Reviewerly/exHarmony huggingface dataset:
From reflective observation to active learning: A mobile experiential learning approach for environmental science education

Abstract Several previous studies have emphasized the importance of situating students in authentic learning or problem‐solving contexts to enhance their learning performances. However, some challenges of situated learning have been stated, such as the lack of effective learning support or concrete objective design to improve students’ learning engagement, problem‐solving competences and their active thinking. In this study, a mobile technology‐supported experiential learning system was developed to improve students’ problem‐solving competences as well as their learning performances, attitudes and collective efficacy. Moreover, an experiment was conducted on an environmental science course in an elementary school to compare the effects of this method with those of the conventional situated mobile learning approach on students’ learning effectiveness. The experimental results display that the implemented approach can significantly enhance students’ learning achievements, environmental attitudes and collective efficacy; furthermore, the students who learned with the implemented approach showed higher problem‐solving competence than those who learned with the conventional situated mobile learning approach, implying a noticeable reference for conducting experiential learning activities in environmental science education. Furthermore, the promotion of students’ active thinking in the mobile technology‐supported experiential learning is discussed. Practitioner Notes What is already known about this topic The importance of situating students in authentic learning or problem‐solving contexts to enhance their learning performances has been emphasized. Some challenges of situated learning have been stated, such as the lack of effective learning support or concrete objective design to improve students’ learning engagement, problem‐solving competences and active thinking. Properly integrating technology into instruction is most effective in an authentic learning context. What this paper adds A mobile technology‐supported experiential learning system was designed to conduct an authentic learning activity. The proposed approach can significantly enhance students’ learning achievements, environmental attitudes and collective efficacy. The students who learned with the proposed approach showed higher problem‐solving competence than those who learned with the conventional situated mobile learning approach. The promotion of students’ active thinking while learning with the mobile technology‐supported experiential learning system is discussed. Implications for practice and/or policy The mobile technology‐supported experiential learning system is beneficial to students in an authentic learning environment as it triggers their active thinking. Such an approach can be designed with authentic learning activities in other courses by replacing the authentic objects and learning materials. It would be interesting to probe students’ social interactions and learning patterns in an experiential collective learning activity.
LIRME

Information retrieval (IR) models often employ complex variations in term weights to compute an aggregated similarity score of a query-document pair. Treating IR models as black-boxes makes it difficult to understand or explain why certain documents are retrieved at top-ranks for a given query. Local explanation models have emerged as a popular means to understand individual predictions of classification models. However, there is no systematic investigation that learns to interpret IR models, which is in fact the core contribution of our work in this paper. We explore three sampling methods to train an explanation model and propose two metrics to evaluate explanations generated for an IR model. Our experiments reveal some interesting observations, namely that a) diversity in samples is important for training local explanation models, and b) the stability of a model is inversely proportional to the number of parameters used to explain the model.
Visualizing Student Opinion Through Text Analysis

Contribution: An automated methodology that provides visualizations of students' free text comments from course satisfaction surveys. Focusing on sentiment, these visualizations reveal learning and teaching aspects of the course that either may require improvement or are performing well. They provide educators with a simple, systematic way to monitor their courses and make pedagogically sound decisions on teaching strategies. Background: Student course satisfaction surveys often solicit free text comments. This feedback can provide invaluable insights for educators, but because these comments often contain a large amount of data, they cannot easily be acted upon. Existing visualization methods are not suitable for this application, and needed additional capabilities. Research Questions: How can large quantities of student satisfaction data be summarized and visualized? How can these visualizations be used to learn meaningful information about courses? What are the recurring themes across semesters? Methodology: Several methods based on machine learning and text analysis techniques were used to visualize student satisfaction comments. The latent Dirichlet allocation (LDA) statistical method was used to identify aspects of student opinion of a course. The sentiment of the student comments were also identified. This information was then presented visually for educators in a case study that gives examples of these visualizations. Findings: The visualization methods explored provide educators with an overview of aspects and their associated sentiment. The summary visualizations allow easy comparison to be made between courses, or between teaching periods in the same course.
Age from Faces in the Deep Learning Revolution

Face analysis includes a variety of specific problems as face detection, person identification, gender and ethnicity recognition, just to name the most common ones; in the last two decades, significant research efforts have been devoted to the challenging task of age estimation from faces, as witnessed by the high number of published papers. The explosion of the deep learning paradigm, that is determining a spectacular increasing of the performance, is in the public eye; consequently, the number of approaches based on deep learning is impressively growing and this also happened for age estimation. The exciting results obtained have been recently surveyed on almost all the specific face analysis problems; the only exception stands for age estimation, whose last survey dates back to 2010 and does not include any deep learning based approach to the problem. This paper provides an analysis of the deep methods proposed in the last six years; these are analysed from different points of view: the network architecture together with the learning procedure, the used datasets, data preprocessing and augmentation, and the exploitation of additional data coming from gender, race and face expression. The review is completed by discussing the results obtained on public datasets, so as the impact of different aspects on system performance, together with still open issues.
Time-domain characterization and correction of on-chip distortion of control pulses in a quantum processor

We introduce Cryoscope, a method for sampling on-chip baseband pulses used to dynamically control qubit frequency in a quantum processor. We specifically use Cryoscope to measure the step response of the dedicated flux control lines of two-junction transmon qubits in circuit QED processors with the temporal resolution of the room-temperature arbitrary waveform generator producing the control pulses. As a first application, we iteratively improve this step response using optimized real-time digital filters to counter the linear-dynamical distortion in the control line, as needed for high-fidelity, repeatable one- and two-qubit gates based on dynamical control of qubit frequency.
Using Blockchain for Improved Video Integrity Verification

A video record plays a crucial role in providing evidence for crime scenes or road accidents. However, the main problem with the video record is that it is often vulnerable to various video tampering attacks. Although visual evidence is required to conduct an integrity verification before investigations, it is still difficult for human vision to detect a forgery. In this paper, we propose a novel video integrity verification method (IVM) that takes advantage of a blockchain framework. The proposed method employs an effective blockchain model in centralized video data, by combining a hash-based message authentication code and elliptic curve cryptography to verify the integrity of a video. In our method, video content with a predetermined size (segments) is key-hashed in a real-time manner and stored in a chronologically chained fashion, thus establishing an irrefutable database. The verification process applies the same procedure to the video segment and generates a hash value that can be compared with the hash in the blockchain. The proposed IVM is implemented on a PC environment, as well as on an accident data recorder-embedded system for verification. The experimental results show that the proposed method has better detection capabilities and robustness toward various kinds of tampering, such as copy–move, insert, and delete, as compared to other state-of-the-art methods. An analysis based on execution time along with an increase in the number of blocks within the blockchain shows a minimal overhead in the proposed method.
Better Safe Than Sorry

The arm race between spambots and spambot-detectors is made of several cycles (or generations): a new wave of spambots is created (and new spam is spread), new spambot filters are derived and old spambots mutate (or evolve) to new species. Recently, with the diffusion of the adversarial learning approach, a new practice is emerging: to manipulate on purpose target samples in order to make stronger detection models. Here, we manipulate generations of Twitter social bots, to obtain - and study - their possible future evolutions, with the aim of eventually deriving more effective detection techniques. In detail, we propose and experiment with a novel genetic algorithm for the synthesis of online accounts. The algorithm allows to create synthetic evolved versions of current state-of-the-art social bots. Results demonstrate that synthetic bots really escape current detection techniques. However, they give all the needed elements to improve such techniques, making possible a proactive approach for the design of social bot detection systems.
LAEO-Net: Revisiting People Looking at Each Other in Videos

Capturing the ‘mutual gaze’ of people is essential for understanding and interpreting the social interactions between them. To this end, this paper addresses the problem of detecting people Looking At Each Other (LAEO) in video sequences. For this purpose, we propose LAEO-Net, a new deep CNN for determining LAEO in videos. In contrast to previous works, LAEO-Net takes spatio-temporal tracks as input and reasons about the whole track. It consists of three branches, one for each character’s tracked head and one for their relative position. Moreover, we introduce two new LAEO datasets: UCO-LAEO and AVA-LAEO. A thorough experimental evaluation demonstrates the ability of LAEO-Net to successfully determine if two people are LAEO and the temporal window where it happens. Our model achieves state-of-the-art results on the existing TVHID-LAEO video dataset, significantly outperforming previous approaches.
Survey of Reconfigurable Data Center Networks

Emerging optical technologies introduce opportunities to recon gure network topologies at runtime. The resulting topological exibilities can be exploited to design novel demand-aware and self-adjusting networks. This paper provides an overview of the algorithmic problems introduced by this technology, and surveys rst solutions.
Query-aware sparse coding for web multi-video summarization

Given the explosive growth of online videos, it is becoming increasingly important to relieve the tedious work of browsing and managing the video content of interest. Video summarization aims at providing such a technique by transforming one or multiple videos into a compact one. However, conventional multi-video summarization methods often fail to produce satisfying results as they ignore the users’ search intents. To this end, this paper proposes a novel query-aware approach by formulating the multi-video summarization in a sparse coding framework, where the web images searched by a query are taken as the important preference information to reveal the query intent. To provide a user-friendly summarization, this paper also develops an event-keyframe presentation structure to present keyframes in groups of specific events related to the query by using an unsupervised multi-graph fusion method. Moreover, we release a new public dataset named MVS1K, which contains about 1000 videos from 10 queries and their video tags, manual annotations, and associated web images. Extensive experiments on the MVS1K and TVSum datasets demonstrate that our approaches produce competitively objective and subjective results.
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms

We provide theoretical analyses for two algorithms that solve the regularized optimal transport (OT) problem between two discrete probability measures with at most $n$ atoms. We show that a greedy variant of the classical Sinkhorn algorithm, known as the \emph{Greenkhorn algorithm}, can be improved to $\widetilde{\mathcal{O}}(n^2\varepsilon^{-2})$, improving on the best known complexity bound of $\widetilde{\mathcal{O}}(n^2\varepsilon^{-3})$. Notably, this matches the best known complexity bound for the Sinkhorn algorithm and helps explain why the Greenkhorn algorithm can outperform the Sinkhorn algorithm in practice. Our proof technique, which is based on a primal-dual formulation and a novel upper bound for the dual solution, also leads to a new class of algorithms that we refer to as \emph{adaptive primal-dual accelerated mirror descent} (APDAMD) algorithms. We prove that the complexity of these algorithms is $\widetilde{\mathcal{O}}(n^2\sqrt{\delta}\varepsilon^{-1})$, where $\delta > 0$ refers to the inverse of the strong convexity module of Bregman divergence with respect to $\|\cdot\|_\infty$. This implies that the APDAMD algorithm is faster than the Sinkhorn and Greenkhorn algorithms in terms of $\varepsilon$. Experimental results on synthetic and real datasets demonstrate the favorable performance of the Greenkhorn and APDAMD algorithms in practice.
Deep Frame Prediction for Video Coding

We propose a novel frame prediction method using a deep neural network (DNN), with the goal of improving the video coding efficiency. The proposed DNN makes use of decoded frames, at both the encoder and decoder to predict the textures of the current coding block. Unlike conventional inter-prediction, the proposed method does not require any motion information to be transferred between the encoder and the decoder. Still, both the uni-directional and bi-directional predictions are possible using the proposed DNN, which is enabled by the use of the temporal index channel, in addition to the color channels. In this paper, we developed a jointly trained DNN for both uni-directional and bi-directional predictions, as well as separate networks for uni-directional and bi-directional predictions, and compared the efficacy of both the approaches. The proposed DNNs were compared with the conventional motion-compensated prediction in the latest video coding standard, High Efficiency Video Coding (HEVC), in terms of the BD-bitrate. The experiments show that the proposed joint DNN (for both uni-directional and bi-directional predictions) reduces the luminance bitrate by about 4.4%, 2.4%, and 2.3% in the low delay $P$ , low delay, and random access configurations, respectively. In addition, using the separately trained DNNs brings further bit savings of about 0.3%-0.5%.
Tracking Multiple Vehicles Using a Variational Radar Model

High-resolution radar sensors are able to resolve multiple detections per object and, therefore, provide valuable information for vehicle environment perception. For instance, multiple detections allow us to infer the size of an object or to measure the object's motion more precisely. Yet, the increased amount of data raises the demands on tracking modules; measurement models that are able to process multiple detections for an object are necessary and measurement-to-object associations become more complex. This paper presents a new variational radar model for tracking vehicles using radar detections and demonstrates how this model can be incorporated into a random-finite-set-based multi-object filter. The measurement model is learned from actual data using variational Gaussian mixtures and avoids excessive manual engineering. In combination with the multi-object tracker, the entire process chain from raw measurements to the resulting tracks is formulated probabilistically. The presented approach is evaluated on experimental data, and it is demonstrated that the data-driven measurement model outperforms a manually designed model.
AVID: Adversarial Visual Irregularity Detection

Real-time detection of irregularities in visual data is very invaluable and useful in many prospective applications including surveillance, patient monitoring systems, etc. With the surge of deep learning methods in the recent years, researchers have tried a wide spectrum of methods for different applications. However, for the case of irregularity or anomaly detection in videos, training an end-to-end model is still an open challenge, since often irregularity is not well-defined and there are not enough irregular samples to use during training. In this paper, inspired by the success of generative adversarial networks (GANs) for training deep models in unsupervised or self-supervised settings, we propose an end-to-end deep network for detection and fine localization of irregularities in videos (and images). Our proposed architecture is composed of two networks, which are trained in competing with each other while collaborating to find the irregularity. One network works as a pixel-level irregularity $$\mathcal {I}$$ npainter, and the other works as a patch-level $$\mathcal {D}$$ etector. After an adversarial self-supervised training, in which $$\mathcal {I}$$ tries to fool $$\mathcal {D}$$ into accepting its inpainted output as regular (normal), the two networks collaborate to detect and fine-segment the irregularity in any given testing video. Our results on three different datasets show that our method can outperform the state-of-the-art and fine-segment the irregularity.
Input Prioritization for Testing Neural Networks

Deep neural networks (DNNs) are increasingly being adopted for sensing and control functions in a variety of safety and mission-critical systems such as self-driving cars, autonomous air vehicles, medical diagnostics and industrial robotics. Failures of such systems can lead to loss of life or property, which necessitates stringent verification and validation for providing high assurance. Though formal verification approaches are being investigated, testing remains the primary technique for assessing the dependability of such systems. Due to the nature of the tasks handled by DNNs, the cost of obtaining test oracle data-the expected output, a.k.a. label, for a given input-is high, which significantly impacts the amount and quality of testing that can be performed. Thus, prioritizing input data for testing DNNs in meaningful ways to reduce the cost of labeling can go a long way in increasing testing efficacy. This paper proposes using gauges of the DNN's sentiment derived from the computation performed by the model, as a means to identify inputs that are likely to reveal weaknesses. We empirically assessed the efficacy of three such sentiment measures for prioritization-confidence, uncertainty and surprise-and compare their effectiveness in terms of their fault-revealing capability and retraining effectiveness. The results indicate that sentiment measures can effectively flag inputs that expose unacceptable DNN behavior. For MNIST models, the average percentage of inputs correctly flagged ranged from 88% to 94.8%.
Quantum communication protocols by quantum walks with two coins

We introduce some new perfect state transfer and teleportation schemes by quantum walks with two coins. Encoding the transferred information in coin 1 state and alternatively using two coin operators, we can perfectly recover the information on coin 1 state at target position only by at most two times of flipping operation. Based on quantum walks with two coins either on the line or on the $N$-circle, we can perfectly transfer any qubit state. In addition, using quantum walks with two coins either on complete graphs or regular graphs, we can first implement perfect qudit state transfer by quantum walks. Compared with existing schemes driven by one coin, more general graph structures can be used to perfectly transfer more general state. Moreover, the external control of coin operator during the transmitting process can be decreased greatly. Selecting coin 1 as the sender and coin 2 as the receiver, we also study how to realize generalized teleportation over long steps of walks by the above quantum walk models. Because quantum walks is an universal quantum computation model, our schemes may provide an universal platform for the design of quantum network and quantum computer.
Side window guided filtering

This paper presents an improved version of the popular guided filtering (GF) technique. Although very effective, GF can suffer from artifacts such as edge blurring and halos. Through analyzing GF, we discover that traditional filtering practices of placing the center of the filtering window over the pixel being processed is one of the causes of these artifacts. This leads GF to perform linear combination and averaging operations on pixels on both sides of the edges thus causing edge blurring. Based on this discovery, we introduce the Side Window Guided Filtering (SWGF) technique. Treating each pixel as a potential edge, SWGF aligns the sides or corners of the filtering window with the pixels under processing. There are left, right, upper and bottom windows placed on one side of the pixel and northwest, northeast, southwest and southeast windows placed on one corner of the pixel. The one that closest approximates the current pixel is chosen as the output. We will present mathematical analysis to demonstrate the correctness of this method. SWGF is applied to a variety of image processing applications. Results demonstrate that under exactly the same parameter settings, SWGF consistently improves the edge preservation performances of GF and its other variations.
Distributed optimization for deep learning with gossip exchange

We address the issue of speeding up the training of convolutional neural networks by studying a distributed method adapted to stochastic gradient descent. Our parallel optimization setup uses several threads, each applying individual gradient descents on a local variable. We propose a new way of sharing information between different threads based on gossip algorithms that show good consensus convergence properties. Our method called GoSGD has the advantage to be fully asynchronous and decentralized.