Reviewerly/exHarmony huggingface dataset:
The Implicit Bias of Depth: How Incremental Learning Drives Generalization

A leading hypothesis for the surprising generalization of neural networks is that the dynamics of gradient descent bias the model towards simple solutions, by searching through the solution space in an incremental order of complexity. We formally define the notion of incremental learning dynamics and derive the conditions on depth and initialization for which this phenomenon arises in deep linear models. Our main theoretical contribution is a dynamical depth separation result, proving that while shallow models can exhibit incremental learning dynamics, they require the initialization to be exponentially small for these dynamics to present themselves. However, once the model becomes deeper, the dependence becomes polynomial and incremental learning can arise in more natural settings. We complement our theoretical findings by experimenting with deep matrix sensing, quadratic neural networks and with binary classification using diagonal and convolutional linear networks, showing all of these models exhibit incremental learning.
A Cauchy mutant pigeon-inspired optimization–based multi-unmanned aerial vehicle path planning method

To improve the performance of multi-unmanned aerial vehicle path planning in plateau narrow area, a control strategy based on Cauchy mutant pigeon-inspired optimization algorithm is proposed in this article. The Cauchy mutation operator is chosen to improve the pigeon-inspired optimization algorithm by comparing and analyzing the changing trend of fitness function of the local optimum position and the global optimum position when dealing with unmanned aerial vehicle path planning problems. The plateau topography model and plateau wind field model are established. Furthermore, a variety of control constrains of unmanned aerial vehicles are summarized and modeled. By combining with relative positions and total flight duration, a cooperative path planning strategy for unmanned aerial vehicle group is put forward. Finally, the simulation results show that the proposed Cauchy mutant pigeon-inspired optimization method gives better robustness and cooperative path planning strategy which are effective and advanced as compared with traditional pigeon-inspired optimization algorithm.
Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control

We propose Adaptive Curriculum Generation from Demonstrations (ACGD) for reinforcement learning in the presence of sparse rewards. Rather than designing shaped reward functions, ACGD adaptively sets the appropriate task difficulty for the learner by controlling where to sample from the demonstration trajectories and which set of simulation parameters to use. We show that training vision-based control policies in simulation while gradually increasing the difficulty of the task via ACGD improves the policy transfer to the real world. The degree of domain randomization is also gradually increased through the task difficulty. We demonstrate zero-shot transfer for two real-world manipulation tasks: pick-and-stow and block stacking. A video showing the results can be found at https://lmb.informatik.uni-freiburg.de/projects/curriculum/.
Autonomous Human Detection System Mounted on a Drone

Since the past decade the use of autonomous and semi-autonomous robotic systems for human assistance has gained immense significance in the fields of defense, medical surgery, industrial automation, automobile assembly, etc. In particular, the development of Unmanned Aerial Vehicles (UAVs) has been a boon to human kind because of their agility to survey inaccessible places with ease. Consequently, UAVs could provide prominent and multi-faceted information in a Search and Rescue (SAR) operation. In order to mitigate the number of casualties involved in a natural disaster, rapid localization of the victims involved is of utmost priority. The paper in hand elucidates the development of an automated human detection system mounted on a drone that can provide the rescuers with the vital data required to execute an effective rescue mission. The proposed UAV can autonomously carryout survey over a preplanned region of interest and detect the survivors. It is also capable of assigning a score of confidence for each human detection, from the video captured through the camera mounted on the UAV. Consequently, this would help in drastic reduction of time spent in coming up with an action plan to accomplish the rescue operation. After a few successful trials, results have been acquired with moderate accuracy in detecting humans with a higher level of confidence.
Adversarial Continual Learning

Continual learning aims to learn new tasks without forgetting previously learned ones. We hypothesize that representations learned to solve each task in a sequence have a shared structure while containing some task-specific properties. We show that shared features are significantly less prone to forgetting and propose a novel hybrid continual learning framework that learns a disjoint representation for task-invariant and task-specific features required to solve a sequence of tasks. Our model combines architecture growth to prevent forgetting of task-specific skills and an experience replay approach to preserve shared skills. We demonstrate our hybrid approach is effective in avoiding forgetting and show it is superior to both architecture-based and memory-based approaches on class incrementally learning of a single dataset as well as a sequence of multiple datasets in image classification. Our code is available at \url{this https URL}.
Benefits of commitment in hierarchical inference.

Humans have the tendency to commit to a single interpretation of what has caused some observed evidence rather than considering all possible alternatives. This tendency can explain various forms of biases in cognition and perception. However, committing to a single high-level interpretation seems short-sighted and irrational, and thus it is unclear why humans are motivated to use such strategy. In a first step toward answering this question, we systematically quantified how this strategy affects estimation accuracy at the feature level in the context of 2 common hierarchical inference tasks, category-based perception and causal cue combination. Using model simulations, we demonstrate that although estimation accuracy is generally impaired when conditioned on only a single high-level interpretation, the reduction is not uniform across the entire feature range. Compared with a full inference strategy that considers all high-level interpretations, accuracy is only worse for feature values relatively close to the decision boundaries but is better everywhere else. That is, for feature values for which an observer has a reasonably high chance of being correct about the high-level interpretation of the feature, a full commitment to that particular interpretation is advantageous. We also show that conditioning on an preceding high-level interpretation provides an effective mechanism for partially protecting the evidence from corruption with late noise in the inference process (e.g., during retention in and recall from working memory). Our results suggest that a top-down inference strategy that solely relies on the most likely high-level interpretation can be favorable with regard to late noise and more holistic performance metrics. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Exact mapping between a laser network loss rate and the classical XY Hamiltonian by laser loss control

Abstract Recently, there has been growing interest in the utilization of physical systems as heuristic optimizers for classical spin Hamiltonians. A prominent approach employs gain-dissipative optical oscillator networks for this purpose. Unfortunately, these systems inherently suffer from an inexact mapping between the oscillator network loss rate and the spin Hamiltonian due to additional degrees of freedom present in the system such as oscillation amplitude. In this work, we theoretically analyze and experimentally demonstrate a scheme for the alleviation of this difficulty. The scheme involves control over the laser oscillator amplitude through modification of individual laser oscillator loss. We demonstrate this approach in a laser network classical XY model simulator based on a digital degenerate cavity laser. We prove that for each XY model energy minimum there corresponds a unique set of laser loss values that leads to a network state with identical oscillation amplitudes and to phase values that coincide with the XY model minimum. We experimentally demonstrate an eight fold improvement in the deviation from the minimal XY energy by employing our proposed solution scheme.
The spatial distribution of attention predicts familiarity strength during encoding and retrieval.

The memories we form are determined by what we attend to, and conversely, what we attend to is influenced by our memory for past experiences. Although we know that shifts of attention via eye movements are related to memory during encoding and retrieval, the role of specific memory processes in this relationship is unclear. There is evidence that attention may be especially important for some forms of memory (i.e., conscious recollection), and less so for others (i.e., familiarity-based recognition and unconscious influences of memory), but results are conflicting with respect to both the memory processes and eye movement patterns involved. To address this, we used a confidence-based method of isolating eye movement indices of spatial attention that are related to different memory processes (i.e., recollection, familiarity strength, and unconscious memory) during encoding and retrieval of real-world scenes. We also developed a new method of measuring the dispersion of eye movements, which proved to be more sensitive to memory processing than previously used measures. Specifically, in 2 studies, we found that familiarity strength-that is, changes in subjective reports of memory confidence-increased with (a) more dispersed patterns of viewing during encoding, (b) less dispersed viewing during retrieval, and (c) greater overlap in regions viewed between encoding and retrieval (i.e., resampling). Recollection was also related to these eye movements in a similar manner, though the associations with recollection were less consistent across experiments. Furthermore, we found no evidence for effects related to unconscious influences of memory. These findings indicate that attentional processes during viewing may not preferentially relate to recollection, and that the spatial distribution of eye movements is directly related to familiarity-based memory during encoding and retrieval. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Sharing Health and Wellness Data with Blockchain and Smart Contracts

Increased longevity and people's concern about aging with quality has led to increased health and wellness data. Much of the data is not interoperable because of its divergent structures and semantics, being little used and little protected. Existing standards are complex and lack the adherence of the agents involved to ensure their application. Blockchain technology offers alternatives to unify the standards and their application by consensus algorithm, which considers the validation and secrecy in the insertion of the blocks of transactions in the chain. However, smart contracts can ensure secrecy and the rules of data sharing in blocks in the chain. In this paper, we propose a blockchain architecture with a consensus algorithm that considers data collected in the health and wellness ecosystem, including those obtained by IoT devices and persisted in middleware platforms. It is intended that this architecture be able to answer the questions and establish the concepts for the full and secure sharing of health and wellness data.
StARe: Gaze-Assisted Face-to-Face Communication in Augmented Reality

This research explores the use of eye-tracking during Augmented Reality (AR) - supported conversations. In this scenario, users can obtain information that supports the conversation, without augmentations distracting the actual conversation.We propose using gaze that allows users to gradually reveal information on demand. Information is indicated around user's head, which becomes fully visible when other's visual attention explicitly falls upon the area. We describe the design of such an AR UI and present an evaluation of the feasibility of the concept. Results show that despite gaze inaccuracies, users were positive about augmenting their conversations with contextual information and gaze interactivity. We provide insights into the trade-offs between focusing on the task at hand (i.e., the conversation), and consuming AR information. These findings are useful for future use cases of eye based AR interactions by contributing to a better understanding of the intricate balance between informative AR and information overload.
Opportunities and Challenges in Involving Users in Project-Based HCI Education

Users are fundamental to HCI. However, little is known about how HCI education introduces students to working with users, particularly those different from themselves. To better understand design students' engagement, reactions, and reflections with users, we investigate a case study of a graduate-level 10-week prototyping studio course that partnered with a children's co-design team. HCI students participated in two co-design sessions with children to design a STEM learning experience for youth. We conducted participant observations, interviews with 14 students, and analyzed final artifacts. Our findings demonstrate the communication challenges and strategies students experienced, how students observed issues of power dynamics, and students' perceived value in engaging with users. We contribute empirical evidence of how HCI students directly interact with target users, principles for reflective HCI pedagogy, and highlight the need for more intentional investigation into HCI educational practice.
A survey on domain adaptation theory: learning bounds and theoretical guarantees

All famous machine learning algorithms that comprise both supervised and semi-supervised learning work well only under a common assumption: the training and test data follow the same distribution. When the distribution changes, most statistical models must be reconstructed from newly collected data, which for some applications can be costly or impossible to obtain. Therefore, it has become necessary to develop approaches that reduce the need and the effort to obtain new labeled samples by exploiting data that are available in related areas, and using these further across similar fields. This has given rise to a new machine learning framework known as transfer learning: a learning setting inspired by the capability of a human being to extrapolate knowledge across tasks to learn more efficiently. Despite a large amount of different transfer learning scenarios, the main objective of this survey is to provide an overview of the state-of-the-art theoretical results in a specific, and arguably the most popular, sub-field of transfer learning, called domain adaptation. In this sub-field, the data distribution is assumed to change across the training and the test data, while the learning task remains the same. We provide a first up-to-date description of existing results related to domain adaptation problem that cover learning bounds based on different statistical learning frameworks.
Semi-supervised Clustering via Pairwise Constrained Optimal Graph

In this paper, we present a technique of definitely addressing the pairwise constraints in the semi-supervised clustering. Our method contributes to formulating the cannot-link relations and propagating them over the affinity graph flexibly. The pairwise constrained instances are provably guaranteed to be in the same or different connected components of the graph. Combined with the Laplacian rank constraint, the proposed model learns a Pairwise Constrained structured Optimal Graph (PCOG), from which the specified c clusters supporting the known pairwise constraints are directly obtained. An efficient algorithm invoked by the label propagation is designed to solve the formulation. Additionally, we also provide a compact criterion to acquire the key pairwise constraints for prompting the semi-supervised graph clustering. Substantial experimental results show that the proposed method achieves the significant improvements by using a few prior pairwise constraints.
Privacy-Preserving Genetic Algorithm Outsourcing in Cloud Computing

Genetic Algorithm (GA) has been widely used to solve various optimization problems. As the solving process of GA requires large storage and computing resources, it is well motivated to outsource the solving process of GA to the cloud server. However, the algorithm user would never want his data to be disclosed to cloud server. Thus, it is necessary for the user to encrypt the data before transmitting them to the server. But the user will encounter a new problem. The arithmetic operations we are familiar with cannot work directly in the ciphertext domain. In this paper, a privacy-preserving outsourced genetic algorithm is proposed. The user’s data are protected by homomorphic encryption algorithm which can support the operations in the encrypted domain. GA is elaborately adapted to search the optimal result over the encrypted data. The security analysis and experiment results demonstrate the effectiveness of the proposed scheme.
Syntactically-informed word representations from graph neural network

Most deep language understanding models depend only on word representations, which are mainly based on language modelling derived from a large amount of raw text. These models encode distributional knowledge without considering syntactic structural information, although several studies have shown benefits of including such information. Therefore, we propose new syntactically-informed word representations (SIWRs), which allow us to enrich the pre-trained word representations with syntactic information without training language models from scratch. To obtain SIWRs, a graph-based neural model is built on top of either static or contextualised word representations such as GloVe, ELMo and BERT. The model is first pre-trained with only a relatively modest amount of task-independent data that are automatically annotated using existing syntactic tools. SIWRs are then obtained by applying the model to downstream task data and extracting the intermediate word representations. We finally replace word representations in downstream models with SIWRs for applications. We evaluate SIWRs on three information extraction tasks, namely nested named entity recognition (NER), binary and n-ary relation extractions (REs). The results demonstrate that our SIWRs yield performance gains over the base representations in these NLP tasks with 3–9% relative error reduction. Our SIWRs also perform better than fine-tuning BERT in binary RE. We also conduct extensive experiments to analyse the proposed method.
Can Visual Scanpath Reveal Personal Image Memorability? Investigation of HMM Tools for Gaze Patterns Analysis

Visual attention has been shown as a good proxy for QoE, revealing specific visual patterns considering content, system and contextual aspects of a multimedia applications. In this paper, we propose a novel approach based on hidden markov models to analyze visual scanpaths in an image memorability task. This new method ensures the consideration of both temporal and idiosyncrasic aspects of visual behavior. The study shows promising results for the use of indirect measures for the personalization of QoE assessment and prediction.
Developing Framework for Web Based e-Commerce: Secure-SDLC

Abstract Software development usually follows the stages known as the Software Development Life Cycle (SDLC). Development begins with the requirements phase and then continues with the design, development of test plans, implementation (coding), testing, and deployment. However, the security aspect has not been explicitly reflected in SDLC. In fact security must be present at every stage of the SDLC. Application of security in the process of developing software, especially e-Commerce, one of which uses the Security Development Lifecycle-Agile (SDL-Agile) method. The advantage of the SDL-Agile method is the application development process which incorporates a safety factor in each application development process, which is concise, and emphasizes user satisfaction. This objective of this paper examine the deployment of SDL Agile Method for eCommerce website to measure its confidentiality, integrity, and availability of the systems.
Fluorescent digital image correlation applied for macroscale deformation measurement

Fluorescent digital image correlation (DIC) has been implemented on monocular and binocular fluorescence microscopes for shape and deformation measurements at the microscale and even the nanoscale by tracking fluorescent particles. This technique, however, has not been used with regular optical imaging systems for DIC measurements at the macroscale so far. In this Letter, by combining the photoluminescence nature of fluorescent materials and bandpass filtering imaging, we discover that fluorescent DIC also features some distinct advantages in macroscale characterizations, e.g., specular reflection elimination, high speckle contrast, and the capability to simultaneously observe superficial/internal surfaces of transparent structures. To show these advantages and reveal the potential applications of fluorescent DIC, comparative experiments, including shape, displacement, and deformation measurements, were carried out. Furthermore, some issues accompanied by employing fluorescent imaging in DIC were discussed. This study indicates that upgrading classic DIC to fluorescent DIC can significantly improve its performance, enhance its functions, and expand its applications without introducing additional problems.
How Good is SGD with Random Shuffling

We study the performance of stochastic gradient descent (SGD) on smooth and strongly-convex finite-sum optimization problems. In contrast to the majority of existing theoretical works, which assume that individual functions are sampled with replacement, we focus here on popular but poorly-understood heuristics, which involve going over random permutations of the individual functions. This setting has been investigated in several recent works, but the optimal error rates remain unclear. In this paper, we provide lower bounds on the expected optimization error with these heuristics (using SGD with any constant step size), which elucidate their advantages and disadvantages. In particular, we prove that after $k$ passes over $n$ individual functions, if the functions are re-shuffled after every pass, the best possible optimization error for SGD is at least \Omega\left(1/(nk)^2+1/nk^3\right)$, which partially corresponds to recently derived upper bounds. Moreover, if the functions are only shuffled once, then the lower bound increases to $\Omega(1/nk^2)$. Since there are strictly smaller upper bounds for repeated reshuffling, this proves an inherent performance gap between SGD with single shuffling and repeated shuffling. As a more minor contribution, we also provide a non-asymptotic $\Omega(1/k^2)$ lower bound (independent of $n$) for the incremental gradient method, when no random shuffling takes place. Finally, we provide an indication that our lower bounds are tight, by proving matching upper bounds for univariate quadratic functions.