Reviewerly/exHarmony huggingface dataset:
An HPC-Driven Data Science Platform to Speed-up Time Series Data Analysis of Patients with the Acute Respiratory Distress Syndrome

An increasing number of data science approaches that take advantage of deep learning in computational medicine and biomedical engineering require parallel and scalable algorithms using High-Performance Computing systems. Especially computational methods for analysing clinical datasets that consist of multivariate time series data can benefit from High-Performance Computing when applying computing-intensive Recurrent Neural Networks. This paper proposes a dynamic data science platform consisting of modular High-Performance Computing systems using accelerators for innovative Deep Learning algorithms to speed-up medical applications that take advantage of large biomedical scientific databases. This platform's core idea is to train a set of Deep Learning models very fast to easily combine and compare the different Deep Learning models' forecast (out-of-sample) performance to their past (in-sample) performance. Considering that this enables a better understanding of what Deep Learning models can be useful to apply to specific medical datasets, our case study leverages the three data science methods Gated Recurrent Units, one-dimensional convolutional layers, and their combination. We validate our approach using the open MIMIC-III database in a case study that assists in understanding, diagnosing, and treating a specific condition that affects Intensive Care Unit patients, namely Acute Respiratory Distress Syndrome.
Unsupervised Paraphrasing Consistency Training for Low Resource Named Entity Recognition

Unsupervised consistency training is a way of semi-supervised learning that encourages consistency in model predictions between the original and augmented data.For Named Entity Recognition (NER), existing approaches augment the input sequence with token replacement, assuming annotations on the replaced positions unchanged.In this paper, we explore the use of paraphrasing as a more principled data augmentation scheme for NER unsupervised consistency training.Specifically, we convert Conditional Random Field (CRF) into a multi-label classification module and encourage consistency on the entity appearance between the original and paraphrased sequences.Experiments show that our method is especially effective when annotations are limited.
Correlation Power Analysis Attack Resisted Cryptographic RISC-V SoC With Random Dynamic Frequency Scaling Countermeasure

Cryptographic System-on-Chips (SoCs) are becoming more and more popular. In these systems, cryptographic accelerators are integrated with processor cores to provide users with the software’s flexibility and hardware’s high performance. First, this work aimed to confirm the vulnerability of cryptographic SoCs against several types of power analysis attacks. Then, the novel Random Dynamic Frequency Scaling (RDFS) countermeasure is proposed to improve the resistance of such systems. The proposed RDFS countermeasure improved the power analysis resistance while maintaining low-performance overhead and hardware costs by generating more than 219,000 distinct frequencies for driving only the cryptographic accelerators. The effectiveness of the proposed RDFS countermeasure is demonstrated by conducting realistic Correlation Power Analysis (CPA) attacks, Deep-Learning-based Side-Channel Analysis (DL-SCA) attacks, and Test Vector Leakage Assessment (TVLA) testing methodology. The experimental results show that the RISC-V SoC protected by RDFS countermeasure can withstand CPA attacks and TVLA test with more than five million power traces, which is the best result compared to other related works. The proposed RDFS countermeasure also harden the targeted RISC-V SoC’s resistance against DL-SCA attacks.
Noncompliance as Deviant Behavior: An Automated Black-box Noncompliance Checker for 4G LTE Cellular Devices

The paper focuses on developing an automated black-box testing approach called DIKEUE that checks 4G Long Term Evolution (LTE) control-plane protocol implementations in commercial-off-the-shelf (COTS) cellular devices (also, User Equipments or UEs) for noncompliance with the standard. Unlike prior noncompliance checking approaches which rely on property-guided testing, DIKEUE adopts a property-agnostic, differential testing approach, which leverages the existence of many different control-plane protocol implementations in COTS UEs. DIKEUE uses deviant behavior observed during differential analysis of pairwise COTS UEs as a proxy for identifying noncompliance instances. For deviant behavior identification, DIKEUE first uses black-box automata learning, specialized for 4G LTE control-plane protocols, to extract input-output finite state machine (FSM) for a given UE. It then reduces the identification of deviant behavior in two extracted FSMs as a model checking problem. We applied DIKEUE in checking noncompliance in 14 COTS UEs from 5 vendors and identified 15 new deviant behavior as well as 2 previous implementation issues. Among them, 11 are exploitable whereas 3 can cause potential interoperability issues.
Oblivious Transfer from Trapdoor Permutations in Minimal Rounds

Oblivious transfer (OT) is a foundational primitive within cryptography owing to its connection with secure computation. One of the oldest constructions of oblivious transfer was from certified trapdoor permutations (TDPs). However several decades later, we do not know if a similar construction can be obtained from TDPs in general.
A Privacy Preserving Anomaly Detection Framework for Cooperative Smart Farming Ecosystem

The agriculture sector has seen growing applications of AI and data intensive systems. Typically, individual farm owners join together to form agricultural cooperatives to share resources, data, and domain knowledge. These data intensive cooperatives help generate AI supported insights for member farmers. However, this leads to a rising concern among individual smart farm owners about the privacy of their data, especially while sharing the data with the co-op. In this paper, we present a framework where the individual smart farm owner's privacy is preserved, as it is shared to train robust anomaly detection models at the cooperative level. Here, we preserve the privacy of each farm owner by adding noise to their data through data perturbation techniques such as white Gaussian noise. Our experimental results show that the anomaly detection models can identify various anomalous events even when the training data is transformed with white noise. Further, we evaluate our framework and compare the detection performance on non-transformed and transformed data that belongs to multiple smart farms present in a cooperative.
Unintended Behavior in Learning-Enabled Systems: Detecting the Unknown Unknowns

One of the important certification objectives for airborne software is demonstrating the absence of unintended behavior. In current software development processes, unintended behavior is associated with some identifiable structural feature, such as specific lines of code or a model element. However, in learning-enabled systems (like neural networks or other machine learning approaches), unintended behavior emerges from the data used to train the system. New inputs not encountered during training may result in novel activations in a neural network, leading to unexpected (and potentially dangerous) outputs. In this paper we will first review the rationale and methods for detecting unintended behavior in current airborne software systems, including the use of model based development techniques and formal methods for software verification. Then we will consider the challenges posed by learning-enabled components (LECs) and examine new techniques that are being developed to address these challenges, as well as how these techniques may shape new certification guidance.
Towards Personalized, Dialogue-Based System Supported Learning for MOOCs

Since the start of the pandemic in March 2020 at the latest, e-learning has become a key factor. Many students and teachers had to shift from a traditional campus to online platforms and learn new skills in a very short time. Even before the pandemic, lack of technical skills, poor hardware, and unstable internet connections were not the only limitations of e-learning portals, such as Massive Open Online Course (MOOC) platforms. Many students complain about a lack of self-discipline and self-motivation when learning online. They often feel too stressed, which leads to more frustration. Lack of social interaction between students or the teacher, as well as lack of body language, complicate educational activities. Participants cannot ask teachers for assistance as easily as they can with traditional in-class learning. Alternatives to traditional campus facilities, such as libraries or career counseling, are also limited on e-learning platforms. To reduce or even solve the limitations of MOOC platforms, this paper presents a concept for personalized, automated dialogue-based systems. These systems will have various integrated scaffolding tools to encourage the users in their learning activities. Besides a library function and a quiz mode, the chatbot answers frequently asked questions, recommends tutorials, and helps the users with their learning paths. An integrated to-do list with reminders is designed to help students become more organized and complete coursework on time. Ultimately, such efforts to create more personalized e-learning tools and interactions could reduce drop-out rates and increase educational achievements and satisfaction among users of MOOC platforms.
Re‐ranking with multiple objective optimization in recommender system

Abstract The ranking algorithm in the recommender system aims at optimizing accuracy during training so that it pays too much attention to the relevance of the individual and ignores the mutual influence between the items in the list. In response to this problem, we propose dither, a re‐ranking model for the recommender system. We deploy the re‐ranking algorithm as an independent module after the ranking algorithm to achieve the function of decoupling from it. Our model formalizes the re‐ranking problem as a multi‐objective optimization problem. It re‐ranks the initial ranking list by balancing multiple indicators to generate an improved list and updates the list during frequent user interactions with the system. Through a case study on the MovieLens 100 K data set, the workflow, and effects of the dither model are demonstrated. In addition, the re‐ranking algorithm shows the performance advantages of our model over existing methods.
Defending Medical Image Diagnostics Against Privacy Attacks Using Generative Methods: Application to Retinal Diagnostics

Machine learning (ML) models used in medical imaging diagnostics can be vulnerable to a variety of privacy attacks, including membership inference attacks, that lead to violations of regulations governing the use of medical data and threaten to compromise their effective deployment in the clinic. In contrast to most recent work in privacy-aware ML that has been focused on model alteration and post-processing steps, we propose here a novel and complementary scheme that enhances the security of medical data by controlling the data sharing process. We develop and evaluate a privacy defense protocol based on using a generative adversarial network (GAN) that allows a medical data sourcer (e.g. a hospital) to provide an external agent (a modeler) a proxy dataset synthesized from the original images, so that the resulting diagnostic systems made available to model consumers is rendered resilient to privacy attackers. We validate the proposed method on retinal diagnostics AI used for diabetic retinopathy that bears the risk of possibly leaking private information. To incorporate concerns of both privacy advocates and modelers, we introduce a metric to evaluate privacy and utility performance in combination, and demonstrate, using these novel and classical metrics, that our approach, by itself or in conjunction with other defenses, provides state of the art (SOTA) performance for defending against privacy attacks.
Training algorithm matters for the performance of neural network potential: A case study of Adam and the Kalman filter optimizers

One hidden yet important issue for developing neural network potentials (NNPs) is the choice of training algorithm. In this article, we compare the performance of two popular training algorithms, the adaptive moment estimation algorithm (Adam) and the extended Kalman filter algorithm (EKF), using the Behler-Parrinello neural network and two publicly accessible datasets of liquid water [Morawietz et al., Proc. Natl. Acad. Sci. U. S. A. 113, 8368-8373, (2016) and Cheng et al., Proc. Natl. Acad. Sci. U. S. A. 116, 1110-1115, (2019)]. This is achieved by implementing EKF in TensorFlow. It is found that NNPs trained with EKF are more transferable and less sensitive to the value of the learning rate, as compared to Adam. In both cases, error metrics of the validation set do not always serve as a good indicator for the actual performance of NNPs. Instead, we show that their performance correlates well with a Fisher information based similarity measure.
Application of Machine Learning Techniques in Detecting Fake Profiles on Social Media

Today, almost billions of people use social media sites to share pictures, thoughts online and connect to people worldwide. During this challenging time of pandemics, we are leaning towards social media more than ever, increasing our engagement. It has made us aware of the coronavirus cases worldwide, has entertained us, helped us stay connected to our acquaintances living far away, and even helped many to start and grow their small businesses online. Besides the wide range of advantages that social media offers, it also comes up with many disadvantages of being an online platform. Issues like fake profiles and impersonation have increased on social media platforms, such as computer-generated bots, human-generated, or cyborgs. Such accounts are made with malicious intentions. Moreover, there is no feasible solution to such a problem. The paper aims at developing a model which can detect fake profiles on social media by using machine learning techniques for better prediction and identification. For this paper, Instagram data is considered for the availability of the dataset, and the analysis will be done by implementing the machine learning algorithms, which gives the highest accuracy on the dataset. By accurately detecting such profiles, social media platforms can be made safe for users.
A Real-time Anti-distractor Infrared UAV Tracker with Channel Feature Refinement Module

The unmanned aerial vehicles (UAVs) have been widely used in various application fields, yet unauthorized use of UAVs raises great threats for restricted areas and public security. Therefore, it is urgently necessary to develop a practical anti-UAV target tracking technique. In this paper, we propose a real-time anti-distractor infrared UAV tracker for infrared anti-UAV tasks, which employs a global real-time perception mechanism to find candidate targets, then utilizes spatial-temporal information to obtain the real UAV target. Moreover, we integrate a channel feature refinement module into multi-scale feature fusion to better enhance the representation of the finer features of the UAV targets channel-wisely, thus improving the tracking performance. We test the performance of the proposed method and the other competitive ones on the constructed UAV dataset from ourselves, and eventually verify the validity of the proposed method as the best performing method with a better balance between tracking accuracy and speed.
Improvements in Multi-Document Abstractive Summarization using Multi Sentence Compression with Word Graph and Node Alignment

The present work proposes a scheme for multi-document abstractive text summarization using node-aligned Word Graph based representation of clustered sentences. In the first step, the proposed scheme uses SBERT embedding for representing the sentences as fixed-size vectors. The sentences belonging to the same cluster are then represented using Word Graph, in which words of different sentences are aligned based on their semantic and syntactic similarities. The advantage of the above representation is that it utilizes alignment information of words between pairs of similar sentences to merge nodes in the Word Graph, and thereby facilitating the generation of sentences with multiple chunks of information. A sentence scoring function assisted by an intensification function is used to measure the grammaticality and informativeness of the generated sentences. Integer Linear Programming has been used to make the final selection of the scored sentences for generating the abstract. Experiments conducted for the task of sentence fusion and multi-document summarization demonstrate superior performance in comparison with the state-of-the-art techniques available in the literature.
Quantum Spectral Clustering of Mixed Graphs

Spectral graph partitioning is a well known technique to estimate clusters in undirected graphs. Recent approaches explored efficient spectral algorithms for directed and mixed graphs utilizing various matrix representations. Despite its success in clustering tasks, classical spectral algorithms suffer from a cubic growth in runtime. In this paper, we propose a quantum spectral clustering algorithm for discovering clusters and properties of mixed graphs. Our experimental results based on numerical simulations demonstrate that our quantum spectral clustering outperforms classical spectral clustering techniques. Specifically, our approach leads to a linear growth in complexity, while state-of-the-art classical counterpart leads to cubic growth. In a case study, we apply our proposed algorithm to preform unsupervised machine learning using both real and simulated quantum computers. This work opens an avenue for efficient implementation of machine learning algorithms on directed as well as mixed graphs by making use of the inherent potential quantum speedup.
VR Collaboration in Large Companies: An Interview Study on the Role of Avatars

Collaboration is essential in companies and often physical presence is required, thus, more and more Virtual Reality (VR) systems are used to work together remotely. To support social interaction, human representations in form of avatars are used in collaborative virtual environment (CVE) tools. However, up to now, the avatar representations often are limited in their design and functionality, which may hinder effective collaboration. In our interview study, we explored the status quo of VR collaboration in a large automotive company setting with a special focus on the role of avatars. We collected inter-view data from 21 participants, from which we identified challenges of current avatar representations used in our setting. Based on these findings, we discuss design suggestions for avatars in a company setting, which aim to improve social interaction. As opposed to state-of-the-art research, we found that users within the context of a large automotive company have an altered need with respect to avatar representations.
Multi-featured Cyberbullying Detection Based on Deep Learning

Cyberbullying is a common and harmful social problem. The existing cyberbullying detections used traditional machine learning and deep learning algorithms to extract text features and then classify users’ emotions, but the classification results can hardly meet expectations. This paper proposes a BiGRU-CNN sentiment classification model (GCA: BiGRU+CNN+ATTETION) for cyberbullying detection, which consists of BiGRU layer, attention mechanism layer, CNN layer, full connection layer and classification layer. BiGRU is a bidirectional GRU layer that has the function of combining context to gain the global features. With the join of CNN layer based on a convolution function with 128 kernels of length 5, it can not only obtain the local features more comprehensively, but also improve the learning rate of the model greatly. The attention mechanism layer can grasp representative words and assign weight to words better. We use the text data set from Kaggle and the emoji data set crawled from social networks to train and test the model. The results show that the classification accuracy of the model is better than that of the traditional model, which reaches 91.07%.
Existential time and historicity in interaction design

KEYWORDS: Speculative designinteraction design educationdevelopmenttimetemporalityhistoricityexistentialismSTS
Equivalence of Three Classical Algorithms With Quantum Side Information: Privacy Amplification, Error Correction, and Data Compression

Privacy amplification (PA) is an indispensable component in classical and quantum cryptography. Error correction (EC) and data compression (DC) algorithms are also indispensable in classical and quantum information theory. We here study these three algorithms (PA, EC, and DC) in the presence of quantum side information, and show that they all become equivalent in the one-shot scenario. As an application of this equivalence, we take previously known security bounds of PA, and translate them into coding theorems for EC and DC which have not been obtained previously. Further, we apply these results to simplify and improve our previous result that the two prevalent approaches to the security proof of quantum key distribution (QKD) are equivalent. We also propose a new method to simplify the security proof of QKD.
Blockchain Based Covid Vaccine Booking and Vaccine Management System

Immun izat ion is all about using Block chain for managing and tracing the vaccine stocks, logistics and transparent distribution.Immunization g ives you continuous visibility and enables actionable insights to track vaccine distribution and ensure a fair and equitable distribution.Immunization allows you to book your vaccination appointments and will also allo w you to keep track of the vaccine being distributed.Block chain helps in maintaining the integrity and transparency of the whole process right from inception of the vaccine.
Robot Arm Path Planning Based on Improved RRT Algorithm

There are problems such as large randomness, high space complexity, and inability to generate the optimal path in the path planning of the manipulator in view of the traditional RRT(Rapidly Random Tree) algorithm. In this paper, the traditional RRT algorithm is optimized by fusing the artificial potential field method, so that the manipulator can effectively avoid obstacles in the working process, and ensure the shortest path of the manipulator from the starting point to the target point. The simulation results prove the effectiveness of the method.
The Technology of the Learning Outcomes Test Development

The paper presents the technology for developing a test of learning outcomes applied to engineering students. Considering the evidence, which reflected the practice of learning outcomes testing and tests development, the need to study the problem was revealed. There was a lack of scientifically based recommendations that allow designing valid and reliable assessment tools for the learning outcomes diagnostics under competency-based education. The finding of the current research was formed as a technology of the test development. The substantiated technology included four phases: organization and preparation, design and expertize reflection and diagnostics, and final technological phase. Each phase involved certain stages. The proposed diagnostic procedures made it possible to design valid tests that met the quality criteria in examining the level of learning outcomes for engineering students. When the test developer adheres strictly to the reasonable testing phases, they obtain an algorithm for creating a valid test item, which could be accumulated in a test database and then used to design tests. Following the technology stages, an educator/examiner/test developer could design, justify and evaluate the test item according to a scientific basis rather than their intuition. That enables objective and reliable results of student’s achievements testing.