Reviewerly/exHarmony huggingface dataset:
Lattice based access control for protecting user data in cloud environments with hybrid security

Cloud computing is an aspired reverie of computing that provides a huge capacity of storage. The users of the cloud, store large volumes of their private data, where sometimes they have to suffer vulnerabilities and attacks on it. Since there is only partial control over the stored data, the users require strong and significant protection techniques to guard their data. The protection of data storage is a challenging and formidable task and so the user data should be protected against security threats. There is a great deal of uncertainty on how to provide security for the user data also. Thus, encrypting the data before storage might be the most promising approach. In this paper, a hybrid layered approach is proposed to protect the user's data along with the association of lattice-based security technique. In this proposed model, a new approach has been incorporated for checking roles and responsibilities using lattice model. For this implementation text-based Patient Health care Records (PHR) are taken and significance of the proposed method is tested. Advanced Encryption Standard and Rivest-Shamir-Adleman are employed in this approach to provide further and better security to the sensitive data. The proposed model uses a CloudSim simulator in which virtual machine has been created with balanced configuration and it is implemented as well as tested. Compared to the existing methods, the proposed scheme is more secure and we offers easy interface to the patients for uploading and downloading their records. We have tested the differed parameters of proposed method with state of art approaches. The experimentation results show that the proposed scheme is more efficient.
A Corpus of BTC Tweets in the Era of COVID-19

The coronavirus disease 2019 (COVID-19) outbreak has created a great challenge for many industries, including healthcare systems, E-commerce, and transportation. Cryptocurrencies, such as Bitcoin (BTC), is an alternative class of digital assets primarily used as a medium of exchange. In the financial market, data collected from social media such as tweets, posts, and can assist in an early indication of market sentiment. This has frequently been conducted on Twitter data. In particular, this paper provides a corpus of tweet text for Bitcoin-related tweets during the summer of the COVID-19 era. This dataset is publicly available covering a considerable period of roughly three months, to allow people with a similar interest in Twitter, Bitcoin, sentiment analysis, and the financial sector to perform research unimpeded.
EUCISE-OWL: An ontology-based representation of the Common Information Sharing Environment (CISE) for the maritime domain

The timely and efficient cooperation across sectors and borders during maritime crises is paramount for the safety of human lives. Maritime monitoring authorities are now realizing the grave importance of cross-sector and cross-border information sharing. However, this cooperation is compromised by the diversity of existing systems and the vast volumes of heterogeneous data generated and exchanged during maritime operations. In order to address these challenges, the EU has been driving several initiatives, including several EU-funded projects, for facilitating information exchange across sectors and borders. A key outcome from these efforts is the Common Information Sharing Environment (CISE), which constitutes a collaborative initiative for promoting automated information sharing between maritime monitoring authorities. However, the adoption of CISE is substantially limited by its existing serialization as an XML Schema only, which facilitates information sharing and exchange to some extent, but fails to deliver the fundamental additional benefits provided by ontologies, like the richer semantics, enhanced semantic interoperability and semantic reasoning capabilities. Thus, this paper presents EUCISE-OWL, an ontology representation of the CISE data model that capitalizes on the benefits provided by ontologies and aims to encourage the adoption of CISE. EUCISE-OWL is an outcome from close collaboration in an EU-funded project with domain experts with extensive experience in deploying CISE in practice. The paper also presents a representative example for handling information exchange during a maritime crisis as well as performance results for specific querying tasks that can demonstrate and evaluate the use of the proposed ontology in practice.
Automatic Interest Recognition from Posture and Behaviour

In the last years, the clothing industry has attracted a lot of interest from researchers. Increasing research efforts have been devoted into giving the buyer a way to improve the shopping experience by suggesting meaningful items to purchase. These efforts result in works aiming at suggesting good matches for clothes, but seem to lack one important aspect: understanding the user's interest. In fact, to suggest something it is first necessary to collect the user's personal interests, or something about his or her previous purchases. Without this information, no personalized suggestion can be made. User interest understanding allows to recognize if a user is showing interest in a product he or she is looking at, acquiring precious information that can be later leveraged. Usually user interest is associated to facial expressions, but these are known to be easily falsifiable. Moreover, when privacy is a concern, faces are often impossible to exploit. To address all these aspects, we propose an automatic system that aims to recognize the user's interest towards a garment by just looking at body posture and behaviour. To train and evaluate our system we create a body pose interest dataset, named BodyInterest, which consists of 30 users looking at garments for a total of approximately 6 hours of videos. Extensive evaluations show the effectiveness of our proposed method.
Simple bounds for one-shot pure-state distillation in general resource theories

We present bounds for distilling many copies of a pure state from an arbitrary initial state in a general quantum resource theory. Our bounds apply to operations that are able to generate no more than a $\ensuremath{\delta}$ amount of resource, where $\ensuremath{\delta}\ensuremath{\ge}0$ is a given parameter. To maximize applicability of our upper bound, we assume little structure on the set of free states under consideration besides a weak form of superadditivity of the function ${G}_{\mathrm{min}}(\ensuremath{\rho})$, which measures the overlap between $\ensuremath{\rho}$ and the set of free states. Our bounds are given in terms of this function and the robustness of resource. Known results in coherence and entanglement theory are reproduced in this more general framework.
Performance of prediction models for COVID-19: the Caudine Forks of the external validation

Healthcare systems worldwide have observed significant changes to meet demands due to the coronavirus disease 2019 (COVID-19) pandemic. The uncertainty surrounding optimal treatment, the rapid public health urgency and clinical emergencies have caused a chaotic disruption of the cases and their related contacts at inpatient and outpatient settings. Developing more tailored healthcare plans based on the currently available scientific evidence, could help improve clinical efficacy, treatment outcomes, prognosis, and health efficiency. Existing evidence suggests that none of the COVID -19 prediction models can be supported for clinical use. Here we discuss "what next" in COVID-19 prediction. <https://bit.ly/2SMtoLV>
ConGradetect: Blockchain-based detection of code and identity privacy vulnerabilities in crowdsourcing

At present, various types of code vulnerabilities lead to a huge workload of detection. Although multi-party cooperation detection methods can be applied to improve efficiency, common crowdsourcing models are difficult to guarantee the privacy of code and identity. We design a code and identity privacy vulnerability crowdsourcing detection system ConGradetect. The system is built on a blockchain and solves code privacy issues, identity privacy issues, and reward preemption issues during the crowdsourcing detection process of vulnerabilities. In ConGradetect, users have a dynamic fake identity, which ensures that the identity of the user is not exposed, thereby avoiding the problem of information exposure caused by data accumulation. In the meantime, ConGradetect uses a local code granulation tool to perform off-line code cutting for task publishers, and uses proxy re-encryption to allocate task code blocks to ensure that the complete original code is only known to the code owner. When the task is received, a trusted key conversion is performed using smart contract. Further, we propose a method for anonymous verification of the binding relationship between task and user to prevent reward preemption and ensure that task performers can obtain corresponding legal benefits. Finally, we run the system on the Ethereum blockchain and test its performance. After testing, the feasibility of ConGradetect is proved.
Video Super-Resolution using Multi-scale Pyramid 3D Convolutional Networks

Video super-resolution (SR) aims at generating high-resolution (HR) frames from consecutive low-resolution (LR) frames. The challenge is how to make use of temporal coherence among neighbouring LR frames. Most previous works use motion estimation and compensation based models. However, their performance relies heavily on motion estimation accuracy. In this paper, we propose a multi-scale pyramid 3D convolutional (MP3D) network for video SR, where 3D convolution can explore temporal correlation directly without explicit motion compensation. Specifically, we first apply 3D convolution into a pyramid subnet to extractmulti-scale spatial and temporal features simultaneously from the LR frames, such that it can handle various sizes of motions. We then feed the fused feature maps into an SR reconstruction subnet, where a 3D sub-pixel convolution layer is used for up-sampling. Finally, we append a detail refinement subnet based on the encoder-decoder structure to further enhance texture details of the reconstructed HR frames. Extensive experiments on benchmark datasets and real-world cases show that the proposed MP3D model outperforms state-of-the-art video SR methods in terms of PSNR/SSIM values, visual quality and temporal consistency, respectively.
The Market for Fake Reviews

We study the market for fake product reviews on Amazon.com. These reviews are purchased in large private internet groups on Facebook and other sites. We hand-collect data on these markets to characterize the types of products that buy fake reviews and then collect large amounts of data on the ratings and reviews posted on Amazon for these products, as well as their sales rank, advertising, and pricing behavior. We use this data to assess the costs and benefits of fake reviews to sellers and evaluate the degree to which they harm consumers. The theoretical literature on review fraud shows conditions when they harm consumers and conditions where they function as simply another type of advertising. Using detailed data on product outcomes before and after they buy fake reviews we can directly determine if these are low-quality products using fake reviews to deceive and harm consumers or if they are high-quality products that solicit reviews to establish reputations. We find that a wide array of products purchase fake reviews, including products with many reviews and high average ratings. Buying fake reviews on Facebook leads to a significant increase in average rating and sales rank, but the effect disappears after roughly one month. After firms stop buying fake reviews their average ratings fall significantly and the share of one-star reviews increases significantly, indicating fake reviews are mostly used by low quality products and are deceiving and harming consumers. Finally, we observe that Amazon deletes large numbers of reviews and we document their deletion policy.
Dual Adversarial Network for Unsupervised Ground/Satellite-to-Aerial Scene Adaptation

Recent domain adaptation work tends to obtain a uniformed representation in an adversarial manner through joint learning of the domain discriminator and feature generator. However, this domain adversarial approach could render sub-optimal performances due to two potential reasons: First, it might fail to consider the task at hand when matching the distributions between the domains. Second, it generally treats the source and target domain data in the same way. In our opinion, the source domain data which serves the feature adaption purpose should be supplementary, whereas the target domain data mainly needs to consider the task-specific classifier. Motivated by this, we propose a dual adversarial network for domain adaptation, where two adversarial learning processes are conducted iteratively, in correspondence with the feature adaptation and the classification task respectively. The efficacy of the proposed method is first demonstrated on Visual Domain Adaptation Challenge (VisDA) 2017 challenge, and then on two newly proposed Ground/Satellite-to-Aerial Scene adaptation tasks. For the proposed tasks, the data for the same scene is collected not only by the traditional camera on the ground, but also by satellite from the out space and unmanned aerial vehicle (UAV) at the high-altitude. Since the semantic gap between the ground/satellite scene and the aerial scene is much larger than that between ground scenes, the newly proposed tasks are more challenging than traditional domain adaptation tasks. The datasets/codes can be found at https://github.com/jianzhelin/DuAN.
Ultra-Short-Term Forecasting of Photo-Voltaic Power via RBF Neural Network

With the fast expansion of renewable energy systems during recent years, the stability and quality of smart grids using solar energy have been challenged because of the intermittency and fluctuations. Hence, forecasting photo-voltaic (PV) power generation is essential in facilitating planning and managing electricity generation and distribution. In this paper, the ultra-short-term forecasting method for solar PV power generation is investigated. Subsequently, we proposed a radial basis function (RBF)-based neural network. Additionally, to improve the network generalization ability and reduce the training time, the numbers of hidden layer neurons are limited. The input of neural network is selected as the one with higher Spearman correlation among the predicted power features. The data are normalized and the expansion parameter of RBF neurons are adjusted continuously in order to reduce the calculation errors and improve the forecasting accuracy. Numerous simulations are carried out to evaluate the performance of the proposed forecasting method. The mean absolute percentage error (MAPE) of the testing set is within 10%, which show that the power values of the following 15 min. can be predicted accurately. The simulation results verify that our method shows better performance than other existing works.
Toward Shared Autonomy Control Schemes for Human-Robot Systems: Action Primitive Recognition Using Eye Gaze Features

The functional independence of individuals with upper limb impairment could be enhanced by teleoperated robots that can assist with activities of daily living. However, robot control is not always intuitive for the operator. In this work, eye gaze was leveraged as a natural way to infer human intent and advance action recognition for shared autonomy control schemes. We introduced a classifier structure for recognizing low-level action primitives that incorporates novel three-dimensional gaze-related features. We defined an action primitive as a triplet comprised of a verb, target object, and hand object. A recurrent neural network was trained to recognize a verb and target object, and was tested on three different activities. For a representative activity (making a powdered drink), the average recognition accuracy was 77% for the verb and 83% for the target object. Using a nonspecific approach to classifying and indexing objects in the workspace, we observed a modest level of generalizability of the action primitive classifier across activities, including those for which the classifier was not trained. The novel input features of gaze object angle and its rate of change were especially useful for accurately recognizing action primitives and reducing the observational latency of the classifier.
Memory-Based Network for Scene Graph with Unbalanced Relations

The scene graph which can be represented by a set of visual triples is composed of objects and the relations between object pairs. It is vital for image captioning, visual question answering, and many other applications. However, there is a long tail distribution on the scene graph dataset, and the tail relation cannot be accurately identified due to the lack of training samples. The problem of the nonstandard label and feature overlap on the scene graph affects the extraction of discriminative features and exacerbates the effect of data imbalance on the model. For these reasons, we propose a novel scene graph generation model that can effectively improve the detection of low-frequency relations. We use the method of memory features to realize the transfer of high-frequency relation features to low-frequency relation features. Extensive experiments on scene graph datasets show that our model significantly improved the performance of two evaluation metrics [email protected] and [email protected] compared with state-of-the-art baselines.
Multicamera pedestrian detection using logic minimization

In this paper an algorithm for multicamera pedestrian detection is proposed. The first stage of this work is based on the probabilistic occupancy map framework, in which the ground plane is discretized into a grid and the likelihood of pedestrian presence at each location is estimated by comparing a rectangle, of the average size of the pedestrians standing there, with the foreground silhouettes in all camera views. In the second stage, where we borrowed the idea from the Quine-McCluskey method for logic function minimization, essential candidates are initially identified, each of which covers at least a significant part of the foreground that is not covered by the other candidates. Then non-essential candidates are selected to cover the remaining foregrounds by following an iterative process, which alternates between merging redundant candidates and finding emerging essential candidates. Experiments on benchmark video datasets have demonstrated the improved performance of this algorithm in comparison with some benchmark non-deep or deep multicamera/monocular algorithms for pedestrian detection.
Cap2Seg: Inferring Semantic and Spatial Context from Captions for Zero-Shot Image Segmentation

Zero-shot image segmentation refers to the task of segmenting pixels from specific unseen semantic class. Previous methods mainly rely on historic segmentation tasks, such as using semantic embedding or word embedding of class names to infer a new segmentation model. In this work we describe Cap2Seg, a novel solution of zero-shot image segmentation that harnesses accompanying image captions for intelligently inferring spatial and semantic context for the zero-shot image segmentation task. As our main insight, image captions often implicitly entail the occurrence of a new class in an image and its most-confident spatial distribution. We define a contextual entailment question (CEQ) that tailors BERT-like text models. In specific, the proposed networks for inferring unseen classes consists of three branches (global / local / semi-global), which infer labels of unseen class from image level, image-stripe level or pixel level respectively. Comprehensive experiments and ablation studies are conducted on two image benchmarks, COCO-stuff and Pascal VOC. All clearly demonstrate the effectiveness of the proposed Cap2Seg, including a set of hardest unseen classes (i.e., image captions do not literally contain the class names and direct matching for inference fails).
A Unified Framework for Detecting Audio Adversarial Examples

Adversarial attacks have been widely recognized as the security vulnerability of deep neural networks, especially in deep automatic speech recognition (ASR) systems. The advanced detection methods against adversarial attacks mainly focus on pre-processing the input audio to alleviate the threat of adversarial noise. Although these methods could detect some simplex adversarial attacks, they fail to handle robust complex attacks especially when the attacker knows the detection details. In this paper, we propose a unified adversarial detection framework for detecting adaptive audio adversarial examples, which combines noise padding with sound reverberation. Specifically, a well-designed adaptive artificial utterances generator is proposed to balance the design complexity, such that the artificial utterances (speech with reverberation) are efficiently determined to reduce the false positive rate and false negative rate of detection results. Moreover, to destroy the continuity of the adversarial noise, we develop a novel multi-noise padding strategy, which implants the Gaussian noises in the silent fragments of the input speech by the voice activity detector. Furthermore, our proposed method can effectively tackle the robust adaptive attacks in an adaptive learning manner. Importantly, the conceived system is easily embedded into any ASR models without requiring additional retraining or modification. The experimental results show that our method consistently outperforms the state-of-the-art audio defense methods, even for the adaptive and robust attacks.
Progressive Domain Adaptation for Robot Vision Person Re-identification

Person re-identification has received much attention in the last few years, as it enhances the retrieval effectiveness in the video surveillance networks and video archive management. In this paper, we demonstrate a guiding robot with person followers system, which recognizes the follower using a person re-identification technology. It first adopts existing face recognition and person tracking methods to generate person tracklets with different IDs. Then, a classic person re-identification model, pre-trained on the surveillance dataset, is adapted to the new robot vision condition incrementally. The demonstration showcases the quality of robot follower focusing.