Reviewerly/exHarmony huggingface dataset:
Membership-Function-Dependent Stability Analysis for Polynomial-Fuzzy-Model-Based Control Systems via Chebyshev Membership Functions

In this article, we investigate the stability analysis of a polynomial-fuzzy-model-based control system by employing a new form of approximate membership functions called Chebyshev membership functions (CMFs) based on the Lyapunov stability theory. The membership-function-dependent method shows that the approximate functions can introduce useful information to relax the conservativeness of stability analysis. Therefore, in the case of the same order and operating domain, how to design the approximate functions to introduce more information to relax stability is a concern. In this article, given the Chebyshev norm relationship between the approximate membership functions and the original ones, CMFs can be obtained by the Chebyshev approximation theory and the Remez/Remez-type iterative algorithm, which own the minimum of the maximum absolute approximation error. Together with CMFs, the boundary information of premise variables on the overall operating domain is considered to reduce the conservativeness of stability analysis. Furthermore, to further relax the stability analysis without increasing the order of CMFs, the operating domain is divided into subdomains, and piecewise Chebyshev membership functions (PCMFs) are proposed to facilitate the stability analysis. Together with PCMFs, the subdomain boundary information of premise variables is introduced into stability conditions. A simulation example is given to demonstrate the effectiveness of the proposed approach.
A formal security analysis of ZigBee (1.0 and 3.0)

The rapid increase in the number of IoT devices in recent years indicates how much financial investment and efforts the tech-industries and the device manufacturers have put in. Unfortunately, this aggressive competition can give rise to poor quality IoT devices that are prone to adversarial attacks. To make matter worse, these attacks can compromise not only security but also safety, since an IoT device can directly operate on the physical world. Many recently reported attacks are due to the insecurity present in the underlying communication protocol stacks, and ZigBee is one of them. Considering the emergence and adoption of ZigBee 3.0 and the current market share of ZigBee 1.0, it is essential to study and analyze these protocol stacks at their specification level so that any insecurity at the specification level should be identified and fixed before they go into production.
Meta-Learning for Query Conceptualization at Web Scale

Concepts naturally constitute an abstraction for fine-grained entities and knowledge in the open domain. They enable search engines and recommendation systems to enhance user experience by discovering high-level abstraction of a search query and the user intent behind it. In this paper, we study the problem of query conceptualization, which is to find the most appropriate matching concepts for any given search query from a large pool of pre-defined concepts. We propose a coarse-to-fine approach to first reduce the search space for each query through a shortlisting scheme and then identify the matching concepts using pre-trained language models, which are meta-tuned to our query-concept matching task. Our shortlisting scheme involves using a GRU-based Relevant Words Generator (RWG) to first expand and complete the context of the given query and then shortlisting the candidate concepts through a scoring mechanism based on word overlaps. To accurately identify the most appropriate matching concepts for a query, even when the concepts may have zero verbatim overlaps with the query, we meta-fine-tune a BERT pairwise text-matching model under the Reptile meta-learning algorithm, which achieves zero-shot transfer learning on the conceptualization problem. Our two-stage framework can be trained with data completely derived from a search click graph, without requiring any human labelling efforts. For evaluation, we have constructed a large click graph based on more than $7$ million instances of the click history recorded in Tencent QQ browser and performed the query conceptualization task based on a large ontology with $159,148$ unique concepts. Results from a range of evaluation methods, including an offline evaluation procedure on the click graph, human evaluation, online A/B testing and case studies, have demonstrated the superiority of our approach over a number of competitive pre-trained language models and fine-tuned neural network baselines.
Automated SMS Classification and Spam Analysis using Topic Modeling

A huge amount of illegitimate short message service (SMS) messages flood users' mobile phone inbox and worsen users' experiences. This paper presents an automated framework for filtering SMS spam with the use of various classifiers. The text of SMS is preprocessed to generate a large number of tokens. These tokens individually or combinedly act as a feature and generate a large number of features. The comparative analysis of different machine learning algorithms with and without feature selection methods is performed. The classifiers are categorized into three groups based on their working namely ensemble-based, decision tree-based and others. The used classifiers are assessed using metrics such as accuracy, precision, recall, f-score and area under the receiver operating characteristics curve (AUC-ROC)). The random forest along with the chi-square feature selection method achieved the best performance. The major categories of SMS spam using topic modeling approaches such as latent Dirichlet allocation (LDA) and non-matrix factorization (NMF) are discussed.
Applications of Blockchain Technology for Smart Cities

The exponential growth of urbanization has impacted the living and working conditions of people in various sectors such as, education, health, and transport. Nevertheless, it also has a negative impact on citizens’ life as they encounter issues like pollution, traffic congestion, environmental resource limitations. Thus, the idea of a smart city was developed to enhance the services provided by transportation, health, energy, and education sectors. However, to provide such services, the smart city uses IoT devices to collect the data and draw conclusions from the data to manage resources effectively. But such a centralized network produces various challenges such as security, privacy, trust, and cost. Blockchain technology, because of its salient features such as transparency and decentralization, can be used to address these challenges. This paper presents a technical review of using blockchain as a solution for Internet of Things (IoT) based smart cities. It also presents the current challenges faced by blockchain technology.
Efficient Hardware Implementations of Deep Neural Networks: A Survey

Deep learning develops a significant breakthrough in numerous Artificial Intelligence (AI) applications. It is widely used in many state-of-the-art applications such as image classification, speech recognition, object detection and market prediction. However, deep learning models require extensive calculations and high computation power to perform a task. Therefore, there is a need to explore efficient hardware for Deep Neural Networks (DNNs) without affecting accuracy and its output. Hardware cost is also playing an essential role to affect the DNNs usage in portable or standalone devices. In this paper, we aim to providing detailed survey about recent trends in efficient hardware processing of DNNs. The overview of deep learning approaches, various hardware architectures and key advances in efficient computation of DNNs via reducing hardware size and changing hardware driven algorithms is also analyzed. Furthermore, we have explained the key comparisons, tradeoffs between different hardware platforms and various deep learning techniques for the efficient utilization of resources.
Blockchain and Smart Contract Engineering

Blockchains help to build trust among a decentralized network of unknown and untrusted peers who need to agree on a common protocol and trust the correctness and compatibility of the corresponding software implementations. The software engineering discipline cannot ignore this trend, as it fundamentally affects the way software is designed, developed, deployed, and delivered.1 As with the emergence of the Internet, software smart contracts for solving new classes of real-world problems, as opposed to introducing blockchains everywhere, where they may be unnecessary, or provide an inefficient and environmentally unsound solution.4
A Study of Bitcoin De-Anonymization: Graph and Multidimensional Data Analysis

Bitcoin was designed to be a decentralized global electronic payment system that does not require verification by a third-party intermediary platform and can be used by anyone originally. Due to its anonymity and globalization, bitcoin has achieved great success and attracted the attention of various illegal traders. In recent years, the number of illegal transactions of bitcoin has been increasing. Although bitcoin can support a certain amount of privacy, the bitcoin users and entity information can be linked by tracking the on-chain information of bitcoin users and combining the public off-chain information. Through bitcoin users de-anonymization, we can obtain some valuable intelligence information, which plays an important role in combating bitcoin-related crimes. In this paper, we build a visual analysis system for bitcoin transactions based on a graph database and use real-world multi-dimensional data sources to analyze the entity information of bitcoin transactions on the chain to achieve the effect of de-anonymization. Besides, we adopt a supervised learning method in our system to predict the legitimacy of unknown bitcoin transactions. Experiments and analyses show that our system can achieve good correlation analysis and de-anonymization. Finally, we put forward the future research direction of the bitcoin de-anonymization field.
Understanding the effects of real-time sentiment analysis and morale visualisation in backchannel systems: A case study

When presenting to a large group of students, either in an amphitheatre or through an online platform, effectively connecting to the audience – understanding how well the audience is following the presentation and taking appropriate actions promptly if they experience difficulties – is a serious challenge. Backchannel systems are sometimes deployed to address this issue by allowing audience to give feedback to the presenter without interrupting the current discourse. However, these systems are not designed to immediately aggregate and present the audience's feedback to the presenter in a meaningful way that is easy to quickly digest. To fill this gap, we have explored a proof-of-concept method for analysing the emotions and sentiments from the audience's feedback in real time and displaying to the presenter a morale graph showing a trend of the audience's overall reaction over time. This allows a presenter to effectively connected to their audience in real time, knowing whether their presentation is going well and what issues their audience may have in common at any specific moment. We have further implemented this method in an educational context, using a prototype backchannel system, known as ClasSense, for a lecturer to effectively connect to their students. This paper presents the evaluation of this system, which shows that lecturers accept and prefer the morale graph based user interface developed over other backchannel user interfaces that display all posts in chronological order. Students also positively expressed their agreement that the system not only makes their feedback an important part of the class but also increases their interactions with the lecturers. This is further confirmed with a Markov chain predicting the probability that students’ and lecturers’ survey results lead to their overall positive sentiment towards the tool. The flexibility of the ClasSense system suggests it may also be suitable in contexts other than education.
Approach to Sentiment Analysis and Business Communication on Social Media

Social media sites, namely Facebook and Twitter, have gained popularity in current times. These sites generate huge data that expresses thoughts and opinions of its users on issues. In this context, private individuals become the sources of information through online sharing of opinions and thoughts. These thoughts and opinions can be extracted to show patterns on sentiments of its users. Sentiment analysis, also referred as opinion mining, studies the “opinions, attitudes and emotions” of peoples. Sentiment analysis is a text classification problem, and identifying and extracting the right sentiment from huge data on social media sites are challenge. In this chapter, we presented different approaches to sentiment analysis on social media sites and social media business communication.
Discovering Functional Dependencies from Mixed-Type Data

Given complex data collections, practitioners can perform non-parametric functional dependency discovery (FDD) to uncover relationships between variables that were previously unknown. However, known FDD methods are applicable to nominal data, and in practice non-nominal variables are discretized, e.g., in a pre-processing step. This is problematic because, as soon as a mix of discrete and continuous variables is involved, the interaction of discretization with the various dependency measures from the literature is poorly understood. In particular, it is unclear whether a given discretization method even leads to a consistent dependency estimate. In this paper, we analyze these fundamental questions and derive formal criteria as to when a discretization process applied to a mixed set of random variables leads to consistent estimates of mutual information. With these insights, we derive an estimator framework applicable to any task that involves estimating mutual information from multivariate and mixed-type data. Last, we extend with this framework a previously proposed FDD approach for reliable dependencies. Experimental evaluation shows that the derived reliable estimator is both computationally and statistically efficient, and leads to effective FDD algorithms for mixed-type data.
A Hybrid Chinese Conversation model based on retrieval and generation

Conversation generation is an important natural language processing task and has attracted much attention in recent years. The realization of the conversation model is also of great significance to the field of social computing, helping to build artificial intelligence robots on social networks. The open domain conversation model is fundamentally data-driven, which can be roughly divided into retrieval models and generation models. Although remarkable progress has been achieved in recent years, it is still difficult to get responses that are grammatically and semantically appropriate. We propose the Rerank of Retrieval-based and Transformer-based Conversation model (RRT), a novel conversation model that combines the retrieval model with the generation model for the purpose of obtaining context–appropriate response. The context–response pairs with the highest similarity from training set are retrieved using traditional retrieval method, and further ranked to obtain the retrieval candidate response. We replaced the traditional sequence-to-sequence models for conversation generation by the transformer model and achieved better results with less training time. Finally, the post-reranking module is used to rank the retrieved candidate and the generated one to obtain the final response. We conducted detailed experiments on two datasets and the results show that compared with the traditional generation model, our model has a significant improvement in each metric, and the training time is decreased by a factor of 5. Furthermore, our model is more informative and relevant to the input context than the retrieval model.
A Survey on Approaches and Applications of Knowledge Representation Learning

Knowledge representation learning (KRL) is one of the important research topics in artificial intelligence and Natural language processing. It can efficiently calculate the semantics of entities and relations in a low-dimensional space, and effectively solve the problem of data sparsity, which significantly improve the performance of knowledge acquisition, fusion and reasoning and so on. Starting from the five perspectives of distance-based, semantic matching, bilinear-based, neural network model and additional information model, this paper first introduces the overall framework and specific model design, and then correspondingly introduces the experimental evaluation tasks, metrics and benchmark datasets of each model. On this basis, how to apply KRL to various downstream tasks is summarized.
Generalization Error Bounds via mth Central Moments of the Information Density

We present a general approach to deriving bounds on the generalization error of randomized learning algorithms. Our approach can be used to obtain bounds on the average generalization error as well as bounds on its tail probabilities, both for the case in which a new hypothesis is randomly generated every time the algorithm is used-as often assumed in the probably approximately correct (PAC)-Bayesian literature-and in the single-draw case, where the hypothesis is extracted only once.For this last scenario, we present a novel bound that is explicit in the central moments of the information density. The bound reveals that the higher the order of the information density moment that can be controlled, the milder the dependence of the generalization bound on the desired confidence level.Furthermore, we use tools from binary hypothesis testing to derive a second bound, which is explicit in the tail of the information density. This bound confirms that a fast decay of the tail of the information density yields a more favorable dependence of the generalization bound on the confidence level.
Design and Application of Deep Belief Network Based on Stochastic Adaptive Particle Swarm Optimization

Due to the problem of poor recognition of data with deep fault attribute in the case of traditional superficial network under semisupervised and weak labeling, a deep belief network (DBN) was proposed for deep fault detection. Due to the problems of deep belief network (DBN) network structure and training parameter selection, a stochastic adaptive particle swarm optimization (RSAPSO) algorithm was proposed in this study to optimize the DBN. A stochastic criterion was proposed in this method to make the particles jump out of the original position search with a certain probability and reduce the probability of falling into the local optimum. The RSAPSO-DBN method used sample data to train the DBN and used the final diagnostic error rate to construct the fitness value function of the particle swarm algorithm. By comparing the minimum fitness value of each particle to determine the advantages and disadvantages of the model, the corresponding minimum fitness value was selected. Using the number of network nodes, learning rate, and momentum parameters, the optimal DBN classifier was generated for fault diagnosis. Finally, the validity of the method was verified by bearing data from Case Western Reserve University in the United States and data collected in the laboratory. Comparing BP (BP neural network), support vector machine, and heterogeneous particle swarm optimization DBN methods, the proposed method demonstrated the highest recognition rates of 87.75% and 93.75%. This proves that the proposed method possesses universality in fault diagnosis and provides new ideas for data identification with different fault depth attributes.
Bootstrapping Fully Homomorphic Encryption over the Integers in Less than One Second

One can bootstrap LWE-based fully homomorphic encryption (FHE) schemes in less than one second, but bootstrapping AGCD-based FHE schemes, also known as FHE over the integers, is still very slow. In this work we propose a fast bootstrapping method for FHE over the integers, closing thus this gap between these two types of schemes. We use a variant of the AGCD problem to construct a new GSW-like scheme that can natively encrypt polynomials, then, we show how the single-gate bootstrapping method proposed by Ducas and Micciancio (EUROCRYPT 2015) can be adapted to FHE over the integers using our scheme, and we implement a bootstrapping that, using around 400 MB of key material, runs in less than one second in a common personal computer.
Enhanced K-Means Clustering Algorithm for Feasibility Assessment of ACC

Contracts formulation with a minimalistic intervention by developers is crucial to methodically bring about optimized contracts providing extended and enhanced software testing. The proposed scheme to derive the contracts in an automated mode encompasses of shaping the behavior related and structural dependency details as constraints on a Decision tree and subsequently concretize them as Automated Code Contracts (ACC). A Contract refinement is thereafter pursued to harness reinforced feasible contracts. This is performed by tailoring the K-means Clustering algorithm using Neural Networks related Activation functions to construct precise clusters of analogous contracts and at the same time seek finer Computational and memory performances with efficiency gains over bug detecting ability.
Design of Temperature Remote Monitoring System Based on STM32

This paper introduces the design and implementation of a remote temperature data monitoring system based on STM32. The system uses PT100 and STM32F103VET6 main control boards to collect the measured ambient temperature. Zigbee will pass the tested data through the RS232 interface, and deliver the data to the gateway based on the MODBUS communication protocol. The gateway will upload the data to the server through the MQTT communication protocol, and the server will receive the data for caching, storage and analysis, and display it through the web page. At the same time, in order to deal with the problem of network failure, the InfluxDB standby database is deployed in the middle of the gateway.
AJIT

New applications such as remote surgery and connected cars, which are being touted as use cases for 5G and beyond, are mission-critical. As such, communications infrastructure needs to support and enforce stringent and guaranteed levels of service before such applications can take off. However, from an operator's perspective, it can be difficult to provide uniformly high levels of service over long durations or large regions. As network conditions change over time, or when a mobile end point goes to regions with poor coverage, it may be difficult for the operator to support previously agreed upon service agreements that are too stringent. Second, from a consumer's perspective, purchasing a stringent service level agreement with an operator can also be expensive. Finally, failures in mission critical applications can lead to disasters, so infrastructure should support assignment of liabilities when a guaranteed service level is reneged upon - this is a difficult problem because both the operator and the customer have an incentive to lay the blame on each other to avoid liabilities of poor service.