Reviewerly/exHarmony huggingface dataset:
Deep representation method for radar emitter signal using wavelet packets decomposition

Signal representation is to transform the intercepted original signal into feature parameters, which is the basis for the electronic reconnaissance system to analyse and identify a radar. The traditional method is to manually design a limited number of features with prior knowledge, which can only achieve the desired results in specific applications. Therefore, it is necessary to study signal representation methods that are more adaptable, reasonable, and effective. This study proposes a feature-level design scheme that extracts multi-level signal feature parameters to obtain deep essential feature representations, which is consistent with deep-learning frameworks. Through the decomposition through wavelet packet transform (WPT), the best sub-band tree structure of the signal can be obtained, and the depth representation of the signal can be well achieved, which can meet requirements of time–frequency analysis automatically. Here, Symlets wavelet packet decomposition (WPD) is used as an example to implement multi-layer signal feature representation. The combination of characteristic parameters at different levels can reflect the different information contained in different radar signals. The simulation results show that the method can effectively identify the modulation mode of radar signals and enhance the information mining ability from radar signals.
Development, Validation and Testing of the Bayesian Network to Evaluate the National Law Enforcement Agencies’ work

The paper proposes a methodology for using static Bayesian networks (BN) to assess the factors influencing the assessment of the quality of the national law enforcement agencies work. The methods for constructing the BN structure, their parametric learning, validation, sensitivity analysis and scenario analysis "What-if" are considered.
An Efficient Online Signature Verification Based on Feature Fusion and Interval Valued Representation of Writer Specific Features

Online Signature Verification (OSV) is a pattern recognition problem, which involves analysis of discrete-time signals of signature samples to classify them as genuine or forgery. One of the core difficulties in designing online signature verification (OSV) system is the inherent intra-writer variability in genuine handwritten signatures, combined with the likelihood of close resemblances and dissimilarities of skilled forgeries with the genuine signatures. To address this issue, in this manuscript, we emphasize the concept of writer dependent parameter fixation (i.e. features, decision threshold and feature dimension) using interval valued representation grounded on feature fusion. For an individual writer, a subset of discriminative features is selected from the original set of features using feature clustering techniques. This is at variance with the writer independent models in which common features are used for all the writers. To practically exhibit the efficiency of the proposed model, thorough experiments are carried out on benchmarking online signature datasets MCYT-100 (DB1), MCYT-330 (DB2) consist of signatures of 100, 330 individuals respectively. Experimental result confirms the efficiency of writer specific parameters for online signature verification. The EER value, the model computes, is lower compared to various latest signature verification models.
SATOS: Storage Agnostic Tokens over Opaque and Substructural Types

Public blockchains in support of Smart Contracts (SC), like Ethereum enable everyone to represent scarce, valuable resources (like cryptocurrencies) as so-called tokens. Token issuing and management was the first blockchain use case. However, programming languages and runtime systems used in the current blockchains for their SCs lack a secure and straightforward way to implement and handle tokens. The unnecessary complexity in doing so can lead to erroneous implementation of tokens and applications built on top of these, including the loss or theft of tokens as it happened. The most known attack was "TheDAO" attack which led to the "loss" of tokens, valued at that time at approximately 60 M US Dollar. A better and secure token representation directly embedded into a SC runtime and SC programming language could prevent loss of tokens. Thus this paper presents an approach including parts of a programming language using it. The core of the model is to use opaque and substructural data types together with an onchain soundness checker to generically represent tokens securely as values similar to integers and booleans. Such opaque data types enforce that only a designated piece of code can create values of that type. The substructural data types allow arbitrary values to express scarcity by preventing the duplication and elimination of values. The on-chain soundness checker ensures that the deployed code does not violate guarantees given by the type system, which includes opaque and substructural data types.
Synthesis of quantum images using phase rotation

A topic about synthesis of quantum images is proposed, and a specific phase rotation transform constructed is adopted to theoretically realize the synthesis of two quantum images. The synthesis strategy of quantum images comprises three steps, which include: (1) in the stage of phase extraction, we obtain the phases of the state of the quantum image by transforming the state of the quantum image to prepare the conditions for multiple phases extraction. (2) In the stage of rotation operator construction, the phases obtained in the first stage are used to construct the rotation operator where a mechanism is introduced into it to reduce the phase overflow. (3) In the stage of application of the rotation operator, we apply the operator constructed in the second stage on the state of quantum image to get a goal state. Additionally, numerical analysis gives the joint uncertainty relation of the pixel of the synthesized quantum image. The analysis result about the compression ratio indicates that the phase rotation transform and the overflow control mechanism are effective.
FPN++: A Simple Baseline for Pedestrian Detection

Our observation shows that pedestrians' heights greatly affect pedestrian detection performance, and for small pedestrians, their context information is useful for localizing and recognizing these pedestrians. Based on our observation, a FPN++ framework, which is an extension of Feature Pyramid Network (FPN) is proposed. It improves the FPN from the following aspects: i) we modify the backbone of FPN by reducing the stride of convolution from 2 to 1 in FPN from earlier layers, which allows the network to detect smaller pedestrians with more semantically meaningful features extracted from deeper layers, then we replace the convolution with dilated convolution to increase the local receptive fields and facilitate the detection on pedestrians of all scales; ii) a context-aware detection module is introduced in the predictor head of FPN to leverage context information for detection. Extensive experiments on the CityPersons and Caltech pedestrian datasets show that our FPN++ achieves state-of-the-art performance and significantly improves the performance for small pedestrians. Our solution can be readily extended to other detection tasks, and experiments on the VOC2007 benchmark also validate the effectiveness of our solution.
Training Virtual Environment for Teaching Simulation and Control of Pneumatic Systems

It is clear that in recent years have emerged countless simulators in all possible work areas for reaching the goals of Industry 4.0. Unity Pro software focuses on studying the options and possibilities offered virtual environments for the teaching in schools and universities. The aim of this paper is intended to create a practical training environment for the simulation and control of a pneumatic process for the students of Industrial Process Engineering of Automation. This virtual environment uses MQTT protocol into a Raspberry Pi to transmit instruction for control a real FESTO pneumatic lab based on what students develop during their virtual classes, granting and facilitating the student and teacher a new method that accelerates the learning process.
Discriminative Group Collaborative Competitive Representation for Visual Classification

In pattern recognition, the representation-based classification (RBC) has attracted much attention recently. As a representative one of RBC, collaborative representation-based classification (CRC) and its variants have achieved promising classification performance in many visual classification tasks. However, most of the CRC methods cannot directly consider the class discrimination information of data that is very important for classification. To fully use the class discrimination information, we propose a novel discriminative group collaborative competitive representation-based classification method (DGCCR) in this paper. In the designed DGCCR model, the discriminative competitive relationships of classes, the discriminative decorrelations among classes and the weighted class-specific group constraints are simultaneously taken into account for strengthening the power of pattern discrimination. Experiments on three visual classification data sets demonstrate that the proposed DGCCR out-performs state-of-the-art RBC methods.
Research on Human Action Recognition Based on Convolutional Neural Network

This paper proposes a human action recognition (HAR) algorithm based on convolutional neural network, which is used for human semaphore motion recognition. First, collecting datas in three scenarios and Deep Convolution Generative Adversarial Networks (DCGAN) is used to implement data enhancement to generate the dataset (DataSR). Then, the 1*1 and 3*3 convolution kernels are used to design the full convolution network and the model is further compressed using the group convolution to obtain the new model HARNET. Experiments show that the mAP of HARNET on the DataSR dataset is 94.36%, and the model size is 76M, which is 30% of the size of the YOLOv3 model.
The Roving Proxy Framewrok for SMS Spam and Phishing Detection

This paper presents the details of the roving proxy framework for SMS spam and SMS phishing (SMishing) detection. The framework aims to protect organizations and enterprises from the danger of SMishing attacks. Feasibility and functionality studies of the framework are presented along with an update process study to define the minimum requirements for the system to adapt with the latest spam and SMishing trends.
Automatic image annotation using model fusion and multi-label selection algorithm

Automatic Image Annotation (AIA) aims to provide a semantic description for the content of image by assigning a set of textual labels. The recent approaches mainly focus on the improvement of single model and neglect the potential advantages of different models. In order to make full use of the adv antages of different annotation models, Dual Model based on Multi-Label Selection Algorithm(DM-SA) is proposed in this research which combines a discriminative model with a nearest-neighbor-based model. The algorithm takes consideration of the advantages of each model, thus provides better annotation performance. A deep Convolutional Neural Network (CNN) is used to obtain visual representation of images first, then a discriminative model, CNN with Label Smoothing (CNN-LS), and a nearest-neighbor-based model, 2PKNN with Canonical Correlation Analysis (2PKNN-CCA) generate candidate label set respectively. Finally, a multi-label selection algorithm based on inverse document frequency is adopted to assign the final labels from two candidate label sets. Experimental results based on Corel5K and IAPRTC-12 datasets show that the proposed method can achieve state-of-the-art performance for average recall, 0.52 and 0.42 on Corel5K and IAPRTC-12 respectively.
Knowledge Distillation with Category-Aware Attention and Discriminant Logit Losses

Deep neural networks (DNNs) usually suffer large amount of storage and computation, limiting their deployment on resource constrained platforms. Knowledge distillation is an effective way to address the above limitation by transferring knowledge from a large while accurate teacher model to a small yet fast student model. In this paper, we propose two objective functions to optimize the knowledge transferring process. First, we propose a category-aware attention loss which works at the convolutional feature level and catches object localization information. Second, we propose a discriminant logit loss at fully-connected feature level to capture classification information. The combined two objective functions are able to integrate different level features and guide the training of the student. We demonstrate the effectiveness of our approach on several CNN models across various datasets, and show consistent performance gain with the proposed method.
Deep variational auto-encoder for text classification

Dimensionality reduction is an important technique in machine learning and data mining, which makes the processing of high dimensional data faster. An efficient method for dimensionality reduction can find a low-dimension feature subset extracting the most relevant information. The dimensionality reduction methods based on neural network are applied to all kinds of data, especially computer vision data. In this paper, we focus on the text data with high sparse and high dimension, then reduce its dimension by using the variational auto-encoder. The performance of variational auto-encoder in dimensionality reduction is observed by comparison test. First, unstructured text data is converted to computer-processable vectors using term frequencyCinverse document frequency. Then variational auto-encoder is used to reduce the dimensionality. Finally, the experiment verifies the efficiency of variational auto-encoder by comparing seven commonly used dimensionality reduction methods.
Performance Evolution of Newcomers in Large-Scale Distributed Software Projects: An Industrial Case Study

Large-scale distributed software projects with long life cycles often involve a considerable amount of complex legacy code. The combination of scale and distribution challenges and the difficulty in acquiring knowledge about massive amounts of complex legacy code may make the onboarding of new developers/teams problematic. These problems may lead to extended periods of low performance. The primary objective of this paper is to investigate the performance evolution of offshore newcomers onboarded in a large-scale globally distributed project and how it relates to the employed onboarding strategy. To achieve our objective, we conducted a case study in Ericsson. We identified that the following aspects in the onboarding strategy employed in the investigated case seem to be related to the unexpectedly low performance evolution: i) the distance to mentors; ii) the used formal training approach, which did not fit the sociocultural background of the newcomers; iii) allocation of large and distributed tasks in the early stages of the onboarding process; and iv) team instability. We conclude that the onboarding of newcomers in globally distributed projects must be planned well ahead and should consider avoiding the aspects mentioned above.
Blockchain Technology in the Registration and Protection of Digital Copyright

With the rapid development of digital publishing field, the situation of digital copyright infringement is becoming more and more serious, and the frequent tortious cases dampen the enthusiasm of the original creators seriously. As an emerging information protection technology, blockchain has attracted extensive attention and become an important means of information protection over the years. By combining the blockchain technology with the registration protection of digital rights, the digital copyright information is stored in the block and each block is connected into the block chain in the form of time stamp by the hash pointer to the hash value of the previous block, which ensures that the digital copyright information cannot be tampered with. In the blockchain, technologies such as consensus mechanism, asymmetric encryption and intelligent contract are adopted to realize the security and credibility of information in the process of copyright registration, storage, verification, authorization and transfer, which protects digital copyright effectively.
A New Approach to Constructing Digital Signature Schemes

A new hash-based, server-supported digital signature scheme was proposed recently in [7]. We decompose the concept into forward-resistant tags and a generic cryptographic time-stamping service. Based on the decomposition, we propose more tag constructions which allow efficient digital signature schemes with interesting properties to be built. In particular, the new schemes are more suitable for use in personal signing devices, such as smart cards, which are used infrequently. We define the forward-resistant tags formally and prove that (1) the discussed constructs are indeed tags and (2) combining such tags with time-stamping services gives us signature schemes.
Software as a Service operation model in cloud based ERP systems

The operation model for cloud-based application has already became the new standard in ERP implementations. The model has introduced a new software abstraction layer, which hides the data center model from the customers. This layer covers the architecture, operating system, database and elements. When the layer covers the complexity of the middleware layers, allows the owners to concentrate on implementing the core business logic only. The risk is usually that more and more resources are allocated to the infrastructure; with this model the resources can be balanced with the business side. In the end this will lead to extended application lifecycle, while the core functions of the business logic are totally separated from the recently changing infrastructure layer. Standard merge and release management, starting from pre alpha state and describes the way until the gold release, was changed in this operational model with an agile way release process. This results the overall possibility of always using the latest version of the application always, when using the Software as a Service cloud model. Code refactoring, reusability is a key factor, as having another abstraction layer between the platform specific and the platform independent model. Having the proper abstract transformation layer between these two keeps the application roles separated, and the end user does not have to create a whole IT support organization, because the abstract layer will hide the low level infrastructure. This article focuses mainly to the change management questions which arise when an organization moves from standard data center solution to a cloud based (SaaS) model.The ERP solutions should be redesigned, reconstructed and sometimes rebuilt from scratch to provide continuous versioning, and be able to offer customer services. As the goal is always to be able to provide services at a lower operational cost there must be a solid reusability method in the application. Code refactoring and reusability methods should be changed also, using and implemented on a separated abstraction layer. The transformation layer has to be able to maintain the goals of the reliability of the software product during its whole lifecycle. These middleware layer have to be able to maintain the reusability of the 3rd party software elements, but the commonly used parts have to be in the same format as in the base application.The article shows different ways and possibilities which all can be used for building up an application environment in the cloud using software as a service and the other different service bus like methodologies, and focuses on switching the operation model from the on premise datacenter application to the cloud based SaaS model.
A Secure Permissioned Blockchain Based System for Trademarks

A trademark may be a word, phrase, symbol, sound, color, scent or design, or combination of these, that identifies and distinguishes the products or services of a particular source from those of others. Obtaining a trademark is a complex, time intensive and costly process that involves varied steps before the trademark can be registered including searching prior trademarks, filing of the trademark application, review of the trademark application and final publication for opposition by the public. Currently, the process of trademark registration, renewal and validation faces numerous challenges such as the requirement for registration in different jurisdictions, maintenance of centralized databases in different jurisdictions, proving the authenticity of the physical trademark documents, identifying the violation and abuse of the intellectual property etc. to name a few. Recently, blockchain technology has shown great potential in a variety of industries such as finance, education, energy and resource management, healthcare, due to its decentralization and non-tampering features. Furthermore, in the recent years, smart contracts have attracted increased attention due to the popularity of blockchains. In this study, we have utilized Hyperledger fabric as the permissioned blockchain framework along with smart contracts to provide solution to the financial, procedural, enforcement and protection related challenges of the current trademark system. Our blockchain based application seeks to provide a secure, decentralized, immutable trademark system that can be utilized by the intellectual property organizations across different jurisdictions for easily and effectively registering, renewing, validating and distributing digital trademark certificates.
Proof-Theoretic Aspects of Hybrid Type-Logical Grammars

This paper explores proof-theoretic aspects of hybrid type-logical grammars, a logic combining Lambek grammars with lambda grammars. We prove some basic properties of the calculus, such as normalisation and the subformula property and also present a proof net calculus for hybrid type-logical grammars. In addition to clarifying the logical foundations of hybrid type-logical grammars, the current study opens the way to variants and extensions of the original system, including but not limited to a non-associative version and a multimodal version incorporating structural rules and unary modes.
Word Embeddings Improvement via Echo State Networks

The paper continues investigations on the application of bidirectional echo state networks (BiESN) to the task of word sense disambiguation (WSD). Motivated by observations that the quality of the embedding vectors used to train the models influences to a significant degree their accuracy, here we propose the application of a single ESN reservoir to generate new potentially better embedding vectors with different dimensions. BiESN models for WSD of various reservoir sizes were trained using various combinations of new and original embeddings models for the input and/or output steps; the achieved accuracy is reported here. The results demonstrate increased WSD accuracy in several cases of newly derived embedding sets.
A prototype of Audio-Visual Broadcast Transcription System

This paper focuses on the use of methods and algorithms from the area of speech processing and recognition and from the area of machine vision for designing of system for automatic audio-visual broadcast transcription. The resulting audio-visual system has been designed and created mainly for transcription of huge video databases with TV recordings in this work. The visual signal processing and recognition is usually several times computationally more demanding than audio signal processing and recognition. Therefore, all applied machine vision methods and algorithms were considered with respect to low computing time as well as the highest possible recognition rate. Our proposed broadcast transcription system was extended by several modules for visual signal segmentation, for TV channel identification, for face detection and identification and for Optical Character Recognition (OCR).