Reviewerly/exHarmony huggingface dataset:
An analytical threshold for combining Bayesian Networks

Structural learning of Bayesian Networks (BNs) is an NP-hard problem, and the use of sub-optimal strategies is essential in domains involving many variables. One approach is the generation of multiple approximate structures and then reduce the ensemble to a representative structure. This can be performed by using the occurrence frequency (on the structures ensemble) as the criteria for accepting a dominant directed edge between two nodes and thus obtaining the single structure. In this paper, an analogy with an adapted one-dimensional random-walk was used for analytically deducing an appropriate decision threshold to such occurrence frequency. The obtained closed-form expression has been validated across the synthetic datasets applying the Matthews Correlation Coefficient (MCC) as the performance metric. In the experiments using a recent medical dataset, the resulting BN from the analytical cutoff-frequency captured the expected associations among nodes and also achieved better prediction performance than the BNs learned with neighbours thresholds to the computed.
SEPTIC: Detecting Injection Attacks and Vulnerabilities Inside the DBMS

Databases continue to be the most commonly used backend storage in enterprises, but they are often integrated with vulnerable applications, such as web frontends, which allow injection attacks to be performed. The effectiveness of such attacks stems from a semantic mismatch between how SQL queries are believed to be executed and the actual way in which databases process them. This leads to subtle vulnerabilities in the way input validation is done in applications. In this paper, we propose SEPTIC, a mechanism for DBMS attack prevention, which can also assist on the identification of the vulnerabilities in the applications. The mechanism was implemented in MySQL and evaluated experimentally with various applications and alternative protection approaches. Our results show no false negatives and no false positives with SEPTIC, on the contrary to other solutions. They also show that SEPTIC introduces a low performance overhead, in the order of 2.2%.
Bain: a program for Bayesian testing of order constrained hypotheses in structural equation models

This paper presents a new statistical method and accompanying software for the evaluation of order constrained hypotheses in structural equation models (SEM). The method is based on a large sample approximation of the Bayes factor using a prior with a data-based correlational structure. An efficient algorithm is written into an R package to ensure fast computation. The package, referred to as Bain, is easy to use for applied researchers. Two classical examples from the SEM literature are used to illustrate the methodology and software.
Deep convolution neural network with scene-centric and object-centric information for object detection

In recent years, Deep Convolutional Neural Network (CNN) has shown an impressive performance on computer vision field. The ability of learning feature representations from large training dataset makes deep CNN outperform traditional hand-crafted features approaches on object classification and detection. However, computations for deep CNN models are time consuming due to their high complexity, which makes it hardly applicable to real world application, such as Advance Driver Assistance System (ADAS). To reduce the computation complexity, several fast object detection frameworks in the literature have been proposed, such as SSD and YOLO. Although these kinds of method can run at real-time, they usually struggle with dealing of small objects due to the difficulty of handling smaller input image size. Based on our observation, we propose a novel object detection framework which combines the feature representations learned from object-centric and scene-centric datasets with an aim to improve the accuracy on detecting especially small objects. The experimental results on MSCOCO dataset show that our method can actually improve the detection accuracy of small objects, which leads to better overall results. We also evaluate our method on PASCAL VOC 2012 datasets, and the results show that our method not only can achieve state-of-the-art accuracy but also most importantly presents in real-time.
An innovative design of a hybrid chain coding algorithm for bi-level image compression using an agent-based modeling approach

In this article, the researcher introduces a hybrid chain code for shape encoding, as well as lossless and lossy bi-level image compression. The lossless and lossy mechanisms primarily depend on agent movements in a virtual world and are inspired by many agent-based models, including the Paths model, the Bacteria Food Hunt model, the Kermack–McKendrick model, and the Ant Colony model. These models influence the present technique in three main ways: the movements of agents in a virtual world, the directions of movements, and the paths where agents walk. The agent movements are designed, tracked, and analyzed to take advantage of the arithmetic coding algorithm used to compress the series of movements encountered by the agents in the system. For the lossless mechanism, seven movements are designed to capture all the possible directions of an agent and to provide more space savings after being encoded using the arithmetic coding method. The lossy mechanism incorporates the seven movements in the lossless algorithm along with extra modes, which allow certain agent movements to provide further reduction. Additionally, two extra movements that lead to more substitutions are employed in the lossless and lossy mechanisms. The empirical outcomes show that the present approach for bi-level image compression is robust and that compression ratios are much higher than those obtained by other methods, including JBIG1 and JBIG2, which are international standards in bi-level image compression. Additionally, a series of paired-samples t-tests reveals that the differences between the current algorithms’ results and the outcomes from all the other approaches are statistically significant.
Using Blockchain to Enhance and Optimize IoT-based Intelligent Traffic System

Smart city blueprint is constructed by increasing deployment of various Internet of Things devices. In this paper, we focus on data transmission and request for lane property right under the domain of an intelligent traffic system. IoT devices are usually deployed in a heterogeneous environment, with the natural gene of decentralization. Compared with cloud-based centralized architecture, in this paper, we introduce blockchain as a decentralized technology to let vehicles jointly collaborate without having to go through a central computing node authority. Data transmission between vehicles uses a peer-to-peer network in which every node communicates directly with every other node, verified by their relevant end-point nodes; lane's acquisition right is approved by all relevant vehicle nodes, as well as with consensus agreed upon via smart contract.
Preimage Attacks on Round-Reduced Keccak-224/256 via an Allocating Approach

We present new preimage attacks on standard Keccak-224 and Keccak-256 that are reduced to 3 and 4 rounds. An allocating approach is used in the attacks, and the whole complexity is allocated to two stages, such that fewer constraints are considered and the complexity is lowered in each stage. Specifically, we are trying to find a 2-block preimage, instead of a 1-block one, for a given hash value, and the first and second message blocks are found in two stages, respectively. Both the message blocks are constrained by a set of newly proposed conditions on the middle state, which are weaker than those brought by the initial values and the hash values. Thus, the complexities in the two stages are both lower than that of finding a 1-block preimage directly. Together with the basic allocating approach, an improved method is given to balance the complexities of two stages, and hence, obtains the optimal attacks. As a result, we present the best theoretical preimage attacks on Keccak-224 and Keccak-256 that are reduced to 3 and 4 rounds. Moreover, we practically found a (second) preimage for 3-round Keccak-224 with a complexity of $$2^{39.39}$$ .
Analysis of multimodal Bayesian nonparametric autoregressive hidden Markov models for process monitoring in robotic contact tasks

Robot introspection aids robots to understand what they do and how they do it. Previous robot introspection techniques have often used parametric hidden Markov models or supervised learning techniques, implying that the number of hidden states or classes is defined a priori and fixed through the entire modeling process. Fixed parameterizations limit the modeling power of a process to properly encode the data. Furthermore, first-order Markov models are limited in their ability to model complex data sequences that represent highly dynamic behaviors as they assume observations are conditionally independent given the state. In this work, we contribute a Bayesian nonparametric autoregressive Hidden Markov model for the monitoring of robot contact tasks, which are characterized by complex dynamical data that are hard to model. We used a nonparametric prior that endows our hidden Markov models with an unbounded number of hidden states for a given robot skill (or subtask). We use a hierarchical Dirichlet stochastic process prior to learn an hidden Markov model with a switching vector autoregressive observation model of wrench signatures and end-effector pose for the manipulation contact tasks. The proposed scheme monitors both nominal skill execution and anomalous behaviors. Two contact tasks are used to measure the effectiveness of our approach: (i) a traditional pick-and-place task composed of four skills and (ii) a cantilever snap assembly task (also composed of four skills). The modeling performance or our approach was compared with other methods, and classification accuracy measures were computed for skill and anomaly identification. The hierarchical Dirichlet stochastic process prior to learn an hidden Markov model with a switching vector autoregressive observation model was shown to have excellent process monitoring performance with higher identification rates and monitoring ability.
Auto-calibration and real-time external parameter correction for stereo digital image correlation

Calibrating stereo digital image correlation (stereo-DIC) is crucial for 3D deformation measurements. Existing stereo-DIC calibration methods mainly rely on a well-made planar target with additional specific coding to perform pre-calibration. In this paper, a new method is proposed to calibrate a stereo-DIC system automatically with feature correspondences in an unconstrained scene. In contrast to traditional methods, the proposed method is scale-independent and does not require assumptions such as planarity. More importantly, it is capable of correcting disturbed external parameters in real time. With inverse depth parameterization, a stereo-DIC system can be effectively calibrated by a carefully designed bundle adjustment framework. Subsequently, a fast correction strategy based on massive-point subset tracking is derived to recalibrate the disturbed stereo-DIC system. The proposed method overcomes the predicament of reduced measurement accuracy due to the unavoidable disturbance of external parameters in dynamic deformation measurements. Experiments were conducted to verify the effectiveness of the proposed method.
Microgrid Group Trading Model and Solving Algorithm Based on Blockchain

With the development of the energy Internet and the integration of multi-type energy situations, it is of great significance to study the competition game of a multi-agent microgrid group system for its development. As an emerging distributed database technology, blockchain technology has great application potential in the field of energy trading. Firstly, blockchain technology is coupled with the microgrid group transaction, and the information flow transaction model of a microgrid group based on blockchain technology is established. Aiming at this complex multi-objective optimization problem, an improved ant colony optimization algorithm is proposed to solve the model. Finally, the competitive trading model and solving algorithm are simulated and analyzed. The relevant results show that the near global optimum price strategy of each time based on the proposed model can effectively balance the efficiency of each subject in the market. In addition, the model ensures that there is no high-income and low-cost phenomenon in the trading process, therefore the security and quality of the market are guaranteed.
Towards Attack and Defense Views of Rational Delegation of Computation

Rational delegation of computation is an important technology of mobile Internet at present, which is significant to the construction of intelligent urban computing. In order to close to practical applications, many tasks need cooperation with edge computing and cloud computing. The delegated computation scheme with rational players guarantees the reliability of the calculation results by the utility function. This paper studies the rational delegation of computation and the security attack-defense from attack and defense. Combining with the concept of average mutual information in information theory, this paper presents the attack and defense model for rationally delegating the computation. The main method of modeling is to convert the attack or defense of rationally delegated computation into communication problems in information theory and construct the attack channel and defense channel for the client and the calculator respectively. The channel capacity of the attack channel represents the attack capacity limitation of the adversary in the delegated computing environment, and that of the defense channel denotes the defense capacity limitation of clients in the delegated computation. Then, we discuss the relationship between attack limitation and defense limitation, and it shows that there are different strengths and weaknesses between the calculator and the client under different mixed strategies. Finally, the numerical analysis of experiments demonstrates that the optimal strategy of attack and defense is the strategy when the game between client and calculator reaches the equilibrium point.
Yes, we GAN: Applying adversarial techniques for autonomous driving

Generative Adversarial Networks (GAN) have gained a lot of popularity from their introduction in 2014 till present. Research on GAN is rapidly growing and there are many variants of the original GAN focusing on various aspects of deep learning. GAN are perceived as the most impactful direction of machine learning in the last decade. This paper focuses on the application of GAN in autonomous driving including topics such as advanced data augmentation, loss function learning, semi-supervised learning, etc. We formalize and review key applications of adversarial techniques and discuss challenges and open problems to be addressed.
Nested Dilation Network (NDN) for Multi-Task Medical Image Segmentation

The deep convolutional network has shown excellent performance in medical image analysis.However, almost all network variants are presented for one specific task, e.g., segment pancreas on computerized tomography (CT).In this paper, we propose a nested dilation network (NDN) which is applied to multiple segmentation tasks even for different modalities, including CT, magnetic resonance imaging (MRI), and endoscopic images.We design residual blocks nested with dilations (RnD Blocks) that catch larger receptive field in the first few layers to boost shallow semantic information.Besides, we apply the modified focal loss to help the network to provide more accurate segmentation results.We evaluate our method on five subtasks from medical segmentation decathlon challenge and GIANA2018, and the results show that our method achieves a better performance than the latest methods in each task.A lot of research works have been done recently to strengthen the learning power of the convolutional neural network (CNN) to get better performance.
There Is Virtually No Excuse: The Frequency and Predictors of College Students’ Bystander Intervention Behaviors Directed at Online Victimization

Research shows that a large percentage of college students have experienced online victimization. However, bystander intervention behaviors directed at online contexts are absent from both the online victimization and bystander intervention research. With a sample of undergraduate college students, the current study explores the frequency and predictors of bystander intervention behaviors in response to online situations. Results show that a majority of students intervened during the past academic year when faced with the opportunity. Individuals with high self-control, who previously experienced online victimization, and/or witnessed positive peers’ norms offline are more likely to intervene.
Reinforced Cross-Media Correlation Learning by Context-Aware Bidirectional Translation

The heterogeneity gap leads to inconsistent distributions and representations between image and text, which rises a challenging task to measure their similarities and construct cross-media correlation between them. The existing works mainly model the cross-media correlation in a common subspace, which causes insufficient correlation modeling in such third-party subspace with intermediate unidirectional transformation. Inspired by the recent advances of neural machine translation, which aims to establish a corresponding relationship between two entirely different languages, we can naturally discover that it has striking common characteristic with cross-media correlation learning to consider image and text as bilingual pairs, where the image is treated as a special kind of language to provide visual description, so that bidirectional transformation can be conducted between image and text to effectively explore cross-media correlation in the feature space of each media type. Thus, we propose a reinforced cross-media bidirectional translation (RCBT) approach to model the correlation between visual and textual descriptions. First, cross-media bidirectional translation mechanism is proposed to conduct direct transformation between the bilingual pairs of visual and textual descriptions bidirectionally, where the cross-media correlation can be effectively captured in both feature spaces of image and text through bidirectional translation training. Second, cross-media context-aware network with residual attention is proposed to exploit the rich spatial and temporal context hints with cross-media convolutional recurrent neural network, which can lead to more precise correlation learning for promoting bidirectional translation process. Third, cross-media reinforcement learning is proposed to perform a two-agent communication game played as a round between image and text to boost the bidirectional translation process, and we further extract inter-media and intra-media reward signals to provide complementary clues for learning cross-media correlation. Extensive experiments are conducted on cross-media retrieval to verify the effectiveness of our proposed RCBT approach, compared with 11 state-of-the-art methods on three cross-media datasets.
Verifiable Smart Contract Portability

With the advent of blockchain technologies, the idea of decentralized applications has gained traction. Smart contracts permit the implementation of application logic to foster distributed systems that are capable of removing intermediaries. Hereby, lock in effects originating from isolated data storage and central authorities are mitigated. Yet, smart contracts deployed to a ledger generate dependencies on the underlying blockchain. Over time, requirements regarding contract execution may detach from the utilized blockchain due to contradicting incentives and security or performance issues. To avoid a novel form of lock in effect towards a host blockchain, we introduce a concept for smart contract portability that permits any user to migrate contract logic and state between blockchains in a flexible and verifiable manner. As the Ethereum Virtual Machine (EVM) is supported by a multitude of blockchain implementations, it poses a common execution environment for smart contracts. We provide a toolbox that facilitates smart contract portability between EVM-compatible blockchains without trust requirements in the entity executing the migration process. To prove the concept's soundness, we transfer token contracts based on the ERC20 standard as well as applications containing dependencies to other smart contracts. Our evaluation shows the validity of ported applications including their current states.
Examining Gentle Rivalry: Decision-Making in Blockchain Systems

The blockchain comes with the promise of being a disruptive technology with the potential for novel ways of interaction in a wide range of applications. Although scholarly interest in the technology is growing, a comprehensive analysis of blockchain applications from a governance perspective lacks to date. This research pays special attention to the governance of blockchain systems and illustrates core governance decisions on 15 blockchain implementations from four application domains. Additionally, this research sheds light on changes brought by the blockchain in terms of governance. Based on academic literature, semi-structured-interviews with representatives from those companies, and content analysis of grey literature, different blockchain governance decisions have been derived and their enactment described. The identification of those enriches the scarce body of knowledge on blockchain-based implementations with a better understanding of how key governance decisions are enacted.
Characterization of a Bayesian network‐based radiotherapy plan verification model

The current process for radiotherapy treatment plan quality assurance relies on human inspection of treatment plans, which is time-consuming, error prone and oft reliant on inconsistently applied professional judgments. A previous proof-of-principle paper describes the use of a Bayesian network (BN) to aid in this process. This work studied how such a BN could be expanded and trained to better represent clinical practice.We obtained 51 540 unique radiotherapy cases including diagnostic, prescription, plan/beam, and therapy setup factors from a de-identified Elekta oncology information system from the years 2010-2017 from a single institution. Using a knowledge base derived from clinical experience, factors were coordinated into a 29-node, 40-edge BN representing dependencies among the variables. Conditional probabilities were machine learned using expectation maximization module using all data except a subset of 500 patient cases withheld for testing. Different classes of errors that were obtained from incident learning systems were introduced to the testing set of cases which were withheld from the dataset used for building the BN. Different sizes of datasets were used to train the network. In addition, the BN was trained using data from different length epochs as well as different eras. Its performance under these different conditions was evaluated by means of Areas Under the receiver operating characteristic Curve (AUC).Our performance analysis found AUCs of 0.82, 0.85, 0.89, and 0.88 in networks trained with 2-yr, 3-yr 4-yr and 5-yr windows. With a 4-yr sliding window, we found AUC reduction of 3% per year when moving the window back in time in 1-yr steps. Compared to the 4-yr window moved back by 4 yrs (2010-2013 vs 2014-2017), the largest component of overall reduction in AUC over time was from the loss of detection performance in plan/beam error types.The expanded BN method demonstrates the ability to detect classes of errors commonly encountered in radiotherapy planning. The results suggest that a 4-yr training dataset optimizes the performance of the network in this institutional dataset, and that yearly updates are sufficient to capture the evolution of clinical practice and maintain fidelity.