Reviewerly/exHarmony huggingface dataset:
Hybrid Differential Evolution and Particle Swarm Optimization Algorithm Based on Random Inertia Weight

A new hybrid differential evolution and particle swarm optimization algorithm called RWDEPSO is proposed, which combines the advantages of particle swarm optimization (PSO) with fast convergence speed and differential evolution (DE) with high search accuracy. In the new algorithm, the random inertia weight is introduced to strengthen the global exploration ability and local exploition ability of the PSO optimization process. Then, the optimized individuals of PSO and DE are cross-operated to generate new individuals, which inherit the dominant characteristics of both algorithms. Comparing with the simulations of the other intelligent algorithms in six typical Benchmark functions, the results show that the proposed algorithm RWDEPSO has faster convergence speed and stronger global research ability.
Image classification and captioning model considering a CAM‐based disagreement loss

Image captioning has received significant interest in recent years, and notable results have been achieved. Most previous approaches have focused on generating visual descriptions from images, whereas a few approaches have exploited visual descriptions for image classification. This study demonstrates that a good performance can be achieved for both description generation and image classification through an end-to-end joint learning approach with a loss function, which encourages each task to reach a consensus. When given images and visual descriptions, the proposed model learns a multimodal intermediate embedding, which can represent both the textual and visual characteristics of an object. The performance can be improved for both tasks by sharing the multimodal embedding. Through a novel loss function based on class activation mapping, which localizes the discriminative image region of a model, we achieve a higher score when the captioning and classification model reaches a consensus on the key parts of the object. Using the proposed model, we established a substantially improved performance for each task on the UCSD Birds and Oxford Flowers datasets.
Solving systems of polynomial equations over GF(2) by a parity-counting self-reduction

We consider the problem of finding solutions to systems of polynomial equations over a finite field. Lokshtanov et al. [SODA'17] recently obtained the first worst-case algorithms that beat exhaustive search for this problem. In particular for degree-d equations modulo two in n variables, they gave an O^*(2^{(1-1/(5d))n}) time algorithm, and for the special case d=2 they gave an O^*(2^{0.876n}) time algorithm.We modify their approach in a way that improves these running times to O^*(2^{(1-1/(2.7d))n}) and O^*{2^{0.804n}), respectively. In particular, our latter bound - that holds for all systems of quadratic equations modulo 2 - comes close to the O^*(2^{0.792n}) expected time bound of an algorithm empirically found to hold for random equation systems in Bardet et al. [J. Complexity, 2013]. Our improvement involves three observations: 1) The Valiant-Vazirani lemma can be used to reduce the solution-finding problem to that of counting solutions modulo 2. 2) The monomials in the probabilistic polynomials used in this solution-counting modulo 2 have a special form that we exploit to obtain better bounds on their number than in Lokshtanov et al. [SODA'17]. 3) The problem of solution-counting modulo 2 can be embedded in a smaller instance of the original problem, which enables us to apply the algorithm as a subroutine to itself.
Texture features based microscopic image classification of liver cellular granuloma using artificial neural networks

Automated classification of Schistosoma mansoni granulomatous microscopic images of mice liver using Artificial Intelligence (AI) technologies is a key issue for accurate diagnosis and treatment. In this paper, three grey difference statistics-based features, namely three Gray-Level Co-occurrence Matrix (GLCM) based features and fifteen Gray Gradient Co-occurrence Matrix (GGCM) features were calculated by correlative analysis. Ten features were selected for three-level cellular granuloma classification using a Scaled Conjugate Gradient Back-Propagation Neural Network (SCG-BPNN) in the same performance. A cross-entropy is then calculated to evaluate the proposed Sigmoid input and the ten-hidden layer network. The results depicted that SCG-BPNN with texture features performs high recognition rate compared to using morphological features, such as shape, size, contour, thickness and other geometry-based features for the classification. The proposed method also has a high accuracy rate of 87.2% compared to the Back-Propagation Neural Network (BPNN), Back-Propagation Hopfield Neural Network (BPHNN) and Convolutional Neural Network (CNN).
The Possibility of Applying Rumen Research at the Projective Plane PG (2,17)

One of the main objectives of this research is to use a new theoretical method to find arcs and Blocking sets. This method includes the deletion of a set of points from some lines under certain conditions explained in a paragraph 2.In this paper we were able to improve the minimum constraint of the (256,16) &amp;ndash; arc in the projection plane PG(2,17).Thus , we obtained a new {50,2}-blocking set for size Less than 3q , and according to the theorem (1.3.1),we obtained the linear 257,3,24117&amp;nbsp;&amp;nbsp;&amp;nbsp; code, theorem( 2.1.1 ) giving some examples on arcs of the Galois field GF(q);q=17.&amp;quot;
Feasibility of fast non local means filter in pediatric chest x-ray for increasing of pulmonary nodule detectability with 3D printed lung nodule phantom

General x-ray images have a lower probability of nodule detection than other modalities. Especially in children, the probability of nodule detection can likely drop due to poor image quality from using low radiation dose. To demonstrate the effectiveness of fast non-local means (FNLM) filter to increase the probability of nodule detection in pediatric chest x-ray images and reduce radiation dose while maintaining image quality. Quantitative assessment of normalised noise power spectrum (NNPS), coefficient of variation (COV) and contrast to noise ratio (CNR) were performed after applying four filters (median, Wiener, total variation and FNLM) on a 1-year-old child phantom. A 3D-printed patient nodule phantom was inserted into the phantom. Assessment was performed on AP and LAT view images acquired with the tube voltage reduced to 38 and 27%, and tube current reduced to 84 and 61%, respectively. The results showed the lowest NNPS and COV values and the highest CNR value when the FNLM filter applied. Moreover, the AP view results showed 37% decrease in COV and 30% increase in CNR in images with the FNLM filter applied (images exposed with the tube voltage and current reduced to 29% and 50%, respectively). The LAT view results showed 5% decrease in COV and 36% increase in CNR in images with the FNLM filter applied (images exposed with the tube current reduced by 27%). By applying the FNLM filter, the probability of nodule detection could be increased by denoising and contrast enhancement. Moreover, using the FNLM filter could reduce cancer risk in pediatric patients by reducing radiation dose about 30% to 44%.
Design and Implementation of Mobile Active Two-Axis Solar Tracker with Reflector Based on Particle Swarm Fuzzy Controller

Sun tracking system, or solar tracker, is an electronic device consisting of several electrical and mechanical elements, which serves to guide the solar panels in order to make them follow the sun position accurately and to generate the maximum solar energy reception. Sun tracking system is a solution offered in order to use solar energy optimally. The two-axis solar tracking system can absorb solar energy better than a single axis solar tracking or PV fixed system. The two-axis active sun tracking system uses an LDR (Light Dependent Resistor) sensor in order to capture the powerful solar lighting to be received by PV (photovoltaic). Four LDR sensors are used to represent the position of the sun that is north, south, west, and east. This research applies a modified particle swarm fuzzy-based control to be used in the system so that the solar panel system is more effective and responsive to sunlight position changes. The results show that the control method is capable of applying well with a 70-degree reflector. This can be noticed from the results of the steady-state error performance index in the pitch and the yaw tests that are 0.638% and 0.312%, whereas for the other performance indexes including rise time, settling time and Maximum Overshoot (MOV) for both tests are 7 seconds, 9 seconds, 0% and 4 seconds, 6 seconds and 0% respectively. The mobile active solar tracker built can increase energy gain of 43.01% compared to fixed system.
Gender Inference for Facebook Picture Owners

Social media such as Facebook provides a new way to connect, interact and learn. Facebook allows users to share photos and express their feelings by using comments. However, its users are vulnerable to attribute inference attacks where an attacker intends to guess private attributes (e.g., gender, age, political view) of target users through their online profiles and/or their vicinity (e.g., what their friends reveal). Given user-generated pictures on Facebook, we explore in this paper how to launch gender inference attacks on their owners from pictures meta-data composed of: (i) alt-texts generated by Facebook to describe the content of pictures, and (ii) comments posted by friends, friends of friends or regular users. We assume these two meta-data are the only available information to the attacker. Evaluation results demonstrate that our attack technique can infer the gender with an accuracy of 84% by leveraging only alt-texts, 96% by using only comments, and 98% by combining alt-texts and comments. We compute a set of sensitive words that enable attackers to perform effective gender inference attacks. We show the adversary prediction accuracy is decreased by hiding these sensitive words. To the best of our knowledge, this is the first inference attack on Facebook that exploits comments and alt-texts solely.
Medical Image Compression for Telemedicine Applications

Transferring medical images from one center to another is common use in telemedicine. These high-quality images stored in DICOM format require higher bandwidth for transmission and large storage space in PACS (Picture Archiving and Communication System) memory. Therefore, reducing the image size by preserving diagnostic information has become a need.
Is Bigger Better? A Fitts’ Law Study on the Impact of Display Size on Touch Performance

Touch-sensitive surfaces are already a standard form of interaction. These surfaces come in many different sizes like tablets or touch walls. However, there is little research to characterize the impact of surface size on touch performance. We conducted a Fitts’ Law study of three display sizes (13.5 $$^{\prime \prime }$$ tablet, 28 $$^{\prime \prime }$$ monitor, 69.5 $$^{\prime \prime }$$ large monitor), comparing various performance measures. We found that the smallest size (13.5 $$^{\prime \prime }$$ ) is problematic both objectively (high error rate) and subjectively (imprecise, difficult-to-use). In contrast, both the medium (28 $$^{\prime \prime }$$ ) and the large display (69.5 $$^{\prime \prime }$$ ) perform equally well. However, small displays allow for greater interaction speed compared to very large screens. Our results can help interaction designers and automatic algorithms to optimize current and future touch devices.
Dynamic modeling of process support by the influence of various factors on the activities of firms in a competitive environment

Abstract The paper builds and explores a dynamic model of a company’s development under the influence of both negative and positive factors in a competitive environment. It is assumed that the head of the company is interested in obtaining the maximum profit from his company in a finite period of time and, acting on the basis of his interests, wants to find the optimal strategy. The task of finding the optimal policy for maximum profit is considered. Labor resources are considered as positive factors in the competitive environment.
Greedy InfoMax for Biologically Plausible Self-Supervised Representation Learning.

We propose a novel deep learning method for local self-supervised representation learning that does not require labels nor end-to-end backpropagation but exploits the natural order in data instead. Inspired by the observation that biological neural networks appear to learn without backpropagating a global error signal, we split a deep neural network into a stack of gradient-isolated modules. Each module is trained to maximally preserve the information of its inputs using the InfoNCE bound from Oord et al. [2018]. Despite this greedy training, we demonstrate that each module improves upon the output of its predecessor, and that the representations created by the top module yield highly competitive results on downstream classification tasks in the audio and visual domain. The proposal enables optimizing modules asynchronously, allowing large-scale distributed training of very deep neural networks on unlabelled datasets.
Towards Hardware-Aware Tractable Learning of Probabilistic Models

Smart portable applications increasingly rely on edge computing due to privacy and latency concerns. But guaranteeing always-on functionality comes with two major challenges: heavily resource-constrained hardware; and dynamic application conditions. Probabilistic models present an ideal solution to these challenges: they are robust to missing data, allow for joint predictions and have small data needs. In addition, ongoing efforts in field of tractable learning have resulted in probabilistic models with strict inference efficiency guarantees. However, the current notions of tractability are often limited to model complexity, disregarding the hardware's specifications and constraints. We propose a novel resource-aware cost metric that takes into consideration the hardware's properties in determining whether the inference task can be efficiently deployed. We use this metric to evaluate the performance versus resource trade-off relevant to the application of interest, and we propose a strategy that selects the device-settings that can optimally meet users' requirements. We showcase our framework on a mobile activity recognition scenario, and on a variety of benchmark datasets representative of the field of tractable learning and of the applications of interest.
Deflectometry in cylindrical coordinates using a conical mirror: principles and proof of concept

Engineering applications for precision cylindrical parts have required increasingly tight geometric tolerances to guarantee their correct functionality. In addition to demanding better machine tools and manufacturing processes, it is also important to have the means to verify if the qualities of precision cylindrical components meet the requirements. Optical measurement techniques are excellent options for this task since they are contactless, fast and precise. This paper proposes the concept of deflectometry in cylindrical coordinates for measuring roundness in precision cylindrical parts. Deflectometry is an optical technique that indirectly measures the shape deviations of a mirror-like surface from the distortions in reflected images by the measured surface. Flat or smooth surfaces with good surface finishes are among the main applications. Measurement uncertainties of a few nanometers have been reported in the literature. The new concept presented in this paper uses deflectometry through a 45° conical mirror. The conical mirror turns the cylindrical surface into a planar image by an optical transformation, making possible to measure the geometric deviations using deflectometry. This paper presents the proposed optical configuration, the required sequence of fringes to be reflected, measurement and reconstruction algorithms and the results of proof-of-concept experiments. The preliminary results show very good performance for roundness measurements of cross sections. However, it was not possible to measure efficiently the cylinder generatrix straightness. The concept presented here may give rise to a new family of optical roundness measurement machines.
Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL

One key component in text-to-SQL is to predict the comparison relations between columns and their values.To the best of our knowledge, no existing models explicitly introduce external common knowledge to address this problem, thus their capabilities of predicting comparison relations are limited beyond training data.In this paper, we propose to leverage adjective-noun phrasing knowledge mined from the web to predict the comparison relations in text-to-SQL.Experimental results on both the original and the re-split Spider dataset show that our approach achieves significant improvement over state-of-the-art methods on comparison relation prediction.
Noisy Parallel Corpus Filtering through Projected Word Embeddings

We present a very simple method for parallel text cleaning of low-resource languages, based on projection of word embeddings trained on large monolingual corpora in high-resource languages.In spite of its simplicity, we approach the strong baseline system in the downstream machine translation evaluation.
Publicly-Verifiable Proofs of Storage Based on the Discrete Logarithm Problem

With the rapid development of cloud computing platforms, cloud storage services are becoming widespread in recent years.Based on these services, clients are able to store data on remote cloud servers and thereby saving their local storage.This greatly reduces the burden of clients, while it also brings certain security risks to the outsourced data.Among the risks, a critical one is data corruption, for example cloud servers may delete some rarely used outsourced data for cost saving.To prevent this risk, proof of storage (PoS) schemes are invented, which can validate the integrity of cloud data without downloading the entire data.The existing PoS schemes, however, mostly either involve complex operations e.g.bilinear pairings, or don't support public verifiability.To fill this gap, in this paper we construct a new PoS scheme that is publicly verifiable and only requires simple cryptographic computations.We prove that our scheme is secure under the discrete logarithm assumption, in the random oracle model.Furthermore, we also show how to extend the scheme to support data updates.Finally, we implement our scheme.The simulation results demonstrate that our scheme is more computationally-efficient than the publicly-verifiable PoS schemes of Shacham and Waters (Journal of Cryptology 2013).
Digital Image Forgery Detection Techniques: A Comprehensive Review

The image forgery techniques are used to provide the particular image in the form of computerized pictures with no errors in capturing of digital image information which shows the original image. While compared to normal images the edited images are difficult to find out the forged images. The image should maintain the authenticity and integrity to secure the picture from unauthorized users. In this paper, we have compared the digital image forgery and JPEG is the most common format used by the photographic images and the digital camera devices. These operations are performed in an adobe photo-shop using with the content of image security to restore some digital image with an authenticity and integrity to detect the digital image forgery using active and passive techniques.
A Somatic Approach to Combating Cybersickness Utilising Airflow Feedback

Abstract This paper presents a novel somatosensory approach towards reducing the risk of cybersickness during virtual reality locomotion in a 3D environment. We start by presenting theories regarding the cause of cybersickness which led to the proposal and construction of a prototype airflow-based feedback system. The solution proposed by this paper builds on the concept of sensory misalignment, where the body struggles understand its state due to conflicting sensory feedback and consequently generates negative health symptoms and discomfort. To evaluate the work an experiment was carried out where 40 participants drive a simulated car around a virtual environment. In one condition the participants had additional somatosensory feedback regarding their motion, provided by a fan synchronised to their speed in the virtual world. In the second condition there was no additional feedback. We evaluated both conditions for cybersickness and presence, and showed a statistically significant improvement for both in the condition using airflow feedback. We conclude with a discussion of the results, and present a direction for possible future research.
Least Inferable Policies for Markov Decision Processes

In a variety of applications, an agent's success depends on the knowledge that an adversarial observer has or can gather about the agent's decisions. It is therefore desirable for the agent to achieve a task while reducing the ability of an observer to infer the agent's policy. We consider the task of the agent as a reachability problem in a Markov decision process and study the synthesis of policies that minimize the observer's ability to infer the transition probabilities of the agent between the states of the Markov decision process. We introduce a metric that is based on the Fisher information as a proxy for the information leaked to the observer and using this metric formulate a problem that minimizes expected total information subject to the reachability constraint. We proceed to solve the problem using convex optimization methods. To verify the proposed method, we analyze the relationship between the expected total information and the estimation error of the observer, and show that, for a particular class of Markov decision processes, these two values are inversely proportional.
BERT Masked Language Modeling for Co-reference Resolution

This paper explains the TALP-UPC participation for the Gendered Pronoun Resolution shared-task of the 1st ACL Workshop on Gender Bias for Natural Language Processing.We have implemented two models for mask language modeling using pre-trained BERT adjusted to work for a classification problem.The proposed solutions are based on the word probabilities of the original BERT model, but using common English names to replace the original test names.
Improving Sentiment Classification in Slovak Language

Using different neural network architectures is widely spread for many different NLP tasks.Unfortunately, most of the research is performed and evaluated only in English language and minor languages are often omitted.We believe using similar architectures for other languages can show interesting results.In this paper, we present our study on methods for improving sentiment classification in Slovak language.We performed several experiments for two different datasets, one containing customer reviews, the other one general Twitter posts.We show comparison of performance of different neural network architectures and also different word representations.We show that another improvement can be achieved by using a model ensemble.We performed experiments utilizing different methods of model ensemble.Our proposed models achieved better results than previous models for both datasets.Our experiments showed also other potential research areas.