Reviewerly/exHarmony huggingface dataset:
Using a multidimensional approach to examine TPACK among teacher candidates

The Technological Pedagogical and Content Knowledge (TPACK) conceptual framework captures the complex nature of teacher knowledge essential for effective teaching in the digital age. TPACK is difficult to assess, however, particularly among teacher candidates whose pedagogical knowledge is developing. This study aimed to examine multiple ways to assess teacher candidates' TPACK development and its application in response to a technology-focused coursework. Using a mixed-method, multi-assessment design, the study sampled 26 teacher candidates enrolled in a graduate-level course. TPACK survey at two time points and multiple course artifacts including course reflection, lesson delivery reflection and unit plan design were used for analysis. Quantitative survey data pointed to the significant growth in all domains of TPACK except for PK. Qualitative reflection data revealed themes supporting the growth in key TPACK domains. However, unit plan analysis revealed some inconsistency with the self-reported survey results, showing the limitation of TPACK survey. Unit plan data revealed variability and limited application of TPACK knowledge among teacher candidates who reported biggest TPACK growth, suggesting the necessity for providing more opportunities for acquiring TPACK in meaningful application. The study extends earlier research findings about the necessity of using multiple sources of information to assess teacher candidates' TPACK development.
Evaluating Accuracy of Eye Gaze Controlled Interface in Military Aviation Environment

Eye gaze controlled interfaces allow us to directly manipulate a graphical user interface just by looking at it. It has already been explored for assistive technology. Eye gaze controlled interface has great potential use in military aviation. In particular, operating different displays in situations where pilots' hands are occupied with flying the aircraft. This paper reports two studies on analyzing accuracy of eye gaze controlled interface inside aircraft undertaking representative flying missions. We reported that pilots can undertake representative pointing and selection tasks at less than 2 sees on average and the accuracy of COTS eye gaze tracking glass is less than 5° of visual angle up to +3G, although it is less accurate at ‒1G and +5G. We used these results in developing a multimodal head and eye gaze movement based HMDS and in a user study involving standard ISO 9241 pointing task. The average pointing and selection times was significantly lower in the proposed system compared to traditional TDS.
Saec

Embedding methods are commonly used in recommender systems to represent features about user and item. An impeding practical challenge is that the large number of embedding vectors incurs substantial memory footprint for serving queries, especially as the number of features continues to grow. We propose an embedding compression system called Saec to address this challenge. Saec exploits the similarity among features within a field as they represent the same attribute of user or item, and uses clustering to compress the embeddings. We propose a new fast clustering method that relies on the empirical heavy-tailed nature of features to drastically reduce the clustering overhead. We implement a prototype of Saec on a production system and evaluate it with private feature datasets from a large Internet company. Testbed experiments show that Saec consistently reduces the memory footprint of embedding vectors from 4.46 GB to 161 MB. The fast clustering method we design achieves 32x speedup compared to the baseline method.
Scalable Pseudorandom Quantum States

Efficiently sampling a quantum state that is hard to distinguish from a truly random quantum state is an elementary task in quantum information theory that has both computational and physical uses. This is often referred to as pseudorandom (quantum) state generator, or PRS generator for short. In existing constructions of PRS generators, security scales with the number of qubits in the states, i.e. the (statistical) security parameter for an n-qubit PRS is roughly n. Perhaps counter-intuitively, n-qubit PRS are not known to imply k-qubit PRS even for $$k<n$$ . Therefore the question of scalability for PRS was thus far open: is it possible to construct n-qubit PRS generators with security parameter $$\lambda $$ for all $$n, \lambda $$ . Indeed, we believe that PRS with tiny (even constant) n and large $$\lambda $$ can be quite useful. We resolve the problem in this work, showing that any quantum-secure one-way function implies scalable PRS. We follow the paradigm of first showing a statistically secure construction when given oracle access to a random function, and then replacing the random function with a quantum-secure (classical) pseudorandom function to achieve computational security. However, our methods deviate significantly from prior works since scalable pseudorandom states require randomizing the amplitudes of the quantum state, and not just the phase as in all prior works. We show how to achieve this using Gaussian sampling.
Ensuring safe and consistent coengineering of cyber‐physical production systems: A case study

Abstract In today's engineering projects, companies continuously have to adapt their systems to changing customers or dynamic market requirements. This requires a flexible, iterative development process in which different parts of the system under construction are built and updated concurrently. However, concurrent engineering becomes quite challenging in domains where different engineering artifacts from different disciplines come into play, such as safety‐critical cyber‐physical systems, where the involved engineering artifacts are quite heterogeneous in nature. In such systems, it is of utmost importance that different artifacts remain consistent in order to guarantee a correctly functioning end product. In this article, we discuss our experiences (with a leading company working in the areas of production automation and product processing) in maintaining the consistency between electrical models and the corresponding software controller, when both are subject to continuous changes. The article discusses how we let engineers describe the relationships between electrical models and the corresponding software controller code in the form of links and consistency rules. Additionally, we demonstrate that how our approach, through a process of continuous consistency checking, notifies engineers about the erroneous impact of their changes in various engineering artifacts.
Ontology Evaluation and Multilingualism

Many ontologies have been developed recently, and evaluation is important for researchers or users when searching ontologies needed in their work. Ontology evaluation also is important phase of ontology development, refinement or evolution process. Automatically-developed ontologies as a result of ontology learning also need from evaluation. So, ontology evaluation is important both for selecting ontologies, meeting some requirements, and as a part of ontology life cycle. In this paper we propose deep analysis of ontology evaluation and discuss how multilingualism affects ontology evaluation. We classify ontology evaluation approaches, methods and metrics according to several dimensions. Our analysis and conclusions will be helpful for development of good ontologies and for finding or selection of needed ontologies.
A Joint Model for Aspect-Category Sentiment Analysis with TextGCN and Bi-GRU

In the age of Internet, online customers express their opinions on products by posting reviews. It is critical to do sentiment analysis on customers’ review data to help subsequent customers make their purchasing decisions and guide companies to improve their products. Aspect-Category Sentiment Analysis(ACSA) is a subtask of sentiment analysis, which aims to detect the aspect categories mentioned in the text and identify their corresponding sentiment polarities. Most of the previous methods regard Aspect Category Detection(ACD) and AspectCategory Sentiment Classification(ACSC) as two separate tasks, which could lead to error propagation so as to lack practicality. However, as far as we know, joint modeling of aspects and polarities has not yet received widespread attention. The only few existing joint models do not work well with representing the relation of aspect categories and its corresponding sentiment words. To address these problems, we propose a novel model (TG-GRU) based on the combination of GCN and Bi-GRU for detecting aspect categories and corresponding sentiment polarities simultaneously. Experiments are conducted on one Chinese dataset and two English datasets. Experimental results show that our model outperforms the state-of-the-art methods of joint models.
Prune it Yourself: Automated Pruning by Multiple Level Sensitivity

Deep neural network pruning is to reduce the model size by removing redundant structures and weights. Existing methods focus on single layer information which ignore other layers. And pruning progress is simply removing all weights at the same time. To address these limitations, we propose Prune it Yourself (PIY) framework. First, we collect both filter and channel sensitivity information. Then combine them to decide the structure to be pruned. At last we use the gradual pruning algorithm to reduce the accuracy loss without extra hyper-parameters. We use VGG-16 and ResNet to perform experiments on CIFAR-10 and ImageNet. The experimental results prove the effectiveness of our method.
Communication Efficient and Byzantine Tolerant Distributed Learning

We develop a communication-efficient distributed learning algorithm that is robust against Byzantine worker machines. We propose and analyze a distributed gradient-descent algorithm that performs a simple thresholding based on gradient norms to mitigate Byzantine failures. We show the (statistical) error-rate of our algorithm matches that of Yin et al., 2018, which uses more complicated schemes (like coordinate-wise median or trimmed mean). Furthermore, for communication efficiency, we consider a generic class of δ-approximate compressors from Karimireddy et al., 2019, that encompasses sign-based compressors and top-k sparsification. Our algorithm uses compressed gradients and gradient norms for aggregation and Byzantine removal respectively. We establish the statistical error rate of the algorithm for arbitrary (convex or non-convex) smooth loss function. We show that, in certain regime of δ, the rate of convergence is not affected by the compression operation. We have experimentally validated our results and shown good performance in convergence for convex (least-square regression) and non-convex (neural network training) problems.
A Collaborative Intrusion Detection System Using Deep Blockchain Framework for Securing Cloud Networks

Security solutions, especially intrusion detection and blockchain, have been individually employed in the cloud for detecting cyber threats and preserving private data. Both solutions demand ensembled models-based learning that can alert the campaign of complex malicious events and concurrently accomplish data privacy. Such models would also provide additional security and privacy to the live migration of Virtual Machines (VMs) in the cloud. This would allow the secure transfer of one or more VMs between datacentres or cloud providers in real-time. This paper proposes a Deep Blockchain Framework (DBF) designed to offer security-based distributed intrusion detection and privacy-based blockchain with smart contracts in the cloud. The intrusion detection method is employed yet using a Bidirectional Long Short-Term Memory (BiLSTM) deep learning algorithm to deal with sequential network data and is assessed using the dataset of UNSW-NB15. The Privacy-based blockchain and smart contract methods are developed using the Ethereum library to provide privacy to the distributed intrusion detection engines. The DBF framework is compared with compelling privacy-preserving intrusion detection models, and the empirical results reveal that DBF outperforms the compelling models. The framework has the potential to be used as a decision support system that can assist users and cloud providers in securely and timely migrating their data in a fast and reliable manner.
Comparing Two Methods of Usability Testing in Saudi Arabia: Concurrent Think-Aloud vs. Co-Discovery

This paper presents the results of a study that aimed to compare the utility and validity of the traditional concurrent think-aloud and the co-discovery usability testing methods. The study was conducted in Saudi Arabia and involved three points of comparison: number and nature of usability problems discovered, test participants’ experiences, and overall task performance. The results show significant differences between the two types of testing methods. The co-discovery method led to the detection of a greater number of minor usability problems relating to layout and functionality. The participants also found the co-discovery method to be easier and less tiring to perform and more natural for them than the concurrent think-aloud method. No difference was found between the methods in terms of participants’ task performance. The study concludes that the co-discovery method seems to be appropriate for identifying numerous minor issues and ensuring that the usability testing experience is as natural as possible for participants. However, the classic method seems to be a more cost-effective method, as it is equally useful in revealing high-severity problems and requires only one participant per test session.
Directions and Speeds of Mouse Movements on a Website and Reading Patterns

Mouse activity is known as an important indicator of user attention and interest on a web page. Many modern commercial web analytics services record and report mouse activity of users on websites. The position of the mouse cursor on the screen is the main source of information, as studies show a correlation between the cursor position during mouse activity and the user's eye gaze. This study focuses on mouse movement directions and speeds, and what they indicate, rather than on the mouse cursor position. Statistical analysis of mouse movements on a technical-educational website, which was selected for this study, sheds light on several interesting patterns. For example, most mouse movements in the examined usage data are either approximately horizontal or approximately vertical, horizontal mouse movements are more frequent than vertical mouse movements, and horizontal movements to the left and to the right are not equivalent in terms of moving time and speed. As this study shows, these statistical findings are related to the reading patterns and behaviors of web users. Associating mouse movements with text reading may potentially highlight content that most users tend to skip, and therefore, might not interest the website audience, and content that many readers read more than once or slowly, meaning it is possibly unclear. This could be useful in locating issues in textual content, in websites in general, and especially in online learning and educational technology applications.
Grassmannian Frames in Composite Dimensions by Exponentiating Quadratic Forms

Grassmannian frames in composite dimensions D are constructed as a collection of orthogonal bases where each is the element-wise product of a mask sequence with a generalized Hadamard matrix. The set of mask sequences is obtained by exponentiation of a q-root of unity by different quadratic forms with m variables, where q and m are the product of the unique primes and total number of primes, respectively, in the prime decomposition of D. This method is a generalization of a well-known construction of mutually unbiased bases, as well as second-order Reed-Muller Grassmannian frames for power-of-two dimension D = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> , and allows to derive highly symmetric nested families of frames with finite alphabet. Explicit sets of symmetric matrices defining quadratic forms leading to constructions in non-prime-power dimension with good distance properties are identified.
On the Persistence of Persistent Identifiers of the Scholarly Web

Scholarly resources, just like any other resources on the web, are subject to reference rot as they frequently disappear or significantly change over time. Digital Object Identifiers (DOIs) are commonplace to persistently identify scholarly resources and have become the de facto standard for citing them. We investigate the notion of persistence of DOIs by analyzing their resolution on the web. We derive confidence in the persistence of these identifiers in part from the assumption that dereferencing a DOI will consistently return the same response, regardless of which HTTP request method we use or from which network environment we send the requests. Our experiments show, however, that persistence, according to our interpretation, is not warranted. We find that scholarly content providers respond differently to varying request methods and network environments and even change their response to requests against the same DOI. In this paper we present the results of our quantitative analysis that is aimed at informing the scholarly communication community about this disconcerting lack of consistency.
Methodology for resume parsing and job domain prediction

With the rapid growth in on-line based recruiting system, candidates apply for job on web portal by uploading their resumes. Due to internet based recruiting systems, candidates participate in large volumes and hence it becomes a challenge task for recruiter to filter candidates for the required role. The resumes uploaded by the candidate are varied in format such as font, colour, font size, etc. and it is difficult for the recruiters to find the best match for a job role. Natural Language Processing (NLP) helps to deal with such problems and help recruiters to extract detailed information of the candidates required to carry forward their candidature. In this work, we propose to use named entity recognition of Stanford CoreNLP system to extract information relevant for recruiting process. Moreover, on the basis of skills set of candidate, the resume of the candidate is assigned a genre such as Computer Science, Statistics, Business Development etc. In this paper we propose to design an intelligent resume parser system capable of converting the unstructured data into a structured format which enables the recruiter to filter the right candidates for the desired job role. The overall resume prediction of our system is 91.47%.
Uplink Cost Adjustable Schemes in Secure Distributed Matrix Multiplication

In secure distributed matrix multiplication (SDMM) the multiplication AB from two private matrices A and B is outsourced by a user to N distributed servers. In ℓ-SDMM, the goal is to design a joint communication-computation procedure that optimally balances conflicting communication and computation metrics without leaking any information on both A and B to any set of ℓ ≤ N servers. To this end, the user applies coding with Ã <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> and B̃ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> representing encoded versions of A and B destined to the i-th server. Now, SDMM involves multiple tradeoffs. One such tradeoff is the tradeoff between uplink (UL) and downlink (DL) costs. To find a good balance between these two metrics, we propose two schemes which we term USCSA and GSCSA that are based on secure cross subspace alignment (SCSA). We implement schemes from the literature, in addition to USCSA and GSCSA, and test their performance on Amazon EC2. Our numerical results show that USCSA and GSCSA establish a good balance between the time spend on the communication and computation in SDMMs. This is because they combine advantages of polynomial codes, namely low time for the upload of (Ã <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> , B̃ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ) <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i=1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sup> and the computation of O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> = Ã <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> , B̃ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> , with those of SCSA, being a low timing overhead for the download of (O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ) <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i=1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sup> and the decoding of AB.
PolyShard: Coded Sharding Achieves Linearly Scaling Efficiency and Security Simultaneously

Today's blockchain designs suffer from a trilemma claiming that no blockchain system can simultaneously achieve decentralization, security, and performance scalability. For current blockchain systems, as more nodes join the network, the efficiency of the system (computation, communication, and storage) stays constant at best. A leading idea for enabling blockchains to scale efficiency is the notion of sharding: different subsets of nodes handle different portions of the blockchain, thereby reducing the load for each individual node. However, existing sharding proposals achieve efficiency scaling by compromising on trust - corrupting the nodes in a given shard will lead to the permanent loss of the corresponding portion of data. In this paper, we settle the trilemma by demonstrating a new protocol for coded storage and computation in blockchains. In particular, we propose PolyShard: "polynomially coded sharding" scheme that achieves information-theoretic upper bounds on the efficiency of the storage, system throughput, as well as on trust, thus enabling a truly scalable system.
Research on Active Jamming Recognition in Complex Electromagnetic Environment

With the development of electronic jamming technology, more and more active jamming is applied in electronic warfare. Because there are many kinds of jamming in complex electromagnetic environment, it is difficult for radar to recognize all kinds of jamming effectively. In order to solve this problem, seven typical suppression jamming and deception jamming are modeled and simulated in this paper. Then, a set of highly separable features are extracted from the time domain, frequency domain and time-frequency domain, which are sent into decision tree and support vector machine (SVM) for jamming recognition and their recognition accuracy are compared. The simulation results show that under the condition of feature extraction, the recognition accuracy of decision tree for active jamming is higher.