on digital Baker map is proposed to show its potential application. The proposed PRBG is completely constructed by the digital chaotic map, without any other complex operations. Several numerical results indicate that this PRBG has good randomness and high complexity level.
Optical development in the zebrafish eye lens

The optics of the eye is the key to a functioning visual system. The exact nature of the correlation between ocular optics and eye development is not known because of the paucity of knowledge about the growth of a key optical element, the eye lens. The sophisticated optics of the lens and its gradient of refractive index provide the superior optical quality that the eye needs and which, it is thought, has a major influence on the development of proper visual function. The nature of a gradient refractive index lens, however, renders accurate measurements of its development difficult to make and has been the reason why the influence of lens growth on visual function remains largely unknown. Novel imaging techniques have made it possible to investigate growth of the eye lens in the zebrafish. This study shows measurements using X-ray Talbot interferometry of three-dimensional gradient index profiles in eye lenses of zebrafish from late larval to adult stages. The zebrafish lens shows evidence of a gradient of refractive index from the earliest stages measured and its growth suggests an apparent coincidence between periods of rapid increase in refractive index in the lens nucleus and increased expression of a particular crystallin protein group.
Understanding and Enhancing Mixed Sample Data Augmentation.

Mixed Sample Data Augmentation (MSDA) has received increasing attention in recent years, with many successful variants such as MixUp and CutMix. By studying the mutual information between the function learned by a VAE on the original data and on the augmented data we show that MixUp distorts learned functions in a way that CutMix does not. We further demonstrate this by showing that MixUp acts as a form of adversarial training, increasing robustness to attacks such as Deep Fool and Uniform Noise which produce examples similar to those generated by MixUp. We argue that this distortion prevents models from learning about sample specific features in the data, aiding generalisation performance. In contrast, we suggest that CutMix works more like a traditional augmentation, improving performance by preventing memorisation without distorting the data distribution. However, we argue that an MSDA which builds on CutMix to include masks of arbitrary shape, rather than just square, could further prevent memorisation whilst preserving the data distribution in the same way. To this end, we propose FMix, an MSDA that uses random binary masks obtained by applying a threshold to low frequency images sampled from Fourier space. These random masks can take on a wide range of shapes and can be generated for use with one, two, and three dimensional data. FMix improves performance over MixUp and CutMix, without an increase in training time, for a number of models across a range of data sets and problem settings, obtaining a new single model state-of-the-art result on CIFAR-10 without external data. Finally, we show that a consequence of the difference between interpolating MSDA such as MixUp and masking MSDA such as FMix is that the two can be combined to improve performance even further. Code for all experiments is provided at this https URL .
BG-SAC: Entity relationship classification model based on Self-Attention supported Capsule Networks

To date, deep learning techniques, especially the combination of convolutional neural networks and recurrent neural networks with the attention mechanism, have been the state-of-the-art solutions for processing relation extraction and classification tasks. However, the neural network model constructed by this method cannot make full use of the labeled entities and their positional information in the relation classification, or even performs poorly on the small sample dataset. To address these issues, this paper proposes an entity relationship classification model BG-SAC, which combines BiGRU, Self-Attention mechanism and Capsule Networks. BG-SAC primarily uses BiGRU to obtain sentence sequential information and context-based semantic information, and then is coupled with the Self-Attention mechanism to get the correlation between words. Capsule Networks are used to acquire the positional information of entities. Eventually the probability that entities belong to a certain relationship category is calculated through the length of a capsule, so as to determine the relationship between entities and realize the classification of entity relationship. The experimental results show that the proposed model can effectively capture the word positional information and improve the classification effect with small sample datasets.
Non-Local SVD Denoising of MRI Based on Sparse Representations

Magnetic Resonance (MR) Imaging is a diagnostic technique that produces noisy images, which must be filtered before processing to prevent diagnostic errors. However, filtering the noise while keeping fine details is a difficult task. This paper presents a method, based on sparse representations and singular value decomposition (SVD), for non-locally denoising MR images. The proposed method prevents blurring, artifacts, and residual noise. Our method is composed of three stages. The first stage divides the image into sub-volumes, to obtain its sparse representation, by using the KSVD algorithm. Then, the global influence of the dictionary atoms is computed to upgrade the dictionary and obtain a better reconstruction of the sub-volumes. In the second stage, based on the sparse representation, the noise-free sub-volume is estimated using a non-local approach and SVD. The noise-free voxel is reconstructed by aggregating the overlapped voxels according to the rarity of the sub-volumes it belongs, which is computed from the global influence of the atoms. The third stage repeats the process using a different sub-volume size for producing a new filtered image, which is averaged with the previously filtered images. The results provided show that our method outperforms several state-of-the-art methods in both simulated and real data.
Under false flag: using technical artifacts for cyber attack attribution

Abstract The attribution of cyber attacks is often neglected. The consensus still is that little can be done to prosecute the perpetrators – and unfortunately, this might be right in many cases. What is however only of limited interest for the private industry is in the center of interest for nation states. Investigating if an attack was carried out in the name of a nation state is a crucial task for secret services. Many methods, tools and processes exist for network- and computer forensics that allow the collection of traces and evidences. They are the basis to associate adversarial actions to threat actors. However, a serious problem which has not got the appropriate attention from research yet, are false flag campaigns, cyber attacks which apply covert tactics to deceive or misguide attribution attempts – either to hide traces or to blame others. In this paper we provide an overview of prominent attack techniques along the cyber kill chain. We investigate traces left by attack techniques and which questions in course of the attribution process are answered by investigating these traces. Eventually, we assess how easily traces can be spoofed and rate their relevancy with respect to identifying false flag campaigns.