Reviewerly/exHarmony huggingface dataset:
Hybrid Architecture Based on Blockchain Technology and Smart Contracts for Healthcare Applications

The goal of this paper is to design a hybrid architecture based on blockchain technology and smart contracts for healthcare applications. The problem that will be addressed is how to achieve a balance between the three standard concepts of the success of the health system, which are: “privacy,” “security,” and “efficiency and effectiveness of data usage.” The authors proposed a hybrid architecture for a more balanced approach by storing the light information in the blockchain (distributed and decentralized storage), except for large media files, which are in the central storage, which reduces the cost of storing large files and raises the protection of critical information. For demonstration, an application was developed based on the proposed architecture and using smart contracts with the help of an IPFS server as a document storage method, and using MetaMask application as an authentication tool.
Learning to Throw With a Handful of Samples Using Decision Transformers

Throwing objects by a robot extends its reach and has many industrial applications. While analytical models can provide efficient performance, they require accurate estimation of system parameters. Reinforcement Learning (RL) algorithms can provide an accurate throwing policy without prior knowledge. However, they require an extensive amount of real world samples which may be time consuming and, most importantly, pose danger. Training in simulation, on the other hand, would most likely result in poor performance on the real robot. In this letter, we explore the use of Decision Transformers (DT) and their ability to transfer from a simulation-based policy into the real-world. Contrary to RL, we re-frame the problem as sequence modelling and train a DT by supervised learning. The DT is trained off-line on data collected from a far-from-reality simulation through random actions without any prior knowledge on how to throw. Then, the DT is fine-tuned on an handful ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 5$</tex-math></inline-formula> ) of real throws. Results on various objects show accurate throws reaching an error of approximately 4 cm. Also, the DT can extrapolate and accurately throw to goals that are out-of-distribution to the training data. We additionally show that few expert throw samples, and no pre-training in simulation, are sufficient for training an accurate policy.
Human Activity Segmentation employing Ml Algorithms and Cellular phone Sensor Data

Recognizing human behavior has been shown to be helpful in a variety of fields, including human-machine interaction, creating smart devices with unique properties, and comprehending human nature. One of the intelligent devices that a person uses all day long is a smartphone. Smartphones are equipped with several sensors that can read the angular motion, acceleration, position, and tri-axial (3-d) motion data with great accuracy. Machine learning techniques have already proven their capability to work with data on a large scale and have a large number of features. In this project, we will identify human activity based on information gathered by cellphone sensors using a sensor read value data set. We displayed the precision, recall, and f1-score together with the related confusion matrix to allow for additional analysis of the model's performance. Support vector classification outperforms the other five machine learning models with 97% training and 96% testing accuracy, whereas the decision tree struggles to handle a large number of features and performs poorly with only 41% training and testing accuracy. In the case of SGD, a similar situation is evident. However, Ridge CV and logistic regression perform remarkably well.
Handling Exit Node Vulnerability in Onion Routing with a Zero-Knowledge Proof

Onion routing is a method for anonymous data transfer in a communication network. It is mostly used for the deep web via TOR's (The Onion Router) different services such as TOR browser, Atlas, Orbot, and more. In this network, messages are encrypted with several layers, each layer connects only with its predecessor. An anonymity problem arises at the destination point, the exit node, which is the final node in the onion chain of routing. In the exit node the final layer is decrypted, and the message is delivered to the recipient. The possible vulnerability is that if the exit node is compromised in some way, the attacker can receive the raw data that is being delivered, potentially including sensitive or personal information. There are some ways of handling this issue, such as SSL encryption or secure HTTP, but these are not anonymous by nature as certificates include personal verification. This paper suggests a new method for solving the Exit Node Vulnerability issue that keeps the privacy and anonymity of the network. The method uses Zero-Knowledge Proof (ZKP), an encryption scheme in which one party (the prover) can validate specific information to another side (the verifier) without disclosing any additional private information. The paper presents the full scheme that solves the Exit Node Vulnerability in Onion routing.
AR-based Sustainability in Food Development

This paper presents the design, prototype implementation and primary evaluation of an AR-based mobile application for promoting sustainable food development mainly from a customer point of view. Related work shows that AR may raise awareness of customers, travelers and food professionals in sustainability food issues but there is no application that expresses them in a holistic way. After discussing what constitutes food sustainable development, the qualitative characteristics of the application are defined. A partially functionable prototype is presented that contains the main interactivity and gaming activities. Primary evaluation results show that end-users find useful the application and favor its complete implementation.
iGROWL: Improved Group Detection With Link Prediction

One of the main challenges robots need to overcome is crowd analysis. Crowd analysis deals with the detection of individuals and interaction groups as well as the recognition of their activities. This paper focuses on the detection of conversational groups, where there have been a number of approaches addressing this problem in both supervised and unsupervised ways. Supervised bottom-up approaches primarily relied on pairwise affinity matrices and were limited to static, third-person views. In this work, we present our approach based on Graph Neural Networks (GNNs) to the problem of interaction group detection, called improved Group Detection With Link Prediction (iGROWL). iGROWL utilises the fact that interaction groups exist in certain inherent spatial configurations and improves its predecessor, GROWL, by introducing an ensemble learning-based sample balancing technique to the algorithm. Our results show that iGROWL outperforms other state-of-the-art methods by 16.7% and 26.4% in terms of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{1}$ </tex-math></inline-formula> -score when evaluated on the Salsa Poster Session and Cocktail Party datasets, respectively. Moreover, we show that sample balancing with GNNs is not trivial, but consistent results can be achieved by employing ensemble learning.
REBench: Microbenchmarking Framework for Relation Extraction Systems

In recent years, several relation extractions (RE) models have been developed to extract knowledge from natural language texts. Accordingly, several benchmark datasets have been proposed to evaluate these models. These RE datasets consisted of natural language sentences with a fixed number of relations from a particular domain. Albeit useful for general-purpose RE benchmarking, they do not allow the generation of customized microbenchmarks according to user-specified criteria for a specific use case. Microbenchmarks are key to testing the individual functionalities of a system and hence pinpoint component-based insights. This article proposes REBench, a framework for microbenchmarking RE systems, which can select customized relation samples from existing RE datasets from diverse domains. The framework is flexible enough to choose relation samples of different sizes and according to the user-defined criteria on essential features to be considered for RE benchmarking. We used various clustering algorithms to generate microbenchmarks. We evaluated the state-of-the-art RE systems using different RE benchmarking samples. The evaluation results show that specialized microbenchmarking is crucial for identifying the limitations of various RE models and their components. Resource Type: Evaluation benchmarks or Methods Repository: https://github.com/dice-group/REBench License: GNU General Public License v3.0
MedViTGAN: End-to-End Conditional GAN for Histopathology Image Augmentation with Vision Transformers

Deep learning networks have demonstrated competitive performance for various tasks on medical images. However, obtaining promising results requires a large amount of annotated data for supervised training, which is labor-intensive. Recently, the increasing interest in transformers has suggested their robust performance on computer vision tasks, including generative adversarial networks (GANs). In this paper, we propose a conditional GAN built on pure transformer-based architectures, named MedViTGAN, to assist in generating synthetic histopathology images for data augmentation in an end-to-end manner. The presented model adopts a conditioned training strategy by incorporating a transformer-based auxiliary classifier to facilitate the discriminative image generation process. We further introduce an adaptive hybrid loss weighting mechanism to balance multiple losses over sources and classes to stabilize the training. Extensive experiments on the histopathology datasets show that leveraging MedViTGAN generated images results in a significant and consistent improvement in classification performance.
Nested Multi-Axis Learning Network for Single Image Super-Resolution

Real-world images usually contain rich features at different granularity levels, such as illumination, edges, and textures. Performance of a deep-learning method for single image super-resolution (SISR) could be degraded if it fails to extract features over all granularity levels. To address this problem, a novel deep-learning network is proposed, which consists of a set of nested multi-axis learning blocks (NMLBs) and is thus termed the nested multi-axis learning network (NMLNet). Each NMLB has an outer multi-axis structure that contains 3 axes dedicated to extracting coarse-, medium-, and fine-grained features, respectively. With the concern that our human visual system is more sensitive to the medium-grained features (e.g., edges), the medium-grained axis further has an inner multi-axis structure, by which edge features are captured at a wide range of network depths. By transmitting image features via the nested multi-axis structure, efficient information propagation is achieved throughout our developed network. To boost the network performance, a two-tier attention block is also proposed, which adaptively rescales the extracted features in both channel and spatial domains to maximize the representation capacity of our network. Extensive experimental results show that the proposed NMLNet can deliver superior performance over a number of state-of-the-art SISR methods, especially with respect to the reconstruction quality of image edges.
LordBERT: Embedding Long Text by Segment Ordering with BERT

Although BERT has achieved significant improvements on many downstream NLP tasks, it has difficulty handling long text because of its quadratic computation complexity. A typical approach to this issue is splitting the input into shorter segments and utilizing order-independent attention mechanism to conduct intersegment interaction, but the approach ignores the segment order information, which is greatly beneficial for capturing implicit relations across different segments. To address this problem, we propose a novel multi-task learning framework, named LordBERT, which fully exploits both intra- and inter-segment information in long text by segment ordering with BERT. LordBERT learns segment-level representations from segments through BERT and a reasoner, and utilizes an auxiliary segment ordering module to reorder disordered segments. With this module, the model implicitly encodes intersegment relations and global information of long text into segment representations. The downstream task and the ordering task are jointly optimized during training, while for inferencing we mainly conduct the downstream task. Experimental results show that LordBERT outperforms the state-of-the-art models by up to 0.58% in accuracy for text classification tasks on long text.
A Discrete Interval-Based Multi-Objective Memetic Algorithm for Scheduling Workflow With Uncertainty in Cloud Environment

In consideration of the uncertainty of the scientific workflows, an interval-based multi-objective cloud workflow scheduling problem is investigated, which widely exists in the cloud environment. This problem aims at allocating the workflow to the public cloud environment. The uncertain workload and communication data of workflow as well as the processing ability and bandwidth of the resources are represented by an interval number, which models the uncertainty of these variables. The objectives are to minimize the total execution time and cost. To address this problem, a discrete interval-based multi-objective memetic algorithm (DIMOMA) is proposed. A hybrid initial strategy is employed to generate the potential population. With the contribution-based selection mechanism, the self-adaptive genetic operators are designed to perform a global search in the problem space. Then, a novel local search procedure is incorporated to perform intensification and accelerate the convergence. A comprehensive computational experiment and comparisons with several meta-heuristics adapted from the related problems are conducted based on an extended benchmark set. The simulated results reveal that the proposed method can achieve better trade-off fronts between the execution time and cost of workflow. On the performance metric hypervolume which measures both execution time and cost, the proposed DIMOMA can improve by 3.90%, 9.30%, 6.25%, and 7.74% compared with EMS-C, MOACS, ch-PICEA-g, and I_MaOPSO, respectively. Besides, DIMOMA can achieve better robustness, which means the difference between the lower and upper bounds of the execution time and cost of the solutions obtained by DIMOMA are over smaller than the state-of-the-art algorithms.
Arbitrarily Oriented Object Detection in Remote Sensing Images Based on Improved YOLOv4-CSP

Arbitrarily oriented object detection in remote sensing images is a challenging task.At present, most of the algorithms are dedicated to improving the detection accuracy, while ignoring the detection speed.In order to further improve the detection accuracy and provide a more efficient model for scenes that require real-time detection, we propose an improved YOLOv4-CSP network for rotating object detection in remote sensing images.There are mainly three contributions in our approach.First, we design a new bounding box regression loss function, which is distance and angle-intersection over union (DAIoU).This loss function is formed by adding a distance penalty term and an angle penalty term on the basis of intersection over union.It is suitable for arbitrarily oriented object detection networks.Second, we develop an adaptive angle setting method for anchors based on k-means clustering algorithm.This method can obtain representative angles for better representing the distribution of the angle set.By assigning representative angles to all anchors for training, it is beneficial to reduce the complexity of the network to adjust anchors to GT bounding boxes.Finally, we improve the YOLOV4-CSP network and make it suitable for detection scenarios based on rotated anchors by applying rotation transformations.We combine the above methods and use the final network to perform the detection task.The experimental results on three remote sensing datasets, i.e., HRSC2016, UCAS-AOD, and SSDD+, validate the effectiveness of our method.Comparison results with state-of-the-arts methods demonstrate that our method can be used to significantly improve the detection accuracy with a higher detection speed.
Tamil Speech Recognition Using XLSR Wav2Vec2.0 &amp; CTC Algorithm

Automatic Speech Recognition is a promising research topic with lots of real-world applications like virtual assistants, aids for physically challenged etc. Tamil language speech recognition could be potentially challenging due to the fact that there are many possible dialects, slangs and accents. This paper proposes an ASR system based on cross-lingual transfer learning in combination with CTC algorithm. The pretrained model from Facebook AI viz. XLSR Wav2Vec2.0 is used. The dataset used in this work is Common Voice Tamil, which is a crowd-sourced dataset provided by Mozilla. Our system achieves a Word Error Rate of 0.58 and Character Error Rate of 0.11.
Heterogeneous Image Classification with Multi-Stage Conditional Adversarial Domain Adaptation Between SAR and Optical Imagery

In this paper, we deal with the problem of heterogeneous image classifiers transferring between SAR and optical im-agery through a novel multi-stage domain adaptation technique. The problem of transferring the optical image classifier to SAR and vice-versa is of practical importance because it allows us to leverage plenty of labelled data in the source do-main for the target domain task, but gains little attention. Be-cause there is a drastic distribution-gap between both the opti-cal and SAR imaging modalities, it is non-trivial to apply do-main adaption directly. We propose a multi-stage adversarial feature alignment procedure that firstly performs global ad-versarial feature alignment and then a class-conditional adver-sarial feature alignment is conducted to further enable class-discriminative feature adaption. The proposed method is val-idated on the SEN12MS dataset, and some discussions are provided about heterogeneous domain adaption between SAR and optical imagery.
Visualizing Code Smells: Tables or Code Cities? A Controlled Experiment

This paper presents a study in which we compared the visualization of code smells in Code Cities with classical tabular representations. We conducted a controlled experiment with 20 participants who had to solve six tasks in both environments. We evaluated the results of our experiment statistically and came to the following conclusions: In four tasks the completion time was significantly lower when using Code Cities (the remaining two tasks did not show any statistically significant differences for any of the two environments). Also, the perceived effort was significantly lower in three tasks when the participants used Code Cities over the tabular representation (again, the remaining tasks did not show any statistically significant differences). However, with regard to the perceived usability (which was measured across all tasks) and the correctness of the supplied answers, the tabular representation performed better. In particular, in two tasks the correctness was significantly better in the tabular environment and in one task the correctness was significantly better in the Code Cities environment. Based on our results, our verdict is as follows: Code Cities are better suited to get a quick overview of the code smells of a software, whereas tabular representations are better suited to analyze code smells in more detail.Experiment data, evaluation scripts and supplemental material: https://github.com/uni-bremen-agst/VISSOFT2022/archive/ refs/tags/1.0.0.zip (see README.md in the ZIP archive)
Fitness Freaks: A System for Detecting Definite Body Posture Using OpenPose Estimation

Nowadays, because of busy schedules, people have no time to go to the gym, and even if they manage to find a gym nearby having a gym trainer besides all the time to correct postures while doing exercise is impossible unless people do not opt for a personal trainer. Even if they assist a personal trainer for them, they would have to adjust their time accordingly, and both of these methods are quite costly, and not everyone can afford it; also, during this global pandemic, people are stuck at home and have no access to go to the gym or can’t even take a risk of getting in contact with a personal trainer. Performing exercises let it be exercise or doing Yoga proper body posture is important; if not performed properly, it can lead to crucial problems such as poor joint alignment, increased shear forces on the spine, compression of disks and joints, less space for nerves to course through the body due to compression, and reduced blood flow to prevent such injuries and pains, and to track gym exercises repetitions, we came up with a system called Fitness Freaks. Fitness Freaks is an AI fitness tracker. It tracks users’ body movements using human pose estimation. This in turn keeps track of repetitions of gym exercises and detection of wrong body posture while doing Yoga.
The Behavioral Code: Recommender Systems and the Technical Code of Behaviorism

Our lives are increasingly mediated, regulated and produced by algorithmically-driven software; often invisible to the people whose lives it affects. Online, much of the content that we consume is delivered to us through algorithmic recommender systems (“recommenders”). Although the techniques of such recommenders and the specific algorithms that underlie them differ, they share one basic assumption: that individuals are “users” whose preferences can be predicted through past actions and behaviors. While based on a set of assumptions that may be largely unconscious and even uncontroversial, we draw upon Andrew Feenberg’s work to demonstrate that recommenders embody a “formal bias” that has social implications. We argue that this bias stems from the “technical code” of recommenders – which we identify as a form of behaviorism. Studying the assumptions and worldviews that recommenders put forth tells us something about how human beings are understood in a time where algorithmic systems are ubiquitous. Behaviorism, we argue, forms the episteme that grounds the development of recommenders. What we refer to as the “behavioral code” of recommenders promotes an impoverished view of what it means to be human. Leaving this technical code unchallenged prevents us from exploring alternative, perhaps more inclusive and expansive, pathways for understanding individuals and their desires. Furthermore, by problematizing formations that have successfully rooted themselves in technical codes, this chapter extends Feenberg’s critical theory of technology into a domain that is both ubiquitous and undertheorized.