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Digital electronics course is one of the very fundamental courses for the students of undergraduate programme of electrical and electronic engineering (EEE) and the other undergraduate engineering disciplines. Therefore, "digital electronics" shall be taught effectively, so that students can apply the knowledge learned to solve their real-life engineering problems. A teacher needs to adopt new teaching methodologies to attract current generation of students, and thus, to prepare them with practical knowledge and skills. Skills in the cognitive domain of Bloom's taxonomy revolve around knowledge, comprehension and critical thinking of a particular topic. This makes teaching and learning more effective and efficient. In this paper, the teaching method of 'digital electronics' course for the undergraduate EEE students in the cognitive domain has been described with an example. Class performance evaluation in two different cohorts shows that the students' results improve after using this approach.Keywords: Bloom's taxonomy, cognitive domain, digital electronics course, teaching methods.

Functional Encryption (FE) expands traditional public-key encryption in two different ways: it supports fine-grained access control and allows learning a function of the encrypted data. In this paper, we review all FE classes, describing their functionalities and main characteristics. In particular, we mention several schemes for each class, providing their security assumptions and comparing their properties. To our knowledge, this is the first survey that encompasses the entire FE family., To appear in Advances in Mathematics of Communications, https://www.aimsciences.org/article/doi/10.3934/amc.2021049

Deep learning methods have achieved significant progress in the presence of correctly annotated datasets in instance segmentation. However, object classes in large-scale datasets are sometimes ambiguous, which easily causes confusion. Besides, limited experience and knowledge of annotators can lead to mislabeled object semantic classes. To solve this issue, a novel method is proposed in this paper, which considers different roles of noisy class labels in different sub-tasks. Our method is based on two basic observations: firstly, the foreground-background annotation of a sample is correct even though its class label is noisy. Secondly, symmetric loss benefits the model robustness to noisy labels but harms the learning of hard samples, while cross entropy loss is the opposite. Based on the two basic observations, in the foreground-background sub-task, cross entropy loss is used to fully exploit correct gradient guidance. In the foreground-instance sub-task, symmetric loss is used to prevent incorrect gradient guidance provided by noisy class labels. Furthermore, we apply contrastive self-supervised loss to update features of all foreground, to compensate for insufficient guidance provided by partially correct labels especially in the highly noisy setting. Extensive experiments conducted with three popular datasets (i.e., Pascal VOC, Cityscapes and COCO) have demonstrated the effectiveness of our method in a wide range of noisy class label scenarios.

This article is concerned with the problem of the $H_{∞}$ output feedback control for a class of event-triggered networked systems subject to multiple cyber attacks. Two dynamic event-triggered generators are equipped at sensor and observer sides, respectively, to lower the frequency of unnecessary data transmission. The sensor-to-observer (STO) channel and observer-to-controller (OTC) channel are subject to deception attacks and Denial-of-Service (DoS) attacks, respectively. The aim of the addressed problem is to design an output feedback controller, with the consideration of the effects of dynamic event-triggered schemes (DETSs) and multiple cyber attacks. Sufficient condition is derived, which can guarantee that the resulted closed-loop system is asymptotically mean-square stable (AMSS) with a prescribed $H_{∞}$ performance. Moreover, we provide the desired output feedback controller design method. Finally, the effectiveness of the proposed method is demonstrated by an example.

In recent years, we have witnessed the proliferation of mobile devices such as smart phones, tablets, smart watches, etc., the majority of which are based on the Android operating system. However, because these Android-based mobile devices are becoming increasingly popular, they are now the primary target of mobile malware, which could cause both privacy leakage and property loss. To address the rapidly deteriorating security issues caused by mobile malware, various research efforts have been made to develop novel and effective detection mechanisms to identify and battle them. Nevertheless, in order to avoid being caught by these malware detection mechanisms, malware authors are inclined to launch adversarial example attacks by tampering with mobile applications. In this paper, several types of adversarial example attacks are investigated and a feasible approach is proposed to fight against them. First, we look at adversarial example attacks on the Android system and prior solutions that have been proposed to address these attacks. Then, we specifically focus on the data poisoning attack and evasion attack models, which may mutate various application features, such as API calls, permissions and the class label, to produce adversarial examples. Then, we propose and design a malware detection approach which is resistant to the adversarial examples. To observe and investigate how the malware detection system is impacted by the adversarial example attacks, we conduct experiments on some real Android application datasets which are composed of both malware and benign applications. Experimental results clearly indicate that the performance of Android malware detection is severely degraded when facing the adversarial example attacks.

Entity resolution (ER) identifies data instances referring to the same real-world entity and has received enormous research attention. In this paper, we examine the task of ER from a broader perspective, with its input extended from textual records, which are conventionally studied in the literature, to other modalities such as check-in sequences, GPS trajectories and surveillance video frames to generate new applications. Our goal in this paper is to design an effective model to uniformly support all these ER applications with different input formats. Technically, we fully exploit the semantic contexts of embedding vectors for the pair of input instances. In particular, we propose an integrated multi-context attention framework that takes into account self-attention, pair-attention and global-attention from three types of context. The idea can be further extended to incorporate attribute attention in order to support structured datasets. We conduct extensive experiments on a diverse class of entity resolutions tasks, including tasks on unstructured, structured and dirty textual records, check-in sequences, GPS trajectories and surveillance video frames. The experimental results verified the effectiveness and generality of our model. When compared with strong baselines in these applications, our model can achieve superior or comparative performance.

As a class of context-aware systems, context-aware service recommendation aims to bind high-quality services to users while taking into account their context requirements, including invocation time, location, social profiles, connectivity, and so on. However, current CASR approaches are not scalable with the huge amount of service data (QoS and context information, users reviews and feedbacks). In addition, they lack a rich representation of contextual information as they adopt a simple matrix view. Moreover, current CASR approaches adopt the traditional user-service relation and they do not allow for multi-relational interactions between users and services in different contexts. To offer a scalable and context-sensitive service recommendation with great analysis and learning capabilities, we provide a rich and multi-relational representation of the CASR knowledge, based on the concept of knowledge graph. The constructed context-aware service knowledge graph (C-SKG) is, then, transformed into a low-dimentional vector space to facilitate its processing. For this purpose, we adopt Dilated Recurrent Neural Networks to propose a context-aware knowledge graph embedding, based on the principles of first-order and subgraph-aware proximity. Finally, a recommendation algorithm is defined to deliver the top-rated services according to the target user's context. Experiments have proved the accuracy and scalability of our CASR approach.

International audience; This paper uses the recent glottal flow model for iterative adaptive inverse filtering to analyze recordings from dysfunctional speakers, namely those with larynx-related impairment such as laryngectomy. The analytical model allows extraction of the voice source spectrum, described by a compact set of parameters. This single model is used to visualize and better understand speech production characteristics across impaired and nonimpaired voices. The analysis reveals some discriminative aspects of the source model which map to a physiological class description of those impairments. Furthermore, being based on analysis of source parameters only, it is complementary to any existing techniques of vocal-tract or phonetic analysis. The results indicate the potential for future automated speech reconstruction systems that adapt to the method of reconstruction required, as well as being useful for mainstream speech systems, such as ASR, in which front-end analysis can direct back-end models to suit characteristics of impaired speech.

The ON-OFF controlled very-high-frequency Class E dc-dc converter has been extensively investigated for achieving high power density and fast dynamic response. However, the basic ON-OFF control cannot limit the output current at overload or short-circuit condition. This letter proposes an ON-OFF control with current limitation function by adding an output current closed-loop for reducing the output voltage reference when overload or short-circuit occurs. The design considerations for the output current regulator are given. A prototype of 30-MHz, 10-W Class E dc-dc converter is fabricated and tested in the lab, and the experimental results are provided to verify the effectiveness of the proposed control scheme.

Design is an inseparable part of most scientific and engineering tasks, including real and simulation-based experimental design processes and parameter/hyperparameter tuning/optimization. Several model-based experimental design techniques have been developed for design in domains with partial available knowledge about the underlying process. This article focuses on a powerful class of model-based experimental design called the mean objective cost of uncertainty (MOCU). The MOCU-based techniques are objective-based, meaning that they take the main objective of the process into account during the experimental design process. However, the lack of scalability of MOCU-based techniques prevents their application to most practical problems, including large discrete or combinatorial spaces. To achieve a scalable objective-based experimental design, this article proposes a graph-based MOCU-based Bayesian optimization framework. The correlations among samples in the large design space are accounted for using a graph-based Gaussian process, and an efficient closed-form sequential selection is achieved through the well-known expected improvement policy. The proposed framework's performance is assessed through the structural intervention in gene regulatory networks, aiming to make the network away from the states associated with cancer.

Class imbalance is a prevalent phenomenon in various real-world applications and it presents significant challenges to model learning, including deep learning. In this work, we embed ensemble learning into the deep convolutional neural networks (CNNs) to tackle the class-imbalanced learning problem. An ensemble of auxiliary classifiers branching out from various hidden layers of a CNN is trained together with the CNN in an end-to-end manner. To that end, we designed a new loss function that can rectify the bias toward the majority classes by forcing the CNN's hidden layers and its associated auxiliary classifiers to focus on the samples that have been misclassified by previous layers, thus enabling subsequent layers to develop diverse behavior and fix the errors of previous layers in a batch-wise manner. A unique feature of the new method is that the ensemble of auxiliary classifiers can work together with the main CNN to form a more powerful combined classifier, or can be removed after finished training the CNN and thus only acting the role of assisting class imbalance learning of the CNN to enhance the neural network's capability in dealing with class-imbalanced data. Comprehensive experiments are conducted on four benchmark data sets of increasing complexity (CIFAR-10, CIFAR-100, iNaturalist, and CelebA) and the results demonstrate significant performance improvements over the state-of-the-art deep imbalance learning methods.

Stochastic optimization methods have become a class of popular optimization tools in machine learning. Especially, stochastic gradient descent (SGD) has been widely used for machine learning problems such as training neural networks due to low per-iteration computational complexity. In fact, the Newton or quasi-newton methods leveraging second-order information are able to achieve a better solution than the first-order methods. Thus, stochastic quasi-Newton (SQN) methods have been developed to achieve the better solution efficiently than the stochastic first-order methods by utilizing approximate second-order information. However, the existing SQN methods still do not reach the best known stochastic first-order oracle (SFO) complexity. To fill this gap, we propose a novel faster stochastic quasi-Newton method (SpiderSQN) based on the variance reduced technique of SIPDER. We prove that our SpiderSQN method reaches the best known SFO complexity of $\mathcal{O}(n+n^{1/2}\epsilon^{-2})$ in the finite-sum setting to obtain an $\epsilon$-first-order stationary point. To further improve its practical performance, we incorporate SpiderSQN with different momentum schemes. Moreover, the proposed algorithms are generalized to the online setting, and the corresponding SFO complexity of $\mathcal{O}(\epsilon^{-3})$ is developed, which also matches the existing best result. Extensive experiments on benchmark datasets demonstrate that our new algorithms outperform state-of-the-art approaches for nonconvex optimization., Comment: 11 pages, accepted for publication by TNNLS. arXiv admin note: text overlap with arXiv:1902.02715 by other authors

This paper designs formation–containment control algorithms for a class of second-order nonlinear multi-agent systems governed by Euler–Lagrange dynamics with communication delays. The formation–containment problem consists of leader agents’ formation control and follower agents’ containment control. Firstly, to make the leaders form a desired formation and move collectively with a constant velocity, a coordinated formation control algorithm is designed and the variable-gain technique is used to eliminate the effect of communication delays on the leaders’ formation control. Secondly, considering that only the leaders have access to the desired moving velocity, we propose distributed velocity estimators for followers in which the communication delays also exist in the followers’ information interaction. By using the estimated velocity information, coordinated containment control laws are designed for the followers to drive them asymptotically converge to the convex hull spanned by all leaders. Furthermore, to increase the system robustness against uncertainties and external disturbances, the adaptive updating laws are designed for all agents. Finally, simulations are given to demonstrate these obtained results.

We present a technique to automatically generate search heuristics for dynamic symbolic execution. A key challenge in dynamic symbolic execution is how to effectively explore the program's execution paths to achieve high code coverage in a limited time budget. Dynamic symbolic execution employs a search heuristic to address this challenge, which favors exploring particular types of paths that are most likely to maximize the final coverage. However, manually designing a good search heuristic is nontrivial and typically ends up with suboptimal and unstable outcomes. The goal of this paper is to overcome this shortcoming of dynamic symbolic execution by automatically learning search heuristics. We define a class of search heuristics, namely a parametric search heuristic, and present an algorithm that efficiently finds an optimal heuristic for each subject program. Experimental results with industrial-strength symbolic execution tools (e.g., KLEE) show that our technique can successfully generate search heuristics that significantly outperform existing manually-crafted heuristics in terms of branch coverage and bug-finding.

This article studies the pinning synchronization problem with edge-based decentralized adaptive schemes under link attacks. The link attacks considered here are a class of malicious attacks to break links between neighboring nodes in complex networks. In such an insecure network environment, two kinds of edge-based decentralized adaptive update strategies (synchronous and asynchronous) on coupling strengths and gains are designed to realize the security synchronization of complex networks. Moreover, by virtue of the edge pinning technique, the corresponding secure synchronization problem is considered under the case where only a small fraction of coupling strengths and gains is updated. These designed adaptive strategies do not require any global information, and therefore, the obtained results in this article are developed in a fully decentralized framework. Finally, a numerical example is provided to verify the availability of the achieved theoretical outcomes.

Objectives: This study aimed to discover the extent to which medical students can evaluate medical websites, evaluation criteria used, factors affecting their abilities and whether a teaching intervention could rectify problems. Medical students and practitioners are required to evaluate medical information available on the Internet. Most current medical students are familiar with the Internet, but their ability to evaluate material may require improvement. Methods: A class of undergraduate medical students evaluated an unreliable medical website, received a teaching intervention on website evaluation criteria and re-evaluated the same site. This mixed-methods study was conducted at Sultan Qaboos University, Muscat, Oman, from September to December 2018. Results: A total of 149 (response rate: 82.3%) students participated. Students spent, on average, 4.69 hours per day on the Internet. No significant correlations were found between demographic indicators and Internet time. On a 10-point Likert scale, students’ scores ranged from 5–6, with no significant differences between the pre- and post-intervention evaluations, except for increased polarisation away from the mean. Qualitative comments indicated an awareness of relevant criteria but an overall inability to critically apply them. Conclusion: The results indicate that one cannot make a blanket statement about medical students’ ability to evaluate medical websites despite their familiarity with technology. Moreover, website evaluation should be viewed primarily from the information perspective and that critical thinking ability may play a major role. Due to these overriding factors, short interventions are unlikely to have an impact, and other educational strategies should be developed. These are necessary to ensure that medical students can function independently as life-long learners and medical professionals. Keywords: Internet; Medical Students; Oman.

The control of underactuated Euler–Lagrange systems with uncertain and switched parameters is an important problem whose solution has many applications. The problem is challenging as standard adaptive control techniques do not extend to this class of systems due to structural constraints that lead to parameterization difficulties. This note proposes an adaptive switched control framework that handles the uncertainty and switched dynamics without imposing structural constraints. A case study inspired by autonomous vessel operations is used to show the effectiveness of the proposed approach.

Few-shot learning (FSL) for human-object interaction (HOI) aims at recognizing various relationships between human actions and surrounding objects only from a few samples. It is a challenging vision task, in which the diversity and interactivity of human actions result in great difficulty to learn an adaptive classifier to catch ambiguous interclass information. Therefore, traditional FSL methods usually perform unsatisfactorily in complex HOI scenes. To this end, we propose dynamic graph-in-graph networks (DGIG-Net), a novel graph prototypes framework to learn a dynamic metric space by embedding a visual subgraph to a task-oriented cross-modal graph for few-shot HOI. Specifically, we first build a knowledge reconstruction graph to learn latent representations for HOI categories by reconstructing the relationship among visual features, which generates visual representations under the category distribution of every task. Then, a dynamic relation graph integrates both reconstructible visual nodes and dynamic task-oriented semantic information to explore a graph metric space for HOI class prototypes, which applies the discriminative information from the similarities among actions or objects. We validate DGIG-Net on multiple benchmark datasets, on which it largely outperforms existing FSL approaches and achieves state-of-the-art results.

Recently, a multitude of methods for image-to-image translation have demonstrated impressive results on problems, such as multidomain or multiattribute transfer. The vast majority of such works leverages the strengths of adversarial learning and deep convolutional autoencoders to achieve realistic results by well-capturing the target data distribution. Nevertheless, the most prominent representatives of this class of methods do not facilitate semantic structure in the latent space and usually rely on binary domain labels for test-time transfer. This leads to rigid models, unable to capture the variance of each domain label. In this light, we propose a novel adversarial learning method that: 1) facilitates the emergence of latent structure by semantically disentangling sources of variation and 2) encourages learning generalizable, continuous, and transferable latent codes that enable flexible attribute mixing. This is achieved by introducing a novel loss function that encourages representations to result in uniformly distributed class posteriors for disentangled attributes. In tandem with an algorithm for inducing generalizable properties, the resulting representations can be utilized for a variety of tasks such as intensity-preserving multiattribute image translation and synthesis, without requiring labeled test data. We demonstrate the merits of the proposed method by a set of qualitative and quantitative experiments on popular databases such as MultiPIE, RaFD, and BU-3DFE, where our method outperforms other state-of-the-art methods in tasks such as intensity-preserving multiattribute transfer and synthesis.

Zero-shot learning (ZSL) is a pretty intriguing topic in the computer vision community since it handles novel instances and unseen categories. In a typical ZSL setting, there is a main visual space and an auxiliary semantic space. Most existing ZSL methods handle the problem by learning either a visual-to-semantic mapping or a semantic-to-visual mapping. In other words, they investigate a unilateral connection from one end to the other. However, the connection between the visual space and the semantic space are bilateral in reality, that is, the visual space depicts the semantic space; the semantic space, on the other hand, describes the visual space. In this article, therefore, we investigate the bilateral connections in ZSL and present a novel model, called Boomerang-GAN, by taking advantage of conditional generative adversarial networks (GANs). Specifically, we generate unseen visual samples from their category semantic embeddings by a conditional GAN. Different from the existing generative ZSL methods that only consider generating visual features from class descriptions, our method also considers that the generated visual features can be translated back to their corresponding semantic embeddings by introducing a multimodal cycle-consistent loss. Extensive experiments of both ZSL and generalized ZSL on five widely used datasets verify that our method is able to outperform previous state-of-the-art approaches in both recognition and segmentation tasks.

In the fall of 1978 I was consultant to a video project called "The Unemployment Tapes," designed to explore through talks with local people the human costs, the causes, and the possible cures of unemployment in an old industrial area of Connecticut. At the time, I was also reading and thinking about class, language, and the theories of Basil Bernstein. I began to notice in the taped interviews a close correspondence to Bernstein's cen­tral distinction between "restricted" and "elaborated" codes: almost all the peo­ple interviewed on the streets spoke in the restricted code that Bernstein attrib­utes to the working class, while managers and officials used the elaborated code of what Bernstein calls the "middle class." . . . The power relations of a society permeate speech and shape it, while speech reproduces or challenges the pow­er relations of the society. The way we talk is not just an artifact of class, any more than class is an artifact of the ways we talk. Speech takes place in society, but society also takes place "in" speech. The point is well illustrated, I believe, by what happened in those two interviews. A Bernsteinian explanation of their con­trasts badly misrepresents the social forces at work in them, assigning to static "class," differences in speech that ex­press dynamic and changeable power relations. . . . Movements toward worker self-management, co-ops, progressive credit unions, consumer movements, union organizing, populist movements of many kinds, are all fertile soil in which elaborated codes (put to better use than by the mayor, I hope) may grow along with the habit of democracy.

We present an approach to analyse learning outcomes in a broad class of misspecified environments, spanning both single-agent and social learning. We introduce a novel “prediction accuracy” order over subjective models and observe that this makes it possible to partially restore standard martingale convergence arguments that apply under correctly specified learning. Based on this, we derive general conditions to determine when beliefs in a given environment converge to some long-run belief either locally or globally (i.e. from some or all initial beliefs). We show that these conditions can be applied, first, to unify and generalize various convergence results in previously studied settings. Second, they enable us to analyse environments where learning is “slow”, such as costly information acquisition and sequential social learning. In such environments, we illustrate that even if agents learn the truth when they are correctly specified, vanishingly small amounts of misspecification can generate extreme failures of learning.

While the interactional conditions and timing of applause in audience response to public speeches has received attention in conversation analysis research, little research has been done on applause in educational contexts. The nature of applause, however, can vary depending on the context. This paper examines classroom-specific applause and focuses on where in classroom interaction the applause can occur, who initiates the applause, and what the applause accomplishes in the interaction. The data come from 14 audio and video-recorded Japanese primary school EFL class sessions. The analysis reveals that the applause in the data was a typically teacher-initiated action and it regularly occurred in sequence closing position as a positive assessment to the students’ success in carrying out the teachers’ oriented-to expectations.

This paper examines how orientation to cultural identities in an Irish language class in the United States is used to negotiate issues of authenticity and linguistic and cultural authority. The data were recorded in a beginning level Irish language class in Southern California, in which the teacher and all but one student were American. The Irish identity of the remaining student was highly salient to the other students and to the teacher, conferring authenticity and linguistic authority on him. The teacher's evaluations of the students ascribe authenticity and linguistic authority to the Irish student based on perceptions about his identity, in spite of his rejection of such authority. Thus, even when participants do not claim identity based Statuses, they may be imposed by others in a way that is consequential for interaction. Keywords: Identity; Authenticity; Irish; Linguistic authority.

This study deals with Formulaic Speech (FS) usage in the context of the partial L2 immersion class. It tries to define and classify FS according to its functions. The fact that learners resort to FS shows that second language production is not only based on the construction of sentences from scratch but also on the integration of formulaic sequences in discourse. But what is the use of FS? What are the possible functions it performs? We attempt to show that FS makes up for a lack of structural knowledge and might therefore be used as a learning strategy in the acquisition of structure. Then we consider the psycholinguistic function of FS and try to demonstrate that it might be a pre-planning strategy and a way for the learner to economize effort on processing and thus focus on his or her learning of the language. Finally, the paper analyzes the communicative function of FS and its role in the relation between speaker and hearer, and suggests that it might play a part in the development of pragmatic competence. Keywords: Formulaic Speech, Second language acquisition, Second language production, Partial immersion, Communicative competence.

API misuses are well-known causes of software crashes and security vulnerabilities. However, their detection and repair is challenging given that the correct usages of (third-party) APIs might be obscure to the developers of client programs. This paper presents the first empirical study to assess the ability of existing automated bug repair tools to repair API misuses, which is a class of bugs previously unexplored. Our study examines and compares 14 Java test-suite-based repair tools (11 proposed before 2018, and three afterwards) on a manually curated benchmark (APIREPBENCH) consisting of 101 API misuses. We develop an extensible execution framework (APIARTY) to automatically execute multiple repair tools. Our results show that the repair tools are able to generate patches for 28% of the API misuses considered. While the 11 less recent tools are generally fast (the median execution time of the repair attempts is 3.87 minutes and the mean execution time is 30.79 minutes), the three most recent are less efficient (i.e., 98% slower) than their predecessors. The tools generate patches for API misuses that mostly belong to the categories of missing null check, missing value, missing exception, and missing call. Most of the patches generated by all tools are plausible (65%), but only few of these patches are semantically correct to human patches (25%). Our findings suggest that the design of future repair tools should support the localisation of complex bugs, including different categories of API misuses, handling of timeout issues, and ability to configure large software projects. Both APIREPBENCH and APIARTY have been made publicly available for other researchers to evaluate the capabilities of repair tools on detecting and fixing API misuses.

The Department of Clinical Laboratory Sciences at Virginia Commonwealth University incorporated the use of a game-based learning platform into a junior level Immunology course. The primary goal of this addition was to increase student engagement during review sessions. In previous years, each Immunology class was started with a review of material from an earlier lecture. The review consisted of short answer and multiple choice questions as a means to highlight important concepts and allow additional opportunities for students to ask questions. During these review periods it was observed that class participation ranged from 3-20%. Additionally, those who did participated were the same students in each class period. The incorporation of the game-based learning pAlatform into review sessions involved asking review questions in multiple choice format with 30 seconds of time for students to answer after each question was asked. This new format resulted in class participation increasing to 95-100%. Review of student answers also allowed the instructor to determine which areas the students needed additional assistance with before exams. Student feedback indicated that they enjoyed the ability to be anonymous when answering questions while still getting immediate feedback. This new review format will be utilized in additional courses, specifically Immunohematology. Furthermore, this game-based platform will be used to review material with senior students in their advanced senior level courses.

The wireless sensor networks (WSNs) are special network which has purpose of gathering information in certain area. Multipath routing is the paramount path of addressing QoS and energy balancing concerns in low power and lossy networks (LLNs) especially in IoT technology. Most of the extant efforts bring the limited number of disjoint paths into play, and intersect the traffic among them pursuant to a compound metric or centralized optimization problem. This paper proposes a multi-class multipath routing protocol for LLNs (called M2RPL), that construct a braided multipath routing graph based on the standard RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks) protocol, and an energy balanced optimal rate assignment mechanism (EBORA) that solves a local multi class optimization problem for minimum cost traffic rate assignment. Moreover the algorithm tries to maximize network lifetime by equalizing the energy dissipation rates of equi-level nodes. The simulation results expose the efficiency of the proposed framework, leading to an average 15% increase in lifetime, and improved QoS performance in terms of delay and reliability, compared to some well-known algorithms.

This paper presents and analyzes a case study of a five-week study abroad course called Inequality in Brazil: An exploration of race, class, gender, sexuality, and geography. The course was constructed to teach social inequality in the context of Brazil by using place-based and experiential learning within the framework of critical pedagogy (Freire, 1989). By examining inequality through the lens of culture and geography, students were empowered to become student-teachers in their explorations of race, class, gender, and sexuality as they linked theory to practice and lived experience. This paper provides an example of how study abroad can be used to teach about issues of inequality by partnering with community members to build learning environments where students and community members can all benefit.

With increasing usage of face recognition algorithms, it is well established that external artifacts and makeup accessories can be applied to different facial features such as eyes, nose, mouth, and cheek, to obfuscate one’s identity or to impersonate someone else’s identity. Recognizing faces in the presence of these artifacts comprises the problem of disguised face recognition, which is one of the most arduous covariates of face recognition. The challenge becomes exacerbated when disguised faces are captured in real-time environment, with low resolution images. To address the challenge of disguised face recognition, this paper first proposes a novel multi-objective encoder-decoder network, termed as DED-Net. DED-Net attempts to learn the class variations in the feature space generated by both disguised as well non-disguised images, using a combination of Mahalanobis and Cosine distance metrics, along with Mutual Information based supervision. The DED-Net is then extended to learn from the local and global features of both disguised and non-disguised face images for efficient face recognition, and the complete framework is termed as Disguise Resilient (D-Res) framework. The efficacy of the proposed framework has been demonstrated on two real-world benchmark datasets: Disguised Faces in the Wild (DFW) 2018 and DFW2019 competition datasets. In addition, this research also emphasizes on the importance of recognizing disguised faces in low resolution settings and proposes three experimental protocols to simulate the real-world surveillance scenario. To this effect, benchmark results have been shown on seven protocols for three low resolution settings (32×32, 24×24, and 16×16) of the two DFW benchmark datasets. The results demonstrate superior performance of the D-Res framework, in comparison with benchmark algorithms. For example, an improvement of around 3% is observed on the Overall protocol of the DFW2019 dataset, where the D-Res framework achieves 96.3%. Experiments have also been performed on benchmark face verification datasets (LFW, YTF, and IJB-B), where the D-Res framework achieves improved verification accuracy.

This article investigates a class of finite-time cooperative tracking problems of heterogeneous mixed-order multiagent systems (MASs) with higher-order dynamics. Different from the previous works of heterogeneous MASs, the agents in this study are considered to have different first-, second-, or even higher-order nonlinear dynamics. It means that, according to different tasks and situations, the following agents can have nonidentical orders or different numbers of states to be synchronized, which is more general for the practical cooperative applications. The leader is a higher-order nonautonomous system and contains full state information to be synchronized for all agents with mixed-order dynamics. Accordingly, the spanning tree is defined based on the specific state rather than on the agent to guarantee that each following agent can receive adequate state information. Distributed control protocols are designed for all agents to achieve the ultimate state synchronization to the leader in finite time. The Lyapunov approach is used for the stability analysis and a practical example of mixed-order mechanical MASs verifies the effectiveness and performance of the proposed distributed control protocols.

The emergence of wearable devices has opened up new potentials for egocentric activity recognition. Although some methods integrate attention mechanisms into deep neural networks to capture fine-grained human-object interactions in a weak-supervision manner, they either ignore exploiting the temporal consistency or generate attention based on considering appearance cues only. To address these limitations, in this paper, we propose an enhanced attention-tracking method, combined with multi-branch network (EAT-MBNet), for egocentric activity recognition. Specifically, we propose class-aware attention maps (CAAMs) by employing a self-attention-based module to refine the class activation maps (CAMs). Our proposed method can enhance the semantic dependency between the activity categories and the feature maps. To highlight the discriminative features from the regions of interest across frames, we propose a flow-guided attention-tracking (F-AT) module, by simultaneously leveraging historical attention and motion patterns. Furthermore, we propose a cross-modality modeling branch based on an inter-active GRU module, which captures the time-synchronized long-term relationships between the appearance and motion branches. Experimental results on four egocentric activity benchmarks demonstrate that the proposed method achieves state-of-the-art performance.

New class detection and effective model expansion are of great importance in incremental data mining. In open incremental data environments, data often come with novel classes, e.g., the emergence of new classes in image classification or new topics in opinion monitoring, and is denoted as class-incremental learning (C-IL) in literature. There are two main challenges in C-IL: how to conduct novelty detection and how to update the model with few novel class instances. Most previous methods pay much attention to the former challenge while ignoring the problem of efficiently updating models. To solve this problem, we propose a novel framework to handle the incremental new class, named learning to classify with incremental new class (LC-INC), which can process these two challenges automatically in one unified framework. In detail, LC-INC utilizes a novel structure network to consider the prototype information between class centers of known classes and newly incoming instances, which can dynamically combine the prediction information with structure information to detect novel class instances efficiently. On the other hand, the proposed structure network can also act as a meta-network, which can learn to expand the model much faster and more efficiently with inadequate novel class instances. Experiments on synthetic and real-world datasets successfully validate the effectiveness of our proposed method.

Multi-label learning deals with training examples each represented by a single instance while associated with multiple class labels. Due to the exponential number of possible label sets to be considered by the predictive model, it is commonly assumed that label correlations should be well exploited to design an effective multi-label learning approach. On the other hand, class-imbalance stands as an intrinsic property of multi-label data which significantly affects the generalization performance of the multi-label predictive model. For each class label, the number of training examples with positive labeling assignment is generally much less than those with negative labeling assignment. To deal with the class-imbalance issue for multi-label learning, a simple yet effective class-imbalance aware learning strategy called cross-coupling aggregation (COCOA) is proposed in this article. Specifically, COCOA works by leveraging the exploitation of label correlations as well as the exploration of class-imbalance simultaneously. For each class label, a number of multiclass imbalance learners are induced by randomly coupling with other labels, whose predictions on the unseen instance are aggregated to determine the corresponding labeling relevancy. Extensive experiments on 18 benchmark datasets clearly validate the effectiveness of COCOA against state-of-the-art multi-label learning approaches especially in terms of imbalance-specific evaluation metrics.

It is challenging to deploy check-in nodes optimally in a complex network so as to perform specific check-in like services, e.g., fuel supplements etc., but crucial in many real-world applications. In this article, we propose and study the new optimization problem of placing check-in nodes with the minimum cost, i.e., the problem of finding the minimum-cost check-in nodes (MCCN), which is of great interests for real-world application situations, but even more difficult. We motivate the new algorithms through three typical worse cases by the often-used greedy-type algorithms, i.e., One-to-Many, Many-to-One, and Duplicate-Overrides. With this respect, the proposed novel optimization algorithms utilize a novel metric of contribution density for selecting check-in nodes iteratively, which successfully avoid the occurrence of the two worse cases of One-to-Many and Many-to-One. We also introduce an extra backward extraction step in one of new algorithms, which overcomes the crucial worse case of ``duplicate overrides'' and largely improves the algorithmic performance in solving the introduced optimization problem of MCCN. Meanwhile, we extend the new contribution density metric to a more general class of functions and study their effectiveness to eliminate the two worse cases of One-to-Many and Many-to-One; also, a detailed analysis on complexity and performance of the proposed algorithms is presented to show their numerical efficiency and accuracy. Extensive experiments over two classical artificial networks, i.e., BA network and ER network, and ten real-world networks, under two typical cost setups, show the proposed algorithms significantly outperform the four state-of-the-art algorithms. We also demonstrate that our proposed algorithms are much reliable and robust with different experiment settings of deployment cost and network-type and scale.

To fill the gap and add literature on systematic reflections in the area of teacher identity developments, this research, which is a part of a larger case study, described how reflection class followed by pre-service teachers of English language in a teacher development program in Indonesia (PPG Pra-jabatan) was conducted. Using a single case study with embedded design and multiple data sources (observations, interviews, and document analysis), this research found that in its effort to help pre-service teachers' identity development, the reflection class followed a certain procedure that comprises of two phases (i.e. reflection phase and publication preparation phase). In addition, this research also found some protocols that were adhered by the reflection class (i.e. involving social reflection, providing a safe and secure learning environment, feedback provision to the reflections, and giving appropriate prompts). This research recommends the teacher education and teacher professional development programs give more attention to how reflection is conducted. In particular, it is suggested that some protocols of conducting a good reflection are considered so that the pre-service teachers can make the most of the reflective activities.

This research aimed to design a reflection book as a set of problem-based learning (PBL) for a supplementary material in Microteaching class. The book was developed not only to assist pre-service English teachers (PSETs) undergoing Microteaching class to understand real problems in school context, but also to help them enhance their problem-solving skill through critical reflection and discussion. This book was designed by implementing ADDIE model, consisting of five phases, namely Analysis, Design, Development, Implementation, and Evaluation. This product was formatively evaluated within the Analysis, Design, Development and Implementation phases and was field tested in Microteaching class comprising of 19 PSETs from the English Language Education Study Program in Tuladha University (pseudonym). In the last stage, summative evaluation was conducted by two subject matter experts whose background was both English education lecturers and book authors. Product validation included narrative story aspect and reflective activities. The validation result showed that the book was appropriate and practical for enhancing PSETs’ problem-solving skill.

Recently, developing students’ thinking, especially critical thinking (CT), has become a hot issue. Critical thinking has been claimed to have an important impact on learners’ reading comprehension because it can help them analyze, evaluate, construct their thinking, solving problems and reasoning (Ennis, 1989). However, the extent that teachers’ classroom activities contribute to developing students’ critical thinking has rarely been researched. The current case study was conducted with six EFL high school teachers and 10 reading lessons in Vietnam to explore the teachers’ use of questions and to analyze if these questions could facilitate the students’ critical thinking. Classroom observations and the cognitive domain of Bloom’s taxonomy were adapted as the research instruments. The study results reveal common types of questions are often used by high school teachers in their reading lessons. Suggestions are made on types of questions that teachers should function more in their class in order to enhance students’ critical thinking.

To have good acquisition and awareness in reading, the learners need a long and continuous process, and therefore, they are required to have autonomy in learning reading. This study aims to promote learner autonomy in reading class by combining learner-centered reading teaching and extensive reading teaching. Learner-centered reading teaching was carried out through group discussion, presentation, and language awareness activities. Meanwhile, extensive reading teaching was done to review the learners‘ materials in presentation and reinforce their acquisition. Those two different approaches were applied due to differences on learner's characteristics and needs. The result showed some success in the practice of autonomy, indicated by changes on learners' attitude. However, many learners showed that they focused more on obtaining score than on developing their language acquisition. By implementing the approach, the teacher can assist learners to be aware of their ability to learn independently and equip them with the skill needed for long-life learning. Keywords: autonomous learning, learner-centered in teaching reading, Extensive reading teaching

The role of grammar instruction is inevitably important for second and/or foreign language mastery. Unfortunately, in practice, many English teachers in foreign language contexts such as in Indonesia still teach grammar deductively; that is, learners are provided the grammar rules and examples, are told to memorize them, and then are asked to apply the rules to other examples. This particular method of teaching is believed to produce learners who cannot use the language communicatively although they have considerable knowledge of grammar rules. It has also been seen as contributing to learners’ passive participation and negative feelings towards grammar learning. The aims of this paper are to give an alternative solution to that problem by offering some ideas of game-based activities and to investigate students’ perception of the implementation of communicative game-based grammar learning. Using pre- and post-study questionnaire, the research study was conducted with 30 adult EFL learners taking an after-class English course. The findings indicated that these activities were effective in creating a non-threatening and more relaxing grammar classes, changing students’ negative perception of grammar learning and improving their perceived communicative skills.

Contact with the carceral state—ranging from police stops to prison time—is a frequent experience in the United States, particularly in communities marginalized on the basis of race and class. In recent years, political scientists have sought to measure the impacts of these encounters on individuals’ and communities’ political engagement. This review describes the main sources of evidence in this literature and what we learn from them. I present a series of stylized facts about the carceral state and political behavior, highlighting places where we know a great deal (such as the relative underrepresentation of people with criminal convictions among voters) and places where more work is needed (such as nonvoting participation and community spillovers). Then, I discuss policy proposals that seek to mitigate the political impacts of the carceral state, and what is and is not yet known about what they might accomplish.

Contribution: While design project courses offer first-year students a practical introduction to engineering, a portion of class time is usually spent on lecturing foundational knowledge instead of practicing engineering design. This article presents a blended design-based learning (bDBL) approach that makes class time more efficient and explores the changes in students' design competencies and intrinsic motivations.

Decentralized decision-making is becoming more ubiquitous in different organizations that often follow a hierarchical structure. To model these problems, multi-level programming has been suggested as a suitable methodology for modeling the interaction between the different levels of decisions. However, multi-level programming, even for the case of bi-levels, is known to be strongly N P -hard. To address this computational challenge, we develop three different heuristic-based approaches for solving a specific class of tri-level programming problems, in which the leader has direct control over the follower’s decisions to a certain extent, with a common objective function shared at all levels. As expected, each heuristic type offers a trade-off between solution quality and computational time. To illustrate our solution approach, we present an application for defending critical infrastructure to improve its resilience against intentional attacks. In this context we use a defender-attacker-defender model and apply it to electrical power grids. We also propose a modified implementation of a widely adopted enumeration algorithm in this area, with a warm-starting solution technique that significantly enhanced the computational performance of the enumeration algorithm. We test our solution approaches on three electrical transmission networks and present the results of our numerical computations as well as some insights.

This study investigates a parameterization of all output-rectifying retrofit controllers for distributed design of a structured controller. It has been discovered that all retrofit controllers can be characterized as a constrained Youla parameterization, which is difficult to solve analytically. For synthesis, a tractable and insightful class of retrofit controllers, referred to as output-rectifying retrofit controllers, has been introduced. An unconstrained parameterization of all output-rectifying retrofit controllers can be derived under a technical assumption on measurability of particular signals. The aim of this note is to reveal the structure of all output-rectifying retrofit controllers in the general output-feedback case. It is found out that the existing developments can be generalized based on the notions of state projection and an inverse system. The result leads to the conclusion that output-rectifying retrofit controllers can readily be designed even in the general case., to be published at IEEE Transactions on Automatic Control