Reviewerly/exHarmony huggingface dataset:
Fast and Energy-Efficient Block Ciphers Implementations in ARM Processors and Mali GPU

With the proliferation of the internet of things (IoT) and device-to-device (D2D) communications enabled by the boom of mobile computing technology, secure high-speed communication has now become indispensable in our daily life. This is especially true when potentially private data are being continually sensed by and communicated among mobile devices as they exist in a world of interconnected inanimate objects, which is also one of the main themes of the upcoming 5G revolution. As the amount of data to be secured for high-speed communications is vast, there is a need to ensure that the block ciphers used for encryption are deployed without incurring significant computational cost. In this paper, we present fast implementations of recent industry standard block ciphers in typical embedded platforms, consisting of multi-core CPU (ARM A15 and A7) and GPU (Mali T628). We implemented the conventional block cipher (AES) and lightweight block ciphers (CLEFIA, SIMON, SPECK and PRESENT) optimized for fast computation. We also analyze the energy efficiency of these block ciphers computation in CPU and GPU, as low power consumption is crucial for the embedded system. Our experimental results show that the embedded GPU is not only able to compute block ciphers faster than conventional CPU but also consumes significantly less power.
Application of Improved BM Algorithm in String Approximate Matching

In the paper, we will propose a new algorithm to improve the error tolerance and flexibility of the exact matching by combining the bad characters and good suffix rules in Boyer-Moore algorithm. First of all, using the binary sequence function to control the output range of the target segment; secondly, matching the target segment with partial similar features. The experimental results show that the improved algorithm can improve the accuracy of the target segment and the number of segments matched can be increased to more than 7 times of the exact matching segment.
Monocular 3D Head Reconstruction via Prediction and Integration of Normal Vector Field

Reconstructing the geometric structure of a face from a single input image is a challenging active research area in computer vision. In this paper, we present a novel method for reconstructing 3D heads from an input image using a hybrid approach based on learning and geometric techniques. We introduce a deep neural network trained on synthetic data only, which predicts the map of normal vectors of the face surface from a single photo. Afterward, using the network output we recover the 3D facial geometry by means of weighted least squares. Through qualitative and quantitative evaluation tests, we show the accuracy and robustness of our proposed method. Our method does not require accurate alignment due to the image-to-image translation network and also successfully recovers 3D geometry for real images, despite the fact that the model was trained only on synthetic data.
Blockchain Enabled Trustless API Marketplace

There has been an unprecedented surge in the number of service providers offering a wide range of machine learning prediction APIs for tasks such as image classification, language translation, etc. thereby monetizing the underlying data and trained models. Typically, a data owner (API provider) develops a model, often over proprietary data, and leverages the infrastructure services of a cloud vendor for hosting and serving API requests. Clearly, this model assumes complete trust between the API Provider and cloud vendor. On the other hand, a malicious/buggy cloud vendor may copy the APIs and offer an identical service, under-report model usage metrics, or unfairly discriminate between different API providers by offering them a nominal share of the revenue. In this work, we present the design of a blockchain based decentralized trustless API marketplace that enables all the stakeholders in the API ecosystem to audit the behavior of the parties without having to trust a single centralized entity. In particular, our system divides an AI model into multiple pieces and deploys them among multiple cloud vendors who then collaboratively execute the APIs. Our design ensures that cloud vendors cannot collude with each other to steal the combined model, while individual cloud vendors and clients cannot repudiate their input or model executions.
A Robotic Control System Using Robot Operating System and MATLAB for Sensor Fusion and Human-Robot Interaction

Human-robot interaction is a key aspect in the area of robotics. In this paper, the implementation of a sensor fusion based robotic control system architecture using Robot Operating System (ROS) and MATLAB is presented. Also, a few test cases under different scenarios is also highlighted for a robot navigation task and using the proposed architecture. The robotic system architecture was previously proposed and the work in this paper focuses on the implementation aspect of this architecture. This paper is a work in progress and more sensors and modules will continue to be interfaced and implemented as the research progresses.
An Intelligent Informative Totem Application Based on Deep CNN in Edge Regime

Giammatteo, Paolo Valente, Giacomo D’Ortenzio, AlessandroIn this paper we present an application targeting an informative totem, with a discussion about its possible usage and the requirements it needs to satisfy. In this regard, we propose a Machine Learning algorithm, a Convolutional Neural Network, performing computation on images taken from a camera on an edge-computing platform. Performance tests on two different edge processors are reported, respectively for a CPU and a GPU, and a comparison with the principal competitors is provided. Our final goal is to lay the foundation for the application of an informative totem in an edge computing regime, which is able to recognize the age and the gender of the person approaching it in order to give a better presentation of its contents.
Mining Keywords from Short Text Based on LDA-Based Hierarchical Semantic Graph Model

Extracting keywords from a text set is an important task. Most of the previous studies extract keywords from a single text. Using the key topics in the text collection, the association relationship between the topic and the topic in the cross-text, and the association relationship between the words and the words in the cross-text has not played an important role in the previous method of extracting keywords from the text collection. In order to improve the accuracy of extracting keywords from text collections, using the semantic relationship between topics and topics in texts and highlighting the semantic relationship between words and words under the key topics, this article proposes an unsupervised method for mining keywords from short text collections. In this method, a two level semantic association model is used to link the semantic relations between topics and the semantic relations between words, and extract the key words based on the combined action. First, the text is represented with LDA; the authors used word2vec to calculate the semantic association between topic and topic, and build a semantic relation graph between topics, that is the upper level graph, and use a graph ranking algorithm to calculate each topic score. In the lower layer, the semantic association between words and words is calculated by using the topic scores and the relationship between topics in the upper network allow a graph to be constructed. Using a graph sorting algorithm sorts the words in short text sets to determine the keywords. The experimental results show that the method is better for extracting keywords from the text set, especially in short articles. In the text, the important topics, the relationship between topics and the correlation between words can improve the accuracy of extracting keywords from the text set.
The Law Commission presumption concerning the dependability of computer evidence

In this paper Peter Bernard Ladkin, Bev Littlewood, Harold Thimbleby and Martyn Thomas CBE consider the condition set out in section 69(1)(b) of the Police and Criminal Evidence Act 1984 (PACE 1984) that reliance on computer evidence should be subject to proof of its correctness, and compare it to the 1997 Law Commission recommendation that a common law presumption be used that a computer operated correctly unless there is explicit evidence to the contrary (LC Presumption). The authors understand the LC Presumption prevails in current legal proceedings. They demonstrate that neither section 69(1)(b) of PACE 1984 nor the LC presumption reflects the reality of general software-based system behaviour.&#x0D; Index words: Law Commission; presumption computers are reliable; software impossible to be correct
Comparing the Parameter Complexity of Hypernetworks and the Embedding-Based Alternative.

In the context of learning to map an input $I$ to a function $h_I:\mathcal{X}\to \mathbb{R}$, we compare two alternative methods: (i) an embedding-based method, which learns a fixed function in which $I$ is encoded as a conditioning signal $e(I)$ and the learned function takes the form $h_I(x) = q(x,e(I))$, and (ii) hypernetworks, in which the weights $\theta_I$ of the function $h_I(x) = g(x;\theta_I)$ are given by a hypernetwork $f$ as $\theta_I=f(I)$. We extend the theory of~\cite{devore} and provide a lower bound on the complexity of neural networks as function approximators, i.e., the number of trainable parameters. This extension, eliminates the requirements for the approximation method to be robust. Our results are then used to compare the complexities of $q$ and $g$, showing that under certain conditions and when letting the functions $e$ and $f$ be as large as we wish, $g$ can be smaller than $q$ by orders of magnitude. In addition, we show that for typical assumptions on the function to be approximated, the overall number of trainable parameters in a hypernetwork is smaller by orders of magnitude than the number of trainable parameters of a standard neural network and an embedding method.
Humanising AR in Design: Introducing Digital and Physical People to an Augmented Reality Design Visualisation Process for Theatre

What is the effect on the theatrical production design process when a design team uses holographic AR visualisations to view the scenery before it has been commissioned to be built? Bringing digital images into the physical world may provide new opportunities to test ideas and create new ones. This question is being tested on a professional production team as they develop the new work; Rejoicing at Her Wondrous Vulva, The Young Lady Applauded Herself at the Ovalhouse Theatre in Oval, London.
Attention-Based Feature Pyramid Network for Object Detection

Attention mechanism and feature pyramid have been widely used in various fields of deep learning in recent years. Especially, Feature Pyramid Network(FPN)becomes a popular object detection network since it is put forward in 2017, which is embedded into many well-known networks.However, FPN takes a suboptimal approach to fuse feature and detects small objects on low-level features that fused with high-level features which contain redundant information. There are very few articles discussing the way of feature fusion.So in this paper we propose a novel Attention-based Feature Pyramid Network(AFPN) which can not only enable better integration of high-level and low-level feature maps but also increase accurate semantic information of low-level features. In particular, the AFPN consists of two modules: the Feature Fusion Module(FFM) and the Feature Enhance Module(FEM). Because our model is a lightweight and general module, it is end-to-end trainable along with base CNNs. We validate our AFPN through extensive experiments on VOC and COCO detection datasets. Our experiments show consistent improvements in detection performances.
Construction of Knowledge Map of Continuous Fiber Reinforced Ceramic Matrix Composites

The development of new materials is a complex process that requires a long development cycle. In order to shorten the research and development cycle and save the cost of new material research and development, the United States proposed the Materials Genome Initiative (MGI) in 2011, in which the development of data sharing and computing tools is critical to the success of MGI. We need to design suitable methods for knowledge organization and storage of ceramic matrix composite (CMC) in order to achieve data sharing, improve the existing status of collecting and integrating relevant data from relevant literature and high error rate by relying on manual methods. In this paper, we propose a data organization method based on a complete experimental data record for CMC. At the same time, we use knowledge maps to store data. On the one hand, data sharing is solved and a visual operation interface is provided. On the other hand, we lay a foundation for semantic search of material database.
Non-Markovian entanglement transfer to distant atoms in a coupled superconducting resonator*

We investigate the non-Markovian effects on the entanglement transfer to the distant non-interacting atom qubits, which are embedded in a coupled superconducting resonator. The master equation governing the dynamics of the system is derived by the non-Markovian quantum state diffusion (NMQSD) method. Based on the solution, we show that the memory effect of the environment can lead to higher entanglement revival and make the entanglement last for a longer time. That is to say, the non-Markovian environment can enhance the entanglement transfer. It is also found that the maximum entanglement transferred to distant atoms can be modified by appropriately selecting the frequency of the modulated inter-cavity coupling. Moreover, with the initial anti-correlated state, the entanglement between the cavity fields can be almost completely transferred to the separated atoms. Lastly, we show that the memory effect has a significant impact on the generation of entanglement from the initial non-entangled states.
Stopping Criteria for, and Strong Convergence of, Stochastic Gradient Descent on Bottou-Curtis-Nocedal Functions

Stopping criteria for Stochastic Gradient Descent (SGD) methods play important roles from enabling adaptive step size schemes to providing rigor for downstream analyses such as asymptotic inference. Unfortunately, current stopping criteria for SGD methods are often heuristics that rely on asymptotic normality results or convergence to stationary distributions, which may fail to exist for nonconvex functions and, thereby, limit the applicability of such stopping criteria. To address this issue, in this work, we rigorously develop two stopping criteria for SGD that can be applied to a broad class of nonconvex functions, which we term Bottou-Curtis-Nocedal functions. Moreover, as a prerequisite for developing these stopping criteria, we prove that the gradient function evaluated at SGD's iterates converges strongly to zero for Bottou-Curtis-Nocedal functions, which addresses an open question in the SGD literature. As a result of our work, our rigorously developed stopping criteria can be used to develop new adaptive step size schemes or bolster other downstream analyses for nonconvex functions.
Cultural Globalization and Human Security

We live in a world of great opportunities, but also of boundless demands. It is generally agreed that the 21st century would be a century of culture. Globalization of culture is an important element of social globalization. This process should be understood as a formation of various relationships and dependencies between societies and their cultures. Globalization of culture entails a change in values and norms, a disturbance of social memory, and shallow culture. Presently culture has become a consumer culture, and it is created by the world of media and the Internet. The article is an important voice in a wider discussion on the impact of cultural globalism on human security. The author is convinced that cultural globalism to the greatest extent affects human personality and social hazards. An important part of it is the description of personal security, in which human subjectivity, freedom and responsibility of cultural threats play a significant role.
Tweet with a smile: The selection and use of emoji on Twitter in the Netherlands and England

Emoji, colourful pictographs showing faces, creatures and objects, have seen a surge in popularity and number in recent years. This exploratory study strives to answer the following question: how and why are emoji used on Twitter in the Netherlands and England? By combining quantitative and qualitative methods, we identified three important factors explaining emoji usage: the individual’s purpose on Twitter, the perceived functionality of emoji and the individual’s selection criteria for emoji. Overall, emoji play an important role in online communication and their use is more complex than their light-hearted appearance may suggest.
Visualization of Information in the Educational Process: Current Trends

It is known that eighty percent of people perceive and absorb experience that exists in a certain field of activity, mainly with the help of visual images. Reliance on the visual image provides the individual with the effectiveness of studying, reproducing and further developing his own experience, thinking, and activity. Thinking is a higher process of cognition, which is associated with sensory perception and is based on sensations and perceptions of the surrounding reality. It follows that the stimulus for thought processes is visually perceived objects, visual educational materials.
Continuous Control in Deep Reinforcement Learning with Direct Policy Derivation from Q Network

The reinforcement learning approach allows learning desired control policy in different environments without explicitly providing system dynamics. A model-free deep Q-learning algorithm is proven to be efficient on a large set of discrete-action tasks. Extension of this method to the continuous control task usually solved with actor-critic methods which approximate a policy function with additional actor network and uses Q function to speed up policy network training. Another approach is to discretize action space which will not give a smooth policy and is not applicable for large action spaces. A direct continuous policy derivation from the Q network leads to optimization of action on each inference and training step which is not efficient but provides optimal and continuous action. Time-efficient Q function input optimization is required in order to apply this method in practice. In this work, we implement efficient action derivation method which allows using Q-learning in real-time continuous control tasks. In addition, we test our algorithm on robotics control tasks from robotics gym environments and compare this method with modern continuous RL methods. The results have shown that in some cases proposed approach learns smooth continuous policy keeping the implementation simplicity of the original discreet action space Q-learning algorithm.
Dynamic Range Compression and the Semantic Descriptor Aggressive

In popular music productions, the lead vocal is often the main focus of the mix and engineers will work to impart creative colouration onto this source. This paper conducts listening experiments to test if there is a correlation between perceived distortion and the descriptor “aggressive”, which is often used to describe the sonic signature of Universal Audio 1176, a much-used dynamic range compressor in professional music production. The results from this study show compression settings that impart audible distortion are perceived as aggressive by the listener, and there is a strong correlation between the subjective listener scores for distorted and aggressive. Additionally, it was shown there is a strong correlation between compression settings rated with high aggressive scores and the audio feature roughness.
Mathematical analysis of a quantum random number generator based on the time difference between photon detections

A quantum random number generation method based on measuring the time differences between photon detections is analyzed mathematically in terms of bit generation efficiency and bit generation rate. It was found that these two values cannot be maximized simultaneously by altering the incident photon rate of a light source with Poisson statistics, but the generation rate can be increased at the expense of efficiency. Analytical results are presented for both an idealized and a practical case. The detector’s dead time has been taken into consideration in the latter case, implying that, for certain combinations of the dead time and the measurement clock period, the generation rate becomes a two-peaked function of the incident photon rate. The theoretical results are confirmed by simulations, showing excellent agreement between the two.
Balanced joint maximum mean discrepancy for deep transfer learning

Recent studies have revealed that deep networks can learn transferable features that generalize well to novel tasks with little or unavailable labeled data for domain adaptation. However, justifying which components of the feature representations can reason about original joint distributions using JMMD within the regime of deep architecture remains unclear. We present a new backpropagation algorithm for JMMD called the Balanced Joint Maximum Mean Discrepancy (B-JMMD) to further reduce the domain discrepancy. B-JMMD achieves the effect of balanced distribution adaptation for deep network architecture, and can be treated as an improved version of JMMD’s backpropagation algorithm. The proposed method leverages the importance of marginal and conditional distributions behind multiple domain-specific layers across domains adaptively to get a good match for the joint distributions in a second-order reproducing kernel Hilbert space. The learning of the proposed method can be performed technically by a special form of stochastic gradient descent, in which the gradient is computed by backpropagation with a strategy of balanced distribution adaptation. Theoretical analysis shows that the proposed B-JMMD is superior to JMMD method. Experiments confirm that our method yields state-of-the-art results with standard datasets.
A Comparative Study of HMMs and CNN Acoustic Model in Amazigh Recognition System

In this paper, we apply two acoustic models to build the Amazigh speech recognition system; the first system based on hidden Markov models (HMMs) using the open-source CMU Sphinx-4, from the Carnegie Mellon University, the second system based on the convolution neural network CNN is a particular form of neural network implementing in TensorFlow and GPU computation. The two systems evaluated use mel frequency cepstral coefficients to extract the MFFc features. The corpus consists of 9900 audio files. The system obtained the best results when trained using CNN produced 92% of accuracy.
Using logistic regression for persona segmentation in tourism: A case study

I introduced a method for persona segmentation in the tourism industry to identify representative subgroups with different motivations or goals. Data from 496 key opinion leaders of groups representing 7,965 travel service users were analyzed with a logistic regression model of user characteristics and tourism motivation. I found that logistic regression is an integrated method of persona segmentation that balances precision and accuracy, and yields replicable and valid results. Three subgroups for persona segmentation based on logistic regression models are proposed.