Reviewerly/exHarmony huggingface dataset:
Mobile Learning in Higher Education: Structural Equation Model for Good Teaching Practices

Mobile learning is a methodology that involves the use of mobile devices to carry out the teaching-learning process.In exceptional situations such as that experienced during the COVID-19 pandemic in Spain, virtual training methods take on great importance, being the main route for the education of students.The purposes of this paper were to analyse the degree of implementation of the mobile learning methodology in Spanish universities and to check the sociodemographic factors that influence the development of good teaching practices in mobile learning.Ten hypothetical relationships were established and contrasted using a structural equation model.The sample was made up of 1544 university professors from 59 Spanish universities who were asked to complete a questionnaire designed to evaluate mobile learning practices.The results indicated that the degree of implementation of mobile devices was almost 73% of the population surveyed.While the sociodemographic factors that significantly influenced the development of good teaching practices were: teacher status; type of institution; educational technology research; implementing pedagogical innovations on a regular basis; agree that mobile devices are appropriate; belief in the expansion of mobile learning.Finally, the main findings and practical implications derived from the data obtained were discussed.
Extending Quantum Links: Modules for Fiber‐ and Memory‐Based Quantum Repeaters

Abstract Elementary building blocks for quantum repeaters based on fiber channels and memory stations are analyzed. Implementations are considered for three different physical platforms, for which suitable components are available: quantum dots, trapped atoms and ions, and color centers in diamond. The performances of basic quantum repeater links for these platforms are evaluated and compared, both for present‐day, state‐of‐the‐art experimental parameters as well as for parameters that can in principle be reached in the future. The ultimate goal is to experimentally explore regimes at intermediate distances—up to a few 100 km—in which the repeater‐assisted secret key transmission rates exceed the maximal rate achievable via direct transmission. Two different protocols are considered, one of which is better adapted to the higher source clock rate and lower memory coherence time of the quantum dot platform, while the other circumvents the need of writing photonic quantum states into the memories in a heralded, nondestructive fashion. The elementary building blocks and protocols can be connected in a modular form to construct a quantum repeater system that is potentially scalable to large distances.
Amplifying Key Cues for Human-Object-Interaction Detection

Human-object interaction (HOI) detection aims to detect and recognise how people interact with the objects that surround them. This is challenging as different interaction categories are often distinguished only by very subtle visual differences in the scene. In this paper we introduce two methods to amplify key cues in the image, and also a method to combine these and other cues when considering the interaction between a human and an object. First, we introduce an encoding mechanism for representing the fine-grained spatial layout of the human and object (a subtle cue) and also semantic context (a cue, represented by text embeddings of surrounding objects). Second, we use plausible future movements of humans and objects as a cue to constrain the space of possible interactions. Third, we use a gate and memory architecture as a fusion module to combine the cues. We demonstrate that these three improvements lead to a performance which exceeds prior HOI methods across standard benchmarks by a considerable margin.
A comparison between chatbot and human service: customer perception and reuse intention

Purpose Chatbot users’ communication experience with disembodied conversational agents was compared with instant messaging (IM) users’ communication experience with human conversational agents. The purpose of this paper is to identify what affects users’ intention to reuse and whether they perceive any difference between the two. Design/methodology/approach A conceptual model was developed based on computer-mediated communication (CMC) and interpersonal communication theories. Data were collected online from four different continents (North America, Europe, Asia and Australia). Partial least squares structural equation modeling was applied to examine the research model. Findings The findings mainly reveal that media richness and social presence positively influence trust and reuse intention through task attraction and social attraction; IM users reported significantly higher scores in terms of communication experience, perceived attractiveness of the conversational agent, and trust than chatbot users; users’ trust in the conversational agents is mainly determined by perceived task attraction. Research limitations/implications Customers’ evaluation of the communication environment is positively related to their perceived competence of the conversational agent which ultimately affect their intention to reuse chatbot/IM. The findings reveal determinants of chatbot/IM adoption which have rarely been mentioned by previous work. Practical implications Practitioners should note that consumers in general still prefer to interact with human conversational agents. Practitioners should contemplate how to combine chatbot and human resources effectively to deliver the best customer service. Originality/value This study goes beyond the Computer as Social Actor paradigm and Technology Acceptance Model to understand chatbot and IM adoption. It is among one of the first studies that compare chatbot and IM use experience in the tourism and hospitality literature.
Label-Efficient Breast Cancer Histopathological Image Classification

The automatic classification of breast cancer histopathological images has great significance in computer-aided diagnosis. Recently, deep learning via neural networks has enabled pattern detection and prediction using large, labeled datasets; whereas, collecting and annotating sufficient histological data using professional pathologists is time consuming, tedious, and extremely expensive. In the proposed paper, a deep active learning framework is designed and implemented for classification of breast cancer histopathological images, with the goal of maximizing the learning accuracy from very limited labeling. This method involves manual annotation of the most valuable unlabeled samples, which are then integrated into the training set. The model is then iteratively updated with an increasing training set. Here, two selection strategies are discussed for the proposed deep active learning framework: An entropy-based strategy and a confidence-boosting strategy. The proposed method has been validated using a publicly available breast cancer histopathological image dataset, wherein each image patch is binarily classified as benign or malignant. The experimental results demonstrate that, compared with a random selection, our proposed framework can reduce annotation costs up to 66.67%, with higher accuracy and less expensive annotation than standard query strategy.
Location Privacy Preservation for Mobile Users in Location-Based Services

Because location-based cyber services are increasingly found in mobile applications (e.g., social networking and maps), user location privacy preservation is essential and remains one of the several ongoing research challenges. In this paper, we propose a region-of-interest division-based algorithm to Preserve the location Privacy of mobile device users in location-based Cyber Services (PPCS). Unlike existing methods, our proposed PPCS approach generates dummy locations while considering the semantic information of those locations. The PPCS algorithm enables the generated locations to exclude or reduce the exposure of a user's real location. In our analysis, we demonstrate that PPCS is resilient to both colluding attacks and inference attacks. We also evaluate the efficiency and demonstrate the utility of our proposed approach through extensive simulations.
Metric Learning for Adversarial Robustness

Deep networks are well-known to be fragile to adversarial attacks. We conduct an empirical analysis of deep representations under the state-of-the-art attack method called PGD, and find that the attack causes the internal representation to shift closer to the ``false'' class. Motivated by this observation, we propose to regularize the representation space under attack with metric learning to produce more robust classifiers. By carefully sampling examples for metric learning, our learned representation not only increases robustness, but also detects previously unseen adversarial samples. Quantitative experiments show improvement of robustness accuracy by up to 4% and detection efficiency by up to 6% according to Area Under Curve score over prior work. The code of our work is available at https://github.com/columbia/Metric_Learning_Adversarial_Robustness.
Practical Adversarial Attacks Against Speaker Recognition Systems

Unlike other biometric-based user identification methods (e.g., fingerprint and iris), speaker recognition systems can identify individuals relying on their unique voice biometrics without requiring users to be physically present. Therefore, speaker recognition systems have been becoming increasingly popular recently in various domains, such as remote access control, banking services and criminal investigation. In this paper, we study the vulnerability of this kind of systems by launching a practical and systematic adversarial attack againstX-vector, the state-of-the-art deep neural network (DNN) based speaker recognition system. In particular, by adding a well-crafted inconspicuous noise to the original audio, our attack can fool the speaker recognition system to make false predictions and even force the audio to be recognized as any adversary-desired speaker. Moreover, our attack integrates the estimated room impulse response (RIR) into the adversarial example training process toward practical audio adversarial examples which could remain effective while being played over the air in the physical world. Extensive experiment using a public dataset of $109$ speakers shows the effectiveness of our attack with a high attack success rate for both digital attack ($98%$) and practical over-the-air attack ($50%$).
Calamari and Falafl: Logarithmic (Linkable) Ring Signatures from Isogenies and Lattices

We construct efficient ring signatures (RS) from isogeny and lattice assumptions. Our ring signatures are based on a logarithmic OR proof for group actions. We instantiate this group action by either the CSIDH group action or an MLWE-based group action to obtain our isogeny-based or lattice-based RS scheme, respectively. Even though the OR proof has a binary challenge space and therefore requires a number of repetitions which is linear in the security parameter, the sizes of our ring signatures are small and scale better with the ring size N than previously known post-quantum ring signatures. We also construct linkable ring signatures (LRS) that are almost as efficient as the non-linkable variants. The isogeny-based scheme produces signatures whose size is an order of magnitude smaller than all previously known logarithmic post-quantum ring signatures, but it is relatively slow (e.g. 5.5 KB signatures and 79 s signing time for rings with 8 members). In comparison, the lattice-based construction is much faster, but has larger signatures (e.g. 30 KB signatures and 90 ms signing time for the same ring size). For small ring sizes our lattice-based ring signatures are slightly larger than state-of-the-art schemes, but they are smaller for ring sizes larger than $$N \approx 1024$$ .
RGB2Hands

Tracking and reconstructing the 3D pose and geometry of two hands in interaction is a challenging problem that has a high relevance for several human-computer interaction applications, including AR/VR, robotics, or sign language recognition. Existing works are either limited to simpler tracking settings ( e.g. , considering only a single hand or two spatially separated hands), or rely on less ubiquitous sensors, such as depth cameras. In contrast, in this work we present the first real-time method for motion capture of skeletal pose and 3D surface geometry of hands from a single RGB camera that explicitly considers close interactions. In order to address the inherent depth ambiguities in RGB data, we propose a novel multi-task CNN that regresses multiple complementary pieces of information, including segmentation, dense matchings to a 3D hand model, and 2D keypoint positions, together with newly proposed intra-hand relative depth and inter-hand distance maps. These predictions are subsequently used in a generative model fitting framework in order to estimate pose and shape parameters of a 3D hand model for both hands. We experimentally verify the individual components of our RGB two-hand tracking and 3D reconstruction pipeline through an extensive ablation study. Moreover, we demonstrate that our approach offers previously unseen two-hand tracking performance from RGB, and quantitatively and qualitatively outperforms existing RGB-based methods that were not explicitly designed for two-hand interactions. Moreover, our method even performs on-par with depth-based real-time methods.
RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning

We study unsupervised video representation learning that seeks to learn both motion and appearance features from unlabeled video only, which can be reused for downstream tasks such as action recognition. This task, however, is extremely challenging due to 1) the highly complex spatial-temporal information in videos and 2) the lack of labeled data for training. Unlike representation learning for static images, it is difficult to construct a suitable self-supervised task to effectively model both motion and appearance features. More recently, several attempts have been made to learn video representation through video playback speed prediction. However, it is non-trivial to obtain precise speed labels for the videos. More critically, the learned models may tend to focus on motion patterns and thus may not learn appearance features well. In this paper, we observe that the relative playback speed is more consistent with motion patterns and thus provides more effective and stable supervision for representation learning. Therefore, we propose a new way to perceive the playback speed and exploit the relative speed between two video clips as labels. In this way, we are able to effectively perceive speed and learn better motion features. Moreover, to ensure the learning of appearance features, we further propose an appearance-focused task, where we enforce the model to perceive the appearance difference between two video clips. We show that jointly optimizing the two tasks consistently improves the performance on two downstream tasks (namely, action recognition and video retrieval) w.r.t the increasing pre-training epochs. Remarkably, for action recognition on the UCF101 dataset, we achieve 93.7% accuracy without the use of labeled data for pre-training, which outperforms the ImageNet supervised pre-trained model. Our code, pre-trained models, and supplementary materials can be found at https://github.com/PeihaoChen/RSPNet.
PatchAttack: A Black-Box Texture-Based Attack with Reinforcement Learning

Patch-based attacks introduce a perceptible but localized change to the input that induces misclassification. A limitation of current patch-based black-box attacks is that they perform poorly for targeted attacks, and even for the less challenging non-targeted scenarios, they require a large number of queries. Our proposed PatchAttack is query efficient and can break models for both targeted and non-targeted attacks. PatchAttack induces misclassifications by superimposing small textured patches on the input image. We parametrize the appearance of these patches by a dictionary of class-specific textures. This texture dictionary is learned by clustering Gram matrices of feature activations from a VGG backbone. PatchAttack optimizes the position and texture parameters of each patch using reinforcement learning. Our experiments show that PatchAttack achieves $${>}99\%$$ success rate on ImageNet for a wide range of architectures, while only manipulating $$3\%$$ of the image for non-targeted attacks and $$10\%$$ on average for targeted attacks. Furthermore, we show that PatchAttack circumvents state-of-the-art adversarial defense methods successfully. The code is publicly available here .
hIPPYlib

We present an extensible software framework, hIPPYlib, for solution of large-scale deterministic and Bayesian inverse problems governed by partial differential equations (PDEs) with (possibly) infinite-dimensional parameter fields (which are high-dimensional after discretization). hIPPYlib overcomes the prohibitively expensive nature of Bayesian inversion for this class of problems by implementing state-of-the-art scalable algorithms for PDE-based inverse problems that exploit the structure of the underlying operators, notably the Hessian of the log-posterior. The key property of the algorithms implemented in hIPPYlib is that the solution of the inverse problem is computed at a cost, measured in linearized forward PDE solves, that is independent of the parameter dimension. The mean of the posterior is approximated by the MAP point, which is found by minimizing the negative log-posterior with an inexact matrix-free Newton-CG method. The posterior covariance is approximated by the inverse of the Hessian of the negative log posterior evaluated at the MAP point. The construction of the posterior covariance is made tractable by invoking a low-rank approximation of the Hessian of the log-likelihood. Scalable tools for sample generation are also discussed. hIPPYlib makes all of these advanced algorithms easily accessible to domain scientists and provides an environment that expedites the development of new algorithms.
Systematic Literature Review (SLR) on Social Media and the Digital Transformation of Drug Trafficking on Darkweb

A proposed study provides an annotated list of SLR (Systematic literature Review) for use by dark web (DW) researchers, cyber experts, and security agencies in the investigation and detection of criminal activities targeting the international drug market via online social networking (OSN) platforms. To update prior studies, this research employed a broad systematic search. We used an AS (automated search) to look for SLRs that were produced between 2015 (February 1) and 2021(March 30). These SLRs are compared to those detected in the original study in terms of number, quality, and source. In our extensive investigation, we found a total of (30) SLRs linked to (28) separate evaluations. (15) of the papers appeared to be relevant, with another (10) having the potential to be useful to practitioners. The quality of articles given at workshops and conferences improved as more academics adopted SLR norms. SLRs have advanced beyond the level where they are fully utilized by innovators, but they are still not commonly used in criminal investigations involving the DW & DT (drug trafficking). We’ll investigate into and analyze on the DW’s influence in diverse socioeconomic scenarios, such as DT. Yet there are still gaps, such as primary research that frequently fails to assess quality and coverage of focused research on DT and other DW crime incidents, covering wide range of issues, including online illicit business, organized criminal events, and potential illegal markets.
On Reinforcement Learning for Full-Length Game of StarCraft

StarCraft II poses a grand challenge for reinforcement learning. The main difficulties include huge state space, varying action space, long horizon, etc. In this paper, we investigate a set of techniques of reinforcement learning for the full-length game of StarCraft II. We investigate a hierarchical approach, where the hierarchy involves two levels of abstraction. One is the macro-actions extracted from expert’s demonstration trajectories, which can reduce the action space in an order of magnitude yet remain effective. The other is a two-layer hierarchical architecture, which is modular and easy to scale. We also investigate a curriculum transfer learning approach that trains the agent from the simplest opponent to harder ones. On a 64×64 map and using restrictive units, we train the agent on a single machine with 4 GPUs and 48 CPU threads. We achieve a winning rate of more than 99% against the difficulty level-1 built-in AI. Through the curriculum transfer learning algorithm and a mixture of combat model, we can achieve over 93% winning rate against the most difficult noncheating built-in AI (level-7) within days. We hope this study could shed some light on the future research of large-scale reinforcement learning.
Deep Neural Network Hyperparameter Optimization with Orthogonal Array Tuning

Deep learning algorithms have achieved excellent performance lately in a wide range of fields (e.g., computer version). However, a severe challenge faced by deep learning is the high dependency on hyper-parameters. The algorithm results may fluctuate dramatically under the different configuration of hyper-parameters. Addressing the above issue, this paper presents an efficient Orthogonal Array Tuning Method (OATM) for deep learning hyper-parameter tuning. We describe the OATM approach in five detailed steps and elaborate on it using two widely used deep neural network structures (Recurrent Neural Networks and Convolutional Neural Networks). The proposed method is compared to the state-of-the-art hyper-parameter tuning methods including manually (e.g., grid search and random search) and automatically (e.g., Bayesian Optimization) ones. The experiment results state that OATM can significantly save the tuning time compared to the state-of-the-art methods while preserving the satisfying performance.
Coronavirus lockdowns have changed the way Earth moves

A reduction in seismic noise because of changes in human activity is a boon for geoscientists. A reduction in seismic noise because of changes in human activity is a boon for geoscientists.
Role of Telecom Network to Manage COVID-19 in India: Aarogya Setu

Concerted Research, Development and Innovation have enabled telecom and Internet reach most of the 1.3 billion people in India at a price-point that they can afford. This telecom network is being used for a whole lot of application today providing a large variety of services like education, health care, financial inclusion and disaster management. In almost all crisis situation like that created by coronavirus, one finds that telecom network can be leveraged to offer help to the needy, administration and coordinators to better manage the disaster. Aarogya Setu for smartphones and Aarogya Setu for feature phones are two such efforts, which are potentially going to help in this difficult situation.
Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking

Zero-shot transfer learning for multi-domain dialogue state tracking can allow us to handle new domains without incurring the high cost of data acquisition.This paper proposes new zero-short transfer learning technique for dialogue state tracking where the in-domain training data are all synthesized from an abstract dialogue model and the ontology of the domain.We show that data augmentation through synthesized data can improve the accuracy of zero-shot learning for both the TRADE model and the BERT-based SUMBT model on the MultiWOZ 2.1 dataset.We show training with only synthesized in-domain data on the SUMBT model can reach about 2/3 of the accuracy obtained with the full training dataset.We improve the zero-shot learning state of the art on average across domains by 21%.