Reviewerly/exHarmony huggingface dataset:
Algorithmic Approach to the Design of e-Learning Courses

The article discusses the problem of transition to e-learning from a cybernetic perspective. Of course, the learning process is a purposeful and controlled process. In the traditional approach, the process is guided by the teacher, seeking to achieve the learning goal. With the transition to e-learning, an additional tool appears between the teacher and the trainee: an electronic training course, in which the teacher lays the goal of learning. The article examines what features and limitations are imposed by an electronic training course. An approach is being developed to the e-course as an algorithmic process, therefore, to the design of an e-course as the construction of a learning algorithm. The problem, however, is how to combine the algorithmic approach with the spontaneity of human behavior. It is demonstrated that the algorithmic approach to building an electronic course can be combined with human spontaneity in teaching. At the same time, the use of an algorithmic approach simplifies the course design process for the teacher and ensures the achievement of the intended learning goals. #CSOC1120
Non-native acoustic modeling for mispronunciation verification based on language adversarial representation learning

Non-native mispronunciation verification is designed to provide feedback to guide language learners to correct their pronunciation errors in their further learning and it plays an important role in the computer-aided pronunciation training (CAPT) system. Most existing approaches focus on establishing the acoustic model directly using non-native corpus thus they are suffering the data sparsity problem due to time-consuming non-native speech data collection and annotation tasks. In this work, to address this problem, we propose a pre-trained approach to utilize the speech data of two native languages (the learner’s native and target languages) for non-native mispronunciation verification. We set up an unsupervised model to extract knowledge from a large scale of unlabeled raw speech of the target language by making predictions about future observations in the speech signal, then the model is trained with language adversarial training using the learner’s native language to align the feature distribution of two languages by confusing a language discriminator. In addition, sinc filter is incorporated at the first convolutional layer to capture the formant-like feature. Formant is relevant to the place and manner of articulation. Therefore, it is useful not only for pronunciation error detection but also for providing instructive feedback. Then the pre-trained model serves as the feature extractor in the downstream mispronunciation verification task. Through the experiments on the Japanese part of the BLCU inter-Chinese speech corpus, the experimental results demonstrate that for the non-native phone recognition and mispronunciation verification tasks (1) the knowledge learned from two native languages speech with the proposed unsupervised approach is useful for these two tasks (2) our proposed language adversarial representation learning is effective to improve the performance (3) formant-like feature can be incorporated by introducing sinc filter to further improve the performance of mispronunciation verification.
Scalable Storage Scheme for Blockchain-Enabled IoT Equipped Food Supply Chains

Blockchain is an innovative technology which enabled new applications for solving numerous problems in distributed environments such as the internet of things (IoT) equipped food supply chains (FSCs). In FSCs, the large volume of IoT data such as audio, video, images, and sensor data will be transferred to ensure the traceability of the food to its source. When blockchain technology is used in an FSC, the storage requirements in the nodes will grow with time, since blockchain only allows information adding, without deleting the already stored information. Therefore, it is evident that offchain storage offers more flexibility than onchain storage in IoT equipped supply chains. This paper proposes a scalable storage scheme using offchain storage, where the data from IoT devices in the supply chain will store offchain. To reduce the growth of offchain storage, we exploit the fact that some information regarding a particular food item may not be required after the expiration date. The scalability of the proposed scheme is validated through numerical and experimental results.
Multi‐innovation Newton recursive methods for solving the support vector machine regression problems

Abstract The support vector machine has been widely used in binary classification applications because of the simplicity of its implementation. This article proposes an online identification method based on the support vector machine in the field of parameter identification. By substituting the constraint item into the original criterion function to form a new criterion function about the weight vector and the bias item, and then on the basis of the recursive identification methods, the newly established criterion function is minimized by using the Newton search to derive the Newton recursive support vector machine algorithm. Additionally, in order to improve the performance of the Newton recursive support vector machine algorithm, the multi‐innovation identification theory is applied to optimize the algorithm, and a multi‐innovation Newton recursive support vector machine algorithm is derived. In addition, the convergence and the calculation amount of the proposed algorithms are carefully analyzed in this article. Finally, a numerical simulation example is given to compare the Newton recursive algorithm and the corresponding multi‐innovation recursive algorithm. The results show that the algorithms and optimization strategy studied in this article are effective.
Mass Flow Measurement of Molten Iron From Blast Furnace, Based on Trusted Region Stacking Using Single High-Speed Camera

Accurately measuring the mass flow of molten iron at the bottom of a blast furnace is of great significance for energy savings and efficient operation in the iron making process. However, the lack of texture information on the surface of molten iron will severely limit the accuracy of conventional displacement reconstruction algorithms. To this end, this article presents a methodology based on trusted region stacking, which can estimate an accurate displacement distribution of the molten iron surface through automatic location, tracking, and stacking of the trusted region. Subsequently, a falling motion model of the molten iron was proposed, and combined with the displacement distribution of the molten iron surface, the accurate measurement of the molten iron velocity at the outlet of the swing nozzle was realized even if the image of the molten iron there is missing. Therefore, the mass flow of molten iron is determined with the a priori information of the outlet shape of the swing nozzle. Industrial experiments illustrate that the proposed method can stably and accurately measure the molten iron mass flow and estimate the weight of molten iron in the iron ladle.
SoundFence: Securing Ultrasonic Sensors in Vehicles Using Physical-Layer Defense

Autonomous vehicles (AVs), equipped with numerous sensors such as camera, LiDAR, radar, and ultrasonic sensor, are revolutionizing the transportation industry. These sensors are expected to sense reliable information from a physical environment, facilitating the critical decision-making process of the AVs. Ultrasonic sensors, which detect obstacles in a short distance, play an important role in assisted parking and blind spot detection events. However, due to their weak security level, ultrasonic sensors are particularly vulnerable to signal injection attacks, when the attackers inject malicious acoustic signals to create fake obstacles and intentionally mislead the vehicles to make wrong decisions with disastrous aftermath. In this paper, we systematically analyze the attack model of signal injection attacks toward moving vehicles. By considering the potential threats, we propose SoundFence, a physical-layer defense system which leverages the sensors' signal processing capability without requiring any additional equipment. SoundFence verifies the benign measurement results and detects signal injection attacks by analyzing sensor readings and the physical-layer signatures of ultrasonic signals. Our experiment with commercial sensors shows that SoundFence detects most (more than 95%) of the abnormal sensor readings with very few false alarms, and it can also accurately distinguish the real echo from injected signals to identify injection attacks.
An FPGA Implementation of a RISC-V Based SoC System for Image Processing Applications

The Laplacian filter is one of the fundamental applications in image processing. In our work, the Laplacian filter has been applied to an image, and both hardware and software implementation of the filter has been studied. Our system consists of an OV7670 Camera module, Nexys 4 DDR FPGA board and VGA monitor to display the processed video stream. Mentioned process has forwarding tasks: camera module captures raw RGB data and writes to RAM, Laplacian filter IP processes raw image and the results written back to memory. VGA modules show output images to monitor. The Laplacian filter part considered in hardware and software implementation is compared in terms of time and area.
Learning Engineering as an Ethical Framework

The advances in technology continually push at the boundary of what is possible in online learning environments. Digital learning generates data at scale that can be analyzed to gain new insights into the learning process, which in turn sparks further changes in technology. Adaptive instructional systems have been growing in type and complexity, which also increases the scope of the technology and the teams who work to develop them. In this time of intense exploration and innovation in learning science and technology, it is also imperative to put in place a system of ethics to center this innovative spirit on the intended user: the learner. I propose that learning engineering provides a purpose to advocate for the best interests of the learner as the Learning Engineering Process is carried out. This practitioner purpose can help the learning engineer develop an ethical voice and engage in a dialogic ethic as new technology is being developed. I use a case study on the development and improvement of adaptive activities to illustrate both the Learning Engineering Process as well as how this process supports an ethical practice. In this paper I situate learning engineering in an ethical framework and provide a contextual example to spark discussion on the role of ethics in an increasingly complex learning ecosystem.
Machine Learning Model Drift Detection Via Weak Data Slices

Detecting drift in performance of Machine Learning (ML) models is an acknowledged challenge. For ML models to become an integral part of business applications it is essential to detect when an ML model drifts away from acceptable operation. However, it is often the case that actual labels are difficult and expensive to get, for example, because they require expert judgment. Therefore, there is a need for methods that detect likely degradation in ML operation without labels. We propose a method that utilizes feature space rules, called data slices, for drift detection. We provide experimental indications that our method is likely to identify that the ML model will likely change in performance, based on changes in the underlying data.
A Study on Modeling Techniques in Software Fault Prediction

Software fault prediction is a key area in the field of software engineering. Fault can occur in any stage of software development but if it is not correctly identified and removed, it may lead to system failure. The main purpose of software fault prediction is to find faults prior to the testing phase, which will further decrease the time and cost of developing software and upgrade the quality of software. In this paper, we review machine learning and ensemble-based modeling techniques concerning software fault prediction. This paper will help the researchers to choose the best modeling technique with respect to datasets and performance measures. The findings reveal that the presence of various data quality issues related to datasets degrades the accuracy of fault prediction and also find that ensemble-based techniques are more efficient as compared to machine learning techniques.
Training Error Approximation Through the State-Space Representation of the Fuzzy Model

Various algorithms for fuzzy model identification have been introduced in the literature, and the focus is mostly on proper rule generation and parameter updating. However, irrespective of the optimization algorithm in the design step, the structural properties of fuzzy models, e.g., the number of fuzzy rules, highly affects the quality of the nonlinear function approximation to reach the balance between the accuracy in model training and the complexity of the model for real application. In this paper, we focus on the issue of error approximation in smooth fuzzy systems, upon which the required number of fuzzy rules can be determined. In the proposed approach, the designer will not need to assign the optimum parameters of the fuzzy model to estimate the error in the approximation of the nonlinear systems with the smooth fuzzy model. Therefore, the designer will skip the iteration of optimal parameter seeking and fuzzy model training before finding the fuzzy model's approximation error. Instead, through the state-space representation of the fuzzy model, one would estimate the error in the fuzzy approximation of the nonlinear system using the extended Kalman filter first. In the next step and after estimating the error in the approximation of the fuzzy model, the designer will endeavor to find the fuzzy model's optimum parameters straightforward. The proposed method has been illustrated in an example.
Science Fiction—An Untapped Opportunity in HCI Research and Education

This work open up a conversation about the opportunities that Science Fiction offers to Human-Computer Interaction (HCI) and Design research. In doing so, first, it briefly challenges the term Design Fiction, an emerging concept with increasing popularity in HCI and Design research. Design Fictions are either, manifest design propositions, or intangible, imagined future interfaces, speculative interactions and would-be environments. Design Fictions as useful as they are, however, are not necessarily something new nor innovative in themselves. Speculative design and evaluation methods all have been staple methods of Computer Science research since the establishment of HCI as a discrete field sometime in the 1980s. Second, the paper proceeds to propose that the domain of Science Fiction could not only be useful, but rightly considered, as a legitimate field of creative inquire in HCI and Design education. By reframing the notion of Science Fiction from an 'anecdotal gimmick' toward an 'area of study' a novel, wide-ranging space for design inspiration, ingenuity and creativity opens up for conceptual exploration. We believe that such an elevation of Science Fiction as a serious research topic has strong potential to inform the Computer Science, HCI and Design research application and education endeavours of the future.
Low-Cost Eye Tracking Calibration: A Knowledge-Based Study

Subject calibration has been demonstrated to improve the accuracy in high-performance eye trackers. However, the true weight of calibration in off-the-shelf eye tracking solutions is still not addressed. In this work, a theoretical framework to measure the effects of calibration in deep learning-based gaze estimation is proposed for low-resolution systems. To this end, features extracted from the synthetic U2Eyes dataset are used in a fully connected network in order to isolate the effect of specific user’s features, such as kappa angles. Then, the impact of system calibration in a real setup employing I2Head dataset images is studied. The obtained results show accuracy improvements over 50%, probing that calibration is a key process also in low-resolution gaze estimation scenarios. Furthermore, we show that after calibration accuracy values close to those obtained by high-resolution systems, in the range of 0.7°, could be theoretically obtained if a careful selection of image features was performed, demonstrating significant room for improvement for off-the-shelf eye tracking systems.
Achieve space-efficient key management in lightning network

The low transaction throughput, high transaction latency, and unfriendly micropayment are the main obstacles hindering Bitcoin use in time-sensitive environments. To mitigate these problems, various solutions have been proposed. The lightning network (LN) is considered one of the most promising ones, and it has been widely deployed in different versions. However, the LN itself is subject to a scalability problem due to the used channel state revocation technique. It especially requires O(n̄) storage cost to store the private keys, where n̄ is the number of transactions that happened in the channel and expected to be infinite. Though there are some techniques to resolve this problem, none of them is compatible with the current Bitcoin system. Aiming at solving this dilemma, in this paper, we propose two space-efficient Bitcoin-compatible key management schemes for the LN, based on the hash function and trapdoor one-way function, respectively. Both schemes reduce the storage complexity from O(n̄) to O(1). The detailed security analysis shows that our schemes hold the security level of the original LN or its variants. The extensive experimental results demonstrate that our proposed schemes are efficient and feasible, with a significant reduction in storage overhead.
DiMBERT: Learning Vision-Language Grounded Representations with Disentangled Multimodal-Attention

Vision-and-language (V-L) tasks require the system to understand both vision content and natural language, thus learning fine-grained joint representations of vision and language (a.k.a. V-L representations) is of paramount importance. Recently, various pre-trained V-L models are proposed to learn V-L representations and achieve improved results in many tasks. However, the mainstream models process both vision and language inputs with the same set of attention matrices. As a result, the generated V-L representations are entangled in one common latent space . To tackle this problem, we propose DiMBERT (short for Di sentangled M ultimodal-Attention BERT ), which is a novel framework that applies separated attention spaces for vision and language, and the representations of multi-modalities can thus be disentangled explicitly. To enhance the correlation between vision and language in disentangled spaces, we introduce the visual concepts to DiMBERT which represent visual information in textual format. In this manner, visual concepts help to bridge the gap between the two modalities. We pre-train DiMBERT on a large amount of image–sentence pairs on two tasks: bidirectional language modeling and sequence-to-sequence language modeling. After pre-train, DiMBERT is further fine-tuned for the downstream tasks. Experiments show that DiMBERT sets new state-of-the-art performance on three tasks (over four datasets), including both generation tasks (image captioning and visual storytelling) and classification tasks (referring expressions). The proposed DiM (short for Di sentangled M ultimodal-Attention) module can be easily incorporated into existing pre-trained V-L models to boost their performance, up to a 5% increase on the representative task. Finally, we conduct a systematic analysis and demonstrate the effectiveness of our DiM and the introduced visual concepts.
Momentum based Projection Free Stochastic Optimization Under Affine Constraints

This paper considers stochastic convex optimization problems with affine constraints, in addition to other deterministic convex constraints on the domain of the variables. Usually, to solve the stochastic optimization problems, the gradient based approaches make use of stochastic gradients of the objective arriving sequentially over iterations and then project back the iterate on the feasible domain. However, there are scenarios where carrying out projection step onto the convex domain constraints at every iteration is not viable. Traditionally, the projection operation can be avoided by considering the Frank-Wolfe (FW) a.k.a. conditional gradient variant of the algorithm. Existing approaches towards solving the problems having costly projections along with affine constraints combine homotopy and smoothing techniques under the conditional gradient framework. The state-of-the-art stochastic FW variants to solve such problems achieve a convergence rate of O(k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1/3</sup> ) on the optimality gap and O(k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5/1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) on the feasibility gap. In this work, we propose the Momentum based Stochastic Homotopy Frank-Wolfe (MSHFW) algorithm that tracks gradient using momentum technique and guarantees an optimal convergence rate of O(k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1/2</sup> ) in terms of both optimality and feasibility gap. The efficacy of the proposed algorithm is tested on relevant problems of K-means clustering and sparse matrix estimation.
DeepCC: Bridging the Gap Between Congestion Control and Applications via Multiobjective Optimization

The increasingly complicated and diverse applications have distinct network performance demands, e.g., some desire high throughput while others require low latency. Traditional congestion controls (CC) have no perception of these demands. Consequently, literatures have explored the objective-specific algorithms, which are based on either offline training or online learning, to adapt to certain application demands. However, once generated, such algorithms are tailored to a specific performance objective function. Newly emerged performance demands in a changeable network environment require either expensive retraining (in the case of offline training), or manually redesigning a new objective function (in the case of online learning). To address this problem, we propose a novel architecture, DeepCC. It generates a CC agent that is generically applicable to a wide range of application requirements and network conditions. The key idea of DeepCC is to leverage both offline deep reinforcement learning and online fine-tuning. In the offline phase, instead of training towards a specific objective function, DeepCC trains its deep neural network model using multi-objective optimization. With the trained model, DeepCC offers near Pareto optimal policies w.r.t different user-specified trade-offs between throughput, delay, and loss rate without any redesigning or retraining. In addition, a quick online fine-tuning phase further helps DeepCC achieve the application-specific demands under dynamic network conditions. The simulation and real-world experiments show that DeepCC outperforms state-of-the-art schemes in a wide range of settings. DeepCC gains a higher target completion ratio of application requirements up to 67.4% than that of other schemes, even in an untrained environment.