Reviewerly/exHarmony huggingface dataset:
Identification of Urdu Ghazal Poets using SVM

Urdu literature has a rich tradition of poetry, with many forms, one of which is Ghazal. Urdu poetry structures are mainly of Arabic origin. It has complex and different sentence structure compared to our daily language which makes it hard to classify. Our research is focused on the identification of poets if given with ghazals as input. Previously, no one has done this type of work. Two main factors which help categorize and classify a given text are the contents and writing style. Urdu poets like Mirza Ghalib, Mir Taqi Mir, Iqbal and many others have a different writing style and the topic of interest. Our model caters these two factors, classify ghazals using different classification models such as SVM (Support Vector Machines), Decision Tree, Random forest, Naïve Bayes and KNN (K-Nearest Neighbors). Furthermore, we have also applied feature selection techniques like chi square model and L1 based feature selection. For experimentation, we have prepared a dataset of about 4000 Ghazals. We have also compared the accuracy of different classifiers and concluded the best results for the collected dataset of Ghazals.
An Adaptive Random Path Selection Approach for Incremental Learning

In a conventional supervised learning setting, a machine learning model has access to examples of all object classes that are desired to be recognized during the inference stage. This results in a fixed model that lacks the flexibility to adapt to new learning tasks. In practical settings, learning tasks often arrive in a sequence and the models must continually learn to increment their previously acquired knowledge. Existing incremental learning approaches fall well below the state-of-the-art cumulative models that use all training classes at once. In this paper, we propose a random path selection algorithm, called Adaptive RPS-Net, that progressively chooses optimal paths for the new tasks while encouraging parameter sharing between tasks. We introduce a new network capacity measure that enables us to automatically switch paths if the already used resources are saturated. Since the proposed path-reuse strategy ensures forward knowledge transfer, our approach is efficient and has considerably less computation overhead. As an added novelty, the proposed model integrates knowledge distillation and retrospection along with the path selection strategy to overcome catastrophic forgetting. In order to maintain an equilibrium between previous and newly acquired knowledge, we propose a simple controller to dynamically balance the model plasticity. Through extensive experiments, we demonstrate that the Adaptive RPS-Net method surpasses the state-of-the-art performance for incremental learning and by utilizing parallel computation this method can run in constant time with nearly the same efficiency as a conventional deep convolutional neural network.
Replica Exchange for Non-Convex Optimization

Gradient descent (GD) is known to converge quickly for convex objective functions, but it can be trapped at local minimums. On the other hand, Langevin dynamics (LD) can explore the state space and find global minimums, but in order to give accurate estimates, LD needs to run with small discretization stepsize and weak stochastic force, which in general slow down its convergence. This paper shows that these two algorithms can collaborate through a simple exchange mechanism, in which they swap their current positions if LD yields a lower objective function. This idea can be seen as the singular limit of the replica exchange technique from the sampling literature. We show that this new algorithm converges to the global minimum linearly with high probability, assuming the objective function is strongly convex in a neighborhood of the unique global minimum. By replacing gradients with stochastic gradients, and adding a proper threshold to the exchange mechanism, our algorithm can also be used in online settings. We further verify our theoretical results through some numerical experiments, and observe superior performance of the proposed algorithm over running GD or LD alone.
Connecting the Dots: Reader Ratings, Bibliographic Data, and Machine-Learning Algorithms for Monograph Selection

Recommender systems, a subclass of information filtering designed to predict the rating or preference of a user, are among the most successful examples of machine learning in action. Drawing inspiration from the benefits of using recommender systems for business, and their success in heightening the perceived utility of recommendations, this project was developed using Python to optimize collection recommendations and to help librarians make collection decisions using a recommender system. This paper illustrates several examples of building recommender systems using a variety of recommendation techniques to aid in the selection of monographs. It also points out possible future uses of recommender systems in libraries.
The Synergetic Impact of AI, IoT, and 5G on Information Literacy and Education

Purpose/Thesis: The purpose of the study is to argue, explain and justify that a troika of AI (Artificial Intelligence), IoT (Internet of Things), and 5G broadband cooperating with each other has the trans­formative power that can provide a synergetic impact on the information literacy and education. The troika can provide new methodologies and tools that will unleash and boost the spirit of innovation and trigger energy, passion, and creativity resulting in developing better education ecosystems.Approach/Methods: The method adopted in the study is twofold, namely: (i) it presents and examines the features of AI, IoT, and 5G and the way they can cooperate with each other in order to develop new education models and patterns, and then analyses the issue of e-literacy by breaking it apart into e-skills, information literacy, and media literacy; (ii) it shows how AI, IoT, and 5G can support information literacy and education.Results and conclusions: The outcome of the research is the presentation and examination of AI, IoT, and 5G collaborating with each other towards transforming education models and processes. It concludes that the AI, IoT, and 5G troika has a great potential to boost teaching and learning, and to inspire and support the development of new generation educational ecosystems. However, in the digital age information literacy and education is too serious a matter to be left solely to educators and ICT technologists. Learners, students, and concerned citizens need to be involved in the trans­formation processes.Originality/Value: The originality and value of the research is to prove that while working together AI, IoT, and 5G can provide a significant support for transforming the present education patterns, in particular regarding the information literacy that is a condition sine qua non of effective education in the digital era. This can help scholars and education communities to unleash innovativeness aimed at developing new generations of educational ecosystems, and thereby make learning and teaching processes more efficient and available to significantly larger audiences.
Empirical Analysis of Fictitious Play for Nash Equilibrium Computation in Multiplayer Games

While fictitious play is guaranteed to converge to Nash equilibrium in certain game classes, such as two-player zero-sum games, it is not guaranteed to converge in non-zero-sum and multiplayer games. We show that fictitious play in fact leads to improved Nash equilibrium approximation over a variety of game classes and sizes than (counterfactual) regret minimization, which has recently produced superhuman play for multiplayer poker. We also show that when fictitious play is run several times using random initializations it is able to solve several known challenge problems in which the standard version is known to not converge, including Shapley's classic counterexample. These provide some of the first positive results for fictitious play in these settings, despite the fact that worst-case theoretical results are negative.
Detecting structured sources in noisy images via Minkowski maps

Astronomy, biophysics, and material science often depend on the possibility to extract information out of faint spatial signals.Here we present a morphometric analysis technique to quantify the shape of structural deviations in greyscale images.It identifies important features in noisy spatial data, especially for short observation times and low statistics.Without assuming any prior knowledge about potential sources, the additional shape information can increase the sensitivity by 14 orders of magnitude compared to previous methods.Rejection rates can increase by an order of magnitude.As a key ingredient to such a dramatic increase, we accurately describe the distribution of the homogeneous background noise in terms of the density of states Ω(A, P, χ) for the area A, perimeter P , and Euler characteristic χ of random black-and-white images.The technique is successfully applied to data of the H.E.S.S. experiment for the detection of faint extended sources.
Teaching Machines to Converse

The ability of a machine to communicate with humans has long been associated with the general success of AI. This dates back to Alan Turing's epoch-making work in the early 1950s, which proposes that a machine's intelligence can be tested by how well it, the machine, can fool a human into believing that the machine is a human through dialogue conversations. Many systems learn generation rules from a minimal set of authored rules or labels on top of hand-coded rules or templates, and thus are both expensive and difficult to extend to open-domain scenarios. Recently, the emergence of neural network models the potential to solve many of the problems in dialogue learning that earlier systems cannot tackle: the end-to-end neural frameworks offer the promise of scalability and language-independence, together with the ability to track the dialogue state and then mapping between states and dialogue actions in a way not possible with conventional systems. On the other hand, neural systems bring about new challenges: they tend to output dull and generic responses; they lack a consistent or a coherent persona; they are usually optimized through single-turn conversations and are incapable of handling the long-term success of a conversation; and they are not able to take the advantage of the interactions with humans. This dissertation attempts to tackle these challenges: Contributions are two-fold: (1) we address new challenges presented by neural network models in open-domain dialogue generation systems; (2) we develop interactive question-answering dialogue systems by (a) giving the agent the ability to ask questions and (b) training a conversation agent through interactions with humans in an online fashion, where a bot improves through communicating with humans and learning from the mistakes that it makes.
Case Study on Seat Management of University Library Based on WeChat Public Number Client—Taking Jianghan University Library as an Example

University library is an important learning place for students, but it is always affected by seat occupancy problem. In order to solve the long-term occupancy problem of self-study space in the library, the library of Jianghan University introduced the WeChat Public Number Seat Reservation Management System, and achieved obvious results through its operation.
A Meta-model for context-aware adaptive Business Process as a Service in collaborative cloud environment

With the emergence of cloud computing, building and developing a new Software by SaaS composition becomes a major concern in today’s cloud business. First, the business analyst establishes the Business Process (BP) of the new SaaS. Then, the SaaS composer, based on functional and non-functional requirements, discovers and selects the potential SaaS components that can fill into each of the defined tasks in the BP. Currently, enterprises need more and more flexible SaaS business process as an adaptive composite SaaS from process vendors, which interest on context changes and needs evolutions. A meta-model is proposed in this paper dealing with different concepts involved in business cloud layer. This meta-model, in which both business process and service composition are considered, is accompanied by a case study. Then, an ontology has been developed and an extract of instances has been presented to demonstrate the adaptation process.
Implementation of K-Means and crossover ant colony optimization algorithm on multiple traveling salesman problem

Abstract Multiple Traveling Salesman Problem (MTSP) is a generalization of the Traveling Salesman Problem (TSP). MTSP is an optimization problem to find the minimum total distance of m salesmen tours to visit several cities in which each city is only visited exactly by one salesman, starting from origin city called depot and return to depot after the tour is completed. In this paper, K-Means and Crossover Ant Colony Optimization (ACO) are used to solve MTSP. The implementation is observed on three datasets from TSPLIB with 2, 3, 4, and 8 salesmen. Analysis of results using K-Means and Crossover ACO will be compared. The effect of selecting a city as depot on the total travel distance of tour will also be analyzed.
SpWMM: A High-Performance Sparse-Winograd Matrix-Matrix Multiplication Accelerator for CNNs

In recent years, many CNN accelerators are proposed to exploit the sparsity of the networks to enjoy the benefits of both computation and memory reduction. However, these accelerators either cannot exploit the sparsity of both activations and weights, or cannot achieve stable performance with a static scheduling strategy, which is vulnerable to the sparsity distribution. This paper proposes a dynamic scheduling strategy and a balanced compressed sparse row (BCSR) format to efficiently address these two issues. A set-associate structure is presented to tradeoff the load balance and logic overhead. We propose SpWMM to accelerate the CNN inference, which is the first work to implement both sparse Winograd convolution and sparse fully-connected (FC) layers. On contemporary neural networks, this work achieves: (1) 2.6Top/s for Winograd convolution and 525Gop/s for 1×1 convolution and FC layers in the 4-way association design on Xilinx ZC706 platform, (2) 6.5 Top/s for Winograd convolution and 1.2Top/s for 1×1 convolution and FC layers in the 16-way association design on Xilinx VCU1525 platform. Compared with the state-of-the-art works on the same platform, the 4-way design achieves 2.0× speedup.
Asymptotics of Cross-Validation

Cross validation is a central tool in evaluating the performance of machine learning and statistical models. However, despite its ubiquitous role, its theoretical properties are still not well understood. We study the asymptotic properties of the cross validated-risk for a large class of models. Under stability conditions, we establish a central limit theorem and Berry-Esseen bounds, which enable us to compute asymptotically accurate confidence intervals. Using our results, we paint a big picture for the statistical speed-up of cross validation compared to a train-test split procedure. A corollary of our results is that parametric M-estimators (or empirical risk minimizers) benefit from the "full" speed-up when performing cross-validation under the training loss. In other common cases, such as when the training is done using a surrogate loss or a regularizer, we show that the behavior of the cross-validated risk is complex with a variance reduction which may be smaller or larger than the "full" speed-up, depending on the model and the underlying distribution. We allow the number of folds to grow with the number of observations at any rate.
The Effectiveness of Implementation of the MGMP Revitalization Program as a Media Increasing English Teacher Competency in Pidie District

This research was conducted to determine the effectiveness of the revitalization program for Musyawarah Guru Mata Pelajaran Teacher's Deliberation of Course of Study (MGMP) as a medium to improve English teacher competency. The objectives of this study are (1) Analyzing the reaction of each participant to the English Subject Teacher Consultation activity in Pidie District. (2) Explain knowledge and skills regarding the theoretical and philosophical foundations of English subjects after participating in the English Subject Teacher Consultation activities in the Pidie District. (3) Analyze the motivation of each participant in the English Subject Teacher Meeting to transfer knowledge and skills into the English learning process. (4) Analyzing the impact of the activities of the English Subject Teachers Conference on the development of English teacher competencies in Pidie District. The research method applied in this study is evaluative research based on (Arikunto 2007: 222) evaluative research can be interpreted as a process carried out in order to determine policy by first considering the positive values and benefits of a program, as well as considering the processes and techniques that have been used to do a research. The results showed that each participant's positive reaction to the implementation of the program could significantly influence the increase in knowledge and skills for each participant / program participant in relation to the philosophical and theoretical basis of learning English in secondary schools. However, increasing knowledge and skills did not always have an impact significant at the practical level, namely the quality of the learning process carried out by teachers, participants in the MGMP revitalization program, in the classroom. Teachers still tend to use conventional patterns in the process of learning English. . In general, the implementation of the MGMP revitalization program has been able to improve the participants' knowledge and skills discourse on the philosophical and theoretical foundations of English learning. The obstacle that arises then is the lack of motivation of some participants to apply knowledge and skills in the context of their performance. The lack of motivation to apply this knowledge and skills has resulted in learning practices that have not changed much. This shows that the forum has not been able to effectively and optimally carry out its role and function as a means for the development and development of teaching professionalism.
Spatial-Adaptive Network for Single Image Denoising

Previous works have shown that convolutional neural networks can achieve good performance in image denoising tasks. However, limited by the local rigid convolutional operation, these methods lead to oversmoothing artifacts. A deeper network structure could alleviate these problems, but more computational overhead is needed. In this paper, we propose a novel spatial-adaptive denoising network (SADNet) for efficient single image blind noise removal. To adapt to changes in spatial textures and edges, we design a residual spatial-adaptive block. Deformable convolution is introduced to sample the spatially correlated features for weighting. An encoder-decoder structure with a context block is introduced to capture multiscale information. With noise removal from the coarse to fine, a high-quality noisefree image can be obtained. We apply our method to both synthetic and real noisy image datasets. The experimental results demonstrate that our method can surpass the state-of-the-art denoising methods both quantitatively and visually.
Digital Education in the International Pedagogical Discourse

Abstract The article analyzes the essence of the concept of “digital education”, the way of its formation in the international pedagogical discourse. The modern international tendencies of development of digital education which are shown in the conditions of acceleration of processes of digital transformation of a society and their realization in educational establishments are characterized. An important consequence of the digital revolution is the explosives growth of accessible and potentially useful information in various forms – not only traditionally textual, but also visual, audio. All paper is gradually converted to electronic format. One of the most significant innovations is the active implementation of digital education. The results of the comparative analysis have highlighted a number of major components of the digital education system. It has done the analysis of modern trends, which are today the main ones in the implementation of digital education in different educational institutions. The essence and basic qualities of distance and online learning are revealed. The role and main spectrum of tasks that automated testing in the control of knowledge and skills of education recipients is analyzed. A number of important areas are highlighted, which greatly increase the effectiveness of teaching and at the same time require a revision of traditional approaches to learning using mobile devices. Here are some of the key features of Learning Management Systems (LMS). The technology of adapting computer game techniques to non-game processes and events is discussed to increase the involvement of participants in the educational process. A number of benefits of digital education, combined with adaptive learning and personalization, have been highlighted, which are able to help each educator to achieve an optimal level of intellectual development according to his or her natural abilities and inclinations. It is established that the process of following modern trends and tendencies in the field of digital education will change the traditional plane of problems of education, the process of obtaining education will be wider and more flexible.
Supervised Class Distribution Learning for GANs-Based Imbalanced Classification

Class imbalance is a challenging problem in many real-world applications such as fraudulent transactions detection in finance and diagnosis of rare diseases in medicine, which has attracted more and more attention in the community of machine learning and data mining. The main issue is how to capture the fundamental characteristics of the imbalanced data distribution. In particular, whether the hidden pattern can be truly mined from minority class is still a largely unanswered question after all it contains limited instances. The existing methods provide only a partial understanding of this issue and result in the biased and inaccurate classifiers. To overcome this issue, we propose a novel imbalanced classification framework with two stages. The first stage aims to accurately determine the class distributions by a supervised class distribution learning method under the Wasserstein auto-encoder framework. The second stage makes use of the generative adversarial networks to simultaneously generate instances according to the learnt class distributions and mine the discriminative structure among classes to train the final classifier. This proposed framework focuses on Supervised Class Distribution Learning for Generative Adversarial Networks-based imbalanced classification (SCDL-GAN). By comparing with the state-of-the-art methods, the experimental results demonstrate that SCDL-GAN consistently benefits the imbalanced classification task in terms of several widely-used evaluation metrics on five benchmark datasets.