Reviewerly/exHarmony huggingface dataset:
Extending LADM to support workflows and process models

In order to be able to guarantee the overall correctness of data, updating of Land administration system's data must be executed following a formally and technically clearly defined and controllable procedure (process). In general technical sense, correctness of data primarily depends on its consistency. Databases natively support enforcing consistency of transactions over the data but lack the flexibility needed for modelling business processes and do not natively support operating in heterogeneous IT environments (web services, various external and loosely coupled systems). WorkFlow Management Systems (WFMS) with integrated transactional properties provide a technological platform which supports enforcing consistency within heterogeneous IT environments. Within the scope of our previous work and in order to provide a standardised means for modelling of dynamic component of Land Administration Systems (LASs), we developed an extension to LADM for integrating processes into LADM. We propose to extend the LADM with a new class LA_Process, as a sub-class of the existing LADM class LA_Source. Also, LA_Process is associated to descendants of LA_Source (spatial and administrative source) and versioned object. Associating those classes to LA_Process enables executing processes over instances of LADM classes in a versioning supported manner and linked to underlying formal documents. This extension is proposed to be included in the second edition of the LADM which is under development (Lemmen et al., 2019). Within this work we further revise LA_Process in order to broaden its scope and to consider processes on LADM classes outside of the SpatialUnit package. In addition to this proposed extension of the LADM we give an example of hierarchical modelling of LAS-processes. We define topmost two levels of processes with a basic set of activities which are applicable to any LAS. As the granularity of activities increases, processes may become dependent on a particular LAS but the general concepts from topmost level should be followed. We use UML (Unified Modelling Language) Activity Diagrams to model processes in LAS.
Dynamic Object Tracking for Self-Driving Cars Using Monocular Camera and LIDAR

The detection and tracking of dynamic traffic participants (e.g., pedestrians, cars, and bicyclists) plays an important role in reliable decision-making and intelligent navigation for autonomous vehicles. However, due to the rapid movement of the target, most current vision-based tracking methods, which perform tracking in the image domain or invoke 3D information in parts of their pipeline, have real-life limitations such as lack of the ability to recover tracking after the target is lost. In this work, we overcome such limitations and propose a complete system for dynamic object tracking in 3D space that combines: (1) a 3D position tracking algorithm based on monocular camera and LIDAR for the dynamic object; (2) a re-tracking mechanism (RTM) that restore tracking when the target reappears in camera's field of view. Compared with the existing methods, each sensor in our method is capable of performing its role to preserve reliability, and further extending its functions through a novel multimodality fusion module. We perform experiments in the real-world self-driving environment and achieve a desired 10Hz update rate for real-time performance. Our quantitative and qualitative analysis shows that this system is reliable for dynamic object tracking purposes of self-driving cars.
Personalized Visualization Recommendation

Visualization recommendation work has focused solely on scoring visualizations based on the underlying dataset, and not the actual user and their past visualization feedback. These systems recommend the same visualizations for every user, despite that the underlying user interests, intent, and visualization preferences are likely to be fundamentally different, yet vitally important. In this work, we formally introduce the problem of personalized visualization recommendation and present a generic learning framework for solving it. In particular, we focus on recommending visualizations personalized for each individual user based on their past visualization interactions (e.g., viewed, clicked, manually created) along with the data from those visualizations. More importantly, the framework can learn from visualizations relevant to other users, even if the visualizations are generated from completely different datasets. Experiments demonstrate the effectiveness of the approach as it leads to higher quality visualization recommendations tailored to the specific user intent and preferences. To support research on this new problem, we release our user-centric visualization corpus consisting of 17.4k users exploring 94k datasets with 2.3 million attributes and 32k user-generated visualizations.
Load Balancing in Cloud Computing Environment: A Broad Perspective

Cloud computing is a recent buzzword in the field of information technology (IT) that has changed the way of computing from personal computing to virtual computing done with the help of cloud service providers via the Internet. It is evolved from distributed computing included with some other technologies such as virtualization, utility computing, service-oriented architecture, and data center automation. It is based upon the concept of computing delivered as a utility encapsulated with various other characteristics such as elasticity, scalability, on-demand access, and some other prominent features. To keep these properties intact during high demand for services, the load must be balanced among the available resources in the cloud environment. This load can be of various types such as CPU load, network load, memory load, etc., and balanced by executing a load balancing mechanism after detecting the overloaded and underloaded nodes. To achieve this researchers design different types of load-balancing algorithms for optimizing different performance parameters. The paper deals with a broad perspective of various load-balancing approaches done in the field by assuming the different performance metrics. The authors discuss that these approaches are multi-objective and there should be a good trade-off among these metrics to improve the performance.
FLaPS: Federated Learning and Privately Scaling

Federated learning (FL) is a distributed learning process where the model (weights and checkpoints) is transferred to the devices that posses data rather than the classical way of transferring and aggregating the data centrally. In this way, sensitive data does not leave the user devices. FL uses the FedAvg algorithm, which is trained in the iterative model averaging way, on the non-iid and unbalanced distributed data, without depending on the data quantity. Some issues with the FL are, 1) no scalability, as the model is iteratively trained over all the devices, which amplifies with device drops; 2) security and privacy trade-off of the learning process still not robust enough and 3) overall communication efficiency and the cost are higher. To mitigate these challenges we present Federated Learning and Privately Scaling (FLaPS) architecture, which improves scalability as well as the security and privacy of the system. The devices are grouped into clusters which further gives better privacy scaled turn around time to finish a round of training. Therefore, even if a device gets dropped in the middle of training, the whole process can be started again after a definite amount of time. The data and model both are communicated using differentially private reports with iterative shuffling which provides a better privacy-utility trade-off. We evaluated FLaPS on MNIST, CIFAR10, and TINY-IMAGENET-200 dataset using various CNN models. Experimental results prove FLaPS to be an improved, time and privacy scaled environment having better and comparable after-learning-parameters with respect to the central and FL models.
A Context-Aware Approach to Detect Abnormal Human Behaviors

Abnormal human behaviors can be signs of a health issue or the occurrence of a hazardous incident. Detecting such behaviors is essential in Ambient Intelligent (AmI) systems to enhance the safety of people. While detecting abnormalities has been extensively explored in different domains, there are still some challenges for developing efficient approaches dealing with the limitations of data-driven approaches to detect abnormal human behaviors in AmI systems. In this paper, a novel approach is proposed to detect such behaviors exploiting the contextual information of human behaviors. Machine-learning models are firstly used to recognize human activities, locations, and objects. Different contexts of human behaviors are then extracted in terms of the duration, frequency, time of the day, locations, used objects, and sequences of the frequent recognized activities. An ontology, called Human ACtivity ONtology (HACON), is proposed to conceptualize the contexts of human behaviors. Finally, a probabilistic version of ASP, a high-level expressive logic-based formalism, is proposed to detect abnormal behaviors through a set of rules based on the HACON ontology. The proposed approach is evaluated in terms of precision, recall, F-measure, and accuracy using two datasets, namely Orange4Home dataset and HAR dataset using smartphones. The evaluation results demonstrate the ability of the proposed approach to detect abnormal human behaviors.
Voice Liveness Detection for Voice Assistants using Ear Canal Pressure

With the success of voice recognition techniques, users can easily control any device in smart home environments by simply saying a voice command. Based on this idea, a new group of smart devices are designed and released, which are called voice assistant. However, the voice itself is not secure and can be attacked in many ways. To defend against various types of voice replay attacks, we present a new voice liveness detection system. The basic insight of our system is that mouth opening movements will change the space size in the ear canal, which further changes the air pressure in ear canals. In this paper, we propose solutions to detect mouth opening movements using the noisy air pressure data and match them with the voices to validate the liveness of the voice source. To evaluate the effectiveness of our system, we develop a prototype on Raspberry Pi and conduct comprehensive evaluations. Experiments with ten volunteers show that our system can accurately accept voice commands from legitimate users with an accuracy of 91.72%. Moreover, our system can effectively defend current voice assistant devices from replay attacks with an accuracy of 97.2%.
Serverless Straggler Mitigation using Error-Correcting Codes

Inexpensive cloud services, such as serverless computing, are often vulnerable to straggling nodes that increase the end-to-end latency for distributed computation. We propose and implement simple yet principled approaches for straggler mitigation in serverless systems for matrix multiplication and evaluate them on several common applications from machine learning and high-performance computing. The proposed schemes are inspired by error-correcting codes and employ parallel encoding and decoding over the data stored in the cloud using serverless workers. This creates a fully distributed computing framework without using a master node to conduct encoding or decoding, which removes the computation, communication and storage bottleneck at the master. On the theory side, we establish that our proposed scheme is asymptotically optimal in terms of decoding time and provide a lower bound on the number of stragglers it can tolerate with high probability. Through extensive experiments, we show that our scheme outperforms existing schemes such as speculative execution and other coding theoretic methods by at least 25%.
A Cluster-based Approach for Finding Domain wise Experts in Community Question Answering System

Abstract Community Question Answering (CQA) systems is an emerging web-based information service. CQA enables web users to get precise answers to questions from experts of the specific domain.CQA is used in wide areas such as biomedicine, information technology, tourism, etc. This paper focused on finding experts in community question answering system using unsupervised machine learning technique. Our Proposed system consists of three phases namely i) Clustering tags ii) Determining Experts for the unanswered questions iii) Finding experts for the given Question. By doing the tag analysis process, identified similar tags in a particular domain, and formed a cluster. By doing question analysis, we found the unanswered questions in each domain and identified the experts. For the given question, suggest the expert by doing pattern matching technique. The results section proves that our system predicts the experts for the given question with good accuracy.
Towards Transparent Robotic Planning via Contrastive Explanations

Providing explanations of chosen robotic actions can help to increase the transparency of robotic planning and improve users' trust. Social sciences suggest that the best explanations are contrastive, explaining not just why one action is taken, but why one action is taken instead of another. We formalize the notion of contrastive explanations for robotic planning policies based on Markov decision processes, drawing on insights from the social sciences. We present methods for the automated generation of contrastive explanations with three key factors: selectiveness, constrictiveness and responsibility. The results of a user study with 100 participants on the Amazon Mechanical Turk platform show that our generated contrastive explanations can help to increase users' understanding and trust of robotic planning policies, while reducing users' cognitive burden.
Assessment of Waste Management Practices and its Impact on the Village Ecosystem in Himachal Pradesh, India

Solid waste management is a persistent global issue that begs critical attention. India oversees an estimated 960 million tonnes of solid waste generation annually. The lesser developed Indian rural villages have no systematic means of solid waste management, exposing the village communities to severe health repercussions. The purpose of this study is to conduct an impact analysis of the current waste management and sanitation practices followed in the village Indpur, Himachal Pradesh, India. Participatory research methods were adopted based on Human Centered Design approach to deeply understand the persistent challenge, its impact on the community and propose a sustainable intervention to address the identified problem. The study proposes a novel solution in the form of plastic blocks that is capable of addressing the twin challenge of plastic waste management and sanitation. Done as part of the Live-in-Labs <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> program, the study manages to address a multi domain rural challenge through a human centric and sustainable development approach that concerns various Sustainable Development Goals of the UN.
Optical character recognition system for Baybayin scripts using support vector machine

In 2018, the Philippine Congress signed House Bill 1022 declaring the Baybayin script as the Philippines’ national writing system. In this regard, it is highly probable that the Baybayin and Latin scripts would appear in a single document. In this work, we propose a system that discriminates the characters of both scripts. The proposed system considers the normalization of an individual character to identify if it belongs to Baybayin or Latin script and further classify them as to what unit they represent. This gives us four classification problems, namely: (1) Baybayin and Latin script recognition, (2) Baybayin character classification, (3) Latin character classification, and (4) Baybayin diacritical marks classification. To the best of our knowledge, this is the first study that makes use of Support Vector Machine (SVM) for Baybayin script recognition. This work also provides a new dataset for Baybayin, its diacritics, and Latin characters. Classification problems (1) and (4) use binary SVM while (2) and (3) apply the multiclass SVM classification. On average, our numerical experiments yield satisfactory results: (1) has 98.5% accuracy, 98.5% precision, 98.49% recall, and 98.5% F1 Score; (2) has 96.51% accuracy, 95.62% precision, 95.61% recall, and 95.62% F1 Score; (3) has 95.8% accuracy, 95.85% precision, 95.8% recall, and 95.83% F1 Score; and (4) has 100% accuracy, 100% precision, 100% recall, and 100% F1 Score.
UniRoam: An Anonymous and Accountable Authentication Scheme for Cross-Domain Access

In recent years, cross-domain roaming through Wi-Fi is ubiquitous, and the number of roaming users has increased dramatically. It is essential to authenticate users belonging to different institutes to ensure network privacy and security. Existing systems, such as eduroam, have centralized and hierarchical structure on indorse accounts that create privacy and security issues. We have proposed UniRoam, a blockchain-based cross-domain authentication scheme that provides accountability and anonymity without any trusted authority. Unlike traditional centralized approaches, UniRoam provides access authentication for its servers and users to provide anonymity and accountability without any privacy leakage issues efficiently. By using the sovrin identifier as an anonymous identity, we integrate our system with Hyperledger and Intel SGX to authenticate users that preserves both anonymity and trust when the user connects to the network. Therefore, UniRoam is highly “faulted-tolerant” to deal with different attacks and provides an effective solution that can be deployed easily in different environments.
TrustedChain: A Blockchain-based Data Sharing Scheme for Supply Chain

Supply chain involves mutual independent and distrusted stakeholders and large of sensitive order data. Sharing data among stakeholders is a essential project because that improves efficiency for various workflow among stakeholders. This paper proposes TrustedChain, a blockchain-based data sharing scheme for supply chain, which has two advantages: (a) trusted: we present a trusted environment, Trusted Environment (TE), based on blockchain to allow mutually distrusted stakeholders manage data collaboratively. (b) secure: we provide a secure design that first stores order forms in Distributed Database (DDB) and then records URI in Contract Account (CA) of TE. In addition, Supply-Business Contract Management (SCM) manages all CA and Node Communication (NC) allows communication over the network. The security analysis and evaluation prove the effectiveness of TrustedChain.
Expert-novice classification of mobile game player using smartphone inertial sensors

The gaming industry has seen a tremendous growth in the last decade due to an exponential increase in the number of smartphone users. Embedded smartphone sensors provide solutions for automatic game controls during game-play. In this paper, we present an experimental study for the expertise classification of a game (mobile-based) player using smartphone inertial sensors, while they are simultaneously used for game controls. The game expertise level of participants is either labeled as expert or novice using game scores. Towards this end, data from 38 participants are curated during Traffic Racer game-play (in three different trials) using the embedded gyroscope and accelerometer sensors of the smartphone. These signals are pre-processed using Savitzky-Golay smoothing filter to remove noise. Twenty time domain features are extracted from the pre-processed data and are subjected to the wrapper-based feature selection method to select an optimum subset of features. Three classifiers, including k-nearest neighbor (k-NN), random forest, and the Naive Bayes, are evaluated towards the classification of player’s expertise level, i.e., expert and novice. The best average accuracy of 92.1% is achieved with k-NN classifier using the fusion of gyroscope and accelerometer data, which outperforms the existing state-of-the-art methods.
Binary team game algorithm based on modulo operation for knapsack problem with a single continuous variable

For solving the knapsack problem with a single continuous variable (KPC), a binary team game algorithm (TGA) with one-way mutation strategy is proposed. Firstly, without changing the evolution mode of TGA, three basic operations are reconstructed based on modulo 2 operation. Then, a binary TGA (BTGA) suitable for solving binary optimization problem is proposed. In order to use BTGA to solve KPC problem effectively, a one-way mutation strategy to improve individual quality is subsequently developed. Finally, based on BTGA and the existing repair and optimization algorithm of eliminating infeasible solutions, a novel algorithm MOBTGA for solving KPC problem is proposed. For validating the performance of MOBTGA, Kruskal–Wallis test is used to determine the reasonable values of its parameters. The comparison of experimental results obtained by different algorithms for two sets of KPC instances is subsequently executed. The comparison results show that MOBTGA has better performance than the existing heuristic algorithms ETDE, S-HBDE and B-HBDE in terms of solution accuracy and stability for solving KPC. The same time, the solving speed of MOBTGA also has a certain degree of competitiveness. Thus, MOBTGA is a quick and efficient heuristic algorithm for solving large-scale KPC instances.
A novel text-style sequential modeling method for ultrasonic rail flaw detection

Integrity of rails is the foundation of safe rail transportation. It is critical to detect internal rail flaws in time, and one popular solution to this issue is ultrasonic techniques. On the other hand, long short-term memory (LSTM) has been proven in text classification to which we think the ultrasonic rail flaw detection can be quite similar. In this context, this paper proposes a novel text-style sequential modeling method for ultrasonic rail flaw data and a LSTM-based deep learning model for rail flaw detection. Comparative experiments proved the feasibility and remarkable computational efficiency of the proposed modeling method and model.
Use of a Deep Neural Network for Object Detection in a Mobile Robot Application

This paper presents the implementation of a deep neural network for object detection in a mobile robot application. A mobile robot was designed and developed to work in dangerous environments. The mobile robot will move either tele-operated through the image transmitted by the video camera to a tablet or computer, or it will move autonomously in the environment in which it operates. In autonomous mode the mobile robot must recognize certain objects or people in the operating environment. For this reason, we implemented with good results an object detection algorithm based on deep neural network.