Reviewerly/exHarmony huggingface dataset:
OLCD: Delivering an Exciting Future for Flexible Displays

Abstract The development of glass‐free organic LCDs (OLCDs) promises a future of thin, lightweight, and shatterproof screens that are scalable to large areas and capable of sidestepping flat screens' limitations to embrace curved surfaces.
Recursive enumerability and elementary frame definability in predicate modal logic

We investigate the relationship between recursive enumerability and elementary frame definability in first-order predicate modal logic. On the one hand, it is well-known that every first-order predicate modal logic complete with respect to an elementary class of Kripke frames, i.e., a class of frames definable by a classical first-order formula, is recursively enumerable. On the other, numerous examples are known of predicate modal logics, based on `natural' propositional modal logics with essentially second-order Kripke semantics, that are either not recursively enumerable or Kripke incomplete. This raises the question of whether every Kripke complete, recursively enumerable predicate modal logic can be characterized by an elementary class of Kripke frames. We answer this question in the negative, by constructing a normal predicate modal logic which is Kripke complete, recursively enumerable, but not complete with respect to an elementary class of frames. We also present an example of a normal predicate modal logic that is recursively enumerable, Kripke complete, and not complete with respect to an elementary class of rooted frames, but is complete with respect to an elementary class of frames that are not rooted.
Safety and Robustness of Deep Neural Networks Object Recognition Under Generic Attacks

Embedding machine or deep learning software into safety-critical systems such as autonomous vehicles requires software verification and validation. Such software adds non traceable hazards to traditional hardware and sensors failures, not to mention attacks that fool the prediction of a DNN and hampers its robustness. Formal methods from computer science are now applied to deep neural networks to assess the local and global robustness of a given DNN. Typically static analysis with Abstract Interpretation or SAT solvers approaches are applied to neural networks and leverages the important progress of formal methods over the last decades. Such approaches estimate bounds on the perturbation of the inputs and formally guarantee the same DNN prediction within these bounds. However formal methods over DNN for image perception system have only been applied to simple image attacks (2D rotation, brightness). In this work, we extend the definition of Lower and Upper Bounds to assess the robustness of a DNN perception system against more generic attacks. We propose a general method to verify object recognition systems using Abstract Interpretation theory. Another major contribution is the adaptation of Upper and Lower Bounds with the abstract intervals to support more complex attacks. We consider the three following classes: convolutional attacks, occlusion attacks and geometrical transformations. For the last one, we generalize the geometrical transformations with displacements in the three-dimensional space.
The predictive distributions of thinning‐based count processes

Abstract This paper shows that the term structure of conditional (i.e. predictive) distributions allows for closed form expression in a large family of (possibly higher order or infinite order) thinning‐based count processes such as INAR( p ), INARCH( p ), NBAR( p ), and INGARCH(1,1). Such predictive distributions are currently often deemed intractable by the literature and existing approximation methods are usually time consuming and induce approximation errors. In this paper, we propose a Taylor's expansion algorithm for these predictive distributions, which is both exact and fast. Through extensive simulation exercises, we demonstrate its advantages with respect to existing methods in terms of the computational gain and/or precision.
Learning-Augmented Data Stream Algorithms

The data stream model is a fundamental model for processing massive data sets with limited memory and fast processing time. Recently Hsu et al. (2019) incorporated machine learning techniques into the data stream model in order to learn relevant patterns in the input data. Such techniques were encapsulated by training an oracle to predict item frequencies in the streaming model. In this paper we explore the full power of such an oracle, showing that it can be applied to a wide array of problems in data streams, sometimes resulting in the first optimal bounds for such problems. Namely, we apply the oracle to counting distinct elements on the difference of streams, estimating frequency moments, estimating cascaded aggregates, and estimating moments of geometric data streams. For the distinct elements problem, we obtain the first memory-optimal algorithms. For estimating the $p$-th frequency moment for $0 2$ we obtain a quadratic saving in memory. We empirically validate our results, demonstrating also our improvements in practice.
Distance education in the training system of highly qualified personnel

At the present stage of the society development, continuous education programs are being actively implemented. One way to implement continuous education is to use various forms of distance education. The use of distance education in the training of highly qualified personnel is associated with specific features due to legal, methodological and other factors. This article is devoted to the study of these features and the development of a conceptual approach to the implementation of distance forms of training highly qualified personnel. The problem of the implementation of distance education in the training of highly qualified personnel is investigated. The concept of distance training of postgraduate student and doctoral students is considered taking into account existing methodological approaches and features of the current state of society. The implementation of the proposed distance education system is demonstrated by the example of training foreign highly qualified personnel. The developed model of postgraduate student training quality management is presented. It is concluded that the achievement of a high-quality educational process is possible only with the effective work of the teaching staff, modern methodological support of the educational process, the use of models and quality management systems. All these components are aimed at achieving a single result - the training of a highly qualified specialist in demand in modern conditions.
Stacking of SVMs for Classifying Intangible Cultural Heritage Images

Our investigation aims at classifying images of the intangible cultural heritage (ICH) in the Mekong Delta, Vietnam. We collect an images dataset of 17 ICH categories and manually annotate them. The comparative study of the ICH image classification is done by the support vector machines (SVM) and many popular vision approaches including the handcrafted features such as the scale-invariant feature transform (SIFT) and the bag-of-words (BoW) model, the histogram of oriented gradients (HOG), the GIST and the automated deep learning of invariant features like VGG19, ResNet50, Inception v3, Xception. The numerical test results on 17 ICH dataset show that SVM models learned from Inception v3 and Xception features give good accuracy of 61.54% and 62.89% respectively. We propose to stack SVM models using different visual features to improve the classification result performed by any single one. Triplets (SVM-Xception, SVM-Inception-v3, SVM-VGG19), (SVM-Xception, SVM-Inception-v3, SVM-SIFT-BoW) achieve 65.32% of the classification correctness.
The Right to Internet Access

When dealing with the issue at the heart of this chapter, a fundamental question has to be tackled in greater depth: is the right of access to the Internet a human right (or a fundamental right – below is my attempt to introduce a terminological clarification in this regard) which enjoys semantic, conceptual and constitutional autonomy? In other words, is access to the Internet an autonomous right or only a precondition for enjoying, among other rights, freedom of expression? Why does the classification as a free-standing or derived right matter? Does it carry normative implications or is it primarily a rhetorical tool?
HARGRURNN: Human activity recognition using inertial body sensor gated recurrent units recurrent neural network

Internet of Things (IoT), wearable devices, and smartphones are more ubiquitous and available at a reasonable cost. Fitbit, Microsoft Band, Apple Watch, MI band, and Smartphone apps namely Strava and Runkeeper are some of the commercial products which are already available in the market. These products are embedded with the sensors that facilitate them to regularly sense and capture the environmental, physiological, functional data for applications in healthcare, wellbeing, and sports. These sensing devices have mediocre computing capabilities for data processing and transfer. The size of devices is so compact that can be worn on the body put in the pocket or locate in the house. This large scale recorded a wealth of physiological data require an efficient and meaningful interpretation as well as the proper and proficient method of analysis and the classification of data.Human Activity Recognition is classified into two features say Shallow and Deep features. Shallow features are extracted conventionally with the help of a simple machine learning approach. The large-scale time series situ data which require high computation power, processing capabilities, activities, and real-time classification, Deep Learning (DL) is the promising technique to deal with these activities.Weexercise the triaxial Accelerometers and triaxial Gyroscope to capture the data for HAR. The data set measured by the inertial sensor is then divided into the segments of 4 to 10s. The performance will be shown on the parameters of precision (%), Recall (%) and Accuracy (%).Parameter optimization and extraction of the segments are the two areas where we can carry forward our research.
A quick look at impredicativity

Type inference for parametric polymorphism is wildly successful, but has always suffered from an embarrassing flaw: polymorphic types are themselves not first class. We present Quick Look, a practical, implemented, and deployable design for impredicative type inference. To demonstrate our claims, we have modified GHC, a production-quality Haskell compiler, to support impredicativity. The changes required are modest, localised, and are fully compatible with GHC's myriad other type system extensions.
Faster p-norm minimizing flows, via smoothed q-norm problems

We present faster high-accuracy algorithms for computing lp-norm minimizing flows. On a graph with m edges, our algorithm can compute a (1 + 1/poly(m))-approximate unweighted lp-norm minimizing flow with [MATH HERE] operations, for any p ≥ 2, giving the best bound for all p ≳ 5.24. Combined with the algorithm from the work of Adil et al. (SODA '19), we can now compute such flows for any 2 ≤ p ≤ mo(1) in time at most O(m1.24). In comparison, the previous best running time was Ω(m1.33) for large constant p. For p ~ Δ−1 log m, our algorithm computes a (1 + Δ)-approximate maximum flow on undirected graphs using m1+o(1) Δ−1 operations, matching the current best bound, albeit only for unit-capacity graphs. We also give an algorithm for solving general lp-norm regression problems for large p. Our algorithm makes [MATH HERE] calls to a linear solver. This gives the first high-accuracy algorithm for computing weighted lp-norm minimizing flows that runs in time o(m1.5) for some p = mΩ(1). Our key technical contribution is to show that smoothed lp-norm problems introduced by Adil et al., are interreducible for different values of p. No such reduction is known for standard lp-norm problems.
Detecting Malicious Social Bots: Story of a Never-Ending Clash

Recently, studies on the characterization and detection of social bots were published at an impressive rate. By looking back at over ten years of research and experimentation on social bots detection, in this paper we aim at understanding past, present, and future research trends in this crucial field. In doing so, we discuss about one of the nastiest features of social bots – that is, their evolutionary nature. Then, we highlight the switch from supervised bot detection techniques – focusing on feature engineering and on the analysis of one account at a time – to unsupervised ones, where the focus is on proposing new detection algorithms and on the analysis of groups of accounts that behave in a coordinated and synchronized fashion. These unsupervised, group-analyses techniques currently represent the state-of-the-art in social bot detection. Going forward, we analyze the latest research trend in social bot detection in order to highlight a promising new development of this crucial field.
Multi-path learnable wavelet neural network for image classification

Despite the remarkable success of deep learning in pattern recognition, deep network models face the problem of training a large number of parameters. In this paper, we propose and evaluate a novel multi-path wavelet neural network architecture for image classification with far less number of trainable parameters. The model architecture consists of a multi-path layout with several levels of wavelet decompositions performed in parallel followed by fully connected layers. These decomposition operations comprise wavelet neurons with learnable parameters, which are updated during the training phase using the back-propagation algorithm. We evaluate the performance of the introduced network using common image datasets without data augmentation except for SVHN and compare the results with influential deep learning models. Our findings support the possibility of reducing the number of parameters significantly in deep neural networks without compromising its accuracy.
High-Capacity Robust Image Steganography via Adversarial Network

Steganography has been successfully employed in various applications, e.g., copyright control of materials, smart identity cards, video error correction during transmission, etc. Deep learning-based steganography models can hide information adaptively through network learning, and they draw much more attention.However, the capacity, security, and robustness of the existing deep learning-based steganography models are still not fully satisfactory.In this paper, three models for different cases, i.e., a basic model, a secure model, a secure and robust model, have been proposed for different cases.In the basic model, the functions of high-capacity secret information hiding and extraction have been realized through an encoding network and a decoding network respectively.The high-capacity steganography is implemented by hiding a secret image into a carrier image having the same resolution with the help of concat operations, InceptionBlock and convolutional layers.Moreover, the secret image is hidden into the channel B of carrier image only to resolve the problem of color distortion.In the secure model, to enhance the security of the basic model, a steganalysis network has been added into the basic model to form an adversarial network.In the secure and robust model, an attack network has been inserted into the secure model to improve its robustness further.The experimental results have demonstrated that the proposed secure model and the secure and robust model have an overall better performance than some existing high-capacity deep learning-based steganography models.The secure model performs best in invisibility and security.The secure and robust model is the most robust against some attacks.
Comparative Study of Machine Learning Algorithms Using the Breast Cancer Dataset

Breast cancer is the second most common cancer overall and the most common cancer in women worldwide. To better diagnose and predict the development of breast cancer, current medicine uses several techniques and tools based on very powerful and advanced methods such as machine learning algorithms. This work consists to produce a comparative study between 11 machine learning algorithms using the Breast Cancer Wisconsin (Diagnostic) Dataset, and by measuring their classification test accuracy. We have elaborated this study to define the best method to create two classifiers that must define benign from malignant breast lumps based on the features of the dataset which have been extracted from diagnostic images of a fine needle aspirate of a breast mass. The results of the classification experimentation show that the best accuracy in this paper was achieved by the Neural Network algorithm, which had, in its best configuration, 96.49% of accuracy.
Usefulness of Mobile Assisted Language Learning in Primary Education

Literacy & Numeracy Drive (LND) is a mobile application that is used in public sector primary schools in Punjab province, Pakistan to teach students of Grade 03 on a tablet for learning languages and Mathematics.Persons designated the role of a Monitoring & Evaluation Assistant (MEA) visit every school allocated by authorities once in a month and select 07-10 students randomly to evaluate them on his own tablet by asking multiple questions related to English, Urdu and Mathematics.After the evaluation, MEA has to upload the result on the official portal for the respective school.This study aims to evaluate the effectiveness of LND for its usefulness, usability, accessibility, content, and assessments by involving students and teachers using this application in different schools.A mixedmethod study has been adopted in which 57 teachers and nearly 300 students from different locations of the district and from different schools have been selected, to measure the effectiveness of LND and evaluate the effectiveness with the help of interviews and questionnaires.The result reveals, in its current form, the LND application is not effective and needs improvement in usability, design, content, accessibility, infrastructure, and assessment.Furthermore, teachers recommend that game-based learning consists of an interactive interface, phonics, animations.As the more interactive and attractive presentation of the content and variations in the assessment may increase students' involvement and will make this application more effective and will produce good results.
AI shortcuts speed up simulations by billions of times

With little training, neural networks create accurate emulators for physics, astronomy, and earth science.
LaProp: a Better Way to Combine Momentum with Adaptive Gradient

We identity a by-far-unrecognized problem of Adam-style optimizers which results from unnecessary coupling between momentum and adaptivity. The coupling leads to instability and divergence when the momentum and adaptivity parameters are mismatched. In this work, we propose a method, Laprop, which decouples momentum and adaptivity in the Adam-style methods. We show that the decoupling leads to greater flexibility in the hyperparameters and allows for a straightforward interpolation between the signed gradient methods and the adaptive gradient methods. We experimentally show that Laprop has consistently improved speed and stability over Adam on a variety of tasks. We also bound the regret of Laprop on a convex problem and show that our bound differs from that of Adam by a key factor, which demonstrates its advantage.
Face Recognition Based on The Improved MobileNet

Within the average pooling area of the lightweight MobileNet network, The problem that the global feature performance is insufficient due to the difference of contribution of each element, A weighted pooling method based on sensitive location is proposed. Different adaptive contribution values are assigned to the elements in the pooling area, which makes the spatial information more fully expressed and reduces the information loss. At the same time, the improved MobileNet model is applied to face recognition. The experimental training set is derived from the CASIA-WebFace data set, and the improved MobileNet algorithm is validated by LFW data set. Through comparing with the original model, it is found that the improved MobileNet algorithm makes the model more effective and obtains a better recognition accuracy, which can be increased by 1% at most.
Organizational Culture and Entrepreneurial Orientation: Mediating Role of Entrepreneurial Leadership

The purpose of this study was to investigate the mediating role of entrepreneurial leadership between different types of organizational cultures and entrepreneurial orientation. A total of 181 online responses from the small and medium size industries were gathered with the help of chamber of commerce and were analyzed using Partial Least Square (PLS). The result revealed that adhocracy culture and clan culture significantly affected the entrepreneurial orientation only if entrepreneurial leadership style acted as a mediator showing innovative solution development and mentorship were highly dependent on entrepreneurial leadership style. However, the relationship between market culture and entrepreneurial orientation became insignificant with the mediation of entrepreneurial leadership showing lack of external focus towards the market. The role of entrepreneurial leadership style remained significant in hierarchical culture to enhance entrepreneurial orientation suggesting higher internal focus and control. This study will help researchers and practitioners to understand the entrepreneurial leadership style in different organizational perspectives to generate entrepreneurial orientation.
EXPLORING EFL STUDENT TEACHERS READINESS AND GENDER DIFFERENCES OF LEARNER AUTONOMY

Despite its importance for successful English as a Foreign Language (EFL) language learning and the abundance of scientific works of learner autonomy, this construct has not received decent attention in the Indonesian context. Further, gender differences in learner autonomy as an essential variable that has not been well-researched were also included in this study. The authors conducted a mix-method study to explore the fourth-year student teachers’ conceptualization, attitudes, and readiness for learning autonomy by employing a 43 items questionnaire survey adapted from Karabiyik (2008) to a total of 120 participants (156 male and 164 female) and interview questions to 6 volunteers. The qualitative data implied that the participants had an insufficient understanding of learner autonomy even though they exhibited a positive attitude towards this construct. The overall results of quantitative data suggested that the student teachers were not ready for learner autonomy indicated by the tendency to teacher-centered teaching which might be accounted for by low proficiency in English reflected by low engagement virtually in the majority of the autonomous learning activities. Furthermore, in terms of gender differences in learner autonomy, the t-test results demonstrated that no significant differences between male and female student teachers in terms of the responsibilities of autonomous learning, decision-making abilities, and engagement of autonomous learning outside the class. However, a significant difference was found between genders and engagement in autonomous activities in class which favored male students than their female counterparts suggesting that the male students performed more than male students in asking questions to teachers and taking opportunities to practice their English with their peers. The authors concluded that the student teachers were not ready for autonomous learning even though they had a positive attitude. Thus, the authors recommended the teachers to implement teaching methods, for instance, Project-Based Learning which might help to promote learner autonomy in the Indonesian context.