Reviewerly/exHarmony huggingface dataset:
Dependable Industrial Crypto Computing

This paper presents a work on how to assure the dependability of a crypto system built across on and off the blockchain by using the proposed adaptive checkpoint and rollback algorithm, and a prototype is developed for demonstration purpose.The theoretical background of the proposed checkpoint and rollback algorithm is studied to characterize the variables affecting the dependability such as security, authenticity and reliability with respect to the rates of hit by any events of those issues, the rates to detect and diagnose, and then the rate to vote for a consensus whether to trigger a rollback or not. Based on the variables characterization in a stochastic manner, then steady state probabilities and state transition probabilities are derived in order to assure the ultimate effective dependability of each individual dependability variable (i.e., security, authenticity and reliability), then finally to assure the dependability in a compound manner with each variable assigned a weight depending on the nature of the systems specifications.Based on the theoretical study, a protype of a crypto system is built to demonstrated the underlying architecture and operations and to justify the need for such system to take synergistic advantages from both on- and off-chain blockchains, with an experimental result of a benefit in gas fee which is the most exigently addressed issue today in blockchain systems especially in Ethereum network of blockchains. An astonishing gas fee saving results are demonstrated. It is observed that the crypto system benefits more if more computationally intensive transactions are executed off-chain while vice versa.
A Semi-Supervised Short Text Classification Method Based on Weighted Word Vector Representation

With the characteristics of growing dynamic, short and large, lack of semantic information and class labels, high-dimensional features and sparse problem, supervised learning has become an important research field to solve short text classification. Aiming at the defects of existing short text classification algorithms, a semi-supervised short text classification algorithm based on weighted word vector representation is proposed. Firstly, the strong category feature set representing the category information is extracted from the label short text dataset based on the improved expected cross entropy; Secondly, the eigenvector of short text is obtained by Worde2Vec training, and the mean value of eigenvector of short text, containing the strong feature word, is used as the virtual class center to calculate the cosine similarity between all short text and the virtual class center, after normalization. After transformation, the real center class of short text is calculated according to the weight of each short text calculated by introducing the normalization of above similarity. Finally, unlabeled data is classified according to the similarity between its' cluster center and the real class center of labeled data. The experimental data show that compared with the classical semi-supervised learning classification algorithm, the algorithm has higher classification accuracy and accuracy through simple weighting processing.
Performance Characterization of Single and Multi GPU Training of U-Net Architecture for Medical Image Segmentation Tasks

The rapid advancements in high performance computing hardware and corresponding rise in deep convolutional neural network (CNN) architectures have led to state-of-the-art results in several biomedical image segmentation tasks. Recently, U-Net, a modified fully convolutional network, has become the state-of-the-art in various two-dimensional and three-dimensional semantic (pixellevel) segmentation tasks related to medicine. U-Net has achieved most success in terms of dealing with datasets where the well annotated ground truth is beyond reach. However, there has not been a detailed analysis on the impact of computing configurations, input data type, preprocessing, and data augmentation techniques on U-Nets training speed and end-to-end computation pipeline. In this paper, we trained U-Net on multiple configurations of GPU (single vs. multi-node) hardware in terms of memory and time efficiency towards finding optimal set up for U-Net segmentation tasks.
CCA-Secure Leakage-Resilient Identity-Based Key-Encapsulation from Simple (Not $$\mathtt {q}$$ -type) Assumptions

In this paper, we propose a new leakage-resilient identity-based encryption (IBE) scheme that is secure against chosen-ciphertext attacks (CCA) in the bounded memory leakage model. It is the first CCA-secure leakage-resilient IBE scheme which does not depend on $$\mathtt {q}$$ -type assumptions. More precisely, it is secure under the DLIN assumption for symmetric bilinear groups and under the XDLIN assumption for asymmetric bilinear groups, respectively.
Rain-Density Squeeze-and-Excitation Residual Network for Single Image Rain-removal

The removal of rain streaks in a single image is an extremely challenging task due to the uneven rainfall density in the image. Methods based on deep learning have boosted the performance of rain removal significantly in recent years. However, most of these methods have a certain demand for different density of rain marks in the training data, which prevent them to further improve the performance in some outdoor scenarios. In this paper, we present a novel Rain-Density Squeeze-and-Excitation residual network (RDSER-NET), which adopts the squeeze-and-excitation blocks into the ResNet framework. The proposed network remove rain streaks based on single density of rain marks in the training data, reducing the limitation of multi-density proposals and achieving better results. Extensive experiments on synthetic and real datasets demonstrate that the proposed network outperform the recent state-of-the-art methods greatly.
Robust Sensor-based Human Activity Recognition with Snippet Consensus Neural Networks

Sensor-based human activity recognition is an important problem in pervasive computing, which has attracted lots of attention from the research community in the past few years. The existing relevant studies focused on using handcrafted features or machine learning-based methods to tackle this problem. However, these methods are usually limited to specific datasets, such that the generality is limited. Some methods are also limited to strict experimental environments, which do not take stability into consideration. In this paper, we propose a robust and novel deep learning-based framework, named Snippet Consensus Neural Networks (SCNet), which aims to conquer these challenges. Through a series of experiments, the proposed framework is verified to outperform seven state-of-the-art methods on five datasets in terms of not only accuracy but also generality and stability, averagely improving 10% on mean accuracy.
Gabor Features Assist Semantic Feature Learning for Handwritten Formula Symbol Recognition

At present, the recognition of formula symbols is very challenging. Since a large number of similarities and a variety of styles in the standard library, the overall recognition rate of formula symbols is difficult to further improve. In order to deal with these problems, this paper proposes an off-line multi-directional feature fusion decision discriminant algorithm, called MFFD. The novelty lies in the construction of the MMFD based on the convolutional neural network. In addition, we explore the directional gradient feature that facilitates the classification of handwritten formula symbols. The standard mathematical formula symbol library provided by the CROHME would verify the proposed algorithm. The error rates of CROHME2014 and CROHME2016 are 8.28% and 6.88%, respectively, which is higher than that of existing algorithms.
Understanding Visual Information Processing for American vs. Saudi Arabian Users

Cultural differences may affect user experiences in many different ways. This may range from having to adapt to different data formats to understanding novel directions of reading and writing. Analyzing data presented in various forms are crucial for both information assimilation and dissemination. Different information presentation methods can substantially affect task performances. To date, limited research has been reported that investigates how users from different cultural backgrounds respond to various information presentation methods while conducting visual information processing tasks. To fill in that gap, we conducted an experiment to better understand the relationship between American and Saudi Arabian users when interacting with different information presentation methods in the context of visual information processing. The specific information presentation methods investigated in the study are textual presentation, graphical presentation, and tabular presentation. The results suggest that information representation has significant impact on task efficiency for both groups of participants. However, information representation only affected the answer quality of the American group.
Identifying Tumor in Whole-Slide Images of Breast Cancer Using Transfer Learning and Adaptive Sampling

Deep learning is widely used in medical applications in view of the excellent performance it achieved in image processing. Early methods of diagnosis on whole slide images (WSIs) is usually based on dense sampling which is time-consuming and requires a lot of memory to handle it. In this paper, we propose an adaptive sampling method that classify WSI of breast biopsies into two categories (cancer area and normal area) complied by transfer learning. This method involves: i) an adaptive sampling method based on probability gradient map. ii) a classifier which contain feature extraction part and classifier part to divide WSI into two categories. We tried nine different transfer learning models based on TensorFlow and Keras platform and apply the model to execute classification in WSI under three different magnifications (x5, x20, x40). The results showed that (1) the transfer learning combined with SVM or NN is enough to detect the cancer area which achieved an average test accuracy of 97.07% under x20 magnification, and (2) the adaptive sampling method is an effective strategy to deal with WSI with good performance (achieve the Dice coefficient of 80%) and far fewer samples (less than 5% of samples when use uniform sampling method).
DDT

To achieve better network performance, the cloud service providers are widely deploying the ECN-based transport protocols (i.e., DCTCP) in their data center networks (DCN). In multi-tenant environment, however, the newly introduced ECN-enabled TCP greatly impairs the performance of applications with out-dated and miscon figured TCP stacks. The reason is that the ECN-enabled datacenter switch fails to treat the mixed TCP traffic fairly, causing the distinguished performance gap between the ECN-enabled and ECN-disabled TCPs. This paper proposes DDT (Dual Dynamic Thresholds), an active queue management algorithm (AQM) that aims to achieve the flow-level fairness when the heterogeneous TCP traffic coexists. DDT monitors the switch queue in real time, and dynamically tunes the distance between ECN-marking and packet-dropping thresholds to mitigate the competitiveness difference between the ECN-enabled and ECN-disabled TCP. Our preliminary real implementations and testing results show that DDT elegantly fills the competitiveness gap of heterogeneous TCP traffic without disturbing their own control loops, while only introducing acceptable deployment overhead at the switch.
Modular norm models: practical representation and analysis of contractual rights and obligations

Compliance analysis requires legal counsel but is generally unavailable in many software projects. Analysis of legal text using logic-based models can help developers understand requirements for the development and use of software-intensive systems throughout its lifecycle. We outline a practical modeling process for norms in legally binding agreements that include contractual rights and obligations. A computational norm model analyzes available rights and required duties based on the satisfiability of situations, a state of affairs, in a given scenario. Our method enables modular norm model extraction, representation, and reasoning. For norm extraction, using the theory of frame semantics, we construct two foundational norm templates for linguistic guidance. These templates correspond to Hohfeld’s concepts of claim-right and its jural correlative, duty. Each template instantiation results in a norm model, encapsulated in a modular unit which we call a super-situation that corresponds to an atomic fragment of law. For hierarchical modularity, super-situations contain a primary norm that participates in relationships with other norm models. Norm compliance values are logically derived from its related situations and propagated to the norm’s containing super-situation, which in turn participates in other super-situations. This modularity allows on-demand incremental modeling and reasoning using simpler model primitives than previous approaches. While we demonstrate the usefulness of our norm models through empirical studies with contractual statements in open source software and privacy domains, its grounding in theories of law and linguistics allows wide applicability.
Automatic Patients' Vital Sign Monitoring by Single Board Computer (SBC) Based MPI Cluster

Periodic monitoring of patients' physiological data is important in order to maintain in-ward patients' wellbeing under control. However, such an approach is exhaustive and may not be suitable for patients, because a slight negligence from medicinal services staff may lead to serious ramifications for the patients. This paper proposes a Patients' Monitoring System (PMS) in which multiple patients' vital sign data may be monitored automatically or on demand without human intervention. The suggested system is designed around Single Board Computer (SBC) based Message Passing Interface (MPI) Cluster. Low-cost Raspberry Pis are used as SBC. A prototype of the proposed system has been presented. Development steps have been discussed.
Data Clustering Using the Cooperative Search Based Artificial Bee Colony Algorithm

Data clustering is a significant and strong data analysis technique which has a broad range of applications in many domains. In this paper, the artificial bee colony algorithm (ABC) is adopted to partition data sets into K clusters. To trade off the global and local searching ability of ABC algorithm, two kinds of cooperative search based ABC algorithms are proposed, that is N2ABC and WCABC. Then, the proposed algorithms are combined with K-means to deal with data clustering. For the purpose of demonstrating the efficiency of two hybrid clustering algorithms (N2ABCC and WCABCC), one artificial data set and six benchmark data sets are selected to test clustering results. Meanwhile, five algorithms, namely K-means, PSOC, ABCC, GABCC and CABCC, are chosen for comparison. The clustering results indicate that the proposed algorithms have better clustering validity than other algorithms.
Thangka Realization Based on MR

Virtual Reality, Augmented Reality and hybrid Reality technologies have been deeply applied in various fields. This study selects the Thangka with Tibetan cultural characteristics as the research object, and combines VR, AR and MR technologies to complete the Thangka scene display, which brings the viewers an immersive feeling. Thus effectively solve the intangible cultural heritage exhibition and protection of the "non-renewable heritage resources" between the contradiction.
One Step Towards Dynamically Programmable Things: an Implementation Using Beacons

The number of devices connected to the internet is constantly increasing. This large amount of devices can be used to improve people's interaction with their environment. However, this also implies an increase in the complexity when interacting with so many heterogeneous devices. Automating this process is key to keep up with this growth. This is the purpose of the People as a Service model (PeaaS), which works towards developing virtual profiles for every user in their own mobile devices under their full control. These profiles allow to establish preferences and predefined parameters, which are then used by the devices they connect to. By integrating both the information in the users' virtual profiles and the information given by the devices they connect to, we can create a context in which to make smart decisions and apply them to the devices, all of this in a decentralised way. This allows for a programmatically configuration of the devices without the need of the user's intervention. In order to put the proposal in action, we have developed a treasure hunting game as a proof of concept to bring to the spotlight the utility of an environment with programmatically adapted devices.
A Low-Budget Approach for Vehicle Detection and Occlusion Removal on Traffic Videos

A huge increase in traffic flow in large cities has created serious problems for urban planning and transit authorities, as they seek for solutions for handling increasingly larger traffic jams. In this paper, we describe part of an automated system designed to extract information from videos captured by closed-circuit surveillance camera systems to perform traffic monitoring. The main contributions of this paper are a combination of known methods for performing robust vehicle detection and counting as well as the adaptation of the Viola and Jones framework to deal with occlusion events. We tested our system on videos acquired at different times of the day and the results show a dear improvement over the traditional Viola and Jones method. We also compared the results of two versions of our approach to YOLO V3, a state-of-the-art method for detecting and classifying objects, on three different computers, and achieved slightly lower occlusion solving rates. We emphasize that YOLO V3 worked at very low FPS rates for one computer when not using the GPU, when compared to the Frames Per Second (FPS) rates achieved by our approach. Moreover, YOLO could not be executed on the notebook used in the experiments or a low-budget device Raspberry Pi 2, while our approaches achieved processing rates between 8 and 10 FPS on the Raspberry Pi 2 model.
Graph Planning with Expected Finite Horizon

Graph planning gives rise to fundamental algorithmic questions such as shortest path, traveling salesman problem, etc. A classical problem in discrete planning is to consider a weighted graph and construct a path that maximizes the sum of weights for a given time horizon T. However, in many scenarios, the time horizon is not fixed, but the stopping time is chosen according to some distribution such that the expected stopping time is T. If the stopping time distribution is not known, then to ensure robustness, the distribution is chosen by an adversary, to represent the worst-case scenario. A stationary plan for every vertex always chooses the same outgoing edge. For fixed horizon or fixed stopping-time distribution, stationary plans are not sufficient for optimality. Quite surprisingly we show that when an adversary chooses the stopping-time distribution with expected stopping time T, then stationary plans are sufficient. While computing optimal stationary plans for fixed horizon is NP-complete, we show that computing optimal stationary plans under adversarial stopping-time distribution can be achieved in polynomial time. Consequently, our polynomial-time algorithm for adversarial stopping time also computes an optimal plan among all possible plans.
A Fusion of Cuckoo Search and Multiscale Adaptive Smoothing Based Unsharp Masking for Image Enhancement

Image enhancement means to improve the visual appearance of an image by increasing its contrast and sharpening the features. This article presents a fusion of cuckoo search optimization-based image enhancement (CS-IE) and multiscale adaptive smoothing based unsharping method (MAS-UM) for image enhancement. The fusion strategy is introduced to improve the deficiency of enhanced image that suppresses the saturation and over-sharpness artefacts in order to obtain a visually pleasing result. The ideology behind the selection of fusion images (candidate) is that one image should have high sharpness or contrast with maximum entropy and other should be high Peak Signal-to-Noise Ratio (PSNR) sharp image, to provide a better trade-off between sharpness and noise. In this article, the CS-IE and MAS-UM results are fused to combine their complementary advantages. The proposed algorithms are applied to lathe tool images and some natural standard images to verify their effectiveness. The results are compared with conventional enhancement techniques such as Histogram equalization (HE), Linear contrast stretching (LCS), Contrast-limited adaptive histogram equalization (CLAHE), standard PSO image enhancement (PSO-IE), Differential evolution image enhancement (DE-IE) and Firefly algorithm-based image enhancement (FA-IE) techniques.
Multi-Resolution Hierarchical Structure for Efficient Data Aggregation and Mining of Big Data

Big data analysis is essential for modern applications in areas such as healthcare, assistive technology, intelligent transportation, environment and climate monitoring. Traditional algorithms in data mining and machine learning do not scale well with data size. Mining and learning from big data need time and memory efficient techniques, albeit the cost of possible loss in accuracy. We have developed a data aggregation structure to summarize data with large number of instances and data generated from multiple data sources. Data are aggregated at multiple resolutions and resolution provides a trade-off between efficiency and accuracy. The structure is built once, updated incrementally, and serves as a common data input for multiple mining and learning algorithms. Data mining algorithms are modified to accept the aggregated data as input. Hierarchical data aggregation serves as a paradigm under which novel data representations and algorithms work together for analysis and mining of big data. To evaluate its performance, we have implemented a multi-resolution Naive Bayes Classifier on the data aggregation structure. Experimental results show that the proposed structure helps the classifier to reduce computation time to 25% on average and reduce the memory usage while preserving the accuracy of results.
A Survey of Anonymous Communication Methods in Internet of Things

The research on anonymous communication has been going on for more than 30 years, but due to the limitations of Internet of Things (IoT) devices, traditional methods cannot be directly applied to the IoT environment. In view of this situation, this paper first analyzes the characteristics of anonymous communication in the IoT environment, then explores the advantages and disadvantages of methods based on computational offloading and lightweight cryptography and so on. In addition, this paper also discusses anonymous authentication process before the user enters the system and explores the future research hotspots of anonymous communication in IoT.
G2Q: Haskell constraint solving

Constraint solvers give programmers a useful interface to solve challenging constraints at runtime. In particular, SMT solvers have been used for a vast variety of different, useful applications, ranging from strengthening Haskell's type system to verifying network protocols.