Reviewerly/exHarmony huggingface dataset:
Application of the InterCriteria Analysis Method to a Data of Malignant Melanoma Disease for the Burgas Region for 2014–2018

The aim of this paper is to analyze a statistical data for the registered patients with malignant melanoma in Burgas region for the period 2014–2018. The InterCriteria Analysis approach is applied. The relations between gender, marital status and the type of malignant melanoma; relations between year of registration of patient and type of malignant melanoma are studied. The obtained results by InterCriteria Analysis method are confirmed by statistical analysis according to Pearson, Kendall and Spearman.
Stealing Machine Learning Parameters via Side Channel Power Attacks

Side-channel attacks target system implementation statistics, such as the electricity required to run a cryptographic function. Deriving cryptographic keys, such as AES keys, has become such a simplified process that extracting sensitive information from an otherwise secure algorithm requires only a $35USD microcontroller. While cryptographic algorithms indicate the presence of sensitive data, making them a preferable target, other systems hold valuable data with significantly less protection. Due to the ubiquity and rigidity of machine learning algorithms, the ability to infer model parameters has drastic security implications. This investigation extracted information from machine learning models through the use of traditional side-channel techniques. Specifically, a side-channel power analysis was performed using a ChipWhisperer Lite to extract information from Neural Networks and Linear Regression models running on a target microcontroller. Then, time series classification tasks were performed on the resultant power traces to determine the differences between the two models and their varied hyperparameters. Three such classification tasks were tested. In the first, a neural network was differentiated from a linear regression model with 100% accuracy. In the second, two neural networks with different sized hidden layers are classified with 97.92% accuracy. In the third, two virtually identical linear regression models are compared that differ only in the initial value of one hyperparameter. These models were only classified with 67.92% accuracy. Although the accuracy decreases as the models become more alike, these results indicate that machine learning model parameters can be inferred from power-based side-channel attacks.
Optimal Rates of Teaching and Learning Under Uncertainty

In this paper, we consider a recently-proposed model of teaching and learning under uncertainty, in which a teacher receives independent observations of a single bit corrupted by binary symmetric noise, and sequentially transmits to a student through another binary symmetric channel based on the bits observed so far. After a given number <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> of transmissions, the student outputs an estimate of the unknown bit, and we are interested in the exponential decay rate of the error probability as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> increases. We propose a novel block-structured teaching strategy in which the teacher encodes the number of 1s received in each block, and show that the resulting error exponent is the binary relative entropy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D\left({\frac {1}{2}\|\max (p,q)}\right)$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> are the noise parameters. This matches a trivial converse result based on the data processing inequality, and settles two conjectures of [Jog and Loh, 2021] and [Huleihel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2019]. In addition, we show that the computation time required by the teacher and student is linear in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> . We also study a more general setting in which the binary symmetric channels are replaced by general binary-input discrete memoryless channels. We provide an achievability bound and a converse bound, and show that the two coincide in certain cases, including (i) when the two channels are identical, and (ii) when the student-teacher channel is a binary symmetric channel. More generally, we give sufficient conditions under which our learning rate is the best possible for block-structured protocols.
Development and deployment of a generative model-based framework for text to photorealistic image generation

The task of generating photorealistic images from their textual descriptions is quite challenging. Most existing tasks in this domain are focused on the generation of images such as flowers or birds from their textual description, especially for validating the generative models based on Generative Adversarial Network (GAN) variants and for recreational purposes. However, such work is limited in the domain of photorealistic face image generation and the results obtained have not been satisfactory. This is partly due to the absence of concrete data in this domain and a large number of highly specific features/attributes involved in face generation compared to birds or flowers. In this paper, we propose an Attention Generative Adversarial Network (AttnGAN) for a fine-grained text-to-face generation that enables attention-driven multi-stage refinement by employing Deep Attentional Multimodal Similarity Model (DAMSM). Through extensive experimentation on the CelebA dataset, we evaluated our approach using the Frechet Inception Distance (FID) score. The output files for the Face2Text Dataset are also compare with that of the T2F Github project. According to the visual comparison, AttnGAN generated higher-quality images than T2F. Additionally, we compare our methodology with existing approaches with a specific focus on CelebA dataset and demonstrate that our approach generates a better FID score facilitating more realistic image generation. The application of such an approach can be found in criminal identification, where faces are generated from the textual description from an eyewitness. Such a method can bring consistency and eliminate the individual biases of an artist drawing the faces from the description given by the eyewitness. Finally, we discuss the deployment of the models on a Raspberry Pi to test how effective the models would be on a standalone device to facilitate portability and timely task completion.
Fine-Grained Multi-label Sexism Classification Using a Semi-Supervised Multi-level Neural Approach

Abstract Sexism, a permeate form of oppression, causes profound suffering through various manifestations. Given the increasing number of experiences of sexism shared online, categorizing these recollections automatically can support the battle against sexism, since it can promote successful evaluations by gender studies researchers and government representatives engaged in policy making. In this paper, we examine the fine-grained, multi-label classification of accounts (reports) of sexism. To the best of our knowledge, we consider substantially more categories of sexism than any related prior work through our 23-class problem formulation. Moreover, we present the first semi-supervised work for the multi-label classification of accounts describing any type(s) of sexism. We devise self-training-based techniques tailor-made for the multi-label nature of the problem to utilize unlabeled samples for augmenting the labeled set. We identify high textual diversity with respect to the existing labeled set as a desirable quality for candidate unlabeled instances and develop methods for incorporating it into our approach. We also explore ways of infusing class imbalance alleviation for multi-label classification into our semi-supervised learning, independently and in conjunction with the method involving diversity. In addition to data augmentation methods, we develop a neural model which combines biLSTM and attention with a domain-adapted BERT model in an end-to-end trainable manner. Further, we formulate a multi-level training approach in which models are sequentially trained using categories of sexism of different levels of granularity. Moreover, we devise a loss function that exploits any label confidence scores associated with the data. Several proposed methods outperform various baselines on a recently released dataset for multi-label sexism categorization across several standard metrics.
Hello Quantum! How Quantum Computing Will Change the World

The world of information technology is buzzing around the possibilities that quantum computers might open up for organizations. But what are quantum computers and what could be their business potential, eventually? Part of why quantum computers are steeped in mystery is because they rely on some very strange properties of the microcosm. From quantum interference to quantum entanglement, not to mention some of the underlying philosophical quantum implications like the possibilities for multiple universes and the foundations of quantum physics that revolve around peculiar mathematical formulations, quantum computers have been under development for decades, with IBM, D-Wave, and Google being amongst a handful of companies investing in this space.
Component-based metric learning for fully automatic kinship verification

This paper introduces a fully automatic method for kinship verification from facial images. Recently, a number of methods have been proposed to verify kinship from facial images, however, most of these methods are needed to exactly align face images before feature extraction in a manual manner. Unlike these methods, our method does not depend on face alignment. Firstly, we localize several facial feature points by utilizing a facial feature detector to extract SIFT descriptor around each feature point of a face image. Lastly, two ways, feature combination and distance metric learning, are used to verify the kinship of a pair of face images. For feature combination, three simple strategies of feature combination and support vector machine classifier are used for kinship verification. For metric learning, we propose a component-based metric learning (CML) method to measure the distance of each face pair, which jointly learns multiple local distance metrics, and one specific distance metric for each facial feature point. Experimental results show the effectiveness of our proposed approach on two popular kinship datasets.
Scale-Localized Abstract Reasoning

We consider the abstract relational reasoning task, which is commonly used as an intelligence test. Since some patterns have spatial rationales, while others are only semantic, we propose a multi-scale architecture that processes each query in multiple resolutions. We show that indeed different rules are solved by different resolutions and a combined multi-scale approach outperforms the existing state of the art in this task on all benchmarks by 5-54%. The success of our method is shown to arise from multiple novelties. First, it searches for relational patterns in multiple resolutions, which allows it to readily detect visual relations, such as location, in higher resolution, while allowing the lower resolution module to focus on semantic relations, such as shape type. Second, we optimize the reasoning network of each resolution proportionally to its performance, hereby we motivate each resolution to specialize on the rules for which it performs better than the others and ignore cases that are already solved by the other resolutions. Third, we propose a new way to pool information along the rows and the columns of the illustration-grid of the query. Our work also analyses the existing benchmarks, demonstrating that the RAVEN dataset selects the negative examples in a way that is easily exploited. We, therefore, propose a modified version of the RAVEN dataset, named RAVEN-FAIR. Our code and pretrained models are available at https://github.com/yanivbenny/MRNet.
Model-driven Development for ESP-based IoT Systems

The large variety of low-end devices and their programming environment increases the development complexity of embedded software for the Internet of Things. Therefore, developing IoT-based systems has been found to be a complex process. As a consequence, the development of these systems becomes more error-prone, time-consuming and costly. ESP (ESP8266 and ESP32) is one of the preferred micro-controllers for education, industrial projects, and prototyping. Arduino, a well-known firmware, is used in ESP which makes it easy-to-use. However, learning the device-specific configurations, using network features, and implementing IoT applications for ESP on the Arduino platform are still burdensome and time-consuming tasks. Raising the abstraction level can help to decrease the complexity and address this problem. To this end, in this paper, a model-driven approach is proposed for the development of Arduino-based programmable ESP micro-controllers. First, a meta-model is designed for the Arduino-based ESP micro-controllers. Based on this meta-model, a Domain-specific Language (DSL) is developed to graphically represent the domain models. To gain more functionality for the language, domain rules are defined as constraints. Also, partial system codes are automatically generated from the instance models. In this way, the development of IoT systems based on ESP modules is supported by code synthesis which increases the performance and reduces the number of errors. Finally, a motion-sensitive thief detector and a servo-motor based room temperature control system are implemented to evaluate the proposed DSL.
Detail Me More: Improving GAN’s photo-realism of complex scenes

Generative models can synthesize photo-realistic images of a single object. For example, for human faces, algorithms learn to model the local shape and shading of the face components, i.e., changes in the brows, eyes, nose, mouth, jaw line, etc. This is possible because all faces have two brows, two eyes, a nose and a mouth, approximately in the same location. The modeling of complex scenes is however much more challenging because the scene components and their location vary from image to image. For example, living rooms contain a varying number of products belonging to many possible categories and locations, e.g., a lamp may or may not be present in an endless number of possible locations. In the present work, we propose to add a "broker" module in Generative Adversarial Networks (GAN) to solve this problem. The broker is tasked to mediate the use of multiple discriminators in the appropriate image locales. For example, if a lamp is detected or wanted in a specific area of the scene, the broker assigns a fine-grained lamp discriminator to that image patch. This allows the generator to learn the shape and shading models of the lamp. The resulting multi-fine-grained optimization problem is able to synthesize complex scenes with almost the same level of photo-realism as single object images. We demonstrate the generability of the proposed approach on several GAN algorithms (BigGAN, ProGAN, StyleGAN, StyleGAN2), image resolutions (256 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to 1024 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), and datasets. Our approach yields significant improvements over state-of-the-art GAN algorithms.
Personalized Learning Strategy as a Tool to Improve Academic Performance and Motivation of Students

The purpose of the research is to study and scientifically substantiate personalized strategies as the basis for personalized learning aimed at improving academic performance and student motivation. The study involved 65 teachers and 700 students of various specialties from three Russian and Kazakhstan universities. The survey analysis showed that the modern generation of students is no longer motivated to receive standardized educational services, and in this context, personalized strategies as a form of personalized learning are a key trend that has permeated all aspects of professional training. Unfortunately, currently, universities find it difficult to implement such an approach as personalized learning makes educational modules highly adapted and individualized to meet the needs of each particular student. To apply personalized strategies in the educational process, it is necessary to study the structure of a particular institution, to analyze educational programs, to conduct classes based on personalized learning strategies.
Personalization of Medical Treatment Decisions: Simplifying Complex Models while Maintaining Patient Health Outcomes

Personalizing medical treatments based on patient-specific risks and preferences can improve patient health. However, models to support personalized treatment decisions are often complex and difficult to interpret, limiting their clinical application.We present a new method, using machine learning to create meta-models, for simplifying complex models for personalizing medical treatment decisions. We consider simple interpretable models, interpretable ensemble models, and noninterpretable ensemble models. We use variable selection with a penalty for patient-specific risks and/or preferences that are difficult, risky, or costly to obtain. We interpret the meta-models to the extent permitted by their model architectures. We illustrate our method by applying it to simplify a previously developed model for personalized selection of antipsychotic drugs for patients with schizophrenia.The best simplified interpretable, interpretable ensemble, and noninterpretable ensemble models contained at most half the number of patient-specific risks and preferences compared with the original model. The simplified models achieved 60.5% (95% credible interval [crI]: 55.2-65.4), 60.8% (95% crI: 55.5-65.7), and 83.8% (95% crI: 80.8-86.6), respectively, of the net health benefit of the original model (quality-adjusted life-years gained). Important variables in all models were similar and made intuitive sense. Computation time for the meta-models was orders of magnitude less than for the original model.The simplified models share the limitations of the original model (e.g., potential biases).Our meta-modeling method is disease- and model- agnostic and can be used to simplify complex models for personalization, allowing for variable selection in addition to improved model interpretability and computational performance. Simplified models may be more likely to be adopted in clinical settings and can help improve equity in patient outcomes.
Integrating Knowledge Distillation With Learning to Rank for Few-Shot Scene Classification

Few-shot learning (FSL) has great potential for automatic interpretation of remote sensing (RS) images. In this article, we make a study of few-shot RS scene classification. Many recently proposed FSL models adopt a very effective episode-based training procedure, where each episode is contrived to mimic the few-shot task by dividing training examples into support images and query images. However, these models can only judge whether or not a support image has the same class membership as a given query image. Such a yes-or-no prediction is rather rough, so we set a more demanding training objective, enforcing FSL models to rank support images according to support-query similarity and, hence, endowing them with better generalization ability. To this end, first, we propose ranking-preserving knowledge distillation (KD), which encourages a student network to rank support images in the same way as teacher networks. Then, integrating multiteacher KD with learning to rank, we construct a novel distillation loss using the Plackett&#x2013;Luce distributions and build a novel few-shot classification model called ranking network. Extensive evaluation on two public RS datasets shows that the ranking network achieves the state of the art by a wide margin.
Analysis of the Tools to Support Systematic Literature Review in Software Engineering

Abstract A thorough and accurate review of the literature is a necessary condition for relevant research. However, the literature review is a laborious, error-prone process. For this reason, various software tools are being developed to support researchers in conducting literature reviews. In this paper, three tools to support a systematic literature review in software engineering were analysed and evaluated using the DESMET methodology. Tools were found by conducting mapping study and were selected since they were most promising to support an overall systematic literature review process. Although the tools do not yet support the whole systematic review process on a high level, they provide a reasonable basis for further development. It is needed to set features that need to be supported by the tools.
Fast prediction of complicated temperature field using Conditional Multi-Attention Generative Adversarial Networks (CMAGAN)

• Widely applicable strategies of temperature prediction via computer vision technology. • Presenting a conditional multi-attention module inspired by self-attention module. • Obtaining temperature field rapidly from the structure by building a new GAN model. • Improving stability, accuracy and overall perception compared with former studies. • Exploring our GAN's performance in expressing thermodynamics phenomenon. Generative Adversarial Networks (GANs) have been applied to solve simple physics and engineering problems without complex governing equations. However, existing models can not characterize details and overall dependence in predicting a temperature field. In this paper, we specially propose a new GAN, conditional multi-attention generative adversarial network (CMAGAN), for temperature prediction. CMAGAN can provide reasonably accurate results of complicated heat transfer phenomena rapidly. In our network, a conditional multi-attention module (CMAM) is proposed to captures long-range dependencies between temperature in different regions. An improved multi-scale discriminator is designed to improve the accuracy of temperature results and reduce the randomness by judging from multiple scales. We evaluated the proposed model by simulating the temperature field in a bare sandy land scene with random distribution of signal stations. The temperature images generated by CMAGAN show high accuracy in wake regions (MAPE < 0.079%), shadow regions (MPAE < 0.083%) and signal station regions (MAPE < 0.173%). Compared with previous networks, our GAN has the best performance in temperature prediction via different image similarity evaluation criteria. Finally, CMAGAN's performance in expressing thermodynamics phenomenon is discussed via the visualization of CMAM in our experiment case. The results show that CMAGAN can directly generate robust solutions to complicated heat transfer phenomena.
Characterizing the Relationship Between Application QoE and Network QoS for Real-Time Services

Real-time services require a good interplay between network and application dynamics in order to deliver a satisfactory user experience. The traditional monitoring and optimization of Quality of Service (QoS) parameters in the network lacks of knowledge of user's Quality of Experience (QoE) and, as a result, of efficiency in improving user experience.