Reviewerly/exHarmony huggingface dataset:
Nonlinear information data mining based on time series for fractional differential operators

The use of mathematical methods has become an indispensable research tool and method in the establishment and improvement of many disciplines. Therefore, mathematical methods have also been included in the intelligence analysis system of public security information science. Intelligence is a summary of information that exists in all aspects of our lives. This information is distributed according to time based on certain rules. The application of mathematical analysis methods can more accurately extract effective information and predict future trends. As we all know, classical calculus is a powerful tool for dealing with many dynamic processes in the field of applied science. However, there are many complex systems in nature that cannot be characterized by classical integer-order calculus models, especially in information processing analysis. The fractional-order system model can better describe its system performance. This paper introduces the time series analysis method into the public security intelligence analysis system, combines the fractional differential operator to construct the mathematical model, analyzes the network intelligence, predicts the future occurrence of the case, and compares the predicted data with the actual data to verify. The method is predictive of the true credibility of the results.
Durandal: A Rank Metric Based Signature Scheme

We describe a variation of the Schnorr-Lyubashevsky approach to devising signature schemes that is adapted to rank based cryptography. This new approach enables us to obtain a randomization of the signature, which previously seemed difficult to derive for code-based cryptography. We provide a detailed analysis of attacks and an EUF-CMA proof for our scheme. Our scheme relies on the security of the Ideal Rank Support Learning and the Ideal Rank Syndrome problems and a newly introduced problem: Product Spaces Subspaces Indistinguishability, for which we give a detailed analysis. Overall the parameters we propose are efficient and comparable in terms of signature size to the Dilithium lattice-based scheme, with a signature size of 4 kB for a public key of size less than 20 kB.
Augmented reality enhancing place satisfaction for heritage tourism marketing

The current study proposes and empirically verifies a conceptual framework, explicating why and how the LBAR (location-based augmented reality) application may enhance place satisfaction for heritage tourism. As the research result suggests, the LBAR application forms immersive experience generating positive impact on place satisfaction of heritage tourists. Partial mediation of user engagement and perceived authenticity magnifies such impact.
Research of a Sign Language Translation System Based on Deep Learning

Sign language is a visual language that uses hand gesture, change of hand shape and track information to express meaning, and is the main communication tool for people with hearing and language impairment. Sign language recognition can improve the problem that the number of people who need to use sign language is large but the popularity of sign language is poor, and provide a more convenient way of study, work and life for people with hearing and language impairment. Hand locating and sign language recognition methods can generally be divided into traditional methods and deep learning methods. In recent years, with the brilliant achievements of deep learning in the field of computer vision, it has been proved that the method based on deep learning has many advantages, such as rich feature extraction, strong modeling ability and intuitive training. Therefore, this paper studies hand locating and sign language recognition of common sign language based on neural network, and the main research contents include: 1. A hand locating network based on the Faster R-CNN is established to recognize the sign language video or the part of the hand in the picture, and the result of recognition is handed over to subsequent processing; 2. A 3D CNN feature extraction network and a sign-language recognition framework based on long and short time memory (LSTM) coding and decoding network are constructed for the sign language images of sequence sequences. The framework can improve the recognition accuracy by learning the context of sign language; 3. In order to solve the problem of RGB sign language image or video recognition in practical problems, this paper combines hand locating network, 3D CNN feature extraction network and LSTM encoding and decoding to build the recognition algorithm. Experimental results show that the recognition rate of this method is up to 99% in common vocabulary data set, which is better than other known methods.
Body Pose Prediction Based on Motion Sensor Data and Recurrent Neural Network

Mixed reality environments give better chances to provide constant help to the people in need. Applied there artificial intelligence models will provide ad hoc monitoring measures, which may be the best chance to protect life in dangerous conditions. In this article, we present our research on mixed reality system developed to detect symptoms of unusual poses at work, home, or other environments. Recurrent neural network is using sensor readings to evaluate the situation by the minimum necessary number of body sensors working as safe indicators. Research results show that the developed system is working with very high accuracy of 99.89% using just two body sensors working in a separate mode. The system can work without any special infrastructure or development in various environments to help workers and elder people in dangerous situations.
A new unified image encryption algorithm based on a lifting transformation and chaos

Image encryption is one of the most effective means to ensure the security of image information in network communications. A new unified image encryption system with identical encryption and decryption algorithms was proposed in this paper. In this system, an external key and the Hénon map are used to generate the equivalent secret keys. Then, a kind of lifting-like transformation is employed to diffuse the image information to fulfill the image encryption. On the computer configured with i7-9750H CPU, the unified image cryptosystem realized by C# program has the encryption/decryption speed of 59 Mbps. Meanwhile the proposed cryptosystem possesses strong sensitivities of secret key, plain image and ciphered image for the maximum relative error of their test indexes is only 0.55%. Thus the proposed can be used as a candidate scheme of network image information security.
A Blockchain Based Solution for Medication Anti-Counterfeiting and Traceability

Medication quality and safety are crucial to the health of the public. Responding to the urgent need for medication information provenance and anti-counterfeiting, this study proposes a blockchain based method for medication information storage, inquiry, and anti-counterfeiting along a medication supply chain. Leveraging the features of decentralization, tamper-proof, traceability, and participative node maintenance of blockchain technology, the proposed method can assure the transparency and openness of medication supply chains. An access control policy model based on smart contract is designed to prevent medication information from being altered or disclosed at nodes of the blockchain. In addition, a point-accumulation upgrade/downgrade mechanism is introduced to improve the consensus mechanism. The proposed solution eliminates the needs for centralized institutions and third-party organizations, and provides a full record of the medication circulation process. Our simulation results show that efficiency and security are enhanced by the improved consensus algorithm and access control mechanism. As a result, our method can render high level of security and privacy protection that is critical to the integrity of a medication information management system.
Remittances, finance and industrialisation in Africa

The paper assesses how remittances directly and indirectly affect industrialisation using a panel of 49 African countries for the period 1980–2014. The indirect impact is assessed through financial development channels. The empirical evidence is based on three interactive and non-interactive simultaneity-robust estimation techniques, namely: (i) Instrumental Fixed Effects (FE) to control for the unobserved heterogeneity; (ii) Generalised Method of Moments (GMM) to control for persistence in industrialisation and (iii) Instrumental Quantile Regressions (QR) to account for initial levels of industrialisation. The non-interactive specification elucidates direct effects of remittances on industrialisation whereas interactive specifications explain indirect impacts. The findings broadly show that for certain initial levels of industrialisation, remittances can drive industrialisation through the financial development mechanism. Policy implications are discussed.
Applying Reinforcement Learning towards automating energy efficient virtual machine consolidation in cloud data centers

Energy awareness presents an immense challenge for cloud computing infrastructure and the development of next generation data centers. Virtual Machine (VM) consolidation is one technique that can be harnessed to reduce energy related costs and environmental sustainability issues of data centers. In recent times intelligent learning approaches have proven to be effective for managing resources in cloud data centers. In this paper we explore the application of Reinforcement Learning (RL) algorithms for the VM consolidation problem demonstrating their capacity to optimize the distribution of virtual machines across the data center for improved resource management. Determining efficient policies in dynamic environments can be a difficult task, however, the proposed RL approach learns optimal behavior in the absence of complete knowledge due to its innate ability to reason under uncertainty. Using real workload data we provide a comparative analysis of popular RL algorithms including SARSA and Q-learning. Our empirical results demonstrate how our approach improves energy efficiency by 25% while also reducing service violations by 63% over the popular Power-Aware heuristic algorithm.
UniRE: A Unified Label Space for Entity Relation Extraction

Many joint entity relation extraction models setup two separated label spaces for the two sub-tasks (i.e., entity detection and relation classification).We argue that this setting may hinder the information interaction between entities and relations.In this work, we propose to eliminate the different treatment on the two sub-tasks' label spaces.The input of our model is a table containing all word pairs from a sentence.Entities and relations are represented by squares and rectangles in the table.We apply a unified classifier to predict each cell's label, which unifies the learning of two sub-tasks.For testing, an effective (yet fast) approximate decoder is proposed for finding squares and rectangles from tables.Experiments on three benchmarks (ACE04, ACE05, SciERC) show that, using only half the number of parameters, our model achieves competitive accuracy with the best extractor, and is faster.
Tied Transformers: Neural Machine Translation with Shared Encoder and Decoder

Sharing source and target side vocabularies and word embeddings has been a popular practice in neural machine translation (briefly, NMT) for similar languages (e.g., English to French or German translation). The success of such wordlevel sharing motivates us to move one step further: we consider model-level sharing and tie the whole parts of the encoder and decoder of an NMT model. We share the encoder and decoder of Transformer (Vaswani et al. 2017), the state-of-the-art NMT model, and obtain a compact model named Tied Transformer. Experimental results demonstrate that such a simple method works well for both similar and dissimilar language pairs. We empirically verify our framework for both supervised NMT and unsupervised NMT: we achieve a 35.52 BLEU score on IWSLT 2014 German to English translation, 28.98/29.89 BLEU scores on WMT 2014 English to German translation without/with monolingual data, and a 22.05 BLEU score on WMT 2016 unsupervised German to English translation.
What do you learn from context? Probing for sentence structure in contextualized word representations

Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline. We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline.
Learn-as-you-go with Megh: Efficient Live Migration of Virtual Machines

Cloud providers leverage live migration of virtual machines to reduce energy consumption and allocate resources efficiently in data centers. Each migration decision depends on three questions: when to move a virtual machine, which virtual machine to move and where to move it? Dynamic, uncertain, and heterogeneous workloads running on virtual machines make such decisions difficult. Knowledge-based and heuristics-based algorithms are commonly used to tackle this problem. Knowledge-based algorithms, such as MaxWeight scheduling algorithms, are dependent on the specifics and the dynamics of the targeted Cloud architectures and applications. Heuristics-based algorithms, such as MMT algorithms, suffer from high variance and poor convergence because of their greedy approach. We propose an online reinforcement learning algorithm called Megh. Megh does not require prior knowledge of the workload rather learns the dynamics of workloads as-it-goes. Megh models the problem of energy- and performance-efficient resource management during live migration as a Markov decision process and solves it using a functional approximation scheme. While several reinforcement learning algorithms are proposed to solve this problem, these algorithms remain confined to the academic realm as they face the curse of dimensionality. They are either not scalable in real-time, as it is the case of MadVM, or need an elaborate offline training, as it is the case of Q-learning. These algorithms often incur execution overheads which are comparable with the migration time of a VM. Megh overcomes these deficiencies. Megh uses a novel dimensionality reduction scheme to project the combinatorially explosive state-action space to a polynomial dimensional space with a sparse basis. Megh has the capacity to learn uncertain dynamics and the ability to work in real-time without incurring significant execution overhead. Megh is both scalable and robust. We implement Megh using the CloudSim toolkit and empirically evaluate its performance with the PlanetLab and the Google Cluster workloads. Experiments validate that Megh is more cost-effective, converges faster, incurs smaller execution overhead and is more scalable than MadVM and MMT. An empirical sensitivity analysis explicates the choice of parameters in experiments.
Explainable Agents and Robots: Results from a Systematic Literature Review

Humans are increasingly relying on complex systems that heavily adopts Artificial Intelligence (AI) techniques. Such systems are employed in a growing number of domains, and making them explainable is an impelling priority. Recently, the domain of eXplainable Artificial Intelligence (XAI) emerged with the aims of fostering transparency and trustworthiness. Several reviews have been conducted. Nevertheless, most of them deal with data-driven XAI to overcome the opaqueness of black-box algorithms. Contributions addressing goal-driven XAI (e.g., explainable agency for robots and agents) are still missing. This paper aims at filling this gap, proposing a Systematic Literature Review. The main findings are (i) a considerable portion of the papers propose conceptual studies, or lack evaluations or tackle relatively simple scenarios; (ii) almost all of the studied papers deal with robots/agents explaining their behaviors to the human users, and very few works addressed inter-robot (inter-agent) explainability. Finally, (iii) while providing explanations to non-expert users has been outlined as a necessity, only a few works addressed the issues of personalization and context-awareness.
Term Weighting for Feature Extraction on Twitter: A Comparison Between BM25 and TF-IDF

Feature extraction is to transform a text document from any format into a list of features that can be easily processed by text classification techniques. Feature extraction is one of significant preprocessing techniques in data mining and text classification that computes features value in documents. Hence, efficient feature extraction techniques like the BM25 and term frequency-inverse document frequency (TF-IDF) techniques are normally utilized in term weighting. Nevertheless, BM25 is not a single function that is utilized to exceedingly correct very long documents. This problem cannot denote the helpfulness or importance of confident features, and decreases the efficiency of classification. This paper presents a comparative study of feature extraction techniques. Two techniques were evaluated BM25 and TF-IDF to weight the terms on Twitter. In this paper, TF-IDF feature extraction technique is presented to compare between the two techniques. The experiments show that TF-IDF improves the performance evaluation of feature extraction according to the maximum value of F1-measure is 89.77 for TF-IDF and 89.16 for BM25.
Scaling Distance Labeling on Small-World Networks

Distance labeling approaches are widely adopted to speed up the online performance of shortest distance queries. The construction of the distance labeling, however, can be exhaustive especially on big graphs. For a major category of large graphs, small-world networks, the state-of-the-art approach is Pruned Landmark Labeling (PLL). PLL prunes distance labels based on a node order and directly constructs the pruned labels by performing breadth-first searches in the node order. The pruning technique, as well as the index construction, has a strong sequential nature which hinders PLL from being parallelized. It becomes an urgent issue on massive small-world networks whose index can hardly be constructed by a single thread within a reasonable time. This paper scales distance labeling on small-world networks by proposing a Parallel Shortest-distance Labeling (PSL) scheme and further reducing the index size by exploiting graph and label properties. PSL insightfully converts the PLL's node-order dependency to a shortest-distance dependence, which leads to a propagation-based parallel labeling in D rounds where D denotes the diameter of the graph. Extensive experimental results verify our efficiency on billion-scale graphs and near-linear speedup in a multi-core environment.
Smart irrigation system for environmental sustainability in Africa: An Internet of Everything (IoE) approach

Water and food are two of the most important commodities in the world, which makes agriculture crucial to mankind as it utilizes water (irrigation) to provide us with food. Climate change and a rapid increase in population have put a lot of pressure on agriculture which has a snowball effect on the earth's water resource, which has been proven to be crucial for sustainable development. The need to do away with fossil fuel in powering irrigation systems cannot be over emphasized due to climate change. Smart Irrigation systems powered by renewable energy sources (RES) have been proven to substantially improve crop yield and the profitability of agriculture. Here we show how the control and monitoring of a solar powered smart irrigation system can be achieved using sensors and environmental data from an Internet of Everything (IoE). The collected data is used to predict environment conditions using the Radial Basis Function Network (RBFN). The predicted values of water level, weather forecast, humidity, temperature and irrigation data are used to control the irrigation system. A web platform was developed for monitoring and controlling the system remotely.
Experimental learning of quantum states

The number of parameters describing a quantum state is well known to grow exponentially with the number of particles. This scaling limits our ability to characterize and simulate the evolution of arbitrary states to systems, with no more than a few qubits. However, from a computational learning theory perspective, it can be shown that quantum states can be approximately learned using a number of measurements growing linearly with the number of qubits. Here, we experimentally demonstrate this linear scaling in optical systems with up to 6 qubits. Our results highlight the power of the computational learning theory to investigate quantum information, provide the first experimental demonstration that quantum states can be "probably approximately learned" with access to a number of copies of the state that scales linearly with the number of qubits, and pave the way to probing quantum states at new, larger scales.
OntoKin: An Ontology for Chemical Kinetic Reaction Mechanisms

An ontology for capturing both data and the semantics of chemical kinetic reaction mechanisms has been developed. Such mechanisms can be applied to simulate and understand the behavior of chemical processes, for example, the emission of pollutants from internal combustion engines. An ontology development methodology was used to produce the semantic model of the mechanisms, and a tool was developed to automate the assertion process. As part of the development methodology, the ontology is formally represented using a web ontology language (OWL), assessed by domain experts, and validated by applying a reasoning tool. The resulting ontology, termed OntoKin, has been used to represent example mechanisms from the literature. OntoKin and its instantiations are integrated to create a knowledge base (KB), which is deployed using the RDF4J triple store. The use of the OntoKin ontology and the KB is demonstrated for three use cases—querying across mechanisms, modeling atmospheric pollution dispersion, and as a mechanism browser tool. As part of the query use case, the OntoKin tools have been applied by a chemist to identify variations in the rate of a prompt NOx formation reaction in the combustion of ammonia as represented by four mechanisms in the literature.
Rare Words: A Major Problem for Contextualized Embeddings and How to Fix it by Attentive Mimicking

Pretraining deep neural network architectures with a language modeling objective has brought large improvements for many natural language processing tasks. Exemplified by BERT, a recently proposed such architecture, we demonstrate that despite being trained on huge amounts of data, deep language models still struggle to understand rare words. To fix this problem, we adapt Attentive Mimicking, a method that was designed to explicitly learn embeddings for rare words, to deep language models. In order to make this possible, we introduce one-token approximation, a procedure that enables us to use Attentive Mimicking even when the underlying language model uses subword-based tokenization, i.e., it does not assign embeddings to all words. To evaluate our method, we create a novel dataset that tests the ability of language models to capture semantic properties of words without any task-specific fine-tuning. Using this dataset, we show that adding our adapted version of Attentive Mimicking to BERT does substantially improve its understanding of rare words.