Reviewerly/exHarmony huggingface dataset:
An index-based joint multilingual/cross-lingual text categorization using topic expansion via BabelNet

The majority of the state-of-the-art text categorization algorithms are supervised and therefore require prior training. Besides the rigor involved in developing training datasets and the requirement for repetition of training for different texts, working with multilingual texts poses additional unique challenges. One of these challenges is that the developer is required to have many different languages involved. Term expansion such as query expansion has been applied in numerous applications; however, a major drawback of most of these applications is that the actual meaning of terms is not usually taken into consideration. Considering the semantics of terms is necessary because of the polysemous nature of most natural language words. In this paper, as a specific contribution to the document index approach for text categorization, we present a joint multilingual/cross-lingual text categorization algorithm (JointMC) based on semantic term expansion of class topic terms through an optimized knowledge-based word sense disambiguation. The lexical knowledge in BabelNet is used for the word sense disambiguation and expansion of the topics' terms. The categorization algorithm computes the distributed semantic similarity between the expanded class topics and the text documents in the test corpus. We evaluate our categorization algorithm using a multilabel text categorization problem. The multilabel categorization task uses the JRC-Acquis dataset. The JRC-Acquis dataset is based on subject domain classification of the European Commission's EuroVoc microthesaurus. We compare the performance of the classifier with a model of it using the original class topics. Furthermore, we compare the performance of our classifier with two state-of-the-art supervised algorithms (each for multilingual and cross-lingual tasks) using the same dataset. Empirical results obtained on five experimental languages show that categorization with expanded topics shows a very wide performance margin when compared to usage of the original topics. Our algorithm outperforms the existing supervised technique, which used the same dataset. Cross-language categorization surprisingly shows similar performance and is marginally better for some of the languages.
Gender Stereotyping’s Influence on the Perceived Competence of Siri and Co.

Some users express frustration with regard to virtual assistants due to their lack of perceived competence.To address this negative perception, we believe that technology companies should be aware of gender stereotypes.More specifically, it has been shown that males are attributed with rational competence more often than females.Drawing from the CASA paradigm, which states that people regularly assign human traits to computers, we expect that this stereotype might also be present for virtual assistants, i.e., male-voice virtual assistants are perceived as being more competent than female-voice virtual assistants.We test this hypothesis by conducting a controlled experiment which simulates a realistic interaction with differently voiced virtual assistants.The results indicate that gender stereotypes indeed play a role in the perception of the interaction.Male-voiced assistants are perceived more competent than their female-voiced counterpart which has practical implications in the design and development of devices that utilize these assistants.
Improvement of Image Steganography Using Discrete Wavelet Transform

Recently the Discrete-Wavelet-Transform (DWT) has been represented as signal processing powerful tool to separate the signal into its band frequency components. In this paper, improvement of the steganography techniques by hiding the required message into the suitable frequency band is presented. The results show that the increase of the message length will reduce the Peak Signal to Noise Ratio (PSNR), while the PSNR increases with the increasing the DWT levels. It should be noted that the PSNR reduction was from -13.8278 to -17.77208 when increasing the message length from 161 to 505 characters. In this context, the PSNR is increased from -13.8278 to 7.0554 and from -17.7208 to 1.7901 when the DWT increased from level (1) to level (2).
OnionGraph: Hierarchical topology+attribute multivariate network visualization

Hierarchical abstraction is a scalable strategy to deal with large networks. Existing visualization methods have allowed to aggregate the network nodes into hierarchies based on the node attributes or network topology, each of which has its own advantage. Very few previous system has the capability to enjoy the best of both worlds. This paper presents OnionGraph, an integrated framework for the exploratory visual analysis of heterogeneous multivariate networks. OnionGraph allows nodes to be aggregated based on either node attributes, topology, or a hierarchical combination of both. These aggregations can be split, merged and filtered under the focus+context interaction model, or automatically traversed by the information-theoretic navigation method. Node aggregations that contain subsets of nodes are displayed by the onion metaphor, indicating the level and details of the abstraction. We have evaluated the OnionGraph tool in three real-world cases. Performance experiments demonstrate that on a commodity desktop, our method can scale to million-node networks while preserving the interactivity for analysis.
Build a compact binary neural network through bit-level sensitivity and data pruning

Due to the high computational complexity and memory storage requirement, it is hard to directly deploy a full-precision convolutional neural network (CNN) on embedded devices. The hardware-friendly designs are needed for resource-limited and energy-constrained embedded devices. Emerging solutions are adopted for the neural network compression, e.g., binary/ternary weight network, pruned network and quantized network. Among them, binary neural network (BNN) is believed to be the most hardware-friendly framework due to its small network size and low computational complexity. No existing work has further shrunk the size of BNN. In this work, we explore the redundancy in BNN and build a compact BNN (CBNN) based on the bit-level sensitivity analysis and bit-level data pruning. The input data is converted to a high dimensional bit-sliced format. In the post-training stage, we analyze the impact of different bit slices to the accuracy. By pruning the redundant input bit slices and shrinking the network size, we are able to build a more compact BNN. Our result shows that we can further scale down the network size of the BNN up to 3.9x with no more than 1% accuracy drop. The actual runtime can be reduced up to 2x and 9.9x compared with the baseline BNN and its full-precision counterpart, respectively.
Finding Safety in Numbers with Secure Allegation Escrows

For fear of retribution, the victim or witness of a crime may be willing to report it only if other victims of the same perpetrator also step forward.Examples include 1) identifying oneself as the victim of sexual harassment, especially by a person in a position of authority or 2) accusing an influential politician, an authoritarian government, or one's own employer of corruption.To handle such situations, legal literature has proposed the concept of an allegation escrow: a neutral third-party that collects allegations, matches them against each other, and discloses them only after reveal thresholds (in terms of number of co-allegers), pre-specified by the allegers, are reached.Until then, allegations and allegers' identities are kept confidential.An allegation escrow can be realized as a single trusted third party; however, this party must then be trusted to keep the identity of the alleger and content of the allegation private despite any threats or coercion from perpetrators.To address this problem, this paper introduces Secure Allegation Escrows (SAE, pronounced "say").A SAE is a group of parties with independent interests and motives, acting jointly as an allegation escrow.By design, SAEs provide a very strong property: No less than a majority of parties constituting a SAE can de-anonymize or disclose the content of an allegation without a sufficient number of matching allegations (even in collusion with any number of other allegers).Once a sufficient number of matching allegations exist, the joint escrow discloses the allegation and the allegers' identities.We describe how SAEs can be securely constructed using a distributed authentication protocol and a novel allegation matching and bucketing algorithm and evaluate a prototype implementation, demonstrating feasibility in practice.
A Novel Message Authentication Scheme With Absolute Privacy for the Internet of Things Networks

With the rapid development and massive deployment of the Internet of things (IoT) networks, security related issues in the IoT networks have been paid more and more attention to. Among all the security concerns, message authentication is critical in preventing the unauthorized messages from being transmitted in the IoT networks. Many message authentication schemes have been proposed based on the public-key cryptosystem, where the key management is simple and scalable. Identity based cryptosystem is a special type of public-key cryptosystem and can further ease the process of the key management since the public keys can be obtained easily. In this paper, we devise an efficient message authentication with enhanced privacy (IMAEP) scheme using the identity based signature. Our proposed scheme can provide both unconditional privacy as well as the enhanced privacy under full key exposure attack. Our proposed scheme can also provide existential unforgeability under the adaptive chosen-message-and-identity attack. Compared with the scheme that has the same level of anonymity and security, our proposed scheme has much lower computational overhead, and can provide extra unconditional privacy. Next we propose an extended IMAEP (EIMAEP) scheme for the general access structures where the message is signed by a group of users instead of one user. We also conduct comprehensive analysis and demonstrate that the EIMAEP scheme can achieve the same level of privacy and unforgeability as the IMAEP scheme.
Support vector machine parameter tuning based on particle swarm optimization metaheuristic

This paper introduces a method for linear support vector machine parameter tuning based on particle swarm optimization metaheuristic, which is used to find the best cost (penalty) parameter for a linear support vector machine to increase textual data classification accuracy. Additionally, majority voting based ensembling is applied to increase the efficiency of the proposed method. The results were compared with results from our previous research and other authors’ works. They indicate that the proposed method can improve classification performance for a sentiment recognition task.
ARTIFICIAL INTELLIGENCE INFLUENCE IN EDUCATION 4.0 TO ARCHITECTURE CLOUD BASED E-LEARNING SYSTEM

Business Application Layer in the Architecture of E-learning cloud is an important part, because it is the part that differentiates it from the application of cloud in other fields. The development of education today recognizes the term Education 4.0 which is an adaptation of the Industrial era 4.0 where in this era the role of Artificial Intelligent is important. In this paper the author will review a part of the cloud-based architecture of E-Learning which will correspond with Education 4.0. The aim will be to produce a Cloud-Based E-learning system Architecture design that can be used as a guideline in the direction of Education 4.0.
Nonlinear observer design on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e194" altimg="si2.svg"><mml:mrow><mml:mi mathvariant="normal">SL</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mn>3</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math> for homography estimation by exploiting point and line correspondences with application to image stabilization

Although homography estimation from correspondences of mixed-type features, namely points and lines, has been relatively well studied with algebraic approaches by the computer vision community, this problem has never been addressed with nonlinear observer paradigms. In this paper, a novel nonlinear observer on the Special Linear group SL(3) applied to homography estimation is developed. The key advance with respect to similar works on the topic is the formulation of observer innovation that exploits directly point and line correspondences as input without requiring prior algebraic reconstruction of individual homographies. Rigorous observability and stability analysis are provided. A potential application to image stabilization in presence of very fast camera motion, severe occlusion, specular reflection, image blur, and light saturation is demonstrated with very encouraging results.
Task-Agnostic Object Recognition for Mobile Robots through Few-Shot Image Matching

To assist humans with their daily tasks, mobile robots are expected to navigate complex and dynamic environments, presenting unpredictable combinations of known and unknown objects. Most state-of-the-art object recognition methods are unsuitable for this scenario because they require that: (i) all target object classes are known beforehand, and (ii) a vast number of training examples is provided for each class. This evidence calls for novel methods to handle unknown object classes, for which fewer images are initially available (few-shot recognition). One way of tackling the problem is learning how to match novel objects to their most similar supporting example. Here, we compare different (shallow and deep) approaches to few-shot image matching on a novel data set, consisting of 2D views of common object types drawn from a combination of ShapeNet and Google. First, we assess if the similarity of objects learned from a combination of ShapeNet and Google can scale up to new object classes, i.e., categories unseen at training time. Furthermore, we show how normalising the learned embeddings can impact the generalisation abilities of the tested methods, in the context of two novel configurations: (i) where the weights of a Convolutional two-branch Network are imprinted and (ii) where the embeddings of a Convolutional Siamese Network are L2-normalised.
Predicting primary site of secondary liver cancer with a neural estimator of metastatic origin

Purpose: Pathologists rely on relevant clinical information, visual inspection of stained tissue slide morphology, and sophisticated molecular diagnostics to accurately infer the biological origin of secondary metastatic cancer. While highly effective, this process is expensive in terms of time and clinical resources. We seek to develop and evaluate a computer vision system designed to reasonably infer metastatic origin of secondary liver cancer directly from digitized histopathological whole slide images of liver biopsy. Approach: We illustrate a two-stage deep learning approach to accomplish this task. We first train a model to identify spatially localized regions of cancerous tumor within digitized hematoxylin and eosin (H&E)-stained tissue sections of secondary liver cancer based on a pathologist's annotation of several whole slide images. Then, a second model is trained to generate predictions of the cancers' metastatic origin belonging to one of three distinct clinically relevant classes as confirmed by immunohistochemistry. Results: Our approach achieves a classification accuracy of 90.2% in determining metastatic origin of whole slide images from a held-out test set, which compares favorably to an established clinical benchmark by three board-certified pathologists whose accuracies ranged from 90.2% to 94.1% on the same prediction task. Conclusions: We illustrate the potential impact of deep learning systems to leverage morphological and structural features of H&E-stained tissue sections to guide pathological and clinical determination of the metastatic origin of secondary liver cancers.
Simulation-based Blockchain Design to Secure Biopharmaceutical Supply Chain

Bio-drugs grow rapidly and become one of the key drivers of personalized medicine and advancement of life sciences. Driven by the unique challenges in biopharmaceutical supply chain management and also built on two-layer QuarkChain, we introduce a blockchain enabled interoperability framework and it has the reputation based Proof-of-Authority (PoA) smart contract. Given the real-time monitoring data streams of distributed systems, blockchain can improve the transparency and integrity of delivery processes. We introduce stochastic simulation to guide the blockchain design so that we can protect drug products from theft, temperature diversion, and counterfeiting, while improving the supply chain reliability, efficiency, and responsiveness. The preliminary empirical study demonstrates that our approach has promising performance.
Complete classification of trapping coins for quantum walks on the two-dimensional square lattice

One of the unique features of discrete-time quantum walks is called trapping, meaning the inability of the quantum walker to completely escape from its initial position, albeit the system is translationally invariant. The effect is dependent on the dimension and the explicit form of the local coin. A four state discrete-time quantum walk on a square lattice is defined by its unitary coin operator, acting on the four dimensional coin Hilbert space. The well known example of the Grover coin leads to a partial trapping, i.e., there exists some escaping initial state for which the probability of staying at the initial position vanishes. On the other hand, some other coins are known to exhibit strong trapping, where such escaping state does not exist. We present a systematic study of coins leading to trapping, explicitly construct all such coins for discrete-time quantum walks on the 2D square lattice, and classify them according to the structure of the operator and the manifestation of the trapping effect. We distinguish three types of trapping coins exhibiting distinct dynamical properties, as exemplified by the existence or non-existence of the escaping state and the area covered by the spreading wave-packet.
Detecting a logarithmic nonlinearity in the Schrödinger equation using Bose-Einstein condensates

We study the effect of a logarithmic nonlinearity in the Schr\"odinger equation (SE) on the dynamics of a freely expanding Bose-Einstein condensate (BEC). The logarithmic nonlinearity was one of the first proposed nonlinear extensions to the SE which emphasized the conservation of important physical properties of the linear theory, e.g., the separability of noninteracting states. Using this separability, we incorporate it into the description of a BEC obeying a logarithmic Gross-Pitaevskii equation. We investigate the dynamics of such BECs by using variational and numerical methods and find that, by using experimental techniques like $\ensuremath{\delta}$-kick collimation, experiments with extended free-fall times as available on microgravity platforms could be able to lower the bound on the strength of the logarithmic nonlinearity by at least one order of magnitude.
Cycle-Consistent Adversarial GAN: The Integration of Adversarial Attack and Defense

In image classification of deep learning, adversarial examples where input is intended to add small magnitude perturbations may mislead deep neural networks (DNNs) to incorrect results, which means DNNs are vulnerable to them. Different attack and defense strategies have been proposed to better research the mechanism of deep learning. However, those researches in these networks are only for one aspect, either an attack or a defense. There is in the improvement of offensive and defensive performance, and it is difficult to promote each other in the same framework. In this paper, we propose Cycle-Consistent Adversarial GAN (CycleAdvGAN) to generate adversarial examples, which can learn and approximate the distribution of the original instances and adversarial examples, especially promoting attackers and defenders to confront each other and improve their ability. For CycleAdvGAN, once the Generator<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>A</mml:mi></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>D</mml:mi></mml:math> are trained, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:msub><mml:mrow><mml:mi>G</mml:mi></mml:mrow><mml:mrow><mml:mi>A</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> can generate adversarial perturbations efﬁciently for any instance, improving the performance of the existing attack methods, and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mrow><mml:msub><mml:mrow><mml:mi>G</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> can generate recovery adversarial examples to clean instances, defending against existing attack methods. We apply CycleAdvGAN under semiwhite-box and black-box settings on two public datasets MNIST and CIFAR10. Using the extensive experiments, we show that our method has achieved the state-of-the-art adversarial attack method and also has efficiently improved the defense ability, which made the integration of adversarial attack and defense come true. In addition, it has improved the attack effect only trained on the adversarial dataset generated by any kind of adversarial attack.
KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification

Lexical relations describe how concepts are semantically related, in the form of relation triples. The accurate prediction of lexical relations between concepts is challenging, due to the sparsity of patterns indicating the existence of such relations. We propose the Knowledge-Enriched Meta-Learning (KEML) framework to address lexical relation classification. In KEML, the LKB-BERT (Lexical Knowledge Base-BERT) model is first presented to learn concept representations from text corpora, with rich lexical knowledge injected by distant supervision. A probabilistic distribution of auxiliary tasks is defined to increase the model's ability to recognize different types of lexical relations. We further propose a neural classifier integrated with special relation recognition cells, in order to combine meta-learning over the auxiliary task distribution and supervised learning for LRC. Experiments over multiple datasets show KEML outperforms state-of-the-art methods.
A Power Analysis Attack Resistant Multicore Platform With Effective Randomization Techniques

Aimed at improving the resistance against power analysis attacks, a systematic and architectural design approach for multicore processors is proposed in this article and is demonstrated in an eight-core prototype platform with low performance overhead and hardware cost. In order to introduce randomness in both the time dimension and the amplitude dimension and make realignment extremely difficult, the proposed multicore platform leverages several methods together, such as random task scheduling (RTS), random insertion of operations (RIO), and frequency and phase randomization (FPR). Moreover, a power state monitoring and control (PSMC) scheme is proposed to defend against power analysis attacks by keeping enough background noises. A test chip of the proposed multicore processor is fabricated in Taiwan Semiconductor Manufacturing Company (TSMC) 65-nm CMOS LP technology and can operate at up to 800 MHz with a 1.2-V supply. The Advanced Encryption Standard (AES) algorithm with these randomization methods is implemented on the processor. Measurement results show that the correlation power analysis (CPA) attacks and the power analysis attacks based on convolutional neural networks (CNNs) are unsuccessful even with 2 000 000 power traces when all the countermeasures are used.
Special Issue on Advances in Digital Image Correlation (DIC)

Digital Image Correlation (DIC) has become the most popular full field measurement technique in experimental mechanics [...]