Reviewerly/exHarmony huggingface dataset:
Blockchain: Post-Quantum Security &amp;amp; Legal Economics

Blockchain technology is subject to security vulnerabilities resulting from recent developments in quantum computing and cryptography. All the while, the technical complexities of blockchain’s peer-to-peer system require the intervention of third-party intermediaries to facilitate financial transactions across blockchain networks. Current legal scholarship describes blockchain technology as a peer-to-peer system without a central authority, supporting the secure decentralization of economies and financial markets. Yet, with consideration to recent advancements in quantum computing, blockchain technology is not secure. Further, the technology’s legality is subject to the will of the central authorities whose economic systems it seeks to decentralize. This article contributes the first post-quantum analysis of the intersection of blockchain security and law. Additionally, this is the first piece of legal scholarship to analyze the complex relationships between Congress, the Federal Reserve, and blockchain technology in the creation of money. In doing so, this article challenges conventional assumptions relating to blockchain technology’s decentralizing economic impact. Ultimately, this article takes an interdisciplinary approach, drawing on informatics, law, and economics scholarship, to argue blockchain fails to provide a legal or secure means of establishing a peer-to-peer payment system.
A counterexample on contractible transformations on graphs

Ivashchenko proposed the study of contractible transformations on graphs because of their applications to computer image analysis, theory of molecular spaces, and digital topology. He published five papers on this subject. Contractible transformations have recently been applied to topological data analysis. This paper presents a counterexample to several results that appeared in one of Ivashchenko's works.
Personalized Preference Collaborative Filtering: Job Recommendation for Graduates

It is challenging for graduates to find a proper job. Unlike those with occupational history, graduates generally are short of work experience and the support from social network, so they have to face hundreds of recruitment companies. It is very helpful to recommend a few most suitable jobs to graduates. Collaborative filtering (CF) method is currently the most frequently adopted and effective recommendation algorithm, but it cannot be directly applied to job recommendation for graduates because graduates generally have no historical records on employment. Besides, job recommendation should take into account graduate preferences for jobs, such as enterprise types and company locations, which are crucial to job choices. To address these challenges, we propose a Personalized Preference Collaborative Filtering Recommendation Algorithm (P2CF), which can not only recommend jobs for graduates through massive campus records, but also identify graduate personal preferences for jobs. Graduates are first clustered into different groups according to their academic performances and family economic conditions. Then Bayesian personalized ranking (BPR) method is introduced to calculate the scores of graduate groups to jobs. Finally the scores and graduate personalized preferences are combined to recommend a few potential jobs. P2CF is a recommendation algorithm with hierarchical structure, which takes account of both the group records of job choices and the individual preferences for jobs. Experimental results show that P2CF on job recommendation outperforms state-of-the-art CF methods and identifies graduate personalized preference for jobs accurately.
Adaptive protocol generation for group collaborative in smart medical waste transportation

This article addresses the challenge of collective decision making among the smart components in medical waste transportation. We search for an optimized alternation to generate a commitment according to the capabilities belonging to heterogeneous agents under ever-changing context conditions. we propose a Goal-Capability-Commitment based Executable Tree (GCC-ETree) approach to generate an adaptive protocol. We illustrate our approach in a real-world medical waste automated guided vehicle transportation scenario and evaluate the feasibility of our approach by comparison with other common approaches. Our work makes two main contributions. First, we show how ontology-based matching and calculation can be used to enact a semantic understanding of the alignment of environment, goal, capability and commitment. Second, we introduce run-time goal modeling into collective decision making to capture the real-time requirements and provide execution flow information for generating a decision flow at runtime.
Adaptable Text Corpus Development for Specific Linguistic Research

Thesis deals with automated corpus development techniques for specific linguistic research purposes. Different corpora and corpus toolboxes available today have been overviewed. Corpus tool functionality for processing custom and private text purposes and automated corpora building system capabilities has been classified. Lack of analytical means which decrease effectiveness, multifunctionality and adaptability in overviewed corpora has been illustrated (impossibility of FrameNet usage, lack of detailed statistics and other relevant issues). Main stages of source data text processing have been reviewed. Natural language processing and natural language understanding methods and tools for adaptable corpora building have been examined and justified. General workflow and design of adaptable text corpus software tool are suggested. Adaptable text corpus software prototype for specific linguistic research purposes has been developed. Big Data and text search engine approaches have been implemented in the prototype.
3d Deformation Signature for Dynamic Face Recognition

This work proposes a novel 3D Deformation Signature (3DS) to represent a 3D deformation signal for 3D Dynamic Face Recognition. 3DS is computed given a non-linear 6D-space representation which guarantees physically plausible 3D deformations. A unique deformation indicator is computed per triangle in a triangulated mesh as a ratio derived from scale and in-plane deformation in the canonical space. These indicators, concatenated, construct the 3DS for each temporal instance. There is a pressing need of non-intrusive bio-metric measurements in domains like surveillance and security. By construction, 3DS is a non-intrusive facial measurement that is resistant to common security attacks like presentation, template and adversarial attacks. Two dynamic datasets (BU4DFE and COMA) were examined, in a standard classification framework, to evaluate 3DS. A first rank recognition accuracy of 99.9%, that outperforms existing literature, was achieved. Assuming an open-world setting, 99.97% accuracy was attained in detecting unseen distractors.
Privacy-Preserving Pattern Recognition Using Encrypted Sparse Representations in L0 Norm Minimization

In this paper, we propose a privacy-preserving pattern recognition method that uses encrypted sparse representations in L0 norm minimization. We prove, theoretically, that the proposal has exactly the same dictionary and sparse coefficient estimation performance as the Label Consistent K-Singular Value Decomposition (LC-KSVD) algorithm for non-encrypted signals. It can be directly implemented by the LC-KSVD algorithm without any modification. Finally, we demonstrate its excellent recognition performance and security strength for the face recognition task using the Extended YaleB database.
Dynamic Temporal Residual Learning for Speech Recognition

Long short-term memory (LSTM) networks have been widely used in automatic speech recognition (ASR). This paper proposes a novel dynamic temporal residual learning mechanism for LSTM networks to better explore temporal dependencies in sequential data. The temporal residual learning mechanism is implemented by applying shortcut connections with dynamic weights to temporally adjacent LSTM outputs. Two types of dynamic weight generation methods are proposed: using a secondary network and using a random weight generator. Experimental results on Wall Street Journal (WSJ) speech recognition dataset reveal that our proposed methods have surpassed the baseline LSTM network.
Global and Local Discriminative Patches Exploiting for Action Recognition

Recent human action recognition models mainly focus on exploiting human features, such as pose or skeleton features. However, most of these methods do not pay enough attention to action-related backgrounds. In this work we propose a novel multi-stream features fusion framework based on discriminative patch exploiting. Unlike existing part-based or attention-based multi-stream methods, our work improves the recognition accuracy by 1) Paying more attention to exploiting of global and local discriminative patches, which include not only the acting human but also the interactive scenes. 2) Proposing an effective multi-stream feature pooling and fusion mechanism: 2D and 3D features from RGB frames and discriminative patches are combined to enhance spatial-temporal feature representation ability. Our framework is evaluated on two widely used video action benchmarks, where it outperforms other state-of-the-art methods: the accuracy up to 87.8% at HMDB51, and 98.8% at UCF101.
Towards making big data applications network-aware in edge-cloud systems

The amount of data collected in various IT systems has grown exponentially in the recent years. So the challenge rises how we can process those huge datasets with the fulfillment of strict time criteria and of effective resource consumption, usually posed by the service consumers. This problem is not yet resolved with the appearance of edge computing as wide-area networking and all its well-known issues come into play and affect the performance of the applications scheduled in a hybrid edge-cloud infrastructure. In this paper, we present the steps we made towards network-aware big data task scheduling over such distributed systems. We propose different resource orchestration algorithms for two potential challenges we identify related to network resources of a geographically distributed topology: decreasing end-to-end latency and effectively allocating network bandwidth. The heuristic algorithms we propose provide better big data application performance compared to the default methods. We implement our solutions in our simulation environment and show the improved quality of big data applications.
A Conceptual Knowledge Visualisation Framework for Transfer of Knowledge: An Organisational Context

Revolutionary advances in science and technology enables organisations to apply and optimise a world of visual and experiential learning in order to enhance the skills and knowledge of their employees. Furthermore, the volume and complexity of knowledge and information are such that unless a reporting structure is overlaid upon it, it may remain meaningless. Knowledge visualisation uses graphical representations to convey organisational knowledge, enabling employees to share and recall relevant knowledge. However, in order to assist organisations to create and transfer knowledge more effectively through knowledge visualisation, the aim of this study is to provide a conceptual knowledge visualisation framework for the transfer of knowledge for organisations. A conceptual knowledge visualisation framework was designed through a systematic literature review process where 15 organisational knowledge visualisation elements were identified. The 15 elements were grouped and presented in a 4-layered, embedded conceptual framework that organisations may apply to their knowledge visualisation efforts. By using such a framework, organisations may optimise learning and improve knowledge and skills of its employees.
Intelligent Resource Discovery in Inter-Cloud using Blockchain

Efficient and secure resource discovery in the inter-cloud remains non-trivial, especially in non-federated environments. It involves balancing conflicting requirements in a dynamic multi-party scenario based on multi-factor optimization while overcoming security concerns. Various P2P based resource discovery schemes for the inter-cloud environment have been proposed by researchers but challenges including efficient resource discovery and selection, trust/reputation management, transaction record management and seamless financial settlements persist. We propose a Blockchain-based Inter-cloud Resource Discovery (BIRD) framework for the non-federated inter-cloud model which addresses the shortcomings of existing approaches. It is the first demonstration of using the blockchain idea to the inter-cloud resource discovery process alleviating the need of a trusted third-party or broker between participating cloud service providers (CSPs), while ensuring optimal and secure resource discovery and selection.
Human Activity Recognition from Video Clip

The recognition of human activities from video files has been a challenging issue due to background clutter, viewpoint, appearance, and illumination. The human actions are characterized by various actions like walking, standing, and running. The surveillance system lets us to classify a human action either as malicious or non-malicious activities. This paper presents a real-time action recognition system where the camera captured frames are classified using KNN algorithm. The performance of the system is tested against the standard KTH dataset.
33us, 94uJ Optimal Ate Pairing Engine on BN Curve over 254b Prime Field in 65nm CMOS FDSOI

We have designed optimal ate paring engine on BN curve over 254bit prime field in 65nm CMOS FDSOI process. Algorithm is broken down into 2nd extension of the prime field (Fp2) and optimized for pipelined multiplier of Fp2, results in global optimum design. Measurement results demonstrate world fastest implementation of 33us paring time with as low as 94uJ per paring. Energy consumption can be minimized down to 13.7uJ per pairing for 490mV operation. The design consists of 2.8MG and 85% of functional units are active throughout the paring operation. The demonstrated results are promising to realize functional encryptions such like ID based encryption, attribute-based encryption and searchable encryption.
A news image captioning approach based on multimodal pointer‐generator network

Summary News image captioning aims to generate captions or descriptions for news images automatically, serving as draft captions for creating news image captions manually. News image captions are different from generic captions as news image captions contain more detailed information such as entity names and events. Therefore, both images on news and the accompanying text are the source of generating caption of news image. Pointer‐generator network is a neural method defined for text summarization. This article proposes the Multimodal pointer‐generation network by incorporating visual information into the original network for news image captioning. The multimodal attention mechanism is proposed by splitting attention into visual attention paid to the image and textual attention paid to the text. The multimodal pointer mechanism is proposed by using both textual attention and visual attention to compute pointer distributions, where visual attention is first transformed into textual attention via the word‐image relationships. The multimodal coverage mechanism is defined to reduce repetitions of attentions or repetitions of pointer distributions. Experiments on the DailyMail test dataset and the out‐of‐domain BBC test dataset show that the proposed model outperforms the original pointer‐generator network, the generic image captioning method, the extractive news image captioning method, and the LDA ‐based method according BLEU , METEOR , and ROUGL ‐ L evaluations. Experiments also show that the proposed multimodal coverage mechanisms can improve the model, and that transforming visual attention to pointer distributions can improve the model.
State of the Art of Deep Learning Applications in Sentiment Analysis: Psychological Behavior Prediction

With the explosion of web 2.0, we are witnessing a sharp increase in Internet users such as a vertiginous evolution of social media. These media constitute a source of rich and varied information for researchers in sentiment analyses. This paper describes a tool for sifting through and synthesizing reviews by identifying the main deep learning techniques applied for sentiment analysis especially when it comes to psychological behavior prediction.
On the Importance of Vocal Tract Constriction for Speaker Characterization: The Whispered Speech Study

Characterizing speakers under stressed condition is a challenge because speakers deviate from the normal speech production process. Whispered speech is one among them that is produced by abducting the vocal folds to pass the air out of mouth. During this process, the airflow thus passed is influenced by the physiological structure of vocal tract, that can be described by the level of constriction in vocal tract during speech production. We believe such information may be helpful for capturing speaker traits in such scenarios. The vocal tract constriction evidence in combination with mel frequency cepstral coefficients is investigated for speaker recognition experiments. The studies are conducted on CHAINS corpus that includes both neutral and whispered speech. We find that considering the constriction evidence from vocal tract improves the speaker characterization for whispered speech and under different mismatched scenarios.
Blind Modulation Classification via Accelerated Deep Learning

State-of-the-art modulation classifiers often require matching channel model and perfect knowledge of channel state information to mitigate lose in classification accuracy. In this paper, we propose an accelerated deep learning algorithm to achieve the same goal without the aforementioned requirements. The proposed algorithm is able to extract the hide features in raw signal samples before proceeding to obtain the classification decision. While delivering accuracy close to the maximum likelihood classifier in AWGN channels, the resulting solution provides superior performance in fading channels with various levels of carrier phase offset.
Enhanced global attention upsample decoder based on enhanced spatial attention and feature aggregation module for semantic segmentation

This work designs a simple but effective decoder structure called Enhanced Global Attention Upsample Decoder (EGAUD) based on enhanced spatial attention (SA) and feature aggregation module for semantic segmentation, which is used to recover the prediction rich in detail and semantic information. First, Enhanced SA (ESA) and Feature Aggregation Module (FAM) are used to enhance the details and semantic information of shallow and deep features, respectively. ESA integrates the SA module and residual enhancement module to enrich spatial information while ensuring the effectiveness of backpropagation. FAM uses three parallel pooling operations to expand the receptive field of features, to optimize the semantic information. Then, the authors use Global Attention Upsample (GAU) to recover the feature resolution. From bottom to top, GAU utilises two pooling operations to fully integrate the features of adjacent layers. The results on two datasets, PASCAL VOC 2012 and PASCAL-Person-Part, show the effectiveness of the authors’ model.
Unsupervised Variational Bayesian Kalman Filtering For Large-Dimensional Gaussian Systems

This paper considers the unsupervised filtering problem for large-dimensional linear and Gaussian systems, a setup in which the optimal Kalman filter (KF) might not be usable due to the exorbitant computational cost and storage requirements. For this problem, we propose two efficient algorithms based on the variational Bayesian (VB) approach. The first algorithm is an extension of a recent VBKF algorithm to the unsupervised framework, whereas the second algorithm is an accelerated version of the first one, derived based on the adaptation of subspace optimization methods in Hilbert spaces into the space of probability density functions. Furthermore, both algorithms account for the sparsity in the state and observations through heavy-tailed Student-t priors. Results of numerical experiments conducted on a dynamical tomography problem to assess the performances of the proposed schemes are presented.
A Novel Orthogonality Loss for Deep Hierarchical Multi-Task Learning

In this paper, a novel loss function is proposed to measure the correlation among different learning tasks and select useful feature components for each classification task.Firstly, the knowledge map we proposed is used for organizing the affiliation relationship between objects in natural world.Secondly, a novel loss function-orthogonality loss is proposed to make the deep features more discriminative by removing useless feature components.Furthermore, in order to prevent the extracted feature maps from being too divergent and causing over-fitting which will reduce network performance, this paper also added the orthogonal distribution regularization term to constrain the distribution of network parameters.Finally, the proposed orthogonality loss is applied in a multi-task network structure to learn more discriminative deep feature, and also to evaluate the validity of the proposed loss function.The results show that compared with the traditional deep convolutional neural network and a multi-task network without orthogonality loss, the multi -task based orthogonality loss is significantly better than the other two types of networks on image classification.
GROSE: Optimal group size estimation for broadcast proxy re-encryption

In this paper, we propose GROSE — a scheme to determine the optimal group size of the receiver in Broadcast Proxy Re-encryption. GROSE uses the Rubinstein–Ståhl bargaining approach, so that both the sender and the receiver are mutually benefited and the total payoff increases. Proxy re-encryption is an important scheme to send cloud data securely to another user. For secured sharing of data to more than one users, the concept of broadcast proxy re-encryption was introduced. However, it poses an overhead on the receiver, if the receiver group is large, as each of the receivers in the group has to calculate the additional expensive algebraic operations, which linearly increases with the number of users in the receiver group. We model the problem using a bargaining perspective, and address it as the Rubinstein–Ståhl bargaining game for finite version of the game and then extend it to the infinite horizon version of it. We define the payoff of the sender and the receiver, and discuss how the payoff depends on different factors. Finally, we implement GROSE and compare it with the previously proposed schemes to depict the efficiency of GROSE. It is observed that GROSE is more efficient than the traditional proxy re-encryption and broadcast proxy re-encryption schemes.