Reviewerly/exHarmony huggingface dataset:
An Open and Modular Architecture for Autonomous and Intelligent Systems

Over the past few decades, remarkable progress has been made in the the field of Artificial Intelligence (AI). For some tasks such as games, natural language processing, and image classification, AI powered applications can match or surpass human performance. Despite this progress, truly Autonomous and Intelligent Systems (AIS) serving the needs of, and sharing the environment with humans, are yet to become a commercial reality. AIS engineering requires considerable integration efforts that must be disciplined and guided by a reference model enabling reuse and concurrent design: an open and modular architecture. While several specialized architectures have been developed over the course of few decades, there is a need for a unified approach that supports multi-agency, learning, knowledge representation, reasoning, planning, and run-time verification. In this paper, we propose an open and modular architecture for autonomous and intelligent systems. We start by defining the three primary modules of the architecture, namely situational assessment, knowledge repository and management, and decision making. We then refine each module into functional units and we describe possible interaction patterns among them.
Ops-Scale: Scalable and Elastic Cloud Operations by a Functional Abstraction and Feedback Loops

Recent research has proposed new techniques to streamline the autoscaling of cloud applications, but little effort has been made to advance configuration management (CM) systems for such elastic operations. Existing practices use CM systems, from the DevOps paradigm, to automate operations. However, these practices still require human intervention to program ad hoc procedures to fully automate reconfiguration. Moreover, even after careful programming of cloud operations, the backing models are insufficient for re-running such programs unchanged in other platforms-which implies an overhead in rewriting the programs. We argue that CM programs can be designed to be deployment-agnostic and highly elastic with well-defined abstractions. In this paper, we introduce our abstraction based on declarative functional programming, and we demonstrate it using a feedback loop control mechanism. Our proposal, called Ops-Scale, is a family of cloud operations that are derived by making a functional abstraction over existing configuration programs. The hypothesis in this paper is twofold: 1) it should be possible to make a highly declarative CM system rich enough to capture fine-grained reconfigurations of autoscaling automatically, and; 2) that a program written for a specific deployment can be re-used in other deployments. To test this hypothesis, we have implemented an open source configuration engine called Karamel that is already used in industry for large-scale cluster deployments. Results show that at scale Ops-Scale can capture a polynomial order of reconfiguration growth in a fully automated manner. In practice, recent deployments have demonstrated that Karamel can provision clusters of 100 virtual machines consisting of many-layers distributed services on Google's IaaS Cloud in 'less than 10 minutes'.
A Multiview Representation Framework for Micro-Expression Recognition

Multiview representation has become important due to its good performance for machine learning problems. In this paper, a multiview representation framework based on transfer learning is proposed for micro-expression recognition. The framework takes macro-expression as the auxiliary domain and micro-expression as the target domain, and assists the identification of micro-expressions by transferring the rich information extracted from the auxiliary domain, which effectively addresses the small sample problem of micro-expression recognition. The proposed algorithm mainly consists of three parts. Firstly, the features of the two domains are projected into a common space and the dictionaries of each domain are studied respectively. Then the dictionary of micro-expression domain is linearly reconstructed. Finally, in order to improve the comprehensive utilization of feature information, the most representative features from four different micro-expression feature sets are selected by multiview representation. The experiments and evaluation are carried out on three different databases, and the performance comparison of the proposed algorithm with other advanced methods are given. The experimental results show that the proposed algorithm has the better performance than other related methods.
Data Quality Transaction on Different Distributed Ledger Technologies

Data quality is a bottleneck for efficient machine-to-machine communication without human intervention in Industrial Internet of Things (IIoT). Conventional centralised data quality management (DQM) approaches are not tamper-proof. They require trustworthy and highly skilled intermediation, and can only access and use data from limited data sources. This does not only impacts the integrity and availability of the IIoT data, but also makes the DQM process time and resource consuming. To address this problem, a blockchain based DQM platform is proposed in this paper, which aims to enable tamper-proof data transactions in a decentralised and trustless environment. To fit for different quality requirements, our platform supports customisable smart contracts for quality assurance. And to improve our platform’s performance, we discuss and analyze different distributed ledger technologies.
Learning Transparent Object Matting

This paper addresses the problem of image matting for transparent objects. Existing approaches often require tedious capturing procedures and long processing time, which limit their practical use. In this paper, we formulate transparent object matting as a refractive flow estimation problem, and propose a deep learning framework, called TOM-Net, for learning the refractive flow. Our framework comprises two parts, namely a multi-scale encoder-decoder network for producing a coarse prediction, and a residual network for refinement. At test time, TOM-Net takes a single image as input, and outputs a matte (consisting of an object mask, an attenuation mask and a refractive flow field) in a fast feed-forward pass. As no off-the-shelf dataset is available for transparent object matting, we create a large-scale synthetic dataset consisting of 178 K images of transparent objects rendered in front of images sampled from the Microsoft COCO dataset. We also capture a real dataset consisting of 876 samples using 14 transparent objects and 60 background images. Besides, we show that our method can be easily extended to handle the cases where a trimap or a background image is available. Promising experimental results have been achieved on both synthetic and real data, which clearly demonstrate the effectiveness of our approach.
Zero-Shot Learning with Few Seen Class Samples

Zero-shot learning (ZSL) is originally designed to address the small sample size problem often encountered in computer vision by recognizing unseen object classes without any training samples. Existing ZSL models (particularly deep ones) often assume that hundreds of labelled samples are collected from each seen class. In real-world applications, this assumption tends to become invalid. Therefore, a new ZSL setting is concerned in this paper: each seen class only has few labelled samples, while each unseen class still has no samples. This is more challenging yet more useful/practical than the conventional ZSL setting. To overcome the extreme label scarcity, we choose to obtain more training samples from image search engine for data augmentation: the name of each seen class is used as the query of Google, and the top returned images can be viewed as the noisy labelled samples for this seen class. With the augmented but noisy labelled training data, a novel inductive ZSL model is proposed by formulating label noise reduction (LNR) and semantic projection learning (SPL) within a unified framework: (1) LNR aims to refine the noisy labelled samples for projection learning; (2) SPL aims to learn the projection function with the refined training data. Extensive experiments show that our ZSL model outperforms the state-of-the-art alternatives.
Double sums of Kloosterman sums in finite fields

We bound double sums of Kloosterman sums over a finite field Fq, with one or both parameters ranging over an affine space over its prime subfield Fp⊆Fq. These are finite fields analogues of a series of recent results by various authors in finite fields and residue rings. Our results are based on recent advances in additive combinatorics in arbitrary finite field.
Defining semantic networks using association-oriented metamodel

Knowledge representation is one of the most explored areas in nowadays computer science research. In this paper authors pursue definition and semantics of semantic networks that are defined as part of Semantic Knowledge Base being a hybrid knowledge oriented system. The approach presented in here aims at introducing advanced properties of networks such as cardinality, partitioning or certainty at the same time using simple structure based on two operands and operators. Following paper is an extension of a conference publication that introduced advanced aspects of semantic networks modelling with the use of Association-Oriented Metamodel. The extension includes a discussion related to the formal description of the structure, as well as the description and use of association-oriented design patterns.
A Fuzzy Reasoning Process for Conversational Agents in Cognitive Cities

Facing the challenges in a city that is to be understood as a complex construct, this article presents a solution approach for the further development of existing conversational agents, which should be used particularly in cities, for instance, as a source of information. The proposed framework consists of a fuzzy analogical reasoning process (based on structure-mapping theory) and a network-like memory (i.e., fuzzy cognitive maps stored in graph databases) as additions to the general architecture of a chatbot. Thus, it represents a concept of a global fuzzy reasoning process, which allows conversational agents to emulate human information processing by using cognitive computing (consisting of soft computing methods and cognition and learning theories). The framework is already in the third iteration of its development. Three experiments were conducted to examine the stability of the theoretical foundation as well as the potential of the framework.
LPHD: A Large-Scale Head Pose Dataset for RGB Images

Head pose estimation has attracted many research interest in recent years. With the advent of deep learning, it is possible to predict the head pose accurately from the RGB images without the help of facial landmarks or depth information. However, existing head pose datasets often lack large pose head images, which extremely limits the development of head pose estimation algorithms. In this paper, we build the largescale head pose dataset (LHPD) including more than 140,000 images with the diverse and accurate head poses. The LHPD dataset includes the head images recorded from different shooting angles between the camera and the human body for the first time, which greatly expands the range of head pose compared to previous datasets. Therefore, the range of head pose can cover +/-90° for each Euler angle. The accurate and reliable head pose annotation is labeled by the motion capture system and careful calibration procedures. We then propose a head pose estimation method through fine-tuning the ResNet on the LHPD dataset when using the Euclidean distance of quaternions as the loss function. The results show that our method achieves better performance than current state-of-theart algorithms.
A Scalable Text Classification Using Naive Bayes with Hadoop Framework

Automated text classification is the labeling of documents to the predefined class label or category using machine learning algorithms. It is one of the important domains in machine learning where the algorithm is applied to classify documents to the appropriate category or genre of the document. For example, the document might be news items and the class/category/genre might be business news, sport news, health news, financial news and social news. Due to the volume of this textual data and its presumed exponential growth, classical data mining techniques may not provide optimal performance in terms of efficiency. To this end, scalable machine learning library apache mahout with hadoop can be used to improve the performance of the algorithm and computation time. In this study Naïve Bayes classification algorithm is implemented on top of hadoop to build automatic document categorizer using Mapreduce programing model. Addis Ababa university institutional repository/Electronic thesis and dissertations text document is used for training and evaluation dataset. The proposed model achieved an accuracy of 79.06%. The result shows that the system can categorize large thesis documents into its predefined class with promising accuracy.
A High Current efficiency Stacked Digital Low Dropout Array with True-Random-Noise Injection and Ultralow Output Ripple for Power-Side Channel Attack Protection

This paper proposes a stacked digital low dropout (DLDO) array with three stacked groups to improve security and efficiency, consuming 1/3 of the input current in the prior art. The security is improved by two mechanisms. The advanced encryption standard (AES) engine can be one of point of loads (POLs) hidden in the deeper levels to minimize the disturbance from the AES to the input current. The other is the digital balanced interleave control (DBIC) receives random sources from internal leakage current frequency generator (LCFG) to generate randomly noise current to further hide the current interference caused by the AES. Due to DBIC and LCFG techniques, the correlation between input current and AES current is low to 0.006, which is 150 times lower than that of conventional DLDO.
A Complex-Valued Encoding Moth-Flame Optimization Algorithm for Global Optimization

The real-valued moth-flame optimization algorithm (MFO) is a new bio-inspired algorithm. It simulates the navigation mechanism of moth lateral positioning under moonlight. MFO has excellent performance in solving optimization problems and has strong ability in solving power optimization combination. In order to improve the global search ability of the algorithm, a complex-valued encoding moth-flame optimization algorithm (CMFO) is proposed. The real and imaginary parts of the population are updated by using the diploid structure of complex-valued encoding. The diversity of the population was increased. The effectiveness of CMFO algorithm has been verified by 4 benchmark problems. Statistically significant results and analysis show that the proposed complex-valued encoding moth-flame optimization algorithm is very promising and occasionally competitive compared with other well-established meta-heuristic techniques.
Exploit Hierarchical Label Knowledge for Deep Learning

In this paper we propose a methodology based on a complex deep learning network topology, named Hierarchical Deep Neural Network (HDNN), applied to eXtreme Multi-label Text Classification (XMTC) problem. The HDNN topology reproduces the label hierarchy. The main idea arises directly from the assumption that, if the label-set structure is defined, forcing this information into the network topology could improve classification performances and results interpretation. In this way, we define a method to force prior knowledge into the DNN. We perform the experimental assessment on a XMTC task related to a real application domain problem, namely the automatic labelling of biomedical scientific literature extracted from PubMed. The obtained preliminary results show that, despite the very high computational time needed to update the network weights, a slight performance improvement is obtained, with respect to a classical approach based on Convolution Neural Network. Some considerations will be drawn out to figure out possible key readings.
Combining evolution and self-organization to find natural Boolean representations in unconventional computational media

Designing novel unconventional computing systems often requires the selection of the computational structure as well as choosing the right symbol encoding. Several approaches apply heuristic search and evolutionary algorithms to find both computational structure and symbol encoding, which is time consuming because they depend on each other. Here, we present a novel approach that combines evolution with self-organization, in particular we evolve the computational structure but let the symbol encoding emerge through self-organization. This should not only be more efficient but should also lead to a more "natural" symbol encoding. We successfully demonstrate the potential of the technique, using an evolutionary algorithm to optimize the parameters of two non-linear media to perform as NAND-gates: a continuous-time recurrent neural network (CTRNN) and a computational model of BZ-droplet-based computing (DropSim). In both cases, the technique identified representations for TRUE and FALSE, and system configurations that performed successfully as NAND-gates. The effectiveness of the evolved NAND gates was further evaluated by their performance in half-adder networks, where again, both evolved systems performed correctly, producing the correct output for all possible inputs and for all possible transitions between inputs. We conclude that beyond the specific applications demonstrated here, combining evolution with self-organization could be a promising strategy widely applicable.
Analysis of network issues and their impact on Cloud Storage

Cloud computing renders highly scalable and flexible computing and storage resources on pay-per-use policy. Cloud Computing's services for computation and storage are getting increasingly popular and many organizations are now moving their data from in-house data centers to the Cloud Storage Providers (CSPs). Virtualization and distributed storage allocation are one of the many storage methodologies in the cloud paradigm. However, increasing user base and remote storage of data introduces several network issues and impact the storage methodologies. In this paper, we provide a review of state-of-the-art solutions to tackle the current issues in the cloud computing paradigm.
Monies, economies and democracy: cultivating ambivalence in the co-design of digital currencies

Systematised collaborative design of complementary currencies is still a largely unexplored area that offers underutilised opportunities for supporting a fair and sustainable sharing economy. Future currency design necessitates attention to the systemic factors and the social particularities in which new monetary alternatives, i.e. ‘monies’, and the technological solutions that serve these alternatives, are created. This paper argues for a socially and contextually sensitive design of complementary currency innovation. It considers the technological conditions and the tendencies to exploitation from contemporary capitalism. Based on the literature review, we propose collaborative design of complementary currencies with particularist and inclusive approaches. New directions for open-ended economic innovation are explored in finding opportunities in the future-oriented Transition Design, supported by an empathic and ambivalent design mindset.
Generative Models for Fashion Industry using Deep Neural Networks

The progress of deep learning models in image and video processing leads to new artificial intelligence applications in Fashion industry. We consider the application of Generative Adversarial Networks and Neural Style Transfer for Digital Fashion presented as Virtual fashion for trying new clothes. Our model generate humans in clothes with respect to different fashion preferences, color layouts and fashion style. We propose that the virtual fashion industry will be highly impacted by accuracy of generating personalized human model taking into account different aspects of product and human preferences. We compare our model with state-of-art VITON model and show that using new perceptual loss in deep neural network architecture lead to better qualitative results in generating humans in clothes.
Development of a system for collecting and processing sky images and meteorological data used for weather prediction

Weather prediction is a crucial element for power management in photovoltaic power plants (PVPP). In this paper, we propose a novel system for collecting essential data used for local short-term weather prediction. Image data consists of all-sky ground-based images obtained by an all-sky camera system with a fish-eye lens. Our proposed weather station collects meteorological data into database. The data include air temperature, humidity, wind speed, relative pressure, and spectrum of solar radiation. First, the whole setup for obtaining all-sky images is described, and setup for weather station is proposed. Then, our all-sky image database is characterized. Finally, to test sky images an experiment was performed to determine sky condition (clear sky, partly cloudy, mostly cloudy, overcast) with the use of a deep convolutional neural network (CNN). The accuracy of this method reached 97,80%.
Training-ValueNet: Data Driven Label Noise Cleaning on Weakly-Supervised Web Images

Manually labelling new datasets for image classification remains expensive and time-consuming. A promising alternative is to utilize the abundance of images on the web for which search queries or surrounding text offers a natural source of weak supervision. Unfortunately the label noise in these datasets has limited their use in practice. Several methods have been proposed for performing unsupervised label noise cleaning, the majority of which use outlier detection to identify and remove mislabeled images. In this paper, we argue that outlier detection is an inherently unsuitable approach for this task due to major flaws in the assumptions it makes about the distribution of mislabeled images. We propose an alternative approach which makes no such assumptions. Rather than looking for outliers, we observe that mislabeled images can be identified by the detrimental impact they have on the performance of an image classifier. We introduce training-value as an objective measure of the contribution each training example makes to the validation loss. We then present the training-value approximation network (Training-ValueNet) which learns a mapping between each image and its training-value. We demonstrate that by simply discarding images with a negative training-value, Training-ValueNet is able to significantly improve classification performance on a held-out test set, outperforming the state of the art in outlier detection by a large margin.
An Empirical Study of Incorporation of Augmented Reality into Civic Education

This paper attempts to examine the effectiveness of educational application of augmented reality into civic education class. 166 college freshmen students were investigated in a randomized field teaching experiment to determine the statistical difference between the traditional teaching mode and new one which connects AR course materials to civic education course with academic emotions, learning strategies and environment as research variables. As suggested by the results of Independent t-test and Pearson correlation, we found that students' positive academic emotions in the class with AR based teaching materials increased with more self-regulated learning strategies adopted and improved classroom environment. The study has implications for the development of civic education curriculum, which encourages the incorporation of today's AR technology into civic education.