Reviewerly/exHarmony huggingface dataset:
Research and Application of Intelligent Voice Food Ordering System

With the rapid development and popularization of Internet applications, online food ordering systems can make people's lives more convenient. However, how to choose the favorite food among various foods conveniently has become the focus of the development of online food ordering systems. Due to the rapid development of artificial intelligence technology and popularization of intelligent applications, more and more intelligent systems have been proposed. However, artificial intelligence exploiting both speech recognition and semantic understanding technology is rarely used to develop intelligent food ordering systems. We hereby propose the intelligent voice food ordering system (IVFOS), which is designed and implemented on the basis of Iflytek AIUI platform, combining speech recognition and semantic understanding technology so as to make the food ordering more convenient. We have further introduced a personalized recommendation mechanism to serve customers based on their historical ordering records. The practical application shows that our intelligent voice food ordering system has the characteristics of convenient usage and highly personalized food ordering experience.
Credit Decision System of Small and Medium Sized Micro Enterprises Based on Big Data Technology and Risk Assessment Thinking

In order to solve the common credit decision-making problem of most small and medium-sized enterprises, this paper proposes a new credit decision-making system for small and medium-sized enterprises based on big data technology and risk assessment thinking. The decision-making system fully analyzes the problems existing in the credit decision-making of most small and medium-sized micro enterprises, and gives full play to the powerful function of big data technology in data processing. Based on this, the decision-making system also constructs a comprehensive evaluation model, cluster analysis model, adds matlab algorithm, and from the perspective of risk assessment thinking, insists on minimizing the risk of credit decision-making. The results show that the decision-making system can analyze the credit problems of most small and medium-sized enterprises from a comprehensive and scientific perspective, and obtain the optimal credit strategy of enterprises through qualitative and quantitative analysis.
Clinical Report Classification: Continually Learning from User Feedback

Hospitals have to deal with continually arriving clinical data. In this paper, we present a neural network approach that can aide the process medical document classification. In this scenario a human annotator can correct the model predictions for a new training sample which can then be used for training. This data needs to be classified into ICD categories and the newly obtained knowledge should be captured by the model with minimal loss of already acquired knowledge. More specifically, different strategies are proposed and evaluated for constructing a replay dataset in a continual learning setting. The presented methodology alternates an incremental learning phase with a full retraining of all training samples seen so far. In this manner, a balance can be found where most of the time newly obtained knowledge can immediately be added to the model, but not to the extent where it loses a vital part of previously obtained knowledge.
On Cascaded Federated Learning for Multi-tier Predictive Models

The performance prediction of user equipment (UE) metrics has many applications in the 5G era and beyond. For instance, throughput prediction can improve carrier selection, adaptive video streaming's quality of experience (QoE), and traffic latency. Many studies suggest distributed learning algorithms (e.g., federated learning (FL)) for this purpose. However, in a multi-tier design, features are measured in different tiers, e.g., UE tier, and gNodeB (gNB) tier. On one hand, neglecting the measurements in one tier results in inaccurate predictions. On the other hand, transmitting the data from one tier to another improves the prediction performance at the expense of increasing network overhead and privacy risks. In this paper, we propose cascaded FL to enhance UE throughput prediction with minimum network footprint and privacy ramifications (if any). The idea is to introduce feedback to conventional FL, in multi-tier architectures. Although we use cascaded FL for UE prediction tasks, the idea is rather general and can be used for many prediction problems in multi-tier architectures, such as cellular networks. We evaluate the performance of cascaded FL by detailed and 3GPP compliant simulations of London's city center. Our simulations show that the proposed cascaded FL can achieve up to 54% improvement over conventional FL in the normalized gain, at the cost of 1.8 MB (without quantization) and no cost with quantization.
Linear-Time Calculation of the Expected Sum of Edge Lengths in Random Projective Linearizations of Trees

Abstract The syntactic structure of a sentence is often represented using syntactic dependency trees. The sum of the distances between syntactically related words has been in the limelight for the past decades. Research on dependency distances led to the formulation of the principle of dependency distance minimization whereby words in sentences are ordered so as to minimize that sum. Numerous random baselines have been defined to carry out related quantitative studies on lan- guages. The simplest random baseline is the expected value of the sum in unconstrained random permutations of the words in the sentence, namely, when all the shufflings of the words of a sentence are allowed and equally likely. Here we focus on a popular baseline: random projective per- mutations of the words of the sentence, that is, permutations where the syntactic dependency structure is projective, a formal constraint that sentences satisfy often in languages. Thus far, the expectation of the sum of dependency distances in random projective shufflings of a sentence has been estimated approximately with a Monte Carlo procedure whose cost is of the order of Rn, where n is the number of words of the sentence and R is the number of samples; it is well known that the larger R is, the lower the error of the estimation but the larger the time cost. Here we pre- sent formulae to compute that expectation without error in time of the order of n. Furthermore, we show that star trees maximize it, and provide an algorithm to retrieve the trees that minimize it.
A New Fast Framework for Anonymizing IoT Stream Data

The Internet of Things (IoT) plays an important role in human life today. Millions of devices generate and transmit vast amounts of data. Exploring this data without compromising privacy practices may expose to risk of users' identities. One of the measures used to protect data privacy is anonymity methods. IoT data anonymization is not possible using traditional methods because such data, unlike database data, are not static and are very large. In this paper, we propose a new framework that can anonymize the received stream data by considering their expiration time. This anonymization is performed using a new clustering method using a streaming data processing engine. The introduced clustering method has a significant effect on reducing data delay. It supports both numerical and categorical data types too. Also, merging remaining clusters at the end of the method has minimized information loss. Comparing the performance results of the introduced method with similar methods shows that the proposed method performs better in terms of information loss and data delay.
Generative locally linear embedding: A module for manifold unfolding and visualization

Data often have nonlinear patterns in machine learning. One can unfold the nonlinear manifold of a dataset for low-dimensional visualization and feature extraction. Locally Linear Embedding (LLE) is a nonlinear spectral method for dimensionality reduction and manifold unfolding. It embeds data using the same linear reconstruction weights as in the input space. In this paper, we propose an open source module which not only implements LLE, but also includes implementations of two generative LLE algorithms whose linear reconstruction phases are stochastic. Using this module, one can generate as many manifold unfoldings as desired for data visualization or feature extraction.
Blockchain and publishing: towards a publisher-centred distributed ledger for the book publishing industry

There is a small but growing volume of scholarly research and technological experiments in relation to blockchain and book publishing. However, to date, appropriate and feasible blockchain models for rights management and copyright exploitation remain largely untested. Furthermore, extant research has focussed on author-centred models of intellectual property and royalty management, with limited focus on publisher-centred models. Consequently, there are few practical prototypes and little understanding of blockchain models that are mutually beneficial for both writers and publishers. This article presents the findings of a two-year research project, No Point in Stopping, that explores a publisher-centred model of blockchain publishing that creates value for micro-publishers. The project developed a custom blockchain prototype and digital ledger to manage intellectual property agreements, royalty payments, and the tracking of both physical in-store and online purchases. The authors argue that for publishers, blockchain can create and monetise new digital publishing products from the drafting and editing process, thereby using intellectual property that would normally be an input into the publishing process rather than a commercial output. Blockchain can also create a new contract and revenue system that disrupts the current fee-for-service model by paying royalties to all creative professionals involved in this publishing process.
Evolving neural selection with adaptive regularization

Over-parameterization is one of the inherent characteristics of modern deep neural networks, which can often be overcome by leveraging regularization methods, such as Dropout. Usually, these methods are applied globally and all the input cases are treated equally. However, given the natural variation of the input space for real-world tasks such as image recognition and natural language understanding, it is unlikely that a fixed regularization pattern will have the same effectiveness for all the input cases. In this work, we demonstrate a method in which the selection of neurons in deep neural networks evolves, adapting to the difficulty of prediction. We propose the Adaptive Neural Selection (ANS) framework, which evolves to weigh neurons in a layer to form network variants that are suitable to handle different input cases. Experimental results show that the proposed method can significantly improve the performance of commonly-used neural network architectures on standard image recognition benchmarks. Ablation studies also validate the effectiveness and contribution of each component in the proposed framework.
Towards a pragmatic detection of unreliable accounts on social networks

In recent years, the problem of unreliable content in social networks has become a major threat, with a proven real-world impact in events like elections and pandemics, undermining democracy and trust in science, respectively. Research in this domain has focused not only on the content but also on the accounts that propagate it, with the bot detection task having been thoroughly studied. However, not all bot accounts work as unreliable content spreaders (p.e. bot for news aggregation), and not all human accounts are necessarily reliable. In this study, we try to distinguish unreliable from reliable accounts, independently of how they are operated. In addition, we work towards providing a methodology capable of coping with real-world situations by introducing the content available (restricting it by volume- and time-based batches) as a parameter of the methodology. Experiments conducted on a validation set with a different number of tweets per account provide evidence that our proposed solution produces an increase of up to 20% in performance when compared with traditional (individual) models and with cross-batch models (which perform better with different batches of tweets).
Full Image Inference Conditionally upon Available Pieces Transmitted into Limited Resources Context

In a context marked by the proliferation of smartphones and multimedia applications, the processing and transmission of images have become a real problem.Image compression is the first approach to address this problem, it nevertheless suffers from its inability to adapt to the dynamics of limited environments, consisting mainly of mobile equipment and wireless networks.In this work, we propose a stochastic model to gradually estimate an image upon information on its pixels that are transmitted progressively.We consider this transmission as a dynamical process, where the sender pushes the data in decreasing significance order.In order to adapt to network conditions and performances, instead of truncating the pixels, we suggest a new method called Fast Reconstruction Method by Kalman Filtering (FRM-KF) consisting of recursive inference of the not yet received layers belonging to a sequence of bitplanes.After empirical analysis, we estimate parameters of our model which is a linear discrete Kalman Filter.We assume the initial law of information to be the uniform distribution on the set [0, 255] corresponding to the range of gray levels.The performances of FRM-KF method have been evaluated in terms of the ratios in the quality of data image/size sent and in the quality of image/time required for treatment.A high quality was reached faster with relatively small data (less than 10% of image data is needed to obtain up to the sixth-quality image).The time for treatment also decreases faster with number of received layers.However, we found that the time of image treatment might be large starting from a image resolution of 1024 * 1024.Hence, we recommend FRM-KF method for resolutions less or equal to
A temporal–spatial attention-based action recognition method for intelligent fault diagnosis

The intelligent fault diagnosis of video data has become a demanding task in industrial applications. However, existing models require expensive computational cost and memory demand, which makes this technology applied in factories impossible. To address this problem, a temporal-spatial attention-based action recognition method (TARM) integrating TAB (temporal-attention-based frame splitting model), SAB (spatial-attention-based agent focusing mode) and LSB (long-short term feature learning mode) is proposed. TAB first extracts important frames from raw videos. Then, SAB refines video data by reinforcing their essential features and weakening unnecessary features. Furthermore, LSB monitors action type of video data by establishing recurrent convolutional architectures. Finally, the performance of TARM in terms of training time and fault diagnosis accuracy are validated by comparing with six state-of-the-art video diagnosis methods.
User-Centric Explainability in Fintech Applications

Fintech applications such as robo-financial advisors (RAs) are complex algorithmic decision making systems, which gained prominence with their claim to democratize finance. Lack of transparency and explanations for these automated decisions leads to a trust deficit for users, limiting the potential of these applications. Our research aims to analyse the effectiveness of user-centric explanations in conveying the decision-making logic of complex algorithmic systems. Our user study tests techniques from explainable AI, varying in complexity and transparency. The quantitative aspects of our study determine the efficacy and usability of explanations and the qualitative aspects measure the effect of explanations on users and system usability. Our study finds trust and confidence of users in the system is positively correlated with comprehension and transparency provided by the presence of an explanation. There is a notable reduction in comprehension and trust between transparent white and opaque black box explanations of algorithms. This study is designed to aid policymakers and regulators in order to understand user needs which are crucial to designing better policies around algorithmic explainability for RAs.
Crime Monitor: Monitoring Criminals from Trajectory Data

The movement of criminals is an important factor used in detecting crimes. Individuals sentenced to house arrest who wears an ankle monitor have their trajectories collected periodically. Each offender using an ankle monitor must adhere to a set of rules, for instance, be at his/her home during the night. Unfortunately, some of them break such rules, also some end up committing crimes again. In this demonstration <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , we present a prototype system called Crime Monitor to monitor offenders in a semi-open regime. Crime Monitor reports the illegal activities to the police department in real-time based on trajectory features. Thus, the police can effectively prevent crimes from happening and handle them efficiently when they occur. We tackled the trajectory classification problem and used a deep learning model combining embedding with a recurrent neural network to classify illegal activities and learn the pattern regardless of who the criminal user is. We conduct experiments on a real dataset, and we show that DeepeST outperforms other approaches from state- of-the-art.
Low complexity iterative decoding of Reed–Solomon convolutional concatenated codes

Summary The power limitation is the predominant challenge for achieving a reliable communication transmission for aerospace systems. Therefore, it is appealing to use robust channel coding techniques with a low decoding complexity. The robust forward error correction scheme that uses a Reed–Solomon as an outer code concatenated with a convolutional code as an inner code is an attractive scheme whose applications are widely used in wireless and space communications. However, iterative soft‐decision decoding of that concatenated code is still an open research challenge. This paper proposes a reduced complexity iterative decoding algorithm for this concatenated coding scheme. The soft‐output adaptive Viterbi algorithm with a dynamic discarding threshold has been adopted to decode the inner convolutional code while the outer decoder will be based on a bit‐level modified Chase algorithm. We have used the Hamming metric instead of the Euclidean metric, which is not only much less complex but also overcomes the lack of channel information on the outer decoder input. Simulation results using the proposed soft information exchange decoding mechanism show that a considerable performance enhancement over the classical decoding scheme as well as a significant reduction in complexity over the existing decoding algorithms that use an iterative process to decode this concatenated coding scheme. The adaptive decoding of the inner convolutional code can gain a complexity reduction of 90 % after 5 iterations compared to the soft‐output Viterbi algorithm while maintaining the small performance loss from the maximum a posteriori decoding algorithm.
Challenges for Recommender Systems Evaluation

Many businesses and web portals adopt Recommender Systems (RSs) to help their users to tame information overload and make better choices. Despite the fact that RSs should support user decision making, academic researchers, when evaluating the effectiveness of a RS, largely adopt offline rather than live user studies methods. We discuss the relationships between these evaluation methods by considering a tourism RS case study. We then suggest future directions to be taken by HCI and RS research to better assess the user’s value of RSs.
Application of Deep Learning in Medical Imaging

With the rapid evolution of the field of computer science, automatic diagnosis of medical images has become possible. This paper introduces the latest research progress and application of deep learning in the medical field. First of all, this paper introduces several common diseases, including the Alzheimer's disease, lung cancer, breast cancer, glaucoma, and diabetic eye disease, etc. Secondly, it introduces the basic methods of deep learning, including the convolutional neural network and multiple perceptron model Moreover, it introduces the application of these methods in the above disease diagnosis, mainly about the development process, data set and accuracy rate. Finally, the research orientation is summarized and further development of deep learning in medical imaging is proposed.
Research on the evaluation method of business English informatization teaching level based on BSC

In order to solve the problems of low evaluation accuracy and high evaluation time-consuming of traditional business English informatization teaching level evaluation methods, this paper proposes a new business English informatization teaching level evaluation method based on BSC. Firstly, Markov chain is used to collect business English information teaching data. Based on the collected teaching data, this paper uses the Balanced Scorecard to construct the evaluation model of business English informatization teaching level, calculates the index weight, and completes the evaluation of business English informatization teaching level. The experimental results show that compared with the traditional evaluation method, the evaluation accuracy and time-consuming of this method are significantly improved.
Application of Graphological Coincidence Applied in the Field of Speech Therapy in Children with Motor Difficulties

The proposal of an application to measure the graphological coincidence according to the activities proposed in the speech therapy in children with motor difficulties is presented, for which the data from The Quick, Draw! Google Creative Lab dataset is obtained from the game The Quick, Draw! And made available to developers, this base The Quick, Draw! Dataset provides 50 million drawings from 345 different categories. For the project, the database of the circle (contains 118805 drawings of circles), square (120538 drawings of squares), and Triangle (contains 120499 drawings of triangles) was downloaded in the format of simplified drawings in Numpy bitmaps, which contain images 28 × 28 grayscale. The existing database was used to obtain an acceptable level of recognition, with the data obtained, training was carried out with respect to the drawings made by regular children and with motor difficulties using an ergonomic pencil. For the experiment procedure, a certain amount of data is extracted from each figure for training and testing. With 4000 training data and 1000 test data, they are enough to obtain optimal results in the recognition of the 3 figures. Keras was used for the neural network because it has more development tools to use and is optimal for rapid prototyping compared to TensorFlow which is high performance. The Classification Script is used to carry out the process, it uses the Preparation Script to obtain the data from the Training and Test folders by the label and prepares them to be used in the Keras model.