Reviewerly/exHarmony huggingface dataset:
A blockchain approach to digital archiving: digital signature certification chain preservation

Purpose The short lifespan of digital signatures presents a challenge to the long-term preservation of digitally signed records. It can undermine attempts to presume, verify or assess their authenticity. This paper aims to investigate the challenges of the expiration of digital signatures in the context of digital archiving. Design/methodology/approach The paper identifies requirements for the long-term preservation of digitally signed records and compares them with the existing approaches. The characteristics, operational procedures and requirements of the technologies used for digital signatures are combined with the archival requirements to design a new model. Findings The paper proposes a new model of a blockchain-based system, which can be combined with any digital archive to assist the process of long-term preservation of digitally signed records. Practical implications The proposed model offers a new alternative to the current practice in the long-term preservation of digitally signed records, such as periodic resigning procedures or periodic wrapping of digitally signed records with archival timestamps. Originality/value The proposed TrustChain 2.0 model is based on previous research conducted as part of the InterPARES Trust project. It builds on TrustChain 1.0 by including digital signature certificate chain validity information in a blockchain thus avoiding the issues concerning records confidentiality and privacy information disclosure. The paper contributes not only to the development of archival science but also shows archival institutions on how to approach long-term preservation of digitally signed records.
Accuracy, Training Time and Hardware Efficiency Trade-Offs for Quantized Neural Networks on FPGAs

Neural networks have proven a successful AI approach in many application areas. Some neural network deployments require low inference latency and lower power requirements to be useful e.g. autonomous vehicles and smart drones. Whilst FPGAs meet these requirements, hardware needs of neural networks to execute often exceed FPGA resources. Emerging industry led frameworks aim to solve this problem by compressing the topology and precision of neural networks, eliminating computations that require memory for execution. Compressing neural networks inevitably comes at the cost of reduced inference accuracy. This paper uses Xilinx’s FINN framework to systematically evaluate the trade-off between precision, inference accuracy, training time and hardware resources of 64 quantized neural networks that perform MNIST character recognition. We identify sweet spots around 3 bit precision in the quantization design space after training with 40 epochs, minimising both hardware resources and accuracy loss. With enough training, using 2 bit weights achieves almost the same inference accuracy as 3–8 bit weights.
MannequinChallenge: Learning the Depths of Moving People by Watching Frozen People

We present a method for predicting dense depth in scenarios where both a monocular camera and people in the scene are freely moving (right). Existing methods for recovering depth for dynamic, non-rigid objects from monocular video impose strong assumptions on the objects' motion and may only recover sparse depth. In this paper, we take a data-driven approach and learn human depth priors from a new source of data: thousands of Internet videos of people imitating mannequins, i.e., freezing in diverse, natural poses, while a hand-held camera tours the scene (left). Because people are stationary, geometric constraints hold, thus training data can be generated using multi-view stereo reconstruction. At inference time, our method uses motion parallax cues from the static areas of the scenes to guide the depth prediction. We evaluate our method on real-world sequences of complex human actions captured by a moving hand-held camera, show improvement over state-of-the-art monocular depth prediction methods, and demonstrate various 3D effects produced using our predicted depth.
The SOTA approach to engineering collective adaptive systems

The emergence of collective adaptive systems—i.e., computational systems made up of an ensemble of autonomous components that have to operate in a coordinated and adaptive way in open-ended and unpredictable environments—calls for innovative modeling and software engineering tools, to support their systematic and rigorous design and development. In this paper, we present a general model for collective adaptive systems called SOTA ("State Of The Affairs"). SOTA brings together the lessons of goal-oriented requirements modeling, context-aware system modeling, and dynamical systems modeling. It has the potential for acting as a general reference model to help tackling some key issues in the design and development of collective adaptive systems. In particular, as we will show with reference to a scenario of collectives of autonomous vehicles, SOTA enables: early verification of requirements, identification of knowledge requirements for self-adaptation, and the identification of the most suitable architectural patterns for self-adaptation.
Automatic generation of specialized direct convolutions for mobile GPUs

Convolutional Neural Networks (CNNs) are a powerful and versatile tool for performing computer vision tasks in both resource constrained settings and server-side applications. Most GPU hardware vendors provide highly tuned libraries for CNNs such as Nvidia's cuDNN or ARM Compute Library. Such libraries are the basis for higher-level, commonly-used, machine-learning frameworks such as PyTorch or Caffe, abstracting them away from vendor-specific implementation details. However, writing optimized parallel code for GPUs is far from trivial. This places a significant burden on hardware-specific library writers which have to continually play catch-up with rapid hardware and network evolution.
Custom Block Chain Based Cyber Physical System for Solid Waste Management

The aim of this paper is to develop an intelligent cyber-physical system for the waste management in a locality which will not only keep records of the use of the trash bins in the but also ensure they have an additional blockchain based verification system which will ensure the proper working even in the presence of an adversary. The proposed system is an ensemble of the database server, blockchain server, embedded system based clients and an android app. This is inspired by the recent global trend in smartphone popularity which has piqued interest in the above mentioned division. The advent of smartphone popularity helps us to create an ideal system for a plethora of reasons such as processing capability and native storage etc. Furthermore, blockchain technology, introduced by Satoshi Nakamoto in 2009 ensures decentralization and security of transactions due to its use of cryptographic hash functions and public key cryptographic system. To further excite the situation, India recently revised its solid waste management policy in 2016 after 16 long years. All of the above factors together pointed to creation of a smart waste management system to prevent further exacerbation of India’s existing waste management crisis.
A Blockchain-Based Hierarchical Reputation Management Scheme in Vehicular Network

The vehicular reputation management scheme based on blockchain has been studied with the rapidly development of blockchain technology. Existing works mainly focus on provisioning the security of the reputation management scheme by using the blockchain technology. However, the broadcast latency of blocks and the throughput capacity of the blockchain also have significant impacts on the performance of the reputation management scheme. This paper proposes a hierarchical blockchain based reputation management scheme in vehicular networks. Our hierarchical blockchain architecture assures that each vehicle can obtain the latest reputation value of any neighboring vehicle in a short time. Moreover, our designed blockchain structure aims at reducing the confirmation time of reputation values and increasing the capacity of blockchain. We finally conduct simulation experiments to compare our proposed scheme with the existing schemes.
Accent Classification of the Three Major Nigerian Indigenous Languages Using 1D CNN LSTM Network Model

Accent identification and classification pose a major challenge for speech recognition systems, as various pronunciations of the same words by speakers of different races are recognized differently by speech recognition systems. Similarly, in most cases, it is difficult for native speakers of the same dialect to understand each other perfectly, especially, if one or more of the speakers has a thick accent. This paper therefore investigates the most accent sensitive words of the three major Nigerian indigenous languages and in addition uses machine learning (ML) to solve the problem of accent classification (AC) of the three languages. A speech-based algorithm was designed and implemented with Python. Speech data were acquired from 300 speakers and mel-frequency cepstral coefficient (MFCC) was employed to extract distinct features which are used to distinguish speakers of the three native languages. The acquired speech data were used to train a combination of a one-dimensional convolutional neural network (1D CNN) and a long short-term memory (LSTM) network model (1D CNN LSTM). Experimental results show a classification accuracy of 94.9%.
Enabling Self-defense in Small Drones

Drones are breaking away from their role as recreational toys and are emerging as trusted delivery systems and tools for law enforcement. This new role makes them targets of midair attacks for profit and vandalism. Despite tremendous advancement in robotic navigation and control, drones are still vulnerable to collisions with dynamic objects. While projectile detection and direction-of-arrival estimation techniques are mature in theory and fully functional in modern aircrafts' defense systems, these solutions could not make their ways to these resource-constrained flying robots. We envision to develop an ecosystem of lightweight and low-power defense modules for small drones. This paper explores the viability of this vision and as a proof of concept, develops a single microphone-based acoustic sensing mechanism capable of identifying the direction of approaching projectiles thrown at the drone. Experiments on the developed prototype of the sensing systems show perfect accuracy for detecting cases where a dynamic object is about to hit the drone. The low latency of our system detects an approaching object around 100 ms before the event giving sufficient time for the drone to dodge it. Despite being far from a complete defense solution, our prototype bolsters the possibility of developing sensing-inferencing modules for extreme resource-constrained scenarios.
A Low-Rank and Sparse Decomposition-Based Method of Improving the Accuracy of Sub-Pixel Grayscale Centroid Extraction for Spot Images

In order to improve the accuracy of sub-pixel grayscale centroid extraction for noise-containing spot images, an image denoising method based on low-rank and sparse decomposition (LRSD) was proposed in this paper. Relative total variation (RTV) was introduced into the method on the basis of the weighted nuclear norm minimization (WNNM) model to construct a new RTV-WNNM model, so as to enhance the denoising accuracy and detail-preserving capability of LRSD method. Alternating direction multiplier method (ADMM) was used to solve this convex problem iteratively. Moreover, in view that the existing LRSD denoising method had insufficient detail-preserving capability, an adaptive selection method of LRSD singular value threshold based on the improved elitist clone selection algorithm (ECSA) was also raised in this paper. The constructed RTV-WNNM model taken as the objective function, the improved ECSA could realize equilibrium between search in depth and optimization in breadth through the adaptive chaotic mutation operator and antibody reselection strategy based on antibody concentration and antigen affinity moment. Furthermore, it could reach rapid self-adaption to acquire a low-rank threshold and then form an image denoising fusion algorithm, which had both denoising and detail-preserving capabilities. Simulation and experimental results show that the proposed method can reach better results in both quantity measure and visual quality than the state-of-the-art image denoising methods, and it has also remarkably improved the accuracy of sub-pixel grayscale centroid extraction for noise-containing spot images.
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks

Deep neural networks (DNNs) have demonstrated impressive performance on many challenging machine learning tasks. However, DNNs are vulnerable to adversarial inputs generated by adding maliciously crafted perturbations to the benign inputs. As a growing number of attacks have been reported to generate adversarial inputs of varying sophistication, the defense-attack arms race has been accelerated. In this paper, we present MODEF, a cross-layer model diversity ensemble framework. MODEF intelligently combines unsupervised model denoising ensemble with supervised model verification ensemble by quantifying model diversity, aiming to boost the robustness of the target model against adversarial examples. Evaluated using eleven representative attacks on popular benchmark datasets, we show that MODEF achieves remarkable defense success rates, compared with existing defense methods, and provides a superior capability of repairing adversarial inputs and making correct predictions with high accuracy in the presence of black-box attacks.
Strengthening the perception of the virtual worlds in a virtual reality environment

Virtual reality is becoming more and more improved primarily due to numerous applications and the powers of mobile devices. Using various sensors, precise displays and high computing powers smartphone are becoming devices that make the boost in technology. Now it is necessary to efficiently use various sensors without affecting system operation and improve control abilities for various purposes. Especially in practical applications received by mass users such as games and any kind of experience. In this article, we propose a system that allows to extend the perception of the virtual world by conveying information about the user's movements in reality into the supervised model. The system retrieves data from several sources, quickly analyzes them using artificial intelligence techniques, and returns information to the mobile phone about the activity that is being processed. The concept extends the understanding of today's virtual reality by allowing the user to move and perform simple gestures in a specially designed room. Moreover, we propose multiplayer mode in virtual reality, where players are in different places. The proposed architecture of the system has been tested on simple applications, and the results show high potential for implementations in various apps by achieving almost 90% efficiency in changing player direction in real time and only 7.5% of collision cases.
Tiny-BDN: An Efficient and Compact Barcode Detection Network

This paper presents a novel approach for accurate barcodes detection in real and challenging environments using compact deep neural networks. Our approach is based on Convolutional Neural Network (CNN) and neural network compression, which can detect the four vertexes coordinates of a barcode accurately and quickly. Our approach consists of four stages: (i) feature extraction by a base network, (ii) region proposal network (RPN) training, (iii) barcode classification and coordinates regression, and (iv) weights pruning and recoding. The model is trained in the first three stages and then compressed in the fourth stage to reduce the size of the trained model. In order to remove the effect of geometric distortion during barcode decoding, we add a distortion removal module to the end of the trained model. In experiments, we validate our approach on a challenging large-scale dataset. Compared with previous methods, our method can locate the coordinates of a barcode accurately and quickly and enhance decoding rate through distortion removal. In addition, the storage and memory overheads of our model are reduced through model compression, which shows great potential in industrial applications.
Spectral Graph Based Vertex-Frequency Wiener Filtering for Image and Graph Signal Denoising

In this article, we propose and develop a spectral graph based vertex varying Wiener filtering framework in the joint vertex-frequency domain for denoising of graph signals defined on weighted, undirected and connected graphs. To this end, we first extend the Zadeh time-frequency filter concept to graph signals and obtain vertex-frequency transfer function of the proposed Wiener filter by transforming its vertex varying impulse response that minimizes the mean square error between original and recovered signals. To facilitate the derived Wiener filter, we present a detailed derivation of a recently proposed graph Rihaczek vertex-frequency signal distribution (GRD) so as to match the structure of the proposed graph Zadeh filter, based on a graph translation operator defined by generalized convolution with a delta signal. We express the filter transfer function in terms of this graph transform of the original, noiseless signal. The form of the obtained Wiener filter is, interestingly, different than those of time-frequency Wiener filters prevalent in the classical signal processing. We also investigate the invertibility of the employed GRD. Assuming that the original graph signal of interest can be viewed as deterministic, we propose two algorithms to implement the proposed vertex-frequency Wiener filter from a single realization of a noisy, input signal. We derive mean and variance of the GRD of the noisy signal, since they are required in one of these algorithms. We apply proposed Wiener filter algorithms to denoise a standard set of images and three irregularly structured graph signals, and demonstrate their competitiveness with compared high performance denoising methods.
Dynamic traceable CP‐ABE with revocation for outsourced big data in cloud storage

Summary Ciphertext‐policy attribute‐based encryption (CP‐ABE) is the recommended best practice for outsourced big data access control in the cloud environment. However, most of the existing CP‐ABE schemes do not address the issue of tracing and revoking the malicious user who leaks the secret key for profit, which in turn reduces the security of the CP‐ABE schemes. In this paper, we propose a dynamic traceable CP‐ABE with revocation (DTCP‐ABE) for outsourced big data in cloud storage. DTCP‐ABE scheme dynamically traces who decrypts the ciphertext during the outsourced decryption process, which helps to find the malicious user who leaks the secret key. Our scheme also automatically revokes the malicious users once they are identified. We prove that our scheme is secure against chosen‐plaintext, secret key forging, user collision, and proxy attacks. Furthermore, our scheme also achieves backward revocation security. Performance evaluation proves that our DTCP‐ABE scheme is efficient than other existing schemes.
Instance Segmentation of Newspaper Elements Using Mask R-CNN

Newspaper digitization has gained wide interest around the world. Archives of digitized newspapers contain a wealth of information that spans decades. To extract this abundance of information, optical character recognition (OCR) techniques are used. However, as a first step, the newspaper pages should be logically deconstructed into articles to gain meaningful knowledge. This is difficult due to the complex layout of newspapers and the various styles, shapes, and languages of newspaper articles. Newspaper pages also contain other elements besides articles, such as advertisements that come in multiple shapes and forms, and top headers that contain information about the newspaper's issue and page. Therefore, it is important to detect these elements before information extraction begins. In this paper, we present a deep learning solution for the problem of newspaper page semantic segmentation of the main newspaper elements (articles, advertisements, and page headers). We employed the instance segmentation method mask R-CNN mask_rcnn to create a language-agnostic model that logically deconstructs a newspaper page raw image into its main elements based only on its visual features. We show the results of experiments that display the accuracy and robustness of our model.
Exploring Demographics and Personality Traits in Recommendation System to Address Cold Start Problem

Several different approaches in recommender system have been suggested based on underlying rating history, but the majority of them suffer from the cold start problem (i.e., an inability to draw inferences to recommend items to new users. In this paper, a hybrid method has been proposed that combines personality traits using myPersonality application created by Facebook and the demographic characteristics into the traditional rating-based similarity computation. Majorly, personality information has been categorized into five personality traits: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. Aforesaid personality characteristics can efficiently address the new user problem as it will accurately predict the similarity between users.
Load Balancing in Cloud Computing: Survey

Today cloud computing is the most rising innovation due to its ground-breaking and significant power. It is a type of Internet-based computing which enables to provide various services in cost-effective manner on user’s demand. Virtualization, grid technique and utility computing are the most popular emerged technologies, which are used in cloud computing and make it most powerful. But still cloud computing has number of critical issues, such as security, load adjusting, adaptation to non-critical failure and so forth. The gigantic development of cloud computing will cause the overburden on the server. In this way, it will lead to degrade the performance of networks. A decent load balancing adjusting can make cloud computing increasingly productive and improves execution of client fulfilment. This paper exhibits the far-reaching survey of cloud computing and load balancing techniques.
A Privacy Framework for Charging Connected Electric Vehicles Using Blockchain and Zero Knowledge Proofs

With the increasing interest in connected vehicles along with electrification opportunities, there is an ongoing effort to automate the charging process of electric vehicles (EVs). However, charging EVs takes time and thus in-advance scheduling is needed. This, however, raises privacy concerns since frequent scheduling will expose the charging pattern of the EV to the service providers. Nevertheless, the EV needs to be authenticated which means some information will need to be provided anyway. While there have been many studies to address the problem of privacy-preserving authentication, such solutions will be void if charging payments are made through traditional means. In this paper, we tackle this problem by utilizing distributed applications enabled by Blockchain and smart contracts. We adapt zero-knowledge proofs to Blockchain for enabling privacy-preserving authentication while removing the need for a central authority. The evaluation indicates that the overhead of this process is affordable to enable real-time charging operations for connected EVs.