Miracle-dz/UniFAR-FTUs huggingface dataset:
Blended Learning Technologies in the Automotive Industry Specialists' Training

Today, business requires engineers with sufficient level of professionalism that mean not only high qualification, but also that students must be able to use personal, interpersonal and system competences in professional sphere. Therefore the system of engineering education in the 21st century should embrace the innovative principles, methods and teaching technologies. The analysis of applied forms of education shows that Blended Learning has advantages over traditional learning and E-Learning. For its successful implementation an intelligent learning environment, including such technologies, as gamification, virtual and augmented reality, has to be created. The specific feature of proposed approach is to use such modeling environment and software tools for education process, which are applied at manufacturing site as well as allow to model the systems, with which the future engineer will work in practices. Examples of interaction between Kazan Federal University and the Public Corporation "KAMAZ" in engineers training are presented.
Cost-Optimized Submarine Cables Using Massive Spatial Parallelism

We study the techno-economics of submarine systems constrained by a fixed electrical power supply. We show significant cost savings for high-capacity submarine systems using massive space-division multiplexing (SDM), even without assuming any savings from SDM-specific subsystem integration. Systems with about 100 parallel optical paths, e.g., $\sim$50 fiber pairs are shown to provide minimum cost/bit, operating at reduced spectral efficiencies and deep within the linear regime. While advanced nonlinearity-optimized fibers and digital nonlinearity compensation schemes provide little to no gain in such systems, SDM integration of amplifiers and transponders is shown to be a source for significant additional cost savings. We further examine the permissible cost premium for multicore fibers in such massively parallel systems and revisit various design tradeoffs for optical amplifiers, showing that a reduced noise figure can be traded for better power conversion efficiency. We also evaluate potential gains from increasing the available electrical supply power and discuss reliability aspects of massively parallel submarine systems.
Saturated Models in Mathematical Fuzzy Logic

This paper considers the problem of building saturated models for first-order graded logics. We define types as pairs of sets of formulas in one free variable which express properties that an element is expected, respectively, to satisfy and to falsify. We show, by means of an elementary chains construction, that each model can be elementarily extended to a saturated model where as many types as possible are realized. In order to prove this theorem we obtain, as by-products, some results on tableaux (understood as pairs of sets of formulas) and their consistency and satisfiability, and a generalization of the Tarski--Vaught theorem on unions of elementary chains.
eBrowser: Making Human-Mobile Web Interactions Energy Efficient with Event Rate Learning

Due to the limited screen size of mobile devices, finger movements on touchscreen, such as scrolling and pinching (i.e., zooming in or out), are frequently used on mobile Web browsers and WebView-based apps, consuming considerable energy on mobile devices. While existing works on mobile Web browsers focus on reducing the power consumption or optimizing the performance of webpage loading, the power consumption of mobile Web interactions, especially after webpage loading, has received comparatively little attention. Motivated by an empirical study of the power consumption and user experience survey of human-mobile interactions, we design and implement eBrowser, an energy-efficient mobile Web interaction framework. It leverages a cloud-based machine learning model to enable personalized interaction event rate for individual users according to the interaction speed of their finger movement and the content of rendered webpages. To adapt to user behavior changes, eBrowser continuously monitors the interaction experience on each mobile device and periodically updates the personalized event rate model with incremental learning in the cloud. We implement eBrowser in Chromium and deploy the event rate model in a remote Aliyun cloud instance. Experimental results show that eBrowser reduces the energy consumption of mobile Web interactions by up to 43.8% with negligible runtime overhead, while guaranteeing user satisfaction on both mobile browsers and WebView-based apps.
Ejection Fraction Classification in Transthoracic Echocardiography Using a Deep Learning Approach

Cardiovascular diseases are the leading cause of death worldwide. These diseases are related with a broad range of factors but usually show high correlation with diminished left ventricle function, which can be evaluated by measuring the ventricular ejection fraction through transthoracic echocardiography (TTE), a cost-effective and highly portable first-line diagnosing technique. Ejection fraction (EF) is currently determined through a semi-automatic process that requires manual delineation of the left ventricle area both in a diastolic and systolic frame of the patient's exam. To remove this manual annotation step, which is both time-consuming and user dependent, automatic Computer-Aided Diagnosis (CAD) systems can be used. Herein, we propose the first steps for such a system that classifies ejection fraction in four classes, based on TTE exams, with the objective of automatically providing valuable information to physicians. Our classification method is based on a 3D-Convolutional Neural Network (3D-CNN) trained on a dataset constructed with exams from a cardiology reference center. The dataset creation consisted of three main steps: firstly, for each exam, cine-loops showing the apical 4 chambers view were manually selected; then, 30 sequential frames were extracted from each cine-loop; finally, each frame was pre-processed to mask burned-in metadata. The neural network was designed to explore concepts such as convolutions using asymmetric filters and residual learning blocks. The model was trained on a dataset with 4000 TTE exams and tested on a separate dataset containing 1600 TTE cases. We obtained an accuracy of 78% and a F1 score of 71.3% for unhealthy EF (below 45%), 63.3% for intermediate EF (45-55%), 72.3% for healthy EF (55-75%) and 54.6% for abnormally high EF (above 75%). These results are promising and show that convolutional neural networks can be applied to this domain. Furthermore, this work will serve as a foundation for future research where other relevant cardiac metrics will be determined.
New Incremental Learning Algorithm for Semi-Supervised Support Vector Machine

Semi-supervised learning is especially important in data mining applications because it can make use of plentiful unlabeled data to train the high-quality learning models. Semi-Supervised Support Vector Machine (S3VM) is a powerful semi-supervised learning model. However, the high computational cost and non-convexity severely impede the S3VM method in large-scale applications. Although several learning algorithms were proposed for S3VM, scaling up S3VM is still an open problem. To address this challenging problem, in this paper, we propose a new incremental learning algorithm to scale up S3VM (IL-S3VM) based on the path following technique in the framework of Difference of Convex (DC) programming. The traditional DC programming based algorithms need multiple outer loops and are not suitable for incremental learning, and traditional path following algorithms are limited to convex problems. Our new IL-S3VM algorithm based on the path-following technique can directly update the solution of S3VM to converge to a local minimum within one outer loop so that the efficient incremental learning can be achieved. More importantly, we provide the finite convergence analysis for our new algorithm. To the best of our knowledge, our new IL-S3VM algorithm is the first efficient path following algorithm for a non-convex problem (i.e., S3VM) with local minimum convergence guarantee. Experimental results on a variety of benchmark datasets not only confirm the finite convergence of IL-S3VM, but also show a huge reduction of computational time compared with existing batch and incremental learning algorithms, while retaining the similar generalization performance.
TurboStream: Towards Low-Latency Data Stream Processing

Data Stream Processing (DSP) applications are often modelled as a directed acyclic graph: operators with data streams among them. Inter-operator communications can have a significant impact on the latency of DSP applications, accounting for 86% of the total latency. Despite their impact, there has been relatively little work on optimizing inter-operator communications, focusing on reducing inter-node traffic but not considering inter-process communication (IPC) inside a node, which often generates high latency due to the multiple memory-copy operations. This paper describes the design and implementation of TurboStream, a new DSP system designed specifically to address the high latency caused by inter-operator communications. To achieve this goal, we introduce (1) an improved IPC framework with OSRBuffer, a DSP-oriented buffer, to reduce memory-copy operations and waiting time of each single message when transmitting messages between the operators inside one node, and (2) a coarse-grained scheduler that consolidates operator instances and assigns them to nodes to diminish the inter-node IPC traffic. Using a prototype implementation, we show that our improved IPC framework reduces the end-to-end latency of intra-node IPC by 45.64% to 99.30%. Moreover, TurboStream reduces the latency of DSP by 83.23% compared to JStorm.
A Subword-Based Deep Learning Approach for Sentiment Analysis of Political Tweets

The successful use of online material in political campaigns over the past two decades has motivated the inclusion of social media platforms—such as Twitter—as an integral part of the political apparatus. Political analysts are increasingly turning to Twitter as an indicator of public opinion. We are interested in learning how positive and negative opinions propagate through Twitter and how important events influence public opinion. In this paper, we present a neural network-based approach to analyse the sentiment expressed on political tweets. First, our approach represents the text by dense vectors comprising subword information to better detect word similarities by exploiting both morphology and semantics. Then, a Convolutional Neural Network is trained to learn how to classify tweets depending on sentiment, based on an available labelled dataset. Finally, the model is applied to perform the sentiment analysis of a collection of tweets retrieved during the days prior to the latest UK General Election. Results are promising and show that the neural network approach represents an improvement over lexicon-based approaches for positive/negative sentence classification.
The regulatory interplay between Oct-1 isoforms contributes to hematopoiesis and the isoforms imbalance correlates with a malignant transformation of B cells

Oct-1(POU2F1) is a DNA-binding transcription regulator and its level being highly increased in many human cancers. Oct-1 is present in the human cells as a family of functionally different isoforms which are transcribed from alternative promoters. Here, we have demonstrated that expression patterns of Oct-1 isoforms change during differentiation of hematopoetic progenitor cells (CD34+) (HPCs) to the B (CD19+) and T (CD3+) cells. While Oct-1L is expressed at a high level in the CD34+ HPCs, its expression level drops dramatically during the T-cell differentiation, although remains nearly the same in B-cells. We have described the novel human Oct-1R isoform which is conserved in mammals and is B cell-specific. Oct-1R was found in B cells, but not in HPCs. Oct-1R is transcribed from the same promoter as Oct-1L, another lymphocyte-specific isoform. Overexpression of Oct-1R and Oct-1L in the Namalwa cells leads to the repression of many genes involved in B-lymphocyte differentiation and signal transduction. Thus these isoforms may regulate the particular stages of development of normal B cells and maintain their proper differentiation status. However the extremely high level of Oct-1L isoform observed in the B-lymphoblast tumor cell lines indicated that the excess of Oct-L seem likely to considerably decrease the differentiation ability of these cells. Oct-1 may serve as a therapeutic target for many tumors, but it should be noted that in a tumor the content of a certain isoform Oct-1, rather than the total Oct-1 protein, can be increased.
Decolorization-Based Hyperspectral Image Visualization

Image decolorization is known to be an effective way in transferring a color image into a gray one while well preserving the major information of all three bands. In this paper, a simple yet effective hyperspectral image visualization framework based on decolorization, named decolorization based hyperspectral visualization, is proposed, which enables us to fully exploit the benefits of decolorization technique. The proposed framework consists of the following two main steps. First, the hyperspectral image is partitioned into nine subsets of adjacent hyperspectral bands and the averaged band of each subset is calculated. Then, the dimension reduced image is further divided into three groups of adjacent bands, and the bands in each group are fused by using an image decolorization method. The main contribution of this paper is that the strong correlations in two different fields, i.e., image decolorization and hyperspectral image visualization, are first built. Experiments performed on several real hyperspectral data sets demonstrate that the proposed framework can obtain outstanding visualization performance in terms of both subjective and objective evaluations.
LogTracker: Learning Log Revision Behaviors Proactively from Software Evolution History

Log statements are widely used for postmortem debugging. Despite the importance of log messages, it is difficult for developers to establish good logging practices. There are two main reasons for this. First, there are no rigorous specifications or systematic processes to guide the practices of software logging. Second, logging code co-evolves with bug fixes or feature updates. While previous works on log enhancement have successfully focused on the first problem, they are hard to solve the latter. For taking the first step towards solving the second problem, this paper is inspired by code clones and assumes that logging code with similar context is pervasive in software and deserves similar modifications. To verify our assumptions, we conduct an empirical study on eight open-source projects. Based on the observation, we design and implement LogTracker, an automatic tool that can predict log revisions by mining the correlation between logging context and modifications. With an enhanced modeling of logging context, LogTracker is able to guide more intricate log revisions that cannot be covered by existing tools. We evaluate the effectiveness of LogTracker by applying it to the latest version of subject projects. The results of our experiments show that LogTracker can detect 199 instances of log revisions. So far, we have reported 25 of them, and 6 have been accepted.
QUEST: Quadriletral Senary Bit Pattern for Facial Expression Recognition

Facial expression has significant role to analyzing human cognitive state. Deriving an accurate facial appearance representation is critical task for an automatic facial expression recognition application. This paper provides a new feature descriptor named as Quadrilateral Senary bit Pattern for facial expression recognition. The QUEST pattern encoded the intensity changes by emphasizing relationship between neighboring and reference pixels by dividing them into two quadrilaterals in local neighborhood. Thus, the resultant gradient edges reveal the transitional variation information, that improves the classification rate by discriminating expression classes. Moreover, it also enhances the capability of the descriptor to deal with view point variations and illumination changes. The trine relationship in quadrilateral structure helps to extract the expressive edges and suppressing noise elements to enhance the robustness to noisy conditions. The QUEST pattern generates a six-bit compact code, which improve the efficiency of the FER system with more discriminability. The effectiveness of proposed method is evaluated by conducting several experiments on four benchmark datasets: MMI, GEMEP-FERA, OULU-CASIA and ISED. The experimental results show better performance of the proposed method as compared to existing state-art-the approaches.
Winner's Curse: Bias Estimation for Total Effects of Features in Online Controlled Experiments

Online controlled experiments, or A/B testing, has been a standard framework adopted by most online product companies to measure the effect of any new change. Companies use various statistical methods including hypothesis testing and statistical inference to quantify the business impact of the changes and make product decisions. Nowadays, experimentation platforms can run as many as hundreds or even more experiments concurrently. When a group of experiments is conducted, usually the ones with significant successful results are chosen to be launched into the product. We are interested in learning the aggregated impact of the launched features. In this paper, we investigate a statistical selection bias in this process and propose a correction method of getting an unbiased estimator. Moreover, we give an implementation example at Airbnb's ERF platform (Experiment Reporting Framework) and discuss the best practices to account for this bias.
DCS-ctrl: A Fast and Flexible Device-Control Mechanism for Device-Centric Server Architecture

Modern high-performance servers leverage a large number of emerging peripheral devices (e.g., data processing accelerators, non-volatile memory storage, high-bandwidth network cards) to meet ever-increasing performance demands of server applications. However, as such servers experience severe kernel overhead due to frequently invoked device operations (e.g., buffer management and data copy), server architects have proposed various hardware and software approaches to enable direct communications among the devices. Unfortunately, existing direct device-to-device (D2D) communication schemes still suffer from low performance and the lack of flexibility. First, software-based schemes depend on complicated kernel routines and necessitate multiple hardware-software and user-kernel boundary crossings, which significantly limit the performance improvement opportunities from direct D2D communications. On the other hand, hardware-based schemes require tight integration and custom-built devices, preventing architects from flexibly adding off-the-shelf devices. In this paper, we propose DCS-ctrl, a novel Hardware-based Device-Control (HDC) mechanism for Device-Centric Server (DCS) architecture to provide fast and CPU-efficient direct D2D communications among a large number of off-the-shelf peripheral devices. The key idea of DCS-ctrl is to implement a low-cost and flexible device-control mechanism on an independent FPGA device called HDC Engine. As HDC Engine manages all data and control transfers among devices at the hardware level, the server achieves high performance, scalability, and flexibility. First, optimizing both data and control paths at the hardware level minimizes the latency of inter-device communications. Second, implementing FPGA-based reconfigurable device controllers enables direct D2D communications among commodity devices and thus improves per-device flexibility. Third, merging heterogeneous device operations with intermediate data processing supports creates more opportunities for direct inter-device communications in server applications. Our DCS-ctrl prototype reduces the latency of software-based direct D2D communications by 42% and the CPU utilization by 52%.
SQR: Balancing Speed, Quality and Risk in Online Experiments

Controlled experimentation, also called A/B testing, is widely adopted to accelerate product innovations in the online world. However, how fast we innovate can be limited by how we run experiments. Most experiments go through a "ramp up" process where we gradually increase the traffic to the new treatment to 100%. We have seen huge inefficiency and risk in how experiments are ramped, and it is getting in the way of innovation. This can go both ways: we ramp too slowly and much time and resource is wasted; or we ramp too fast and suboptimal decisions are made. In this paper, we build up a ramping framework that can effectively balance among Speed, Quality and Risk (SQR). We start out by identifying the top common mistakes experimenters make, and then introduce the four SQR principles corresponding to the four ramp phases of an experiment. To truly scale SQR to all experiments, we develop a statistical algorithm that is embedded into the process of running every experiment to automatically recommend ramp decisions. Finally, to complete the whole picture, we briefly cover the auto-ramp engineering infrastructure that can collect inputs and execute on the recommendations timely and reliably.
Accurate Prediction and Analysis of Electromagnetic Fields and Forces in Flux-Focusing Eddy Current Coupling With Double Slotted Conductor Rotors

Permanent magnet eddy current couplings are promising devices for torque and speed transmission without any mechanical contact. In this paper, flux-focusing permanent magnet eddy current couplings with double slotted conductor rotors are proposed and investigated. Given the drawback of the accurate 3-D finite element method, the purpose of this paper lies to establish an accurate and fast analytical model to evaluate the electromagnetic field and torque of such devices with non-homogeneous boundary conditions. Thus, based on the 2-D sub-domain method, the magnetic vector potential in each sub-domain is formulated and solved by the separation of variables method. Taking into account the eddy current effects and slotting effects, the closed-form expressions of magnetic field, induced current, electromagnetic force, and torque for such devices are obtained. Finally, the 3-D finite element method is employed to validate the analytical results.
Symmetric Key-Based Lightweight Authentication Protocols for RFID Security

Radio Frequency Identification (RFID) is being used in many industries from inventory tracking to human implants. However, there are many security issues in RFID systems. Many attacks like privacy related attacks, eavesdropping, replay attack, man-in-the-middle attack, de-synchronization attack, etc. are possible in RFID systems. Due to these security problems, an adversary can get important information which can be used to track the location of a person, steal information, etc. Many researchers have proposed protocols to cater for security. However, there are several drawbacks in existing protocols. For example, they do not support both active and passive tags. In many protocols, reader is not authenticated by server. Many protocols cannot be implemented in practice due to limitations in RFID tag computing ability. This motivated us to explore new protocols which can satisfy all security requirements of authentication protocols for RFID security at low cost. In this paper, we propose a suite of lightweight authentication protocols for RFID security which provide security against all known attacks. We have compared the security provided by the proposed protocols with those of the existing protocols, and shown that the proposed protocols meet most of the security requirements of authentication protocols for RFID security. Essentially, we propose two protocols with tag anonymity as an option. The first protocol ensures all security requirements at low cost. In the second and improved protocol, communication between server and reader is greatly reduced.