Reviewerly/exHarmony huggingface dataset:
Adaptive chaotic satin bowerbird optimisation algorithm for numerical function optimisation

The Satin Bowerbird Optimisation (SBO) was inspired by the Satin Bowerbirds living in Australia’s rainforests and other mesic habitats. Like other meta-heuristic algorithms, the main problem faced by the SBO is that it has been empirically demonstrated to become easily trapped into local optimal solutions, creating low precision and slow convergence speeds. To overcome these deficiencies, we propose herein the Adaptive Chaotic Satin Bowerbird Optimisation algorithm (AC-SBO). Within the AC-SBO algorithm, a chaotic map is introduced to modify the search process, with which to enhance global convergence speeds, and to obtain better performance. We introduced the chaos theory into the SBO optimisation process, in order to replace the main parameter’s greatest step size (α), which assists in controlling the balance between both exploration and exploitation. The search accuracy and performance of the AC-SBO algorithm were verified on ten classical benchmark functions. In addition, in the experimental CEC2014 results showed that for almost all functions, the AC-SBO technique proved superior to the other comparative algorithms optimisations. The Wilcoxon rank-sum statistical test was performed in order to judge the significance of the results, and further demonstrated the improved performance of the proposed AC-SBO algorithm.
Information Hiding in Industrial Control Systems: An OPC UA based Supply Chain Attack and its Detection

Industrial Control Systems (ICS) help to automate various cyber-physical systems in our world. The controlled processes range from rather simple traffic lights and elevators to complex networks of ICS in car manufacturing or controlling nuclear power plants. With the advent of industrial Ethernet ICS are increasingly connected to networks of Information Technology (IT). Thus, novel attack vectors on ICS are possible. In IT networks information hiding and steganography is increasingly used in advanced persistent threats to conceal the infection of the systems allowing the attacker to retain control over the compromised networks. In parallel ICS are more and more a target for attacks as well. Here, simple automated attacks as well as targeted attacks of nation state actors with the intention of damaging components or infrastructures as a part of cyber crime have already been observed. Information hiding could bring such attacks to a new level by integrating backdoors and hidden/covert communication channels that allow for attacking specific processes whenever it is deemed necessary. This paper sheds light on potential attack vectors on Programmable Logic Controllers (PLCs) using OPC Unified Architecture (OPC UA) network protocol based communication. We implement an exemplary supply chain attack consisting of an OPC UA server (Bob, B) and a Siemens S7-1500 PLC as OPC UA client (Alice, A). The hidden storage channel is using source timestamps to embed encrypted control sequences allowing for setting digital outputs to arbitrary values. The attack is solely relying on the programming of the PLC and does not require firmware level access. Due to the potential harm to life caused by attacks on cyber-physical systems any presentation of novel attack vectors need to present suitable mitigation strategies. Thus, we investigate potential approaches for the detection of the hidden storage channel for a warden W as well as potential countermeasures in order to increase the warden-compliance. Our machine learning based detection approach using a One-Class-Classifier yields a detection performance of 89.5% with zero false positives within an experiment with 46,159 OPC UA read responses without a steganographic message and 7,588 OPC UA read responses with an embedded steganographic message.
Face Detection System Based Viola-Jones Algorithm

Face detection using biometric facial face image details is a very challenging field that uses the basic face detection algorithm for face detection and many other algorithms to support that use. This paper proposed system consist of many levels that is used for face detection effectively using many algorithm for feature extraction (LDA algorithm) and for feature reduction (BAT and chicken algorithms), classification (J48 algorithms) as well as viola jones for main face detection algorithm. Accuracy increased from 97.59 with (LDA-J48) to 97.88 with (LDA-BAT/CHICKEN-J48).
Data-Augmented Hybrid Named Entity Recognition for Disaster Management by Transfer Learning

This research aims to build a Mandarin named entity recognition (NER) module using transfer learning to facilitate damage information gathering and analysis in disaster management. The hybrid NER approach proposed in this research includes three modules: (1) data augmentation, which constructs a concise data set for disaster management; (2) reference model, which utilizes the bidirectional long short-term memory–conditional random field framework to implement NER; and (3) the augmented model built by integrating the first two modules via cross-domain transfer with disparate label sets. Through the combination of established rules and learned sentence patterns, the hybrid approach performs well in NER tasks for disaster management and recognizes unfamiliar words successfully. This research applied the proposed NER module to disaster management. In the application, we favorably handled the NER tasks of our related work and achieved our desired outcomes. Through proper transfer, the results of this work can be extended to other fields and consequently bring valuable advantages in diverse applications.
Human Action Recognition in Video Using DB-LSTM and ResNet

Human action recognition in video is one of the most widely applied topics in the field of image and video processing, with many applications in surveillance (security, sports, etc.), activity detection, video-content-based monitoring, man-machine interaction, and health/disability care. Action recognition is a complex process that faces several challenges such as occlusion, camera movement, viewpoint move, background clutter, and brightness variation. In this study, we propose a novel human action recognition method using convolutional neural networks (CNN) and deep bidirectional LSTM (DB-LSTM) networks, using only raw video frames. First, deep features are extracted from video frames using a pre-trained CNN architecture called ResNet152. The sequential information of the frames is then learned using the DB-LSTM network, where multiple layers are stacked together in both forward and backward passes of DB-LSTM, to increase depth. The evaluation results of the proposed method using PyTorch, compared to the state-of-the-art methods, show a considerable increase in the efficiency of action recognition on the UCF 101 dataset, reaching 95% recognition accuracy. The choice of the CNN architecture, proper tuning of input parameters, and techniques such as data augmentation contribute to the accuracy boost in this study.
EMC2: Energy-efficient and multi-resource- fairness virtual machine consolidation in cloud data centres

The rapid rise in the cloud service adoption reflects the growth of Cloud Data Centers' (CDCs) number, size, energy consumption and eco-unfriendly carbon footprints. In CDCs, Virtual Machine Consolidation (VMC) plays a significant role in reducing their energy consumption and thereby reducing the carbon footprints. The state-of-the-art VMC heuristics based on First-Fit Decreasing (FFD) and Dominant Residual Resource (DRR) called DRR-FFD and DRR-BinFill are grouping the VMs based on single VM resource. We attempt to further reduce the energy consumption of CDCs through the proposed EMC2, an energy-efficient VMC framework that employs our multi-resource-fairness based VM selection heuristics, namely VMNeAR-H (Hierarchical), VM NeAR- D (Directed Hierarchical) and VM NeAR-E (Euclidean Distance). A dataset extracted from ENERGY STAR® containing the heterogeneous physical machine resource capacities and their estimated energy consumptions is utilised in the simulation experiments. The proposed EMC2-VMNeAR-D heuristic dominates the existing DRR heuristics in terms of total energy consumed by all the physical machines in the CDC (3.318 % energy savings on average of 40 schedules = 185107 kWh).
Turning Cost-Based Steganography into Model-Based

Abstract Most modern steganographic schemes embed secrets by minimizing the total expected cost of modifications. However, costs are usually computed using heuristics and cannot be directly linked to statistical detectability. Moreover, as previously shown by Ker at al., cost-based schemes fundamentally minimize the wrong quantity that makes them more vulnerable to knowledgeable adversary aware of the embedding change rates. In this paper, we research the possibility to convert cost-based schemes to model-based ones by postulating that there exists payload size for which the change rates derived from costs coincide with change rates derived from some (not necessarily known) model. This allows us to find the steganographic Fisher information for each pixel (DCT coefficient), and embed other payload sizes by minimizing deflection. This rather simple measure indeed brings sometimes quite significant improvements in security especially with respect to steganalysis aware of the selection channel. Steganographic algorithms in both spatial and JPEG domains are studied with feature-based classifiers as well as CNNs.
Mining Top- <i>k</i> pairs of correlated subgraphs in a large network

We investigate the problem of correlated subgraphs mining (CSM) where the goal is to identify pairs of subgraph patterns that frequently co-occur in proximity within a single graph. Correlated subgraph patterns are different from frequent subgraphs due to the flexibility in connections between constituent subgraph instances and thus, existing frequent subgraphs mining algorithms cannot be directly applied for CSM. Moreover, computing the degree of correlation between two patterns requires enumerating and finding distances between every pair of subgraph instances of both patterns - a task that is both memory-intensive as well as computationally demanding. To this end, we propose two holistic best-first exploration algorithms: CSM-E (an exact method) and CSM-A (a more efficient approximate method with near-optimal quality). To further improve efficiency, we propose a top- k pruning strategy, while to reduce memory footprint, we develop a compressed data structure called R eplica, which stores all instances of a subgraph pattern on demand. Our empirical results demonstrate that the proposed algorithms not only mine interesting correlations, but also achieve good scalability over large networks.
Security Evaluation of Cryptosystems based on Orthogonal Transformation

Proposed approach for security evaluation of some cipher systems based on orthogonal transformation is introduced in this paper. This approach based on using the Discrete Fourier transform (DFT) as a main processor in the evaluation process. The mean value of the DFT coefficients of the ciphered outputs is considered as an evaluation parameter for evaluating the security of the considered cipher system. This approach was tested on symmetric and asymmetric cryptosystems. For symmetric one, we choose a stream cipher system to be tested by this method. While for a symmetric one we chose the RSA (public key cryptosystem). These different choices gave a wide spread use for this proposed method in comparison with other evaluation methods. The results were encouraging, and can be considered as a suitable approach to enhance the security evaluation tasks. That is really considered as a hard problem facing the cryptosystem design.
SLA as a mechanism to manage risks related to chatbot services

Intelligent Chatbot services become one of the mainstream applications in user help and many other areas. Apart from bringing numerous benefits to users these services may bring additional risks to the companies that employ them. The study starts with the review of the scale of chatbot industry and common use cases by focusing on their applications & industry tendencies. Review of functionality and architecture of typical chatbot services shows the potential risks associated with chatbots. Analysis of such risks in the paper helped to build a checklist that security managers can use to assess risks prior to chatbot implementation. The proposed checklist was tested by reviewing a number of Service Level Agreements (SLA) of real chatbot providers.
Deep Learning for Effective Refugee Tent Extraction Near Syria–Jordan Border

Rukban is a desert area crossing the border between Syria and Jordan, and thousands of Syrian refugees fled into this area since the Syrian civil war in 2014. In the past few years, the number of refugee shelters for the forcibly displaced Syrian refugees in this area has increased rapidly. Estimating the location and number of refugee tents has become a key factor to maintain the sustainability of the refugee shelter camps. Manually counting the shelters is labor-intensive and sometimes prohibitive given the large quantities. In addition, these shelters/tents are usually small in size, irregular in shape, and sparsely distributed in a very large area and could be easily missed by the traditional image-analysis techniques, making the image-based approaches also challenging. In this letter, we proposed a deep fully convolutional neural network (FCN) model to extract automatically the refugee shelters/tents in the worldview-2 (WV-2) satellite images. In addition, we transferred knowledge in the pretrained VGG-16 model to improve the detection accuracy and network training convergence. We compared the proposed approach with the traditional spectral angle mapper (SAM) method, deep convolutional neural network (CNN) models, and the mask Region-based CNN (R-CNN) model. The experimental results show that the FCN model improved the overall accuracy by 4.49%, 3.54%, and 0.88% compared with the CNNs, SAM, and mask R-CNN models, and improved the precision by 34.61%, 41.99%, and 11.87%, respectively.
Adaptive Fading Bayesian Unscented Kalman Filter and Smoother for State Estimation of Unmanned Aircraft Systems

This paper proposes an adaptive fading Bayesian unscented Kalman filter (AF-BUKF) and explores its application for state estimation of unmanned aircraft systems (UASs).In the AF-BUKF, the state and noise densities are approximated as finite Gaussian mixtures, in which the mean and covariance for each component are recursively estimated using the UKF.To avoid the prohibitive computational complexity caused by the exponential growth of mixture components, a Gaussian mixture simplification algorithm is employed.Moreover, the AF-BUKF algorithm employs a novel adaptive fading strategy to recursively update the Gaussian components, so that the adverse effect of inexact knowledge of the state and measurement noise covariance can be mitigated.An AF-BUK Smoother (AF-BUKS) is also proposed by extending the AF-BUKF algorithm using the concept of optimal Bayesian smoothing and the Rauch-Tung-Striebel Smoother to improve estimation accuracy.Experimental results on simulated and real UAS data show that the proposed AF-BUKF/S algorithms can achieve better performance compared with the conventional methods.Thus, they can serve as attractive alternative approaches for nonlinear state estimation of UASs and other problems.
Designing to engage children in monitoring indoor air quality

Indoor air quality (IAQ) is especially important for children because they are more susceptible to the deleterious impacts of poor air quality compared to adults. While devices to monitor IAQ are increasingly available, these are designed primarily for adults, and little attention has been paid to their potential use by children. This paper describes an effort to engage children directly in the design of an IAQ visualization interface for children. In engaging children in participatory workshops, we found that they rely heavily on visual, olfactory, and thermal cues to perceive and assess IAQ. Reflecting on these findings and based on design principles for technology for children, we created and tested child-friendly interface prototypes for IAQ visualization. Based on children's input, we designed a final set of visual interfaces that will be implemented in the IAQ monitor. The next study will test and deploy the monitor in the real world.
Are You Still Watching? Streaming Video Quality and Engagement Assessment in the Crowd

As video streaming accounts for the majority of Internet traffic, monitoring its quality is of importance to both Over the Top (OTT) providers as well as Internet Service Providers (ISPs). While OTTs have access to their own analytics data with detailed information, ISPs often have to rely on automated network probes for estimating streaming quality, and likewise, academic researchers have no information on actual customer behavior. In this paper, we present first results from a large-scale crowdsourcing study in which three major video streaming OTTs were compared across five major national ISPs in Germany. We not only look at streaming performance in terms of loading times and stalling, but also customer behavior (e.g., user engagement) and Quality of Experience based on the ITU-T P.1203 QoE model. We used a browser extension to evaluate the streaming quality and to passively collect anonymous OTT usage information based on explicit user consent. Our data comprises over 400,000 video playbacks from more than 2,000 users, collected throughout the entire year of 2019. The results show differences in how customers use the video services, how the content is watched, how the network influences video streaming QoE, and how user engagement varies by service. Hence, the crowdsourcing paradigm is a viable approach for third parties to obtain streaming QoE insights from OTTs.
Multi-mass breast cancer classification based on hybrid descriptors and memetic meta-heuristic learning

Among the causes of death in the world, breast cancer is considered the most common cause of mortality among women to the extent that one in five deaths among women is attributed to the incidence of this cancer. In this paper, we introduce a computer-aided detection approach to multiple classifications of breast masses. We tried to separate and intelligently recognize different masses in the breast cancer by means of mammograms so that in the first step, with the pre-processing, pectoral region is segmented from other parts and different areas are primarily clustered by K-means method. In the next step, using aggregation of efficient features such as texture features, Pseudo–Zernike moments, and wavelet features will be extracted from the input image and simulated annealing algorithm will reduce the size of feature vector. The final step will be the classification of possible masses in mammogram and the assessment of its severity based on memetic meta-heuristic adaptive neuro-based fuzzy inference system in which the optimizer is shuffled frog-leaping algorithm. The proposed method is evaluated using 322 mammogram images taken from Mini-MIAS database, which contain a variety of possible masses in mammograms. We compare our model with similar algorithms and several state-of-the-art methods through a comprehensive set of experiments. In this approach, the focus is on providing a hybrid algorithm for accurate detection and extraction of masses in mammography, with the approach that the physician can predict both the potential disease stage and type of tumor.
A survey on implementation of lightweight block ciphers for resource constraints devices

Lightweight cryptography is essential for securing resource-limited devices like smart card, RFID (radio-frequency identification)tags, sensor network and embedded system. In this paper, we present a survey of lightweight block ciphers from both software and hardware perspectives. This paper show the analysis of lightweight block ciphers on the basis of block size, key size, number of rounds, area in gate equivalent, speed, logic process as metrics for comparison purpose. Also, this comprehensive survey highlights the strengths and some of the security related issues in terms of key length and attacks in lightweight cryptography.
Effects of trade and financial integration on structural transformation in Africa: New evidence from a sample splitting approach

Improving on structural transformation is one of the major preoccupations of countries in Africa. However, structural transformation process has been unimpressive despite the efforts of countries to promote transformation. While evidence abound on the macroeconomic determinants of structural transformation, issues around whether or not countries in Africa should open their economies to international markets for improved transformation have not received much attention in the literature. This study therefore examines the threshold effects of trade and financial integration on structural transformation in 32 African countries using a sample splitting estimation approach over the period 1985 to 2015. Our evidence suggests that, trade and financial integration effects do not evolve independently but complement each other in explaining cross-country differences in countries’ level of structural transformation in Africa. However, the transformation-inducing effect of trade (financial) openness slows as financial (trade) integration progresses above the estimated optimal threshold levels. To foster deeper integration in Africa, this study recommends that, efforts to address non-tariff bottlenecks should be a major preoccupation of countries.
An Automated Pipeline for Character and Relationship Extraction from Readers Literary Book Reviews on Goodreads.com

Reader reviews of literary fiction on social media, especially those in persistent, dedicated forums, create and are in turn driven by underlying narrative frameworks. In their comments about a novel, readers generally include only a subset of characters and their relationships, thus offering a limited perspective on that work. Yet in aggregate, these reviews capture an underlying narrative framework comprised of different actants (people, places, things), their roles, and interactions that we label the “consensus narrative framework”. We represent this framework in the form of an actant-relationship story graph. Extracting this graph is a challenging computational problem, which we pose as a latent graphical model estimation problem. Posts and reviews are viewed as samples of sub graphs/networks of the hidden narrative framework. Inspired by the qualitative narrative theory of Greimas, we formulate a graphical generative Machine Learning (ML) model where nodes represent actants, and multi-edges and self-loops among nodes capture context-specific relationships. We develop a pipeline of interlocking automated methods to extract key actants and their relationships, and apply it to thousands of reviews and comments posted on Goodreads.com. We manually derive the ground truth narrative framework from SparkNotes, and then use word embedding tools to compare relationships in ground truth networks with our extracted networks. We find that our automated methodology generates highly accurate consensus narrative frameworks: for our four target novels, with approximately 2900 reviews per novel, we report average coverage/recall of important relationships of >80% and an average edge detection rate of >89%. These extracted narrative frameworks can generate insight into how people (or classes of people) read and how they recount what they have read to others. 1