Reviewerly/exHarmony huggingface dataset:
A New Text Classification Model Based on Contrastive Word Embedding for Detecting Cybersecurity Intelligence in Twitter

Detecting cybersecurity intelligence (CSI) on social media such as Twitter is crucial because it allows security experts to respond cyber threats in advance. In this paper, we devise a new text classification model based on deep learning to classify CSI-positive and -negative tweets from a collection of tweets. For this, we propose a novel word embedding model, called contrastive word embedding, that enables to maximize the difference between base embedding models. First, we define CSI-positive and -negative corpora, which are used for constructing embedding models. Here, to supplement the imbalance of tweet data sets, we additionally employ the background knowledge for each tweet corpus: (1) CVE data set for CSI-positive corpus and (2) Wikitext data set for CSI-negative corpus. Second, we adopt the deep learning models such as CNN or LSTM to extract adequate feature vectors from the embedding models and integrate the feature vectors into one classifier. To validate the effectiveness of the proposed model, we compare our method with two baseline classification models: (1) a model based on a single embedding model constructed with CSI-positive corpus only and (2) another model with CSI-negative corpus only. As a result, we indicate that the proposed model shows high accuracy, i.e., 0.934 of F1-score and 0.935 of area under the curve (AUC), which improves the baseline models by 1.76∼6.74% of F1-score and by 1.64∼6.98% of AUC.
Sequential Segment-based Level Generation and Blending using Variational Autoencoders

Existing methods of level generation using latent variable models such as VAEs and GANs do so in segments and produce the final level by stitching these separately generated segments together. In this paper, we build on these methods by training VAEs to learn a sequential model of segment generation such that generated segments logically follow from prior segments. By further combining the VAE with a classifier that determines whether to place the generated segment to the top, bottom, left or right of the previous segment, we obtain a pipeline that enables the generation of arbitrarily long levels that progress in any of these four directions and are composed of segments that logically follow one another. In addition to generating more coherent levels of non-fixed length, this method also enables implicit blending of levels from separate games that do not have similar orientation. We demonstrate our approach using levels from Super Mario Bros., Kid Icarus and Mega Man, showing that our method produces levels that are more coherent than previous latent variable-based approaches and are capable of blending levels across games.
Using Blockchain Technologies to Improve Security in Federated Learning Systems

The potential of Federated Learning (FL) deployment increases rapidly as the number of connected devices increases, the value of artificial intelligence is recognized and networking technologies and edge computing evolves. However, as in any distributed system, a set of security issues arise in FL systems. In this paper, we discuss the use of blockchain technology to address diverse security aspects of FL systems and focus on the model poisoning attack for which we propose a novel Blockchain-based defense scheme. An assessment using data from the MNIST database has shown that the proposed approach, which has been designed to be implemented on blockchain technology, offers significant protection against adversaries attempting model poisoning attacks. The approach adopts a novel algorithm for evaluating the model updates, by verifying each model update separately against a verification dataset, without requiring information about the training dataset size, which is often unavailable or easily falsified.
Applying deep learning to reduce large adaptation spaces of self-adaptive systems with multiple types of goals

When a self-adaptive system needs to adapt, it has to analyze the possible options for adaptation, i.e., the adaptation space. For systems with large adaptation spaces, this analysis process can be resource- and time-consuming. One approach to tackle this problem is using machine learning techniques to reduce the adaptation space to only the relevant adaptation options. However, existing approaches only handle threshold goals, while practical systems often need to address also optimization goals. To tackle this limitation, we propose a two-stage learning approach called Deep Learning for Adaptation Space Reduction (DLASeR). DLASeR applies a deep learner first to reduce the adaptation space for the threshold goals and then ranks these options for the optimization goal. A benefit of deep learning is that it does not require feature engineering. Results on two instances of the DeltaIoT artifact (with different sizes of adaptation space) show that DLASeR outperforms a state-of-the-art approach for settings with only threshold goals. The results for settings with both threshold goals and an optimization goal show that DLASeR is effective with a negligible effect on the realization of the adaptation goals. Finally, we observe no noteworthy effect on the effectiveness of DLASeR for larger sizes of adaptation spaces.
Privacy-Preserving Wandering Behavior Sensing in Dementia Patients Using Modified Logistic and Dynamic Newton Leipnik Maps

The health status of an elderly person can be identified by examining the additive effects of aging along disease linked to it and can lead to the 'unstable incapacity'. This health status is essentially determined by the apparent decline of independence in Activities of Daily Living (ADLs). Detecting ADLs provide possibilities of improving the home life of elderly people as it can be applied to fall detection systems.. This article looks at Radar images to detect large scale body movements. Using a publicly available Radar spectogram dataset, Deep Learning and Machine Learning techniques are used for image classification of Walking, Sitting, Standing, Picking up Object, Drinking Water and Falling Radar spectograms. The Machine Learning algorithm used were Random Forest, K Nearest Neighbours and Support Vector Machine. The Deep Learning algorithms used in this article were Long Short Term Memory, Bi-directional Long Short-Term Memory and Convolutional Neural Network. In addition to using Machine Learning and Deep Learning on the spectograms, data processing techniques such as Principal Component Analysis and Data Augmentation is applied to the spectogram images. The work done in this article is divided into 4 experiments. The first experiment applies Machine and Deep Learning to the the Raw images data, the second experiment applies Principal Component Analysis to the Raw image Data, the third experiment applies Data Augmentation to the Raw image data and the fourth and final experiment applies Principal Component Analysis and Data Augmentation to the Raw image data. The results obtained in these experiments found that the best results were obtained using the CNN algorithm with Principal Component Analysis and Data Augmentation together to obtain a result of 95.30 % accuracy. Results also showed how Principal Component Analysis was most beneficial when the training data was expanded by augmentation of the available data.
Real World Scrum A Grounded Theory of Variations in Practice

Scrum, the most popular agile method and project management framework, is widely reported to be used, adapted, misused, and abused in practice. However, not much is known about how Scrum actually works in practice, and critically, where, when, how and why it diverges from Scrum by the book. Through a Grounded Theory study involving semi-structured interviews of 45 participants from 30 companies and observations of five teams, we present our findings on how Scrum works in practice as compared to how it is presented in its formative books. We identify significant variations in these practices such as work breakdown, estimation, prioritization, assignment, the associated roles and artefacts, and discuss the underlying rationales driving the variations. Critically, we claim that not all variations are process misuse/abuse and propose a nuanced classification approach to understanding variations as <i>standard, necessary, contextual,</i> and <i>clear deviations</i> for successful Scrum use and adaptation.
Operational Quantification of Continuous-Variable Quantum Resources

The diverse range of resources which underlie the utility of quantum states in practical tasks motivates the development of universally applicable methods to measure and compare resources of different types. However, many of such approaches were hitherto limited to the finite-dimensional setting or were not connected with operational tasks. We overcome this by introducing a general method of quantifying resources for continuous-variable quantum systems based on the robustness measure, applicable to a plethora of physically relevant resources such as optical nonclassicality, entanglement, genuine non-Gaussianity, and coherence. We demonstrate in particular that the measure has a direct operational interpretation as the advantage enabled by a given state in a class of channel discrimination tasks. We show that the robustness constitutes a well-behaved, bona fide resource quantifier in any convex resource theory, contrary to a related negativity-based measure known as the standard robustness. Furthermore, we show the robustness to be directly observable -- it can be computed as the expectation value of a single witness operator -- and establish general methods for evaluating the measure. Explicitly applying our results to the relevant resources, we demonstrate the exact computability of the robustness for several classes of states.
Multi-label zero-shot learning with graph convolutional networks

The goal of zero-shot learning (ZSL) is to build a classifier that recognizes novel categories with no corresponding annotated training data. The typical routine is to transfer knowledge from seen classes to unseen ones by learning a visual-semantic embedding. Existing multi-label zero-shot learning approaches either ignore correlations among labels, suffer from large label combinations, or learn the embedding using only local or global visual features. In this paper, we propose a Graph Convolution Networks based Multi-label Zero-Shot Learning model, abbreviated as MZSL-GCN. Our model first constructs a label relation graph using label co-occurrences and compensates the absence of unseen labels in the training phase by semantic similarity. It then takes the graph and the word embedding of each seen (unseen) label as inputs to the GCN to learn the label semantic embedding, and to obtain a set of inter-dependent object classifiers. MZSL-GCN simultaneously trains another attention network to learn compatible local and global visual features of objects with respect to the classifiers, and thus makes the whole network end-to-end trainable. In addition, the use of unlabeled training data can reduce the bias toward seen labels and boost the generalization ability. Experimental results on benchmark datasets show that our MZSL-GCN competes with state-of-the-art approaches.
Certificate validation using blockchain

In the digital world, each and everything is digitalized in which the certificate of SSLC, HSC, and academic certificate are digitalized in the educational institution and provided to the students. Students are difficult to maintain their degree certificates. For the organization and institution, verification and validation of certificates are tedious and cumbersome. Our project will help to store the certificate in the blockchain system and provide security. First, the paper certificates are converted into digital certificates. The chaotic algorithm is used to generate the hash code value for the certificate. Then the certificates are store in blockchain. And these certificates are validated by using the mobile application. By using blockchain technology we can provide a more secure and efficient digital certificate validation.
Vuln4Real: A Methodology for Counting Actually Vulnerable Dependencies

Vulnerable dependencies are a known problem in today's free open-source software ecosystems because FOSS libraries are highly interconnected, and developers do not always update their dependencies.Our paper proposes Vuln4Real, the methodology for counting actually vulnerable dependencies, that addresses the over-inflation problem of academic and industrial approaches for reporting vulnerable dependencies in FOSS software, and therefore, caters to the needs of industrial practice for correct allocation of development and audit resources.To understand the industrial impact of a more precise methodology, we considered the 500 most popular FOSS Java libraries used by SAP in its own software.Our analysis included 25767 distinct library instances in Maven.We found that the proposed methodology has visible impacts on both ecosystem view and the individual library developer view of the situation of software dependencies: Vuln4Real significantly reduces the number of false alerts for deployed code (dependencies wrongly flagged as vulnerable), provides meaningful insights on the exposure to third-parties (and hence vulnerabilities) of a library, and automatically predicts when dependency maintenance starts lagging, so it may not receive updates for arising issues.
Quantum circuit optimization using quantum Karnaugh map

Abstract Every quantum algorithm is represented by set of quantum circuits. Any optimization scheme for a quantum algorithm and quantum computation is very important especially in the arena of quantum computation with limited number of qubit resources. Major obstacle to this goal is the large number of elemental quantum gates to build even small quantum circuits. Here, we propose and demonstrate a general technique that significantly reduces the number of elemental gates to build quantum circuits. This is impactful for the design of quantum circuits, and we show below this could reduce the number of gates by 60% and 46% for the four- and five-qubit Toffoli gates, two key quantum circuits, respectively, as compared with simplest known decomposition. Reduced circuit complexity often goes hand-in-hand with higher efficiency and bandwidth. The quantum circuit optimization technique proposed in this work would provide a significant step forward in the optimization of quantum circuits and quantum algorithms, and has the potential for wider application in quantum computation.
An improved BiGAN based approach for anomaly detection

Anomaly detection is considered as a challenging task due to its imbalanced and unlabelled nature. To overcome this challenge, the combination of different machine learning approaches such as supervised, unsupervised, semi-supervised learning are proposed in the literature. With the advent of neural networks and generative models, different methodologies derived from neural networks are applied to anomaly detection tasks. In this study, we use the KDDCUP99 data set, consider it as an anomaly detection task, and implement BiGAN, considering it as a one-class anomaly detection algorithm. Since generator and discriminator are highly dependent on each other in the training phase, to reduce this dependency, in this paper, we propose two different training approaches for BiGAN by adding extra training steps to it. We also demonstrate that proposed approaches increased the performance of BiGAN on anomaly detection task.
Training-Set Distillation for Real-Time UAV Object Tracking

Correlation filter (CF) has recently exhibited promising performance in visual object tracking for unmanned aerial vehicle (UAV). Such online learning method heavily depends on the quality of the training-set, yet complicated aerial scenarios like occlusion or out of view can reduce its reliability. In this work, a novel time slot-based distillation approach is proposed to efficiently and effectively optimize the training-set's quality on the fly. A cooperative energy minimization function is established to score the historical samples adaptively. To accelerate the scoring process, frames with high confident tracking results are employed as the keyframes to divide the tracking process into multiple time slots. After the establishment of a new slot, the weighted fusion of the previous samples generates one key-sample, in order to reduce the number of samples to be scored. Besides, when the current time slot exceeds the maximum frame number, which can be scored, the sample with the lowest score will be discarded. Consequently, the training-set can be efficiently and reliably distilled. Comprehensive tests on two well-known UAV benchmarks prove the effectiveness of our method with real-time speed on single CPU.
Fast and resilient manipulation planning for target retrieval in clutter

This paper presents a task and motion planning (TAMP) framework for a robotic manipulator in order to retrieve a target object from clutter. We consider a configuration of objects in a confined space with a high density so no collision-free path to the target exists. The robot must relocate some objects to retrieve the target without collisions. For fast completion of object rearrangement, the robot aims to optimize the number of pick-and-place actions which often determines the efficiency of a TAMP framework.We propose a task planner incorporating motion planning to generate executable plans which aims to minimize the number of pick-and-place actions. In addition to fully known and static environments, our method can deal with uncertain and dynamic situations incurred by occluded views. Our method is shown to reduce the number of pick-and-place actions compared to baseline methods (e.g., at least 28.0% of reduction in a known static environment with 20 objects).
MAFNet: Multi-style attention fusion network for salient object detection

Salient object detection based on deep learning has become one of the research hotspots in computer vision. How to effectively extract useful information is a key issue for saliency detection. Most of the existing methods integrate features extracted from convolutional neural networks indiscriminately. However, the features of different layers have different characteristics, not all of them are useful for saliency detection and some even cause interferences. To solve above problem, we propose a Multi-style Attention Fusion Network (MAFNet). Specifically, MAFNet is mainly consists of dual-cues spatial attention module (DSA), dual attention intermediate representation module (DAIR), high-level channel attention module (HCA) and multi-level feature fusion module (MLFF). Among them, DSA aims to refine low-level features and filter background noise. DAIR uses two branches to adaptively integrate the spatial and semantic information of middle-level features. HCA obtains the semantic features of high-level blocks through two different channel-wise operations. Besides, MLFF effectively integrates the above multi-level features in a learnable manner. Finally, different from cross-entropy, cross-IOU loss guides the network to pay more attention to local details. Experimental results on six public datasets demonstrate that MAFNet's outperformance is competitive in saliency detection and performs well on small object detection.
Chinese medical named entity recognition based on multi-granularity semantic dictionary and multimodal tree

In recent years, named entity recognition (NER) has attracted significant attention in various fields, especially in the clinical medical field, because NER is essential for useful mining knowledge in the clinical medical area. However, there are still some problems in Chinese named entity recognition, such as the complexity of medical texts, word segmentation errors, and incomplete extraction of semantic information. In this paper, we propose a Chinese NER method based on the multi-granularity semantic dictionary and multimodal tree method, which involves the following steps. First, we extract different semantic words using multimodal trees. Next, we extract the boundary information, and finally, perform the multi-granularity feature fusion. Furthermore, we combine the above methods to complete the entity recognition task. From the results of our experimental verification, our proposed model outperforms the current state-of-the-art results. • In the proposed MSD_DT_NER method, we construct a multimodal word tree. • Our method combines vocabulary information and position information. • Our method outperforms other recently proposed models.
Promoting User Advocacy through Design Thinking in the Age of Automated Writing

This article investigates how design thinking can help solve wicked problems in the design and use of automated writing technologies. First, I argue that problems in developing and using automated writing algorithms are complex and recurrent wicked problems that can hardly be solved with traditional, once-and-for-all solutions. Then I analyze how wicked problems harm user experience and benefits, causing unequal power distribution between users and tech giants. Finally, I argue that design thinking as “a process for addressing complex, messy problems in the world” [1] can help improve the use of AW technologies to avoid technological exploitation and exclusion of users. Design thinking's emphasis on users’ voices, experiences, and needs can help bring the design and use of AW technologies back to human-centeredness.
Explainable Deep CNNs for MRI-Based Diagnosis of Alzheimer’s Disease

Deep Convolutional Neural Networks (CNNs) are becoming prominent models for semi-automated diagnosis of Alzheimer's Disease (AD) using brain Magnetic Resonance Imaging (MRI). Although being highly accurate, deep CNN models lack transparency and interpretability, precluding adequate clinical reasoning and not complying with most current regulatory demands. One popular choice for explaining deep image models is occluding regions of the image to isolate their influence on the prediction. However, existing methods for occluding patches of brain scans generate images outside the distribution to which the model was trained for, thus leading to unreliable explanations. In this paper, we propose an alternative explanation method that is specifically designed for the brain scan task. Our method, which we refer to as Swap Test, produces heatmaps that depict the areas of the brain that are most indicative of AD, providing interpretability for the model's decisions in a format understandable to clinicians. Experimental results using an axiomatic evaluation show that the proposed method is more suitable for explaining the diagnosis of AD using MRI while the opposite trend was observed when using a typical occlusion test. Therefore, we believe our method may address the inherent black-box nature of deep neural networks that are capable of diagnosing AD.
LBA-PAKE: Lattice-Based Anonymous Password Authenticated Key Exchange for Mobile Devices

The recent advancement in quantum computers generates the threat alarm of breaking the security of key exchange protocols, which is based on discrete logarithmic or prime factorization problem in polynomial time. Hence, motivated to develop the key exchange protocol that is secure in the postquantum era, Feng <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> proposed an anonymous authenticated key exchange protocol for mobile devices in a postquantum world. Although the protocol is simple, elegant, and efficient for mobile devices, but it is vulnerable to signal leakage attack, spoofing attack, manipulation-based attacks, and user anonymity violation attack. Therefore, to overcome the above weaknesses, lattice-based anonymous password authenticated key exchange protocol has been proposed for mobile devices. Further, our protocol supports the key reuse, perfect forward secrecy, and anonymity features. Our formal security proof and implementation results show that the proposed protocol is suitable to be used for real-time security of mobile networks.