Reviewerly/exHarmony huggingface dataset:
Structure Learning of High-Order Dynamic Bayesian Networks via Particle Swarm Optimization with Order Invariant Encoding

Dynamic Bayesian networks usually make the assumption that the underlying process they model is first-order Markovian, that is, that the future state is independent of the past given the present. However, there are situations in which this assumption has to be relaxed. When this order increases, the size of the search space grows greatly, not all structure learning algorithms may be suited to learn higher-order networks, and a new appropriate order has to be found. To address the computational issues of huge networks, we propose a structure learning method that uses particle swarm optimization to search in the space of possible structures. To avoid the additional costs of increasing the Markovian order, we provide an order-invariant encoding that represents the networks as vectors of natural numbers whose length remains constant. Due to this encoding, we only need to set a maximum desired order rather than the exact one. Our experimental results show that this method is efficient in high orders and performs better than similar algorithms in both execution time and quality of the obtained networks.
SESCon: Secure Ethereum Smart Contracts by Vulnerable Patterns’ Detection

Ethereum smart contracts have been gaining popularity toward the automation of so many domains, i.e., FinTech, IoT, and supply chain, which are based on blockchain technology. The most critical domain, e.g., FinTech, has been targeted by so many successful attacks due to its financial worth of billions of dollars. In all attacks, the vulnerability in the source code of smart contracts is being exploited and causes the steal of millions of dollars. To find the vulnerability in the source code of smart contracts written in Solidity language, a state-of-the-art work provides a lot of solutions based on dynamic or static analysis. However, these tools have shown a lot of false positives/negatives against the smart contracts having complex logic. Furthermore, the output of these tools is not reported in a standard way with their actual vulnerability names as per standards defined by the Ethereum community. To solve these problems, we have introduced a static analysis tool, SESCon (secure Ethereum smart contract), applying the taint analysis techniques with XPath queries. Our tool outperforms other analyzers and detected up to 90% of the known vulnerability patterns. SESCon also reports the detected vulnerabilities with their titles, descriptions, and remediations as per defined standards by the Ethereum community. SESCon will serve as a foundation for the standardization of vulnerability detection.
Chat or Tap? – Comparing Chatbots with ‘Classic’ Graphical User Interfaces for Mobile Interaction with Autonomous Mobility-on-Demand Systems

In autonomous mobility-on-demand (AMoD) systems, passengers will solely interact with autonomous vehicles via digital user interfaces (UIs). Hence, UIs are crucial for acceptance and user experience (UX). As a foundation for deriving empirically grounded design guidelines, we investigate two approaches for mobile interaction: chatbots and ‘classic’ graphical UIs (GUIs). We evaluated prototypes of both in expert studies (nGUI = 6; nChatbot = 5) and a between-subjects simulator user study (n = 34). The latter enabled participants to experience a complete AMoD journey. While both concepts receive good acceptance and positive UX evaluations, the GUI results in significantly higher attractiveness and user satisfaction ratings. A significant interaction effect reveals a higher intention to use the chatbot in scenarios with a change of plans, but the GUI in ‘happy path’ scenarios. Interview data and emotion curves support this effect. Balancing the concepts‘ advantages and disadvantages, we provide design recommendations and propose to use GUI-based mobile applications with integrated (text-based) conversational elements for future human-AMoD interaction.
Strategies for Deploying a Sensor Network to Explore Planetary Lava Tubes

Recently discovered pits on the surface of the Moon and Mars are theorized to be remnants of lava tubes, and their interior may be in pristine condition. Current landers and rovers are unable to access these areas of high interest. However, multiple small, low-cost robots that can utilize unconventional mobility through ballistic hopping can work as a team to explore these environments. In this work, we propose strategies for exploring these newly discovered Lunar and Martian pits with the help of a mother-daughter architecture for exploration. In this architecture, a highly capable rover or lander would tactically deploy several spherical robots (SphereX) that would hop into the rugged pit environments without risking the rover or lander. The SphereX robots would operate autonomously and perform science tasks, such as getting inside the pit entrance, obtaining high-resolution images, and generating 3D maps of the environment. The SphereX robot utilizes the rover or lander’s resources, including the power to recharge and a long-distance communication link to Earth. Multiple SphereX robots would be placed along the theorized caves/lava tube to maintain a direct line-of-sight connection link from the rover/lander to the team of robots inside. This direct line-of-sight connection link can be used for multi-hop communication and wireless power transfer to sustain the exploration mission for longer durations and even lay a foundation for future high-risk missions.
A Systematic Mapping Study on Approaches for Al-Supported Security Risk Assessment

Effective assessment of cyber risks in the increasingly dynamic threat landscape must be supported by artificial intelligence techniques due to their ability to dynamically scale and adapt. This article provides the state of the art of AI-supported security risk assessment approaches in terms of a systematic mapping study. The overall goal is to obtain an overview of security risk assessment approaches that use AI techniques to identify, estimate, and/or evaluate cyber risks. We carried out the systematic mapping study following standard processes and identified in total 33 relevant primary studies that we included in our mapping study. The results of our study show that on average, the number of papers about AI-supported security risk assessment has been increasing since 2010 with the growth rate of 133% between 2010 and 2020. The risk assessment approaches reported have mainly been used to assess cyber risks related to intrusion detection, malware detection, and industrial systems. The approaches focus mostly on identifying and/or estimating security risks, and primarily make use of Bayesian networks and neural networks as supporting AI methods/techniques.
Efficient common objects localization based on deep hybrid Siamese network

Finding the common objects in a set of images is considered one of the recent challenges in different computer vision tasks. Most of the conventional methods have proposed unsupervised and weakly supervised co-localization methods to find the common objects; however, these methods require producing a huge amount of region proposals. This paper tackles this problem by exploiting supervised learning benefits to localize the common object in a set of unlabeled images containing multiple objects or with no common objects. Two stages are proposed to localize the common objects: the candidate box generation stage and the matching and clustering stage. In the candidate box generation stage, the objects are localized and surrounded by the bounding boxes. The matching and clustering stage is applied on the generated bounding boxes and creates a distance matrix based on a trained Siamese network to reflect the matching percentage. Hierarchical clustering uses the generated distance matrix to find the common objects and create clusters for each one. The proposed method is trained on PASCAL VOC 2007 dataset; on the other hand, it is assessed by applying different experiments on PASCAL VOC 2007 6×2 and Object Discovery datasets, respectively. The results reveal that the proposed method outperforms the conventional methods by 8% to 40% in terms of corloc metric.
DensePILAE: a feature reuse pseudoinverse learning algorithm for deep stacked autoencoder

Abstract Autoencoder has been widely used as a feature learning technique. In many works of autoencoder, the features of the original input are usually extracted layer by layer using multi-layer nonlinear mapping, and only the features of the last layer are used for classification or regression. Therefore, the features of the previous layer aren’t used explicitly. The loss of information and waste of computation is obvious. In addition, faster training and reasoning speed is generally required in the Internet of Things applications. But the stacked autoencoders model is usually trained by the BP algorithm, which has the problem of slow convergence. To solve the above two problems, the paper proposes a dense connection pseudoinverse learning autoencoder (DensePILAE) from reuse perspective. Pseudoinverse learning autoencoder (PILAE) can extract features in the form of analytic solution, without multiple iterations. Therefore, the time cost can be greatly reduced. At the same time, the features of all the previous layers in stacked PILAE are combined as the input of next layer. In this way, the information of all the previous layers not only has no loss, but also can be strengthened and refined, so that better features could be learned. The experimental results in 8 data sets of different domains show that the proposed DensePILAE is effective.
Attention and Adaptive Bilinear Matching Network for Cross-Domain Few-Shot Defect Classification of Industrial Parts

In the task of cross-domain few-shot defect classification on industrial part images, it's a challenging research problem that how to effectively reduce the feature distribution between different parts and increase the feature information distance of different defects of the same part. We propose a simple yet effective approach named attention and adaptive bilinear matching network (AABM). Equipped with Residual Attention Module (RAM) to enrich feature information from different semantic layer, the feature extractor could improve the poor performance caused by the issue that dataset covers too many categories. In addition, the adaptive bilinear matching network (ABM) is utilized to learn the feature information and make forecast for solving the problem of fine-grained image analysis in cross-domain few-shot classification. Specifically, we have inserted RAM behind each residual block in a stacked manner based on RESNET-10 as the backbone of feature encoder, which enables linear enhancement of the feature extractor. After the procedure of passing the feature information through an adaptive bilinear matching network whose weights not share between the sub-network, two different posterior probabilities are calculated and the final prediction result is obtained by merging them with the adaptive parameters from the last feature layer through full connection. The results on benchmark and part image datasets demonstrates that our model achieve better performances on cross-domain few-shot classification and our AABM achieves is competitive compared to the state-of-art approaches.
The effectiveness of Automatic Network Administration (ANA) in network automation simulation at Universitas Pendidikan Ganesha

Abstract Network administration activities pose scalability challenges. Challenges arise when the devices handled are very numerous. Instead of using traditional methods, IT organizations need to implement automation methods. It is very inefficient to administer a large number of network devices by logging into them one by one. Human error is very susceptible to occur, which will cause configuration inconsistencies, to configuration errors, which will have an impact on poor network services provided. The automation method on the network is known as Network Automation (NA). NA uses an algorithmic approach in arranging steps in problem solving. NA is executed in the form of an application program that has the main function of automating routine, complex, repetitive, and comprehensive activities. Network administration activities that can be automated include routing automation, virtual administration and backup or restore. The simulation of NA implemented by ANA. ANA is the test is which was carried out using the Python programming language by utilizing Paramiko and Netmiko Library in a virtual environment using GNS3 and Virtual Box. The purpose of developing ANA was to make network administration activities run more efficiently. The results of applying the ANA can be taken into consideration in network administration activities. The result showed ANA was effective to prove the NA purpose.
An Iterated Local Search Methodology for the Qubit Mapping Problem

The qubit mapping approach serves to transform a quantum logical circuit (LC) into a physical one that satisfies the connectivity constraints imposed by the noisy intermediate-scale quantum (NISQ) devices. The quality of the physical circuit generated by a mapping approach depends largely on the initial mapping, which specifies the correspondence between the qubits in the LC and the qubits on the NISQ device. There are a total of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n!$ </tex-math></inline-formula> different initial mappings for a qubit mapping problem with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> qubits, and among them, there is at least one initial mapping corresponding to the smallest physical circuit that this mapping approach can output. Finding such an initial mapping is very important for reliable computations on the NISQ device. To this end, we propose an iterated local search framework as well as a heuristic circuit mapper. In this framework, we perform multiple local searches on the space of initial mappings, and during each local search, several promising neighborhoods of the current initial mapping are generated and evaluated by invoking the circuit mapper in a forward or a backward manner. This framework provides a way for the qubit mapping approach to find the best physical circuit that it can produce, allowing it to trade time for circuit quality, which is necessary in the NISQ era. The experimental results demonstrate the stability, scalability, and effectiveness of this approach in reducing the number of additional gates. Moreover, although this approach is a multipass circuit mapping process, it can generate a good-quality physical circuit within half an hour, even for the circuit with more than 10 000 gates.
Modeling User Interaction at the Convergence of Filtering Mechanisms, Recommender Algorithms and Advisory Components

A variety of methods is used nowadays to reduce the complexity of product search on e-commerce platforms, allowing users, for example, to specify exactly the features a product should have, but also, just to follow the recommendations automatically generated by the system. While such decision aids are popular with system providers, research to date has mostly focused on individual methods rather than their combination. To close this gap, we propose to support users in choosing the right method for the current situation. As a first step, we report in this paper a user study with a fictitious online shop in which users were able to flexibly use filter mechanisms, rely on recommendations, or follow the guidance of a dialog-based product advisor. We show that from the analysis of the interaction behavior, a model can be derived that allows predicting which of these decision aids is most useful depending on the user's situation, and how this is affected by demographics and personality.
CMIX: Deep Multi-agent Reinforcement Learning with Peak and Average Constraints

In many real-world tasks, a team of learning agents must ensure that their optimized policies collectively satisfy required peak and average constraints, while acting in a decentralized manner. In this paper, we consider the problem of multi-agent reinforcement learning for a constrained, partially observable Markov decision process – where the agents need to maximize a global reward function subject to both peak and average constraints. We propose a novel algorithm, CMIX, to enable centralized training and decentralized execution (CTDE) under those constraints. In particular, CMIX amends the reward function to take peak constraint violations into account and then transforms the resulting problem under average constraints to a max-min optimization problem. We leverage the value function factorization method to develop a CTDE algorithm for solving the max-min optimization problem, and two gap loss functions are proposed to eliminate the bias of learned solutions. We evaluate our CMIX algorithm on a blocker game with travel cost and a large-scale vehicular network routing problem. The results show that CMIX outperforms existing algorithms including IQL, VDN, and QMIX, in that it optimizes the global reward objective while satisfying both peak and average constraints. To the best of our knowledge, this is the first proposal of a CTDE learning algorithm subject to both peak and average constraints.
Real-Time Facial Recognition Using SURF-FAST

Facial recognition is a biometric technology that uses unique facial features to identify a person. Preventing retail crime, smart advertising, unlocking phone, finding missing person, diagnosis of disease, tracking school attendance are some of the many utilizations of face recognition. Recently, novel work has been done to detect facial emotion to understand the state of mind of a learner in an online class which better helps the instructor to design courses. Since there is no contact required like it is required for fingerprints, facial recognition offers a quick, automatic, and seamless verification experience. Most of the face detection techniques use deep learning and neural networks which require huge datasets for training. Using large datasets and matching one by one is a time taking approach. On the other hand, face identification using local feature extraction provides less accuracy than what is expected. Hence, this directs us to handle huge database, focus on developing new real time algorithms with maximum accuracy. The paper proposes a dynamic real time automated face recognition system to identify face in live video using surveillance camera and label a known person by their name and unknown person as "Unknown". In the experiment, stacked-image montage is used to reduce the time for training the images. Multi-feature extraction using Fast Feature, Speeded-Up-Robust Features (SURF) and Sum of Absolute Difference (SAD) have proved beneficial in getting high accuracy using very small dataset for face recognition.
The CIS Common Library Space in the Context of Humanitarian Cooperation

The article reveals the scientific aspects and development practice of the common library space of the Commonwealth of Independent States (CIS CLS), which over 30 years has formed a system of its organization and mechanisms of functioning. There is reflected the role of libraries in solving the humanitarian problems of the Commonwealth. The article considers the CIS CLS as the main part of the Eurasian library space, and studies it as an independent system. There are highlighted the most important principles of building the CIS CLS. The article shows that during the period under study, the path of formation has been passed, and the circle of common interests and relevant areas of activity has been identified. There is formulated the regulatory and legal framework for the development of librarianship in the CIS countries, the model laws designed to harmonize the national legal documents. The formed organizational system of the CLS functioning includes the interrelated links — national libraries of the CIS, the Library Assembly of Eurasia, the Russian State Library as the basic organization for cooperation in the field of librarianship of the CIS member states, the scientific and practical journal “Herald of the Library Assembly of Eurasia”. The article identifies the priority areas of cooperation: building and preserving national library collections, ensuring open and equal access for users to local and world resources through the use of information technologies, promoting the best examples of national culture and literature, cultural and educational events and ethno-cultural library interaction. The author provides examples of the cooperation programs implementation. The article determines that for the sustainable development and stability of the CLS as a system of library interaction, it is necessary to constantly overcome the contradictions accumulated in it, make adjustments to the definition of common interests and organizational structure. The author concludes that the integration activities of CIS libraries to strengthen the CLS, with the unifying role of the Russian language, serves as the basis for the consolidated inclusion of libraries in the common humanitarian and cultural space of the CIS and in the world information space.
Joint Image and Label Self-super-Resolution

We propose a method to jointly super-resolve an anisotropic image volume along with its corresponding voxel labels without external training data. Our method is inspired by internally trained superresolution, or self-super-resolution (SSR) techniques that target anisotropic, low-resolution (LR) magnetic resonance (MR) images. While resulting images from such methods are quite useful, their corresponding LR labels-derived from either automatic algorithms or human raters-are no longer in correspondence with the super-resolved volume. To address this, we develop an SSR deep network that takes both an anisotropic LR MR image and its corresponding LR labels as input and produces both a super-resolved MR image and its super-resolved labels as output. We evaluated our method with 50 T1-weighted brain MR images 4× down-sampled with 10 automatically generated labels. In comparison to other methods, our method had superior Dice across all labels and competitive metrics on the MR image. Our approach is the first reported method for SSR of paired anisotropic image and label volumes.
Channel Metamodeling for Explainable Data-Driven Channel Model

Machine learning can produce accurate data-driven channel models, but their black-box nature makes it harder to explain the models and to understand underlying channel characteristics. In this letter, we propose a channel metamodeling approach for such a black-box data-driven channel model. Our approach enables us to express the data-driven channel model in terms of transparent mathematical expressions based on symbolic function approximation methods. Through experiments with synthetic and real datasets, we demonstrate that our approach produces a channel metamodel of the data-driven channel model for each dataset that is highly accurate and allows us to easily explain the data-driven channel model and to understand the underlying channel characteristics.