Reviewerly/exHarmony huggingface dataset:
Consolidating the student voice using think aloud protocols

This paper reports on the use of Think Aloud Protocols (TAPs) to identify the concepts in consolidated financial statements that students may find difficult to master. Consolidated Financial Statements is a topic taught normally in Intermediate Accounting that is known to pose a challenge to students (Murphy & McCarthy, 2010). In this study, we use TAPs to capture a participant’s immediate thoughts while performing tasks related to consolidated financial statements. TAPs offer the opportunity to hear the student’s voice in their strategies to solve a question and provide a unique insight into concepts, terminology and parts of the consolidation process that students may experience as troublesome. These insights will be used to develop a future project of an adaptive learning resource to support student mastery of threshold financial accounting concepts. This paper extends our understanding of the use of TAPs in Accounting and the research methodology employed in this paper could be applied to other teaching innovations.
Sentiment Analysis of Movie Reviews Using Support Vector Machine Classifier with Linear Kernel Function

Sentiment analysis refers to the process of determining the opinion stated by the user corresponds to positive, and to be negative or considered to be neutral. The mechanism of sentiment analysis is said to be the process of opinion mining which in then resembles the behavior/attitude measurement of the speaker. This is extremely useful in a place which there is a complete need for a recommendation for the user to follow a specific case of action. In public domain, the aspect of sentiment analysis is helpful for the user to state a specific nature of the action. This research work focuses on the analysis of review data to determine the aspect based on sentiments using TF, IDF, and SVM. The model extracts the textual reviews and classifiers them into positive, negative, and neutral cases. The result retrieved with the proposed scheme gives an improved accuracy of about 87.56% determining the positive and negative cases more efficiently. With this proposed approach the classification of review data can be made more efficiently for various sort of recommendation systems which makes the user have good insight for a product review, movie review, and user rating analysis.
Parallel Implementation of kNN Algorithm for Breast Cancer Detection

Current advances in parallel processing technology aims at providing unmatched degree of computational power in upcoming days. Parallel computation is an efficient form of information processing which exploits the concurrency of execution. This paper investigates the use of parallel programming, when applied on k Nearest Neighbors (kNN) algorithm which is intended for classification and prediction of the large dataset. Breast cancer dataset is used for classification and prediction which consists of two labels namely, malignant and benign. kNN is a non-parametric algorithm which makes use of similarity measure to classify the dataset into different categories. The similarity between the data points is computed by using Euclidean distance formula. Multiple threads are created for parallel processing and an appropriate kNN graph is constructed, which helps in easier implementation. Finally, execution speeds for sequential and parallel programs is recorded. The results are verified by using frameworks namely, Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA) highlighting that parallel execution takes less time when compared to sequential execution.
Vehicle Obstacle Avoidance Path Planning Based on Gauss Pseudospectral Method

For the problem of vehicle obstacle avoidance control, a steering obstacle avoidance method based on Gauss pseudospectral method is designed. In this paper, the kinematics and dynamics models of vehicles are established as dynamic constraints, combining with state constraints and path constraint, the obstacle avoidance behavior of intelligent vehicles is summarized as a continuous optimal control problem which takes obstacle avoidance time as the optimal objective. In the research of solving the optimal control problem, Gauss pseudospectral method is adopted which utilizes numerical approximation techniques to transform the continuous optimal control problem into a discrete nonlinear programming problem with series of equality and inequality constraints. The trajectories of control variable and state variable are obtained through solving this nonlinear programming problems numerically. The effectiveness and control performance of this method are illustrated by simulation.
Enriching Knowledge Base by Parse Tree Pattern and Semantic Filter

This paper proposes a simple knowledge base enrichment based on parse tree patterns with a semantic filter. Parse tree patterns are superior to lexical patterns used commonly in many previous studies in that they can manage long distance dependencies among words. In addition, the proposed semantic filter, which is a combination of WordNet-based similarity and word embedding similarity, removes parse tree patterns that are semantically irrelevant to the meaning of a target relation. According to our experiments using the DBpedia ontology and Wikipedia corpus, the average accuracy of the top 100 parse tree patterns for ten relations is 68%, which is 16% higher than that of lexical patterns, and the average accuracy of the newly extracted triples is 60.1%. These results prove that the proposed method produces more relevant patterns for the relations of seed knowledge, and thus more accurate triples are generated by the patterns.
Architectures of Cloud-enabled Cyber Physical Systems — a Systematic Mapping Study

Cloud-enabled Cyber Physical Systems (CCPS) combine embedded systems with highly scalable cloud services. Such systems provide opportunities to offload computing or data analytics tasks which require more resources than an embedded device can offer. The development of a CCPS involves multiple stakeholders as well as engineers and developers from different disciplines, which makes the description and communication of the system architecture a challenging task. Additionally, the architecture design of CCPS has the inherent challenge to determine which functionality should be placed on the device, in the cloud, or on a possible fog/edge device within or close to the system. This systematic mapping study evaluates how CCPS architectures are discussed in the current literature and which topics are associated with cloud computing in CCPS architectures. The results show a significant increase in CCPS publications over the last years, a focus on a specific architectural viewpoint and application areas, and a potential misalignment with the common understanding of cloud computing as a paradigm.
Insulator detection method based on multi-angle region proposal network

Aiming at the direction of insulators are uncertain in the inspection images of transmission lines, and the problems of missed detection and inaccurate positioning due to complex backgrounds and occlusions, a method of insulators detection based on multi-angle region proposal network is proposed. Firstly, using a convolutional neural network, that characteristics of the different levels of features are obtained, and a feature fusion of shallow features and in-depth features is used to obtain richer features. Next, using the region proposal network based on Faster R-CNN, multi-angle factors are added and fine-tuned by non-maximum suppression so that it can generate candidate frames in any orientation to fit the target better. At the same time, the original pooling layer was changed to the RoIAlign layer. Finally, a 1 × 1 convolution layer is added before the fully connected layer of the network in order to reduce the parameters of its feature map and avoid overfitting. The inspection insulator image dataset is constructed and tested. The experimental results show that the final detection result obtained using this method can accurately detect the insulator, and its performance is better than the existing mainstream target detection algorithms and has high engineering application value.
Surrogate Modelling for Efficient Discovery of Emergent Population Dynamics

Outcomes of simulating complex systems models, such as emergent properties and desirable system level behaviours, can be discovered via heuristic techniques such as Genetic Algorithms (GAs). Using simulation as the cost function evaluation for a GA (i.e. simulation guided search) is computationally expensive. Additionally the GA search process may require many generations before high quality solutions can be discovered. As such, simulation guided search can be considered high latency with respect to discovery of a range of high quality solutions. In this paper we experimentally demonstrate that the time to discovery of high quality solutions can be reduced through a low latency, hybrid GA search using a machine learning surrogate model trained to approximate simulation via large amounts of batched parallel simulation data generated in a HPC environment. Using a common population dynamics model optimised for GPU simulation by the FLAME GPU framework, we directly compare the hybrid approach with simulation guided search to understand the relationship between computational cost and quality of prediction. Our results indicate that given equivalent levels of simulation investment, results of equivalent quality can be obtained. The hybrid approach is however able to reduce the latency of the GA search process by shifting the computational cost of simulation to a highly parallel pre-search step used to train surrogate models.
End-to-end kernel learning via generative random Fourier features

Random Fourier features (RFFs) provide a promising way for kernel learning in a spectral case. Current RFFs-based kernel learning methods usually work in a two-stage way. In the first-stage process, learning the optimal feature map is often formulated as a target alignment problem, which aims to align the learned kernel with the pre-defined target kernel (usually the ideal kernel). In the second-stage process, a linear learner is conducted with respect to the mapped random features. Nevertheless, the pre-defined kernel in target alignment is not necessarily optimal for the generalization of the linear learner. Instead, in this paper, we consider a one-stage process that incorporates the kernel learning and linear learner into a unifying framework. To be specific, a generative network via RFFs is devised to implicitly learn the kernel, followed by a linear classifier parameterized as a full-connected layer. Then the generative network and the classifier are jointly trained by solving the empirical risk minimization (ERM) problem to reach a one-stage solution. This end-to-end scheme naturally allows deeper features, in correspondence to a multi-layer structure, and shows superior generalization performance over the classical two-stage, RFFs-based methods in real-world classification tasks. Moreover, inspired by the randomized resampling mechanism of the proposed method, its enhanced adversarial robustness is investigated and experimentally verified.
Analysis and Decision of Optimal Path of Forest Disaster Patrol Based on Beidou Navigation

In this paper, a forest data map returned by Beidou satellites is combined with classic theories such as graph theory and mathematical planning, and the Dijkstra algorithm, which is the most widely used in practice, is the core to propose a forest patrol optimal path planning model. And by comparing with the Floyd path planning algorithm, the correctness and implementability of the model are verified. The experimental results show that the algorithm effectively solves the problem of optimal path decision from a patrol base to an arbitrary forest area in a certain forest, and verifies that the Dijkstra algorithm can be accurately and reliably applied to the optimal path planning field of forest patrol.
Photovoltaic Power Ramp Prediction Based on Sequential Difference

The prediction of photovoltaic power ramp events is one of the important works for the grid to realize intelligent scheduling. A method was proposed to automatically predict the ramp events based on a set of steps including the sequence difference method for predicting cloud motion vectors. First, image preprocessing including mask and image inpainting was performed on the ground-based image to extract the sky area. Second, a cloud detection algorithm that reduced sunlight interference was used to identify clouds in images. Third, a geometric transformation that took into account the relative position of the observation point and the clouds was derived to adjust the position of the image pixels. Radial interpolation consistent with the transformation principle was proposed to repair the image and complete the distortion correction. Fourth, the sequence difference method is proposed to predict cloud motions, and the individual cloud motion vectors were provided to adapt to the characteristics of cloud motion anisotropy. Finally, photovoltaic power ramp events are predicted based on individual cloud motion vectors. The experimental results showed that the distortion correction with precise geometric transformation derivation and radial interpolation improved the accuracy and quality of the results. The sequence difference method improved the accuracy of cloud matching and realized anisotropic cloud motion prediction. Further, the photovoltaic power ramp event was accurately predicted.
Evaluation of the HAVOSS software process maturity model

The HAVOSS (Handling Vulnerabilities in OSS) maturity model describes important processes for managing security vulnerabilities in OSS modules in developed products. So far, the model has not been evaluated in any real assessment process. Here we present a study where the model was evaluated by using it in assessments of processes for two product types in one organization. Each assessment was conducted in a focus group meeting where their procedures were analyzed. The evaluation was conducted by posing specific questions about the model during the focus group meetings and by investigating how difficult it was to assess the maturity of practices from the transcribed text. It was found that some practices were easy to assess, while other could be analysed separately for different parts of the products. Further work can be conducted on how assessments can be conducted and how they can be combined with other software security initiatives.
Fault Diagnosis for Distributed Cooperative System Using Inductive Logic Programming

This paper proposes a learning and diagnosis method that can be applied immediately after a distributed system starts cooperative operation. The proposed method first learns behavioral rules for individual systems from their time series data, which are collected under independent operations. Then, anomality is detected and the system is diagnosed following the cooperative specification. The proposed method learns rules for individual systems based on ACEDIA, which is a kind of inductive logic programming; the rules are either transition rules or relationship rules that hold among variables at the same transition time. In a diagnostic phase, inconsistent rules and inconsistent specifications are obtained with ranking information against the diagnostic data, where ranking is performed through evaluation in terms of the generality on each rule and specification. We demonstrate that the proposed method correctly outputs the rules and specifications that are violated by diagnostic data. Moreover, in a case study on a simplified automotive system consisting of multiple control systems, the rules essentially related to the error were ranked higher.
TECN: Task Selection and Placement in GPU Enabled Clouds Using Neural Networks

Resource scheduling in cloud computing needs to be addressed effectively and efficiently to enable fair share, high throughput and low latency for large numbers of jobs that share the cloud. Currently, there are no known solutions for vGPU enabled VM placement. Virtualized GPUs present many opportunities and challenges; finding an optimal placement for VMs is an NP-hard problem. Our research focusses on using Machine Learning to address task/VM placement in a vGPU enabled cloud.We built a simulator to test and compare different strategies to select and place VMs. In this paper, we describe the simulator and discuss the results of the comparison of different heuristics. We present details of the dense neural networks (DNN) we built that out-perform all the heuristics. The DNNs learns the "best" heuristic at every system configuration and as such are "superior" to any individual heuristic. Our approach to using machine learning to solve the problem of selection and placement starts with heuristics, trains DNNs from these heuristics, and then out-performs them. We did a head-to-head comparison of the task selection by the DNNs with that generated by the heuristics. In this comparison, the DNNs show better task selection results for 76% of the test cases than the heuristics. These results obtained by using DNNs look promising and can be further improved by refining the neural networks.
Pushdown and Lempel-Ziv depth

In previously published work (Jordon and Moser, 2020), notions of finite-state-depth and pushdown-depth were presented. These were based on finite-state transducers and information lossless pushdown compressors. Unfortunately, a complete separation between the two notions was not established. This paper introduces a new formulation of pushdown-depth based on restricting how fast a pushdown compressor's stack can grow. This allows us to do a full comparison by demonstrating the existence of sequences with high finite-state-depth and low pushdown-depth, and vice-versa. A new notion based on the Lempel-Ziv 78 algorithm is also presented. Its difference from finite-state-depth is shown by a Lempel-Ziv deep sequence that is not finite-state deep, and vice versa. Lempel-Ziv-depth's difference from pushdown-depth is shown by building sequences that have a pushdown-depth of roughly 1/2 but low Lempel-Ziv depth, and by a sequence with high Lempel-Ziv depth but low pushdown-depth. Properties of all three notions are also studied.
Visual Analytics in Machine Training Systems for Effective Decision

The approaches to the formation, development of a formal and mental model based on the use of visual analytics are proposed. It is based on the description of model building technologies. An example of information technology that allows getting a formal model based on the transformation of the mental model through the space of formalized universal forms is given. This allows the model to be used in a different usage and execution environment. Model development is carried out using loops the improvement of the base model or transforming the use of the model from another runtime. An example of equipment and tools for the construction and transformation of models is demonstrated.
Financial market sustainable development of Kazakhstan: scenario approach based on fuzzy cognitive maps

Access to financial resources is the main constraint for sustainable economic growth in Kazakhstan. Kazakhstan's financial institutions, being relatively small in size and unbalanced, cannot finance development through market mechanisms. The model that reflects the system's sustainable development was constructed based on the fuzzy cognitive map to consolidate 31 indicators of three types of the subsystem: economic, financial institutions, and financial markets to reflect the viability and balance of the system development. The time-series data of Kazakhstan's economic and financial development of Kazakhstan from 2014 to 2019 with prognosis to 2025 was used. Based on the results of our research, we concluded that the implementation of policies for sustainable development should include not only specific aims for the financial market but also for financial institutions and the economy. Moreover, sustainability is reinforced by the development of integration, innovativeness, and accessibility, which forms the key aspects of financial system state policy development.
A DMPs-based Switching Motion Planning Method for Robots with Obstacles

Recent studies of obstacle avoidance under the Dynamic Movement Primitives (DMPs) framework are limited on the dotted obstacles. A novel obstacle avoidance algorithm based on DMPs is proposed for shaped obstacle avoidance. By combining the steering behavior method and the potential field method with the evaluation of Euclidean distance, while absorbing the switching strategy, this new algorithm performs well in mimicking the learned DMPs trajectory and handles the fluctuations occur in the initial stage of motion. The simulations of 2D DMPs trajectory learning of one-obstacle avoidance and multi-obstacle avoidance show that the algorithm is feasible with figurate obstacles, and acquires great conformability with the learned trajectory and smoothness.
Out of Touch? Challenges in Reconnecting Bodies with Instruments ‘of the Future’

‘Touch’, and the return of the body to digital music-making in general, has taken on a central importance in electronic instrument design and theory in recent decades. ‘Touch’ and lack thereof has been considered central to the challenge of a ‘disconnect’ between musicians’ bodily actions and digital instruments, and as a solution to it that instruments ‘of the future’ can provide. But ‘touch’ is not a universal quantity; it is a construction that varies widely in its application and invocation, across academic and commercial contexts of instrument design. This article examines the complex history of the contemporary concern with ‘touch’, and its role in the framing and reception of instruments such as ROLI’s Seaboard, JazzMutant’s Lemur, Sensel’s Morph and the Joué. With reference to writings on embodied cognition, the recent material turn in the humanities and to Mills and Sterne’s concept of dismediation it seeks to historicise, interrogate, and denaturalise ‘touch’ within instrument design and its surrounding discourse.
Low delay non-binary error correction codes based on Orthogonal Latin Squares

Due to the scaling of technology and the environment effects, such as radiation, memory systems that are on board of spacecraft such as satellites, are more sensitive to errors. This kind of error is difficult to predict and its correction in real-time is crucial for the correct behavior of the whole system. In this paper, a low delay fully parallel non-binary decoder based on orthogonal Latin squares defined in a Galois field is presented. The proposed approach corrects a single symbol in GF(2q) with very low complexity applying a one-step majority logic algorithm that allows reaching high-speed decoding with very low latency. The decoder introduced in this manuscript was implemented for 64 and 128-bit codes over GF(28) in a 45 nm CMOS process. Our implementation outperforms state-of-the-art decoders with the same error correction capacity, achieving lower latency, delay and area in both cases. The results show that the proposed solution can be suitable for the protection of some types of memories like DRAM or 3D-memories and other radioactive environment applications.
Data-Driven Intelligent Tutoring System for Accelerating Practical Skills Development. A Deep Learning Approach

Our data-driven intelligent tutoring system presents promising results in supporting and accelerating the skills acquiring process. For example, mapping of the common latent variables enables the instructors and curricula designers to understand better the relationships between different exercise items and thus to create improved training scenarios. The case study results also reveal significant improvements in accelerating the process of training welders: participants gradually started to improve their welding skills after only 15 trials (approximately 1 hour of training using the system).