Reviewerly/exHarmony huggingface dataset:
Clustering Recurrent and Semantically Cohesive Program Statements in Introductory Programming Assignments

Students taking introductory programming courses are typically required to complete assignments and expect timely feedback to advance their learning. With the current popularity of these courses in both traditional and online versions, graders are seeing themselves overwhelmed by the sheer amount of student programs they have to handle, and the quality of the educational experience provided is often compromised for promptness. Thus, there is a need for automated approaches to effectively increase grading productivity. Existing approaches in this context fail to support flexible grading schemes and customization based on the assignment at hand. This paper presents a data-driven approach for clustering recurrent program statements performing similar but not exact semantics across student programs, which we refer to as core statements. We rely on structural graph clustering over the program dependence graph representations of student programs. Such clustering is performed over the graph resulting from the pairwise approximate graph alignments of programs. Core statements help graders understand solution variations at a glance and, since they group program statements present in individual student programs, can be used to propagate feedback, thus increasing grading productivity. Our experimental results show that, on average, we discover core statements covering more than 50% of individual student programs, and that program statements grouped by core statements are semantically cohesive, which ensures effective grading.
Prediction of Eye States using k-NN Algorithm: A Comparison Study for Different Distance Metrics and Number of Neighbour Parameters

Eye state prediction is the process of determining whether the users' eyes are in opened or closed state. It has been widely employed in human computer interaction, braincomputer interfaces, etc. In this paper, we investigated the classification performances of different distance metrics and number of neighbour parameters on the problem of predicting eye states. We conducted experiments on real-world EEG Eye State Data set which is intended to find appropriate approaches for eye state prediction. The classification performances were evaluated for accuracy measurement using the ten-fold leave-one-out cross-validation. The results demonstrate that the distance metrics and number of neighbour parameters highly affect the performance. Compared to previous works, the following two points were improved: (i) not only the euclidean distance but also other distance metrics' performances were investigated for EEG based eye state prediction and (ii) better classification accuracy rates were achieved compared to previous k-NN based studies.
Enhancement Of Navigation Technology Using Augmented Reality

Abstract Navigation technology has observed a tremendous advancement in the last 10 years with people shifting from using traditional maps to satellite based navigation smartphone apps such as Google maps and Apple maps. These applications mainly focus on outdoor navigation and represent the route in a 2-D layout, which sometimes lacks clarity when it comes to distinguishing between overlapping paths. One more area where this project is focused is indoor navigation. Indoor navigation can be helpful for navigating inside big buildings like hospitals, malls and airports. For example, when a user enters a mall and he or she needs to find a particular showroom, he just needs to put the name of that showroom and he would get 3-D augmented objects on his device which will point to that showroom as he follows the designated path. Augmented reality is used combined with satellite based GPS technology to navigate outdoors as well as indoors. The application uses 3-D markers to point the direction to the user in a real time environment on their devices which will be easy to understand and navigate. At the same time it will augment location based point of interest along the path, which will provide more clarity to the user about his or her environment. From this project we hope to develop a navigation application for users which is easy to use and understand.
Human Action Recognition Based on Multiple Features and Modified Deep Learning Model

In order to improve the accuracy of human action recognition in video and the computational efficiency of large data sets, an action recognition algorithm based on multiple features and modified deep learning model is proposed. First, the deep network pre-training process is used to learn and optimize the RBM parameters, and the deep belief nets (DBN) model is constructed through deep learning. Then, human 13 joint points and critical points of optical flow are automatically extracted by DBN model. Second, these more abstract and more effective human motion features are combined to represent human actions. Ultimately, the entire DBN network structure is fine-tuned by support vector machine (SVM) algorithm to classify human actions. We demonstrate that human 13 joint points and critical points of optical flow are two very effective human action characterizations, our proposed approach greatly reduces the required samples, and shortens the training time of the samples, can efficiently process large data sets and can effectively recognize novel actions. We performed experiments on the KTH data set, Weizmann data set, the ballet data set and UCF101 data set to evaluate the proposed method, the experiment results show that the average recognition accuracy is over 98%, which validates its effectiveness, and show that our results are stable, reliable, and significantly better than the results of two state-of-the-art approaches on four different data sets. So, it lays a good theoretical foundation for practical applications.
Towards a Quality Framework for Immersive Media Experiences: A Holistic Approach

Immersive Media Technologies have emerged as popular media form. Their captivating nature makes them a powerful tool for participation and storytelling in a variety of domains attracting multidisciplinary interest. Existing frameworks for user-perceived quality in immersive media experiences are limited due to their exclusion of narrative dimensions. This research expands upon the current system-centered Quality of Experience framework by including Content Influence Factors based on learnings from IDN. Hence proposing a conceptual framework for measuring immersive media experiences, which comprise of four constructs: Form, Content, User, and Context. These components are interrelated through their overlapping dimensions, which is discussed through the course of this paper.
An Ontology for Failure Interpretation in Automated Planning and Execution

Autonomous indoor robots are supposed to accomplish tasks, like serve a cup, which involve manipulation actions, where task and motion planning levels are coupled. In both planning levels and execution phase, several source of failures can occur. In this paper, an interpretation ontology covering several sources of failures in automated planning and also during the execution phases is introduced with the purpose of working the planning more informed and the execution prepared for recovery. The proposed failure interpretation ontological module covers: (1) geometric failures, that may appear when e.g. the robot can not reach to grasp/place an object, there is no free-collision path or there is no feasible Inverse Kinematic (IK) solution. (2) hardware related failures that may appear when e.g. the robot in a real environment requires to be re-calibrated (gripper or arm), or it is sent to a non-reachable configuration. (3) software agent related failures, that may appear when e.g. the robot has software components that fail like when an algorithm is not able to extract the proper features. The paper describes the concepts and the implementation of failure interpretation ontology in several foundations like DUL and SUMO, and presents an example showing different situations in planning demonstrating the range of information the framework can provide for autonomous robots.
A conceptual framework for inclusive economics

Background: In view of growing concerns about global inequality and economic exclusion within and between countries, the theme of 'inclusive economics' or 'economic inclusivity' is gaining significance. Aim: In discovering a more in-depth understanding of inclusive economics, the article develops a conceptual framework to better understand both the interpretation and application of inclusive economics. Setting: As an ideal setting, the post-global financial crisis period provides fertile ground to explore alternative economic theories and systems such as inclusive economics. Methods: A variety of terms and theories are associated with inclusive economics, hence a conceptual and theoretical analysis, in a descriptive context, is used to clarify the theoretical demarcation of this concept. Results: Since the concept does pose a challenge to conventional economic frameworks, a key result is that it may become a valuable addition to the range of economic thinking and proposals in solving much-needed 21st century economic challenges. Conclusion: The article contributes towards developing a new paradigm for genuine economic progress – in theory and practice.
Dynamic Monitoring of Health Using Smart Health Band

Smart health band would be a smart assistive innovation-focused solution for the detection. It is also a great tool for analysing the health statistics of the person who is considered to be in grave danger. It is also a means for a network-synced ambulant and also considered as a health-monitoring bracelet which is capable enough of reading the user’s human vitals such as (pulse rate and body temperature). In this bustling world, where the activities of human are restricted to sedentary lifestyle and working on their desktops for the all day long, would be evident to create a burden of problems including obesity, heart problems, no exercise hence respiratory problems, and lot more.
Real-World Trajectory Extraction from Aerial Videos - A Comprehensive and Effective Solution

In this paper, we propose an advanced real-world trajectory extraction method to extract vehicle trajectories from aerial videos. The method includes three steps: vehicle identification and tracking, camera rotation and shifting correction, and semi-automatic lane identification. We identify vehicles using the advanced Mask R-CNN model and track vehicles according to their movement properties. We calculate camera rotation and shifting parameters using a binary search correlation-based matching algorithm. And we generate lane structures based on the topographic properties of extracted trajectories semi-automatically. We tested the method using two aerial video examples, one of which captures freeway traffic and another one captures roundabout traffic. The results show that vehicle trajectories are extracted accurately. The proposed method provides a promising solution to extract real-world long vehicle trajectories from aerial videos.
Context-Aware Imputation for Clinical Time Series

Missing data has been widely recognized as a key challenge of clinical time series analysis, which hinders the practical application of data-driven approaches to clinical data analytics [1], [2]. Various methods have been proposed to perform the time series imputation to alleviate this issue, yet most of them impose strong assumptions on the missing data, for instance, locality in Gaussian Process based models [3], lowrankness and temporal regularity in matrix/tensor factorization models [4], etc. More recently, researchers proposed to apply the Recurrent Neural Networks (RNNs) to tackle the missing data imputation problem for time series, where the RNNs try to capture and summarize the temporal dynamics using hidden state vectors [5]–[7].
Scalable FPGA-Based Convolutional Neural Network Accelerator for Embedded Systems

Convolutional neural network(CNN) and related deep learning algorithms represent the state-of-art ability in several computer vision tasks, such as image classification and video analysis. However, model complexity and massive computational operations restrain CNN deployment on embedded systems with limited computing capability and low power budgets including smartphones, drones and robots. In this paper, we propose a scalable FPGA-based CNN hardware accelerator for embedded systems based on 8-bit fixed point approximation of a hardware-friendly CNN model named SqueezeNet v1.1. Proposed FPGA accelerator is implemented with OpenCL framework and achieves 1.9x energy efficiency compared to previous work. Besides, performance and resource occupation of proposed FPGA accelerator are capable of being customized to fit in different FPGA platforms by changing a single hyperparameter.
Transfer of Calibration in Virtual Reality to both Real and Virtual Environments

It is often questioned whether task performance attained in a virtual environment can be transferred appropriately and accurately to the same task in the real world. With advancements in virtual reality (VR) technology, recent research has focused on individuals’ abilities to transfer calibration achieved in a virtual environment to a real-world environment. Little research, however, has shown whether transfer of calibration from a virtual environment to the real world is similar to transfer of calibration from a virtual environment to another virtual environment. As such, the present study investigated differences in calibration transfer to real-world and virtual environments. In either a real-world or virtual environment, participants completed blind walking estimates before and after experiencing perturbed virtual optic flow via a head-mounted virtual display (HMD). Results showed that individuals calibrated to perturbed virtual optic flow and that this calibration carried over to both real-world and virtual environments in a like manner.
A Knowledge-Based System for Disaster Emergency Relief

Natural disasters have had great impact on human beings. Emergency relief is becoming more important with the increase of the frequency and scale of humanitarian emergencies resulting from more natural disasters because of climate change. In this paper, an interesting hybrid knowledge representation method during the development of a KBS for design of emergency relief structures is presented. It encapsulates ill-structured, semi-structured and structured knowledge that is gathered from literature, human expert and even knowledge gleaned during the system development. All routine as well as cumbrous activities in the emergency relief cycle are covered. The system can provide the user with advice on preliminary plan evaluation, plan optimization, plan evaluation, plan summary and miscellaneous. It would be beneficial to the field of disaster emergency relief decision by focusing on the acquisition and organization of expert knowledge through the development of knowledge-based system.
Public Concerns and Response Pattern toward Shared Mobility Security using Social Media Data

Shared mobility has gained widespread popularity as a result of its innovative business models. However, due to incomplete background checks of drivers, not only government authorities (i.e., transport authorities) but also the public has become concerned about service security. Currently, social networks such as Sina Weibo, a Chinese version of Twitter, provide an informative data source for capturing people’s attitudes toward and perceptions of these mobility services. This dataset has the potential to help service platforms and regulators respond better to public demands and realize co-governance of the travel market. In this study, one month of Weibo data before and after a murder committed by a Didi ride-hailing driver was crawled. A hybrid approach integrating Latent Dirichlet Allocation (LDA) model and dictionary-based sentiment analysis was applied to extract topics receiving lots of attention and sentiment fractions, as well as to detect abnormal events. The results demonstrated that top-4 concerns of the public are service of ride-hailing, examination and responsibility definition of platforms, governmental regulations for market entry and capitalization, and case related description. People had negative attitudes toward ride-hailing platforms when talking about their lack of efficient management and crisis response strategies, while they were more positive about the business rivalries which are expected to enhance the quality of service in the travel market. Additionally, both events in the real world and interactions within social networks (i.e., #Boycott events) contributed to the public’s sentiment. These findings can not only be useful for service platforms to detect public concerns about their services in real-time and to improve their business operations but also can be applied as a reference for government authorities to recognize multiple agents’ interests for the co-governance of the urban mobility market.
Contextual and Relational Role-Based Modeling Framework

Model-driven Software Development (MDSD) approaches struggle when modeling context-dependent and dynamic systems, as their underlying metamodels cannot capture context-dependent concepts and relations. By contrast, role-based modeling has been studied for more than 35 years as a promising paradigm to model context-dependent and dynamic systems. Although some approaches have considered the application of roles on the metamodel level, no approach employed a contextual and relational role-based metamodel as the basis of a modeling framework. To remedy this, we employ the Compartment Role Object Model (CROM) which is a contextual and relational role-based modeling language, as the underlying metamodel of a novel Role-based Modeling Framework (RMF). In particular, our framework is able to generate inter-operable Java source code that permits the programmatic creation, manipulation, and persistence of role-based models. We illustrate the applicability of RMF by modeling a small system with context-dependent concepts and relations, generating corresponding Java source code, and employing it to load, manipulate, and store role-based models.
Assessment of text coherence using an ontology‐based relatedness measurement method

Abstract This paper proposes a novel method for assessing text coherence. Central to this approach is an ontology‐based representation of text, which captures the level of relatedness between consecutive sentences via ontologies. Our method encompasses annotating text using ontological concepts and assessing text coherence based on relatedness measurement among these concepts. The ontology‐based relatedness measurement method used in this study considers various types of relationships in ontologies and derived relationships via an inference engine for computing relatedness. We hypothesized that rich variety of relationships and inferred facts in ontologies would improve the success of text coherence assessment. Our results demonstrate that the use of ontologies yields to coherence values that have a higher correlation with human ratings.
Bit vector-coded simple CART structure for low latency traffic classification on FPGAs

Traffic classification is the determination of the application types during real-time flow of internet traffic. Machine learning (ML) based classification approaches that can classify internet traffic using statistical properties of flows are of great interest, due to its ability to work under encrypted traffic conditions. In this paper, we propose a novel data structure, named Bit Vector Coded Simple CART (BC-SC), for ML based internet traffic classification. BC-SC data structure is a scalable solution in terms of the number of application classes while providing a significant improvement in search latency, memory requirement and throughput when compared to the state-of-the-art approaches. We also designed two alternative hardware architectures, namely Pipelined and Discrete Parallel Range Comparators (DPRC)-based, on the Field Programmable Gate Array (FPGA) platform to support BC-SC data structure. Pipelined and DPRC-based architectures can achieve up to 665 and 914 giga bit per second (Gbps) or 2078 and 2857 million classifications per second (MCPS) respectively for the minimum packet size of 40 Byte. Furthermore, the proposed engines both can reach 96.8125% accuracy with eight application classes.
Two-Level Model for Table-to-Text Generation

Table-to-text generation involves using natural language to describe a table which has formal structure and valuable information. This paper introduces a two-level encoder-decoder neural model for table-to-text generation. To make the most of the structure which ordinarily is expressed as a set of field-value records and deal with rare words appearing in a table, this study adopts an improved encoder-decoder approach and uses field information to reprocess words in texts as decoding result. In encoder, two LSTM-RNNs used for combining fields and values that one LSTM-RNN gives priority to fields and the other gives first place to values. In decoder, two-level attention mechanism used on states encoded before to get the relation between words in the text and fields in the table and the relation between words in the text and values in the table. At last the decoding result is transformed to real words. The model is experimented on WIKIBIO and WEATHERGOV, and improves the current state-of-the-art BLEU-4 score from 44.89 to 45.77, and from 61.01 to 62.89 respectively.
Designing futsal match finder application with floyd-warshall algorithm

Abstract Information about the locations where futsal competition is taking place is needed for the futsal team to meet each other and compete. The purpose of designing Futsal Match Finder is to determine the position of futsal field location and help the shortest path to the field by implementing Floyd-Warshall algorithm. Application system design methodology that used in this research is prototype model. System test results show that the Floyd-Warshall algorithm can help find the shortest path to the location of the opposite futsal team. The futsal opponent search system based on the shortest distance can provide a solution to improve the ability of futsal players who have less relationship with other futsal team.
On the Non-existence of Short Vectors in Random Module Lattices

Recently, Lyubashevsky & Seiler (Eurocrypt 2018) showed that small polynomials in the cyclotomic ring $$\mathbb {Z}_q[X]/(X^n+1)$$, where n is a power of two, are invertible under special congruence conditions on prime modulus q. This result has been used to prove certain security properties of lattice-based constructions against unbounded adversaries. Unfortunately, due to the special conditions, working over the corresponding cyclotomic ring does not allow for efficient use of the Number Theoretic Transform (NTT) algorithm for fast multiplication of polynomials and hence, the schemes become less practical. In this paper, we present how to overcome this limitation by analysing zeroes in the Chinese Remainder (or NTT) representation of small polynomials. As a result, we provide upper bounds on the probabilities related to the (non)-existence of a short vector in a random module lattice with no assumptions on the prime modulus. We apply our results, along with the generic framework by Kiltz et al. (Eurocrypt 2018), to a number of lattice-based Fiat-Shamir signatures so they can both enjoy tight security in the quantum random oracle model and support fast multiplication algorithms (at the cost of slightly larger public keys and signatures), such as the Bai-Galbraith signature scheme (CT-RSA 2014), $$\mathsf {Dilithium\text {-}QROM}$$ (Kiltz et al., Eurocrypt 2018) and $$\mathsf {qTESLA}$$ (Alkim et al., PQCrypto 2017). Our techniques can also be applied to prove that recent commitment schemes by Baum et al. (SCN 2018) are statistically binding with no additional assumptions on q.