Reviewerly/exHarmony huggingface dataset:
Features of the System of Advanced Training and Professional Retraining of Educators of Higher Technical Schools in Modern Conditions

The conceptual provisions of professional retraining and advanced training of educators of engineering universities have been formulated. It is shown that the system of pedagogical competencies is a main indicator of the activities of a modern educator. Requirements for the competencies of educators of engineering universities are formulated in accordance with the criteria of the International Society for Engineering Pedagogy («Internationale Geselschaft für Ingenieur Pädagogik»/«International Society for Engineering Pedagogy» (IGIP)). It is shown that the professional and pedagogical competence of a university’s educator is an ability to apply knowledge, skills, personal, social and methodological skills in an educational or work situation in professional and personal development is the result of the development and assignment of universal, general professional and special professional competencies to students. Engineering pedagogy is presented as the basis of the system of professional retraining and advanced training of educators; the main trends of its development are outlined. The goals, content and structure of the professional-pedagogical activity of educators are considered, the priority components of their professionalism are identified. The advantages of the proposed organization of vocational retraining and further training for educators of engineering colleges and the organizational and pedagogical conditions ensuring the effectiveness of their vocational and pedagogical training have been identified.
Convolutional Autoencoder-Based Transfer Learning for Multi-Task Image Inferences

Pattern-recognition algorithms from machine learning play a prominent role in embedded sensing systems to derive inferences from sensor data. Very often, such systems face severe energy constraints, especially when dealing with high-dimensional data, such as images. The focus of this study is on reducing computational energy by exploiting the concept of transfer learning and energy-efficient dataflow accelerators. We show that the use of convolutional autoencoders can enable various opportunities to reduce computational energy and avoid significant reduction in inference performance when multiple task categories are targeted for inference. We validate our approach through a multi-task case study. The study targets a set of pictures with each picture containing four different task categories: gender, smile, glasses, and pose. In order to minimize inference time and computational energy, a convolutional autoencoder is used for learning a generalized representation of the images. Three scenarios are analyzed: transferring layers using convolutional autoencoders, transferring layers using convolutional neural networks trained on different tasks, and no layer transfer. We show that when the convolutional layers with one fully-connected layer are transferred using convolutional autoencoders, we can achieve a reduction of <inline-formula><tex-math notation="LaTeX">$6.58\times$</tex-math></inline-formula> in computational energy, while improving performance by 1.98, 1.88, 4.11, and 1.47 percent for gender, smile, glasses, and pose inferences, respectively, as compared to the no-transfer method, when the number of training samples is small.
Multi-level models for data security in networks and in the Internet of things

Data flow control for security is a mature research area in computer security, and its established results can be adapted to the newer area of data security in the Internet of things or the Cloud. This paper takes a fundamental approach to the problem. It shows that, under reflexivity and transitivity assumptions, any network of communicating entities can be seen as a partial order of equivalence classes of entities, which is a simplification and generalization of current theory based on the lattice concept, where lattices are generated by labelling. Networks of communicating entities can be created in many ways, including routing, access control policies (possibly involving labeling), etc. Their intrinsic partial orders are necessary and sufficient for data security, and in any such network entities will have greater or lower secrecy or integrity according to their position in the partial order. It is shown how labeling systems, capable of expressing many types of security requirements, can be constructed to assign entities to their appropriate positions in network partial orders. Established paradigms in data security, such as conflicts, conglomeration, aggregation, are introduced in examples. Then it is shown how entities can be added, removed or relocated in partial orders, as a result of events such as user or administrative action. A label-based method is described to maintain security requirements through such transformations. Efficient algorithms exist to implement these concepts, they are applications of transitive closure algorithms and strongly connected component algorithms.
CGA: An image processing based software for surface strain analysis in sheet metal forming

Strain analysis is an essential step in sheet forming applications to understand the deformation behaviour of the material. The strain analysis is conventionally performed by Circle Grid Analysis (CGA). In CGA, a grid of circles with 2 mm–5 mm diameter is printed on a flat sheet and deformed into the required shape. The length of major and minor axes of the deformed ellipses are measured to estimate the strains at different locations of the formed component. The major and minor axes length of the deformed ellipses are measured either by using a manual method or automatic methods. The automated methods are faster and accurate compared to manual methods, but to perform strain measurement using commercially available automatic strain measurement system, is very expensive. Therefore, in this paper, an image processing based software has been developed with user-friendly Graphical User Interface (GUI) for strain measurement and analysis in sheet metal forming operations. The software has been tested on images obtained by printing the ellipses of known dimensions on the flat sheet by laser etching, electrochemical etching and screen printing, which are generally used for printing circular grids on flat sheets for strain analysis in sheet metal forming. It is observed that the edges of the laser engraved ellipse are very slim and easily detected by software compared to other grid printing methods. The developed software can measure the ellipse dimensions with a maximum absolute percentage error of 1.975%. Further, the results of CGA software has been compared with the commercial strain measurement system called Grid Pattern Analyzer (GPA). The mean absolute error in strain measurement using CGA and GPA was found to be 0.0106 and 0.0178, respectively. The statistical test results reveal that there is no evidence to support a claim that there is a difference in mean performance between the two methods.
PSO Based Fuzzy-Genetic Optimization Technique for Face Recognition

One of the trending research topics over the world is Face Recognition. There are several methods available to recognize the human faces. Yet new methods are proposed to make the model efficient and accurate. The model is said to be efficient and accurate if the system can recognize the human faces with higher recognition rate with the best time complexity. The algorithms like textural feature descriptors consists of limitations like robustness to noise, the rotation of images, the dimensionality of the dataset which affects the efficiency of the algorithm. We propose a new method to overcome these limitations to give the optimal solution by using optimization techniques. In this paper, the hybrid optimization technique, PSO based fuzzy-genetic optimization technique is proposed to give high level recognition rate better than techniques like local binary patterns, history of gradients and also basic optimization techniques. The recognition rate of the proposed method is 98.84%. This new technique can efficiently find the best fitted features of the image.
Adversarial robustness via attention transfer

Deep neural networks are known to be vulnerable to adversarial attacks. The empirical analysis in our study suggests that attacks tend to induce diverse network architectures to shift the attention to irrelevant regions. Motivated by this observation, we propose a regularization technique which enforces the attentions to be well aligned via the knowledge transfer mechanism, thereby encouraging the robustness. Resultant model exhibits unprecedented robustness, securing 63.81% adversarial accuracy where the prior art is 51.59% on CIFAR-10 dataset under PGD attacks. In addition, we go beyond performance to analytically investigate the proposed method as an effective defense. Significantly flattened loss landscape can be observed, demonstrating the promise of the proposed method for improving robustness and thus the deployment in security-sensitive settings.
A multi-clause dynamic deduction algorithm based on standard contradiction separation rule

In the past decades, automated theorem proving (ATP) for first-order logic has made good progress, in which binary resolution inference rule plays a crucial role. However, as shown in the latest benchmark library of the ATP system, there are still many practical problems that have not been resolved or cannot be effectively resolved. Recently, in order to overcome the limitations of ATP based on binary resolution inference rules, a novel multi-clause dynamic standard contradiction separation (S-CS) inference rule and its automated deduction theory have been proposed. Based on this theory, this paper first clarifies the generality of this S-CS rule by comparing it with some well-known variants of the binary resolution rule, and then focuses on how to design a specific and effective algorithm along with search strategies to realize the S-CS based deductive theory with its implementation. Specifically, the present work proposes a novel S-CS dynamic deduction algorithm (in short SDDA) based on different strategies and summarizes its implementation procedures. In addition, we focus on evaluating whether SDDA, as a novel perspective multi-clause dynamic automatic deduction algorithm, can be applied on top of the current leading ATP system architectures to further improve their performances. Therefore, SDDA is applied to the current leading first-order ATP systems, i.e., Vampire and E, respectively forming two integrated APT systems, denoted as SDDA_V and SDDA_E. Then the capabilities of SDDA_V and SDDA_E are evaluated on the latest benchmark database TPTP, such as the CASC-J9 problems (FOF division) as well as the hard problems with a rating of 1 in the TPTP benchmark database. The experimental results show the effectiveness of SDDA: SDDA_V outperforms Vampire itself, and SDDA_E, outperforms E itself, and the two improved ATP systems have solved a number of hard problems with the rating of 1 in TPTP, that is, some problems in the latest benchmark database TPTP which have not yet been solved by other current first-order ATP systems.
Quantum Algorithm for Approximating Maximum Independent Sets

We present a quantum algorithm for approximating maximum independent sets of a graph based on quantum non-Abelian adiabatic mixing in the sub-Hilbert space of degenerate ground states, which generates quantum annealing in a secondary Hamiltonian. For both sparse and dense graphs, our quantum algorithm on average can find an independent set of size very close to $\alpha(G)$, which is the size of the maximum independent set of a given graph $G$. Numerical results indicate that an $O(n^2)$ time complexity quantum algorithm is sufficient for finding an independent set of size $(1-\epsilon)\alpha(G)$. The best classical approximation algorithm can produce in polynomial time an independent set of size about half of $\alpha(G)$.
Sentiment Analysis for Twitter Data in the Hindi Language

In the past decade, mining the opinions for information extraction and retrieval has been one of the major fields of study. Sentiment Analysis (SA) can be explained as the process of identifying the polarity of a given content for gaining an insight into the hidden information stored in a text or a piece of document. Today, various social networking sites serve as a platform for expression. Twitter, is one such microblogging application that is tremendously used by various users for expressing the true intent about a particular entity in the form of tweets. The ultimate goal of analyzing the sentiments is to mine the useful content from these opinion sources. The Natural Language Processing (NLP) technique has been used for the initial processing of tweets. There are broadly two methods available for further analyzing the opinionated data: Lexicon Based Approach (LBA) and Machine Learning Approach (MLA) based on supervised learning. The Supervised method has a disadvantage of its dependency on the quality and amount of training data. The LBA technique uses an enhanced dictionary i.e. Hindi SentiWordNet as a resource and Hybrid Based Approach (HBA) which combines the LBA and MLA for classifying movie tweets as either positive or negative. The English language has majorly dominated in this area of research. We, on the other hand, have gathered movie tweets in the Hindi language for Sentiment Analysis of Twitter data and led a comparative evaluation for both the techniques: LBA and HBA.
Robowflex: Robot Motion Planning with MoveIt Made Easy

Robowflex is a software library for robot motion planning in industrial and research applications, leveraging the popular Moveit library and Robot Operating System (ROS) middleware. Robowflex provides an augmented API for crafting and manipulating motion planning queries within a single program, making motion planning with Moveit easy. Robowflex's high-level API simplifies many common use-cases while still providing low-level access to the Moveit library when needed. Robowflex is particularly useful for 1) developing new motion planners, 2) evaluating motion planners, and 3) complex problems that use motion planning as a subroutine (e.g., task and motion planning). Robowflex also provides visualization capabilities, integrations to other robotics libraries (e.g., DART and Tesseract), and is complementary to other robotics packages. With our library, the user does not need to be an expert at ROS or Moveit to set up motion planning queries, extract information from results, and directly interface with a variety of software components. We demonstrate its efficacy through several example use-cases.
A Real-Time Semantic Segmentation Algorithm Based on Improved Lightweight Network

This paper mainly studies the semantic segmentation algorithm for smart car scenes. Considering the real-time nature of the semantic segmentation algorithm in smart car scenes, this paper proposes a real-time semantic segmentation model based on an improved lightweight network. On the basis of the lightweight network MobileNet v2, a network with dilated convolution and attention modules is constructed as the backbone network for feature extraction, and pooling operations of different sizes are used on the dilated pyramid pooling module ASPP to encode surrounding Semantic information. On this basis, a lightweight semantic segmentation model LightSeg was constructed. Tested on a single RTX 2080Ti GPU, the model achieves a segmentation accuracy of 73.8% mIoU at a speed of 49.5 frames per second on the Cityscapes dataset.
Global Data Distribution Weighted Synthetic Oversampling Technique for Imbalanced Learning

Imbalanced learning is a common problem in data mining. There is a different distribution of data samples among other classes in the imbalanced datasets. It's a challenge for standard algorithms designed for balanced class distributions. Although there are various strategies to solve this problem, generating artificial data to achieve a relatively balanced class distribution is universal rather than directly modifying specific classification algorithms. The oversampled data can be combined with any user-specified algorithm without any restrictions. In this paper, we present a novel oversampling method, Global Data Distribution Weighted Synthetic Oversampling Technique (GDDSYN). By applying clustering, optimizing the selection criteria of the minority class samples that are used to generate synthetic samples, avoiding generating more noise samples. GDDSYN assigns weights for the number of synthetic samples to tackle the within-class imbalance and between-class imbalance simultaneously, according to the informative level of the sample and the sparsity of the cluster to which the sample belongs. The use of scores with Silhouette Coefficient and Mutual Information helps the k-means algorithm set a reasonable number of clusters for the minority and majority classes respectively so that the clustering effect can be guaranteed. Next, by using clustering information, synthetic samples' generation path is improved to avoid class overlap. Additionally, GDDSYN has been evaluated extensively on 10 artificial and 10 real-world data sets. The empirical results show that our method is outperforms or comparable with some other existing methods in terms of assessment metrics when artificial data generated by GDDSYN are used.
Compiling Graph Applications for GPU s with GraphIt

The performance of graph programs depends highly on the algorithm, the size and structure of the input graphs, as well as the features of the underlying hardware. No single set of optimizations or one hardware platform works well across all settings. To achieve high performance, the programmer must carefully select which set of optimizations and hardware platforms to use. The GraphIt programming language makes it easy for the programmer to write the algorithm once and optimize it for different inputs using a scheduling language. However, GraphIt currently has no support for generating highperformance code for GPUs. Programmers must resort to re-implementing the entire algorithm from scratch in a low-level language with an entirely different set of abstractions and optimizations in order to achieve high performance on GPUs. We propose G2, an extension to the GraphIt compiler framework, that achieves high performance on both CPUs and GPUs using the same algorithm specification. G2 significantly expands the optimization space of GPU graph processing frameworks with a novel GPU scheduling language and compiler that enables combining load balancing, edge traversal direction, active vertexset creation, active vertexset processing ordering, and kernel fusion optimizations. G2 also introduces two performance optimizations, Edge-based Thread Warps CTAs load balancing (ETWC) and EdgeBlocking, to expand the optimization space for GPUs. ETWC improves load balancing by dynamically partitioning the edges of each vertex into blocks that are assigned to threads, warps, and CTAs for execution. EdgeBlocking improves the locality of the program by reordering the edges and restricting random memory accesses to fit within the L2 cache. We evaluate G2 on 5 algorithms and 9 input graphs on both Pascal and Volta generation NVIDIA GPUs, and show that it achieves up to 5.11× speedup over state-of-the-art GPU graph processing frameworks, and is the fastest on 66 out of the 90 experiments.
A Critical Review on Nature Inspired Optimization Algorithms

Abstract Nature-inspired optimization algorithms turned out to be progressively more accepted in modern era, and the majority of these meta-heuristic algorithms, such as ‘Bat algorithm’, ‘Lion Algorithm’, ‘Particle swarm optimization’, ‘Water wave optimization algorithm’, ‘Elephant herding optimization algorithm’, ‘Optics inspired optimization algorithm’, ‘Cuckoo search’, ‘Flower algorithms’, ‘Genetic algorithms’, ‘Differential evolution’, ‘Harmony search’, ‘Simulated annealing’, and many more. This paper attempts to establish the most recent improvements concerning all main ‘Nature-Inspired Algorithms’ and it is strived to coat at least one hottest optimization problem related to these above-mentioned algorithms. Based on their outcomes it is established that there are a few considerable gaps between hypothesis and observe. On one side, nature-inspired algorithms for optimization are extremely successful and can acquire most favourable clarifications in a convincingly realistic time. On the other side, mathematical investigation of key characteristics of these algorithms is missing. It is extremely advantageous that researchers add on some impending into the neutrality of diverse nature-inspired algorithms and hence can obtain the dare to crack key difficulties that must be solved. The paper is extending extensively and intensifying the need to evaluate and review these various optimization algorithms. This paper is organized in a manner to provide complete description of each NIA starting from introduction then review of literature, analysis of various NIAs and finally conclusion.
Activity With Gender Recognition Using Accelerometer and Gyroscope

Recently, the use of the inertia measurement units (IMU), especially the gyroscope and accelerometer sensors, has increased in the human activity recognition (HAR) due to the extensive use of smartwatches and smartphones. In addition to the high quality and efficiency result in by these sensors, they can capture the data of the body dynamic motion as function of time, then the stream of data is analyzed and processed to classify and predict the action being done, the gender, the health status and many other characteristics. Gender and activity recognition have been deeply studied recently, using various ways to recognize either of them through many interfaces, like voice, image, or inertia measurement motion data. All these types of classifications are crucial in many applications such as recommendation systems, speech recognition, sports tracking, security and most importantly in healthcare. In this research, we present two models (hierarchical model and joint distribution model) and compare between two datasets (MoVi and MotionSense), using only two IMU sensors on right and left hand and motion sense dataset using mobile phone, to predict gender with activity and see how every activity reflect on gender, and explore the efficiency on using autocorrelation function as a feature extractor and compare between three classifiers, Random Forest (RF), Support Vector Machine (SVM) and Convolution Neural Network (CNN).
Re-energizing Domain Discriminator with Sample Relabeling for Adversarial Domain Adaptation

Many unsupervised domain adaptation (UDA) methods exploit domain adversarial training to align the features to reduce domain gap, where a feature extractor is trained to fool a domain discriminator in order to have aligned feature distributions. The discrimination capability of the domain classifier w.r.t. the increasingly aligned feature distributions deteriorates as training goes on, thus cannot effectively further drive the training of feature extractor. In this work, we propose an efficient optimization strategy named Re-enforceable Adversarial Domain Adaptation (RADA) which aims to re-energize the domain discriminator during the training by using dynamic domain labels. Particularly, we relabel the well aligned target domain samples as source domain samples on the fly. Such relabeling makes the less separable distributions more separable, and thus leads to a more powerful domain classifier w.r.t. the new data distributions, which in turn further drives feature alignment. Extensive experiments on multiple UDA benchmarks demonstrate the effectiveness and superiority of our RADA.