Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Association Rule Mining Algorithm for Encrypted Data in Cloud Computing

Recently, privacy-preserving association rules mining algorithms have been proposed to support data privacy. However, the algorithms have an additional overhead to insert fake items (or fake transactions) and cannot hide data frequency. In this paper, we propose a privacy-preserving association rule mining algorithm for encrypted data in cloud computing. For association rule mining, we utilize Apriori algorithm by using the Elgamal cryptosystem, without additional fake transactions. Thus the proposed algorithm can guarantee both data privacy and query privacy, while concealing data frequency. We show that the proposed algorithm achieves about 3-5 times better performance than the existing algorithm, in terms of association rule mining time.
Big Data and Analytics in the Age of the GDPR

The new European General Data Protection Regulation places stringent restrictions on the processing of personally identifiable data. The GDPR does not only affect European companies, as the regulation applies to all the organizations that track or provide services to European citizens. Free exploratory data analysis is permitted only on anonymous data, at the cost of some legal risks. We argue that for the other kinds of personal data processing, the most flexible and safe legal basis is explicit consent. We illustrate the approach to consent management and compliance with the GDPR being developed by the European H2020 project SPECIAL, and highlight some related big data aspects.
Situated Visualization in The Decision Process Through Augmented Reality

The decision-making process and the development of decision support systems (DSS) have been enhanced by a variety of methods originated from information science, cognitive psychology and artificial intelligence over the past years. Situated visualization (SV) is a method to present data representations in context. Its main characteristic is to display data representations near the data referent. As augmented reality (AR) is becoming more mature, affordable and widespread, using it as a tool for SV becomes feasible in several situations. In addition, it may provide a positive contribution to more effective and efficient decision-making, as the users have contextual, relevant and appropriate information to endorse their choices. As new challenges and opportunities arise, it is important to understand the relevance of intertwining these fields. Based on a literature analysis, this paper addresses and discusses current areas of application, benefits, challenges and opportunities of using SV through AR to visualize data in context and to support a decision-making process and its importance in future DSS.
Trends and Updated Research Agenda for Autonomous Agile Teams: A Summary of the Second International Workshop at XP2019

To succeed in complex environments and handle the innovation, development and support, organizations have to find ways to support and regulate the autonomy of teams according to the environmental demands and limitations. Furthermore, there is no one-size-fits-all autonomy approach. The process of forming and implementing autonomous teams, as well as the effective functioning of such teams, is not yet adequately addressed and understood in the context of complex knowledge-intensive organizations. The second international workshop on autonomous teams investigated barriers for team autonomy: “What are the real-world problems to be solved for autonomous teams?” and “What concepts from the literature can be used to solve the problems?”
A Hybrid POW-POS Implementation Against 51 percent Attack in Cryptocurrency System

The success and popularity of Bitcoin mainly focuses the underlying blockchain technology which is totally immutable distributed ledger, highly secured by its P2P network consensus named Proof of Work (PoW). One of the worst threats to a Proof-of-Work based cryptocurrency is 51% attack. If one or more dishonest network peer gains more than 50% of resource such as processing power, then they will become the majority decision maker in the network. It is already proved that mixing of two or more existing protocol that is called hybrid protocol can make the network enough resistive to this attack. The recent implementations of hybrid protocols have other limitations and problems that they are facing and striving to resolve. But their main weakness is in distribution of block mining reward to the investors. From the perspective of an investor, an investor invests his hard-earned money in a cryptocurrency for making proper profit from his investment. The main source of this profit is the block reward which is generated and given to the miner on successful mining of a block. So, to ensure this profit is given to proper user on proper time interval, the consistency of block generation time interval is a vital factor. The voting system, ticket system etc. are not time controlled and over all block reward generation interval will not show a uniform distribution of profit. Another big issue is diversifying the peers by creating special committee and groups of validators the concept of P2P network is violated. In this paper we will describe a step by step process to implement a Hybrid PoW-PoS based consensus protocol. In our proposed system, the PoW mining process is only used to regulate the block generation time. The actual block generation is done by the same user with PoS consensus mechanism. There is no voting or validating committee. The entire network will validate each block. This is the major difference with other discussed system. The system will not only be able to tackle the 51% attack, it provides a uniform distribution of mining reward to the stake holders and investors by maintaining a precise block generation interval with difficulty adjustment in PoW mining and probability calculation for stake holders according to their matured staking balance. We will not only show how to make the system non-vulnerable to this attack but also describe in detail about how to validate the transactions and blocks in different stage of creating the block chain.
Segmenting Hepatic Lesions Using Residual Attention U-Net with an Adaptive Weighted Dice Loss

We propose a novel network architecture called Residual Attention U-Net (ResAttU-Net) for segmenting hepatic lesions. Our model incorporates residual blocks that can extract more complex features as compared with traditional convolutional layers combined with a skip-connection attention module that learns to focus on the relevant features for the task of hepatic lesions segmentation. Moreover, we train our model using an adaptive weighted dice loss that prioritizes the pixels of the tumor class over the pixels of the background class. We evaluate our model on the MICCAI Liver Tumor Segmentation (LiTS) benchmark dataset. Our experimental results show that our method significantly improves upon several state-of-the-art baselines for hepatic lesion or liver tumor segmentation.
3D Face Recognition Based on Deep Learning

At present, face recognition is based on two-dimensional face database, Although the accuracy of face recognition is high, the influence of lighting, posture and expression on face recognition still exists. Because 3D face data not only has 2D face information, but also 3D face depth information. Reasonable use of depth information of 3D face can effectively reduce the impact of illumination, attitude and expression on face recognition. Therefore, this paper proposed a 3D face recognition method based on depth learning. Firstly, face image is preprocessed. Secondly, the output of the convolutional neural network is a abstract feature of the image, then, we fuse the abstract features of the 2D face abstract feature and the face depth map, and use the merged image as the input of the fully connected layer. Finally, the output of the fully connected layer is treated as the input of the classifier. Experimental data is two-dimensional face images and face depth maps which converted from 3D point clound data. The experimental results show that the recognition algorithm has strong robustness to the influence factors such as light, attitude and expression.
PriSTE

Location privacy-preserving mechanisms (LPPMs) have been extensively studied for protecting a user's location in location-based services. However, when user's perturbed locations are released continuously, existing LPPMs may not protect users' sensitive spatiotemporal event , such as "visited hospital in the last week" or "regularly commuting between location 1 and location 2 every morning and afternoon" (it is easy to infer that locations 1 and 2 may be home and office). In this demonstration, we demonstrate PriSTE for protecting spatiotemporal event privacy in continuous location release. First, to raise users' awareness of such a new privacy goal, we design an interactive tool to demonstrate how accurate an adversary could infer a secret spatiotemporal event from a sequence of locations or even LPPM-protected locations. The attendees can find that some spatiotemporal events are quite risky and even these state-of-the-art LPPMs do not always protect spatiotemporal event privacy. Second, we demonstrate how a user can use PriSTE to automatically or manually convert an LPPM for location privacy into one protecting spatiotemporal event privacy in continuous location-based services. Finally, we visualize the trade-off between privacy and utility so that users can choose appropriate privacy parameters in different application scenarios.
Towards A Taxonomy of Uncertainties: Analysing Sources of Spatio-Temporal Uncertainty on the Example of Non-Standard German Corpora

Different types of uncertainties occur in almost all datasets and are an inherent property of data across different academic disciplines, including digital humanities (DH). In this paper, we address, demonstrate and analyse spatio-temporal uncertainties in a non-standard German legacy dataset in a DH context. Although the data collection is primarily a linguistic resource, it contains a wealth of additional, comprehensive information, such as location and temporal detail. The addressed uncertainties have manifested because of a variety of reasons, and partly also because of decades of data transformation processes. We here propose our own taxonomy for capturing and classifying the various uncertainties, and show with numerous examples how the remedying but also re-introduction of uncertainties affects DH practices.
Exploiting artificial intelligence for digitally enriched museum visits

Machine learning is constantly proving its capabilities by achieving exceptional results in recognition and classification tasks. Image content recognition has been addressed by Bags of Visual Words coupled with a classification algorithm as well as convolutional neural networks. In this work, we question the applicability of these approaches individually, while proposing two novel hybrids that work in a cooperative and synergistic way in order to achieve better content recognition performances. We focus on a real-world scenario that involves the on-demand digital content enrichment of a museum-visit experience by exploiting mobile devices. The Folklore Museum of Xanthi (Greece) has been selected as our case study. A new benchmark image dataset has been created based on the museum exhibits and used to train our recognition approaches and to quantify their performance under independent, cooperative, and synergistic operational schemata. The experiments reveal that our hybrid approaches improve the recognition performance while composing a robust framework for applications related to cultural thesaurus interaction and more specifically to the on-demand digital content enrichment with minimum infrastructure requirements.
Image-Based Virtual Try-on Network with Structural Coherence

Virtual try-on system could demonstrate the visual effect of wearing certain clothes, which is in a great demand for the online clothing customers. Due to the neglect of the original person structure, the previous try-on schemes usually encounter with the problems like the loss of body parts, the missing of body details and the deviation of clothing style. In this paper, we propose a novel image-based virtual try-on network, which could maintain the structural consistency between the generated image and the original image by human parsing. Our network consists of three components. Given the original person and the clothing images, the target human parsing maps are generated. Then, the parsing maps are matched with the target clothes to generate the warped clothes. Finally, according to the parsing results, the parts to be replaced the original images are intercepted, and more original information is retained as the input of the network to generate the final results. Experiments on an existing benchmark demonstrate our method maintains the consistency of structure and achieves the state-of-the-art performance.
High-Quality Textured 3D Shape Reconstruction with Cascaded Fully Convolutional Networks

We present a learning-based approach to reconstructing high-resolution three-dimensional (3D) shapes with detailed geometry and high-fidelity textures. Albeit extensively studied, algorithms for 3D reconstruction from multi-view depth-and-color (RGB-D) scans are still prone to measurement noise and occlusions; limited scanning or capturing angles also often lead to incomplete reconstructions. Propelled by recent advances in 3D deep learning techniques, in this paper, we introduce a novel computation- and memory-efficient cascaded 3D convolutional network architecture, which learns to reconstruct implicit surface representations as well as the corresponding color information from noisy and imperfect RGB-D maps. The proposed 3D neural network performs reconstruction in a progressive and coarse-to-fine manner, achieving unprecedented output resolution and fidelity. Meanwhile, an algorithm for end-to-end training of the proposed cascaded structure is developed. We further introduce Human10, a newly created dataset containing both detailed and textured full-body reconstructions as well as corresponding raw RGB-D scans of 10 subjects. Qualitative and quantitative experimental results on both synthetic and real-world datasets demonstrate that the presented approach outperforms existing state-of-the-art work regarding visual quality and accuracy of reconstructed models.
Parasitic GAN for Semi-Supervised Brain Tumor Segmentation

In semantic segmentation, researchers face the shortage of pixel-level annotated data. And it is particularly severe in the medical images. On the other hand, the unlabeled data are abundantly produced in the diagnosis routine. In the paper, we introduced the Parasitic GAN for the brain tumor segmentation to exploit the unlabeled data more efficiently. Parasitic GAN is composed of three parts: the segmentor S, the generator G, and the discriminator V. With the label maps produced by the segmentor and the supplementary label maps synthesized by the generator, the discriminator could learn a more precise boundary of ground truth. Thus, the segmentor benefits from the adversarial learning mechanism and the extra supervision provided by the discriminator. This parasitic relationship between the segmentor and the generative adversarial network (G and V) restricts the fitness ability of the segmentor and improves its generalization capacity. In practice, it definitely improved the performance of segmentor in brain tumor segmentation tasks, increasing the dice score 0.010-0.035.
Broadcast-Optimal Two-Round MPC

An intensive effort by the cryptographic community to minimize the round complexity of secure multi-party computation (MPC) has recently led to optimal two-round protocols from minimal assumptions. Most of the proposed solutions, however, make use of a broadcast channel in every round, and it is unclear if the broadcast channel can be replaced by standard point-to-point communication in a round-preserving manner, and if so, at what cost on the resulting security. In this work, we provide a complete characterization of the trade-off between number of broadcast rounds and achievable security level for two-round MPC tolerating arbitrarily many active corruptions. Specifically, we consider all possible combinations of broadcast and point-to-point rounds against the three standard levels of security for maliciously secure MPC protocols, namely, security with identifiable, unanimous, and selective abort. For each of these notions and each combination of broadcast and point-to-point rounds, we provide either a tight feasibility or an infeasibility result of two-round MPC. Our feasibility results hold assuming two-round OT in the CRS model, whereas our impossibility results hold given any correlated randomness.
Calibration, Coherence, and Consilience in Radiometric Measures of Geologic Time

In 2012, the Geological Time Scale, which sets the temporal framework for studying the timing and tempo of all major geological, biological, and climatic events in Earth’s history, had one-quarter of its boundaries moved in a widespread revision of radiometric dates. The philosophy of metrology helps us understand this episode, and it, in turn, elucidates the notions of calibration, coherence, and consilience. I argue that coherence testing is a distinct activity preceding calibration and consilience, and I highlight the value of discordant evidence and trade-offs scientists face in calibration. The iterative nature of calibration, moreover, raises the problem of legacy data.
A Dual-Attention Dilated Residual Network for Liver Lesion Classification and Localization on CT Images

Automatic liver lesion classification on computed tomography images is of great importance to early cancer diagnosis and remains a challenging task. State-of-the-art liver lesion classification algorithms are currently based on manually selected regions of interest (ROIs) or automatically detected ROIs. However, liver lesions usually vary in size and shape, which makes the ROI selection process labor-intensive and also poses an obstacle to automatic lesion detection. In this paper, we propose a dual-attention dilated residual network (DADRN) as a potential solution to lesion classification task without manual ROI selection or automatic lesion detection. We incorporated a novel dual-attention module in order to capture the non-local feature dependencies and help the deep neural network focus on the lesion area by enlarging the difference between the lesion area and nonlesion area. To the best of our knowledge, we are the first to employ the self-attention mechanism to address liver lesion classification task. In addition, the well-trained DADRN can be used for weakly-supervised lesion localization without any architectural change or retraining. Experiment results show that DADRN could achieve a lesion classification accuracy comparable to that of the state-of-the-art ROI-based method and outperformed state-of-the-art attention-based approaches in both liver lesion classification and localization tasks.
Technical-, Social- and Process Debt in Large-Scale Agile: An Exploratory Case-Study

Large-scale agile projects bring inter-teams interaction challenges. Teams need to be autonomous, but often crosscutting concerns affect many teams. If the teams fail to collaborate on these concerns, the negative effects might hinder agility in the medium and long term. In other words, the organization and the system accumulate debt, on which the teams pay a high interest. Such debt must therefore be prioritized and “repaid” timely. We conducted a case study with interviews, observations and document analysis. Via both team- and large-scale retrospectives we investigated how teams coordinate and discuss Technical-, Social- and Process Debts.
A Reinforcement Learning Model Based on Temporal Difference Algorithm

In some sense, computer game can be used as a test bed of artificial intelligence to develop intelligent algorithms. The paper proposed a kind of intelligent method: a reinforcement learning model based on temporal difference (TD) algorithm. And then the method is used to improve the playing power of the computer game of a special kind of chess. JIU chess, also called Tibetan Go chess, is mainly played in places where Tibetan tribes gather. Its play process is divided two sequential stages: preparation and battle. The layout at preparation is vital for the successive battle, even for the final winning. Studies on Tibetan JIU chess have focused on Bayesian network based pattern extraction and chess shape based strategy, which do not perform well. To address the low chess power of JIU chess from the view of artificial intelligence, we developed a reinforcement learning model based on temporal difference (TD) algorithm for the preparation stage of JIU. First, the search range was limited within a 6 × 6 area at the center of the chessboard, and the TD learning architecture was combined with chess shapes to construct an intelligent environmental feedback system. Second, optimal state transition strategies were obtained by self-play. In addition, the results of the reinforcement learning model were output as SGF files, which act as a pattern library for the battle stage. The experimental results demonstrate that this reinforcement learning model can effectively improve the playing strength of JIU program and outperform the other methods.
A Novel Evolutionary Algorithm for Data Classification Problem With Extreme Learning Machines

Machine learning techniques have gained great popularity due to their success in data classification problems. This study proposes a novel evolutionary feature selection algorithm integrated with Single Hidden Layer Feed-forward Neural Networks (SLFN)s. Our main goal is to find out the most efficient subset of features and provide the best prediction accuracy. The algorithm combines the evolutionary technique of genetic algorithms (GA) and calculates the fitness values (prediction accuracy) of each selected subset of features by using Extreme Learning Machines (ELM). The results of the SLFN are calculated in a faster manner, which is very suitable for the GA while optimizing the selection of the best subset of features. The experimental results show that the proposed algorithm provides significant improvements. Competitive results are obtained/verified by comparing our solutions with those of the state-of-the-art data classification algorithms.
Avalanche blockchain protocol for distributed computing security

Platforms for distributed computing are able to solve complex problems such as radio telescope signal analysis or protein design. However, the distributed computing systems are also vulnerable to attacks, especially considering the fact that some distributed projects reward their participants, so cybercriminals have incentive to sabotage the project for their own gain. The article considers question of distributed computing security. We propose to use metastable blockchain protocol to ensure that integrity of data is preserved. We also discuss advantages of metastable blockchain protocol over regular one for the task of mass distributed computing. Prototype distributed data processing program was designed for the task of calculating checksums. The program was tested in a controlled virtual machine environment with different checksum algorithms and different number of computing units.