Reviewerly/exHarmony huggingface dataset:
Teaching strategies in software engineering towards industry interview preparedness

The Software Engineering (SE) curriculum in undergraduate computer science (CS) education is designed to train students in the process of software and systems development. Traditionally, topics such as software development methodologies, industry nomenclatures, and solution analysis are delivered through lectures and group projects. We propose a novel approach in teaching SE that we call MACROVR: <u>MA</u>chine learning to select project team members; <u>C</u>loud technologies required for project control, code versioning, and team communications; <u>RO</u>tational schedules in Agile/Scrum roles; an individual <u>V</u>ideo of the team project story board; and <u>R</u>ubrics for all presentations. Our teaching strategy with this approach utilizes the latest technologies currently employed in industry and corresponds to soft skills commonly assessed in interviews.
Extending Multi-Object Detection Ability using Correlative Filter

Recently, numerous object detection methods are proposed and has been published by many papers and journals. Since the big hit of YOLO object detector in 2014, even lighter (thus faster) or more accurate method has been developed with good success. On the other side of computer vision advancement, the annual Visual Object Tracker (VOT) challenge is presenting us the newest state-of-the-art tracking methods, which are getting better for each year both in terms of accuracy and speed. In an object detection system with a goal to identify the surrounding environment, it is beneficial to take advantages from rapid development in both object detection and object tracking method. The combined method is able to automatically detect and identify which object is which in subsequent frames with a considerable performance for indoor settings.
Polyphase-modulated radar signal recognition based on time-frequency amplitude and phase features

The current recognition method based on time-frequency analysis only uses the amplitude spectrum and ignores the phase spectrum, which leads to the recognition rate of radar signal low. In this paper, we propose an automatic recognition method based on time-frequency amplitude and phase features. For the signal time-frequency analysis, we take the short-time Fourier-based synchrosqueezing transform for radar signals to obtain two-dimensional time-frequency representation with complex values. Then, we extract the time-frequency amplitude spectrum and phase spectrum and take the absolute value of them as the two inputs of the recognition network. Next, we construct a deep convolutional network with two channels for automatic feature extraction and recognition. The simulation results on 5 kinds of polyphase codes show that the proposed method has superior performance in distinguishing the polyphase codes even at low SNR, and the phase features help to improve the recognition rate.
Breaking Down High-Level Robot Path-Finding Abstractions in Natural Language Programming

Natural language programming (NLPr) allows people to program in natural language (NL) for specific domains. It poses great potential since it gives non-experts the ability to develop projects without exhaustive training. However, complex descriptions can sometimes have multiple interpretations, making program synthesis difficult. Thus, if the high-level abstractions can be broken down into a sequence of precise low-level steps, existing natural language processing (NLP) and NLPr techniques could be adaptable to handle the tasks. In this paper, we present an algorithm for converting high-level task descriptions into low-level specifications by parsing the sentences into sentence frames and using generated low-level NL instructions to generate executable programs for pathfinding tasks in a LEGO Mindstorms EV3 robot. Our analysis shows that breaking down the high-level pathfinding abstractions into a sequence of low-level NL instructions is effective for the majority of collected sentences, and the generated NL texts are detailed, readable, and can easily be processed by the existing NLPr system.
An Integrated Approach to Encompass Big Data Cloud Adoption Using Data Mining

Text Mining is one of the most censorious techniques for analysis of data. It processes the unstructured data which has been found to hold nearly 80% of the world’s data. In the present time a majority of industries and big firms use and store massive amount of data sets. The information gets stored in the data Warehouses, and cloud platforms respectively. The data getting stored shows an exponential growth each time since multiple amount of new data is said to get stored every minute from distinct sources. Due to heavy presence of information stored in the platforms it becomes difficult to gather and extract the most relevant part from the data. Thus, text mining and data mining techniques are applied with algorithms such as CNN and RNN supporting it. The algorithm performs deep analysis of data and presents the most relevant piece of information, removing the less important ones. The relevance of the project comes into existence when it’s seen to be integrated into industries such as software markets, social media analysis and formulating of market reports. Thus, providing an extremely comprehensive information which would enhance scope and application of the data getting stored.
University environment today: problems and prospects of spatial organization. INRTU

Abstract When it comes to advancing the higher education system, the Government’s priority is to create a state-of-the-art university environment with a one-of-a-kind innovation space. This paper analyzes the core factors behind this process: the specifics of the University’s life in the era of new technology; interpretation of the learning environment and its spatial organization as part of the University’s image; presenting the Campus as a progressive approach to embedding the University in the real world of today and its problems, as part of the University’s identity. The University herein means Irkutsk National Research Technical University, or INRTU. An important feature of this paper is that it addresses INRTU’s existence in the modern learning space as the learning environment is increasingly digitalized and the University needs to reorganize its environment to improve its quality, architectural and artistic value and significance. The author’s R&amp;D efforts breathe new life into INRTU’s facilities to make them good for studies and research and suitable for commercial and cultural initiatives taken in the university environment; these efforts include creating a special entrance design for better image, designing innovative technology parks and communication spaces (coworking and creativity areas).
Formal Privacy for Modern Nonparametric Statistics

Modern nonparametric (NP) statistics is an increasingly important and expanding tool set in data analytics as more large, complex data are gathered and analyzed. However, corresponding privacy conc...
Understanding and Mapping Digital Literacy for Students with Disability

Digital literacy capability is important for all students, particularly for those with disability, as it can enable access to learning. The challenges in understanding what this capability means for students with disability, and how their learning in this area progresses, has created difficulties for teachers in supporting these students to become digitally literate. To address this challenge, this study sought to define the construct of digital literacy for students with primarily intellectual disability, with the aim of developing an assessment of digital literacy capability. By incorporating the knowledge of those with subject matter expertise, such as experienced specialist teachers, and the assessment data from 1,413 students with disability, the study applied partial credit item response modelling (Masters, Psychometrica 47:149–174, 1982) to develop a progression of digital literacy for these students. The strong evidence drawn from reliability indices, item and person fit statistics, and differential item functioning support multiple arguments for validity. The results may assist teachers to understand the digital literacy capability of students with disability and what they are likely to be ready to learn next, for the purpose of targeting teaching for learning.
Source Coding for Text Transmission using a Deep Neural Network as a Lossy Compression Stage

The continuous growth of traffic in the telecommunication networks has motivated the search for optimal source codes that can achieve high percentages of compression of the information to be transmitted. However, the compression rates are limited in the practice for the type of messages to encode. For this reason, new techniques have been developed in order to improve the compression rates of the traditional algorithms. In particular, source coding techniques based on computational intelligence algorithms are being studied lately. Hence, this paper proposes a new source coding technique for text compression based on two stages: the initial stage uses a deep neural network, called Text Embedding Neural Network, and the second stage uses a Canonical Huffman Code. The deep neural network increases the compression rate by controlling the level of syntax loss allowed in each message through a single adjustable parameter. This combination is able to reduce the size of the transmitted messages up to 30% with relation to only use traditional source coding algorithms.
Improved Conditional Differential Analysis on NLFSR Based Block Cipher KATAN32 with MILP

This paper describes constructing a Mixed Integer Linear Programming (MILP) model for conditional differential cryptanalysis on nonlinear feedback shift register (NLFSR)-based block ciphers, and proposes an approach for detecting the bit with a strongly-biased difference. The model is successfully applied to the block cipher KATAN32 in the single-key scenario, resulting in practical key-recovery attacks covering more rounds than the previous. In particular, we present two distinguishers for 79 and 81 out of 254 rounds of KATAN32. Based on the 81-round distinguisher we recover 11 equivalent key bits of 98-round KATAN32 with the time complexity being less than \(2^{31}\) encryptions of 98-round KATAN32 and recover 13 equivalent key bits of 99-round KATAN32 with the time complexity being less than \(2^{33}\) encryptions of 99-round KATAN32. Thus far, our results are the best known practical key-recovery attacks for the round-reduced variants of KATAN32 as far as the number of rounds and the time complexity. All the results are verified experimentally.
Synthesis of a Visual Feedback Stabilizing Controller for an Inverted Pendulum Using a Fisheye Lens

This study considers the visual feedback stabilizing problem of an inverted pendulum against image distortion. To design the visual feedback controller, a measurement model using the fisheye lens is delivered. The effectiveness of the feedback controller based on the proposed model is verified through our numerical simulations and experimental results.
Chromatic Aberration Correction Using Cross-Channel Prior in Shearlet Domain

Instead of more expensive and complex optics, recent years, many researches are focused on high-quality photography using light-weight cameras, such as single-ball lens, with computational image processing. Traditional methods for image enhancement do not comprehensively address the blurring artifacts caused by strong chromatic aberrations in images produced by a simple optical system. In this paper, we propose a new method to correct both lateral and axial chromatic aberrations based on their different characteristics. To eliminate lateral chromatic aberration, cross-channel prior in shearlet domain is proposed to align texture information of red and blue channels to green channel. We also propose a new PSF estimation method to better correct axial chromatic aberration using wave propagation model, where F-number of the optical system is needed. Simulation results demonstrate our method can provide aberration-free images while there are still some artifacts in the results of the state-of-art methods. PSNRs of simulation results increase at least 2 dB and SSIM is on average 6.29% to 41.26% better than other methods. Real-captured image results prove that the proposed prior can effectively remove lateral chromatic aberration while the proposed PSF model can further correct the axial chromatic aberration.
Software language engineers’ worst nightmare

Many techniques in software language engineering get their first validation by being prototyped to work on one particular language such as Java, Scala, Scheme, or ML, or a subset of such a language. Claims of their generalisability, as well as discussion on potential threats to their external validity, are often based on authors' ad hoc understanding of the world outside their usual comfort zone. To facilitate and simplify such discussions by providing a solid measurable ground, we propose a language called BabyCobol, which was specifically designed to contain features that turn processing legacy programming languages such as COBOL, FORTRAN, PL/I, REXX, CLIST, and 4GLs (fourth generation languages), into such a challenge. The language is minimal by design so that it can help to quickly find weaknesses in frameworks making them inapplicable to dealing with legacy software. However, applying new techniques of software language engineering and reverse engineering to such a small language will not be too tedious and overwhelming. BabyCobol was designed in collaboration with industrial compiler developers by systematically traversing features of several second, third and fourth generation languages to identify the core culprits in making development of compiler for legacy languages difficult.
OASys: Envisioning an Opto-electronic Accelerator for Deep Learning Applications

We propose OASys, an Opto-electronic Accelerator System, for efficient deep learning acceleration. The system takes on a full-stack architectural design approach and combines the power of Fourier optics and conventional FPGAs.
The Use of AR Elements in the Teaching of Mathematics at a Technical University

Virtual and augmented reality are a completely new technologies in the field of education. They are of great interest, but so far they have little use in education. They can complement the learning process, make it more accessible and fun. Schools and universities use elements of these technologies in the teaching of history, literature, biology, chemistry, physics, but there are practically no developments in the field of mathematics. Meanwhile, visualization in mathematics is a necessary condition for understanding the subject, increasing interest in it. The development of modern technology and its cost reduction has led to the fact that almost everyone has a smartphone. This allows the use of applications related to the subject of study. The younger generation is already living and developing surrounded by computer technology, mastering VR/ AR technology in a game form, creating projects in quantoriums. In order to keep up, the modern teacher is constantly engaged in self-education, improving his skills in the field of computer technology. Our research is based on the project method. Creating projects using AR / VR technologies in the study of mathematics at a technical university allows us to solve at least two problems. On the one hand, this is an increase of interest in the subject, a deeper immersion of students in it. It is also the creation of a bank of teaching materials. Such projects are created using AR technology based on Unity with the addition of Vuforia. The experience in creating such projects has shown the promise of this work.
HyVE: A Hybrid Voting-based Eviction Policy for Caches

In computer architecture, multi-level cache hierarchies are invaluable to achieve good system performance. Even multi-MByte last level caches are short on capacity, therefore, set associativity, write-back strategies and cache replacement policies become important runtime management aspects. Numerous eviction policies, such as LRU, LFU, etc., have been investigated, all attempting to minimize the number of cache replacements. Such standalone policies optimize for a single attribute (recency, frequency, etc.), limiting their benefits for applications exhibiting non-uniform memory access patterns. We propose a Hybrid Voting-based Eviction Policy (HyVE), which extends multiple standalone eviction policies with a ranking system and combines them using voting theory methodologies. The goal of HyVE is to make better replacement decisions by creating a consensus among its constituent eviction policies. Analyzing HyVE, we observed its ability to take different replacement decisions compared to its standalone counterparts, making it a unique and new eviction policy. HyVE not only combines standalone policies, but can also be reduced to any of its constituents, making it flexible. We explored different variants of HyVE, and evaluated them using PARSEC and SPLASH-2 workloads on the sniper multi-core simulator. We also compared HyVE to state-of-the-art set-dueling (DRRIP) and learning-based (Hawkeye) policies. Results show that on average, compared to the LRU policy, HyVE reduces cache misses by 7.4%, DRRIP by 5.5% and Hawkeye by 9.2%. Though Hawkeye exhibits better performance on average, HyVE offers a unique advantage for certain workloads by using a voting-based approach to solve the replacement problem. HyVE has been synthesized on an FPGA prototype. We breakdown HyVE's hardware overheads and analyze its impact on timing.
Secure Personal Unmanned Aerial Vehicle Lease Scheme Based on Blockchain

In recent years, the unmanned aerial vehicle (UAV) sharing industry has attracted considerable attention. However, UAV leasing companies have complicated procedures and high costs, making it cumbersome for UAVs to be shared in real time. Meanwhile, the transaction agreement is driven by a legal environment that is unable to control or limit malicious transactions. In this study, we developed a peer-to-peer authorized UAV rental scheme that improved the security and tamper resistance of data. Using blockchain technology, real-time tracking and supervision of UAVs were enabled and malicious transactions were avoided. The scheme also simplified the leasing process and reduced costs by designing smart contracts. We used the Python PyOTA module and built a UAV rental website based on the Flask web framework. Using the website, we successfully achieved peer-to-peer authorization of UAVs between two untrusted individuals without legal or institutional interference. Our method increased the security and traceability of the data, and made transactions simple and cost-effective. Furthermore, it offered protection against malicious transactions and enabled the tracking and supervision of UAVs. These results show that our scheme can be applied to the shared UAV market and can improve the security of transactions.
Hand Detector based on Efficient and Lighweight Convolutional Neural Network

Hand detection and recognition topic has been studied since the last century and is particularly concerned with the development of machine learning today. Inspired by the benefits of a convolution neural network (CNN), this paper proposed an efficient and lightweight architecture to detect the location of hand in the images. This network is deployed with two main blocks which are the feature extraction and the detection block. The feature extraction block starts by convolution layers, CReLU (Concatenated Rectified Linear Unit) module, and max pooling layers alternately. After that, the six inception modules are used and final by four convolution layers. The detection block is constructed by three blocks of two-sibling convolution layers using for classification and regression. The experiment was trained on the combination of EgoHands and Hand dataset. As evaluation, the detector was tested on Egohands test dataset with the results achieved 93.32% of AP (Average Precision). In addition, the speed was tested in real-time by 33.87 fps (frames per second) on Intel Core I7-4770 CPU @ 3.40 GHz.
The troubling trend of academic makerspaces

Technology-centered makerspaces are increasingly being built in academic libraries, typically featuring high tech machines and software that facilitate creation and design. In this essay, Juliana Mestre offers a critique such spaces highlighting several potential downsides including cost, the threat to library autonomy, and an adherence to design epistemology that overvalues the monetization of knowledge products. The essay encourages academic librarians to engage thoughtfully on the possible ramifications of makerspaces.
Robust centralized fusion Kalman predictor for uncertain descriptor system with missing measurements

This paper addresses the design of centralized fusion time-varying Kalman predictor for a class of uncertain multisensor descriptor system with missing measurements. Using the singular value decomposition, and introducing the fictitious noises, the original descriptor system under consideration is transformed into two reduced-order non-descriptor subsystems with uncertain noise variances. According to the minimax robust estimation principle, based on the worst-case subsystems with the conservative upper bounds of the noise variances, centralized fusion time-varying Kalman predictor is presented. Finally, the robust centralized fusion Kalman predictor of the original multisensor descriptor system and prediction error variance are presented. Its robustness is proved by using Lyapunov equation approach, such that its actual prediction error variance is guaranteed to have the corresponding minimal upper bound for all admissible uncertainties. Its accuracy relation is proved. A simulation example shows the effectiveness and correctness of the proposed results.
Diversified Fisher kernel: encoding discrimination in Fisher features to compete deep neural models for visual classification task

Fisher kernels derived from stochastic probabilistic models such as restricted and deep Boltzmann machines have shown competitive visual classification results in comparison to widely popular deep discriminative models. This genre of Fisher kernels bridges the gap between shallow and deep learning paradigm by inducing the characteristics of deep architecture into Fisher kernel, further deployed for classification in discriminative classifiers. Despite their success, the memory and computational costs of Fisher vectors do not make them amenable for large‐scale visual retrieval and classification tasks. This study introduces a novel feature selection technique inspired from the functional characteristics of neural architectures for learning discriminative feature representations to boost the performance of Fisher kernels against deep discriminative models. The proposed technique condenses the large dimensional Fisher features for kernel learning and shows improvement in its classification performance and storage cost on leading benchmark data sets. A comparison of the proposed method with other state‐of‐the‐art feature selection techniques is made to demonstrate its performance supremacy as well as time complexity required to learn in reduced Fisher space.