Reviewerly/exHarmony huggingface dataset:
Low-cost GelSight with UV Markings: Feature Extraction of Objects Using AlexNet and Optical Flow without 3D Image Reconstruction

GelSight sensor has been used to study microgeometry of objects since 2009 in tactile sensing applications. Elastomer, reflective coating, lighting, and camera were the main challenges of making a GelSight sensor within a short period. The recent addition of permanent markers to the GelSight was a new era in shear/slip studies. In our previous studies, we introduced Ultraviolet (UV) ink and UV LEDs as a new form of marker and lighting respectively. UV ink markers are invisible using ordinary LED but can be made visible using UV LED. Currently, recognition of objects or surface textures using GelSight sensor is done using fusion of camera-only images and GelSight captured images with permanent markings. Those images are fed to Convolutional Neural Networks (CNN) to classify objects. However, our novel approach in using low-cost GelSight sensor with UV markings, the 3D height map to 2D image conversion, and the additional non-Gelsight captured images for training the CNN can be eliminated. AlexNet and optical flow algorithm have been used for feature recognition of five coins without UV markings and shear/slip of the coin in GelSight with UV markings respectively. Our results on confusion matrix show that, on average coin recognition can reach 93.4% without UV markings using AlexNet. Therefore, our novel method of using GelSight with UV markings would be useful to recognize full/partial object, shear/slip, and force applied to the objects without any 3D image reconstruction.
Differentially low uniform permutations from known 4-uniform functions

Abstract Functions with low differential uniformity can be used in a block cipher as S-boxes since they have good resistance to differential attacks. In this paper we consider piecewise constructions for permutations with low differential uniformity. In particular, we give two constructions of differentially 6-uniform functions, modifying the Gold function and the Bracken–Leander function on a subfield.
Service Granularity in Industrial Automation and Control Systems

Applying service-oriented concepts in industrial control and automation systems requires developing new architectural concepts to make it more adaptable to this context. In the scope of service-oriented systems one of the main challenges is defining the right granularity for every service in the system. This paper presents a systematic classification of methods to decompose the services. Identifying and using the correct granularity for each service is expected to lead to higher system flexibility, scalability, and in general to improving service deployment and maintenance. To find the best granularity for a service several approaches have been proposed, some of which are based on modeling and others based on service quality analysis. The approach we propose is based on modeling.
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights

Normalization techniques are a boon for modern deep learning. They let weights converge more quickly with often better generalization performances. It has been argued that the normalization-induced scale invariance among the weights provides an advantageous ground for gradient descent (GD) optimizers: the effective step sizes are automatically reduced over time, stabilizing the overall training procedure. It is often overlooked, however, that the additional introduction of momentum in GD optimizers results in a far more rapid reduction in effective step sizes for scale-invariant weights, a phenomenon that has not yet been studied and may have caused unwanted side effects in the current practice. This is a crucial issue because arguably the vast majority of modern deep neural networks consist of (1) momentum-based GD (e.g. SGD or Adam) and (2) scale-invariant parameters. In this paper, we verify that the widely-adopted combination of the two ingredients lead to the premature decay of effective step sizes and sub-optimal model performances. We propose a simple and effective remedy, SGDP and AdamP: get rid of the radial component, or the norm-increasing direction, at each optimizer step. Because of the scale invariance, this modification only alters the effective step sizes without changing the effective update directions, thus enjoying the original convergence properties of GD optimizers. Given the ubiquity of momentum GD and scale invariance in machine learning, we have evaluated our methods against the baselines on 13 benchmarks. They range from vision tasks like classification (e.g. ImageNet), retrieval (e.g. CUB and SOP), and detection (e.g. COCO) to language modelling (e.g. WikiText) and audio classification (e.g. DCASE) tasks. We verify that our solution brings about uniform gains in those benchmarks. Source code is available at https://github.com/clovaai/AdamP.
Performance comparison of least squares, iterative and global L1 norm minimization and exhaustive search methods for outlier detection in leveling networks

Different approaches have been proposed to determine the possible outliers existing in a dataset.The most widely used consists in the application of the data snooping test over the least squares adjustment results.This strategy is very likely to succeed for the case of zero or one outliers but, contrary to what is often assumed, the same is not valid for the multiple outlier case, even in its iterative application scheme.Robust estimation, computed by iteratively reweighted least squares or a global optimization method, is other alternative approach which often produces good results in the presence of outliers, as is the case of exhaustive search methods that explore elimination of every possible set of observations.General statements, having universal validity, about the best way to compute a geodetic network with multiple outliers are impossible to be given due to the many different factors involved (type of network, number and size of possible errors, available computational force, etc.).However, we see in this paper that some conclusions can be drawn for the case of a leveling network, which has a certain geometrical simplicity compared with planimetric or three-dimensional networks though a usually high number of unknowns and relatively low redundancy.Among other results, we experience the occasional failure in the iterative application of the data snooping test, the relatively successful results obtained by both methods computing the robust estimator, which perform equivalently in this case, and the successful application of the exhaustive search method, for different cases that become increasingly intractable as the number of outliers approaches half the number of degrees of freedom of the network.
Full-field stress computation from measured deformation fields: A hyperbolic formulation

Recent developments in imaging techniques and correlation algorithms enable measurement of strain fields on a deforming material at high spatial and temporal resolution. In such cases, the computation of the stress field from the known deformation field becomes an interesting possibility. This is known as an inverse problem. Current approaches to this problem, such as the finite element update method, are generally over-determined and must rely on statistical approaches to minimize error. This provides approximate solutions in some cases, however, implementation difficulties, computational requirements, and accuracy are still significant challenges. Here, we show how the inverse problem can be formulated deterministically and solved exactly in two or three dimensions for large classes of materials including isotropic elastic solids, Newtonian fluids, non-Newtonian fluids, granular materials, plastic solids subject to co-directionality, and some other plastic solids subject to associative or non-associative flow rules. This solution is based on a single assumption of the alignment of the principal directions of stress and strain or strain rate. No further assumptions regarding incompressibility, pressure independence, yield surface shape or the hardening law are necessary. This assumption leads to a closed, first order, linear system of hyperbolic partial differential equations with variable coefficients. The solution of this class of problems is well established and hence the equations can be solved to give the solution for any geometry and loading condition, enabling broad applicability to a variety of problems. We provide a numerical proof-of-principle study of the plastic deformation of a two-dimensional bar with spatially varying yield stress and strain hardening coefficient. The results are validated against the solution of the corresponding forward problem – solved with a commercial finite element solver – indicating the solution is exact up to numerical error (the normalized root mean square error of the stress is 1.63×10−4). No model calibration or material parameters are required. The sensitivity of the solution to error in the input data is also analyzed. Interestingly, this solution procedure lends itself to a simple physical interpretation of stress propagation through the material. Finally, we provide some examples showing how this approach may be analytically applied to both solid and fluid mechanics problems.
When the Good Turns Ugly: Speculating Next Steps for Digital Wellbeing Tools

Concerns surrounding technology use in society has led to the HCI community creating tools for 'digital wellbeing'. These aim to improve users' relationships with technology, but these positively motivated tools may initiate further negative impacts for users e.g. on their privacy or autonomy. Using Pierce's speculative design concepts of 'foot-in-the-door' technologies and focusing on three common digital wellbeing features (time limits and prompts, social 'do not disturb' modes, app and service blocking), I highlight how these tools are a small step away from being used to manipulate users which could enact slow shifts in users accepting such manipulation. Through this and the discussion, I accentuate that positively motivated designs may not explicitly lead to positive interactions by default. I hope this paper will facilitate speculative design and discussion in the digital wellbeing community, to ensure that our designs continue to mitigate negative impacts from technology now and in the future.
MixCo: Mix-up Contrastive Learning for Visual Representation

Contrastive learning has shown remarkable results in recent self-supervised approaches for visual representation. By learning to contrast positive pairs' representation from the corresponding negatives pairs, one can train good visual representations without human annotations. This paper proposes Mix-up Contrast (MixCo), which extends the contrastive learning concept to semi-positives encoded from the mix-up of positive and negative images. MixCo aims to learn the relative similarity of representations, reflecting how much the mixed images have the original positives. We validate the efficacy of MixCo when applied to the recent self-supervised learning algorithms under the standard linear evaluation protocol on TinyImageNet, CIFAR10, and CIFAR100. In the experiments, MixCo consistently improves test accuracy. Remarkably, the improvement is more significant when the learning capacity (e.g., model size) is limited, suggesting that MixCo might be more useful in real-world scenarios. The code is available at: https://github.com/Lee-Gihun/MixCo-Mixup-Contrast.
Focus and contrastive topic in questions and answers, with particular reference to Turkish

Abstract Much recent research has recognized the importance of focus and contrastive topic in assertions for discourse coherence. However, with few exceptions, it has been neglected that focus and contrastive topic also occur in questions, and have a similar role in establishing coherence. We propose a framework of dynamic interpretation based on the notion of Commitment Spaces that show that a uniform interpretation of focus and contrastive topic is possible. The algebraic representation format is rich enough so that a separate introduction of discourse trees is not necessary. The paper discusses these phenomena for Turkish, a language with an explicit focus marker for polar and alternative questions, which distinguishes focus from contrastive topic.
EFL learners’ experience of a MALL-based vocabulary learning tool

Since vocabulary learning plays a crucial role in the journey of language learning, various programs and mobile applications have been developed to assist vocabulary learning. However, the effectiveness of MALL-based (mobile-assisted language learning) tools can be determined by examining the perception of users, who are learners in this case, because, as Beres (2011) emphasized, learners do not always perceive learning tool effectiveness in the same way as educators. Thus, the primary purpose of this study is to investigate the perspectives of learners regarding the use of one of the programs that can be used for vocabulary learning — Quizlet. The perceptions of thirty-eight Saudi EFL (English as a Foreign Language) freshmen college students regarding the use of Quizlet for fourteen weeks were examined using questionnaires and interviews. The main findings of the study show that the students demonstrated positive attitudes towards the use of Quizlet in English vocabulary learning. They perceived it as a useful program that was easy to use, and they reported the intention to use it in the future. The present study highly recommends the use of MALL-based tools for vocabulary learning for their usefulness inside and outside the classroom.
DynamicRec

Recently convolutional networks have shown significant promise for modeling sequential user interactions for recommendations. Critically, such networks rely on fixed convolutional kernels to capture sequential behavior. In this paper, we argue that all the dynamics of the item-to-item transition in session-based settings may not be observable at training time. Hence we propose DynamicRec, which uses dynamic convolutions to compute the convolutional kernels on the fly based on the current input. We show through experiments that this approach significantly outperforms existing convolutional models on real datasets in session-based settings.
Intestinal Parasites Classification Using Deep Belief Networks

Currently, approximately 4 billion people are infected by intestinal parasites worldwide. Diseases caused by such infections constitute a public health problem in most tropical countries, leading to physical and mental disorders, and even death to children and immunodeficient individuals. Although subjected to high error rates, human visual inspection is still in charge of the vast majority of clinical diagnoses. In the past years, some works addressed intelligent computer-aided intestinal parasites classification, but they usually suffer from misclassification due to similarities between parasites and fecal impurities. In this paper, we introduce Deep Belief Networks to the context of automatic intestinal parasites classification. Experiments conducted over three datasets composed of eggs, larvae, and protozoa provided promising results, even considering unbalanced classes and also fecal impurities.
Suspicious Human Activity Detection System

In collaboration with machine learning and artificial intelligence, anomaly detection systems are vastly used in behavioral analysis so that you can help in identity and prediction of prevalence of anomalies. It has applications in enterprise, from intrusion detection to system fitness tracking, and from fraud detection in credit score card transactions to fault detection in running environments. With the growing crime charges and human lack of confidence globally, majority of the countries are adopting precise anomaly detection systems to approach closer to a comfy area. Visualizing the Indian crime index which stands at 42. 38, the adoption of anomaly detection structures is an alarming want of time. Our own cannot be prevented with the aid of CCTV installations. These systems not simplest lead to identification on my own, but their optimized versions can help in prediction of unusual activities as properly.
Recognition of Perspective Distorted QR Codes with a Partially Damaged Finder Pattern in Real Scene Images

QR (Quick Response) codes are one of the most famous types of two-dimensional (2D) matrix barcodes, which are the descendants of well-known 1D barcodes. The mobile robots which move in certain operational space can use information and landmarks from environment for navigation and such information may be provided by QR Codes. We have proposed algorithm, which localizes a QR Code in an image in a few sequential steps. We start with image binarization, then we continue with QR Code localization, where we utilize characteristic Finder Patterns, which are located in three corners of a QR Code, and finally we identify perspective distortion. The presented algorithm is able to deal with a damaged Finder Pattern, works well for low-resolution images and is computationally efficient.
Motion Inference Using Sparse Inertial Sensors, Self-Supervised Learning, and a New Dataset of Unscripted Human Motion

In recent years, wearable sensors have become common, with possible applications in biomechanical monitoring, sports and fitness training, rehabilitation, assistive devices, or human-computer interaction. Our goal was to achieve accurate kinematics estimates using a small number of sensors. To accomplish this, we introduced a new dataset (the Virginia Tech Natural Motion Dataset) of full-body human motion capture using XSens MVN Link that contains more than 40 h of unscripted daily life motion in the open world. Using this dataset, we conducted self-supervised machine learning to do kinematics inference: we predicted the complete kinematics of the upper body or full body using a reduced set of sensors (3 or 4 for the upper body, 5 or 6 for the full body). We used several sequence-to-sequence (Seq2Seq) and Transformer models for motion inference. We compared the results using four different machine learning models and four different configurations of sensor placements. Our models produced mean angular errors of 10–15 degrees for both the upper body and full body, as well as worst-case errors of less than 30 degrees. The dataset and our machine learning code are freely available.
Round-Efficient Byzantine Broadcast Under Strongly Adaptive and Majority Corruptions

The round complexity of Byzantine Broadcast (BB) has been a central question in distributed systems and cryptography. In the honest majority setting, expected constant round protocols have been known for decades even in the presence of a strongly adaptive adversary. In the corrupt majority setting, however, no protocol with sublinear round complexity is known, even when the adversary is allowed to strongly adaptively corrupt only 51% of the players, and even under reasonable setup or cryptographic assumptions. Recall that a strongly adaptive adversary can examine what original message an honest player would have wanted to send in some round, adaptively corrupt the player in the same round and make it send a completely different message instead. In this paper, we are the first to construct a BB protocol with sublinear round complexity in the corrupt majority setting. Specifically, assuming the existence of time-lock puzzles with suitable hardness parameters and that the decisional linear assumption holds in suitable bilinear groups, we show how to achieve BB in $$(\frac{n}{n-f})^2 \cdot \mathsf{poly} \log \lambda $$ rounds with $$1-\mathsf{negl} (\lambda )$$ probability, where n denotes the total number of players, f denotes the maximum number of corrupt players, and $$\lambda $$ is the security parameter. Our protocol completes in polylogarithmically many rounds even when 99% of the players can be corrupt.
BitMix: data augmentation for image steganalysis

Convolutional neural networks (CNN) for image steganalysis demonstrate better performances with employing concepts from high-level vision tasks. The major employed concept is to use data augmentation to avoid overfitting due to limited data. To augment data without damaging the message embedding, only rotating multiples of 90 degrees or horizontally flipping are used in steganalysis, which generates eight fixed results from one sample. To overcome this limitation, we propose BitMix, a data augmentation method for spatial image steganalysis. BitMix mixes a cover and stego image pair by swapping the random patch and generates an embedding adaptive label with the ratio of the number of pixels modified in the swapped patch to those in the cover-stego pair. We explore optimal hyperparameters, the ratio of applying BitMix in the mini-batch, and the size of the bounding box for swapping patch. The results reveal that using BitMix improves the performance of spatial image steganalysis and better than other data augmentation methods.
Fisher-Rao geometry of Dirichlet distributions

In this paper, we study the geometry induced by the Fisher-Rao metric on the parameter space of Dirichlet distributions. We show that this space is geodesically complete and has everywhere negative sectional curvature. An important consequence of this negative curvature for applications is that the Fr{\'e}chet mean of a set of Dirichlet distributions is uniquely defined in this geometry.
Prosody Learning Mechanism for Speech Synthesis System Without Text Length Limit

Recent neural speech synthesis systems have gradually focused on the control of prosody to improve the quality of synthesized speech, but they rarely consider the variability of prosody and the correlation between prosody and semantics together. In this paper, a prosody learning mechanism is proposed to model the prosody of speech based on TTS system, where the prosody information of speech is extracted from the melspectrum by a prosody learner and combined with the phoneme sequence to reconstruct the mel-spectrum. Meanwhile, the sematic features of text from the pre-trained language model is introduced to improve the prosody prediction results. In addition, a novel self-attention structure, named as local attention, is proposed to lift this restriction of input text length, where the relative position information of the sequence is modeled by the relative position matrices so that the position encodings is no longer needed. Experiments on English and Mandarin show that speech with more satisfactory prosody has obtained in our model. Especially in Mandarin synthesis, our proposed model outperforms baseline model with a MOS gap of 0.08, and the overall naturalness of the synthesized speech has been significantly improved.
SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery

Entity set expansion and synonym discovery are two critical NLP tasks.Previous studies accomplish them separately, without exploring their interdependences.In this work, we hypothesize that these two tasks are tightly coupled because two synonymous entities tend to have similar likelihoods of belonging to various semantic classes.This motivates us to design SynSetExpan, a novel framework that enables two tasks to mutually enhance each other.SynSetExpan uses a synonym discovery model to include popular entities' infrequent synonyms into the set, which boosts the set expansion recall.Meanwhile, the set expansion model, being able to determine whether an entity belongs to a semantic class, can generate pseudo training data to fine-tune the synonym discovery model towards better accuracy.To facilitate the research on studying the interplays of these two tasks, we create the first large-scale Synonym-Enhanced Set Expansion (SE2) dataset via crowdsourcing.Extensive experiments on the SE2 dataset and previous benchmarks demonstrate the effectiveness of SynSetExpan for both entity set expansion and synonym discovery tasks.