Reviewerly/exHarmony huggingface dataset:
Forward secure certificateless proxy multi-signature scheme

In order to deal with key exposure problem, we introduce forward secure technique into certificateless proxy multi-signature scheme, and give the formal definition and security model of forward secure certificateless proxy multi-signature. Furthermore, we present a construction of forward secure certificateless proxy multi-signature scheme. Based on the difficulty of computational Diffie-Hellman problem, the proposed scheme is existentially unforgeable against adaptively chosen-message attacks and chosen-warrant attacks in the random oracle model. The proposed scheme does not use bilinear pairs in the key update and generation proxy signature phases, and updated proxy key is easy, thus it is more suitable for mobile environments. Our scheme has effectively dealt with the key exposure problem and certificate management problem.
Detecting phishing pages using the relief feature selection and multiple classifiers

Website phishing is a deception in e-commerce, which attempts to steal user confidential information using similar websites. The classification technique is one of the common ways to detect phishing websites. According to high-volume main data, attribute reduction algorithms play an essential role. This paper presents an appropriate model based on the relief algorithm to reduce dimension. Moreover, the proposed approach uses multiple-classifiers to increase accuracy. The evaluated results show higher accuracy and superiority than conventional methods.
AGA-GRU: An Optimized GRU Neural Network Model Based on Adaptive Genetic Algorithm

Abstract The weight adjustment of gated recurrent unit (GRU) network depends on the gradient descent algorithm heavily, therefore this paper proposes an improved GRU neural network model based on adaptive genetic algorithm (AGA-GRU) to solve this problem. In this model, AGA is used to construct the optimization system, and the parameters of neural network model are optimized to improve the prediction performance. The results on UCI dataset show that the prediction accuracy of AGA-GRU model is significantly improved, and the generalization performance is stronger.
subs2vec: Word embeddings from subtitles in 55 languages

This paper introduces a collection of vector-embeddings models of lexical semantics in 55 languages, trained on a large corpus of pseudo-conversational speech transcriptions from television shows and movies. The models were trained on the OpenSubtitles corpus using the fastText implementation of the skipgram algorithm. Performance comparable with (and in some cases exceeding) models trained on non-conversational (Wikipedia) text is reported on standard benchmark evaluation datasets. A novel evaluation method of particular relevance to psycholinguists is also introduced: prediction of experimental lexical norms in multiple languages. The models, as well as code for reproducing the models and all analyses reported in this paper (implemented as a user-friendly Python package), are freely available at: https://github.com/jvparidon/subs2vec/
Crafting a web-unique value proposition using the concept analysis technique

Online value proposition is an integral part of websites because it identifies the reason why customers will click on, return, register or buy from the website and feel motivated to share their experience. The purpose of this paper is to develop a framework for identifying competitive web value propositions for e-commerce website. This paper outlines a conceptual analysis of value proposition for e-commerce website combined with the value life cycle in a manner that offers managers a tool to create a web unique value proposition. The result of this paper is the four-cycle process which is an exercise an organisation can undertake in its own organisation's context. Such exercise would be a group activity enabling them to achieve a shared understanding while developing a clearer conceptualisation of the online value proposition concept.
Towards linked open government data in Canada

Governments are publishing enormous amounts of open data on the web every day in an effort to increase transparency and reusability. Linking data from multiple sources on the web enables the performance of advanced data analytics, which can lead to the development of valuable services and data products. However, Canada's open government data portals are isolated from one another and remain unlinked to other resources on the web. In this paper, we first expose the statistical data sets in Canadian provincial open data portals as Linked Data, and then integrate them using RDF Cube vocabulary, thereby making different open data portals available through a single search endpoint. We leverage Semantic Web Technologies to publish open data sets taken from two provincial portals (Nova Scotia and Alberta) as RDF (the Linked Data format), and to connect them to one another. The success of our approach illustrates its high potential for linking open government data sets across Canada, which will in turn enable greater data accessibility and improved search results.
How can we achieve a fully open future?

It is time for a new type of mandate. Plan S has catalysed all sorts of action, and confusion, in the world of scholarly publishing. But it lacks teeth. Instead of encouraging libraries and research institutes to continue to prop up a dysfunctional and out-dated system with taxpayer money, research funders should mandate institutes to create a fully open, modern, technical scholarly infrastructure. This would help to overcome so much of the inertia behind the adoption of open research practices, while simultaneously resolving outstanding issues with reliability, affordability, and functionality in scholarly communication.
Cybersecurity Ontology

Ontologies are explicit conceptualizations of subject domains and therefore important methods used, for instance, for knowledge acquisition and knowledge sharing. Furthermore, ontologies are an important methodological approach for knowledge-intensive problem solving that clearly involves reasoning about objects and concepts in a particular domain or information resource. This means that ontologies are of great value for knowledge acquisition because ontologies can be acquired, or reused, or otherwise inserted into a domain model, then the task of acquiring knowledge can be greatly eased.
Secure provenance-based communication using visual encryption

Explicit specification of the historical record of an instance or a data item is called data provenance. It has many applications in various fields with regards to its importance on capturing data flow mechanisms. However, on the other hand, there are good number of security mechanisms in place to withstand the cyber-attacks. Almost all of these algorithms uses complex mathematical calculations in providing security for the systems. Visual cryptography is a peculiar approach which uses concept of secret sharing by dividing the message into transparencies as encryption process. Upon superimposing transparencies one obtains the original message. In this paper, we propose secret sharing as a notion of security onto data provenance. Main inference of this writing is to throw a model combining above two mentioned aspects which gives away an indigenous solution in the area of information security. This proposed model is implemented with specific use case scenarios for substantiation and related analysis. Simulated results and discussion of the same is presented in the paper.
Tech firms unite to fight face checks ban

A coalition of technology industry groups – whose members include Facebook, Google and specialist biometrics firms – are urging US lawmakers to go easy on facial recognition technology (FRT).
Exploration for Byproduct Critical Element Resources: Proxy Development Using a LA–ICP–MS Database

The transition towards zero-CO 2 energy generation, storage and transport will require a range of metals that are often considered critical and are produced as byproducts of the production of other metals. This means that the reliance of some critical elements on the production of main metal commodities, such as Cu and Ni, is a significant source of supply risk. However, how can we evaluate resource scarcity and supply risks for elements that we do not routinely analyze for and characterize in present day mined ores? Here we demonstrate a method for exploring for and assessing the byproduct critical element potential of magmatic sulfide and volcanogenic massive sulfide deposits using a LA–ICP–MS database. Our results indicate there are significant enrichments of Sb, Bi, Cd, Co, Se and Te in pentlandite (Ni sulfide), chalcopyrite (Cu sulfide) and sphalerite (Zn sulfide) within these systems, demonstrating the need for a holistic approach to critical element research with unrecovered byproducts in existing mining supply chains having the potential to solve perceived resource scarcity challenges.
RT-ViT: Real-Time Monocular Depth Estimation Using Lightweight Vision Transformers

The latest research in computer vision highlighted the effectiveness of the vision transformers (ViT) in performing several computer vision tasks; they can efficiently understand and process the image globally unlike the convolution which processes the image locally. ViTs outperform the convolutional neural networks in terms of accuracy in many computer vision tasks but the speed of ViTs is still an issue, due to the excessive use of the transformer layers that include many fully connected layers. Therefore, we propose a real-time ViT-based monocular depth estimation (depth estimation from single RGB image) method with encoder-decoder architectures for indoor and outdoor scenes. This main architecture of the proposed method consists of a vision transformer encoder and a convolutional neural network decoder. We started by training the base vision transformer (ViT-b16) with 12 transformer layers then we reduced the transformer layers to six layers, namely ViT-s16 (the Small ViT) and four layers, namely ViT-t16 (the Tiny ViT) to obtain real-time processing. We also try four different configurations of the CNN decoder network. The proposed architectures can learn the task of depth estimation efficiently and can produce more accurate depth predictions than the fully convolutional-based methods taking advantage of the multi-head self-attention module. We train the proposed encoder-decoder architecture end-to-end on the challenging NYU-depthV2 and CITYSCAPES benchmarks then we evaluate the trained models on the validation and test sets of the same benchmarks showing that it outperforms many state-of-the-art methods on depth estimation while performing the task in real-time (∼20 fps). We also present a fast 3D reconstruction (∼17 fps) experiment based on the depth estimated from our method which is considered a real-world application of our method.
Image Encryption Algorithm Based on Artificial Bee Colony Algorithm and Chaotic System

This article proposes an image encryption algorithm based on a chaotic bit-plane decomposition and optimization algorithm of a crossover operator artificial bee colony algorithm. Firstly, use the SHA-256 hash algorithm to calculate the plaintext image’s hash value as the starting value of the fractional Lorenz hyperchaotic system after operation. Utilize the chaotic sequence to permutate plaintext image in a bit plane to obtain the scrambled image. Secondly, block the scrambled image into four subimages of equal size, and count the hash value of each row of each block by the SHA-256 hash algorithm as the starting value of the Sine-Tent-Logistic chaotic system. Use the obtained chaotic sequence to substitute the images. Then, stitch the four sub-block images to get the final encrypted image, and the population is obtained. Finally, use the information entropy of ciphertext image as the fitness function of the artificial bee colony algorithm based on a crossover operator. Select the ciphertext image with the best information entropy from the population as the optimal encrypted image, and then, return the position value of the best honey source meanwhile. The experimental simulation and security analysis indicate that the scheme has an excellent encryption effect and ability to oppose various general attacks.
Prediction of electricity energy consumption including <scp>COVID</scp>‐19 precautions using the hybrid <scp>MLR‐FFANN</scp> optimized with the stochastic fractal search with fitness distance balance algorithm

Abstract The increase in energy consumption is affected by the developments in technology as well as the global population growth. Increasing energy consumption makes it difficult to ensure electrical energy supply security. Meeting the energy demand can be achieved with the right planning. Proper planning is critical for both economical use of resources and low cost for the end consumer. On the other hand, erroneous estimation of demand may cause waste of resources and energy crisis. Accurate estimation is possible by accurately modeling the factors affecting electricity consumption. Apart from known factors such as seasonal conditions, days of the week and hours, modeling in extreme events such as pandemics that affect all our behaviors increases the success in modeling the future projection. This ensures that the security of electrical energy supply is carried out effectively with limited resources. For this purpose, in this study, a hybrid multiple linear regression‐feedforward artificial neural network (MLR‐FFANN) based algorithm model was proposed, taking into account the estimated impact of the COVID‐19 pandemic on the energy consumption values of Bursa, an industrial city in Turkey. The aim of the hybrid MLR‐FFANN approach was to simultaneously optimize the β polynomial for multiple linear regression and the weight and bias coefficients for the forward propagation neural network using the adaptive guided differential evolution, equilibrium optimizer, slime mold algorithm, and stochastic fractal search with fitness distance balance (SFSFDB) optimization algorithms. The success of the model whose parameters were optimized using the optimization algorithms was determined according to mean absolute error, mean absolute percentage error, and root mean square error evaluation criteria and statistical analysis of these results. According to the results of the analysis, the MLR‐FFANN approach whose parameters were optimized with the SFSFDB algorithm was more successful in the training of the dataset containing the COVID‐19 precautions.
A stability-enhanced CycleGAN for effective domain transformation of unpaired ultrasound images

As a cost-effective, non-invasive and radiation-free medical imaging modality, ultrasonic imaging is widely used in clinical diagnosis. However, database bias is commonplace among different medical centers. Deep learning based ultrasound image analysis algorithms are usually data driven, rendering high requirements on the uniformity of image datasets. In this paper, we propose a stability-enhanced cycle-consistent generative adversarial network (CycleGAN) method with well detail preservation for the domain transformation to normalize ultrasound images from various medical centers. To stabilize the training process of CycleGAN model, we adopt spectral normalization for 1-Lipschitz continuity to reduce model oscillation. Besides, two time-scale update rule and label smoothing strategy are also utilized to maintain the balance between generators and discriminators for further stability enhancement. Moreover, our method applies skip connections to preserve ultrasound image details and prevent resolution loss during the domain transformation process. Experiments were conducted on clinical thyroid and carotid image datasets acquired from several medical centers. Massive results demonstrate that our proposed model is easier to reach a steady state when training, outstanding 50% from the basic CycleGAN model. Compared with representative algorithms, our proposed method reaches the state-of-the-art performance, with a 11.3% decrease in the mean absolute error and a 9.8% increase in the structural similarity. Hence, our proposed algorithm has a strong capacity of the domain transformation in ultrasound images to reduce the database bias for uniformly distributed datasets. We believe that our method can contribute to the development of the ultrasound image analysis and computer aided clinical diagnosis..
Neural Networks Based on Ultrafast Time-Delayed Effects in Exciton Polaritons

We demonstrate that time-delayed nonlinear effects in exciton polaritons can be used to construct neural networks where information is coded in optical pulses arriving consecutively on the sample. The highly nonlinear effects are induced by time-dependent interactions with the excitonic reservoir. These nonlinearities allow the creation of a nonlinear XOR logic gate that can perform operations on the picosecond timescale. An optoelectronic neural network based on the constructed logic gate performs the classification of spoken digits with a high accuracy rate.
Multiscale generative model using regularized skip-connections and perceptual loss for anomaly detection in toxicologic histopathology

Automated anomaly detection is an important tool that has been developed for many real-world applications, including security systems, industrial inspection, and medical diagnostics. Despite extensive use of machine learning for anomaly detection in these varied contexts, it is challenging to generalize and apply these methods to complex tasks such as toxicologic histopathology (TOXPATH) assessment (i.e.,finding abnormalities in organ tissues). In this work, we introduce an anomaly detection method using deep learning that greatly improves model generalizability to TOXPATH data.We evaluated a one-class classification approach that leverages novel regularization and perceptual techniques within generative adversarial network (GAN) and autoencoder architectures to accurately detect anomalous histopathological findings of varying degrees of complexity. We also utilized multiscale contextual data and conducted a thorough ablation study to demonstrate the efficacy of our method. We trained our models on data from normal whole slide images (WSIs) of rat liver sections and validated on WSIs from three anomalous classes. Anomaly scores are collated into heatmaps to localize anomalies within WSIs and provide human-interpretable results.Our method achieves 0.953 area under the receiver operating characteristic on a real-worldTOXPATH dataset. The model also shows good performance at detecting a wide variety of anomalies demonstrating our method's ability to generalize to TOXPATH data.Anomalies in both TOXPATH histological and non-histological datasets were accurately identified with our method, which was only trained with normal data.
3-Multi ranked encryption with enhanced security in cloud computing

Searchable Encryption (SE) enables data owners to search remotely stored ciphertexts selectively. A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users, and even return the top-k most relevant search results when requested. We refer to a model that satisfies all of the conditions a 3-multi ranked search model. However, SE schemes that have been proposed to date use fully trusted trapdoor generation centers, and several methods assume a secure connection between the data users and a trapdoor generation center. That is, they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords, but it will never attempt to use it in any other manner than that requested, which is impractical in real life. In this study, to enhance the security, we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security assumptions. The proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center, thereby preventing attackers from determining whether two different queries include the same keyword. Moreover, we develop a method for managing multiple encrypted keywords from every data owner, each encrypted with a different key. Our evaluation demonstrates that, despite the trade-off overhead that results from the weaker security assumption, the proposed scheme achieves reasonable performance compared to extant schemes, which implies that our scheme is practical and closest to real life.
Outlier Item Detection in Fashion Outfit

In this paper, we introduce the outlier item detection task, which is related to the compatibility prediction. Although, with the ability of measuring the compatibility, we are able to identify items that do not match the overall style of a given outfit, the outlier item detection task has not been well studied before. Most existing methods on compatibility prediction focus on improving the recommendation accuracy by utilizing the underlying high order relationships among items and have achieved promising results. Since these methods are not designed to address the above problem, the performance can be relatively poor. In this paper, we introduce the outlier item detection task and propose an attention-based encoder to learn a permutation equivariant transformation for items. The encoder is independent of the size of the items. An MLP decoder is deployed to detect the outlier item. We conduct experiments on different fashion datasets and the empirical results show that our model achieves superior performance over the state-of-the-art methods.
Towards a Predictable Open Source FaaS

Auto-scaling is the capability of Function as a Service systems, that supports dynamic scaling of the function instances according to the incoming load. Auto-scalers fire scaling events when a certain threshold is exceeded. However, if this threshold is not set properly, the function can suffer from under or over-provisioning. In this paper we introduce an autoscaling solution for compute intensive functions that calculates the scaling threshold according to the user needs and keeps the completion times predictable even when the function is scaled out. The scaling threshold is given by a simulator that determines the completion time distribution of the function for a given load. We also show which load-balancing algorithm is recommended to use for our auto-scaler. We compare our auto-scaler to existing ones implemented in open source serverless projects.
The Air Quality Lens: Ambiguity as Opportunity to Reactivate Environmental Data

The use of low-cost sensors to collect environmental data can enable citizens to express environmental concerns and foster community building and activism. However, when made public, citizen data is often detached from the subjective experiences integral to citizen sensing. Our work with youngsters from diverse backgrounds explores how existing environmental datasets can be reactivated to engage new stakeholders and discussions. We present a research through design project with air quality data and draw attention to the role of ambiguity in our design process. We synthesize our reflections by discussing three design aspects that can make sense of ambiguity and encourage critical engagements with environmental data. Our goal is to offer a design-oriented account of how citizen-generated environmental data can be reactivated to express matters of concern.