Reviewerly/exHarmony huggingface dataset:
A hybrid learning method for the data-driven design of linguistic dynamic systems

In lots of data based prediction or modeling applications, uncertainties and/or noises in the observed data cannot be avoided. In such cases, it is more preferable and reasonable to provide linguistic (fuzzy) predicted results described by fuzzy memberships or fuzzy sets instead of the crisp estimates depicted by numbers. Linguistic dynamic system (LDS) provides a powerful tool for yielding linguistic (fuzzy) results. However, it is still difficult to construct LDS models from observed data. To solve this issue, this paper first presents a simplified LDS whose inputoutput mapping can be determined by closed-form formulas. Then, a hybrid learning method is proposed to construct the data-driven LDS model. The proposed hybrid learning method firstly generates fuzzy rules by the subtractive clustering method, then carries out further optimization of centers of the consequent triangular fuzzy sets in the fuzzy rules, and finally adopts multiobjective optimization algorithm to determine the left and right end-points of the consequent triangular fuzzy sets. The proposed approach is successfully applied to three real-world prediction applications which are: prediction of energy consumption of a building, forecasting of the traffic flow, and prediction of the wind speed. Simulation results show that the uncertainties in the data can be effectively captured by the linguistic (fuzzy) estimates. It can also be extended to some other prediction or modeling problems, in which observed data have high levels of uncertainties.
Getting Started with TensorFlow Deep Learning

TensorFlow is an open-source software Python-based library developed by Google. It has high popularity in machine learning and deep learning area due to its simplicity, flexibility, and compatibility. In this chapter, we introduce the basic syntax of the TensorFlow and its main operations required to construct an artificial neural network. We briefly introduce the codes for building a recurrent neural network and convolutional neural network for example of MNIST based handwritten digits classification problem.
Security and Privacy via Optimised Blockchain

Security of IoT devices and applications can be improved by using the emerging Blockchain Technology which uses decentralised interaction and scalable architectures with security and distributed trust.In this work a review of Lightweight scalable Blockchain is proposed for security and privacy of IoT devices.An IoT scenario of smart home is used for illustrative purposes but this proposed architecture is wellsuited for diverse IoT applications.Effectiveness of this architecture is analysed using common threat models which provides an appropriate assessment for security and privacy implementation for smart home.
On-chip engineering of high-dimensional path-entangled states in a quadratic coupled-waveguide system

High-dimensional quantum entangled states are a highly desired resource for their extended possibilities in quantum information processing. In this paper, we introduce on-chip engineering of a variety of path-entangled states based on the simultaneous enabling of spontaneous parametric down-conversion and efficient coupling between waveguides in a quadratic coupled-waveguide system. By varying the properties of pump beams, the phase-matching conditions, and the coupling coefficients in a three-coupled-waveguide system, it is possible to generate the deterministic and robust three-path biphoton entangled states without any postprocessing, as well as to transform the output photons among different types of path entanglement. This integrated implementation of the entanglement source will become an important building block for practical applications of on-chip quantum technologies.
Position Invariant Spline Curve Based Image Retrieval Using Control Points

In this paper Cubic Bezier curve-based image retrieval system is proposed.This system evaluates similarity of each image in its database to a query image in terms of shape characteristics.Then, returns those images within a desired range of similarity.The proposed system determines nonlinear relationship between image's features for more accurate similarity comparison between query image and existing database images.Among existing approaches to shape feature analysis, statistical approach to extract shape features is adopted here.It works in form of control points of spline curves from both given query image and images of available database.These control points are further used to find out the Fourier Descriptors.Control points and Fourier descriptors are used for image retrieval in proposed system.With the vast number of images available on-line, quality CBIR systems are critical.The performance and results obtained by proposed system are compared to other CBIR systems.Comparison reveals proposed system performance is state of the art.In many parameters it outperforms other CBIR systems.
Benchmark Datasets for Offline Handwritten Gurmukhi Script Recognition

Handwritten character recognition is an imperative issue in the field of pattern recognition and machine learning research. In the recent years, several techniques for handwritten character recognition have been proposed. Due to the lack of publicly accessible benchmark datasets of Gurmukhi script, no extensive comparisons have been undertaken between those techniques, especially for this script. Over the years, datasets and benchmarks have proven their fundamental importance in character recognition research, and objective comparisons in many fields. This paper presents a collection of seven benchmark datasets (HWR-Gurmukhi_1.1, HWR-Gurmukhi_1.2, HWR-Gurmukhi_1.3, HWR-Gurmukhi_2.1, HWR-Gurmukhi_2.2, HWR-Gurmukhi_2.3, and HWR-Gurmukhi_3.1) with different sizes for offline handwritten Gurmukhi character recognition collected from various public places. A few exploratory outcomes based on precision, False Acceptance Rate (FAR), and False Rejection Rate (FRR) using different classification techniques, namely, k-NN, RBF-SVM, MLP, Neural Network, Decision Tree, and Random Forest are also presented in this paper.
Backstepping Sliding Mode Controller Improved with Interval Type-2 Fuzzy Logic Applied to the Dual Star Induction Motor

This paper proposes Interval type-2 Fuzzy sliding mode controller based on Backstepping (IT2FBSMC), to control the speed of a dual star induction machine (DSIM), in order to get a robust performance machine. An appropriate control strategy based on the coupling of three methods (Backstepping, sliding mode and type-2 Fuzzy controller) is used to build a robust controller used to approximate the discontinuous control eliminating the chattering phenomenon and guaranteeing the stability of the machine. Moreover, it forces the rotor angular speed to follow a desired reference signal. The simulation results obtained using Matlab/Simulink behavior are presented and discussed. The obtained results show that the controller can greatly alleviate the chattering effect and enhance the robustness of control systems with high accuracy.
Improving the Usability of a MAS DSML

Context: A significant effort has been devoted to the design and implementation of various domain-specific modeling languages (DSMLs) for the software agents domain. Problem: Language usability is often tackled in an ad-hoc way, with the collection of anecdotal evidence supporting the process. However, usability plays an important role in the productivity, learnability and, ultimately, in the adoption of a MAS DSML by agent developers. Method: In this chapter, we discuss how the principles of The “Physics” of Notations (PoN) can be applied to improve the visual notation of a MAS DSML, called SEA_ML and evaluate the result in terms of usability. Results: The evolved version of the language, SEA_ML++, was perceived as significantly improved in terms of icons comprehensibility, adequacy and usability, as a direct result of employing the principles of PoN. However, users were not significantly more efficient and effective with SEA_ML++, suggesting these 2 properties were not chiefly constrained by the identified shortcomings of the SEA_ML concrete syntax.
Benchmarking discrete optimization heuristics with IOHprofiler

Automated benchmarking environments aim to support researchers in understanding how different algorithms perform on different types of optimization problems. Such comparisons carry the potential to provide insights into the strengths and weaknesses of different approaches, which can be leveraged into designing new algorithms. Carefully selected benchmark problems are also needed as training sets in the context of algorithm selection and configuration. With the ultimate goal to create a meaningful benchmark set for iterative optimization heuristics, we compile and assess in this work a selection of discrete optimization problems that subscribe to different types of fitness landscapes. All problems have been implemented and tested within IOHprofiler, our recently released software built to assess iterative heuristics solving combinatorial optimization problems. For each selected problem we compare performances of eleven different heuristics. Apart from fixed-target and fixed-budget results for the individual problems, we also derive ECDF results for groups of problems. To obtain these, we have implemented an add-on for IOHprofiler which allows aggregation of performance data across different benchmark functions.
Do Smart Speakers Respond to Their Errors Properly? A Study on Human-Computer Dialogue Strategy

As smart speakers with voice interaction capability permeate continuously in the world, more and more people will gradually get used to the new interaction medium–voice. Although speech recognition, natural language processing (NLP) have been greatly improved over the past few years, users still may encounter errors from time to time like “cannot understand”, “no requested audio resource (such as music)”, which can frustrate users. Therefore, when an error message is reported, it is vital that the smart speaker gives an effective and proper response. However, currently the response strategies adopted by leading smart speaker brands in China differed mainly on two dimensions: “apology or not” and “humor or neutral”. We explored user’s preference of response strategies under two error scenarios——“cannot understand” and “no requested audio resource”. A 2 (apology: yes vs. no) × 2 (error message expression tone: humor vs. neutral) within-subjects experiment was conducted. Two dependent variables (satisfaction and perceived sincerity of response) were measured. The results showed that participants were more satisfied and perceived higher sincerity when smart speaker apologized in both error scenarios. In the “no requested audio resource” scenario, humor had no significant impact on the perception of satisfaction and sincerity. But in the “cannot understand” scenario, humorous expression decreased perceived sincerity.
Matched Bipartite Block Model with Covariates

Author(s): Razaee, ZS; Amini, AA; Li, JJ | Abstract: Community detection or clustering is a fundamental task in the analysis of network data. Many real networks have a bipartite structure which makes community detection challenging. In this paper, we consider a model which allows for matched communities in the bipartite setting, in addition to node covariates with information about the matching. We derive a simple fast algorithm for fitting the model based on variational inference ideas and show its effectiveness on both simulated and real data. A variation of the model to allow for degree-correction is also considered, in addition to a novel approach to fitting such degree-corrected models.
Applying Rhetorical Structure Theory to Student Essays for Providing Automated Writing Feedback

We present a package of annotation resources, including annotation guideline, flowchart, and an Intelligent Tutoring System for training human annotators. These resources can be used to apply Rhetorical Structure Theory (RST) to essays written by students in K-12 schools. Furthermore, we highlight the great potential of using RST to provide automated feedback for improving writing quality across genres.
Multi-Label Chinese Comments Categorization: Comparison of Multi-Label Learning Algorithms

Multi-label text categorization refers to the problem of categorizing text through a multi-label learning algorithm. Text classification for Asian languages such as Chinese is different from work for other languages such as English which use spaces to separate words. Before classifying text, it is necessary to perform a word segmentation operation to convert a continuous language into a list of separate words and then convert it into a vector of a certain dimension. Generally, multi-label learning algorithms can be divided into two categories, problem transformation methods and adapted algorithms. This work will use customer's comments about some hotels as a training data set, which contains labels for all aspects of the hotel evaluation, aiming to analyze and compare the performance of various multi-label learning algorithms on Chinese text classification. The experiment involves three basic methods of problem transformation methods: Support Vector Machine, Random Forest, k-Nearest-Neighbor; and one adapted algorithm of Convolutional Neural Network. The experimental results show that the Support Vector Machine has better performance.
A New Type-2 Fuzzy Systems for Flexible-Joint Robot Arm Control

In this paper an adaptive neuro fuzzy inference system based on interval Gaussian type2 fuzzy sets in the antecedent part and Gaussian type-1 fuzzy sets as coefficients of linear combination of input variables in the consequent part is presented. The capability of the proposed method (we named ANFIS2) to function approximation and dynamical system identification is shown. The ANFIS2 structure is very similar to ANFIS, but in ANFIS2, a layer has been added for the purpose of type reduction. An adaptive learning rate based backpropagation with convergence guaranteed is used for parameter learning. Finally, the proposed ANFIS2 are used to control of a flexible joint robot arm that can be used in robot arm. Simulation results shows the proposed ANFIS2 with Gaussian type-1 fuzzy set as coefficients of linear combination of input variables in the consequent part has good performance and high accuracy but more training time. In the simulation, ANFIS2 is compared with conventional ANFIS. The results show that, in abrupt changes, the type-2 fuzzy system proof of efficiency and excellence to the type-1 fuzzy system.
RraR: Robust Recommendation Aggregation using Retraining in Internet of Things

Trust and Reputation Systems (TRS) have been witnessing the most promising solutions to ensure the security of a system in the presence of insider adversaries. The methodology behind TRS is to analyze the behavior of entities in the system to compute trustworthiness on them by the mean of direct (using own experience) and indirect (using recommendations from the others) trust computation. For the Internet of Things (IoT), where devices collaborate with each other to offer multiple services, TRS solve the issues of access control, decision making, reliable service delivery etc. The focus of this paper is towards robust indirect trust computation in a service-oriented, dynamic IoT environment. We propose a robust recommendation aggregation scheme which alleviates the effect of false or dishonest recommendations in indirect trust computation by performing the objective and subjective evaluation. In the objective evaluation, the value of a received recommendation is weighted by computing deviation from the average value of all the recommendation received. Subjective evaluation is performed to weight the recommender based on their age of interaction. The scheme deploys a retraining module which retrains the credibility of a recommender based on the dissimilarity experienced with the outcome of recommendation. The effectiveness of the proposed scheme has been demonstrated by the results of the simulation.
The maximum likelihood threshold of a path diagram

Linear structural equation models postulate noisy linear relationships between variables of interest.Each model corresponds to a path diagram, which is a mixed graph with directed edges that encode the domains of the linear functions and bidirected edges that indicate possible correlations among noise terms.Using this graphical representation, we determine the maximum likelihood threshold, that is, the minimum sample size at which the likelihood function of a Gaussian structural equation model is almost surely bounded.Our result allows the model to have feedback loops and is based on decomposing the path diagram with respect to the connected components of its bidirected part.We also prove that if the sample size is below the threshold, then the likelihood function is almost surely unbounded.Our work clarifies, in particular, that standard likelihood inference is applicable to sparse highdimensional models even if they feature feedback loops.
Rehabilitation Exercise Segmentation for Autonomous Biofeedback Systems with ConvFSM

Segmenting physical movements is a key step for any accelerometry-based autonomous biofeedback system oriented to rehabilitation and physiotherapy activities. Fundamentally, this can be reduced to the detection of recurrent patterns, also called motion primitives, in longer inertial signals. Most of the solutions developed in the literature require extensive domain knowledge, or are incapable of scaling to complex motion patterns and new exercises. In this paper, we explore the capabilities of inertial measurement units for the segmentation of upper limb rehabilitation exercises. To do so, we introduce a novel segmentation technique based on Convolutional Neural Networks and Finite State Machines, called ConvFSM. ConvFSM is able to isolate motion primitives from raw streaming data, using very little domain knowledge. We also investigate different combinations of sensors, in order to identify the most effective and flexible setup that could fit a home-based rehabilitation feedback system. Experimental results are presented, based on a dataset obtained from a combination of common upper limb and lower limb exercises.
Towards Reliable Learning for High Stakes Applications

In this paper, we focus on delivering reliable learning results for high stakes applications such as self-driving, financial investment and clinical diagnosis, where the accuracy of predictions is considered as a more crucial requirement than giving predictions for all query samples. We adopt the learning with reject option framework where the learning model only predict those samples which they convince to give the correct answer. However, for most prevailing deep learning predictors, the confidence estimated by the model themselves are far from reflecting the real generalization performance. To model the reliability of prediction concisely, we propose an exploratory solution called GALVE (Generative Adversarial Learning with Variance Expansion) which adopts generative adversarial learning to implicitly measure the region where the model achieve good generalization performance. By applying GALVE to measure the reliability of predictions, we achieved an error rate less than half of which straightforwardly measured by confidence in CIFAR10 and SVHN computer vision tasks.
Constraint Programming for Mining Borders of Frequent Itemsets

Frequent itemset mining is one of the most studied tasks in knowledge discovery. It is often reduced to mining the positive border of frequent itemsets, i.e. maximal frequent itemsets. Infrequent itemset mining, on the other hand, can be reduced to mining the negative border, i.e. minimal infrequent itemsets. We propose a generic framework based on constraint programming to mine both borders of frequent itemsets.One can easily decide which border to mine by setting a simple parameter. For this, we introduce two new global constraints, FREQUENTSUBS and INFREQUENTSUPERS, with complete polynomial propagators. We then consider the problem of mining borders with additional constraints. We prove that this problem is coNP-hard, ruling out the hope for the existence of a single CSP solving this problem (unless coNP ⊆ NP).
Research Trends for Named Entity Recognition in Hindi Language

Named Entity Recognition (NER) is a process of identification and classification of names into pre-defined categories- person, location, organization, date, time and others. NER serves as one of the most valuable application tools for varying languages and domains. Despite its popularity and successful deployment in English, this area is still exploratory for the Hindi language. NER in Hindi is a challenging task due to the scarceness of language resources and complex morphological structure. An initial impetus to write this chapter constitutes concise research on prevailing NER systems in the Hindi language. To achieve this objective, all-inclusive review and analysis of the research trends for NER in Hindi starting from the year 1999 to till date is conducted from varied articles. These articles include full-length articles, survey papers, dissertations, and guidelines which exploit decision analysis of the NER with respect to six vital aspects. All these aspects are collaborated and visualized in details within the chapter. The future research directions, challenges, and open issues in the field of NER are also presented for keen researchers. One can then design a unified, coherent NER system which can be applied to vivid language processing tasks for the Hindi and other Indian languages.
Optimizing Cloud Caches For Free: A Case for Autonomic Systems with a Serverless Computing Approach

While significant advances have been made towards realizing self-tuning cloud caches, existing products still require manual tuning. These systems are built to serve requests extremely fast and anything that consumes resources not directly related to the request-serving control path is avoided. We show that severless computing platforms can be leveraged to solve complex optimization problems that arise during self-tuning loops, and thus can be used to optimize resources in cloud caches, for free. To show that our approach is feasible and useful, we implement SPREDS (Self-Partitioning REDis), a modified version of Redis that optimizes memory management in the multi-instance Redis scenario. Through this case study and cost analysis, we make a case for implementing the controller of autonomic systems using a serverless computing approach.
MFA is a Waste of Time! Understanding Negative Connotation Towards MFA Applications via User Generated Content.

Traditional single-factor authentication possesses several critical security vulnerabilities due to single-point failure feature. Multi-factor authentication (MFA), intends to enhance security by providing additional verification steps. However, in practical deployment, users often experience dissatisfaction while using MFA, which leads to non-adoption. In order to understand the current design and usability issues with MFA, we analyze aggregated user generated comments (N = 12,500) about application-based MFA tools from major distributors, such as, Amazon, Google Play, Apple App Store, and others. While some users acknowledge the security benefits of MFA, majority of them still faced problems with initial configuration, system design understanding, limited device compatibility, and risk trade-offs leading to non-adoption of MFA. Based on these results, we provide actionable recommendations in technological design, initial training, and risk communication to improve the adoption and user experience of MFA.