Reviewerly/exHarmony huggingface dataset:
Research on image steganography analysis based on deep learning

Although steganalysis has developed rapidly in recent years, it still faces many difficulties and challenges. Based on the theory of in-depth learning method and image-based general steganalysis, this paper makes a deep study of the hot and difficult problem of steganalysis feature expression, and tries to establish a new steganalysis paradigm from the idea of feature learning. The main contributions of this paper are as follows: 1. An innovative steganalysis paradigm based on in-depth learning is proposed. Based on the representative deep learning method CNN, the model is designed and adjusted according to the characteristics of steganalysis, which makes the proposed model more effective in capturing the statistical characteristics such as neighborhood correlation. 2. A steganalysis feature learning method based on global information constraints is proposed. Based on the previous research of steganalysis method based on CNN, this work focuses on the importance of global information in steganalysis feature expression. 3. A feature learning method for low embedding rate steganalysis is proposed. 4. A general steganalysis method for multi-class steganography is proposed. The ultimate goal of general steganalysis is to construct steganalysis detectors without distinguishing specific types of steganalysis algorithms.
Non-equilibrium dynamic games and cyber–physical security: A cognitive hierarchy approach

Abstract This paper considers the problem of identifying the cognitive capabilities of agents having bounded rationality in a cyber–physical system operating in an uncertain and/or adversarial environment. To categorize the adversaries, we introduce an iterative method of optimal responses that determine the policy of an agent with a level- k intelligence. We then, formulate a model-free learning algorithm to train the different intelligence levels without any knowledge about the physics of the system while quantifying the optimality. By sequential interaction with the adversaries, we learn the true distribution of their levels. Rigorous mathematical proofs show stability of the equilibrium point of the closed-loop system and convergence to the Nash equilibrium as the level of intelligence tends to infinity. Finally, simulation results show the efficacy of our proposed approach.
Classroom misbehaviour management: an SVVR-based training system for preservice teachers

Training teachers to manage their instruction and the class is known to be important and challenging. In traditional teacher training programmes, micro-teaching is frequently adopted; however, its effectiveness could be limited if the learners do not have good opportunities to immerse themselves in and interact in the learning contexts. Virtual Reality (VR) technology could be a good approach to coping with this problem. Nevertheless, the development cost of a VR scene is high from the perspectives of time and money. Hence, a spherical view VR-based training system of classroom misbehaviour management (TrainCM2) is proposed in this study. TrainCM2 can situate learners in a real situation at a low cost. To verify the effectiveness of TrainCM2, an experiment was conducted. The experimental results showed that the TrainCM2 learners outperformed the micro-teaching learners in terms of misbehaviour detection rate and processing performance, showing the benefits of the proposed approach.
Energy-efficient Nature-Inspired techniques in Cloud computing datacenters

Cloud computing is a systematic delivery of computing resources as services to the consumers via the Internet. Infrastructure as a Service (IaaS) is the capability provided to the consumer by enabling smarter access to the processing, storage, networks, and other fundamental computing resources, where the consumer can deploy and run arbitrary software including operating systems and applications. The resources are sometimes available in the form of Virtual Machines (VMs). Cloud services are provided to the consumers based on the demand, and are billed accordingly. Usually, the VMs run on various datacenters, which comprise of several computing resources consuming lots of energy resulting in hazardous level of carbon emissions into the atmosphere. Several researchers have proposed various energy-efficient methods for reducing the energy consumption in datacenters. One such solutions are the Nature-Inspired algorithms. Towards this end, this paper presents a comprehensive review of the state-of-the-art Nature-Inspired algorithms suggested for solving the energy issues in the Cloud datacenters. A taxonomy is followed focusing on three key dimension in the literature including virtualization, consolidation, and energy-awareness. A qualitative review of each techniques is carried out considering key goal, method, advantages, and limitations. The Nature-Inspired algorithms are compared based on their features to indicate their utilization of resources and their level of energy-efficiency. Finally, potential research directions are identified in energy optimization in data centers. This review enable the researchers and professionals in Cloud computing datacenters in understanding literature evolution towards to exploring better energy-efficient methods for Cloud computing datacenters.
Electrically Controlled Neuron-Like Spiking Regimes in Vertical-Cavity Surface-Emitting Lasers at Ultrafast Rates

We report experimentally on the electrically controlled, tunable, and repeatable neuron-like spiking regimes generated in an optically injected vertical-cavity surface-emitting laser (VCSEL) operating at the telecom wavelength of 1300 nm. These fast spiking dynamics (obtained at sub-nanosecond speed rates) demonstrate different behaviors observed in biological neurons such as thresholding, phasic and tonic spiking, and spike rate and spike latency coding. The spiking regimes are activated in response to external stimuli (with controlled strengths and temporal duration) encoded in the bias current applied to a VCSEL subject to continuous wave optical injection. These results reveal the prospect for fast (>7 orders of magnitude faster than neurons), novel, electrically controlled spiking photonic modules for future neuromorphic computing platforms.
Binary Differential Evolution Based on Individual Entropy for Feature Subset Optimization

The high dimensionality of data brings great challenges to the classification accuracy and complexity of the algorithm.Feature selection technology can improve the classification performance of the algorithm effectively.In this paper, a novel binary differential evolution based on individual entropy (BDIE) is proposed.First, the individual entropy method is constructed to quantify the diversity of the population, and the relationship between population diversity and convergence is analyzed.Then, the objective function based on individual entropy is designed to evaluate the feature subset.A new binary mutation strategy is proposed, and it can effectively search the global optimal solution.In order to validate the BDIE, the datasets with different sizes and the classifiers of different types are used for testing.In addition, the well-known algorithms are introduced for comparison.The experimental results show that the proposed algorithm can effectively improve the classification performance and reduce the time cost without increasing the size of the feature subset.
Social recommendation based on users’ attention and preference

Attention is the behavioral and cognitive process of selectively concentrating on small fraction of information while ignoring other perceivable information. Thus, user’s attention will influence his decision on the consumption and consequently the performance of recommender systems. When presented a long list of recommendations, users will focus more on the items that draw their attention and skip others even if they suit their tastes. However, existing works in social recommendation mainly generate recommendations based on users’ preference and overlook the importance of users’ attention. To overcome this limitation, in this paper we propose a probabilistic model HTPF that considers both users’ attention and preference in social recommendation. Besides, by analyzing four real-world social recommendation datasets, we observe that social relations have more explicit impact on users’ attention than on their item rating values. That is, user’s attention on items are easily affected by his social relations while his rating values seem somehow indifferent to social influence. It will be more effective to use social network information to infer users’ attention rather than users’ preference. Thus, different from existing methods that model the social influence on user’s rating values, HTPF focuses on exploiting social influence on user’s attention. Further, a scalable variational inference algorithm has been developed for our model to predict how the specific items will draw users’ attention and how the items will suit users’ tastes. Then HTPF generates the recommendation results based on the weighted combination of these two aspects. Extensive experiments conducted on large real-world datasets validate the superiority of our method over state-of-the-art methods.
SADeepSense: Self-Attention Deep Learning Framework for Heterogeneous On-Device Sensors in Internet of Things Applications

Deep neural networks are becoming increasingly popular in Internet of Things (IoT) applications. Their capabilities of fusing multiple sensor inputs and extracting temporal relationships can enhance intelligence in a wide range of applications. However, one key problem is the missing of adaptation to heterogeneous on-device sensors. These low-end sensors on IoT devices possess different accuracies, granularities, and amounts of information, whose sensing qualities are heterogeneous and vary over time. The existing deep learning frameworks for IoT applications usually treat every sensor input equally over time or increase model capacity in an ad-hoc manner, lacking the ability to identify and exploit the sensor heterogeneities. In this work, we propose SADeepSense, a deep learning framework that can automatically balance the contributions of multiple sensor inputs over time by exploiting their sensing qualities. SADeepSense makes two key contributions. First, SADeepSense employs the self-attention mechanism to learn the correlations among different sensors over time with no additional supervision. The correlations are then applied to infer the sensing qualities and to reassign model concentrations in multiple sensors over time. Second, instead of directly learning the sensing qualities and contributions, SADeepSense generates the residual concentrations that are deviated from the equal contributions, which helps to stabilize the training process. We demonstrate the effectiveness of SADeepSense with two representative IoT sensing tasks: heterogeneous human activity recognition with motion sensors and gesture recognition with the wireless signal. SADeepSense consistently outperforms the state-of-the-art methods by a clear margin. In addition, we show that SADeepSense only imposes little additional resource-consumption burden on embedded devices compared to the corresponding state-of-the-art framework.
Quality matters: Evoking subjective norms and coping appraisals by system design to increase security intentions

The efficacy of direct messages such as threats, warnings, reminders, and training for influencing information security intentions is well established. Despite the pervasiveness of these approaches, users' poor adherence to security best practices remains a significant problem. Thus, it is critical that new approaches for improving security intentions are explored. System quality is known to play an important role in many aspects of information systems interactions, however, its effect on security intentions has not been established. In this paper, we develop and test a theoretical model that extends protection motivation theory and the theory of planned behavior to describe how system quality evokes positive social norms, lowers threat appraisal, and increases coping appraisal to influence intentions of secure behavior. The hypotheses were tested using a laboratory experiment. The results suggest that system quality positively influences social norms and coping appraisals to enhance security intentions. Threat appraisals, however, were unaffected. These findings illustrate how system quality can influence users' security intentions and perceptions of the efficacy of their actions without using direct messaging to warn, train, or reassure them. This technique gives researchers and developers new tools that can influence users to engage in desired behaviors.
Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier

Active authentication refers to the process in which users are unobtrusively monitored and authenticated continuously throughout their interactions with mobile devices. Generally, an active authentication problem is modelled as a one class classification problem due to the unavailability of data from the impostor users. Normally, the enrolled user is considered as the target class (genuine) and the unauthorized users are considered as unknown classes (impostor). We propose a convolutional neural network (CNN) based approach for one class classification in which a zero centered Gaussian noise and an autoencoder are used to model the pseudo-negative class and to regularize the network to learn meaningful feature representations for one class data, respectively. The overall network is trained using a combination of the cross-entropy and the reconstruction error losses. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one class classification. Effectiveness of the proposed framework is demonstrated using three publically available face-based active authentication datasets and it is shown that the proposed method achieves superior performance compared to the traditional one class classification methods. The source code is available at : github.com/otkupjnoz/oc-acnn.
A deep forest classifier with weights of class probability distribution subsets

A modification of the Deep Forest or gcForest proposed by Zhou and Feng for solving classification problems is proposed in the paper and called as PM-DF. The main idea for improving classification performance of the Deep Forest is to assign weights to subsets of the class probability distributions at the leaf nodes computed for every training example. The subsets of probability distributions are defined by using Walley's imprecise pari-mutuel model which compactly divides the unit simplex of probabilities into subsets and allows us to simplify the algorithm of the weight calculation. The weights of the distribution subsets can be viewed in this case as second-order probabilities over subsets of the probability simplex. The optimal weights are computed by solving the standard quadratic optimization problem. The numerical experiments illustrate PM-DF.
Key Encapsulation Mechanism with Explicit Rejection in the Quantum Random Oracle Model

The recent post-quantum cryptography standardization project launched by NIST increased the interest in generic key encapsulation mechanism (KEM) constructions in the quantum random oracle (QROM). Based on a OW-CPA-secure public-key encryption (PKE), Hofheinz, Hövelmanns and Kiltz (TCC 2017) first presented two generic constructions of an IND-CCA-secure KEM with quartic security loss in the QROM, one with implicit rejection (a pseudorandom key is return for an invalid ciphertext) and the other with explicit rejection (an abort symbol is returned for an invalid ciphertext). Both are widely used in the NIST Round-1 KEM submissions and the ones with explicit rejection account for 40%. Recently, the security reductions have been improved to quadratic loss under a standard assumption, and be tight under a nonstandard assumption by Jiang et al. (Crypto 2018) and Saito, Xagawa and Yamakawa (Eurocrypt 2018). However, these improvements only apply to the KEM submissions with implicit rejection and the techniques do not seem to carry over to KEMs with explicit rejection. In this paper, we provide three generic constructions of an IND-CCA-secure KEM with explicit rejection, under the same assumptions and with the same tightness in the security reductions as the aforementioned KEM constructions with implicit rejection (Crypto 2018, Eurocrypt 2018). Specifically, we develop a novel approach to verify the validity of a ciphertext in the QROM and use it to extend the proof techniques for KEM constructions with implicit rejection (Crypto 2018, Eurocrypt 2018) to our KEM constructions with explicit rejection. Moreover, using an improved version of one-way to hiding lemma by Ambainis, Hamburg and Unruh (ePrint 2018/904), for two of our constructions, we present tighter reductions to the standard IND-CPA assumption. Our results directly apply to 9 KEM submissions with explicit rejection, and provide tighter reductions than previously known (TCC 2017).
Corking by Forking: Vulnerability Analysis of Blockchain

The great market success of Blockchain makes it an extremely valuable target for attackers. A well-known attack in Blockchain is the forking attack, where divergent blockchains are produced for inserting some new features to facilitate security breaches. The state-of-the-art works mostly focus on how to detect attacks in real-time transactions, which is in hindsight and cannot deter the forking attack from the root. To take precautions, we employ the large deviation theory to study the vulnerability of blockchain networks incurred by intentional forks from a micro point of view, boosting forward-looking and strategic planning mechanisms for resisting the forking attack. Our study is fine-grained, because it offers not only the vulnerability probability of a blockchain network but also its decay speed, through which we find setting the parameter related to the robust level has more power than enhancing the computer power in speeding up the failure of attacks. This finding is valuable since it renders an opportunity to improve the robustness of a blockchain network in a cost-efficient way. Our analysis is complementary, since it studies both the impacts of the computational power as well as the number of confirmations on the vulnerability of a blockchain network, providing a theoretical basis to design reasonable schemes for invigorating a blockchain network from technical as well as managerial levels. Extensive experiments carried out on a large-scale cloud platform running the Ethereum protocol show the experimental and analytical results match well, verifying the effectiveness of our analysis.
Mixed reality in production and logistics: Discussing the application potentials of Microsoft HoloLensTM

In 2016, Microsoft released the mixed reality (MR) head-mounted display (HMD) HoloLensTM. As augmented reality (AR) devices, the HoloLensTM can enrich the user’s perceived environment with virtual information. However, Microsoft does not consider the HoloLensTM as AR device, but as the first MR device, since it has some additional features compared to competing AR devices. Especially to mention is the possibility to interact with virtual objects and vice versa. This paper shall provide an insight about the application potentials of MR in the field of production and logistics. For this purpose, we present the findings of a literature review about the current state of research concerning the application of Microsoft HoloLensTM in the field of production and logistics. Furthermore, we present a small HoloLensTM application, which we have developed to evaluate the capabilities of the device. Several persons tested our application and gave us feedback concerning the utility and usability of the HoloLensTM. We provide an evaluation of the user experiences at the end of the paper.
Towards accurate tiny vehicle detection in complex scenes

Large variance of scales is still a notable challenge for vehicle detection in complex traffic scenes. Though Faster RCNN based two-stage detectors have demonstrated superiority on generic object detection, its effectiveness on vehicle detection in real applications is not aware of, especially for vehicles of tiny scales. In this paper, a novel two-stage detector is proposed to perform tiny vehicle detection with high recall. It consists of a backward feature enhancement network (BFEN) and a spatial layout preserving network (SLPN). At the first stage, less attention has been paid for generating proposals with high recall, which, however, plays a significant role in recalling vehicles of tiny scales. By fully exploiting the feature representation power of a backbone network, the BFEN aims at generating high-quality region proposals for vehicles of various scales. Even with only 100 proposals, the proposed BFEN achieves an encouraging recall rate over 99%. For a better localization of tiny vehicles, we argue that the spatial layout of the ROI features also plays a significant role in the second stage. Accordingly, a light-weight detection sub-network called SLPN is designed to progressively integrate ROI features, while preserving the spatial layouts. Experiments done on the challenging DETRAC vehicle detection dataset show that the proposed method significantly improves a competitive baseline (ResNet-50 based Faster RCNN) by 16.5% mAP. Comparative performance to the state of the arts are also achieved on both the DETRAC and KITTI benchmarks.
On IOTA as a potential enabler for an M2M economy in manufacturing

Recently, the manufacturing industry became aware of distributed ledger technologies, a protocol that, amongst other things, allows trustless transactions between machines. In this paper, we investigate whether an M2M economy would be feasible within the IOTA network, a popular cryptocurrency for IoT. We build and present a simple industrial lot-size one production system involving three agents that cooperate to create an artistic painting. Payments between agents and users are autonomously executed via IOTA. Amongst other points of criticism, we found that an M2M economy would benefit from the support of smart-contracts and conclude that IOTA is not a fitting solution.
YogaNet: 3-D Yoga Asana Recognition Using Joint Angular Displacement Maps With ConvNets

Representing 3-D motion-capture sensor data with 2-D color-coded joint distance maps (JDMs) as input to a deep neural network has been shown to be effective for 3-D skeletal-based human action recognition tasks. However, the joint distances are limited by their ability to represent rotational joint movements, which account for a considerable amount of information in human action classification tasks. Moreover, for the subject, view and time invariance in the recognition process, the deep classifier needs training on JDMs along different coordinate axes from multiple streams. To overcome the above shortcomings of JDMs, we propose integrating joint angular movements along with the joint distances in a spatiotemporal color-coded image called a joint angular displacement map (JADM). In the literature, multistream deep convolutional neural networks (CNNs) have been employed to achieve invariance across subjects and views for 3-D human action data, which is achieved by sacrificing training time for accuracy. To improve the recognition accuracy with reduced training times, we propose to test our JADMs with a single-stream deep CNN model. To test and analyze the proposed method, we chose video sequences of yoga. The 3-D motion-capture data represent a complex set of actions with lateral and rotational spatiotemporal variations. We validated the proposed method using 3-D traditional human action data from the publicly available datasets HDM05 and CMU. The proposed model can accurately recognize 3-D yoga actions, which may help in building a 3-D model-based yoga assistant tool.