Reviewerly/exHarmony huggingface dataset:
Mitigating Implanted Medical Device Cybersecurity Risks

Cybersecurity vulnerabilities in medical devices have been widely documented. These issues have been described in hacking conferences as well as bulletins from the United States Food and Drug Administration. These issues present a serious threat to implantable medical devices. While the literature is replete with discussions of these vulnerabilities, there is less literature on a broad-based solution that would be applicable to all such devices. In fact, there is a substantial gap in the literature regarding how to mitigate the threat to implanted medical devices. This paper describes a specific firmware solution which can apply to any implantable medical device in order to mitigate security concerns.
A Survey on Data Security using Blockchain: Merits, Demerits and Applications

Network security is consequential prospect while working over the internet, LAN or other networks. Data security must be high precedence when it comes to network setup. Data security assures that shared data is kept secure. In the network, data security is crucial against attackers and hackers. The data security is all about first to secure the information from pirated access and the second one is to protect from thwart hackers. The objective of network security is to keep the network running and safe for all legitimate users. The target of this paper is to present the merits and demerits of blockchain over data security in different sectors. The blockchain technology is used in various applications to store exchanges of values via transactions. All transactions made by nodes are signed digitally with the preceding transaction hash and the public key of the destination node; this scheme guarantees that transactions are tamper-proof. A block containing transactions will be approved by specific blockchain nodes. In block-chain technology, each page in a ledger of transactions forms a block. That block affects the next block or page through cryptographic hashing. In other words, when a block is finally completed, it creates a special secure code, that links to the next page or block, creating a chain of blocks, or block-chain. Data security can be accomplished using the block-chain concept. The hash tables of raw data files are stored on the blockchain, and other copies are validated by a hashing technique. Thus any interference with the data will be instantly found because the initial hash tables are stored on thousands of nodes.
Consortium Blockchain-Based Architecture for Cyber-attack Signatures and Features Distribution

One of the effective ways of detecting malicious traffic in computer networks is intrusion detection systems (IDS). Though IDS identify malicious activities in a network, it might be difficult to detect distributed or coordinated attacks because they only have single vantage point. To combat this problem, cooperative intrusion detection system was proposed. In this detection system, nodes exchange attack features or signatures with a view of detecting an attack that has previously been detected by one of the other nodes in the system. Exchanging of attack features is necessary because a zero-day attacks (attacks without known signature) experienced in different locations are not the same. Although this solution enhanced the ability of a single IDS to respond to attacks that have been previously identified by cooperating nodes, malicious activities such as fake data injection, data manipulation or deletion and data consistency are problems threatening this approach. In this paper, we propose a solution that leverages blockchain's distributive technology, tamper-proof ability and data immutability to detect and prevent malicious activities and solve data consistency problems facing cooperative intrusion detection. Focusing on extraction, storage and distribution stages of cooperative intrusion detection, we develop a blockchain-based solution that securely extracts features or signatures, adds extra verification step, makes storage of these signatures and features distributive and data sharing secured. Performance evaluation of the system with respect to its response time and resistance to the features/signatures injection is presented. The result shows that the proposed solution prevents stored attack features or signature against malicious data injection, manipulation or deletion and has low latency.
Multi-agent system based sequential energy management strategy for Micro-Grid using optimal weighted regularized extreme learning machine and decision tree

Renewable energies are fundamentally changing the traditional power grid. Their integration in micro grid constitutes the best way to produce clean energy in a large scale. However, classical control methods based centralized approaches are not effic
Stochastic Mirror Descent on Overparameterized Nonlinear Models

Most modern learning problems are highly overparameterized, i.e., have many more model parameters than the number of training data points. As a result, the training loss may have infinitely many global minima (parameter vectors that perfectly "interpolate" the training data). It is therefore imperative to understand which interpolating solutions we converge to, how they depend on the initialization and learning algorithm, and whether they yield different test errors. In this article, we study these questions for the family of stochastic mirror descent (SMD) algorithms, of which stochastic gradient descent (SGD) is a special case. Recently, it has been shown that for overparameterized linear models, SMD converges to the closest global minimum to the initialization point, where closeness is in terms of the Bregman divergence corresponding to the potential function of the mirror descent. With appropriate initialization, this yields convergence to the minimum-potential interpolating solution, a phenomenon referred to as implicit regularization. On the theory side, we show that for sufficiently-overparameterized nonlinear models, SMD with a (small enough) fixed step size converges to a global minimum that is "very close" (in Bregman divergence) to the minimum-potential interpolating solution, thus attaining approximate implicit regularization. On the empirical side, our experiments on the MNIST and CIFAR-10 datasets consistently confirm that the above phenomenon occurs in practical scenarios. They further indicate a clear difference in the generalization performances of different SMD algorithms: experiments on the CIFAR-10 dataset with different regularizers, l1 to encourage sparsity, l2 (SGD) to encourage small Euclidean norm, and l∞ to discourage large components, surprisingly show that the l∞ norm consistently yields better generalization performance than SGD, which in turn generalizes better than the l1 norm.
Integrated Machine Learning Model for Prediction of Lung Cancer Stages from Textual data using Ensemble Method

Research and Development on cancer detection is more on imaging than textual data. With the help of documented symptoms in the form of text and Machine Learning (ML) techniques, it is possible to predict the lung cancerstages effectively. This paper conjectures the oeuvre modelwhich is efficient in predicting the stages of lung carcinoma by applying the concepts of ML algorithms. The proposed model is combination of K-Nearest Neighbours, Decision Tree and Neural Networks modelsalong with bagging ensemble method for enhancing the accuracy of the overall prediction. The predictedresults of the suggested model are showing better accuracy compared to individual algorithms.
Traceable Ring Signatures with Post-quantum Security

Traceable ring signature (TRS), a variant of ring signature, allows a signer to sign a message anonymously labeled with a tag on behalf of a group of users, but may reveal the signer's identity if he creates two signatures with the same tag. TRS provides accountable anonymity for users, and serves as an important role in e-voting systems and e-coupon services. However, current TRS schemes are built on hard problems in number theory that cannot resist quantum attackers. To address this issue, first, we propose a general framework of TRS, by using a non-interactive zero-knowledge proof of knowledge, a collision-resistant hash function, and a pseudorandom function with some additional properties. Then, we construct an efficient TRS scheme in the quantum random oracle model, by instantiating the framework with appropriate lattice-based building blocks. Moreover, the signature size of the lattice-based TRS is logarithmic in the ring size.
Conditional Perceptual Adversarial Variational Autoencoder for Age Progression and Regression on Child Face

Recent works have shown that Generative Adversarial Networks (GAN) and Variational Auto-Encoder (VAE) can construct synthetic images of remarkable visual fidelity. In this paper, we propose a novel architecture based on GAN and VAE with Perceptual loss termed as Conditional Perceptual Adversarial Variational Autoencoder (CPAVAE), a model for face aging and rejuvenation on children face. CPAVAE performs face aging and rejuvenation by learning manifold constrained with conditions such as age and gender, which allows it to preserve face identity. CPAVAE uses six networks; these networks are an Encoder (E) and Sampling (S) which maps the child face to latent vector, Generator (G) takes the latent vector z as input along with age conditioned vector and tries to reconstruct the input image, a perceptual loss network Φ, a pre-trained very deep convolution network, discriminator on the encoder (D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</sub> ) smoothen's the age transformation, discriminator on the image (D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">img</sub> ) forces the generator to produce human realistic images. Here D and E are based on Variational Auto-encoder (VAE) architecture, VGGNet is used as perceptual loss network (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">loss</sub> ), D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</sub> and D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">img</sub> are convolutional neural networks. We represent child face progression and regression on the Children Longitudinal Face(CLF) dataset containing 10752 faces images in the age group [0 : 20]. This dataset contains 6164 and 4588 images of boys and girls respectively.
A Comprehensive Evaluation of Webpage Content Features for Detecting Malicious Websites

Security researchers have used features gathered from webpage content (both HTML and JavaScript) to detect malicious websites. Although webpage content features have shown promise, little emphasis is placed on finding new webpage content features for malicious website detection. We address this gap by conducting a comprehensive evaluation of webpage content features to assess whether additional webpage content features improve detection of malicious websites. We study webpage content features from 5,931 malicious websites from the Cisco Talos Intelligence Group including websites associated with phishing, drive-by downloads, and command and control infrastructure and 34,778 benign websites from the Alexa list. We collect 1,865 webpage content features from these websites and select 26 for additional analysis. Among these, 9 have been studied in prior research, while the remaining 17 have not. We build eight supervised learning models and observe that the ones built from the 26 features outperform models built with features from prior research and do so with 48% fewer features. We apply two feature transformation techniques and further show the ability of the 26 features to detect malicious websites. From our study, we postulate that new, unstudied, webpage content features will improve detection of malicious websites.
Topology-Preserving and Geometric Feature-Correction Watermarking of Vector Maps

Digital watermarking is an effective method of vector map copyright protection. However, the topology and geometric features are potentially influenced by the disturbances of vertices caused by watermark embedding, thus reducing the availability of watermarked vector maps. In this research, we propose a post-correction method for two-dimensional vector map watermarking, by which the topology and geometric features of the input data can be preserved, while using only conventional watermarking techniques. In the proposed method, the Maximum Perturbation Regions (MPR) of vertices and direction angle constraints methods of adjacent lines are adopted to undergo multi-azimuth checks of watermarked vertices, which may cause changes of the geometric features and topology. After that, the coordinate adjustment method which is based on the homonymous vertices topology association and the MPR are combined for topology and geometric feature correction. For watermark embedding, two classic frequency domain watermarking techniques, Discrete Fourier Transformation (DFT) and Discrete Wavelet Transformation (DWT), phase based and low-frequency coefficient based respectively, are also adopted. The scheme was conducted on a building vector map and a road vector map. The experimental results show that the proposed method can ensure the topology consistency and geometric feature similarity of vector maps before and after embedding watermarks. Moreover, this method has little interference with the maps, which improves the usability of the watermarked vector map.
An Improved Oversampling Method for imbalanced Data–SMOTE Based on Canopy and K-means

Synthetic Minority Oversampling Technique (SMOTE) is a preferable method used to solve the imbalanced data classification issues. However, its efficiency for resolving the issues of minority sample classification still need to be improved. In order to balance its value and shortcome, we designed a perfected algorithm called “C-K-SMOTE’', which is a mixture clustering algorithm of the Canopy and K-means. For the final purpose of obtaining an approximately balanced data, first we use Canopy to achieve the approximate clustering, then use the K-means to obtain the accurate clustering, and after that we apply the SMOTE to increase the number of minority samples. The referential imbalanced data sets used in the article are selected from KEEL (Knowledge Extraction on Evolutionary Learning). By adopting random forest disaggregated model to carry experiments, SMOTE's efficiency of balancing the imbalanced databases is verified.
Thermal and intrinsic decoherence effects on the dynamics of two three-level moving atomic system

We investigate the quantum Fisher information (QFI) and quantum entanglement (QE) dynamics of moving two three-level atoms interacting with a thermal field. The time evolution of the quantum system is studied numerically under the effect of intrinsic decoherence (ID) in a thermal environment. ID and thermal environment play a critical role during the time evolution of the quantum system. It is found that QE changes drastically when ID increases in the absence of atomic motion. However, a periodic behavior of QE is observed in the case of moving atoms. The thermal environment is seen to have a prominent effect on QFI in the presence of ID. Moreover, the decay of QE is further suppressed at larger time-scales which ensures the enhanced influence of ID on the system’s dynamics in a thermal environment. However, QFI and QE saturate to a lower level at larger time-scales under these environments. The behavior of QFI and QE is opposite to each other under these environments. Furthermore, it is seen that the thermal environment induces a faster decay of QE as compared to the decay induced by ID. Therefore, ID diminishes the degree of QE for higher-dimensional quantum systems. Moreover, the system’s dynamics changes remarkably by increasing the mean number of photons.
Research on Unmanned Vessel Surface Object Detection Based on Fusion of SSD and Faster-RCNN

This paper presents a real-time obstacle detection algorithm for obstacle detection on the sea surface for Unmanned Surface Vehicle (USV). The algorithm uses Kalman filtering to fuse SSD and Faster-RCNN model. Simulation tests have been taken and shown that compared with the traditional algorithm, this algorithm can significantly improve the speed, stability and accuracy.
Revised Version of Block Cipher CHAM

CHAM is a family of lightweight block ciphers published in 2017 [22]. The CHAM family consists of three ciphers, CHAM-64/128, CHAM-128/128, and CHAM-128/256. CHAM can be implemented with a remarkably low area in hardware compared to other lightweight block ciphers, and it also performs well on software. We found new (related-key) differential characteristics and differentials of CHAM using a SAT solver. Although attacks using the new characteristics are limited to the reduced rounds of CHAM, it is preferable to increase the number of rounds to ensure a sufficient security margin. The numbers of rounds of CHAM-64/128, CHAM-128/128, and CHAM-128/256 are increased from 80 to 88, 80 to 112, and 96 to 120, respectively. We provide strong evidence that CHAM with these new numbers of rounds is secure enough against (related-key) differential cryptanalysis. Because increasing the number of rounds does not affect the area in low-area hardware implementations, the revised CHAM is still excellent in lightweight hardware implementations. In software, the revised CHAM is still comparable to SPECK, one of the top-ranked algorithms in software.
Learning entity-centric document representations using an entity facet topic model

Learning semantic representations of documents is essential for various downstream applications, including text classification and information retrieval. Entities, as important sources of information, have been playing a crucial role in assisting latent representations of documents. In this work, we hypothesize that entities are not monolithic concepts; instead they have multiple aspects, and different documents may be discussing different aspects of a given entity. Given that, we argue that from an entity-centric point of view, a document related to multiple entities shall be (a) represented differently for different entities (multiple entity-centric representations), and (b) each entity-centric representation should reflect the specific aspects of the entity discussed in the document. In this work, we devise the following research questions: (1) Can we confirm that entities have multiple aspects, with different aspects reflected in different documents, (2) can we learn a representation of entity aspects from a collection of documents, and a representation of document based on the multiple entities and their aspects as reflected in the documents, (3) does this novel representation improves algorithm performance in downstream applications, and (4) what is a reasonable number of aspects per entity? To answer these questions we model each entity using multiple aspects (entity facets1), where each entity facet is represented as a mixture of latent topics. Then, given a document associated with multiple entities, we assume multiple entity-centric representations, where each entity-centric representation is a mixture of entity facets for each entity. Finally, a novel graphical model, the Entity Facet Topic Model (EFTM), is proposed in order to learn entity-centric document representations, entity facets, and latent topics. Through experimentation we confirm that (1) entities are multi-faceted concepts which we can model and learn, (2) a multi-faceted entity-centric modeling of documents can lead to effective representations, which (3) can have an impact in downstream application, and (4) considering a small number of facets is effective enough. In particular, we visualize entity facets within a set of documents, and demonstrate that indeed different sets of documents reflect different facets of entities. Further, we demonstrate that the proposed entity facet topic model generates better document representations in terms of perplexity, compared to state-of-the-art document representation methods. Moreover, we show that the proposed model outperforms baseline methods in the application of multi-label classification. Finally, we study the impact of EFTM’s parameters and find that a small number of facets better captures entity specific topics, which confirms the intuition that on average an entity has a small number of facets reflected in documents.
Sentiment-Aware Short Text Classification Based on Convolutional Neural Network and Attention

Danmaku is an emerging socio-digital media paradigm that puts anonymous, asynchronous user-generated comments on videos with rich sentiment information. This study focuses on a new type of challenging short text classification task - sentiment-aware Danmaku classification to understand the user's opinion through collective intelligence. Currently, Convolutional neural network(CNN) and Long Short Term Memory(LSTM) network-based models have poor performance in the classification of Danmaku due to the ignoration of the word's position information in a sentence and data sparsity problem. To address these limitations, this paper proposes an Attention and CNN based sentiment-aware short text classifier to advance the state-of-the-art of short-text classification. Firstly, we define a classification criterion of Danmaku intent with six categories, considering three polarities of sentiment and two language types. Then, by introducing the attention mechanism to the Bi-LSTM based model, in which the position information is preserved during training, we realize accurate Danmaku sentiment classification and generate the sentiment embedding. Lastly, to achieve accurate sentiment-aware Danmaku intent classification, a sentiment Embedding with Channel-attention Layer is introduced to a CNN based sentence classifier using the generated Danmaku sentiment embedding. Experiment results show that the Attention and Bi-LSTM based Danmaku sentiment classifier achieves a sentiment classification accuracy of 76%. Furthermore, compared with baseline models, our proposed intent classifier delivers superior performance in classifying the six intents of Danmaku. To the best of our knowledge, this is the first study that leverages deep learning and attention mechanism for the sentiment-aware Danmaku intent classification.
Real Time Implementation of Speech Steganography

Information hiding with a cover object is referred as steganography. Here the cover object might be an image, video or speech. Speech steganography is a process of hiding the message data into a cover speech without degrading the quality of cover speech. This article has introduced a spread spectrum representation-based speech steganography using decimated wavelet transform (DWT), which decomposes the cover speech signal into approximated and detail coefficients i.e., low frequency and high frequency. Our proposed speech steganography provides enhanced imperceptibility since DWT reconstructs the decomposed information without degrading the quality of speech. Our proposed approach is an extended version of existing Fast fourier transform (FFT) based steganography, where there is a lack of imperceptibility. Simulation results proved that the proposed algorithm is superior to the conventional algorithms. Also performed good enough simulations with low bit error rate and excellent imperceptibility.