Reviewerly/exHarmony huggingface dataset:
An Accurate Deep Learning–Based System for Automatic Pill Identification: Model Development and Validation

Medication errors account for a large proportion of all medical errors. In most homes, patients take a variety of medications for a long period. However, medication errors frequently occur because patients often throw away the containers of their medications.We proposed a deep learning-based system for reducing medication errors by accurately identifying prescription pills. Given the pill images, our system located the pills in the respective pill databases in South Korea and the United States.We organized the system into a pill recognition step and pill retrieval step, and we applied deep learning models to train not only images of the pill but also imprinted characters. In the pill recognition step, there are 3 modules that recognize the 3 features of pills and their imprints separately and correct the recognized imprint to fit the actual data. We adopted image classification and text detection models for the feature and imprint recognition modules, respectively. In the imprint correction module, we introduced a language model for the first time in the pill identification system and proposed a novel coordinate encoding technique for effective correction in the language model. We identified pills using similarity scores of pill characteristics with those in the database.We collected the open pill database from South Korea and the United States in May 2022. We used a total of 24,404 pill images in our experiments. The experimental results show that the predicted top-1 candidates achieve accuracy levels of 85.6% (South Korea) and 74.5% (United States) for the types of pills not trained on 2 different databases (South Korea and the United States). Furthermore, the predicted top-1 candidate accuracy of our system was 78% with consumer-granted images, which was achieved by training only 1 image per pill. The results demonstrate that our system could identify and retrieve new pills without additional model updates. Finally, we confirmed through an ablation study that the language model that we emphasized significantly improves the pill identification ability of the system.Our study proposes the possibility of reducing medical errors by showing that the introduction of artificial intelligence can identify numerous pills with high precision in real time. Our study suggests that the proposed system can reduce patients' misuse of medications and help medical staff focus on higher-level tasks by simplifying time-consuming lower-level tasks such as pill identification.
Build Your World – Meaningful Choices in a Hybrid Stage Play

This paper examines the design of meaningful choices for theater audiences embedded in a hybrid play using digital tools and systems. A case study is presented that is established on a hybrid stage performance called ALIENATION based on the so-far unpublished eponymous novel by Corinna Antelmann, which premiered in early 2022 at the Landestheater Linz. The audience could take part in the performance via a real-time rendering engine. Apart from describing the case study in detail, we outline further applications and opportunities to facilitate audience participation and engagement. In general, ALIENATION's design and production positively impacted the audience's attention, especially regarding younger demographics.
Construction of Recursive MDS Matrices Using DLS Matrices

Many block ciphers and hash functions use MDS matrices because of their optimal branch number. On the other hand, MDS matrices generally have a high implementation cost, which makes them unsuitable for lightweight cryptographic primitives. In this direction, several sparse matrix structures like companion, GFS, and DSI matrices are proposed to construct recursive MDS matrices. The key benefit of these matrices is their low fixed XOR, and the diffusion layer can be made by recursively executing the implementation of the matrices, which takes a few clock cycles. In this paper, we propose a new class of sparse matrices called Diagonal-like sparse (DLS) matrices and the DSI matrix is a particular type of DLS matrix. We prove that for an n-MDS DLS matrix of order n, the fixed XOR (say $$\mathcal {K}$$ ) should be at least equal to the $$\left\lceil \frac{n}{2}\right\rceil $$ . We also show that an n-MDS DLS matrix over $$\mathbb {F}_{2^r}$$ with $$\mathcal {K}=\left\lceil \frac{n}{2}\right\rceil $$ is a permutation similar to some n-MDS sparse DSI matrix. We propose another type of sparse matrices called generalized DLS (GDLS) matrices. Next, we introduce some lightweight recursive MDS matrices of orders 4, 5, 6, and 7, using GDLS matrices, that can be implemented with 22, 30, 31, and 45 XORs over $$\mathbb {F}_{2^8}$$ , respectively. The results match the best known lightweight recursive MDS matrices of orders 4 and 6 and beat the best known matrices of orders 5 and 7. Also, the proposed 4-MDS GDLS matrix over $$\mathbb {F}_{2^4}$$ has a XOR count of 10, which meets the best known result.
Communication-efficient Federated Learning with Cooperative Filter Selection

Federated learning, as a distributed machine learning framework that a shared global model that is obtained through frequent local training parameter interaction on each participated device. However, the limited communication bandwidth of participating IoT and edge devices will have a conflict between the frequent parameter-interaction learning mode of federated learning and impact communication and learning efficiency. In this paper, a communication efficiency enhanced federated learning technique is presented by proposing a cooperative filter selection method. The Geometric Median of each layer in the global model is adopted as the criterion to cooperatively select important filters in the local model, and then the corresponding parameters interact with other nodes to achieve efficient communication. Experimental results show that our method has a maximum of $2.66\times$ improvements in communication efficiency compared with the state-of-the-art methods.
Using Analogical Proportions for Explanations

In this article, we offer an introduction to the notion of analogical explanations. Because analogical reasoning is a widely used type of reasoning, we take the view that analogy-based explanations will be acceptable for humans. The cornerstone of the approach is the concept of analogical proportion (i.e., statements of the form “a is to b as c is to d”), comparing 2 pairs of items. Analogical proportions are not simply based on similarity but also involve differences between items. The approach applies to the explanation of the label of an item in a repository, whether the couple (item, label) belongs to a sample of a given population or the label is predicted via an algorithm. The output can be in terms of abductive/factual explanations (answering a “why?” question and providing examples having the same label) or contrastive/counter-factual (answering a “why not?” question and providing examples having a different label). For preliminary experiments, we build Boolean data sets where relevant attributes are known. Our results show that analogical proportion-based explanations can be effective.
Using Frequency Attention to Make Adversarial Patch Powerful Against Person Detector

Deep neural networks (DNNs) are vulnerable to adversarial attacks. In particular, object detectors may be attacked by applying a particular adversarial patch to the image. However, because the patch will lose information during the shrinking process, most existing approaches that employ adversarial patches to attack object detectors would diminish the attack success rate on small and medium targets. This paper proposes a Frequency Module(FRAN), a frequency-domain attention module for guiding patch generation to solve this problem. We use FRAN to allow the patched attack to rely on the low-frequency signals which are lost less in the shrinking process. This is the first study to introduce frequency domain attention to optimize the attack capabilities of adversarial patches. Our method increases the attack success rates of small and medium targets by 4.18% and 3.89%, respectively, over the state-of-the-art attack method for fooling the human detector while assaulting YOLOv3 without reducing the attack success rate of big targets.
Real-world clone-detection in go

In the early stage of development, developers copied internal code snippets to enhance developing efficiency. With the rapid growth of Bytedance, similar or equivalent code snippets across different repositories or different product lines' codebases would potentially increase the cost of maintenance and distribute vulnerable code snippets. With the application of Clone-Detection, there are multiple well-established tools or techniques used for detecting similar code snippets in Java, JavaScript, Objective C and etc [1, 3]. We could hardly find similar tools available for Go, a widely-used programming language in the field of server development, especially at Bytedance. For the lack of public and labeled datasets, we utilized a great number of code snippets in Bytedance's codebase and trained an unsupervised model to propose Go-CopyCatch (GoCC), a tool and technique for Clone-Detection in Go.
Kannada Handwritten Character Recognition and Classification Through OCR Using Hybrid Machine Learning Techniques

In many workplaces in Karnataka the documents are in regional language and it is handwritten. Consequently, there is a requirement for a PC based framework to beat the gap among machines and people. There is a lot of challenges faced when converting these handwritten documents to computer editable format. One of the challenges faced is in classifying confounding characters which are many in Kannada which may recognize wrongly due to the way the characters are written. The scanned handwritten document was pre-processed then segmented into line, word and character ouring Edge based segmentation. The feature extracted mostly based on the curviness of the characters using Convolutional Neural Networks. The segmented and feature extracted characters are further classified using Support Vector Machines, K Nearest Neighbors and Random Forest algorithms. The accuracy rates obtained based on 2000 handwritten documents where Random Forest-95%, Support Vector Machine - 96%, K Nearest Neighbors-92%.
Belief Revision with Dishonest Reports

Belief revision operators are used to model the way that an agent's beliefs change when they acquire new information. However, if the new information comes from another agent, then we also need to be concerned with the notion of honesty. In this paper, we present a model in which agents have a memory of all reports they have received, as well as the set of agents that are assumed to be honest. We propose that all reports from dishonest agents should be ignored, while inconsistencies between reports from honest agents should be resolved through iterated revision. We demonstrate how an agent can learn about the honesty of others through direct observations, and we show how this affects the complete trajectory of beliefs. Finally, we consider the case where an agent only has partial information about the honesty of others. We demonstrate that the set of possible belief trajectories is constrained by a tree of possible revisions, which allows us to briefly explore properties that are invariant with respect to the honesty of others.
Learning Visibility for Robust Dense Human Body Estimation

Estimating 3D human pose and shape from 2D images is a crucial yet challenging task. While prior methods with model-based representations can perform reasonably well on whole-body images, they often fail when parts of the body are occluded or outside the frame. Moreover, these results usually do not faithfully capture the human silhouettes due to their limited representation power of deformable models (e.g., representing only the naked body). An alternative approach is to estimate dense vertices of a predefined template body in the image space. Such representations are effective in localizing vertices within an image but cannot handle out-of-frame body parts. In this work, we learn dense human body estimation that is robust to partial observations. We explicitly model the visibility of human joints and vertices in the x, y, and z axes separately. The visibility in x and y axes help distinguishing out-of-frame cases, and the visibility in depth axis corresponds to occlusions (either self-occlusions or occlusions by other objects). We obtain pseudo ground-truths of visibility labels from dense UV correspondences and train a neural network to predict visibility along with 3D coordinates. We show that visibility can serve as 1) an additional signal to resolve depth ordering ambiguities of self-occluded vertices and 2) a regularization term when fitting a human body model to the predictions. Extensive experiments on multiple 3D human datasets demonstrate that visibility modeling significantly improves the accuracy of human body estimation, especially for partial-body cases. Our project page with code is at: https://github.com/chhankyao/visdb .
Network Traffic Classification Method of Power System Based on DNN and K-means

The classification of power system network traffic is the key point of power system network security protection. Therefore, we propose a power system network traffic classification method based on deep learning algorithm and K-means clustering algorithm. Our method uses a deep neural network (DNN) to classify the pre-processed network traffic data to determine whether it is a server, and then use the K-means algorithm to perform secondary classification on the data that has been identified as a server to help users identified the specific server type. Experiments with real power system network data prove that the classification of this method has a high accuracy rate and can meet the needs of power system network traffic classification in real environments.
Distributed Mechanism Design in Continuous Space for Federated Learning Over Vehicular Networks

With the rapid development of the 5G network and the promising of 6G network, vehicles can report a large amount of real-time traffic information to Road-Side Units (RSUs). However, due to the large communication cost, limited computing resources, and privacy leakage risks, centralized data processing by RSUs is not efficient and not secure. Thereby, federated learning is introduced to enable vehicles to train local models and send the model parameters to the RSUs, without the need for revealing their personal data. Nevertheless, without an efficient incentive mechanism, the vehicles, as data owners, may be unwilling to join the federated learning task. In this paper, we adopt contract theory to design an incentive mechanism with asymmetric information and continuum of types for interaction between the RSU and vehicles. The designed mechanism satisfies the incentive compatibility, individual rationality and also maximizes the RSU's profit. The technical challenge is to solve a non-quadratic functional optimization in continuous space with coupling constraints among the vehicles. To address this challenge, we propose two iterative algorithms to find the RSU's optimal strategies as the functions of vehicles' types. In the first algorithm, a combination of the calculus of variation and dual decomposition methods is utilized to achieve an analytical solution for the optimal strategy of RSU, while in the second one, a combination of approximate dynamic programming and neural networks is used to estimate the optimal strategy of RSU.
Rethinking Task-Incremental Learning Baselines

It is common to have continuous streams of new data that need to be introduced in the system in real-world applications. The model needs to learn newly added capabilities (future tasks) while retaining the old knowledge (past tasks). Incremental learning has recently become increasingly appealing for this problem. Task-incremental learning is a kind of incremental learning where task identity of newly included task (a set of classes) remains known during inference. A common goal of task-incremental methods is to design a network that can operate on minimal size, maintaining decent performance. To manage the stability-plasticity dilemma, different methods utilize replay memory of past tasks, specialized hardware, regularization monitoring etc. However, these methods are still less memory efficient in terms of architecture growth or input data costs. In this study, we present an effective Simple Adjustment Network (SAN) for task incremental learning that achieves near state-of-the-art performance while using minimal architectural size without using memory instances compared to previous state-of-the-art approaches. We investigate this approach on both 3D point cloud object (ModelNet40) and 2D image (CIFAR10, CIFAR100, MiniImageNet, MNIST, PermutedMNIST, notMNIST, SVHN, and FashionMNIST) recognition tasks and establish a strong baseline result for a fair comparison with existing methods. On both 2D and 3D domains, we also observe that SAN is primarily unaffected by different task orders in a task-incremental setting.
Towards Quality Assessment of Blind Deconvolution with Shift Compensation

Quantitative assessment of restoration quality in blind deconvolution is not straightforward. The intricacy comes from the opposite shifts occurring during image reconstruction in both the reconstructed image and point-spread function. State of the art procedures attempting alignment lack specificity and might actually induce interpolation-based errors, whereas alignment-free approaches disregard the possibility of shift variability. Other methods introduce a superresolution-based MSE/PSNR measure involving non-integer shifts. We propose a method to estimate non-integer displacements between the ground-truth image and the reconstructed image via the Fourier domain, which enables at the same time to incorporate interpolation-free shift compensation into the computation of MSE/PSNR measures. We tested our method on synthetically shifted images with additive noise as well as deconvolution results with known shift. Results indicate that distortions of error measures are reduced compared to interpolation-based shift compensation methods, getting closer to a fair assessment of restoration quality without wrongly favouring any blind deconvolution method over another. It was observed that PSNR measures corrected with estimated displacements are close to those corrected with real displacements.
BAFL: Federated Learning with Base Ablation for Cost Effective Communication

Federated learning is a distributed machine learning setting in which clients train a global model on their local data and share their knowledge with the server in form of the trained model while maintaining privacy of the data. The server aggregates clients' knowledge to create a generalized global model. Two major challenges faced in this process are data heterogeneity and high communication cost. We target the latter and propose a simple approach, BAFL (Federated Learning for Base Ablation) for cost effective communication in federated learning. In contrast to the common practice of employing model compression techniques to reduce the total communication cost, we propose a fine-tuning approach to leverage the feature extraction ability of layers at different depths of deep neural networks. We use a model pretrained on general-purpose large scale data as a global model. This helps in better weight initialization and reduces the total communication cost required for obtaining the generalized model. We achieve further cost reduction by focusing only on the layers responsible for semantic features (data specific information). The clients fine tune only top layers on their local data. Base layers are ablated while transferring the model and clients communicate parameters corresponding to the remaining layers. This results in reduction of communication cost per round without compromising the accuracy. We evaluate the proposed approach using VGG-16 and ResNet-50 models on datasets including WBC, FOOD-101, and CIFAR-10 and obtain up to two orders of reduction in total communication cost as compared to the conventional federated learning. We perform experiments in both IID and Non-IID settings and observe consistent improvements.
No Time like the Present: Effects of Language Change on Automated Comment Moderation

The spread of online hate has become a significant problem for newspapers that host comment sections. As a result, there is growing interest in using machine learning (ML) and natural language processing (NLP) for (semi-) automated abusive language detection to avoid manual comment moderation costs or having to shut down comment sections altogether. However, much of the past work on abusive language detection assumes that classifiers operate in a static language environment, despite language and news being in a state of constant flux. In this paper, we show using a new German newspaper comments dataset that the classifiers trained with naive ML techniques like a random test-train split will underperform on future data, and that a time-stratified evaluation split is more appropriate. We also show that a classifier's performance rapidly degrades when evaluated on data from a different period than the training data. Our findings suggest that it is necessary to consider the temporal dynamics of language when developing an abusive language detection system or risk deploying a model that will quickly become defunct.
SMS-MPC: Adversarial Learning-based Simultaneous Prediction Control with Single Model for Mobile Robots

Model predictive control is a promising method in robot control tasks. How to design an effective model structure and efficient prediction framework for model predictive control is still an open challenge. To reduce the time consumption and avoid compounding-error of the multi-step prediction process in model predictive control, we propose a single-model simultaneous framework, which uses single dynamics model to predict the entire prediction horizon simultaneously by taking all control actions with the current state as inputs. Based on this framework, we further propose an adversarial dynamics model that contains two parts. The generator provides a dynamics model for the prediction process, while the discriminator provides constraints that are hard to describe by manually defined loss. This adversarial dynamics model can accelerate training and improve model accuracy in unstructured environments. Experiments conducted in Gazebo simulator and on a real mobile robot demonstrate the efficiency and accuracy of the single-model simultaneous framework with an adversarial dynamics model.
Attributes regrouping by genetic algorithm in fuzzy inference systems

Fuzzy Rule-Based Classification Systems (FRBCS) are a very popular and useful tool due to their interpretable classification models based on linguistic variables. However, a high number of attributes leads to an explosion of the number of classification rules. In this paper, we are interested in ensemble methods applied to FRBCS. An attributes regrouping method based on association rules and called SIFRA was proposed to solve this problem. It determines the relevant groups of related attributes that will be treated separately by FRBCSs. We propose a Genetic Algorithm-based method to select final groups of related attributes determined by SIFRA. In our approach, the genetic algorithm is used to filter the groups of related attributes and conserve the best ones, while maintaining a good classification rate and a reduced number of rules. We lead experiments to compare our contribution with previous methods in the same context; the experimental results are satisfactory.