Reviewerly/exHarmony huggingface dataset:
Instant and global consistency checking during collaborative engineering

Abstract Engineering projects involve a variety of artifacts such as requirements, design, or source code. These artifacts, many of which tend to be interdependent, are often manipulated concurrently. To keep artifacts consistent, engineers must continuously consider their work in relation to the work of multiple other engineers. Traditional consistency checking approaches reason efficiently over artifact changes and their consistency implications. However, they do so solely within the boundaries of specific tools and their specific artifacts (e.g., consistency checking between different UML models). This makes it difficult to examine the consistency between different types of artifacts (e.g., consistency checking between UML models and the source code). Global consistency checking can help addressing this problem. However, it usually requires a disruptive and time-consuming merging process for artifacts. This article presents a novel, cloud-based approach to global consistency checking in a multi-developer/-tool engineering environment. It allows for global consistency checking across all artifacts that engineers work on concurrently. Moreover, it reasons over artifact changes immediately after the change happened, while keeping the (memory/CPU) cost of consistency checking minimal. The feasibility and scalability of our approach were demonstrated by a prototype implementation and through an empirical validation.
Automated Detection of Multitype Landforms on Mars Using a Light-Weight Deep Learning-Based Detector

Intification of geological salient landforms is a primary requirement for spacecraft motion estimation and obstacle avoidance. As a large volume of high-resolution images are acquired by the Mars reconnaissance orbiter, growing number of approaches are proposed to develop automated approaches to detect a particular landform on Mars. However, most existing objective detection models are limited to sliding window-based and morphology-based algorithms, which require complicated preprocessing operations and can hardly be generalized to detecting different types of landforms. In this article, we aimed at developing a multitype landform detection system based on a light-weight deep learning framework, which has a quite small model size but presents excellent performance. This specific deep learning-based framework is named as mini shot multibox detector (SSD), by downsizing and modifying the existing single SSD. In the mini-SSD, some components are further optimized to adapt to this domain specific problem. A pretraining strategy is well-designed and merged into the entire model training process. In the performance evaluation tests, the proposed framework was trained and tested on images with different scales collected from different locations in high resolution imaging science experiment database. Results demonstrate that the introduced Adam optimizer and pretraining strategy can form positive effective to both model training and inference performance. The proposed framework and strategy combination outperforms the original SSD300, faster R-CNN, and YOLO series models as well as all state-of-the-art sliding window-based detectors in the field, namely AdaBoost with LBP features, AdaBoost with Haar features, and support vector machines with histogram of oriented gradient (HOG) features, in two different testing sets. Additionally, it shows high resilience on detecting target landforms in different environments with various sizes and shapes from the qualitative analysis, and can be generalized as a tool for relevant applications.
On Dynamic Resource Allocation for Blockchain Assisted Federated Learning over Wireless Channels

The blockchain technology has been extensively studied to enable distributed and tamper-proof data processing in federated learning (FL). Most existing blockchain assisted FL (BFL) frameworks have employed a third-party blockchain network to decentralize the model aggregation process. However, the decentralized model aggregation is vulnerable to pooling and collusion attacks from the third-party blockchain network. Driven by this issue, we propose a novel BFL framework that features the integration of training and mining at the client side. In our framework, the client first transmits its trained model to other clients, performs global aggregation upon receiving others&#x0027; models, and competes to mine a block for aggregated model verification without the intervention of any third-party blockchain network. Considering the model transmission over time-varying wireless channels, we propose a dynamic training client scheduling to meet stringent latency requirement in FL, where clients with qualified channel conditions are scheduled to train their models in each communication round. Furthermore, we formulate a joint optimization problem of the training client scheduling and dynamic resource allocation (i.e., the transmit and computation power at the client side) under the constraint of long-term time-average (LTA) energy consumption. The objective of this optimization problem is to maximize the LTA training data size and thereby optimize the learning performance of FL. To this end, we obtain the closed-form solution by using the Lyapunov optimization method. Our experimental results show that the optimal solution can outperform baseline schemes in terms of learning accuracy and convergence time.
Combine Object Detection with Skeleton-Based Action Recognition to Detect Smoking Behavior

This paper detects violations of smoking in non-smoking areas by construction workers, uses the YOLO object detection algorithm combined with the Kalman filter to track the human body, then uses Alphapose's human pose estimation to obtain the key points of the human body. We input the key points into Spatial-Temporal Graph Convolutional Networks for preliminary identification of workers’ smoking behavior. However, this will lose the texture feature information of the image, resulting in drinking, scratching, etc. will also be recognized as smoking. Therefore, based on action recognition, the YOLO object detection algorithm is added for the second time to extract the image texture of the workers’ smoking based on a face-gesture-cigarette pattern and achieve the joint determination of posture and texture in order to improve the detection accuracy and robustness of smoking recognition.
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs

Graph Convolutional Networks (GCNs) are a popular method from graph representation learning that have proved effective for tasks like node classification. Recent variants on traditional GCN models aim to classify nodes in dynamic graphs whose topologies and node attributes change over time, e.g., social networks with dynamic relationships. These works, however, do not fully address the challenge of flexibly assigning different importance to snapshots of the graph at different times, which depending on the graph dynamics may have more or less predictive power on the labels. We address this challenge by proposing a new method, GCN-SE, that attaches a set of learnable attention weights to graph snapshots at different times, inspired by Squeeze and Excitation Net (SE-Net). We show that GCNSE outperforms previously proposed node classification methods on a variety of graph datasets. To verify the effectiveness of the attention weight in determining the importance of different graph snapshots, we adapt perturbation-based methods from the field of explainable machine learning to graphical settings and evaluate the correlation between the attention weights learned by GCN-SE and the importance of different snapshots over time.
Hybrid CNN-GRU Framework with Integrated Pre-trained Language Transformer for SMS Phishing Detection

Smartphones are prone to SMS phishing due to the rapid growth in the availability of smart mobile technologies driven by Internet connections. Also, detecting phishing SMS is a challenging task due to the unstructured nature of SMS text data with non-linear complex correlations. In this concern, considering the recent advancements in the domain of cybersecurity, we have proposed a hybrid deep learning framework that extracts robust features from SMS texts followed by an automatic detection of Phishing SMS. Due to combining the potential capability of individual models into one hybrid framework, it has outperformed various other individual machine learning and deep learning models. The proposed Phishing Detection framework is an effective hybrid combination of pretrained transformer model, MPNet (Masked and Permuted Language Modeling), with supervised ConvNets (CNN) and Bi-directional Gated Recurrent Units (GRU). It is intended to successfully detect unstructured short phishing text messages that contain complex patterns.
Capuchin search algorithm based task scheduling in cloud computing environment

Cloud computing is mathematical process that provides more power and flexibility in computing infrastructure. Cloud computing provides internet services using a network of remote services. The core service for any environment is the best business plan that supports better quality of service (QoS). Task scheduling in the cloud is a key issue that needs to be addressed to improve system performance and high customer satisfaction. The task scheduling affects the exact time of operation and the cost of using the system. In this paper, we propose a capuchin search algorithm based task scheduling (CSTS) in cloud computing environment. In CSTS method, first we introduce an improved cuttlefish optimization (ICFO) algorithm for task clustering which groups user task into two set as normal and emergency task. Then, we develop a modified capuchin search (MCS) algorithm for priority based optimal task scheduling which minimize makespan and improve resource utilization. Finally, the simulation results of proposed CSTS method is compared with the existing state-of-art methods in terms of makespan, execution time, deadline violation rate and resource utilization.
Collision-free Trajectory Generation for UAV Swarm Formation Rendezvous

This paper proposes offline and online solutions to deal with a collision-free trajectory generation problem for unmanned aerial vehicle (UAV) swarm formation rendezvous. This method consists of two stages: 1) finding a geometric path based on improved ant colony optimization (ACO) with elite strategy and using minimum-snap algorithm with safe corridors to obtain feasible original trajectories so that the spatial-temporal constraints can be satisfied; 2) incorporating the zeroing control barrier function (ZCBF) constraints and the previous trajectories into the optimal problem to meet safety-critical requirement both for static obstacles and inter-UAV avoidance. The proposed method is tested in numerical simulation and the results demonstrate the efficacy in both dynamic constraints and safety constraints for the swarm.
Machine Learning Advances aiding Recognition and Classification of Indian Monuments and Landmarks

Tourism in India plays a quintessential role in the country's economy with an estimated 9.2% GDP share for the year 2018. With a yearly growth rate of 6.2%, the industry holds a huge potential for being the primary driver of the economy as observed in the nations of the Middle East like the United Arab Emirates. The historical and cultural diversity exhibited throughout the geography of the nation is a unique spectacle for people around the world and therefore serves to attract tourists in tens of millions in number every year. Traditionally, tour guides or academic professionals who study these heritage monuments were responsible for providing information to the visitors regarding their architectural and historical significance. However, unfortunately this system has several caveats when considered on a large scale such as unavailability of sufficient trained people, lack of accurate information, failure to convey the richness of details in an attractive format etc. Recently, machine learning approaches revolving around the usage of monument pictures have been shown to be useful for rudimentary analysis of heritage sights. This paper serves as a survey of the research endeavors undertaken in this direction which would eventually provide insights for building an automated decision system that could be utilized to make the experience of tourism in India more modernized for visitors.
Efficient Reinforced Feature Selection via Early Stopping Traverse Strategy

In this paper, we propose a single-agent Monte Carlo based reinforced feature selection (MCRFS) method, as well as two efficiency improvement strategies, i.e., early stopping (ES) strategy and reward-level interactive (RI) strategy. Feature selection is one of the most important technologies in data prepossessing, aiming to find the optimal feature subset for a given downstream machine learning task. Enormous research has been done to improve its effectiveness and efficiency. Recently, the multi-agent reinforced feature selection (MARFS) has achieved great success in improving the performance of feature selection. However, MARFS suffers from the heavy burden of computational cost, which greatly limits its application in real-world scenarios. In this paper, we propose an efficient reinforcement feature selection method, which uses one agent to traverse the whole feature set, and decides to select or not select each feature one by one. Specifically, we first develop one behavior policy and use it to traverse the feature set and generate training data. And then, we evaluate the target policy based on the training data and improve the target policy by Bellman equation. Besides, we conduct the importance sampling in an incremental way, and propose an early stopping strategy to improve the training efficiency by the removal of skew data. In the early stopping strategy, the behavior policy stops traversing with a probability inversely proportional to the importance sampling weight. In addition, we propose a reward-level interactive strategy to improve the training efficiency via reward-level external advice. Finally, we design extensive experiments on real-world data to demonstrate the superiority of the proposed method.
Quantum entanglement and criticality in a one-dimensional deconfined quantum critical point

We investigate the ground-state quantum entanglement in a one-dimensional incarnation of deconfined quantum critical point by making use of the finite-size density matrix renormalization group method. We observe two distinct behaviors of two-site entanglement calculated on odd and even bonds, and the difference O_{E} is shown to obey conventional scaling relations for order parameters. Accurate deconfined critical point and associated critical exponents are numerically extracted from finite-size scaling analyses. We further notice a close similarity between O_{E} and the valence-bond-solid order parameter and same observations are also obtained for quantum coherence and trace distance. Furthermore, the deconfined critical point is suggested to possess rich quantum entanglement other than the two-site entanglement from the residual entanglement perspective. Our work explores the critical characteristics of the one-dimensional deconfined quantum critical point from the quantum information aspect and provides insights for its ground-state entanglement structure.
Teaching Machines to Read, Answer and Explain

With various Pre-trained Language Models (PLMs) blooming, Machine Reading Comprehension (MRC) systems have embraced significant improvements on various benchmarks and even surpassed human performances. However, most existing works only focus on the accuracy of the answer predictions and neglect the importance of the explanations for the prediction, which is a big obstacle when utilizing these models in real-life applications to convince humans. This paper proposes a novel unsupervised self-explainable framework, called Recursive Dynamic Gating (RDG), for the machine reading comprehension task. The main idea is that the proposed system tries to use less passage information and achieves similar results to the system that uses the whole passage, while the filtered passage is used as text explanations. We carried out experiments on three multiple-choice MRC datasets (including English and Chinese) and found that the proposed system can not only achieve better performance in answer prediction but also provide informative explanations compared to the attention mechanism.
An Empirical Comparison of Portuguese and Multilingual BERT Models for Auto-Classification of NCM Codes in International Trade

Classification problems are common activities in many different domains and supervised learning algorithms have shown great promise in these areas. The classification of goods in international trade in Brazil represents a real challenge due to the complexity involved in assigning the correct category codes to a good, especially considering the tax penalties and legal implications of a misclassification. This work focuses on the training process of a classifier based on bidirectional encoder representations from transformers (BERT) for tax classification of goods with MCN codes which are the official classification system for import and export products in Brazil. In particular, this article presents results from using a specific Portuguese-language-pretrained BERT model, as well as results from using a multilingual-pretrained BERT model. Experimental results show that Portuguese model had a slightly better performance than the multilingual model, achieving an MCC 0.8491, and confirms that the classifiers could be used to improve specialists’ performance in the classification of goods.
A Study of Real-Time Physical Activity Recognition from Motion Sensors via Smartphone Using Deep Neural Network

Recognition of activities by the use of the sensor is one of the context-conscious studies fascinated by many research teams. This paper explains how different types of physical activities are recognized using cell phone’s motion sensors data. Three types of motion sensors (accelerometer, gravity, linear acceleration) data were used for this work. An effective and efficient deep neural network model was proposed for this work that is able to recognize human physical activities from tri-axial motion sensor data in real-time which was later implemented on android-based smartphones using android studio. The sequential model was implemented for this work where LSTM, flatten and the dense layer was added. The model is able to classify seven types of human activities in the real-time data feed. The model achieves 98.8% of classification accuracy while training and testing. Later on, the model has been converted to a smartphone-compatible model using Tensorflow, as the initial deep learning model isn’t compatible to insert into smartphones. The model was successfully converted and implemented to an android based smartphone.
Perception Poisoning Attacks in Federated Learning

Federated learning (FL) enables decentralized training of deep neural networks (DNNs) for object detection over a distributed population of clients. It allows edge clients to keep their data local and only share parameter updates with a federated server. However, the distributed nature of FL also opens doors to new threats. In this paper, we present targeted perception poisoning attacks against federated object detection learning in which a subset of malicious clients seeks to poison the federated training of a global object detection model by sharing perception-poisoned local model parameters. We first introduce three targeted perception poisoning attacks, which have severe adverse effects only on the objects under attack. We then analyze the attack feasibility, the impact of malicious client availability, and attack timing. To safeguard FL systems against such contagious threats, we introduce spatial signature analysis as a defense to separate benign local model parameters from poisoned local model contributions, identify malicious clients, and eliminate their impact on the federated training. Extensive experiments on object detection benchmark datasets validate that the defense-empowered federated object detection learning can improve the robustness against all three types of perception poisoning attacks. The source code is available at https://github.com/git-disl/Perception-Poisoning.
Urdu Named Entity Recognition System Using Deep Learning Approaches

Abstract Named entity recognition (NER) is a fundamental part of other natural language processing tasks such as information retrieval, question answering systems and machine translation. Progress and success have already been achieved in research on the English NER systems. However, the Urdu NER system is still in its infancy due to the complexity and morphological richness of the Urdu language. Existing Urdu NER systems are highly dependent on manual feature engineering and word embedding to capture similarity. Their performance lags if the words are previously unknown or infrequent. The feature-based models suffer from complicated feature engineering and are often highly reliant on external resources. To overcome these limitations in this study, we present several deep neural approaches that automatically learn features from the data and eliminate manual feature engineering. Our extension involved convolutional neural network to extract character-level features and combine them with word embedding to handle out-of-vocabulary words. The study also presents a tweets dataset in Urdu, annotated manually for five named entity classes. The effectiveness of the deep learning approaches is demonstrated on four benchmarks datasets. The proposed method demonstrates notable progress upon current state-of-the-art NER approaches in Urdu. The results show an improvement of 6.26% in the F1 score.
ML-Based Classification of Device Environment Using Wi-Fi and Cellular Signal Measurements

Future spectrum sharing rules very likely will be based on device environment: indoors or outdoors. For example, the 6 GHz rules created different power regimes for unlicensed devices to protect incumbents: “indoor” devices, subject to lower transmit powers but not required to access an Automatic Frequency Control database to obtain permission to use a channel, and “outdoor” devices, allowed to transmit at higher power but required to do so to determine channel availability. However, since there are no reliable means of determining if a wireless device is indoors or outdoors, other restrictions were mandated: reduced power for client devices and indoor access points that cannot be battery powered, have detachable antennas or be weatherized. These constraints lead to sub-optimal spectrum usage and potential for misuse. Hence, there is a need for robust identification of device environments to enable spectrum sharing. In this paper we study automatic indoor/outdoor classification based on the radio frequency (RF) environment experienced by a device. Using a custom Android app, we first create a labeled data set of a number of parameters of Wi-Fi and cellular signals in various indoor and outdoor environments, and then evaluate the classification performance of various machine learning (ML) models on this data set. We find that tree-based ensemble ML models can achieve greater than 99% test accuracy and F1-Score, thus allowing devices to self-identify their environment and adapt their transmit power accordingly.