Reviewerly/exHarmony huggingface dataset:
Accurate and Safe Drug Recommendations based on Singular Value Decomposition

Polypharmacy is the prescription of many drugs for a critically-ill patient, which may have a higher risk of adverse drug reactions among them. Such patients usually follow a treatment that consists of multiple different drugs, which increases the risk of unwanted side-effects and can be occasionally harmful. In this paper, we address the safe drug prescription issues, by integrating the patient's Electronic Health Records (EHRs) with an adversarial Drug-Drug Interaction (DDI) knowledge graph. We study the task of predicting drugs to patients by making use of the real life data set MIMIC III. Our goal is to predict the next drug combination for a patient's therapy and at the same time minimize the drug unwanted side effects. We feed the input data to 6 different algorithms that we later compare in terms of effectiveness and safety in recommending drug combinations. Furthermore, we use the Post Hoc Re-rank technique together with Singular Value Decomposition algorithm to incorporate information in our prediction model from unwanted drug-drug interactions knowledge graph. Our experiments have shown that with a slight loss in the efficacy of the recommendation algorithm, we are able to reduce the toxicity score of the suggested drug combinations.
Early Experiences on using Triplet Networks for Histological Subtype Classification in Non-Small Cell Lung Cancer

Lung cancer has the highest mortality rate among tumours and an accurate pathological assessment is crucial to deliver personalized treatments to patients. The gold standard for pathological assessment requires invasive procedures, which are not always possible and might cause clinical complications. Therefore, in the last years, efforts have been directed towards the development of machine and deep learning approaches for virtual biopsy, which leverage routinely collected CT scans. However, in many cases, the available datasets are limited in size, an issue that limits the training of any model. In this paper, we investigate if triplet networks can cope with this limitation: they are a class of neural networks that uses the same weights while working in tandem on three different input vectors to minimize the loss function. In particular, on a dataset including 87 CT scans collected from patients suffering from non-small cell lung cancer, we experimentally compare triplet networks against plain deep networks when performing histological subtype classification. The results show that the former outperforms the latter in almost all experiments.
Evaluating Classifiers Trained on Differentially Private Synthetic Health Data

The release of differentially private (DP) synthetic data has been proposed as a solution to sharing sensitive individual-level medical data for statistical analysis and machine learning model development. The approach holds promise to generate realistic data that preserves many of the statistical properties of the original data while giving privacy guarantees that bound the risk of leaking any sensitive information about the individuals in the data. However, evaluating the generalization of machine learning models trained on DP-synthetic data remains an open question. A model selected based on its accuracy on synthetic data does not necessarily generalize well to real-world data, leading to poor results and incorrect insights. In this study, we experimentally compare two different protocols for model evaluation and hyperparameter selection for classifiers trained on DP-synthetic medical data. In the first protocol, we use only synthetic data for model selection and final evaluation of selected model, whereas in the second one, we assume limited DP access to a private real validation and test set held by the data curator. Our results provide novel insights into the practical feasibility and utility of different evaluation protocols for classifiers trained on DP synthetic data based on a comprehensive empirical study.
A camouflage target detection method based on local minimum difference constraints

To address the problems of missing inside and incomplete edge contours in camouflaged target detection results, we propose a camouflaged moving target detection algorithm based on local minimum difference constraints (LMDC). The algorithm first uses the mean to optimize the initial background model, removes the stable background region by global comparison, and extracts the edge point set in the potential target region so that each boundary point (seed) grows along the center of the target. Finally, we define the minor difference constraints term, combine the seed path and the target space consistency, and calculate the attributes of each pixel in the potential target area to realize camouflaged moving target detection. The algorithm of this paper is verified based on a public data sofa video and test videos and compared with the five classic algorithms. The experimental results show that the proposed algorithm yields good results based on integrity, accuracy, and a number of objective evaluation indexes, and its overall performance is better than that of the compared algorithms.
Analysis of Semantic Drifting in Diagnostic Texts for Sleep Disorders

Along with the diagnostic process for sleep disorders, a diversity of clinical assessments are required to diagnose the concrete type of sleep disorder. For an accurate diagnosis, a variety of medical examinations are conducted, whose results are documented in clinical records. The textual records form the basis for disease categorization and cohort creation using the International Classification for Sleep Disorder (ICSD-III). However, textual records generated through patient-physician interaction may contain different types of biases caused by the heterogeneous nature of medical specialties and the used vocabulary in the documentation. In this work, we will analyze different types of semantic biases and driftings in the context of sleep disorder diagnosis using the Bern Sleep Database. The database contains documents describing the clinical history, referring reasons and results from different assessments (polysomnography (PSG), multiple sleep latency test (MSLT), maintenance of wakefulness test (MWT), actigraphy (Wrist, PLMS)) from more than 6000 patients. An enhanced text-based ICSD-III classification (Multichannel CNN and Hierachical attentive networks) using a standardized concept representation will be proposed. We will analyze the influence of semantic bias on the accuracy of this automatic classification.
Trajectory Planning of Autonomous Mobile Robot using Model Predictive Control in Human-Robot Shared Workspace

This paper proposes an integrated framework of trajectory planning and control for autonomous mobile robots (AMRs) in an intra-logistic scenario, where humans and robots share the same indoor workspace. The proposed framework consists of perception and trajectory planning. To perceive human motion, information from the RGB-D camera is used to detect human positions. Then a model-based tracking approach is employed to track multiple people’s motion and estimate their speed over a short time horizon. For trajectory planning, a Model Predictive Control (MPC) based scheme is adopted to generate motor commands while taking into account energy efficiency, safety, and addressing human-aware proximity constraints. To convexify the nonlinear collision avoidance constraints, Sequential Convexification Programming is employed. The simulation and experimental results demonstrate that the proposed method can be implemented in real-time and efficiently avoid people in advance.
Design of Crawler and Visual Interactive Interface Based on Scrapy Framework

Nowadays, when studying and analyzing problems, the previous conventional analysis methods are obviously difficult to meet our needs in such a huge data scale environment. At the same time, the emergence of web crawlers solves the problem of data acquisition under big data. This paper completes a series of tasks such as data acquisition, download and storage in the designated website by studying the scrapy framework. At the same time, through the research of pyecharts library, JSON library and other visualization related technologies, we can realize the visual processing and display of the crawling data of the scrapy framework, so as to realize the more intuitive analysis of the data.
Advancement Of Sign Language Recognition Through Technology Using Python And OpenCV

The ability to communicate effectively is critical for every individual in society, but communication can be a challenge for people who are deaf or hard of hearing. Sign language is one of the primary means of communication for this population. However, communication can be difficult for people who need to learn sign language, leading to misunderstandings and frustration. Sign language recognition technology can help bridge this communication gap. Sign Language Recognition (SLR) handles the recognition of hand gestures and continues to generate a text or voice for the corresponding hand gesture. Static and dynamic hand motions are the two different categories. Although static hand gesture recognition is simpler than dynamic hand gesture recognition, both recognitions are essential for human communities. This paper focuses on smart gloves that can detect sign language and on sign language recognition using Python and OpenCV'
Progressive Context-Aware Dynamic Network for Salient Object Detection in Optical Remote Sensing Images

Although remarkable progress has been made for salient object detection (SOD) in optical remote sensing images (RSIs), the static network design paradigm adopted by existing methods would limit their adaptability to large variations in remote sensing scenes as well as object appearances. In contrast, we explore this research issue from the perspective of generating dynamic network filters in which the parameters are conditioned on specific scene- and location-level contexts. In this paper, we propose a Progressive Context-aware Dynamic Network (PCD-Net) for SOD in RSIs, which adaptively captures context information and adjusts its filtering parameters for saliency detection. PCD-Net adopts an encoder-decoder architecture in which encoded feature representations are progressively decoded by a newly proposed dynamic module, namely Pyramid Scene- and Location-sensitive Dynamic filtering module (PSLD), to generate saliency representations. Furthermore, to transfer effective features from the encoder to the decoder, we construct a Dynamic Transfer Attention (DTA) module to control the interference between the encoder and the decoder in a more flexible way. Extensive evaluations on two commonly-used benchmarks demonstrate the superiority of the proposed method against the existing state-of-the-art methods.
Efficient Unmanned Aerial Systems Navigation With Collision Avoidance in Dense Urban Environments

Unmanned Aerial Systems (UAS) are an emerging type of airborne traffic under active research expected to carry cargo and passengers in the future over dense population centers. One challenge is identifying algorithms which can compactly represent and navigate the available airspace while avoiding conflict with buildings and other UAS. In this paper, we explore a decentralized method coupling a medial axis graph for global navigation through the city with local collision avoidance of buildings and other UAS to obtain collision-free efficient navigation through an urban environment. We study trade-offs of using Optimal Reciprocal Collision Avoidance (ORCA), Rapidly-exploring Random Trees (RRT and RRT*), and Fast Markov Decision Process (FastMDP) as the collision avoidance algorithms. We examine low-altitude UAS navigating through a portion of New York City dense with skyscrapers to study the effectiveness of the algorithms in a challenging environment. We show that ORCA, RRT, RRT*, and FastMDP all are fairly efficient for 2D problems, but as the problem becomes more realistic (3D, constrained motion, aware of other UAS), FastMDP provides the best overall performance among the four algorithms studied.
LegoNN: Building Modular Encoder-Decoder Models

State-of-the-art encoder-decoder models (e.g. for machine translation (MT) or automatic speech recognition (ASR)) are constructed and trained end-to-end as an atomic unit. No component of the model can be (re-)used without the others, making it impossible to share parts, e.g. a high resourced decoder, across tasks. We describe LegoNN, a procedure for building encoder-decoder architectures in a way so that its parts can be applied to other tasks without the need for any fine-tuning. To achieve this reusability, the interface between encoder and decoder modules is grounded to a sequence of marginal distributions over a pre-defined discrete vocabulary. We present two approaches for ingesting these marginals; one is differentiable, allowing the flow of gradients across the entire network, and the other is gradient-isolating. To enable the portability of decoder modules between MT tasks for different source languages and across other tasks like ASR, we introduce a modality agnostic encoder which consists of a length control mechanism to dynamically adapt encoders' output lengths in order to match the expected input length range of pre-trained decoders. We present several experiments to demonstrate the effectiveness of LegoNN models: a trained language generation LegoNN decoder module from German-English (De-En) MT task can be reused without any fine-tuning for the Europarl English ASR and the Romanian-English (Ro-En) MT tasks, matching or beating the performance of baseline. After fine-tuning, LegoNN models improve the Ro-En MT task by 1.5 BLEU points and achieve 12.5% relative WER reduction on the Europarl ASR task. To show how the approach generalizes, we compose a LegoNN ASR model from three modules – each has been learned within different end-to-end trained models on three different datasets – achieving an overall WER reduction of 19.5%.
RISE: RISC-V SoC for En/Decryption Acceleration on the Edge for Homomorphic Encryption

Today, edge devices commonly connect to the cloud to use its storage and computing capabilities. This leads to security and privacy concerns about user data. Homomorphic encryption (HE) is a promising solution to address the data privacy problem as it allows arbitrarily complex computations on encrypted data without ever needing to decrypt it. While there has been a lot of work on accelerating HE computations in the cloud, small attention has been paid to the message-to-ciphertext and ciphertext-to-message conversion operations on the edge. In this work, we profile the edge-side conversion operations, and our analysis shows that during conversion error sampling, encryption and decryption operations are the bottlenecks. To overcome these bottlenecks, we present RISE, an area and energy-efficient RISC-V system-on-chip (SoC). RISE leverages an efficient and lightweight pseudorandom number generator (PRNG) core and combines it with fast sampling techniques to accelerate the error sampling operations. To accelerate the encryption and decryption operations, RISE uses scalable data-level parallelism to implement the number theoretic transform (NTT) operation, the main bottleneck within the encryption and decryption operations. In addition, RISE saves area by implementing a unified en/decryption datapath, and efficiently exploits techniques like memory reuse and data reordering to utilize a minimal amount of ON-chip memory. We evaluate RISE using a complete RTL design containing a RISC-V processor interfaced with our accelerator. Our analysis reveals that for message-to-ciphertext and ciphertext-to-message conversions, using RISE leads up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5986.99\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1164.1\times $ </tex-math></inline-formula> more energy-efficient solution, respectively, than when using just the RISC-V processor.
Pufferfish Privacy: An Information-Theoretic Study

Pufferfish privacy (PP) is a generalization of differential privacy (DP), that offers flexibility in specifying sensitive information and integrates domain knowledge into the privacy definition. Inspired by the illuminating formulation of DP in terms of mutual information due to Cuff and Yu, this work explores PP through the lens of information theory. We provide an information-theoretic formulation of PP, termed mutual information PP (MI PP), in terms of the conditional mutual information between the mechanism and the secret, given the public information. We show that MI PP is implied by the regular PP and characterize conditions under which the reverse implication is also true, recovering the relationship between DP and its information-theoretic variant as a special case. We establish convexity, composability, and post-processing properties for MI PP mechanisms and derive noise levels for the Gaussian and Laplace mechanisms. The obtained mechanisms are applicable under relaxed assumptions and provide improved noise levels in some regimes. Lastly, applications to auditing privacy frameworks, statistical inference tasks, and algorithm stability are explored.
Design of Blind Guiding Robot Based on Speed Adaptation and Visual Recognition

With the continuous increase in the number of visually impaired individuals, it has become urgent to address the traffic challenges faced by this group.A blind guiding robot based on speed adaptation and visual recognition was designed to address this problem.The speed adaptation of the robot and the blind person is achieved through feedback control of the distance and speed.Traffic signals are identified using optimized visual recognition method based on YOLOv5 transfer learning, and man-machine interaction is realized by applying multi-module units such as real-time image, speech, and positioning.The experimental results show that the deviation between the relative distance between the man and machine and the set distance was controlled within 13.1%, the relative velocity deviation was controlled within 0.3 m/s, the accuracy of identifying traffic signals reached 91.88%, and when the man-machine distance gap is large, the robot can timely control the man-machine distance to the set distance within 0.7 s, which effectively ensured the travel safety of blind people and provide the groundwork for the practical application of guiding blind robots.
Multimodal Transfer Learning for Oral Presentation Assessment

Oral communication has consistently been ranked as a key skill, with 90 percent of hiring managers and 80 percent of business executives saying it is very important for college graduates to possess, according to a recent survey. Consequently, training and evaluating oral presentation skills remains a priority for educators worldwide, and there are increasing numbers of automated tools developed for providing feedback and assessment of such skills. However, modeling approaches typically require collecting large amounts of data and labels, which can be both expensive and laborious. In this paper, we explore the possibility of transfer learning between two different but related multimodal datasets to benefit the evaluation of oral presentation performance. We utilize knowledge from a job interview dataset as pretraining material and adapt the learned knowledge from the pre-trained model to a small amount of presentation data to improve the learning of the presentation assessment task. We demonstrate the efficacy of our approach, especially in improving performance for inference on small datasets (< 100 data points), and we report our findings. Moreover, we give a comparison between the proposed TL approach and a standard TL method based on a large-scale pre-trained model. Despite the simplicity of our proposed TL approach, the results show that our approach has promise in application to smaller datasets such as ours.
An Approach for Incremental Mining of Clickstream Patterns as a Service Application

Sequential pattern mining in general and one particular form, clickstream pattern mining, are data mining topics that have recently attracted attention due to their potential applications of discovering useful patterns. However, in order to provide them as real-world service applications, one issue that needs to be addressed is that traditional algorithms often view databases as static, although in practice databases often grow over time and invalidate parts of the previous results after updates, forcing the algorithms to rerun from scratch on the updated databases to obtain updated frequent patterns. This can be inefficient as a service application due to the cost in terms of resources, and the returning of results to users can take longer when the databases get bigger. The response time can be shortened if the algorithms update the results based on incremental changes in databases. Thus, we propose PF-CUP (pre-frequent clickstream mining using pseudo-IDList), an approach towards incremental clickstream pattern mining as a service. The algorithm is based on the pre-large concept to maintain and update results and a data structure called a pre-frequent hash table to maintain the information about patterns. The experiments completed on different databases show that the proposed algorithm is efficient in incremental clickstream pattern mining.