Reviewerly/exHarmony huggingface dataset:
Optimisation of camera positions for optical coordinate measurement based on visible point analysis

In optical coordinate measurement using cameras, the number of images, and positions and orientations of the cameras, are critical to object accessibility and the accuracy of a measurement. In this paper, we propose a technique to optimise the number of cameras and the positions of these cameras for the measurement of a given object using visible point analysis of the object's computer aided design data. The visible point analysis technique is based on a hidden point removal approach; this technique is used to detect which surface points on the object are visible from a given camera position. A genetic algorithm is used to find the set of positions that provide optimum surface point density and overlap between views, while minimising the total number of camera images required. The genetic algorithm is used to minimise the measurement data processing time while maintaining optimum surface point density. We test this optimisation procedure on four artefacts and the measurements are shown to be comparable to that from a traceable contact co-ordinate measurement machine. We show that using our procedure improves the measurement quality compared to the more conventional approach of using equally spaced images. This work is part of a larger effort to fully automate and optimise optical coordinate measurement techniques.
I didn’t mean what I wrote! Exploring Multimodality for Sarcasm Detection

Sarcasm detection is, inherently, a non-trivial problem where people express negative sentiment using positive insinuation words. Traditional approaches, in general, rely on the textual information to detect the incongruity between the surface meaning and the actual meaning. However, textual information is not always sufficient, and, often, other sources of information (e.g., visual) provides an important clue for sarcasm detection. In this paper, we propose an effective method based on deep learning that utilizes both textual and visual information for multi-modal sarcasm detection. Our proposed approach is based on the recurrent neural network that aims to exploit the interaction among the input modalities for the prediction. Experimental results suggest that the incorporation of visual modalities plays a decisive role in performance improvement.
An improved YOLOv3 model based on skipping connections and spatial pyramid pooling

The cascaded deep-learning network of YOLOv3 emphasizes on the layer-wise feature extraction. It neglects the sequential influence among the layers that contributes to the subtle features for the objects detection. An improved YOLOv3 model with skipping connections is proposed in this paper for the sufficient utilization of layer-wise features. Firstly, a DenseBlock network is adopted as the fourth and fifth down-sampling layers of YOLOv3. The DenseBlock is characterized of a parallel architecture and capable of transmitting the features backwards among extraction layers. Then, the features of preceding layers are incorporated by a skipping fashion into subsequential layers. Secondly, a spatial pyramid pooling (SPP) module is introduced at the neck of the object detector. It realizes the size-tuning of the model input. Then the multi-scaled region features are generated after the pooling and concatenation operation of the SPP. Finally, the validation experiments have been conducted on a dataset of helmet objects. The results have shown that the proposed YOLOv3 model improves the accuracy effectively. It yields a mean average precision 88.6% on the helmet detection, which is 3.5% higher than the original YOLOv3 network.
Fog Robotics Algorithms for Distributed Motion Planning Using Lambda Serverless Computing

For robots using motion planning algorithms such as RRT and RRT*, the computational load can vary by orders of magnitude as the complexity of the local environment changes. To adaptively provide such computation, we propose Fog Robotics algorithms in which cloud-based serverless lambda computing provides parallel computation on demand. To use this parallelism, we propose novel motion planning algorithms that scale effectively with an increasing number of serverless computers. However, given that the allocation of computing is typically bounded by both monetary and time constraints, we show how prior learning can be used to efficiently allocate resources at runtime. We demonstrate the algorithms and application of learned parallel allocation in both simulation and with the Fetch commercial mobile manipulator using Amazon Lambda to complete a sequence of sporadically computationally intensive motion planning tasks.
Potentials of Blockchain Technologies in Supply Chain Management - A Conceptual Model

Meanwhile the rapidly advancing digitisation affects almost all areas of society and business. This development is based in particular on the omni-present use of digital information technology. One of these technologies awarded with special potential related to business is the distributed ledger technology. The objective of this study is to investigate influences on the potentials of blockchain technology, being one distributed ledger technology, in the area of supply chain management. An empirical, qualitative data collection attained through expert interviews forms the data basis. The conceptual model is derived by using Grounded Theory Methodology to evaluate the interviews. Important influencing factors (trust, efficiency, costs, control, privacy, scalability) for the potential of blockchain technology in supply chain management and moderating effects (use case, knowledge, collaboration, regulations) form the conceptual model in order to deduce recommendations for business.
On the Role of Software Architecture in DevOps Transformation

Development and Operations (DevOps), a particular type of Continuous Software Engineering, has become a popular Software System Engineering paradigm. Software architecture is critical in succeeding with DevOps. However, there is little evidence-based knowledge of how software systems are architected in the industry to enable and support DevOps. Since architectural decisions, along with their rationales and implications, are very important in the architecting process, we performed an industrial case study that has empirically identified and synthesized the key architectural decisions considered essential to DevOps transformation by two software development teams. Our study also reveals that apart from the chosen architecture style, DevOps works best with modular architectures. In addition, we found that the performance of the studied teams can improve in DevOps if operations specialists are added to the teams to perform the operations tasks that require advanced expertise. Finally, investment in testing is inevitable for the teams if they want to release software changes faster.
Word sense disambiguation: an evaluation study of semi-supervised approaches with word embeddings

Word Sense Disambiguation (WSD) is a well-known problem in the field of Natural Language Processing (NLP) related to automatically determining the most appropriate sense of words in context. Several machine learning-based approaches have been proposed to tackle the ambiguity of language, but the lack of labeled data to train supervised models made semi-supervised learning (SSL) appear as an attractive option. Furthermore, the use of word embeddings to enhance the results of NLP tasks was shown to be an efficient strategy. Thus, this paper aims at adapting semi-supervised algorithms for WSD using word embeddings from Word2Vec, FastText, and BERT models combined with part-of-speech tags as input. We conduct a systematic evaluation of four graph-based SSL models analyzing the influence of their hyperparameters on the results, as well as the distances to build the graphs, the percentages of labeled data, and the word embeddings architectural variations. As a result, we show that SSL algorithms which received 10% of labeled data are strong baselines on the subsets of nouns and adjectives. Additionally, these algorithms do not need further training to disambiguate new words, hence being competitive to supervised systems.
A Logging Model for Enabling Digital Forensics in IoT, in an Inter-connected IoT, Cloud Eco-systems

Rapid advancement in the smart device technologies have accelerated the deployment of Internet-of-Things (IoT) to control, monitor or to exchange information of any real world systems and improve the quality of life. The IoT allows to create a network of billions of devices, using multitude of communication and data processing technologies, supporting varying business needs both in civilian settings and sensitive applications. This also creates an increased footprint for security vulnerabilities and cyber-attacks. Therefore, the security and digital forensics capabilities have to be built into the design of the IoT. In this work, we summarized the IoT forensic challenges and defined the IoT forensic requirements. We then proposed a conceptual event logging model and design architecture to support digital forensics in IoT, for an integrated IoT-Cloud computing environment. The model exploits the power of the Cloud computing, same time complements the deficiencies of IoT, utilizing an integrated environment.
Fdflownet: Fast Optical Flow Estimation Using A Deep Lightweight Network

Significant progress has been made for estimating optical flow using deep neural networks. Advanced deep models achieve accurate flow estimation often with a considerable computation complexity and time-consuming training processes. In this work, we present a lightweight yet effective model for real-time optical flow estimation, termed FDFlowNet (fast deep flownet). We achieve better or similar accuracy on the challenging KITTI and Sintel benchmarks while being about 2 times faster than PWC-Net. This is achieved by a carefully-designed structure and newly proposed components. We first introduce an U-shape network for constructing multi-scale feature which benefits upper levels with global receptive field compared with pyramid network. In each scale, a partial fully connected structure with dilated convolution is proposed for flow estimation that obtains a good balance among speed, accuracy and number of parameters compared with sequential connected and dense connected structures. Experiments demonstrate that our model achieves state-of-the-art performance while being fast and lightweight.
Immersive Work Zone Inspection Training using Virtual Reality

Can virtual reality tools be used to train engineers that inspect work zones? In this paper, we share the findings of a research project that developed an interactive and immersive training platform using virtual reality to train state department of transportation (DOT) staff that inspect work zones for compliance. Virtual reality offers an immersive platform that closely replicates the actual experience of an inspector driving through a work zone, but in a safer, cheaper, and quicker way than field visits. The current training practice involves reviewing temporary traffic control procedures, and reports and pictures from previous inspections. The developed platform consists of a learning module and an immersive module. The learning module is founded on the historical knowledge gained by DOT staff from inspections dating back at least 5 years. This knowledge incorporated representative inspection reports from previous years from all DOT districts including photographs of deficiencies. The synthesized knowledge was converted into a concise easy-to-consume format for training. The immersive module places the trainee in a vehicle moving through a work zone, thus providing a realistic experience to the engineer before inspecting a real work zone. The research team developed and tested two immersive scenarios of a freeway work zone. The training platform was tested by 34 individuals that worked for the Missouri Department of Transportation. An overwhelming majority (97%) agreed that virtual reality offered a realistic and effective way to train inspectors.
Extending Compressive Bilateral Filtering For Arbitrary Range Kernel

Smoothing filters have been used for pre/post-processing in various fields, such as computer vision and computer graphics. Bilateral filtering (BF) has a typical edge-preserving filter for such applications. The main issue of BF is its computational cost. Constant-time BF (O(1) BF) is one of the solutions to this problem, and compressive BF is a kind of O(1) BF. Compressive BF has, however, a restriction that we can only use Gaussian kernel as a range kernel until now. In this paper, we propose the method to extend compressive BF to handle arbitrary range kernels. Experimental results show that our extension handles arbitrary range kernels, and becomes the number of convolutions into half.
How are Hybrid Development Approaches Organized?

Agile software development methods promise shorter time-to-market and higher product quality, but lack the ability of long-term planning or coping with large projects. However, software companies often also want the ability of long-term planning, promised by traditional or plan-based methods. To benefit from the strengths of both approaches, software companies often use a combination of agile and plan-based methods, known as hybrid development approaches. These approaches strongly depend on the individual context and are customized. Therefore, companies have to organize their hybrid development approach individually. However, practitioners often have difficulties with the organization of hybrid approaches. The organization considers how the phases, activities, roles, and artifacts are arranged and connected. Research lacks the necessary detailed insight into how hybrid development approaches are organized to support practitioners. To gain better understanding of the organization of hybrid approaches, we conducted a systematic literature review to gather descriptions of hybrid approaches. We analyzed the found papers thoroughly and could identify three general patterns of how hybrid approaches are organized. We found that all these patterns are still based on Royce's waterfall model and use the standard software engineering activities. Our findings shall help to lead further research and help practitioners to better organize their individual development approach.
Cooperative Intelligence - A Humane Perspective

In this contribution, we outline our concept of cooperation between humans and intelligent systems which we denote as cooperative intelligence. We argue from a human perspective and emphasize the advantages of keeping the human in the loop rather than targeting autonomous systems. Our focus is respecting human values such as retaining competences, sharing experiences, and self-esteem. We discuss process-oriented requirements for intuitive cooperation like joint goals and shared intentions and social dimensions like empathy, relations, and trust. Finally, we suggest that cooperative intelligence can be facilitated by integrating interaction episodes across multiple system embodiments and instances, achieving the best holistic service with regard to personal preferences and needs.
The Time Complexity Analysis of Neural Network Model Configurations

The neural network algorithms, such as the deep-learning approach, have been widely applied in dealing with the computer vision problems. The more sophisticated the neural network model is designed; the more computing resources and processing time will be consumed. The time-complexity analysis discloses the training and validating time processed versus the accuracy outcome of the neural network model. This paper proposes a rigorous framework of conducting the time-complexity analysis against the neural network models. From the experiment results against the progressively sophisticated neural network model design, the paper argues that pursuing an unreasonably high accurate result or in persisting in finding the perfect algorithm may not be worth and in practical from the time-complexity perspective if the data quality was not consistently in favor of the algorithm chosen.
Estimating the Efficiency of the Energy Service Market Functioning in Ukraine

The following topics are dealt with: optimisation; learning (artificial intelligence); organisational aspects; economics; probability; neural nets; Internet; fuzzy set theory; regression analysis; cloud computing.
Subjective Quality Assessment For Youtube Ugc Dataset

Due to the scale of social video sharing, User Generated Content (UGC) is getting more attention from academia and industry. To facilitate compression-related research on UGC, YouTube has released a large-scale dataset [1]. The initial dataset only provided videos, limiting its use in quality assessment. We used a crowd-sourcing platform to collect subjective quality scores for this dataset. We analyzed the distribution of Mean Opinion Score (MOS) in various dimensions, and investigated some fundamental questions in video quality assessment, like the correlation between full video MOS and corresponding chunk MOS, and the influence of chunk variation in quality score aggregation.
Usability guidelines and evaluation criteria for conversational user interfaces

Even though conversational user interfaces (CUI) have been studied since the 1950s, it is not yet fully understood what makes them feel natural, intuitive and usable. As a result, their design and evaluation poses major challenges. In this paper, we discuss how CUIs are different from other forms of human computer interaction, and what challenges and opportunities arise from these differences. We provide an overview of relevant linguistic principles for a natural language conversation and look at established high-level usability heuristics to derive a set of 53 technology-agnostic checkpoints specifically for text-based CUIs (a.k.a chatbots). These checkpoints have been evaluated with 15 professionals and academics from the fields of User Experience, Natural Language Processing, Conversation Analysis and linguistics to examine content validity. The resulting list of checkpoints provides both guidelines for the design and criteria for the evaluation of chatbots.
Determining Attention Mechanism for Visual Sentiment Analysis of an Image using SVM Classifier in Deep learning based Architecture

Image (Visual) Sentiment Analysis (ISA) which exhibits the reaction of humans on visual elements for example images and videos, has been an animating and thought provoking problem. ISA demonstrates to apply the fields Computer Vision and Natural Language Processing (NLP) for classifying, extracting, and computing the subjective information in analytical fashion. The accomplishment of existing models can be credited to the progress of solid methodology from Image processing and computer Vision. A large portion of present models were attempted to tackle the problem by highlighting visual features from the complete image or video. Whole image features are the foremost anticipated inputs. For increasing the accuracy of the overall ISA system we proposed a deep learning based model including attention mechanism for consideration instrument for centering local regions of an image determining the required sentiment and adding support vector machine (SVM) in place of soft-max classification layer on deep Convolution Neural Network (CNN). We additionally consider the relevant hash tags of an image to put attention weights on CNN layer indicating by the semantic mapping of image regions and hash tags. Our deep learning based proposed framework is accomplished of spontaneously determining sentimental of specified images and it out spaces existing state-of-the-art approaches to VSA.
YOLOv3 Precision Improvement by the Weighted Centers of Confidence Selection

One of the most popular and widely used object detection algorithm today is the YOLOv3 due to its high performance and speed. However, YOLOv3 is not the best algorithm in terms of precision. This paper introduces a substantial change to the post-processing routine of the YOLOv3 after the prediction to increase its final accuracy. Currently, YOLOv3 uses a Non-Max Suppression algorithm to eliminate multiple detections of the same object. This algorithm is picking the most confident overlaying box on any object to present it as the final prediction. This paper presents a new algorithm called Weighted Centers of Confidence Selection that increases the precision using a confidence-weighted average bounding box as a replacement to the existing bounding boxes without making any changes to the YOLOv3 convolutional neural network. We demonstrate how this algorithm works and compare its results to the results achieved by the YOLO's Non-Max Suppression algorithm, focusing on precision and achieving almost the same frame-speed as the original YOLOv3. This new approach allowed us to improve the average accuracy on the COCO dataset in comparison to the original YOLO's Non-Max Suppression.
FLAGB: Focal Loss based Adaptive Gradient Boosting for Imbalanced Traffic Classification

Machine learning (ML) is widely applied to network traffic classification (NTC), which is an essential component for network management and security. While the imbalance distribution exhibiting in real-world network traffic degrades the classifier's performance and leads to prediction bias towards majority classes, which is always ignored by exiting ML-based NTC studies. Some researches have proposed solutions such as resampling for imbalanced traffic classification. However, most methods don't take traffic characteristics into account and consume much time, resulting in unsatisfactory results. In this paper, we analyze the imbalanced traffic data and propose the focal loss based adaptive gradient boosting framework (FLAGB) for imbalanced traffic classification. FLAGB can automatically adapt to NTC tasks with different imbalance levels and overcome imbalance without the prior knowledge of data distribution. Our comprehensive experiments on two network traffic datasets covering binary and multiple classes prove that FLAGB outperforms the state-of-the-art methods. Its low time consumption during training also makes it an excellent choice for highly imbalanced traffic classification.
KTAN: Knowledge Transfer Adversarial Network

Knowledge distillation was pioneered to transfer the generalization ability of a large teacher deep network to a light-weight student network. The student network can retain the high quality of the teacher network, yet exhibiting low computational complexity and storage requirement, which is attractive for deploying a deep convolution neural network on a resource-constrained mobile device. However, most of the existing methods focus on transferring the probability distribution of a softmax layer in a teacher network and neglect the intermediate representations. However, we find that the intermediate representation is critical for a student network to better understand the transferred generalization as compared to the probability distribution only. In this paper, therefore, we propose such a knowledge transfer adversarial network method which holistically considers both intermediate representations and probability distributions of a teacher network. To transfer the knowledge of intermediate representations, we set high-level teacher feature maps as a target, toward which the method trains student feature maps. Furthermore, to support various structures of a student network, we arrange a novel teacher-to-student layer. Finally, the proposed method employs an adversarial learning process. Specifically, it includes a discriminator network to fully exploit the spatial correlation of feature maps during the training process of a student network. The experimental results demonstrate that the proposed method can significantly improve the performance of a student network on two important vision tasks, image classification and object detection.