Reviewerly/exHarmony huggingface dataset:
RSegNet: A Joint Learning Framework for Deformable Registration and Segmentation

Medical image segmentation and registration are two tasks to analyze the anatomical structures in clinical research. Still, deep-learning solutions utilizing the connections between segmentation and registration remain underdiscovered. This article designs a joint learning framework named RSegNet that can realize concurrent deformable registration and segmentation by minimizing an integrated loss function, including three parts: diffeomorphic registration loss, segmentation similarity loss, and dual-consistency supervision loss. The probabilistic diffeomorphic registration branch could benefit from the auxiliary segmentations available from the segmentation branch to achieve anatomical consistency and better deformation regularity by dual-consistency supervision. Simultaneously, the segmentation performance could also be improved by data augmentation based on the registration with well-behaved diffeomorphic guarantees. Experiments on the human brain 3-D magnetic resonance images have been implemented to demonstrate the effectiveness of our approach. We trained and validated RSegNet with 1000 images and tested its performances on four public datasets, which shows that our method successfully yields concurrent improvements of both segmentation and registration compared with separately trained networks. Specifically, our method can increase the accuracy of segmentation and registration by 7.0% and 1.4%, respectively, in terms of Dice scores. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Registration and segmentation of medical images are two significant tasks in medical research and clinical application. However, most existing approaches consider these two tasks independently while neglecting the potential association between them. Therefore, we suggest a new approach that combines these two tasks into one joint deep learning framework, boosting registration, and segmentation performance by introducing dual-consistency supervision. Besides, our framework could generate outputs within 1 s by taking an affinely aligned medical image pair as input, which is suitable for time-critical requirements in a clinic. We tested it on four public datasets and achieved state-of-the-art performance to demonstrate the proposed method’s feasibility and robustness. Furthermore, our proposed RSegNet is a general learning framework suitable for various image modalities and anatomical structures. Hence, we expect our framework to serve as a practical clinical tool to speed up medical image analysis procedures and improve diagnostic accuracy.
Deep Spatial/temporal-level feature engineering for Tennis-based action recognition

Tennis has becoming an increasingly popular sport throughout the world. Tennis motion recognition based on 3D video has attracted more and more attention in recent years. The algorithm based on dynamic time warping takes into account the timing sequence information of movements and can solve the uncertainty of human movement at temporal level. By increasing the training samples, the efficiency will decrease accordingly. This work presents a tennis action recognition framework based on action standard sequence. The 3D action video samples are incorporated into action sequences by feature extraction, wherein the action standard sequences are encoded as a sequence averaging optimization problem under the dynamic time normalization metric. The dynamic time normalization barycenter averaging algorithm (DBA) is leveraged to solve this problem. For the tennis scenery with significant differences in the action categories, we study the standard sequence learning of multiple actions, and accordingly propose a DBA-K-means clustering algorithm for unsupervised learning. Herein, a human tennis action recognition by integrating feature optimization and image similarity is proposed. The three dimensional reduction methods, including principal component analysis (PCA),PCA + Pearson, and PCA+ Spearman, were compared to prove that PCA+ Pearson correlation coefficient had the best dimensional reduction effect. Meanwhile, the global feature eight-star model is combined with the local feature HOG feature after dimensionally reduced to fully represent human movements. The similarity between pairwise adjacent frames of images was calculated. The statistical weight of single frame SVM classification results within a discriminant period is adaptively allocated, and finally the body pose recognition results are classified twice. Experiments on standard data set KTH show that the recognition accuracy of this algorithm is 94.5%, which is better than other methods. It has a good application value in the field of video human motion recognition. Also, we have demonstrated that this method can further improve the efficiency and accuracy of action recognition. Effective feature extraction is beneficial to improve the accuracy of subsequent human action recognition.
Designing Direct Interactions with Bioluminescent Algae

Living matter is an emerging topic of interest in HCI as researchers are recognizing the unique affordances of biological materials. We explore direct interactions with Dinoflagellates, bioluminescent algae that produce light when exposed to oxygen through physical stimulation. Leveraging Dinoflagellates’ natural feedback mechanism, we propose directly engaging the human user with the organism through physical kinetic interactions. We take an organism-centered design approach, considering the well-being of the organism by focusing first on designing appropriate environments for the organism, then proceeding to exploring the available interactions within these environments. Our framework consists of four components (form, reception, feedback, and control) and can be used to guide designers in their design process with living matter. We demonstrate the framework with environments for Dinoflagellates and three real-life examples: a checkers game, a ball game. and hopscotch. Last, we discuss the constraints and limitations of integrating Dinoflagellates and living matter in interactive systems.
Battery-Free Camera Occupancy Detection System

Occupancy detection systems are commonly equipped with high-quality cameras and a processor with high computational power to run detection algorithms. This paper presents a human occupancy detection system that uses battery-free cameras and a deep learning model implemented on a low-cost hub to detect human presence. Our low-resolution camera harvests energy from ambient light and transmits data to the hub using backscatter communication. We implement the state-of-the-art YOLOv5 network detection algorithm that offers high detection accuracy and fast inferencing speed on a Raspberry Pi 4 Model B. We achieve an inferencing speed of ~ 100ms per image and an overall detection accuracy of >90% with only 2GB CPU RAM on the Raspberry Pi. In the experimental results, we also demonstrate that the detection is robust to noise, illuminance, occlusion, and angle of depression.
Teachers’ perceived information literacy self-efficacy

The aim of this research was to examine the factors that might correlate with variables that may explain teachers’ perceptions of their information literacy self-efficacy level. The study was conducted among 101 teachers during the 2018 academic year. It used the following questionnaires: information literacy self-efficacy level, being a digital newcomer/native, personality variables taken from the Big Five model, mastery of computer applications, personal characteristics, and participation in a digital program. Researchers used a quantitative methodology. Findings present a correlation between teachers’ perceptions of their information literacy self-efficacy level and openness to experience and neuroticism, teachers’ level of digital skills, and teachers’ participation in a digital program. We propose that the ministry of education should focus on developing pre-service teachers’ and teachers’ information literacy skills, thus enhancing their information literacy self-efficacy.
How do we Evaluate Self-adaptive Software Systems?: A Ten-Year Perspective of SEAMS

With the increase of research in self-adaptive systems, there is a need to better understand the way research contributions are evaluated.Such insights will support researchers to better compare new findings when developing new knowledge for the community.However, so far there is no clear overview of how evaluations are performed in self-adaptive systems.To address this gap, we conduct a mapping study.The study focuses on experimental evaluations published in the last decade at the prime venue of research in software engineering for self-adaptive systems-the International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS).Results point out that specifics of self-adaptive systems require special attention in the experimental process, including the distinction of the managing system (i.e., the target of evaluation) and the managed system, the presence of uncertainties that affect the system behavior and hence need to be taken into account in data analysis, and the potential of managed systems to be reused across experiments, beyond replications.To conclude, we offer a set of suggestions derived from our study that can be used as input to enhance future experiments in self-adaptive systems.
Honey Encryption: Fortification Beyond the Brute-Force Impediment

Honey encryption is a technique to protect private data once it is put on the network. The techniques used earlier include password-based encryption techniques which seldom survive brute-force attack. A brute-force attack proceeds by trying the keys starting from one to all on the encrypted text to finally decipher the plain text as well as the key, which may be used to crack the future messages. Honey encryption can help diminish the possibility of brute-force attack. In this research work, we exploit the conception of honey encryption mechanisms, and we apply it to three category of clandestine data embracing simple sentences containing the 5 Ws (that include which, what, why, when, and where), dates, mobile numbers, and debit or credit card passwords.
Data-Efficient Language-Supervised Zero-Shot Learning with Self-Distillation

Traditional computer vision models are trained to predict a fixed set of predefined categories. Recently, natural language has been shown to be a broader and richer source of supervision that provides finer descriptions to visual concepts than supervised "gold" labels. Previous works, such as CLIP, use a simple pretraining task of predicting the pairings between images and text captions. CLIP, however, is data hungry and requires more than 400M image text pairs for training. We propose a data-efficient contrastive distillation method that uses soft labels to learn from noisy image-text pairs. Our model transfers knowledge from pre-trained image and sentence encoders and achieves strong performance with only 3M image text pairs, 133x smaller than CLIP. Our method exceeds the previous SoTA of general zero-shot learning on ImageNet 21k+1k by 73% relatively with a ResNet50 image encoder and DeCLUTR text encoder. We also beat CLIP by 10.5% relatively on zero-shot evaluation on Google Open Images (19,958 classes).
Lexical data augmentation for sentiment analysis

Abstract Machine learning methods, especially deep learning models, have achieved impressive performance in various natural language processing tasks including sentiment analysis. However, deep learning models are more demanding for training data. Data augmentation techniques are widely used to generate new instances based on modifications to existing data or relying on external knowledge bases to address annotated data scarcity, which hinders the full potential of machine learning techniques. This paper presents our work using part‐of‐speech (POS) focused lexical substitution for data augmentation (PLSDA) to enhance the performance of machine learning algorithms in sentiment analysis. We exploit POS information to identify words to be replaced and investigate different augmentation strategies to find semantically related substitutions when generating new instances. The choice of POS tags as well as a variety of strategies such as semantic‐based substitution methods and sampling methods are discussed in detail. Performance evaluation focuses on the comparison between PLSDA and two previous lexical substitution‐based data augmentation methods, one of which is thesaurus‐based, and the other is lexicon manipulation based. Our approach is tested on five English sentiment analysis benchmarks: SST‐2, MR, IMDB, Twitter, and AirRecord. Hyperparameters such as the candidate similarity threshold and number of newly generated instances are optimized. Results show that six classifiers (SVM, LSTM, BiLSTM‐AT, bidirectional encoder representations from transformers [BERT], XLNet, and RoBERTa) trained with PLSDA achieve accuracy improvement of more than 0.6% comparing to two previous lexical substitution methods averaged on five benchmarks. Introducing POS constraint and well‐designed augmentation strategies can improve the reliability of lexical data augmentation methods. Consequently, PLSDA significantly improves the performance of sentiment analysis algorithms.
A competency-based approach to the systematization of mathematical problems in a specialized school

Abstract The issue of searching for new methodological approaches to the systematization that will encourage the increase of students motivation to learn mathematics under the competency-based approach is considered in this article. The research analyzes the existing works on the increase in students motivation to learn Mathematics, in particular, the use of cross-curricular connections while forming students competency. Competency-based problems, systematized according to the topic of the 10th grade Functions, their features and graphics, were determined as the tools to measure students competency and a method to form their motivation to learn mathematics. The use of such methods as the initial research and information gathering, systematization and structural analysis of the problems, data processing allowed the authors of the article to systematize the problems for school subjects of the 10th grade that demonstrate cross-curricular connections of Mathematics with other learning subjects and allow showing the advantages of the mathematical modeling in researching real processes. The research shows the realization of cross-curricular connections in time and such connections as parallel learning, perspective connections, use of the mathematical modeling method are shown. An experiment was held in order to prove the efficiency of implementing a system of the problems to demonstrate the use of the function in different tasks of natural subjects. The results proved that the implemented system of problems considerably influences the increase in students motivation to learn mathematics.
ATNN: Adversarial Two-Tower Neural Network for New Item’s Popularity Prediction in E-commerce

The e-commerce era is witnessing rising new arrivals of items on e-commerce platforms every day. Identifying potential popular items accurately is of great importance in creating commercial value. Click-Through Rate (CTR) is a general indicator to evaluate item popularity. However, existing methods fail in new arrivals prediction because of sparse item features, missing item statistics and high time complexity of computing for all pairs of users and items. To tackle these challenges, we propose a novel Adversarial Two-tower Neural Network (ATNN) model for new arrivals CTR predictions by introducing an adversarial network to a two-tower network. We design a generator and a discriminator to better learn an item vector based on item profiles without item statistics. We also develop a strategy with an O(1) time complexity for a new item's popularity prediction by constructing a user group and utilizing its mean user vector in a time-efficient manner. We implement ATNN on a largescale real-world dataset from one of the world's largest ecommerce platforms, “Tmall.com”. Empirical results show that ATNN is strongly capable of learning item vectors from item profiles for e-commerce. Furthermore, by introducing multi-task learning technology, we extend ATNN to food delivery service. Experimental results on one popular food delivery platform, “Ele.me”, demonstrate that ATNN can recognize attractive and welcoming new restaurants that have higher Value per Page View (VpPV) and generate more Gross Merchandise Volume (GMV).
An Efficient and Fast Softmax Hardware Architecture (EFSHA) for Deep Neural Networks

Deep neural networks are widely used in computer vision applications due to their high performance. However, DNNs involve a large number of computations in the training and inference phase. Among the different layers of a DNN, the softmax layer has one of the most complex computations as it involves exponent and division operations. So, a hardware-efficient implementation is required to reduce the on-chip resources. In this paper, we propose a new hardware-efficient and fast implementation of the softmax activation function. The proposed hardware implementation consumes fewer hardware resources and works at high speed as compared to the state-of-the-art techniques.
OCONet: Image Extrapolation by Object Completion

Image extrapolation extends an input image beyond the originally-captured field of view. Existing methods struggle to extrapolate images with salient objects in the foreground or are limited to very specific objects such as humans, but tend to work well on indoor/outdoor scenes. We introduce OCONet (Object COmpletion Networks) to extrapolate foreground objects, with an object completion network conditioned on its class. OCONet uses an encoder-decoder architecture trained with adversarial loss to predict the object’s texture as well as its extent, represented as a predicted signed-distance field. An independent step extends the background, and the object is composited on top using the predicted mask. Both qualitative and quantitative results show that we improve on state-of-the-art image extrapolation results for challenging examples.
ProtAugment: Intent Detection Meta-Learning through Unsupervised Diverse Paraphrasing

Recent research considers few-shot intent detection as a meta-learning problem: the model is learning to learn from a consecutive set of small tasks named episodes.In this work, we propose PROTAUGMENT, a meta-learning algorithm for short texts classification applied to the intent detection task.PROTAUG-MENT is a novel extension of Prototypical Networks (Snell et al., 2017) that limits over-fitting on the bias introduced by the few-shots classification objective at each episode.It relies on diverse paraphrasing: a conditional language model is first fine-tuned for paraphrasing, and diversity is later introduced at the decoding stage at each meta-learning episode.The diverse paraphrasing is unsupervised as it is applied to unlabelled data and then fueled to the Prototypical Network training objective as a consistency loss.PROTAUGMENT is the stateof-the-art method for intent detection metalearning, at no extra labeling efforts and without the need to fine-tune a conditional language model on a given application domain.
Improving Weakly Supervised Temporal Action Localization by Exploiting Multi-Resolution Information in Temporal Domain

Weakly supervised temporal action localization is a challenging task as only the video-level annotation is available during the training process. To address this problem, we propose a two-stage approach to generate high-quality frame-level pseudo labels by fully exploiting multi-resolution information in the temporal domain and complementary information between the appearance (i.e., RGB) and motion (i.e., optical flow) streams. In the first stage, we propose an Initial Label Generation (ILG) module to generate reliable initial frame-level pseudo labels. Specifically, in this newly proposed module, we exploit temporal multi-resolution consistency and cross-stream consistency to generate high quality class activation sequences (CASs), which consist of a number of sequences with each sequence measuring how likely each video frame belongs to one specific action class. In the second stage, we propose a Progressive Temporal Label Refinement (PTLR) framework to iteratively refine the pseudo labels, in which we use a set of selected frames with highly confident pseudo labels to progressively train two networks and better predict action class scores at each frame. Specifically, in our newly proposed PTLR framework, two networks called Network-OTS and Network-RTS, which are respectively used to generate CASs for the original temporal scale and the reduced temporal scales, are used as two streams (i.e., the OTS stream and the RTS stream) to refine the pseudo labels in turn. By this way, multi-resolution information in the temporal domain is exchanged at the pseudo label level, and our work can help improve each network/stream by exploiting the refined pseudo labels from another network/stream. Comprehensive experiments on two benchmark datasets THUMOS14 and ActivityNet v1.3 demonstrate the effectiveness of our newly proposed method for weakly supervised temporal action localization.
A Gait Energy Image-Based System for Brazilian Sign Language Recognition

Sign language is the main type of communication of the deaf community. However, most people do not know this language, which causes communication problems for many people. Many technological solutions have been proposed to overcome this issue. Some use the concept of wearable devices, but gesture recognition in a video sequence is a cheaper and less intrusive solution. In this work, we address the problem of gesture recognition in video. To do so, we employ a two-step method with feature space mapping and classification. First, the body parts of each subject in a video are segmented through a deep neural network architecture. Then, we use Gait Energy Image to encode the motion of the body parts in a compact feature space. Small datasets are usually a problem in this type of application, leading to sparse representation in the feature space. To contour this problem, we evaluate SMOTE as a data augmentation technique in the feature space and classical dimensionality reduction techniques. We evaluate our method on three challenging Brazilian sign language (Libras) datasets, CEFET/RJ-Libras, MINDS-Libras, and LIBRAS-UFOP, achieving global accuracies of 85.40±3.13%, 84.66±1.78%, and 64.91±3.79%, respectively, with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">singular value decomposition</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">support vector machine</i> .