Reviewerly/exHarmony huggingface dataset:
A new kernel density estimator based on the minimum entropy of data set

The key to probability density function (p.d.f.) estimation is the determination of optimal bandwidth of kernel density estimator (KDE). The typical optimization strategy is to use the unknown p.d.f. to solve the unknown bandwidth, i.e., taking the mean integrated squared error between the unknown p.d.f. and the estimated p.d.f. as the objective function and calculating the optimal bandwidth by minimizing the objective function. Unlike the existing KDEs, this paper proposes a new minimum entropy-based KDE (ME-KDE) which represents the objective function with the re-substitution entropy of given data set. Meanwhile, a new fixed-point iteration algorithm is designed efficiently to determine the optimal bandwidth. ME-KDE reduces the uncertainty when determining the optimal bandwidth and thus enhances the stability of p.d.f. estimation. The experimental results on univariate and multivariate probability distributions demonstrate the feasibility and effectiveness of ME-KDE.
Motion control of joystick interfaced electric wheelchair for improvement of safety and riding comfort

Nowadays powered wheelchairs are widely utilized to help people’s locomotion. However, many commercialized powered wheelchairs still provide unsatisfactory ride quality, which is mainly due to not-well-designed control algorithm. To address this issue, this paper proposes a motion control algorithm that can improve the safety and riding comfort of the user and can be easily implemented on a powered wheelchair. To design the controller in an intuitive way, locomotion mode of a wheelchair is suggested at first, which consists of the longitudinal mode and the rotational mode. Then, the motion reference generator, wheelchair motion observer and control algorithms are designed in this locomotion mode. Moreover, this locomotion mode-based motion control does not require encoders, which makes the implementation of the algorithm affordable for many commercialized wheelchairs. The detail of the controller and state observer is studied, and experimental results to verify the validity of the proposed controller and the state observer are explained.
Artificial intelligence-based platform for online teaching management systems

At present, artificial intelligence has developed rapidly and has been increasingly used in teaching, which has brought about earth-shaking changes in the education of various subjects. To further promote the intelligent development of college online courses, this paper studies the mathematical mo dels and algorithms in the learning management system based on the knowledge forgetting curve. Through the analysis of the current situation of mathematical modeling and application of “knowledge forgetting curve,” the method of fuzzy mathematical knowledge and differential modeling is used, the mathematical model and algorithm design of a new “knowledge forgetting curve” is proposed. And the English word review program test is designed. The results show that the use of the program can overcome the oblivion of the words. It can be seen that the “integrated intelligent teaching network platform” has great application prospects and potential benefits.
Single-Photon Distillation via a Photonic Parity Measurement Using Cavity QED

Single photons with tailored temporal profiles are a vital resource for future quantum networks. Here we distill them out of custom-shaped laser pulses that reflect from a single atom strongly coupled to an optical resonator. A subsequent measurement on the atom is employed to herald a successful distillation. Out of vacuum-dominated light pulses, we create single photons with fidelity 66(1)%, two-and-more-photon suppression 95.5(6)%, and a Wigner function with negative value −0.125(6). Our scheme applied to state-of-the-art fiber resonators could boost the single-photon fidelity to up to 96%.Received 27 December 2018DOI:https://doi.org/10.1103/PhysRevLett.122.133603© 2019 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAtoms, ions, & molecules in cavitiesCavity quantum electrodynamicsPhoton statisticsQuantum states of lightSingle photon sourcesAtomic, Molecular & Optical
Seismic Events Prediction Using Deep Temporal Convolution Networks

Seismic events prediction is a crucial task for preventing coal mine rock burst hazards. Currently, this task attracts increasing research enthusiasms from many mining experts. Considering the temporal characteristics of monitoring data, seismic events prediction can be abstracted as a time series prediction task. This paper contributes to address the problem of long-term historical dependence on seismic time series prediction with deep temporal convolution neural networks (CNN). We propose a dilated causal temporal convolution network (DCTCNN) and a CNN long short-term memory hybrid model (CNN-LSTM) to forecast seismic events. In particular, DCTCNN is designed with dilated CNN kernels, causal strategy, and residual connections; CNN-LSTM is established in a hybrid modeling way by utilizing advantage of CNN and LSTM. Based on these manners, both of DCTCNN and CNN-LSTM can extract long-term historical features from the monitoring seismic data. The proposed models are experimentally tested on two real-life coal mine seismic datasets. Furthermore, they are also compared with one traditional time series prediction method, two classic machine learning algorithms, and two standard deep learning networks. Results show that DCTCNN and CNN-LSTM are superior than the other five algorithms, and they successfully complete the seismic prediction task.
PIM-VR: Erasing Motion Anomalies In Highly-Interactive Virtual Reality World with Customized Memory Cube

With the revolutionary innovations emerging in the computer graphics domain, virtual reality (VR) has become increasingly popular and mainstream for entertainment, medical simulation and education. In the highly-interactive VR world, the motion-to-photon delay (MPD) which represents the delay from users' head motion to the responded image displayed on their head devices, is the most critical factor for a successful VR experience. Long MPD may cause user to experience significant motion anomalies: judder, lagging and sickness. In order to alleviate this negative effect, asynchronous time warp (ATW) has been proposed by VR vendors to map the rendered stereoscopic frame in the correct position using the latest head-motion information. However, after a careful investigation on the efficiency of the current GPU-accelerated ATW through executing real VR applications on modern VR hardware, we observe that the current ATW design on commercial hardware cannot achieve the ideal MPD and often cause ATW to miss the refresh deadline, resulting in motion anomalies and dropped frame rate. This is caused by two major challenges: inefficient VR execution model and intensive off-chip memory accesses. To tackle these, we propose a preemption-free Processing-In-Memory based ATW design which asynchronously executes ATW within a 3D-stacked memory, without interrupting the rendering tasks on the host GPU. We also identify a redundancy reduction mechanism to further simplify and accelerate the ATW operation. A comprehensive evaluation on our proposed design demonstrates that our PIM-based ATW can achieve the ideal MPD and provide superior user experience. Finally, we provide a design space exploration to showcase different design choices for the PIM-based ATW design.
Constant Size Ciphertext Distributed CP-ABE Scheme With Privacy Protection and Fully Hiding Access Structure

In ciphertext-policy attribute-based encryption (CP-ABE) schemes, the computational overhead and the length for the ciphertext are positively correlated with the complexity of the access structure. In addition, the access structure embedded in ciphertext may be a leak sensitive information for the user. In order to solve the above issues, we provide a constant size ciphertext distributed CP-ABE scheme with privacy protection and fully hiding access policy (PPFH-CP-ABE) scheme. Our scheme can protect user's privacy from the following two aspects: 1) A user obtains private keys for his attributes from different authorities who get nothing about user's global identifier (GID) and attributes and 2) Access structure in the ciphertext is fully hidden. In addition, our scheme is able to resist the collusion attacks since the user's GID is nonlinearly bound to his private key from each authority. The presented scheme is proven CPA-secure in selective security model under the decisional DBDH assumption.
Traceable and undeniable ciphertext-policy attribute-based encryption for cloud storage service

Ciphertext-policy attribute-based encryption is a promising mechanism with fine-grained access control for cloud storage system. However, there is a long-lasting problem of key abuse that a user may share its decryption key and a semi-honest authority may illegally issue decryption keys for unauthorized users for profits. To address this problem, we propose an accountable ciphertext-policy attribute-based encryption scheme. In our construction, there are two authorities to issue keys for users, but they cannot decrypt any ciphertexts without collusion. A shared key can be effectively traced, and if the traced identity claims that it is innocent, an auditor can publicly audit who will be responsible for the shared key. Compared with existing accountable ciphertext-policy attribute-based encryption schemes, the proposed scheme is more practical from the two aspects: (1) a user can normally request for a decryption key along with a short signature, and no additional interaction between users and authorities is needed; and (2) the complexity of tracing a masked secret key is reduced to |U| exponent computation, where |U| denotes the number of users in the system. At last, we give the security and experimental analysis.
A novel natural language steganographic framework based on image description neural network

It is a challenge to conduct natural language steganography on Online interactive platforms such as photo-sharing websites since the stego texts should be consistent with the content of the images. In this paper, a novel natural language steganographic framework based on an end-to-end generative network is proposed. A Convolution Neural Network (CNN) combined with Long Short-Term Memory (LSTM) is trained to generate stego descriptions. Word by Word Hiding (WWH) and Sentence by Sentence Hiding (SSH) schemes are proposed to achieve various embedding capacity under the premise of sharing model between the sender and the receiver. Furthermore, a blind extraction scheme called Hash Hiding (HH) is proposed in case that the model is unavailable for data extraction. Comparative experiments show the superiority of the proposed framework. It is verified that the proposed framework is an effective carrier-less steganographic framework with competitive embedding capacity, considerable text quality, and good reversibility.
Personal Data Market Optimization Pricing Model Based on Privacy Level

In the era of the digital economy, data has become a new key production factor, and personal data represents the monetary value of a data-driven economy. Both the public and private sectors want to use these data for research and business. However, due to privacy issues, access to such data is limited. Given the business opportunities that have gaps between demand and supply, we consider establishing a private data market to resolve supply and demand conflicts. While there are many challenges to building such a data market, we only focus on three technical challenges: (1) How to provide a fair trading mechanism between data providers and data platforms? (2) What is the consumer’s attitude toward privacy data? (3) How to price personal data to maximize the profit of the data platform? In this paper, we first propose a compensation mechanism based on the privacy attitude of the data provider. Second, we analyze consumer self-selection behavior and establish a non-linear model to represent consumers’ willingness to pay (WTP). Finally, we establish a bi-level programming model and use genetic simulated annealing algorithm to solve the optimal pricing problem of personal data. The experimental results show that multi-level privacy division can meet the needs of consumers and maximize the profit of data platform.
“He too will become a nation and he too will become great”: The information needs of the “Bnei Menashe” immigrant community in Israel as capability

The “Bnei Menashe” are a marginalized community that immigrated from India to Israel. The community has diverse information needs, yet they are characterized by difficulties that prevent them from acquiring vital information. In this study, we utilized Sen’s (1993) “capabilities approach” as well as Friedland et al. (2012) “critical information needs” for the identification of the Bnei Menashe’s information capabilities, and the ways in which they answer them. The study was conducted using semi-structured individual in-depth interviews. The findings of the study relate that the Bnei Menashe succeed only partially in realizing the capability of being informed. This is a result of the partial technological exclusion that characterizes the community. This is a first-of-its-kind study, as the Bnei Menashe have not been studied methodically. Furthermore, the utilization of the capabilities approach represents a major contribution to existing communication theories, as the approach allows for a more wholesome understanding of the role of information and communication technologies in society.
Oceanic Games: Centralization Risks and Incentives in Blockchain Mining

To participate in the distributed consensus of permissionless blockchains, prospective nodes—or miners—provide proof of designated, costly resources. However, in contrast to the intended decentralization, current data on blockchain mining unveils increased concentration of these resources in a few major entities, typically mining pools. To study strategic considerations in this setting, we employ the concept of Oceanic Games [27]. Oceanic Games have been used to analyze decision making in corporate settings with small numbers of dominant players (shareholders) and large numbers of individually insignificant players, the ocean. Unlike standard equilibrium models, they focus on measuring the value (or power) per entity and per unit of resource in a given distribution of resources. These values are viewed as strategic components in coalition formations, mergers and resource acquisitions. Considering such issues relevant to blockchain governance and long-term sustainability, we adapt oceanic games to blockchain mining and illustrate the defined concepts via examples. The application of existing results reveals incentives for individual miners to merge in order to increase the value of their resources. This offers an alternative perspective to the observed centralization and concentration of mining power. Beyond numerical simulations, we use the model to identify issues relevant to the design of future cryptocurrencies and formulate prospective research questions.
Similarity-preserving hashing based on deep neural networks for large-scale image retrieval

Similarity-preserving hashing has become the mainstream of approximate nearest neighbor (ANN) search for large-scale image retrieval. Recent research shows that deep neural networks can produce efficient feature representation. Most existing deep hashing schemes simply utilize the middle-layer features of the deep neural networks to measure the similarity between query images and database images. However, these visual features are suboptimal for discriminating the semantic information of images, especially for complex images that contain multiple objects. In this paper, a deep framework is employed to learn multi-level non-linear transformations to obtain advanced image features, and then we combine these intermediate features and top layer visual information to implement image retrieval. Three criterions are enforced on these compact codes: (1) minimal quantization loss; (2) evenly distributed binary; (3) independent bits. The experimental results on five public large-scale datasets demonstrate the superiority of our method compared with several other state-of-the-art methods.
Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy

Human papillomavirus (HPV) infection is related to frequent cases of cervical cancer and genital condyloma in humans. Up to now, numerous methods have come into existence for the prevention and treatment of this disease. In this context, this paper aims to help predict the susceptibility of the patient to forms treatment using both cryotherapy and immunotherapy. These studies facilitate the choice of medications, which can be painful and embarrassing for patients who have warts on intimate parts. However, the use of intelligent models generates efficient results but does not allow a better interpretation of the results. To solve the problem, we present the method of a fuzzy neural network (FNN). A hybrid model capable of solving complex problems and extracting knowledge from the database will pruned through F-score techniques to perform pattern classification in the treatment of warts, and to produce a specialist system based on if/then rules, according to the experience obtained from the database collected through medical research. Finally, binary pattern-classification tests realized in the FNN and compared with other models commonly used for classification tasks capture results of greater accuracy than the current state of the art for this type of problem (84.32% for immunotherapy, and 88.64% for cryotherapy), and extract fuzzy rules from the problem database. It was found that the hybrid approach based on neural networks and fuzzy systems can be an excellent tool to aid the prediction of cryotherapy and immunotherapy treatments.
Windowed Attention Mechanisms for Speech Recognition

The usual attention mechanisms used for encoder-decoder models do not constrain the relationship between input and output sequences to be monotonic. To address this we explore windowed attention mechanisms which restrict attention to a block of source hidden states. Rule-based windowing restricts attention to a (typically large) fixed-length window. The performance of such methods is poor if the window size is small. In this paper, we propose a fully-trainable windowed attention and provide a detailed analysis on the factors which affect the performance of such an attention mechanism. Compared to the rule-based window methods, the learned window size is significantly smaller yet the model's performance is competitive. On the TIMIT corpus this approach has resulted in a 17% (relative) performance improvement over the traditional attention model. Our model also yields comparable accuracies to the joint CTC-attention model on the Wall Street Journal corpus.
Differential Spectrum of Kasami Power Permutations Over Odd Characteristic Finite Fields

Functions with low differential uniformity have important applications in cryptography, coding theory, and sequence design. The differential spectrum of a cryptographic function is of great interest for estimating its resistance to some variants of differential cryptanalysis. Finding power permutations (i.e., monomial bijective mappings) over finite fields with low differential uniformity and determining their differential spectra have received a lot of attention over the past two decades. The objective of this paper is to study the differential properties of the well-known Kasami power permutations x p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2k</sup> -p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> +1 over GF(p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ), where p is an odd prime and k is an integer with gcd(n, k) = 1. It turns out that this family of monomials is differentially (p + 1)-uniform. Our result in the case of p = 3 gives an affirmative solution to a recent conjecture by Xu, Cao, and Xu. Most notably, the differential spectrum of this family of power permutations is completely determined.
The role of B2B marketers in increasing cyber security awareness and influencing behavioural change

Although cyberspace is providing business opportunities, it is evident that B2B marketers need to pay attention to how to reduce the vulnerabilities associated with the use of computer technology. To reduce the possibility of a data breach, senior management need to increase threat awareness through the development of a behavioural awareness programme. We address how B2B marketers can contribute to increasing cyber security awareness by relating to an individual's knowledge and attitude that influences their behaviour. By drawing on the information systems management, marketing, communication and motivational research (social psychology) literature, we explain how persuasive communication theory in conjunction with motivation theory can be used to create an awareness programme to influence behavioural change. A qualitative research strategy was deployed and the critical friendship group and the group interview method were utilized. Fourteen highly experienced security experts participated in the study. The findings indicate that the process of knowledge creation can be enhanced by deploying functional theories of attitudes as this provides a basis to categorize staff according to their level of motivation and knowledge. This means that B2B marketers can play the role of co-ordinator when devising and implementing an appropriate cyber security awareness programme to help staff counteract the actions of cyber attackers. By reducing the gap between self-efficacy and perceived expectation, the confidence level of staff will be increased and attitudinal behavioural change will occur throughout the organization.
MedRec: A Network for Personal Information Distribution

MedRec is a simple, distributed system for personal control of identity and distribution of personal information. The work is done in the context of a medical information distribution system where patients retain control over who can access their data. We present a new architecture for the MedRec project, creating a network of trusted data repositories, the access to which are determined by a set of ”smart contracts”. These contracts are stored on a distributed ledger maintained by those who generate data. The distributed nature of the system allows unified access from diverse sources in a single application with no intermediary. This increases patient control while retaining a measure of privacy of both data content and source. MedRec is amenable to extensions for decentralized messaging and distribution of information to third parties such as medical researchers, healthcare proxies, and other institutions. The system is based on a blockchain that contains smart contracts defining user identity and distribution specifics.