Reviewerly/exHarmony huggingface dataset:
DNRTI: A Large-Scale Dataset for Named Entity Recognition in Threat Intelligence

Named entity recognition is an important and challenging problem in Natural language processing. Although the past decade has witnessed major advances in entity recognition in many fields, such successes have been slow to network security field, not only because of the data in the network security field is very professional, but also due to the sensitive information in the data. To advance named entity recognition research in network security field, we introduce a large-scale Dataset for Named Entity Recognition in Threat Intelligence (DNRTI). To this end, we collect more than 300 pieces of threat intelligence. The data in DNRTI is all annotated by experts in threat intelligence interpretation using 13 object categories. The fully annotated DNRTI contains 175220 words. To build a baseline for named entity recognition in the threat intelligence field, we evaluate some deep learning model on DNRTI. Experiments demonstrate that DNRTI well represents the key information in threat intelligence and are quite challenging.
Controlled experimentation in continuous experimentation: Knowledge and challenges

Continuous experimentation and A/B testing is an established industry practice that has been researched for more than 10 years. Our aim is to synthesize the conducted research. We wanted to find the core constituents of a framework for continuous experimentation and the solutions that are applied within the field. Finally, we were interested in the challenges and benefits reported of continuous experimentation. We applied forward snowballing on a known set of papers and identified a total of 128 relevant papers. Based on this set of papers we performed two qualitative narrative syntheses and a thematic synthesis to answer the research questions. The framework constituents for continuous experimentation include experimentation processes as well as supportive technical and organizational infrastructure. The solutions found in the literature were synthesized to nine themes, e.g. experiment design, automated experiments, or metric specification. Concerning the challenges of continuous experimentation, the analysis identified cultural, organizational, business, technical, statistical, ethical, and domain-specific challenges. Further, the study concludes that the benefits of experimentation are mostly implicit in the studies. The research on continuous experimentation has yielded a large body of knowledge on experimentation. The synthesis of published research presented within include recommended infrastructure and experimentation process models, guidelines to mitigate the identified challenges, and what problems the various published solutions solve.
Ein Blick auf aktuelle Entwicklungen bei Blockchains und deren Auswirkungen auf den Energieverbrauch

The enormous power consumption of Bitcoin has led to undifferentiated discussions in science and practice about the sustainability of blockchain and distributed ledger technology in general. However, blockchain technology is far from homogeneous - not only with regard to its applications, which now go far beyond cryptocurrencies and have reached businesses and the public sector, but also with regard to its technical characteristics and, in particular, its power consumption. This paper summarizes the status quo of the power consumption of various implementations of blockchain technology, with special emphasis on the recent 'Bitcoin Halving' and so-called 'zk-rollups'. We argue that although Bitcoin and other proof-of-work blockchains do indeed consume a lot of power, alternative blockchain solutions with significantly lower power consumption are already available today, and new promising concepts are being tested that could further reduce in particular the power consumption of large blockchain networks in the near future. From this we conclude that although the criticism of Bitcoin's power consumption is legitimate, it should not be used to derive an energy problem of blockchain technology in general. In many cases in which processes can be digitised or improved with the help of more energy-efficient blockchain variants, one can even expect net energy savings.
An Efficient and High-Speed Overlap-Free Karatsuba-Based Finite-Field Multiplier for FGPA Implementation

Cryptography systems have become inseparable parts of almost every communication device. Among cryptography algorithms, public-key cryptography, and in particular elliptic curve cryptography (ECC), has become the most dominant protocol at this time. In ECC systems, polynomial multiplication is considered to be the most slow and area consuming operation. This article proposes a novel hardware architecture for efficient field-programmable gate array (FPGA) implementation of Finite-field multipliers for ECC. Proposed hardware was implemented on different FPGA devices for various operand sizes, and performance parameters were determined. Comparing to state-of-the-art works, the proposed method resulted in a lower combinational delay and area-delay product indicating the efficiency of design.
Vision-based hand signal recognition in construction: A feasibility study

In construction fields, it is common for workers to rely on hand signals to communicate and express thoughts due to their simple but effective nature. However, the meaning of these hand signals was not always captured precisely. As a result, construction errors and even accidents were produced. This paper presented a feasibility study on investigating whether the hand signals could be captured and interpreted automatically with computer vision technologies. It starts with the literature review of existing hand gesture recognition methods for sign language understanding, human-computer interaction, etc. It is then followed by creating a dataset containing 11 classes of hand signals in construction. The performance of two state-of-the-art 3D convolutional neural networks is measured and compared. The results indicated that a high classification accuracy (93.3%) and a short inference time (0.17 s/gesture) could be achieved, illustrating the feasibility of using computer vision to automate hand signal recognition in construction.
Adaptive co-optimization of artificial neural networks using evolutionary algorithm for global radiation forecasting

Global radiation is not a regularly measured parameter in any weather station relative to other meteorological parameters due to measurement costs. This study has proposed hybrid artificial neural network models that predicted monthly radiation using typical weather and geographic data. Two datasets and six artificial neural network models were respectively built for indigenous and widespread regions around the world. The referred models co-optimized the artificial neural network properties and feature selection. For this purpose, an adaptive evolutionary algorithm improving prediction performance was developed to train the neural networks. This novel approach has yielded promising results compared to the developed deep learning models in this study. The results revealed that while the indigenous models had common features of longitude, sunshine durations, precipitation, and wind speed, the widespread models involved those of latitude, sunshine durations, and mean daily maximum air temperature. The proposed hybrid model had respectively the best mean absolute percentage errors of 2.45% and 9.93% for validation dataset and 3.75% and 11.03% for testing dataset of the indigenous and widespread regions, respectively. The present findings showed that the proposed hybrid model could be evaluated as a generic model and could improve the forecasting accuracy with the specified optimization parameters.
Mapping changes in artisanal and small-scale mining (ASM) landscape using machine and deep learning algorithms. - a proxy evaluation of the 2017 ban on ASM in Ghana

Artisanal and Small-Scale Mining (ASM) landscapes form integral part of the Land use land cover (LULC) in the developing worlds. However, the spatial, spectral, and temporal footprints of ASM present some challenges for using most of the freely available optical satellite sensors for change analysis. The challenge is even profound in tropical West African countries like Ghana where there is prolonged cloud cover. Whiles very few studies have used Sentinel-2 data to map change analysis in ASM landscape, none examined the contribution of individual S2 bands to the ASM classifications. Also, despite the capabilities of Machine Learning (ML) and Deep Learning (DL) models for LULC classifications, few studies have compared the performances of different classifiers in mapping ASM landscape. This study utilized Sentinel-2 data, four ML and DL models (Artificial Neural Network –ANN, Random Forest – RF, Support Vector Machines –SVM, a pixel-based Convolutional Neural Network-CNN) and image segmentation to examine the performance of S2 bands and ML and DL algorithms for change analysis in ASM landscape, with the Birim Basin in Ghana as a study area. The result of the change analysis was used to assess changes in LULC during the recent ban on the expansion of ASM in the country. It was found out that ANN is a better classifier of ASM achieving the highest overall accuracy (OA) of 99.80% on the segmented Sentinel-2 bands. The study also found out that the Band 5 Vegetation Red Edge (VRE) 1 contributed most to classifying ASM, with the segmented VRE 1 being superlative over the other predictors. In terms of expansion, ASM increased by 59.17 km2 within the period of the study (January 2017 to December 2018), suggesting that ASM still took place under the watch of the ban. The classification results showed that most of the peripheral of forest and farmland have been converted to ASM with little disturbance within the interior of the forest reserves. The study revealed that, the ban was yielding very little or no results due to a number of policy deficiencies including low staff strength, lack of logistics and low remuneration. Enforcement of legal instruments against ASM and farming activities within the forest reserves, improvement in the monitoring systems and intensification of public education on the value of forest and the need to protect it are some of the major recommendations that could control encroachment on the forest reserves.
A Transfer Learning Approach to Heatmap Regression for Action Unit Intensity Estimation

Action Units (AUs) are geometrically-based atomic facial muscle movements known to produce appearance changes at specific facial locations. Motivated by this observation we propose a novel AU modelling problem that consists of jointly estimating their localisation and intensity. To this end, we propose a simple yet efficient approach based on Heatmap Regression that merges both problems into a single task. A Heatmap models whether an AU occurs or not at a given spatial location. To accommodate the joint modelling of AUs intensity, we propose variable size heatmaps, with their amplitude and size varying according to the labelled intensity. Using Heatmap Regression, we can inherit from the progress recently witnessed in facial landmark localisation. Building upon the similarities between both problems, we devise a transfer learning approach where we exploit the knowledge of a network trained on large-scale facial landmark datasets. In particular, we explore different alternatives for transfer learning through a) fine-tuning, b) adaptation layers, c) attention maps, and d) reparametrisation. Our approach effectively inherits the rich facial features produced by a strong face alignment network, with minimal extra computational cost. We empirically validate that our system sets a new state-of-the-art on three popular datasets, namely BP4D, DISFA, and FERA2017.
Computational learning of features for automated colonic polyp classification

Abstract Shape, texture, and color are critical features for assessing the degree of dysplasia in colonic polyps. A comprehensive analysis of these features is presented in this paper. Shape features are extracted using generic Fourier descriptor. The nonsubsampled contourlet transform is used as texture and color feature descriptor, with different combinations of filters. Analysis of variance (ANOVA) is applied to measure statistical significance of the contribution of different descriptors between two colonic polyps: non-neoplastic and neoplastic. Final descriptors selected after ANOVA are optimized using the fuzzy entropy-based feature ranking algorithm. Finally, classification is performed using Least Square Support Vector Machine and Multi-layer Perceptron with five-fold cross-validation to avoid overfitting. Evaluation of our analytical approach using two datasets suggested that the feature descriptors could efficiently designate a colonic polyp, which subsequently can help the early detection of colorectal carcinoma. Based on the comparison with four deep learning models, we demonstrate that the proposed approach out-performs the existing feature-based methods of colonic polyp identification.
IoT ETEI: End-to-End IoT Device Identification Method

The past decades have seen the rapid development of Internet of Things (IoT) in various domains. Identifying the IoT devices connected to the network is a crucial aspect of network security. However, existing work on identifying IoT devices based on manually extracted features and prior knowledge, leading to low efficiency and identification accuracy. In this paper, we propose an automatic end-to-end IoT device identification method (IoT ETEI) based on CNN+BiLSTM deep learning model, which outperforms traditional methods from the perspective of overhead and identify accuracy. We demonstrate the effectiveness and flexibility of the proposed method by deploying IoT ETEI in the face of identifying IoT devices on public datasets with the accuracy rate over 99 %, even for IoT devices that use encryption protocols.
A new heterogeneous neural network model and its application in image enhancement

Based on visual cortical theory of Rybak, a new heterogeneous Rybak neural network (HRYNN) model is proposed for image enhancement. HRYNN is constructed with several Rybak neural network (RYNN) models proposed, which have different parameters corresponding to different neurons. We show that HRYNN can better represent prior information for edge detail enhancement than the logarithmic domain. To capture different resolution texture features of image, a novel receptive field model is proposed to solve the problem of detail enhancement. HRYNN model has excellent enhancement effect on the edge details of image based on the receptive field’s lateral inhibitory characteristics. Moreover, the experimental enhancement results of the colour images from Berkeley image Dataset show the validity and efficiency of the proposed enhancement method. Finally, three evaluation indicators are employed to measure the enhancement result.
Scalable multi-label canonical correlation analysis for cross-modal retrieval

Multi-label canonical correlation analysis (ml-CCA) has been developed for cross-modal retrieval. However, the computation of ml-CCA involves dense matrices eigendecomposition, which can be computationally expensive. In addition, ml-CCA only takes semantic correlation into account which ignores the cross-modal feature correlation. In this paper, we propose a novel framework to simultaneously integrate the semantic correlation and feature correlation for cross-modal retrieval. By using the semantic transformation, we show that our model can avoid computing the covariance matrix explicitly which is a huge save of computational cost. Further analysis shows that our proposed method can be solved via singular value decomposition which has linear time complexity. Experimental results on three multi-label datasets have demonstrated the accuracy and efficiency of our proposed method.
A squeeze and excitation ResNeXt-based deep learning model for Bangla handwritten compound character recognition

In today’s digital era, the recognition of handwritten documents is highly demanding due to the widespread applications. Bangla is one of the most used languages in the world, which consists of 50 basic alphabets. Many compound characters exist in Bangla, which is the combination of two or more basic characters. The recognition of Bangla handwritten characters is a challenging task due to their various sizes, sheer, diversity, lots of turns, similarities between alphabets, and different writing patterns. This paper presents a deep CNN (Convolutional Neural Network) model using the SE-ResNeXt. The squeeze and excitation (SE) blocks are added with existing ResNeXt to address the Bangla handwritten compound character recognition. Usually, CNN extracted the spatial features in the lower-layers and complex features in the upper layers. The SE blocks are added to improve the performance of usual deep CNN by automatically fusing the channel-wise spatial information and inter-channel dependencies through squeezing and excitation within local receptive fields, respectively. To validate the performance of the proposed model, we have utilized Mendeley BanglaLekha-Isolated 2 dataset. The experimental results demonstrate that the proposed model exhibits an average accuracy of 99.82% in recognizing Bangla handwritten compound characters. In addition, the proposed model outperforms the state-of-the-art models by demonstrating higher results.
Deep Consensus Network: Aggregating predictions to improve object detection in microscopy images

Detection of cells and particles in microscopy images is a common and challenging task. In recent years, detection approaches in computer vision achieved remarkable improvements by leveraging deep learning. Microscopy images pose challenges like small and clustered objects, low signal to noise, and complex shape and appearance, for which current approaches still struggle. We introduce Deep Consensus Network, a new deep neural network for object detection in microscopy images based on object centroids. Our network is trainable end-to-end and comprises a Feature Pyramid Network-based feature extractor, a Centroid Proposal Network, and a layer for ensembling detection hypotheses over all image scales and anchors. We suggest an anchor regularization scheme that favours prior anchors over regressed locations. We also propose a novel loss function based on Normalized Mutual Information to cope with strong class imbalance, which we derive within a Bayesian framework. In addition, we introduce an improved algorithm for Non-Maximum Suppression which significantly reduces the algorithmic complexity. Experiments on synthetic data are performed to provide insights into the properties of the proposed loss function and its robustness. We also applied our method to challenging data from the TUPAC16 mitosis detection challenge and the Particle Tracking Challenge, and achieved results competitive or better than state-of-the-art.
IAN: Multi-Behavior Navigation Planning for Robots in Real, Crowded Environments

State-of-the-art approaches for robot navigation among humans are typically restricted to planar movement actions. This work addresses the question of whether it can be beneficial to use interaction actions, such as saying, touching, and gesturing, for the sake of allowing robots to navigate in unstructured, crowded environments. To do so, we first identify challenging scenarios to traditional motion planning methods. Based on the hypothesis that the variation in modality for these scenarios calls for significantly different planning policies, we design specific navigation behaviors as interaction planners for actuated, mobile robots. We further propose a high level planning algorithm for multi-behavior navigation, named Interaction Actions for Navigation (IAN). Through both real-world and simulated experiments, we validate the selected behaviors and the high-level planning algorithm, and discuss the impact of our obtained results on our stated assumptions.
Parameter Optimization of Support Vector Regression Using Harris Hawks Optimization

Support Vector Regression (SVR) is often used in forecasting. Adjustment of parameters in the SVR affects the results of forecasting. This study aims to analyze the SVR method that is optimized using Harris Hawks Optimization (HHO), hereinafter referred to as HHO-SVR. The HHO-SVR was evaluated using five benchmark datasets to determine the performance of this method. The HHO process is also compared based on the type of kernel and other metaheuristic algorithms. The results showed that the HHO-SVR has almost the same performance as other methods but is less efficient in terms of time. In addition, the type of kernel also affects the process and results.
Leveraging semantic features for recommendation: Sentence-level emotion analysis

Personalized recommendation systems can help users to filter redundant information from a large amount of data. Previous relevant researches focused on learning user preferences by analyzing texts from comment communities without exploring the detailed sentiment polarity, which encountered the cold-start problem. To address this research gap, we propose a hybrid personalized recommendation model that extracts user preferences by analyzing user review content in different sentiment polarity at the sentence level, based on jointly applying user-item score matrices and dimension reduction methods. A novel voting mechanism is also designed based on positive preferences from the neighbors of the target user to directly generate the recommendation results. The experimental results of testing the proposed model with a real-world data set show that our proposed model can achieve better recommendation effects than the representative recommendation algorithms. In addition, we demonstrated that fine-grained emotion recognition has good adaptability to a sparse rating matrix with a reasonable and good performance.
Multi-Spectral Image Classification with Quantum Neural Network

Processing Earth observation images to obtain land cover classification is an important task allowing to track changes on the Earth's surface resulting from natural processes, human activity, and climate change. The amount of data acquired from Earth observation satellites is very large and their processing takes large amount of computational resources. We investigate application of quantum circuit based neural network classifiers for multi-spectral data classification aimed at obtaining the land cover information. We show a proof-of-concept of our approach.
Some constructions of quantum MDS codes

We construct quantum MDS codes with parameters $$ [\![ q^2+1,q^2+3-2d,d ]\!] _q$$ for all $$d \leqslant q+1$$ , $$d \ne q$$ . These codes are shown to exist by proving that there are classical generalised Reed–Solomon codes which contain their Hermitian dual. These constructions include many constructions which were previously known but in some cases these codes are new. We go on to prove that if $$d\geqslant q+2$$ then there is no generalised Reed–Solomon $$[n,n-d+1,d]_{q^2}$$ code which contains its Hermitian dual. We also construct an $$ [\![ 18,0,10 ]\!] _5$$ quantum MDS code, an $$ [\![ 18,0,10 ]\!] _7$$ quantum MDS code and a $$ [\![ 14,0,8 ]\!] _5$$ quantum MDS code, which are the first quantum MDS codes discovered for which $$d \geqslant q+3$$ , apart from the $$ [\![ 10,0,6 ]\!] _3$$ quantum MDS code derived from Glynn’s code.