Reviewerly/exHarmony huggingface dataset:
Using multi-task residual network to evaluate image aesthetic quality

With the popularity of cameras, smartphones and other devices, visual content data such as images and videos are increasing day by day, and visual content data processing technology has become a research direction in the research fields of computer vision, computational photography and artificial intelligence. Compared with the semantic features of object recognition, the mystery of human aesthetics has not yet been quantified and scientifically explained. It is difficult to determine the feature points of image aesthetics, which increases the difficulty of image aesthetics quality evaluation. In order to better obtain the feature points of image aesthetics, this paper uses a multi-task residual network model to extract features of the image, and evaluate the aesthetic level of the image according to the extracted features. Using a special aesthetic evaluation data set to test and verify the accuracy of the evaluation model proposed in this paper is 84.68%, which can more accurately evaluate the aesthetic level of images. Therefore, the evaluation model proposed in this paper promotes the development of image aesthetic value evaluation technology.
Partially Monotonic Learning for Neural Networks

In the past decade, we have witnessed the widespread adoption of Deep Neural Networks (DNNs) in several Machine Learning tasks. However, in many critical domains, such as healthcare, finance, or law enforcement, transparency is crucial. In particular, the lack of ability to conform with prior knowledge greatly affects the trustworthiness of predictive models. This paper contributes to the trustworthiness of DNNs by promoting monotonicity. We develop a multi-layer learning architecture that handles a subset of features in a dataset that, according to prior knowledge, have a monotonic relation with the response variable. We use two alternative approaches: (i) imposing constraints on the model's parameters, and (ii) applying an additional component to the loss function that penalises non-monotonic gradients. Our method is evaluated on classification and regression tasks using two datasets. Our model is able to conform to known monotonic relations, improving trustworthiness in decision making, while simultaneously maintaining small and controllable degradation in predictive ability.
D3CyT: Deceptive Camouflaging for Cyber Threat Detection and Deterrence

Even the most secure cyber systems could be compromised, and their data could be stolen. Once the data is stolen, even if it is encrypted or hashed, the attackers can conduct offline brute-forcing on it to recover the plaintext without being disrupted or detected. In this paper, we propose D3CyT, a simple, deceptive approach to camouflage sensitive data against such data thefts. In this approach, we transform a sensitive data value (which could be encrypted or hashed) to a deceptive value, called honeyvalue. The honeyvalue is stored instead of the original value, and the key to retrieve the original value from the honeyvalue is stored on a dedicated and secure server. If the data is stolen, the adversary would only attain the honeyvalues. The honeyvalues would either dissuade the attackers from using them by making stolen data look unimportant, or enable detection of data theft in case the attacker uses them. Through three different case studies focused on camouflaging passwords, QR codes, and logged IP addresses, we show the broad usability of our approach in different domains. We also show that even if our deception fails, the system is still technically more secure and computationally as secure as the original system.
A preliminary experiment combining marine robotics and citizenship engagement using imitation learning

In this paper, we describe a preliminary experiment of citizenship engagement in the context of marine robotics using imitation learning to train a controller that mimics human behavior. The experiment has been carried out during the Festival della Comunicazione in Camogli, Italy, in September 2019. In more detail, citizens have been asked to pilot a small, light, and safe autonomous surface vehicle in front of a crowded public beach with the goal of performing an S-shaped path. The trajectories and controls performed by non-expert human operators have been recorded with the aim of training an imitation system that, after collecting a sufficient number of trajectory-control pairs, has been able to drive the vehicle without human intervention. To learn the human behavior, echo state networks have been employed as approximating architectures. The resulting controller turned out to be very effective in successfully performing the considered experiment with a reduced amount of training trajectories by imitating the human behavior also in unknown situations. The success of this experiment may pave the way to new research processes where citizens are actively engaged.
A Hybrid Visual Tracking Algorithm Based on SOM Network and Correlation Filter

To meet the challenge of video target tracking, based on a self-organization mapping network (SOM) and correlation filter, a long-term visual tracking algorithm is proposed. Objects in different videos or images often have completely different appearance, therefore, the self-organization mapping neural network with the characteristics of signal processing mechanism of human brain neurons is used to perform adaptive and unsupervised features learning. A reliable method of robust target tracking is proposed, based on multiple adaptive correlation filters with a memory function of target appearance at the same time. Filters in our method have different updating strategies and can carry out long-term tracking cooperatively. The first is the displacement filter, a kernelized correlation filter that combines contextual characteristics to precisely locate and track targets. Secondly, the scale filters are used to predict the changing scale of a target. Finally, the memory filter is used to maintain the appearance of the target in long-term memory and judge whether the target has failed to track. If the tracking fails, the incremental learning detector is used to recover the target tracking in the way of sliding window. Several experiments show that our method can effectively solve the tracking problems such as severe occlusion, target loss and scale change, and is superior to the state-of-the-art methods in the aspects of efficiency, accuracy and robustness.
Sanskrit Parsing Following Indian Theories of Verbal Cognition

Pāṇini’s grammar is an important milestone in the Indian grammatical tradition. Unlike grammars of other languages, it is almost exhaustive and together with the theories of śābdabodha (verbal cognition), this grammar provides a system for language analysis as well as generation. The theories of śābdabodha describe three conditions necessary for verbal cognition. They are ākāṅkṣā (expectancy), yogyatā (meaning congruity), and sannidhi (proximity). We examine them from a computational viewpoint and provide appropriate computational models for their representation. Next, we describe the design of a parser following the theories of śābdabodha and present three algorithms for solving the constraints imposed by the theories of śābdabodha . The first algorithm is modeled as a constraint satisfaction problem, the second one as a vertex-centric graph traversal, and the third one as an edge-centric binary join, each one being an improvement over the previous one.
Folding and Friction

Abstract The internet of things (IoT)—the embedding of networked computing into the material world around us—seeks to reshape our everyday lives. To address the IoT is to address the material interface between the global digital networks of the twenty-first-century economy and the mundane doings, affects, and experiences which occupy the great majority of our existence. Taking domestic IoT, the so-called smart home, as a focus, the author argues that the IoT is more than simply an intensification of existing trends, the ongoing extension of computing connectivity which has already jumped from desktop to laptop to smartphone. In breaking out of the constraints of any single personal device, no matter how mobile, the IoT not only further dissolves the spatial and temporal distance between different social domains but also profoundly implicates social life within those domains, between the members of the setting. The IoT is constitutionally social in a way in which no type of social media is. The chapter provides a consideration of the political economy at play in the smart home, before addressing everyday life and the IoT in terms of information management, control, domestic labor, and resistance. In concluding, two key features of the IoT are highlighted: world folding, whereby incommensurate social domains are layered through one another with often problematic—even absurd—results and its misconceived efforts to erase the social frictions of everyday life, which fails to recognize that it is in these frictions that so much of what is socially valuable resides.
Enhancing Dynamic Binary Translation in Mobile Computing by Leveraging Polyhedral Optimization

Dynamic binary translation (DBT) is gaining importance in mobile computing. Mobile Edge Computing (MEC) augments mobile devices with powerful servers, whereas edge servers and smartphones are usually based on heterogeneous architecture. To leverage high-performance resources on servers, code offloading is an ideal approach that relies on DBT. In addition, mobile devices equipped with multicore processors and GPU are becoming ubiquitous. Migrating x86_64 application binaries to mobile devices by using DBT can also make a contribution to providing various mobile applications, e.g., multimedia applications. However, the translation efficiency and overall performance of DBT for application migration are not satisfactory, because of runtime overhead and low quality of the translated code. Meanwhile, traditional DBT systems do not fully exploit the computational resources provided by multicore processors, especially when translating sequential guest applications. In this work, we focus on leveraging ubiquitous multicore processors to improve DBT performance by parallelizing sequential applications during translation. For that, we propose LLPEMU, a DBT framework that combines binary translation with polyhedral optimization. We investigate the obstacles of adapting existing polyhedral optimization in compilers to DBT and present a feasible method to overcome these issues. In addition, LLPEMU adopts static-dynamic combination to ensure that sequential binaries are parallelized while incurring low runtime overhead. Our evaluation results show that LLPEMU outperforms QEMU significantly on the PolyBench benchmark.
Home Network Traffic Control Scheme Based on Two-Level Bargaining Game Model

This article presents a new smart home network (SHN) traffic control scheme to adaptively handle different quality of service (QoS) traffic services. According to their special characteristics, we categorize the SHN traffic services into three classes - class I, II and III data services - to share the limited SHN bandwidth resource. To effectively share the limited resource, we adopt the bridging and iterated egalitarian bargaining solutions, and formulate a two-level cooperative bargaining game. At the over bargaining process, the idea of bridging bargaining solution is used to compromise the conflicting views of real-time and non real-time data services. At the under bargaining process, class I and class II data services share the assigned bandwidth based on the concept of iterated egalitarian bargaining solution. These both processes are implemented to take full advantages of collaborative SHN traffic services. This interactive coordinated paradigm explores the mutual benefits under dynamically changing SHN environments. The most important novelties of our two-level bargaining approach is to compromise diverse QoS requirements while leveraging a reciprocal consensus among different traffic services. In a coordinated manner, we can get a globally desirable solution to challenge the relatively fair-efficient gains. Finally, simulation testbed is constructed and the numerical analysis is conducted to demonstrate the performance improvement of our proposed method.
Domain Adaptive Chinese Word Segmentation Method for power data security classification

Abstract Chinese word segmentation (CWS) is an important task for Chinese NLP, and also an essential pre-processing step to establish a word-root database for security classification of power data, covering different domains such as laws &amp; regulations, power. It is impracticable to label a large number of training corpus for each domain, which brings great challenge to the supervised statistical learning method to carry out effective CWS. Therefore, a Chinese word segmentation approach based on dictionary and semi-supervised conditional random field (SS-CRF ) is presented. At first, a CRF model for CWS is trained with self-training and active learning algorithms and used to conduct CWS task. Then the dictionary features are introduced to correct the result of CRF based segmentation by adopting RMM algorithm. Experiments on a cross domain segmentation task show that the proposed method can effectively improve the domain-adaptive performance of CWS.
Performance and Resource-Aware Virtual Machine Selection using Fuzzy in Cloud Environment

Cloud computing is an on-demand computing which provides elastic resources to users on pay-as-you-go basis. The ever-increasing resource demand of data-driven applications promulgated the deployment of data centers which results in high energy consumption. Therefore, efficient resource management is crucial to serve users’ request. Live migration plays an important role in resource management. However, excessive migrations sometime degrade application performance. Thus, careful virtual machine selection must be done to ensure low migration count. This work develops a virtual machine selection policy: performance and resource-aware virtual machine selection using fuzzy (PRSF) that aims to utilize CPU resources to their maximum in order to reduce migration count. An attempt is also made to decrease migration time of a virtual machine by considering its main memory allocations. The policy implements Mamdani fuzzy controller to optimize the selection decision. The performance evaluation of proposed policy with benchmark algorithms has shown reduction of 32.78% and 81.7% in energy consumption levels and migration count, respectively.
Processing Of Conceptual Models Of Subject Problems For Extracting Knowledge From Technical Documentation

This article presents a formal description of a conceptual model that represents documented technical knowledge with different degrees of formalization. An enlarged algorithm is presented for stratifying the conceptual model for constant information.
General Steganalysis Method of Compressed Speech Under Different Standards

Analysis-by-synthesis linear predictive coding (AbS-LPC) is widely used in a variety of low-bit-rate speech codecs. Most of the current steganalysis methods for AbS-LPC low-bit-rate compressed speech steganography are specifically designed for a specific coding standard or category of steganography methods, and thus lack generalization capability. In this paper, a general steganalysis method for detecting steganographies in low-bit-rate compressed speech under different standards is proposed. First, the code-element matrices corresponding to different coding standards are concatenated to obtain a synthetic code-element matrix, which will be mapped into an intermediate feature representation by utilizing the pre-trained dictionaries. Then, bidirectional long short-term memory is employed to capture long-term contextual correlations. Finally, a code-element affinity attention mechanism is used to capture the global inter-frame context, and a full connection structure is used to generate the prediction result. Experimental results show that the proposed method is effective and better than the comparison methods for detecting steganographies in cross-standard low-bit-rate compressed speech.
Assisted Diagnosis of Real-Virtual Twin Space for Data Insufficiency

This paper proposes a virtual-real twin spatial fusion theory to deal with the problem of insufficient data for intelligent diagnosis of power equipment. First, build a transformer winding fault experiment platform, and use Frequency Response Analysis (FRA) to obtain the measured sample set that contains different fault locations, fault types and severity of the transformer to form an accurate physical space. Then, use COMSOL and matlab to build a transformer digital space model, correspondingly set the fault, obtain the simulation sample set, and form a fuzzy mirror space. Then, the sample data in the two spaces is pre-processed by feature extraction and fused with each other to obtain the virtual and real twin spaces. The fused samples are used as auxiliary training samples of intelligent fault diagnosis network. Finally, based on the proposed method, a variety of intelligent diagnostic networks are applied to diagnose the measured dataset, verifying that the proposed method can effectively improve the diagnosis and positioning effects of small sample data.
Face Manipulation with Generative Adversarial Network

Abstract Generative adversarial network has appeared as an effective image manipulation tool in recent years and has been widely used. The GAN-based manipulation of face images is also possible and tools including DeepFake are already misused. In this paper, we discuss the pros and cons of face manipulation with generative adversarial network. We find that this technique can be very useful for recovering masked face and further improving face recognition accuracy.
A Survey of Social Image Colocalization

Social image colocalization locates the objects belonging to the same category from a set of images. Its goal is to explore the consistent relationship among social entities in the social system, which is conducive to promoting the development of social scene understanding. In a social relationship, consistency is easy to be ignored but is useful for many fields, e.g., natural language processing and computer vision. The social image colocalization aims to utilize the consistent relationship to discover the objects belonging to the same category and locate them by rectangle bounding boxes. The consistency is principally reflected in the category of objects, which can be achieved by a similar appearance or a uniform structure. Currently, state-of-the-art colocalization methods mainly focus on three solving strategies: candidate region proposal selection, saliency maps-based methods, and deep descriptor transformation. In this article, we sort out several kinds of the present mainstream methods and provide a comprehensive review of their fundamentals and performance. We expect that the overview of colocalization would be helpful for the researchers who are new to this area and provide them enlightenments.
Proficiency-aware systems: Designing for user reflection in context-aware systems

Abstract In an increasingly digital world, intelligent systems support us in accomplishing many everyday tasks. With the proliferation of affordable sensing devices, inferring user states from collected physiological data paves the way to tailor-made adaptation. While estimating a user’s abilities is technically possible, such proficiency assessments are rarely employed to benefit the user’s task reflection. In our work, we investigate how to model and design for proficiency estimation as part of context-aware systems. In this paper, we present the definition and conceptual architecture of proficiency-aware systems. The concept is not only applicable to current adaptive systems but provides a stepping stone for systems which actively aid in developing user proficiency during interaction.
Position Identification Using Image Processing for UAV Flights in Martian Atmosphere

This paper presents a method for the position identification of an unmanned aerial vehicle (UAV) in the Martian atmosphere in the future. It uses the image processing of craters captured via an onboard camera of the UAV and database images. The method is composed of two processes: individual crater detection using a cascade object detector and position identification using the recognition Taguchi (RT)-method. In crater detection, objects with shapes that resemble craters are detected regardless of their positions, and the positions of multiple detected craters are identified using the criterion variable D * , which is a normalized Mahalanobis distance. D * is calculated from several feature variables expressing the area ratios and relative positions of the detected craters in the RT-method.
Sparse-Hamiltonian approach to the time-evolution of molecules on quantum computers

Quantum chemistry has been viewed as one of the potential early applications of quantum computing. Two techniques have been proposed for electronic structure calculations: (i) the variational quantum eigensolver and (ii) the phase-estimation algorithm. In both cases, the complexity of the problem increases for basis sets where either the Hamiltonian is not sparse, or it is sparse, but many orbitals are required to accurately describe the molecule of interest. In this work, we explore the possibility of mapping the molecular problem onto a sparse Hubbard-like Hamiltonian, which allows a Green's-function-based approach to electronic structure via a hybrid quantum-classical algorithm. We illustrate the time-evolution aspect of this methodology with a simple four-site hydrogen ring.
A new deep distortion convolutional neural network for semantic segmentation of panoramic images

Abstract Semantic segmentation of panoramic images plays a crucial role in many applications, such as scene understanding, autonomous navigation, and community security. However, the traditional deep learning algorithms achieve lower accuracy for panoramic images due to the serious image distortion. This paper proposes a new deep convolutional neural network to segment panoramic images of outdoor scenes semantically. The proposed network includes two branches: the semantic segmentation sub-branch (SS-branch) and the feature enhancement sub-branch (FE-branch). The SS-branch contains two parts: Encoder and decoder. In the encoder, a new distortion convolution layer (DCL) is defined to reduce the distortion of panoramic images. In the FE-branch, a canny is used to detect the edge and a lightweight network is designed to enhance the features of image boundaries. Finally, a weighted loss function is used. The experimental results show that the proposed semantic segmentation method has better performance for different outdoor scenes.
Optimal Deep Neural Networks by Maximization of the Approximation Power

We propose an optimal architecture for deep neural networks of given size. The optimal architecture obtains from maximizing the minimum number of linear regions approximated by a deep neural network with a ReLu activation function. The accuracy of the approximation function relies on the neural network structure, characterized by the number, dependence and hierarchy between the nodes within and across layers. For a given number of nodes, we show how the accuracy of the approximation improves as we optimally choose the width and depth of the network. More complex datasets naturally summon bigger-sized architectures that perform better applying our optimization procedure. A Monte-Carlo simulation exercise illustrates the outperformance of the optimised architecture against cross-validation methods and gridsearch for linear and nonlinear prediction models. The application of this methodology to the Boston Housing dataset confirms empirically the outperformance of our method against state-of the-art machine learning models.