Quantum Proxy Signature Scheme with Discrete Time Quantum Walks and Quantum One-Time Pad CNOT Operation

The quantum proxy signature is one of the most significant formalisms in quantum signatures. We put forward a quantum proxy signature scheme using quantum walk-based teleportation and quantum one-time pad CNOT (QOTP-CNOT) operation, which includes four phases, i.e., initializing phase, authorizing phase, signing phase and verifying phase. The QOTP-CNOT is achieved by attaching the CNOT operation upon the QOTP and it is applied to produce the proxy signature state. The quantum walk-based teleportation is employed to transfer the encrypted message copy derived from the binary random sequence from the proxy signer to the verifier, in which the required entangled states do not need to be prepared ahead and they can be automatically generated during quantum walks. Security analysis demonstrates that the presented proxy signature scheme has impossibility of denial from the proxy and original signers, impossibility of forgery from the original signatory and the verifier, and impossibility of repudiation from the verifier. Notably, the discussion shows the complexity of the presented algorithm and that the scheme can be applied in many real scenarios, such as electronic payment and electronic commerce

Method of Feature Reduction in Short Text Classification Based on Feature Clustering

One decisive problem of short text classification is the serious dimensional disaster when utilizing a statistics-based approach to construct vector spaces. Here, a feature reduction method is proposed that is based on two-stage feature clustering (TSFC), which is applied to short text classification. Features are semi-loosely clustered by combining spectral clustering with a graph traversal algorithm. Next, intra-cluster feature screening rules are designed to remove outlier feature words, which improves the effect of similar feature clusters. We classify short texts with corresponding similar feature clusters instead of original feature words. Similar feature clusters replace feature words, and the dimension of vector space is significantly reduced. Several classifiers are utilized to evaluate the effectiveness of this method. The results show that the method largely resolves the dimensional disaster and it can significantly improve the accuracy of short text classification

Sentiment-Aware Word Embedding for Emotion Classification

Word embeddings are effective intermediate representations for capturing semantic regularities between words in natural language processing (NLP) tasks. We propose sentiment-aware word embedding for emotional classification, which consists of integrating sentiment evidence within the emotional embedding component of a term vector. We take advantage of the multiple types of emotional knowledge, just as the existing emotional lexicon, to build emotional word vectors to represent emotional information. Then the emotional word vector is combined with the traditional word embedding to construct the hybrid representation, which contains semantic and emotional information as the inputs of the emotion classification experiments. Our method maintains the interpretability of word embeddings, and leverages external emotional information in addition to input text sequences. Extensive results on several machine learning models show that the proposed methods can improve the accuracy of emotion classification tasks

Quantum Dual Signature with Coherent States Based on Chained Phase-Controlled Operations

A novel encryption algorithm called the chained phase-controlled operation (CPCO) is presented in this paper, inspired by CNOT operation, which indicates a stronger correlation among message states and each message state depending on not only its corresponding key but also other message states and their associated keys. Thus, it can prevent forgery effectively. According to the encryption algorithm CPCO and the classical dual signature protocols, a quantum dual signature scheme based on coherent states is proposed in this paper. It involves three participants, the customer Alice, the merchant Bob and the bank Trent. Alice expects to send her order message and payment message to Bob and Trent, respectively. It is required that the two messages must be linked to guarantee the payment is paid for the corresponding order. Thus, Alice can generate a quantum dual signature to achieve the goal. In detail, Alice firstly signs her two messages with the shared secret key. Then She connects the two signatures into a quantum dual signature. Finally, Bob and Trent severally verify the signatures of the order message and the payment message. Security analysis shows that our scheme can ensure its security against forgery, repudiation and denial. In addition, simulation experiments based on the Strawberry Fields platform are performed to valid the feasibility of CPCO. Experimental results demonstrate that CPCO is viable and the expected coherent states can be acquired with high fidelity, which indicates that the encryption algorithm of the scheme can be implemented on quantum devices effectively

An Empirical Study on the Adoption of Online Household e-waste Collection Services in China

Part 2: AdoptionInternational audienceOnline household e-waste collection services are emerging as new solutions to disposing household e-waste in China. This study aims to investigate the adoption of online household e-waste collection services in China. Based on the previous technology diffusion theories (e.g., TAM, UTAUT), a research model with six research hypotheses was proposed in this research. The research model was empirically tested with a sample of 203 users of online household e-waste collection services in China. The results indicated that five of the six research hypotheses were significantly supported. And the most significant determinant for the behavioral intention to use online household e-waste service was effort expectancy. However, facilitating condition did not have significant impact on users’ behavior of using online household e-waste collection services

Optimization of Demand-Response-Based Intelligent Home Energy Management System with Binary Backtracking Search Algorithm

In many nations, limited power from providers and an increase in demand for electricity have created new opportunities that can be used by home energy management systems (HEMSs) systems to enforce proper use of energy. This paper presents a virtual intelligent home with demand response (DR) model home appliances that have an inverter air conditioner, water pump, washing machine, and inverter refrigerator. A binary backtracking search algorithm (BBSA) is proposed to introduce the optimal schedule controller. With the proposed BBSA schedule controller, the highest energy consumption during DR can be reduced by 33.84% during the weekends and by 30.4% daily during the weekdays. The results indicate the effectiveness of the proposed HEMS. Additionally, the model can control the appliances and maintain total residential energy consumption below the defined demand limit

The Effect of Acceleration on Continuous Variable Quantum Key Distribution with Discrete Modulation

The influence of gravity on quantum key distribution cannot be ignored in practical space communication, and this paper provides a thorough analysis of the effectiveness of discrete modulated states. By calculating the acceleration in both inertial and non-inertial reference systems with expectation values of the computational Heisenberg field respectively, the gain coefficient is obtained. Based on this parameter, an improved protocol with a high key rate over a transmission distance of 390 km is proposed, enabling the practical application of quantum key distribution techniques. Furthermore, the results obtained can be extended to the eight-state schemes. This paper not only extends the range of discrete modulation states, but also enhances the impact of relativistic quantum information

China-Australia Personal Income Tax Comparison Research

On August 31, 2018, the Fifth Standing Committee of the 13th National People's Congress (NPC) passed the proposal of revising the Law of Individual Income Tax, indicating the seventh significant revision since its introduction in 1980. Under this background, Chapter 2 in this report concludes the major hot issues of personal income tax (PIT) reform and Chapter 4 applies CPITMS model to analyze the effect of PIT reform on income distribution in China. From residents’ view, PIT reform actually is a part of tax-transfer system. The common goal of PIT and transfer payment policy is to narrowing income inequality and increasing welfare for disadvantaged families. The PIT reform in 2008 (PIT2018) has added the policy of additional special deductions (ASDs), which to some extent is similar to the goal of minimum living standard guarantee system or other income transfer policies. With CPTMS model and STINMOD+ model based on the tax-transfer system in Australia, Chapter 3 compares the behavior of typical households in China and Australia and we hope to provide some new thinking for PIT reform as well as the reform of income transfer policies which target low-income group. According to our research, among all types of ASDs, the largest impact is made by expenditure on children’s education. In fact, issue of children caring and education has always been government’s concern and it is closely related to the commonly existing problems in China, such as child-raising burden and low fertility rate. Chapter 5 introduces the child care subsidy policy in Australia and its impact on common households, aiming at providing references for the same kind of policy decisions in China. Some key findings are as follows: First, the basic function of PIT in China is to adjust income distribution rather than raise fiscal revenue. In 2017, the government’s PIT revenue in China merely accounted for 7.5% of total tax revenues, much lower than that in developed countries. In 2015, PIT contributed to 40.8% in U.S., 27.9% in Britain, 18.9% in France, 24% in OECD countries on average. Second, PIT2018 is expected to reduce PIT revenue by RMB580.4 billion (using income distribution in 2018). Among which, the reduction of RMB46.7 billion comes from ASDs, while children education holds 48.8% of ASDs’ effect. Third, PIT2018 might cause Gini coefficient of Chinese residents to decrease by 0.7% (using income distribution in 2018), 7.6% of which comes from the impact of ASDs. Children education accounts for 36.6% of ASDs’ influence. Fourth, the income redistribution effect of China is weaker than that of Australia. It specifically manifests on two evidences. The first one is that when personal annual income is less than RMB250,000, the average tax rate in China is higher than that in Australia; when over RMB250,000, the average tax rate in China is lower instead. The other one is that when annual income exceeding RMB700,000, its effective marginal tax rate (EMTR, the change of tax payment resulting from income increasing one yuan) in China is lower than that in Australia. While in China, personal income of most employed population is under RMB700,000. Fifth, when making subsidy policy for low-income group, China’s government needs to increase accuracy and fully consider the family income level and the real expenditure burden. Australia has a well-established personal taxation return system and nursery management practices. Before its reform in 2018, the child care subsidy policy could be handled through nursery. Parents needed to provide relevant proof materials to the nursery, so as to deduct the relevant fees directly. After its reform, the child care policy is further tilted towards low-income families. The new policy requires more accurate information on household income and is replaced by reporting by parents directly. If necessary, adjustments can be made when the tax is declared at the end of the year. This depends on a sound personal income management system and a sound child care market, which is worthy of reference for similar policies in China.<br/

Real-Time Movie-Induced Discrete Emotion Recognition from EEG Signals

Recognition of a human&#39;s continuous emotional states in real time plays an important role in machine emotional intelligence and human-machine interaction. Existing real-time emotion recognition systems use stimuli with low ecological validity (e.g., picture, sound) to elicit emotions and to recognise only valence and arousal. To overcome these limitations, in this paper, we construct a standardised database of 16 emotional film clips that were selected from over one thousand film excerpts. Based on emotional categories that are induced by these film clips, we propose a real-time movie-induced emotion recognition system for identifying an individual&#39;s emotional states through the analysis of brain waves. Thirty participants took part in this study and watched 16 standardised film clips that characterise real-life emotional experiences and target seven discrete emotions and neutrality. Our system uses a 2-s window and a 50 percent overlap between two consecutive windows to segment the EEG signals. Emotional states, including not only the valence and arousal dimensions but also similar discrete emotions in the valence-arousal coordinate space, are predicted in each window. Our real-time system achieves an overall accuracy of 92.26 percent in recognising high-arousal and valenced emotions from neutrality and 86.63 percent in recognising positive from negative emotions. Moreover, our system classifies three positive emotions (joy, amusement, tenderness) with an average of 86.43 percent accuracy and four negative emotions (anger, disgust, fear, sadness) with an average of 65.09 percent accuracy. These results demonstrate the advantage over the existing state-of-the-art real-time emotion recognition systems from EEG signals in terms of classification accuracy and the ability to recognise similar discrete emotions that are close in the valence-arousal coordinate space.</p