Reviewerly/exHarmony huggingface dataset:
The AAL/Care Laboratory – a healthcare prevention system for caregivers

The demographic change in Europe causes an imbalance between the potential recipients and providers of the services of healthcare systems. Additionally, the high number of sick leaves and days of inability to work in the profession of elderly care are worsening this situation. Manual patient handling is one of the risk factors and leads to high mechanical stress in the lower back. Understanding how physically demanding activities impact caregivers requires quantification of the potential risk factors. In a laboratory, transfer techniques, ambient assisted living (AAL) systems and tools for analysing the physical burden of caregiving are evaluated. A case study was conducted in a laboratory setting to examine the kinematics, kinetics and muscle activities of a caregiver in different regions of the lower extremity and spine during a standardised manual patient-transfer task. Results showed that with the effect of an ergonomic way of caregiving, the mean muscle activity of the lumbar erector spinae was reduced by 42%. The aim of this exploratory trial was to test the methods for use on a larger scale in future studies. The case study demonstrates the suitability of the utilised sensors for the quantification of musculoskeletal burden among healthcare workers.
SPWalk: Similar Property Oriented Feature Learning for Phishing Detection

Detecting phishing webpages is an essential task that protects legitimate websites and their users from various malicious activities. To classify the suspect webpage as phishing or legitimate, robust and effective features used for classification are in demand. However, recent phishing attacks usually make phishing webpages resemble the legitimate webpages in visual and functional aspects. This poses a greater difficulty for feature extraction. We herein propose SPWalk, an unsupervised feature learning algorithm for phishing detection. In SPWalk, similar property nodes refer to a collection of phishing webpages or legitimate webpages. We first construct a weblink network with nodes representing webpages. The edges between nodes represent the reference relationships that connect webpages through hyperlinks or similar textual content. Then, SPWalk applies the network embedding technique to mapping nodes into a low-dimensional vector space. A biased random walk procedure efficiently integrates both structural information between nodes and URL information of each node. The effectiveness and robustness of SPWalk come from three points. (1). Phishing attackers do not have full control over reference relationships. (2). The structural regularities generated by diverse reference relationships can be exploited to discriminate between phishing and legitimate webpages. (3). Node URL information makes the learned node representations more suited for phishing detection. Using node as numeric features, we conduct experiments to classify webpages as legitimate or phishing. We demonstrate the superiority of SPWalk over state-of-the-art techniques on phishing detection, especially in terms of precision (over 95%). Even in the case that phishing webpages are well camouflaged by attackers for evading detection, SPwalk exhibits better classification efficacy consistently.
Adaptive and separable multiary reversible data hiding in encryption domain

Abstract To ensure the security of digital information, information needs to be stored and processed in the encryption domain during the cloud storage process, during which the user does not allow the cloud provider (CP) to access the image contents, and the CP must embed additional information in the image. Therefore, reversible data hiding in an encryption domain (RDHEI) has emerged. This paper presents an adaptive and separable multiary RDHEI method. First, the image is divided into two parts that consist of reference pixels and non-reference pixels. Next, we compute the prediction errors of the non-reference pixels, followed by a replacement of the original non-reference values with the computed prediction errors. Then, the entire image is encrypted with a specially designed stream cipher strategy so that the CP can embed additional information by modifying the prediction errors without knowing the original image. In addition, although our method vacates space before encryption, the CP can adaptively control the reserved space, unlike traditional RDHEI schemes which also vacate space before encryption. Because our method is separable, the embedded message can be extracted from both encryption and plaintext domains accurately. The experimental results demonstrate the efficacy of the proposed method.
Constructing Knowledge Graph for Social Networks in A Deep and Holistic Way

Online social networks such as Facebook and LinkedIn have been an integrated part of everyday life. To improve the user experience and power the products around the social network, Knowledge Graphs (KG) are used as a standard way to extract and organize the knowledge in social networks. This tutorial focuses on how to build KGs for social networks by developing deep NLP models, and holistic optimization of KGs and the social network. Building KG for social networks poses two challenges: 1) input data for each member in the social network is noisy, implicit and in multilingual, so a deep understanding of the input data is needed; 2) KG and the social network influence each other via multiple organic feedback loops, so a holistic view on both networks is needed.
Improved Dominance Soft Set Based Decision Rules with Pruning for Leukemia Image Classification

Acute lymphoblastic leukemia is a well-known type of pediatric cancer that affects the blood and bone marrow. If left untreated, it ends in fatal conditions due to its proliferation into the circulation system and other indispensable organs. All over the world, leukemia primarily attacks youngsters and grown-ups. The early diagnosis of leukemia is essential for the recovery of patients, particularly in the case of children. Computational tools for medical image analysis, therefore, have significant use and become the focus of research in medical image processing. The particle swarm optimization algorithm (PSO) is employed to segment the nucleus in the leukemia image. The texture, shape, and color features are extracted from the nucleus. In this article, an improved dominance soft set-based decision rules with pruning (IDSSDRP) algorithm is proposed to predict the blast and non-blast cells of leukemia. This approach proceeds with three distinct phases: (i) improved dominance soft set-based attribute reduction using AND operation in multi-soft set theory, (ii) generation of decision rules using dominance soft set, and (iii) rule pruning. The efficiency of the proposed system is compared with other benchmark classification algorithms. The research outcomes demonstrate that the derived rules efficiently classify cancer and non-cancer cells. Classification metrics are applied along with receiver operating characteristic (ROC) curve analysis to evaluate the efficiency of the proposed framework.
SmoothFool: An Efficient Framework for Computing Smooth Adversarial Perturbations

Deep neural networks are susceptible to adversarial manipulations in the input domain. The extent of vulnerability has been explored intensively in cases of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -bounded and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -minimal adversarial perturbations. However, the vulnerability of DNNs to adversarial perturbations with specific statistical properties or frequency-domain characteristics has not been sufficiently explored. In this paper, we study the smoothness of perturbations and propose Smooth-Fool, a general and computationally efficient framework for computing smooth adversarial perturbations. Through extensive experiments, we validate the efficacy of the proposed method for both the white-box and black-box attack scenarios. In particular, we demonstrate that: (i) there exist extremely smooth adversarial perturbations for well-established and widely used network architectures, (ii) smoothness significantly enhances the robustness of perturbations against state-of-the-art defense mechanisms, (iii) smoothness improves the transferability of adversarial perturbations across both data points and network architectures, and (iv) class categories exhibit a variable range of susceptibility to smooth perturbations. Our results suggest that smooth APs can play a significant role in exploring the vulnerability extent of DNNs to adversarial examples. The code is available at https://github.com/alldbi/SmoothFool.
A biologically inspired visual integrated model for image classification

In this paper, we propose a biologically inspired visual integrated model for image classification, called VMVI-CNN. Motivated in part by recent neuroscience progress in revealing integrated functions of human visual system, two bio-inspired visual mechanisms (the visual memory decay mechanism and the visual interaction mechanism) are proposed and built within the VMVI-CNN to (1) control the feature information passing through, and (2) increase the richness of feature information. The proposed method is tested on three benchmark datasets (MNIST, Cifar-10, and Mini-ImageNet) and a real-world industrial dataset. The results demonstrate that the new model can extract distinctive features and exhibit a better recognition performance than the current state-of-the-art approaches.
Eating together while being apart: A pilot study on the effects of mixed-reality conversations and virtual environments on older eaters’ solitary meal experience and food intake

The aim of this study was to investigate the potential of mixed-reality systems to virtually manipulate the eating experience and facilitate increased food intake among older participants. Social isolation is often associated with undernourishment among older adults receiving care services. Mixed-reality systems that blend real elements and a virtual world can conveniently allow older adults to eat a meal in their home while experiencing having a conversation with friends through virtual avatars in a virtual environment. A within-subjects study on thirty older participants investigated whether the mixed-reality illusion of eating in a living room with and without familiar others contributed positively to the meal experience and increased energy intake. The results did not display any significant changes in energy intake but highlighted that the virtual living room had a more energetic and pleasant atmosphere and that meals eaten in the virtual room were perceived to be of a higher quality compared to meals eaten in the real lab environment. Eating while engaging in avatar-based social interactions with three remotely located friends resulted in lower sensations of being alone and positive mood changes. A discussion of the reasons for the absence of increases in energy intake is included.
Filter Pruning Without Damaging Networks Capacity

Due to its over-parameterized design, the deep convolutional neural networks lead to a huge amount of parameters and high computational cost, making it difficult to deploy on some devices with limited computational resources in reality. In this paper, we propose a method of filter pruning without damaging networks capacity to accelerate and compress deep convolutional neural networks. Differently from some existing filter pruning methods, we pay more attention to the damage by filter pruning to model capacity. In order to restore the original model capacity, we generate new feature maps on the basis of the remaining feature maps with lighter structure after pruning the redundant filters that are similar with the others. Experimental results on CIFAR10 and CIFAR100 benchmarks demonstrate the effectiveness of our method. Especially, our method reduces more than 49% FLOPs for VGGNet-16 on CIFAR10 with only 0.07% relative accuracy drop. The relative accuracy has even been increased by 0.13% with reducing more than 24% FLOPs. Moreover, our method accelerates ResNet-110 on CIFAR10 by 22.1% with 0.41% accuracy improvement, which exceeds the previous methods.
Cryptographic Strength Evaluation of Key Schedule Algorithms

Key schedule algorithms play an important role in modern encryption algorithms, and their security is as crucial as the security of the encryption algorithms themselves. Many studies have been performed on the cryptographic strength evaluation of the encryption algorithms; however, strength evaluation of the key schedule algorithms often obtains less attention that can lead towards the possible loophole in the overall encryption process. In this paper, a criterion is proposed to evaluate the cryptographic strength of the key schedule algorithms. This criterion includes different methods of data generation from subkeys and a suitable set of statistical tests. The statistical tests are used to explore the cryptographic properties such as diffusion, confusion, independence, and randomness in the subkeys generated by the key schedule algorithm. The proposed criterion has been applied to some of the key schedule algorithms of different block ciphers. The results confirm that the proposed criterion can effectively differentiate between strong- and weak-key schedule algorithms.
Adaptive online data-driven closed-loop parameter control strategy for swarm intelligence algorithm

Parameter control is critical for the performance of any swarm intelligence algorithm. In this study, we propose an adaptive online data-driven closed-loop parameter control (CLPC) strategy for a swarm intelligence algorithm to solve both single-objective and multi-objective optimization problems with better performance. The proposed CLPC strategy involves three key parts: controller design, feedback selection, and reference determination. First, based on the control theory, we adopt a proportional integral derivative (PID) controller in the CLPC strategy, which can adaptively adjust the value of parameter according to the difference between reference and feedback. Second, to reflect and monitor the evolution state in real time, we use the mean shift clustering method and define the convergence entropy and the extension entropy to generate feedback. Finally, the reference should provide useful guidance for parameter control according to the features of optimization problems. Thus, in single-objective optimization, we propose a new lossless fitness landscape analysis method and design a decision tree to determine the reference; in multi-objective optimization, the range of the convergence entropy and the extension entropy are regarded as the reference. In addition, to illustrate the effectiveness of the CLPC strategy, two groups of experiments are performed based on the particle swarm optimization (PSO) algorithm in single-objective and multi-objective optimization. At the optimization algorithm level, we compare our proposed CLPC-PSO algorithm with three standard PSO algorithms, three decreasing inertia weight PSO algorithms, and two adaptive PSO algorithms. At the optimization problem level, we perform abundant experiments based on five single-objective benchmark functions, five multi-objective benchmark functions, and twelve scheduling instances. The statistical results show that the performance of the proposed CLPC-PSO algorithm is considerably better and more stable than those of the other eight PSO variants when faced with different problems having various features.
Externally induced entanglement amplification in a coherently pumped emission of laser with parametric amplifier and coupled to squeezed vacuum reservoir

Externally induced entanglement amplification in a coherently pumped Correlated Emission Laser (CEL) with parametric amplifier and coupled to a squeezed vacuum reservoir is presented. The combination of the master equation and stochastic differential equation is applied to investigate the entanglement of the correlated two photons generated by the quantum system. The resulting solutions of the correlation properties of noise forces associated with the normal ordering are used to find the mean photon number of the separate cavity modes and their correlation, EPR-pair variables, and smallest eigenvalue of the sympectic matrix. It is found that pumping atoms from the lower energy state to excited state results in a strong entanglement that remains in its maximum strength over a wide range of the strong classical pumping radiation. The introduction of the nonlinear crystal into the laser cavity, and coupling the system to the two-mode squeezed vacuum environment lead to a significant enhancement of entangled cavity light. The entanglement produced is quantified employing logarithmic negativity, Hillary Zubairy and Duan et al criteria. It has been observed that an intense light can be produced where the entanglement is strong. The amount of the entanglement produced is found to be 87% by both Duan et al and logarithmic negativity criteria.
Raw machine translation use by patent professionals

Abstract This article examines the use of raw, unedited machine-translated texts by patent professionals using the framework of distributed cognition. The goals of the study were to evaluate whether the concept of distributed cognition is a useful theoretical lens for examining and explaining raw MT reception, and to contribute to our knowledge of raw MT use through an analysis of a real-life use case. The study revealed that patent professionals often rely on a large network of artifacts and people to help them in the task of understanding raw MT, and therefore the concept of distributed cognition was applicable and useful. The study also contributed new knowledge to our overall understanding of the use of raw MT.
Estimating Network Processes via Blind Identification of Multiple Graph Filters

This paper studies the problem of jointly estimating multiple network processes driven by a common unknown input, thus effectively generalizing the classical blind multi-channel identification problem to graphs. More precisely, we model network processes as graph filters and consider the observation of multiple graph signals corresponding to outputs of different filters defined on a common graph and driven by the same input. Assuming that the underlying graph is known and the input is unknown, our goal is to recover the specifications of the network processes, namely the coefficients of the graph filters, only relying on the observation of the outputs. Being generated by the same input, these outputs are intimately related and we leverage this relationship for our estimation purposes. Two settings are considered, one where the orders of the filters are known and another one where they are not known. For the former setting, we present a least-squares approach and provide conditions for recovery. For the latter scenario, we propose a sparse recovery algorithm with theoretical performance guarantees. Numerical experiments illustrate the effectiveness of the proposed algorithms, the influence of different parameter settings on the estimation performance, and the validity of our theoretical claims.
Experimental Realization of Parrondo's Paradox in 1D Quantum Walks

Abstract The Parrondo effect is a well‐known apparent paradox where a combination of biased random walks displays a counterintuitive reversal in direction. These random walks can be expressed in terms of classical coin tossing games, leading to the surprising result that a combination of losing games can result in a winning game. There is now a large body of literature on quantum walks theoretically analyzing the quantum version of this effect, but to date, there have been no experimental observations of quantum Parrondo walks. Here, the first experimental verification of a quantum Parrondo walk within a quantum optics scenario is demonstrated. Based on the compact large‐scale experimental quantum‐walk platform, two rotation operators are implemented to realize the quantum Parrondo effect. The effect of quantum coherence in a quantum Parrondo walk is also investigated based on a delayed‐choice scheme that cannot be realized with classical light. It is demonstrated that the Parrondo effect vanishes when the quantum walk has a completely decoherent initial state in a delayed‐choice setting. Quantum walks are fundamental to multiple quantum algorithms, and this research provides motivation to expand the results to further explore quantum Parrondo walks.
The role of deterrability for the effect of multi-level sanctions on information security policy compliance: Results of a multigroup analysis

This paper offers a new perspective on the effectiveness of sanctions in influencing information security policy compliance. We observe compliance from a group perspective to identify undeterrable employees who comply based on inner conviction and deterrable employees who comply because of external coercion. Drawing upon survey data, we show that multilevel sanctions (i.e., formal, social, and personal sanctions) have a varying impact on the compliance of inclined and disinclined employees. We also find that multilevel sanction perceptions are differently influenced by information security policy awareness. This group-based deterrability perspective opens up new avenues to study the value of sanctions.
A framework based on (probabilistic) soft logic and neural network for NLP

Deep neural networks have emerged as a flexible framework that achieved state-of-the-art performance in many NLP applications such as machine translation, named entity recognition, sentiment analysis, and part-of-speech tagging. The main advantage of these neural models is their ability to learn useful representations without hand-engineering features. While this success, these models still suffer from the interpretability issue. More recently, probabilistic soft logic (PSL) is a promising framework based on first-order logic that achieves interesting results in both computer vision and NLP by capturing semantic relationships between entities. Moreover, unifying knowledge-driven modeling approaches and data-driven approaches is a promising framework that will have an exciting impact on structured prediction problems. In this paper, we developed NeuralGLogic a generalization framework of the previous model proposed by Huet al. (2016) that combines deep neural networks with logic rules built either using Soft Logic (SL) or Probabilistic Soft Logic (PSL). Furthermore, we evaluate our framework on different neural network architectures applied to two NLP tasks: sentiment classification and part-of-speech tagging. Experimental results showed that we were able to improve the results over the baselines and outperformed all the previous state-of-the-art systems emphasizing the utility of both SL and PSL rules in reducing the uninterpretability of the neural models thus validating our intuition.
A survey on deep learning methods for scene flow estimation

Recently, computer vision has achieved remarkable accomplishments in many domains under the thriving of deep learning. Scene flow estimation turns from the classical manual feature construction to the deep convolutional neural network (DCNN) approaches. In this paper, we review recent works about scene flow, mainly focusing on DCNN methods. We present some milestones of scene flow in recent years, and categorize these methods into supervised and unsupervised based methods. Meanwhile, we also review some multi-task methods related to scene flow. At last, we present a performance comparison among different methods.