Reviewerly/exHarmony huggingface dataset:
A hybrid cryptosystem for digital contents confidentiality based on rotation of quantum spin states

The security of secret information becomes one of global issue in digitally advanced age. Several new digital information forms are now available because of advancement in multimedia tools and applications. In this article, we have projected an efficient digital contents confidentiality technique based on spinning half algebra. We have simulated the image pixels with spin half states sequence for defined range of phase to create confusion capability in our anticipated information confidentiality mechanism. The scope of this article revolved around the development of hybrid information privacy scheme based on the notions of quantum spinning and rotation states effectively.
Joint Super-Resolution and Alignment of Tiny Faces

Super-resolution (SR) and landmark localization of tiny faces are highly correlated tasks. On the one hand, landmark localization could obtain higher accuracy with faces of high-resolution (HR). On the other hand, face SR would benefit from prior knowledge of facial attributes such as landmarks. Thus, we propose a joint alignment and SR network to simultaneously detect facial landmarks and super-resolve tiny faces. More specifically, a shared deep encoder is applied to extract features for both tasks by leveraging complementary information. To exploit representative power of the hierarchical encoder, intermediate layers of a shared feature extraction module are fused to form efficient feature representations. The fused features are then fed to task-specific modules to detect landmarks and super-resolve face images in parallel. Extensive experiments demonstrate that the proposed model significantly outperforms the state-of-the-art in both landmark localization and SR of faces. We show a large improvement for landmark localization of tiny faces (i.e., 16 × 16). Furthermore, the proposed framework yields comparable results for landmark localization on low-resolution (LR) faces (i.e., 64 × 64) to existing methods on HR (i.e., 256 × 256). As for SR, the proposed method recovers sharper edges and more details from LR face images than other state-of-the-art methods, which we demonstrate qualitatively and quantitatively.
Energy Utilization Task Scheduling for MapReduce in Heterogeneous Clusters

Nowadays, energy costs are the most important factor in cloud computing. Therefore, the implementation of energy-aware task scheduling methods is of utmost importance. A task scheduling framework considering deadlines, data locality and resource utilization is proposed to save on energy costs in heterogeneous clusters. The framework consists of task list construction, task scheduling and slot list updating. In terms of deadline constraints, number of job slots allocated and possible processing times of jobs, a new job sequence is proposed to construct an reasonable task list. Tasks are scheduled to promising slots from their rack-local servers, cluster-local servers and remote servers in the produced task scheduling, which greatly improves data locality. After the assignment among tasks and slots, an update of available slots in clusters is proposed not only to find available slots but also to improve server resource utilization using fuzzy logic with the available number of slots according to current CPU, memory and bandwidth utilization. Experimental results show that the proposed heuristic results in lower energy consumption than the adapted existing algorithms with a variable total number of slots.
Novel chaotic grouping particle swarm optimization with a dynamic regrouping strategy for solving numerical optimization tasks

Particle swarm optimization (PSO) has been widely applied to address various optimization problems, since it is easy to implement and only has a few control parameters. However, PSO often suffers from a lack of population diversity during the search process and is ineffective in balancing exploration and exploitation, especially in solving complex numerical optimization tasks. To overcome these disadvantages of PSO for complex numerical optimization problems, a new chaotic grouping PSO algorithm with a dynamic regrouping strategy (CGPSO-DRS) is proposed in this paper. The newly proposed CGPSO-DRS is based on a dynamic multiswarm PSO framework that cooperates with the chaotic grouping mechanism (CGM) and the dynamic regrouping strategy (DRS). First, the CGM divides the entire population into many subswarms via a chaotic sequence. The CGM not only improves the population grouping quality in the search process but also increases the diversity of the population. Second, the DRS is used to guide the regrouping of the population, and the population starts searching with a new configuration. Here, the DRS changes with the number of function evaluations. The DRS facilitates the effective utilization of information to balance the early exploration and the later exploitation performances. In addition, the DRS can increase the diversity of the population in the search process. Experiments have been conducted on 41 benchmark functions, and the numerical results demonstrate that the proposed CGPSO-DRS method outperforms similar population-based approaches and state-of-the-art PSO variants in accelerating the convergence speed and finding the global optimum.
A Comparative Study between Security and Privacy

New technologies like the Internet of Things (IoT), Smart City (SC), and Big data have changed the way we look towards life. These technologies created integration between the physical and virtual world that paved the way for many services that we could not imagine a decade ago for the users. The fuel for these services is the sensed data, which can be collected anytime and anywhere by using sensors. However, these technologies and their services have several challenges on their way ahead. This will decide how they will evolve in the future. The most sensitive and critical challenges are security and privacy of user’s data. Unfortunately, there is no real comprehensive understanding of the two concepts so far. This paper highlights both concepts, definitions, points of interests in each one, distinguishing aspects and compatibility. In addition, few practical cases are discussed with a focus on security and privacy for the IoT environment.
Performance Evaluation Methodology for Long-Term Single-Object Tracking

A long-term visual object tracking performance evaluation methodology and a benchmark are proposed. Performance measures are designed by following a long-term tracking definition to maximize the analysis probing strength. The new measures outperform existing ones in interpretation potential and in better distinguishing between different tracking behaviors. We show that these measures generalize the short-term performance measures, thus linking the two tracking problems. Furthermore, the new measures are highly robust to temporal annotation sparsity and allow annotation of sequences hundreds of times longer than in the current datasets without increasing manual annotation labor. A new challenging dataset of carefully selected sequences with many target disappearances is proposed. A new tracking taxonomy is proposed to position trackers on the short-term/long-term spectrum. The benchmark contains an extensive evaluation of the largest number of long-term trackers and comparison to state-of-the-art short-term trackers. We analyze the influence of tracking architecture implementations to long-term performance and explore various redetection strategies as well as the influence of visual model update strategies to long-term tracking drift. The methodology is integrated in the VOT toolkit to automate experimental analysis and benchmarking and to facilitate the future development of long-term trackers.
Approximate approximation on a quantum annealer

Many problems of industrial interest are NP-complete, and quickly exhaust resources of computational devices with increasing input sizes. Quantum annealers (QA) are physical devices that aim at this class of problems by exploiting quantum mechanical properties of nature. However, they compete with efficient heuristics and probabilistic or randomised algorithms on classical machines that allow for finding approximate solutions to large NP-complete problems.
The regulation of crypto-assets in the EU – investment and payment tokens under the radar

Based on the guidelines issued by the European Securities and Market Authority and by the European Banking Authority, the article deals with the legal qualification of blockchain-based crypto-assets under EU law. Focusing on crypto-assets that function as a) investment instruments (that is, investment tokens) and as b) electronic money (that is, payment tokens), the work outlines shortages and drawbacks in the applicability and enforcement of existing EU legal frameworks regulating investment activities and payment services. With such analysis, the article seeks to inform the ongoing debate within European institutions on the need of regulatory intervention in this area, and it points out pressing questions to be tackled by further research.
Design anthropology for emerging technologies: Trust and sharing in autonomous driving futures

In this article we demonstrate how design anthropology theory, methodology and practice can be mobilised to create interventions in how possible human futures with emerging technologies are understood and imagined. Drawing on our research into Human Experience and Expectations of Autonomous Driving (AD) cars we show how: we engaged ethnographic insights to redefine concepts of trust and sharing which contest dominant problem-solution narratives; and we mobilised these insights in applied contexts, through our AD Futures cards which employ ethnographic quotes and examples to disrupt common assumptions, align stakeholders with everyday experience, and pose new questions.
Bidirectional butterfly optimization algorithm and engineering applications

Butterfly optimization algorithm (BOA) is a recently introduced nature inspired meta-heuristics that mimics the natural foraging and mating behavior of butterflies. The framework of BOA is based on the fragrance emitted by the butterflies, which helps other butterflies in searching food as well as mating partner. BOA performs both the global as well as local search while looking for the optimal solution for the problem. Despite this BOA sometime stuck in local optima that result in slow or poor convergence. This study embeds the bidirectional search in the structure of BOA to enhance the convergence. The proposal is named as BBOA and experimented on a set of seven unimodal benchmark function along with three real world problems.
Assessing the Effects of Photovoltaic Powerplants on Surface Temperature Using Remote Sensing Techniques

The rapid development of photovoltaic (PV) powerplants in the world has drawn attention on their climate and environmental impacts. In this study, we assessed the effects of PV powerplants on surface temperature using 23 largest PV powerplants in the world with thermal infrared remote sensing technique. Our result showed that the installation of the PV powerplants had significantly reduced the daily mean surface temperature by 0.53 °C in the PV powerplant areas. The cooling effect with the installation of the PV powerplants was much stronger during the daytime than the nighttime with the surface temperature dropped by 0.81 °C and 0.24 °C respectively. This cooling effect was also depended on the capacity of the powerplants with a cooling rate of −0.32, −0.48, and −0.14 °C/TWh, respectively, for daily mean, daytime, and nighttime temperature. We also found that the construction of the powerplants significantly decreased the surface albedo from 0.22 to 0.184, but significantly increased the effective albedo (surface albedo plus electricity conversion) from 0.22 to 0.244, suggesting conversion of solar energy to electrical energy is a major contributor to the observed surface cooling. Our further analyses showed that the nighttime cooling in the powerplants was significantly correlated with the latitude and elevation of the powerplants as well as the annual mean temperature, precipitation, solar radiation, and normalized difference vegetation index (NDVI). This means the temperature effect of the PV powerplants depended on regional geography, climate and vegetation conditions. This finding can be used to guide the selection of the sites of PV powerplants in the future.
Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in VideoQA

Videos convey rich information.Dynamic spatio-temporal relationships between people/objects, and diverse multimodal events are present in a video clip.Hence, it is important to develop automated models that can accurately extract such information from videos.Answering questions on videos is one of the tasks which can evaluate such AI abilities.In this paper, we propose a video question answering model which effectively integrates multi-modal input sources and finds the temporally relevant information to answer questions.Specifically, we first employ dense image captions to help identify objects and their detailed salient regions and actions, and hence give the model useful extra information (in explicit textual format to allow easier matching) for answering questions.Moreover, our model is also comprised of duallevel attention (word/object and frame level), multi-head self/cross-integration for different sources (video and dense captions), and gates which pass more relevant information to the classifier.Finally, we also cast the frame selection problem as a multi-label classification task and introduce two loss functions, In-and-Out Frame Score Margin (IOFSM) and Balanced Binary Cross-Entropy (BBCE), to better supervise the model with human importance annotations.We evaluate our model on the challenging TVQA dataset, where each of our model components provides significant gains, and our overall model outperforms the stateof-the-art by a large margin (74.09% versus 70.52%).We also present several word, object, and frame level visualization studies. 1Local Gate Frame Score Margin Inside Frames Outside FramesFrame-Level Att.
MuLaN: Multilingual Label propagatioN for Word Sense Disambiguation

The knowledge acquisition bottleneck strongly affects the creation of multilingual sense-annotated data, hence limiting the power of supervised systems when applied to multilingual Word Sense Disambiguation. In this paper, we propose a semi-supervised approach based upon a novel label propagation scheme, which, by jointly leveraging contextualized word embeddings and the multilingual information enclosed in a knowledge base, projects sense labels from a high-resource language, i.e., English, to lower-resourced ones. Backed by several experiments, we provide empirical evidence that our automatically created datasets are of a higher quality than those generated by other competitors and lead a supervised model to achieve state-of-the-art performances in all multilingual Word Sense Disambiguation tasks. We make our datasets available for research purposes at https://github.com/SapienzaNLP/mulan.
A Virtual Tour of a Hardly Accessible Archaeological Site: The Effect of Immersive Virtual Reality on User Experience, Learning and Attitude Change

Some archaeological sites are not easily accessible by visitors due to mobility or geographical restrictions. Digital technology can make such sites virtually accessible and provide educational information at the same time. Toward this goal, we created a digital reconstruction of the archaeological site of Choirokoitia. Given that a 3D digital reconstruction can be used along with different technologies, we designed and developed an interactive application, where users can navigate and get information about the site, for two different systems: Virtual Reality (VR) systems and desktop computers. A feasibility study was conducted where we compared aspects of the two systems so as to allow the suggestion of the proper technology to utilize according to a user's aims. The results showed higher levels of presence and more positive experience by the participants who used the VR system compared to those who used the desktop version. On the other hand, greater learning gains were demonstrated in participants who used the desktop version compared to those who used the VR version. No differences were shown between the two groups regarding the participants' change of attitudes toward the archaeology of Cyprus.
Design of an integrated model for diagnosis and classification of pediatric acute leukemia using machine learning

Applying artificial intelligence techniques for diagnosing diseases in hospitals often provides advanced medical services to patients such as the diagnosis of leukemia. On the other hand, surgery and bone marrow sampling, especially in the diagnosis of childhood leukemia, are even more complex and difficult, resulting in increased human error and procedure time decreased patient satisfaction and increased costs. This study investigates the use of neuro-fuzzy and group method of data handling, for the diagnosis of acute leukemia in children based on the complete blood count test. Furthermore, a principal component analysis is applied to increase the accuracy of the diagnosis. The results show that distinguishing between patient and non-patient individuals can easily be done with adaptive neuro-fuzzy inference system, whereas for classifying between the types of diseases themselves, more pre-processing operations such as reduction of features may be needed. The proposed approach may help to distinguish between two types of leukemia including acute lymphoblastic leukemia and acute myeloid leukemia. Based on the sensitivity of the diagnosis, experts can use the proposed algorithm to help identify the disease earlier and lessen the cost.
CloudU-Net: A Deep Convolutional Neural Network Architecture for Daytime and Nighttime Cloud Images’ Segmentation

Cloud segmentation is one of the hot tasks in the field of weather forecast, environmental monitoring, site selection for observatory, and other areas. In this letter, we proposed a new deep convolutional neural network architecture called CloudU-Net for daytime and nighttime cloud images&#x2019; segmentation. The net consists of dilated convolution, activation, batch normalization (BN), max pooling, upsampling, skip connection, and fully connected conditional random field (CRF) layers. The benefits of the net architecture are four aspects: First, the dilated convolution increases the receptive field of the filters to obtain more information of the context without increasing the extra amount of computation and the extra number of parameters. Second, the BN layer increases the speed of network training and prevents over-fitting. Third, the fully connected CRF optimizes the output of the front end of the architecture, and finally gets better segmentation results. Finally, the enhanced optimizer Lookahead improves the learning stability and speeds up model convergence. Compared with the current deep-learning-based state-of-the-art cloud images&#x2019; segmentation algorithms, the CloudU-Net demonstrates better segmentation performance for daytime and nighttime cloud images.
Appearance feature enhancement for person re-identification

Person re-identification (Re-ID) has important practical application value in intelligent video analysis. Due to the illumination, occlusion, and pose variation, person Re-ID is still a challenging problem. Some recent Re-ID methods based on ResNet-50 have achieved high accuracy, but performance degradation is caused by pose variation. To address this issue, Pose-Invariant Convolutional Baseline (PICB) embed with the proposed Pooling Fusion Block (PFB) is put forward as a new baseline for person Re-ID task. On the basis of PICB, an end-to-end network named Appearance-Enhanced Feature Learning Network (AEFLN) is proposed to simultaneously learn diversity body features and discriminative part features. Specially, a novel (DBFL) strategy is presented to learn diversity body features, which could alleviate the potential local minima problem generated by optimizing model with randomly initialized parameters in PFB. In addition, uniform part-level feature extractors are applied to learn part features, which compensates for body features' lack of distinguishable local information. In testing phase, body features and part features are integrated to represent the enhanced appearance feature for each person image. Comprehensive experiments have demonstrated that our method can outperform the sate-of-the-art results on several public available datasets, including Market-1501, CUHK03 and DukeMTMC-reID. For instance, we achieve 74.8% (+11.1%) and 76.5% (+19.0%) in Rank-1 accuracy and mAP on CUHK03 dataset.
Privacy-preserving approximate GWAS computation based on homomorphic encryption

Abstract Background One of three tasks in a secure genome analysis competition called iDASH 2018 was to develop a solution for privacy-preserving GWAS computation based on homomorphic encryption. The scenario is that a data holder encrypts a number of individual records, each of which consists of several phenotype and genotype data, and provide the encrypted data to an untrusted server. Then, the server performs a GWAS algorithm based on homomorphic encryption without the decryption key and outputs the result in encrypted state so that there is no information leakage on the sensitive data to the server. Methods We develop a privacy-preserving semi-parallel GWAS algorithm by applying an approximate homomorphic encryption scheme HEAAN. Fisher scoring and semi-parallel GWAS algorithms are modified to be efficiently computed over homomorphically encrypted data with several optimization methodologies; substitute matrix inversion by an adjoint matrix, avoid computing a superfluous matrix of super-large size, and transform the algorithm into an approximate version. Results Our modified semi-parallel GWAS algorithm based on homomorphic encryption which achieves 128-bit security takes 30–40 minutes for 245 samples containing 10,000–15,000 SNPs. Compared to the true p -value from the original semi-parallel GWAS algorithm, the F 1 score of our p -value result is over 0.99. Conclusions Privacy-preserving semi-parallel GWAS computation can be efficiently done based on homomorphic encryption with sufficiently high accuracy compared to the semi-parallel GWAS computation in unencrypted state.
Aspect-Specific Heterogeneous Graph Convolutional Network for Aspect-Based Sentiment Classification

Aspect-based sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence. There are two main problems with existing methods: First, the methods simply take the average of the sentence and aspect word vectors as the sentence and aspect representations for a certain sentence, but they are not explicit representations and will lose considerable useful information. Second, existing models based on graph convolutional networks (GCNs) only use the dependency relationship of a sentence, which cannot fully exploit the potential of the sentence and exert the powerful feature fusion ability of GCNs. To solve these problems, we propose a novel GCN-based model that uses a heterogeneous graph. We explicitly define sentence and aspect nodes to learn the sentence and aspect representations separately and then combine 4 kinds of relationships to construct the heterogeneous graph. In our experiments conducted on 5 public datasets, the experimental results show that our network consistently outperforms the state-of-the-art model on all these datasets.