Reviewerly/exHarmony huggingface dataset:
Reproducibility and pseudo-determinism in log-space

A curious property of randomized log-space search algorithms is that their outputs are often longer than their workspace. This leads to the question: how can we reproduce the results of a randomized log space computation without storing the output or randomness verbatim? Running the algorithm again with new random bits may result in a new (and potentially different) output.We show that every problem in search-RL has a randomized log-space algorithm where the output can be reproduced. Specifically, we show that for every problem in search-RL, there are a pair of log-space randomized algorithms A and B where for every input x, A will output some string tx of size O(log n), such that B when running on (x, tx) will be pseudo-deterministic: that is, running B multiple times on the same input (x, tx) will result in the same output on all executions with high probability. Thus, by storing only O(log n) bits in memory, it is possible to reproduce the output of a randomized log-space algorithm.An algorithm is reproducible without storing any bits in memory (i.e., |tx| = 0) if and only if it is pseudo-deterministic. We show pseudo-deterministic algorithms for finding paths in undirected graphs and Eulerian graphs using logarithmic space. Our algorithms are substantially faster than the best known deterministic algorithms for finding paths in such graphs in log-space.The algorithm for search-RL has the additional property that its output, when viewed as a random variable depending on the randomness used by the algorithm, has entropy O(log n).
A Sequential Set Generation Method for Predicting Set-Valued Outputs

Consider a general machine learning setting where the output is a set of labels or sequences. This output set is unordered and its size varies with the input. Whereas multi-label classification methods seem a natural first resort, they are not readily applicable to set-valued outputs because of the growth rate of the output space; and because conventional sequence generation doesn’t reflect sets’ order-free nature. In this paper, we propose a unified framework—sequential set generation (SSG)—that can handle output sets of labels and sequences. SSG is a meta-algorithm that leverages any probabilistic learning method for label or sequence prediction, but employs a proper regularization such that a new label or sequence is generated repeatedly until the full set is produced. Though SSG is sequential in nature, it does not penalize the ordering of the appearance of the set elements and can be applied to a variety of set output problems, such as a set of classification labels or sequences. We perform experiments with both benchmark and synthetic data sets and demonstrate SSG’s strong performance over baseline methods.
Squared English Word: A Method of Generating Glyph to Use Super Characters for Sentiment Analysis

The Super Characters method addresses sentiment analysis problems by first converting the input text into images and then applying 2D-CNN models to classify the sentiment. It achieves state of the art performance on many benchmark datasets. However, it is not as straightforward to apply in Latin languages as in Asian languages. Because the 2D-CNN model is designed to recognize two-dimensional images, it is better if the inputs are in the form of glyphs. In this paper, we propose SEW (Squared English Word) method generating a squared glyph for each English word by drawing Super Characters images of each English word at the alphabet level, combining the squared glyph together into a whole Super Characters image at the sentence level, and then applying the CNN model to classify the sentiment within the sentence. We applied the SEW method to Wikipedia dataset and obtained a 2.1% accuracy gain compared to the original Super Characters method. For multi-modal data with both structured tabular data and unstructured natural language text, the modified SEW method integrates the data into a single image and classifies sentiment with one unified CNN model.
Calibrating GloVe model on the principle of Zipf’s law

Abstract GloVe is a popular method for learning word representation by fitting log-bilinear model. However, many researchers have observed that the number of co-occurrences of word pairs in Glove generally follow power law distribution with the value of power law exponent larger than 1; and the value becomes increasingly larger with the increase of corpus size, which is inconsistent with the principle of Zipf’s law. Zipf’s law is a power law distribution with the value of power law exponent equal to 1 and is a basic law of corpus linguistics. On the basis of this principle, this study aims to verify whether the performance of GloVe can be improved after processing certain calibrations on the GloVe model. The estimated value of power law exponent is generally obtained via least square estimation in the linear regression for the logarithm of the number of co-occurrences with the logarithm of their ranks. Inspired by processing a suitable power transformation of response variable in linear regression model to improve the model fitting, we implement power transformation to the logarithm of the number of co-occurrences of word pairs [i.e. (logXij)β] and appropriately select β, such that the estimated value of power law exponent approximates to 1. An estimation method of the hyperparameter β is presented for the different sizes of corpus. Then, we use (logXij)β instead of logXij in GloVe to learn the distributed representations of words. We evaluate this new approach on word analogy and word similarity tasks. Experimental results show that our method outperforms GloVe on the word analogy task, and the improvement of performance is significant under McNemar’s test. For word similarity task, the proposed method also outperforms GloVe on five benchmark datasets. Furthermore, our method has a higher convergence speed than GloVe.
First-Order Algorithms Converge Faster than $O(1/k)$ on Convex Problems

It is well known that both gradient descent and stochastic coordinate descent achieve a global convergence rate of $O(1/k)$ in the objective value, when applied to a scheme for minimizing a Lipschitz-continuously differentiable, unconstrained convex function. In this work, we improve this rate to $o(1/k)$. We extend the result to proximal gradient and proximal coordinate descent on regularized problems to show similar $o(1/k)$ convergence rates. The result is tight in the sense that a rate of $O(1/k^{1+\epsilon})$ is not generally attainable for any $\epsilon>0$, for any of these methods.
Recommendation for Cryptographic Key Generation

Cryptography is often used in an information technology security environment to protect data that is sensitive, has a high value, or is vulnerable to unauthorized disclosure or undetected modification during transmission or while in storage. Cryptography relies upon two basic components: an algorithm (or cryptographic methodology) and a cryptographic key. This Recommendation discusses the generation of the keys to be managed and used by the approved cryptographic algorithms.
Uppsala University and Gavagai at CLEF eRISK : Comparing Word Embedding Models

This paper describes an experiment to evaluate the performance of three different types of semantic vectors or word embeddings-random indexing, GloVe, and ELMo-and two different classification arch ...
Mining and Model Understanding on Medical Data

What are the basic forms of healthcare data? How are Electronic Health Records and Cohorts structured? How can we identify the key variables in such data and how important are temporal abstractions? What are the main challenges in knowledge extraction from medical data sources? What are the key machine algorithms used for this purpose? What are the main questions that clinicians and medical experts pose to machine learning researchers?
High Performance Gesture Recognition via Effective and Efficient Temporal Modeling

State-of-the-art hand gesture recognition methods have investigated the spatiotemporal features based on 3D convolutional neural networks (3DCNNs) or convolutional long short-term memory (ConvLSTM). However, they often suffer from the inefficiency due to the high computational complexity of their network structures. In this paper, we focus instead on the 1D convolutional neural networks and propose a simple and efficient architectural unit, Multi-Kernel Temporal Block (MKTB), that models the multi-scale temporal responses by explicitly applying different temporal kernels. Then, we present a Global Refinement Block (GRB), which is an attention module for shaping the global temporal features based on the cross-channel similarity. By incorporating the MKTB and GRB, our architecture can effectively explore the spatiotemporal features within tolerable computational cost. Extensive experiments conducted on public datasets demonstrate that our proposed model achieves the state-of-the-art with higher efficiency. Moreover, the proposed MKTB and GRB are plug-and-play modules and the experiments on other tasks, like video understanding and video-based person re-identification, also display their good performance in efficiency and capability of generalization.
Perceptual Generative Autoencoders

Modern generative models are usually designed to match target distributions directly in the data space, where the intrinsic dimension of data can be much lower than the ambient dimension. We argue that this discrepancy may contribute to the difficulties in training generative models. We therefore propose to map both the generated and target distributions to a latent space using the encoder of a standard autoencoder, and train the generator (or decoder) to match the target distribution in the latent space. Specifically, we enforce the consistency in both the data space and the latent space with theoretically justified data and latent reconstruction losses. The resulting generative model, which we call a perceptual generative autoencoder (PGA), is then trained with a maximum likelihood or variational autoencoder (VAE) objective. With maximum likelihood, PGAs generalize the idea of reversible generative models to unrestricted neural network architectures and arbitrary number of latent dimensions. When combined with VAEs, PGAs substantially improve over the baseline VAEs in terms of sample quality. Compared to other autoencoder-based generative models using simple priors, PGAs achieve state-of-the-art FID scores on CIFAR-10 and CelebA.
A Mobile-Oriented System for Integrity Preserving in Audio Forensics

This paper addresses a problem in the field of audio forensics. With the aim of providing a solution that helps Chain of Custody (CoC) processes, we propose an integrity verification system that includes capture (mobile based), hash code calculation and cloud storage. When the audio is recorded, a hash code is generated in situ by the capture module (an application), and it is sent immediately to the cloud. Later, the integrity of the audio recording given as evidence can be verified according to the information stored in the cloud. To validate the properties of the proposed scheme, we conducted several tests to evaluate if two different inputs could generate the same hash code (collision resistance), and to evaluate how much the hash code changes when small changes occur in the input (sensitivity analysis). According to the results, all selected audio signals provide different hash codes, and these values are very sensitive to small changes over the recorded audio. On the other hand, in terms of computational cost, less than 2 s per minute of recording are required to calculate the hash code. With the above results, our system is useful to verify the integrity of audio recordings that may be relied on as digital evidence.
Effects of Training Strategies on Employees Performance: A Practical Study in Amman's Municipality \ Jordan

This paper aimed at studying the effect of training Strategies on employees performance as a practical study from Amman's Municipality employees' perspective\Jordan, it consisted of (550) individual which is the number of all employees.A random sample of ( 250) individuals was taken & the number of valid feedback questionnaires was (201).The questionnaires' data were processed using (SPSS): the researchers found that training strategies implementation at Amman's Municipality was average level & the employees' performance too an average level.Hypothesis study results have shown a statistical effect at level (α ≤ 0, 05) of training Strategies on employees' performance.Recommendations based on the study findings: To establish training needs regularly to all employees at Amman's Municipality, to increase the budget of Training.
A Theoretical and Algorithmic Analysis of Configurable MDPs

This paper analyzes, from theoretical and algorithmic perspectives, a class of problems recently introduced in the literature of Markov decision processes—configurable Markov decision processes. In this new class of problems we jointly optimize the probability transition function and associated optimal policy, in order to improve the performance of a decision-making agent. We contribute a complexity analysis on the problem from a computational perspective, where we show that, in general, solving a configurable MDP is NP-Hard. We also discuss practical challenges often faced in solving this class of problems. Additionally, we formally derive a gradient-based approach that sheds some light on the correctness and limitations of existing methods. We conclude by demonstrating the application of different parameterizations of configurable MDPs in two scenarios, offering a discussion on advantages and drawbacks from modeling and algorithmic perspectives. Our contributions set the foundation for a better understanding of this recent problem, and the wider applicability of the underlying ideas to different planning problems.
Feature Fusion: H-ELM based Learned Features and Hand-Crafted Features for Human Activity Recognition

Recognizing human activities is one of the main goals of human-centered intelligent systems.Smartphone sensors produce a continuous sequence of observations.These observations are noisy, unstructured and high dimensional.Therefore, efficient features have to be extracted in order to perform an accurate classification.This paper proposes a combination of Hierarchical and kernel Extreme Learning Machine (HK-ELM) methods to learn features and map them to specific classes in a short time.Moreover, a feature fusion approach is proposed to combine H-ELM based learned features with hand-crafted ones.Our proposed method was found to outperform state-of-the-art in terms of accuracy and training time.It gives an accuracy of 97.62% and takes 3.4 seconds as a training time by using a normal Central Processing Unit (CPU).
Interactive Image Generation Using Scene Graphs

Recent years have witnessed some exciting developments in the domain of generating images from scene-based text descriptions. These approaches have primarily focused on generating images from a static text description and are limited to generating images in a single pass. They are unable to generate an image interactively based on an incrementally additive text description (something that is more intuitive and similar to the way we describe an image). We propose a method to generate an image incrementally based on a sequence of graphs of scene descriptions (scene-graphs). We propose a recurrent network architecture that preserves the image content generated in previous steps and modifies the cumulative image as per the newly provided scene information. Our model utilizes Graph Convolutional Networks (GCN) to cater to variable-sized scene graphs along with Generative Adversarial image translation networks to generate realistic multi-object images without needing any intermediate supervision during training. We experiment with Coco-Stuff dataset which has multi-object images along with annotations describing the visual scene and show that our model significantly outperforms other approaches on the same dataset in generating visually consistent images for incrementally growing scene graphs.
EMPLOYING TELEGRAM APPLICATION: LEARNERS’ ATTITUDE, VOCABULARY LEARNING, AND VOCABULARY DELAYED RETENTION

Using smartphones for learning a language has long been recommended by scholars. However, its effectiveness is still the subject of argument. Moreover, due to the limited time in the conventional language classes, students do not have the chance to practice all vocabulary items which may hinder the learning process. Accordingly, a mixed method only-post-test quasi-experimental study was conducted. Also, for the qualitative phase, data were gathered through a focus group interview with the participants chosen through purposive sampling. It was confirmed that there was a significant effect of online practicing on vocabulary learning of the language learners. Furthermore, it was found out that the participants held a positive attitude for practicing target words through Telegram. In addition, the study results showed that using Telegram application does not have a significant effect on the retention of the vocabulary of the learners. From the results of this study, the researcher proposed some pedagogical implications to EFL teachers, practitioners, material developers, policy makers, and other English education stakeholders. Article visualizations:
Improving Students’ Writing Skills through the Application of Synectic Model of Teaching Using Audiovisual Media

Writing might be difficult for some people (Alwasilah, 2007). Some students can speak English fluently and confidently, although since they have difficulties in conveying their ideas in writing. Thus, it needs the effective and efficient model of teaching to improve the teaching learning process. Synectic model of teaching is a model applied to enhance students’ ability to write. This model combines various forms of metaphoric thinking to see old ideas in different ways. In this research, the researcher used audiovisual media to support learning. The purpose of this research is to find out students’ improvement in writing descriptive texts using synectic model of teaching. The sample of the research was 36 students of class IXD in MTsN 2 Pontianak. The research was Classroom Action Research using Kemmis and MC Taggart model with two cycles. The result shows that there is improvement in students writing skill. The mean score of pre-test is 56.01, posttest 1 is 66.5, and posttest 2 is 87.46. It means the application of synectic model of teaching using audiovisual media is able to increase students’ ability in writing descriptive text.
Recapitulating the Violence Detection Systems

Human behavior is the response to the status of internal and external stimuli. It comes out in the form physical actions and observable emotions which may be associated with individual or group of person. Significant contents from human behavior are pulled out for video surveillance, feature extraction and for creating human computer interface. The present research work explores the video surveillance for realization of violence detection system. A novel framework is introduced through which a raw video stream is processed; subsequently the framework decodes correct semantics relating to the violence detection from the received raw image. In this paper, exhaustive literature survey is carried out which discloses up to date technology in the field of video surveillance.
Data mining and visualisation: general discussion

The first page of this article is displayed as the abstract.
Advances of image edge enhancement based on vortex filtering

As an important means of image processing, the edge enhancement techniques play an important role in amplitude-contrast and phase-contrast objects imaging. The vortex filtering techniques based on radial Hilbert transform have attracted much attention because it can achieve isotropic edge enhancement. However, the classical vortex filtering causes background noise and contrast reduction due to diffraction caused by central singularities and sharp edges. In recent years, many research groups have proposed new types of vortex filters for vortex filtering side lobe suppression. In addition, the isotropic and anisotropic edge enhancement techniques based on vortex filtering have also developed rapidly. In this paper, several methods for suppressing vortex side lobes were summarized in recent years, including Laguerre Gaussian amplitude modulation, Bessel-like amplitude modulation, and Airy amplitude modulation. What's more, from two aspects:scalar vortex filtering and vector vortex filtering, the isotropic and anisotropic edge enhancement methods and progress were reviewed.
Fling - A Fluent API Generator (Artifact).

The first general and practical solution of the fluent API problem is presented. We give an algorithm that given a deterministic context free language (equivalently, LR(k), k >= 0 language) encodes it in an unbounded parametric polymorphism type system employing only a polynomial number of types. The theoretical result is employed in an actual tool Fling - a fluent API compiler-compiler in the style of YACC, tailored for embedding DSLs in Java.
Real Time Compressed Video Object Segmentation

Video object segmentation is a challenging task with wide variety of applications. Although recent CNN based methods have achieved great performance, they are far from being applicable for real time applications. In this paper, we propose a propagation based video object segmentation method in compressed domain to accelerate inference speed. We only extract features from I-frames by the traditional deep segmentation network. And the features of P-frames are propagated from I-frames. Apart from feature warping, we propose two effective modules in the process of feature propagation to ensure the representation ability of propagated features in terms of appearance and location. Residual supplement module is used to supplement appearance information lost in warping, and spatial attention module mines accurate spatial saliency prior to highlight the specified object. Compared with recent state-of-the-art algorithms, the proposed method achieves comparable accuracy while much faster inference speed.
‘Mind our mouths and beware our talk’: Stylometric analysis of character dialogue in The Darjeeling Limited

Stylometry uses statistical reasoning to quantify the linguistic attributes of written texts. In this article I draw upon current developments in computer-based stylometric studies to quantify the language of screenplays. I take as my starting point John F. Burrows’s seminal stylometric study of dialogue in Jane Austen’s novels (Computation into Criticism [Burrows 1987]) to identify and quantify the linguistic habits of major screenplay characters, habits that constitute their distinctive voice. Analysis of the dialogue of the three Whitman brothers in The Darjeeling Limited (screenplay by Wes Anderson, Roman Coppola and Jason Schwartzman, dated 22 November 2006) will serve as a preliminary case study. I aim to use the work of Burrows as the starting point in establishing a new research programme within screenplay studies, one based on the stylometric analysis of the language of screenplays.