Reviewerly/exHarmony huggingface dataset:
Ontological Fundamentals of Scientific and Education Portals

The methodology and technological solutions for the design and creation of web-based scientific and educational portals of ontological engineering are described. Web portals are defined as network systems of aggregated representation of subject systems of knowledge. A narrative category is entered, as an integrated representation of descriptions of passive knowledge systems. All stages of implementation are considered - the analysis of subject areas, by the content of which creates all the portal system components, design the structure and function on the basis of ontological modelling of subject areas, highlighting cross-curricular relationships to determine conditions of transition in the portal information environment from one state to another. Mechanisms of dynamic cataloguing of information resources of the scientific-educational portal, cluster formation by thematic search profiles for relevant information are presented. The model of aggregate displaying portal thematic resources is given. Formation means in the use of scientific and educational portals on the base of the component architecture of WEB-semantics services are determined and it describes their mechanisms application with the support of research and educational-cognitive activity of portal users. Based on the variety of descriptions of creative activity of Taras Shevchenko examples of reflection his legacy in the information environment of the scientific-educational portal are given. Ontological means of dialogue with the image of the writer on the basis of establishing intertextual links with his works are presented.
Comparison of Different Computing Platforms for Implementing Parallel Genetic Programming

Genetic programming (GP) is a powerful tool for knowledge discovery and data mining. Over the past decades, GP has been implemented in various parallel computing platforms to reduce its search time. However, these parallel GPs have different design principles and performance characteristics, which makes it difficult for users to choose the proper parallel GP in practice. To address this issue, this paper focuses on comparing and analyzing the characteristics of parallel GPs implemented in different computing platforms, in terms of running time, the speedup ratio, and the scalability. Based on the empirical results, the guidance of selecting different parallel GPs is concluded.
An Ultra-low Power Keyword-Spotting Accelerator Using Circuit-Architecture-System Co-design and Self-adaptive Approximate Computing Based BWN

This paper proposed an ultra-low power keyword-spotting (KWS) accelerator using circuit-architecture-system co-design and precision self-adaptive approximate computing based binarized weight network (BWN). To reduce the power consumption while maintaining the system recognition accuracy for different background noise, we first proposed a bit-by-bit layer-by-layer quantization method to quantize the deep neural network (DNN) to BWN. Then, we proposed a precision self-adaptive approximate addition unit to further reduce the BWN energy consumption. Evaluated under TSMC22nm ULL process technology, this work can support up to 10 keywords real time recognition under different background noise types and SNRs (from 5dB to near microphone) with power consumption of 13.6uW.
Self-tuning Co-Operation of Biology-Inspired and Evolutionary Algorithms for Real-World Single Objective Constrained optimization

Solving single objective constrained real-parameter optimization problems via population-based algorithms has attracted much attention. In this paper, a new self-tuning meta-heuristic approach called Fuzzy Controlled Cooperative Heterogeneous Algorithm (FCHA), which was proposed for constrained optimization, is introduced. The developed approach combines competition and cooperation between biology-inspired and evolutionary algorithms, regulated by fuzzy controller. It should be noted, that the epsilon-constrained method is utilized to handle the constraints for the solved optimization problems. The performance of the proposed FCHA algorithm is evaluated on 57 real-world constrained problems submitted for CEC 2020 special session. Its workability and usefulness are demonstrated; also ways of algorithm improvement are discussed.
Techniques for Complex Analysis of Contemporary Data

Contemporary data objects are typically complex, semi-structured, or unstructured at all. Besides, objects are also related to form a network. In such a situation, data analysis requires not only the traditional attribute-based access but also access based on similarity as well as data mining operations. Though tools for such operations do exist, they usually specialise in operation and are available for specialized data structures supported by specific computer system environments. In contrary, advance analyses are obtained by application of several elementary access operations which in turn requires expert knowledge in multiple areas. In this paper, we propose a unification platform for various data analytical operators specified as a general-purpose analytical system ADAMiSS. An extensible data-mining and similarity-based set of operators over a common versatile data structure allow the recursive application of heterogeneous operations, thus allowing the definition of complex analytical processes, necessary to solve the contemporary analytical tasks. As a proof-of-concept, we present results that were obtained by our prototype implementation on two real-world data collections: the Twitter Higg's boson and the Kosarak datasets.
Applications of Geophysical Surveys for Archaeological Studies in Urban and Rural Areas in Czech Republic and Armenia

The authors of the presented paper tested the possible use of geophysical methods in the field of archaeological and constructional-historical research. It focused on the historical center of Prague, where we took measurements in the built-up areas covered by cobble stones and asphalt. The works were carried out mainly at the Old Town Square, Hradčany Square, Charles Square and Lesser Town Square. Field conditions were completely different at the Armenia sites (intact agricultural areas). We used the methods of shallow geophysical survey, namely geoelectrical methods, gravimetry, seismics and magnetometry. Measurement results from the built-up areas were affected by presence of engineering networks, transportation and field obstacles. Working in the rural areas is generally less demanding in terms of implementation and evaluation, but our results show that proper selection of geophysical methods brings positive results even in urban areas. The research proved that good results can be expected if one uses multiple geophysical methods measuring various physical properties of an environment. The application of only one, albeit sophisticated method, is usually not enough. There is a necessity to improve interpretational software for the purposes of archaeological object detection. The objects are mainly buried within the first meters and are relatively small. Contrast of physical properties between searched objects and their surrounding environment does exist but typically is small and, therefore, it is necessary to measure multiple physical parameters simultaneously. The presented projects aimed not only to archaeology, but the results also had practical outputs for public administration and development.
A Smart Attendance System for Imam Abdulrahman Bin Faisal University Using Facial Recognition

Abstract Students’ attendance is one of the methods used to ensure students success by making sure that a student does not miss many classes. The most dominant method used nowadays in the Saudi universities such as, Imam Abdulrahman Bin Faisal University (IAU), is the traditional method where the instructors are the ones responsible for taking student’s attendance. Developing new and effective methods is critical to improve the traditional way and minimise their errors. A proposed facial recognition based attendance system is one efficient method that can substitute the traditional methods. Installing cameras in classrooms where it detects faces and recognise each face for attendance registration is more competent. The system can also track students’ attendance and warn them if they exceeded the permitted absences rate. The proposed system facilitates the attendance registration process and reduces the time and effort for both students and instructors.
Universal computation with quantum fields

We explore a way of universal quantum computation with particles which cannot occupy the same position simultaneously and are symmetric under exchange of particle labels. Therefore the associated creation and annihilation operators are neither bosonic nor fermionic. In this work we first show universality of our method and numerically address several examples. We demonstrate dynamics of a Bloch electron system from a viewpoint of adiabatic quantum computation. In addition we provide a novel Majorana fermion system and analyze phase transitions with spin-coherent states and the time average of the out-of-time-order correlator (OTOC). We report that a first-order phase transition is avoided when it evolves in a non-stoquastic manner and the time average of the OTOC diagnoses the phase transitions successfully.
Covert and Secret Key Expansion Over Quantum Channels Under Collective Attacks

We consider an enhanced measure of security for a quantum key distribution protocol, in which we require that the adversary not only obtains no information about the key but also remains unaware that a key generation protocol has been executed. When the adversary applies the same quantum channel independently to each transmitted quantum state, akin to a collective attack in the quantum key distribution literature, we propose a protocol that achieves covert and secret key expansion under mild restrictions. A crucial component of the protocol is a covert estimation stage, which is then combined with universal channel coding for reliability and resolvability in the covert regime.
Securing Machine Learning Architectures and Systems

Machine learning (ML), and deep learning in particular, have become a critical workload as they are becoming increasingly applied at the core of a wide range of application spaces. Computer systems, from the architecture up, have been impacted by ML in two primary directions: (1) ML is an increasingly important computing workload, with new accelerators and systems targeted to support both training and inference at scale; and (2) ML supporting computer system decisions, both during design and run times, with new machine learning based algorithms controlling systems to optimize their performance, reliability and robustness. In this paper, we will explore the intersection of security, ML and computing systems, identifying both security challenges and opportunities. Machine learning systems are vulnerable to new attacks including adversarial attacks crafted to fool a classifier to the attacker's advantage, membership inference attacks attempting to compromise the privacy of the training data, and model extraction attacks seeking to recover the hyperparameters of a (secret) model. Architecture can be a target of these attacks when supporting ML (or is supported by ML), but also provides an opportunity to develop defenses against them, which we will illustrate with three examples from our recent work. First, we show how ML based hardware malware detectors can be attacked with adversarial perturbations to the Malware and how we can develop detectors that resist these attacks. Second, we show an example of microarchitectural side channel attacks that can be used to extract the secret parameters of a neural network and potential defenses against it. Finally, we discuss how hardware and systems can be used to make ML more robust against adversarial and other attacks.
IGV Short Scale to Assess Implicit Value of Visualizations through Explicit Interaction

This paper reports the assessment of the infographics-value (IGV) short scale, designed to measure the value in the use of infographics. The scale was made to assess the implicit quality dimensions of infographics. These dimensions were experienced during the execution of tasks in a contextualized scenario. Users were asked to retrieve a piece of information by explicitly interacting with the infographics. After usage, they were asked to rate quality dimensions of infographics, namely, usefulness, intuitiveness, clarity, informativity, and beauty; the overall value perceived from interacting with infographics was also included in the survey. Each quality dimension was coded as a six-point rating scale item, with overall value included. The proposed IGV short scale model was validated with 650 people. Our analysis confirmed that all considered dimensions in our scale were independently significant and contributed to assessing the implicit value of infographics. The IGV short scale is a lightweight but exhaustive tool to rapidly assess the implicit value of an explicit interaction with infographics in daily tasks, where value in use is crucial to measuring the situated effectiveness of visual tools.
Leveraging Personalized Sentiment Lexicons for Sentiment Analysis

We propose a novel personalized approach for the sentiment analysis task. The approach is based on the intuition that the same sentiment words can carry different sentiment weights for different users. For each user, we learn a language model over a sentiment lexicon to capture her writing style. We further correlate this user-specific language model with the user's historical ratings of reviews. Additionally, we discuss how two standard CNN and CNN+LSTM models can be improved by adding these user-based features. Our evaluation on the Yelp dataset shows that the proposed new personalized sentiment analysis features are effective.
Construction and Control Aerial Manipulator Robot

This article presents the construction of an aerial manipulator robot composed of one or two robotic arms on an unmanned aerial vehicle, in order to execute control tasks in an autonomous or tele-operated manner. This aerial manipulator robot can work with one or two arms depending on the application requirements. The arms have been designed to serve several purposes: object manipulation and protect the actuating servos against direct impacts and overloads. Finally, a trajectory tracking algorithm is implemented and the simulation results are presented and discussed, which validate the controller and the proposed modeling.
A Hybrid Surrogate Model for Evolutionary Undersampling in Imbalanced Classification

Data preprocessing is a key stage in data mining that allows machine learning algorithms to obtain meaningful insights. Many preprocessing problems such as feature selection or instance selection can be modelled as optimisation/search problems. Evolutionary algorithms have traditionally excelled in this task when dealing with data of a moderate size. However, their application to large datasets typically involves very high computational costs. In this work, we propose a hybrid surrogate model for evolutionary undersampling in imbalanced classification problems. These are characterised by having a highly skewed distribution of classes in which evolutionary algorithms aim to balance the training data by selecting only the most relevant data. The proposed technique combines a two-stage clustering-based surrogate method with a windowing approach to quickly approximate fitness values of the chromosomes and accelerate the search. The experiments carried out in 44 standard imbalanced datasets show that the proposed hybrid surrogate model highly reduces the computational cost of the evolutionary algorithm without a considerable loss of performance.
Interactive Deep Colorization and its Application for Image Compression

Recent methods based on deep learning have shown promise in converting grayscale images to colored ones. However, most of them only allow limited user inputs (no inputs, only global inputs, or only local inputs), to control the output colorful images. The possible difficulty lies in how to differentiate the influences of different inputs. To solve this problem, we propose a two-stage deep colorization method allowing users to control the results by flexibly setting global inputs and local inputs. The key steps include enabling color themes as global inputs by extracting K mean colors and generating K-color maps to define a global theme loss, and designing a loss function to differentiate the influences of different inputs without causing artifacts. We also propose a color theme recommendation method to help users choose color themes. Based on the colorization model, we further propose an image compression scheme, which supports variable compression ratios in a single network. Experiments on colorization show that our method can flexibly control the colorized results with only a few inputs and generate state-of-the-art results. Experiments on compression show that our method achieves much higher image quality at the same compression ratio when compared to the state-of-the-art methods.
Tree-Shaped Ensemble of Multi-Label Classifiers using Grammar-Guided Genetic Programming

Multi-label classification paradigm has had a growing interest because of the emergence of a large number of classification problems where each of the instances of the data can be associated with several output labels simultaneously. Several ensemble methods were proposed to solve the multilabel classification problem. However, most of them simply create diversity in the ensemble by following a random procedure and give the same importance to all members. In this paper, we propose a Grammar-Guided Genetic Programming algorithm to build ensembles of multi-label classifiers. Given a pool of multilabel classifiers, each of them modeling dependencies among a subset of k labels, they are combined into a tree-shaped ensemble. At each node of the tree, predictions of its children nodes are combined, while each leaf represents a classifier from the pool. We propose two configurations for the method: using a fixed value of k for all classifiers in the pool, or using a variable value of k for each classifier, thus being able to capture relationships among groups of labels of different size in the ensemble. The experiments performed over sixteen multi-label dataset and using five evaluation metrics demonstrated that our method performs significantly better than the state-of-the-art ensembles of multilabel classifiers.
Layers Sequence Optimizing for Deep Neural Networks using Multiples Objectives

Selecting the best architecture for a Deep Neural Network (DNN) is a non-trivial task since there is a massive amount of possible configurations (layers and parameters) and great difficulty in how to choose them. To make this task more independent of human interaction, this work addresses the DNN architecture selection problem as a multi-objective optimization task with different criteria in a combinatorial context. For this, we defined a new way to represent the architecture of DNN (layer sequence) as a solution in the optimization process. The proposed method attempts to find the best composition and sequence of layers for the DNN architecture satisfying two criteria: accuracy and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score. The method was evaluated for performance and compared to the exhaustive and random approaches and state-of-the-art DNN algorithms. The results obtained showed that the proposed method is capable of achieving results close to the optimum, and competitive when compared to those results reached by state of the art algorithms.
MOATcoin

In this paper we present MOATcoin, a gamelike experiment that enabled us to practically investigate the open issues related to the governance and legal facets of smart contract based decentralized applications. After presenting the MOATcoin system architecture, we first tackle the problems of decentralized governance, its limits and the shift of trust that it entails; then, we explore the possible legal implications of the given scenario and, particularly, the consequences of code-written contracts. Finally, we offer taxonomical remarks on the concepts of "token" and "coin" and offer an insight from a regulatory perspective.
AA-ResNet: Energy Efficient All-Analog ResNet Accelerator

High energy efficiency is a major concern for emerging machine learning accelerators designed for IoT edge computing. Recent studies propose in-memory and mixed-signal approaches to minimize energy overhead resulting from frequent memory accesses and extensive digital computation. However, their energy efficiency gain is often limited by the overhead of digital-to-analog and analog-to-digital conversions at the boundary of the compute-memory. In this paper, we propose a new in-memory accelerator that performs all computation in the analog domain for a large, multi-level neural network (NN) for the first time avoiding any digital-to-analog or analog-to-digital conversion overhead. We propose an all-analog ResNet (AAResNet) accelerator in 28-nm CMOS, achieving an energy efficiency of 1.2 µJ/inference and inference rate of 325K images/s for the CIFAR-10 and SVHN datasets in SPICE simulation.
A Social Governance Scheme Based on Blockchain

Abstract Blockchain technology has the characteristics of distributedness, transparency, traceability, and openness. It is suitable for promoting the flatness of social governance structure and the transparency of governance and service processes, thereby improving the security of government social governance data, and promoting intelligent governance and Trustworthy government building. Blockchain combined with big data and artificial intelligence, cutting-edge technology, based on the principles of security, reliability, tamper resistance, anti-counterfeiting, and traceability, a new model of “blockchain + social governance” will be established. This article constructs a social governance scheme based on blockchain, the purpose of which is to solve problems in various areas of existing social governance with the help of the technical advantages of blockchain.
You look like a thing and i love you: how artificial intelligence works and why it’s making the world a weirder place

“The Five Principles of Artificial Intelligence (AI) Weirdness: 1) The danger of AI is not that it’s too smart but that it is not smart enough. 2) AI has the approximate brainpower of a worm. 3) AI...
Mask Transformer: Unpaired Text Style Transfer Based on Masked Language

Currently, most text style transfer methods encode the text into a style-independent latent representation and decode it into new sentences with the target style. Due to the limitation of the latent representation, previous works can hardly get satisfactory target style sentence especially in terms of semantic remaining of the original sentence. We propose a “Mask and Generation” structure, which can obtain an explicit representation of the content of original sentence and generate the target sentence with a transformer. This explicit representation is a masked text that masks the words with the strong style attribute in the sentence. Therefore, it can preserve most of the semantic meaning of the original sentence. In addition, as it is the input of the generator, it also simplified this process compared to the current work who generate the target sentence from scratch. As the explicit representation is readable and the model has better interpretability, we can clearly know which words changed and why the words changed. We evaluate our model on two review datasets with quantitative, qualitative, and human evaluations. The experimental results show that our model generally outperform other methods in terms of transfer accuracy and content preservation.