Reviewerly/exHarmony huggingface dataset:
Fault tolerant controlled quantum dialogue against collective noise*

Quantum system is inevitably affected by the external environment in the real world. Two controlled quantum dialogue protocols are put forward based on logical χ -type states under collective noise environment. One is against collective-dephasing noise, while the other is against collective-rotation noise. Compared with existing protocols, there exist several outstanding advantages in our proposed protocols: Firstly, the χ -type state is utilized as quantum channels, it possesses better entanglement properties than GHZ state, W state as well as cluster state, which make it difficult to be destroyed by local operations. Secondly, two kinds of logical χ -type states are constructed by us in theory, which can be perfectly immune to the effects of collective noise. Thirdly, the controller can be offline after quantum distribution and permission announcement, without waiting for all the participants to complete the information coding. Fourthly, the security analysis illuminates that our protocols can not only be free from the information leakage, but also resist against the intercept-and-resend attack, the entanglement-and-measure attack, the modification attack, the conspiring attack, and especially the dishonest controller’s attacks.
Compressive approaches for cross-language multi-document summarization

The popularization of social networks and digital documents has quickly increased the multilingual information available on the Internet. However, this huge amount of data cannot be analyzed manually. This paper deals with Cross-Language Text Summarization (CLTS) that produces a summary in a different language from the source documents. We describe three compressive CLTS approaches that analyze the text in the source and target languages to compute the relevance of sentences. Our systems compress sentences at two levels: clusters of similar sentences are compressed using a multi-sentence compression (MSC) method and single sentences are compressed using a Neural Network model. The version of our approach using multi-sentence compression generated more informative French-to-English cross-lingual summaries than extractive state-of-the-art systems. Moreover, these cross-lingual summaries have a grammatical quality similar to extractive approaches.
How people use 360-degree cameras

A variety of new kinds of consumer cameras that capture the full 360-degree surroundings in a single panorama photo or video has recently been introduced to the market. In addition to conventional viewing devices, such as smartphones and computers, the 360-degree photos and videos can also be viewed with a virtual reality headset for a more immersive experience. In this paper, we present a field study where 14 consumers used 360-degree cameras freely in their everyday lives for a period of four weeks. We describe the strategies that the participants applied to capture 360-degree content in different situations and discuss the opportunities, challenges, and limitations of using a 360-degree camera compared to conventional cameras. We identify four common practices of consumer 360-degree camera use: Panorama Capture, Experience Capture, Automatic Capture, and Document Capture. We also report on the participants' habits of viewing, sharing, editing, and managing consumer-captured 360-degree content.
Borehole water level model for photovoltaic water pumping systems

Using photovoltaic energy to pump water from aquifers is an interesting solution to circumvent the low electricity grid coverage and provide improved domestic water access in off-grid areas in sub-Saharan Africa. When pumping and during the lifetime of a pumping installation, the borehole water level changes, which impacts the amount of energy required to extract water from the aquifer. In order to address alterations in energy requirements, this article develops a data-driven borehole water level model adapted to photovoltaic water pumping systems (PVWPS). The proposed model is applicable to all types of PVWPS and aquifers. It has been validated against experimental data from a pilot PVWPS located in a rural off-grid village in Burkina Faso having achieved more than 97% accuracy. Thanks to this borehole model, we have been able to assess the influence of the variability of groundwater resources on both the performance of PVWPS and on their optimal sizing. We show that the variation of the static water level can require a increase of the peak power of the PV modules of up to 100%. Nonetheless, the effect of the drawdown due to the pumping is negligible. This study can help companies, governments and non-governmental organizations to better take into account the variability and the sustainability of groundwater resources in the optimal sizing and monitoring of PVWPS.
A Survey: Cloud Data Security Based on Blockchain Technology

The following topics are dealt with: learning (artificial intelligence); data mining; social networking (online); pattern classification; text analysis; security of data; feature extraction; Internet; graph theory; computer network security.
CASA-Crowd: A Context-Aware Scale Aggregation CNN-Based Crowd Counting Technique

The accuracy of object-based computer vision techniques declines due to major challenges originating from large scale variation, varying shape, perspective variation, and lack of side information. To handle these challenges most of the crowd counting methods use multi-columns (restrict themselves to a set of specific density scenes), deploying a deeper and multi-networks for density estimation. However, these techniques suffer a lot of drawbacks such as extraction of identical features from multi-column, computationally complex architecture, overestimate the density estimation in sparse areas, underestimating in dense areas and averaging of feature maps result in reduced quality of density map. To overcome these drawbacks and to provide a state-of-the-art counting accuracy with comparable computational cost, we therefore propose a deeper and wider network: a Context-aware Scale Aggregation CNN-based Crowd Counting method (CASA-Crowd) to obtain the deep, varying scale and perspective varying features. Further, we include a dilated convolution with varying filter size to obtain contextual information. In addition, due to different dilation rates, a variation in receptive field size is more useful to overcome the perspective distortion. The quality of density map is enhanced while preserving the spatial dimension by obtaining a comparable computational complexity. We further evaluate our method on three well-known datasets: UCF_CC_50, ShanghaiTech Part_A, ShanghaiTech Part_B.
Learning to Interactively Learn and Assist

When deploying autonomous agents in the real world, we need effective ways of communicating objectives to them. Traditional skill learning has revolved around reinforcement and imitation learning, each with rigid constraints on the format of information exchanged between the human and the agent. While scalar rewards carry little information, demonstrations require significant effort to provide and may carry more information than is necessary. Furthermore, rewards and demonstrations are often defined and collected before training begins, when the human is most uncertain about what information would help the agent. In contrast, when humans communicate objectives with each other, they make use of a large vocabulary of informative behaviors, including non-verbal communication, and often communicate throughout learning, responding to observed behavior. In this way, humans communicate intent with minimal effort. In this paper, we propose such interactive learning as an alternative to reward or demonstration-driven learning. To accomplish this, we introduce a multi-agent training framework that enables an agent to learn from another agent who knows the current task. Through a series of experiments, we demonstrate the emergence of a variety of interactive learning behaviors, including information-sharing, information-seeking, and question-answering. Most importantly, we find that our approach produces an agent that is capable of learning interactively from a human user, without a set of explicit demonstrations or a reward function, and achieving significantly better performance cooperatively with a human than a human performing the task alone.
Posterior-GAN: Towards Informative and Coherent Response Generation with Posterior Generative Adversarial Network

Neural conversational models learn to generate responses by taking into account the dialog history. These models are typically optimized over the query-response pairs with a maximum likelihood estimation objective. However, the query-response tuples are naturally loosely coupled, and there exist multiple responses that can respond to a given query, which leads the conversational model learning burdensome. Besides, the general dull response problem is even worsened when the model is confronted with meaningless response training instances. Intuitively, a high-quality response not only responds to the given query but also links up to the future conversations, in this paper, we leverage the query-response-future turn triples to induce the generated responses that consider both the given context and the future conversations. To facilitate the modeling of these triples, we further propose a novel encoder-decoder based generative adversarial learning framework, Posterior Generative Adversarial Network (Posterior-GAN), which consists of a forward and a backward generative discriminator to cooperatively encourage the generated response to be informative and coherent by two complementary assessment perspectives. Experimental results demonstrate that our method effectively boosts the informativeness and coherence of the generated response on both automatic and human evaluation, which verifies the advantages of considering two assessment perspectives.
Learning Deep Neural Network Controllers for Dynamical Systems with Safety Guarantees: Invited Paper

There is recent interest in using deep neural networks (DNNs) for controlling autonomous cyber-physical systems (CPSs). One challenge with this approach is that many autonomous CPS applications are safety-critical, and is not clear if DNNs can proffer safe system behaviors. To address this problem, we present an approach to modify existing (deep) reinforcement learning algorithms to guide the training of those controllers so that the overall system is safe. We present a novel verification-in-the-loop training algorithm that uses the formalism of barrier certificates to synthesize DNN-controllers that are safe by design. We demonstrate a proof-of-concept evaluation of our technique on multiple CPS examples.
Unifying Deep Local and Global Features for Efficient Image Search.

Image retrieval is the problem of searching an image database for items that are similar to a query image. To address this task, two main types of image representations have been studied: global and local image features. In this work, our key contribution is to unify global and local features into a single deep model, enabling accurate retrieval with efficient feature extraction. We refer to the new model as DELG, standing for DEep Local and Global features. We leverage lessons from recent feature learning work and propose a model that combines generalized mean pooling for global features and attentive selection for local features. The entire network can be learned end-to-end by carefully balancing the gradient flow between two heads -- requiring only image-level labels. We also introduce an autoencoder-based dimensionality reduction technique for local features, which is integrated into the model, improving training efficiency and matching performance. Comprehensive experiments show that our model achieves state-of-the-art image retrieval on the Revisited Oxford and Paris datasets, and state-of-the-art single-model instance-level recognition on the Google Landmarks dataset v2. Code and models are available at this https URL .
Railway Defender Kill Chain to Predict and Detect Cyber-Attacks

Most organizations focus on intrusion prevention technologies, with less emphasis on prediction and detection. This research looks at prediction and detection in the railway industry. It uses an extended cyber kill chain (CKC) model and an industrial control system (ICS) cyber kill chain for detection and proposes predictive technologies that will help railway organizations predict and recover from cyber-attacks. The extended CKC model consists of both internal and external cyber kill chain; breaking the chain at an early stage will help the defender stop the adversary’s malicious actions. This research incorporates an OSA (open system architecture) for railways with the railway cybersecurity OSA-CBM (open system architecture for condition-based maintenance) architecture. The railway cybersecurity OSACBM architecture consists of eight layers; cybersecurity information moves from the initial level of data acquisition to data processing, data analysis, incident detection, incident assessment, incident prognostics, decision support, and visualization. The main objective of the research is to predict, prevent, detect, and respond to cyber-attacks early in the CKC by using defensive controls called the Railway Defender Kill Chain (RDKC). The contributions of the research are as follows. First, it adapts and modifies the railway cybersecurity OSA-CBM architecture for railways. Second, it adapts the cyber kill chain model for the railway. Third, it introduces the Railway Defender Kill Chain. Fourth, it presents examples of cyber-attack scenarios in the railway system.
GRK-Papyri: A Dataset of Greek Handwriting on Papyri for the Task of Writer Identification

Presenting actual research questions from academia through publishing datasets is a practice of great importance in order to generate relevant solutions. Therefore, we propose a dataset of handwriting on papyri for the task of writer identification. This dataset is derived directly from research questions in the field of Papyrology, and the samples are selected by experts from the respective field of research. This dataset consists of 50 handwriting samples in Greek on papyri approximately from the 6th century A.D., which belong to 10 different scribes. It is prepared and made freely available for non-commercial research along with their confirmed groundtruth information related to the task of writer identification. This paper presents not only the details of the dataset but also its relation to research questions and how the results of computational analysis can support scholars from manuscript research. Some preprocessing and experimentation results are provided as well in order to highlight the difficulties posed by the image degradation of this dataset.
Opposite Structure Learning for Semi-supervised Domain Adaptation

Current adversarial adaptation methods attempt to align the cross-domain features whereas two challenges remain unsolved: 1) conditional distribution mismatch between different domains and 2) the bias of decision boundary towards the source domain. To solve these challenges, we propose a novel framework for semi-supervised domain adaptation by unifying the learning of opposite structures (UODA). UODA consists of a generator and two classifiers (i.e., the source-based and the target-based classifiers respectively) which are trained with opposite forms of losses for a unified object. The target-based classifier attempts to cluster the target features to improve intra-class density and enlarge inter-class divergence. Meanwhile, the source-based classifier is designed to scatter the source features to enhance the smoothness of decision boundary. Through the alternation of source-feature expansion and target-feature clustering procedures, the target features are well-enclosed within the dilated boundary of the corresponding source features. This strategy effectively makes the cross-domain features precisely aligned. To overcome the model collapse through training, we progressively update the measurement of distance and the feature representation on both domains via an adversarial training paradigm. Extensive experiments on the benchmarks of DomainNet and Office-home datasets demonstrate the effectiveness of our approach over the state-of-the-art method.
A Multisecret-Sharing Scheme Based on LCD Codes

Secret sharing is one of the most important cryptographic protocols. Secret sharing schemes (SSS) have been created to that end. This protocol requires a dealer and several participants. The dealer divides the secret into several pieces ( the shares), and one share is given to each participant. The secret can be recovered once a subset of the participants (a coalition) shares their information. In this paper, we present a new multisecret-sharing scheme inspired by Blakley’s method based on hyperplanes intersection but adapted to a coding theoretic situation. Unique recovery requires the use of linear complementary (LCD) codes, that is, codes in which intersection with their duals is trivial. For a given code length and dimension, our system allows dealing with larger secrets and more users than other code-based schemes.
Circular Complex-Valued GMDH-Type Neural Network for Real-Valued Classification Problems

Recently, applications of complex-valued neural networks (CVNNs) to real-valued classification problems have attracted significant attention. However, most existing CVNNs are black-box models with poor explanation performance. This study extends the real-valued group method of data handling (RGMDH)-type neural network to the complex field and constructs a circular complex-valued group method of data handling (C-CGMDH)-type neural network, which is a white-box model. First, a complex least squares method is proposed for parameter estimation. Second, a new complex-valued symmetric regularity criterion is constructed with a logarithmic function to represent explicitly the magnitude and phase of the actual and predicted complex output to evaluate and select the middle candidate models. Furthermore, the property of this new complex-valued external criterion is proven to be similar to that of the real external criterion. Before training this model, a circular transformation is used to transform the real-valued input features to the complex field. Twenty-five real-valued classification data sets from the UCI Machine Learning Repository are used to conduct the experiments. The results show that both RGMDH and C-CGMDH models can select the most important features from the complete feature space through a self-organizing modeling process. Compared with RGMDH, the C-CGMDH model converges faster and selects fewer features. Furthermore, its classification performance is statistically significantly better than the benchmark complex-valued and real-valued models. Regarding time complexity, the C-CGMDH model is comparable with other models in dealing with the data sets that have few features. Finally, we demonstrate that the GMDH-type neural network can be interpretable.
Decentralized Transfer Learning using Blockchain &amp; IPFS for Deep Learning

The era of deep learning has enabled various dimensions of predictive and preventive healthcare in medical science. However, the applications in dimension are limited due to sparse availability of data and computing resources, also the required expertise of data understanding, modeling and training is not common yet. These limitations are forcing a barrier in the widespread use of deep learning applications in healthcare. In this research we propose a decentralized transfer learning mechanism based on smart contracts using private blockchain. The idea is to involve various stakeholders which includes data scientists, deep learning experts, dataset holders and deep learning infrastructure providers into a single trustless deep learning-based analytics system. The framework will allow sharing of data, and expertise in the field of deep learning, along with processing power to solve a single task in a decentralized manner. The sharing mechanism will be based on smart contracts to ensure the intellectual property of an individual remains protected. The framework once deployed on public blockchain can turn transfer learning into a new domain of research and business and will define a new era of shared computing for various domains. The research will directly impact people working in isolation to come together and create opportunities for better analytics and optimized predictions for scientific society.
Algorithm for automatic recognition of PSK and QAM with unique classifier based on features and threshold levels

In this paper, we present a unique modulation classification method that is based on determining an attractive relation between higher-order cumulants (HOCs) using a decision tree-classifier to improve the extracted features employed for the recognition of modulation schemes, such as phase shift keying (PSK) and quadrature amplitude modulation (QAM). A threshold algorithm is applied to the proposed classifier, which consists of sub-classifiers, each comprising a single feature, and each being capable of distinguishing the modulation types individually. In this work, a high-accuracy classifier system is utilized to recognize modulation schemes, such as QAM (16, 32, 64, 128, and 256) and (2, 4, and 8) PSK at a low signal-to-noise ratio (SNR). In this study, 1000 signals are studied for each SNR of –5 dB to 30 dB. The most prominent results of the classifier decisions range from 88% to 100% with regard to distinguishing the same types of PSK and QAM. In the long run, the proposed classifier module will be advantageous in terms of accuracy and computational complexity relative to the other classifiers in the literature. The results demonstrate that the proposed algorithm has a significantly better classification accuracy in comparison with the previously proposed ones.
RFPS: A Robust Feature Points Detection of Audio Watermarking for Against Desynchronization Attacks in Cyber Security

Digital watermarking is an effective covert communication technique in the military field, which can solve the problems in traditional encryption. In general, the Spread Spectrum (SS) algorithm can well satisfy the requirements of concealment and anti-interference in the covert communication process. However, it has a natural defect in resisting desynchronization attacks. In this paper, we propose a Robust Feature Points Scheme (RFPS) against desynchronization attacks by calculating the maximum response value of the second-order derivative of the original audio signal. The maximum response value will be used as a feature point and the audio segments centering at the detected feature points are extracted for both watermark embedding and extraction process. This study mainly focuses on cropping attacks and pitch invariant Time-Scale Modification (TSM) attacks. The experimental results show that under non-uniform cropping attacks, the proposed method can extract the watermark information without any bit error rate. Even in the case of the pitch-invariant TSM attacks, the proposed scheme has great improvement compared with the existing works.