Reviewerly/exHarmony huggingface dataset:
Alternating between Partial and Complete Organization: The Case of Anonymous

This chapter is concerned with further advancing a process perspective on partial organization. More specifically, we address the question of how the different organizational elements of partial organization (i.e. membership, hierarchy, rules, monitoring, and sanctions) interrelate dynamically. Based on the emerging ‘communication as constitutive of organization’ (CCO) perspective in organization studies, we argue that social collectives can alternate between states of organizational ‘partialness’ and ‘completeness’ over time. The temporary and situational completion of partial organization can occur in and through communicative events that demonstrate and ‘celebrate’ a social collective’s ability to mobilize all five elements simultaneously. We illustrate our theoretical considerations by drawing on selected findings from an earlier empirical case study of the hacktivist collective Anonymous.
Collaborative Creation of Engineering Artifacts by Geographically-Distributed Teams

It is common practice today for engineering development projects to be partitioned across multiple, geographically-distributed work locations, often in multiple times zones, and at times, in different countries. Underlying this practice is an implied assumption that the tools and services provided by such the electronic collaboration infrastructure (e.g., electronic mail, audio and video teleconferencing, group on-line editing, sharable file repositories, etc.) used to interconnect the people at these various sites is adequate for supporting such geographically-distributed teams, facilitating their inter-personal communications and collaboration. The goal is for collaborative work to be accomplished with little or no physical travel between these geographically-distributed work sites. In this paper, we describe research conducted to test the validity of this hypothesis, within the domain of projects intended to develop complex, software-intensive systems, of the sort used by the aerospace and energy industries. We discovered important limitations in such electronic collaboration infrastructures, which limit their ability to support such geographically-distributed collaborative development. Specifically, we found that (for large-scale complex systems development) there is evidence that such mechanisms are effective for geographically-distributed review of engineering artifacts, but that they are not effective for geographically-distributed creation of such artifacts. Understanding this distinction is therefore crucial for effective project management. Such an understanding also identifies topics for future research and development: the need to create more effective mechanisms to support the geographically-distributed creation of complex engineering artifacts. To this end, we present findings from our research on how collaborative generation of engineering artifacts occurs, what are the specific challenges that arise from various modes of geographically-distributed project implementation, and why (within the considered problem domain) current electronic collaboration infrastructure tools are not effective to support this geographically-distributed engineering creation. We conclude by postulating mechanisms and methods that are beginning to contribute to the development of effective tools for geographically-distributed creation of complex engineering artifacts.
ACCESSIBILITY, BIASES AND ETHICS IN CHATBOTS AND INTELLIGENT AGENTS FOR EDUCATION

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 8824-8833ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.2196Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Vernon-fenwick at SemEval-2019 Task 4: Hyperpartisan News Detection using Lexical and Semantic Features

In this paper, we present our submission for SemEval-2019 Task 4: Hyperpartisan News Detection.Hyperpartisan news articles are sharply polarized and extremely biased (onesided).It shows blind beliefs, opinions and unreasonable adherence to a party, idea, faction or a person.Through this task, we aim to develop an automated system that can be used to detect hyperpartisan news and serve as a prescreening technique for fake news detection.The proposed system jointly uses a rich set of handcrafted textual and semantic features.Our system achieved 2nd rank on the primary metric (82.0%accuracy) and 1st rank on the secondary metric (82.1% F1-score), among all participating teams.Comparison with the best performing system on the leaderboard 1 shows that our system is behind by only 0.2% absolute difference in accuracy.
Gesture controlled dual six axis robotic arms with rover using MPU

The purpose of this work is to create a gesture controlled dual 6 DOF robotic arm with rover, to perform critical works like bomb, mine defusing and other operations where work like human nature are required. The dual 6 DOF arm make 12 + the rover motion makes 4 = 16 operations can be controlled with the use of single modified ps2 joystick, in which a 6 DOF arm and the rover of the robot can be operated simultaneously. MPU-9250 is mounted on the joystick to recognize the roll and pitch of the joystick along with push button interaction.
Exploring the Potential of Social Annotations for Predictive and Descriptive Analytics

In this paper, we illustrate the successful implementation of a social annotation tool within a content authoring platform, that allows students to discuss learning material with their fellow classmates, and to self-report on their cognitive, metacognitive and affective states-by self-coding the annotations as they journey through the learning material. We explore the predictive potential of such self- reports in reading material against the students completion rate and assessment scores, and also examine how visualisation of these annotation classifications can help instructors easily identify issues and adapt their teaching approach and learning material.
Deep pose consensus networks

In this paper, we address the problem of estimating a 3D human pose from a single image, which is important but difficult to solve due to reasons, such as self-occlusions, wild appearance changes, and inherent ambiguities of 3D estimation from a 2D cue. These difficulties make the problem ill-posed, which have become requiring increasingly complex estimators to enhance the performance. On the other hand, most existing methods try to handle this problem based on a single complex estimator, which might not be good solutions for 3D human pose estimation. In this paper, to resolve this issue, we propose a multiple-partial-hypothesis-based framework for the problem of estimating 3D human pose from a single image, which can be fine-tuned in an end-to-end fashion. We first select several joint groups from a human joint model using the proposed sampling scheme, and estimate the 3D pose of each joint group separately based on deep neural networks. After that, the estimated poses are aggregated to obtain the final 3D pose using the proposed robust optimization formula. The overall procedure can be fine-tuned in an end-to-end fashion, resulting in better estimation performance. In the experiments, the proposed framework shows the state-of-the-art performances on popular benchmark data sets, namely Human3.6M and HumanEva, which demonstrate the effectiveness of the proposed framework.
Building Economic Models and Measures of Search

Economics provides an intuitive and natural way to formally represent the costs and benefits of interacting with applications, interfaces and devices. By using economic models it is possible to reason about interaction, make predictions about how changes to the system will affect behavior, and measure the performance of people's interactions with the system. In this tutorial, we first provide an overview of relevant economic theories, before showing how they can be applied to formulate different ranking principles to provide the optimal ranking to users. This is followed by a session showing how economics can be used to model how people interact with search systems, and how to use these models to generate hypotheses about user behavior. The third session focuses on how economics has been used to underpin the measurement of information retrieval systems and applications using the CWL framework (which reports the expected utility, expected total utility, expected total cost, and so on) -- and how different models of user interaction lead to different metrics. We then show how information foraging theory can be used to measure the performance of an information retrieval system -- connecting the theory of how people search with how we measure it. The final session of the day will be spent building economic models and measures of search. Here sample problems will be provided to challenge participants, or participants can bring their own.
An efficient dynamic switching algorithm for mining colossal closed itemsets from high dimensional datasets

The abundant data across a variety of domains including bioinformatics has led to the formation of dataset with high dimensionality. The conventional algorithms expend most of their time in mining a large number of small and mid-sized itemsets which does not enclose complete and valuable information for decision making. The recent research is focused on Frequent Colossal Closed Itemsets (FCCI), which plays a significant role in decision making for many applications, especially in the field of bioinformatics. The state-of-the-art algorithms in mining FCCI from datasets consisting of a large number of rows and a large number of features are computationally expensive, as they are either pure row or feature enumeration based algorithms. Moreover, the existing preprocessing techniques fail to prune the complete set of irrelevant features and irrelevant rows. The proposed work emphasizes an Effective Improvised Preprocessing (EIP) technique to prune the complete set of irrelevant features and irrelevant rows, and a novel efficient Dynamic Switching Frequent Colossal Closed Itemset Mining (DSFCCIM) algorithm. The proposed DSFCCIM algorithm efficiently switches between row and feature enumeration methods based on data characteristics during the mining process. Further, the DSFCCIM algorithm is integrated with a novel Rowset Cardinality Table, Itemset Support Table, two efficient methods to check the closeness of rowset and itemset, and two efficient pruning strategies to cut down the search space. The proposed DSFCCIM algorithm is the first dynamic switching algorithm to mine FCCI from datasets consisting of a large number of rows and a large number of features. The performance study shows the improved effectiveness of the proposed EIP technique over the existing preprocessing techniques and the improved efficiency of the proposed DSFCCIM algorithm over the existing algorithms.
Making you matter

Virtual Reality (VR) is a transformative medium for narrative storytelling where content creators can place an audience member inside the story, give them a role to play, and ultimately make them matter to the characters. Immersive storytelling is fundamentally different from film and games. It requires a new creative toolset that is still in its infancy compared to other entertainment mediums.
Experimental Microwave Quantum Illumination

Quantum illumination is a powerful sensing technique that employs entangled signal-idler photon pairs to boost the detection efficiency of low-reflectivity objects in environments with bright thermal noise. The promised advantage over classical strategies is particularly evident at low signal powers, a feature which could make the protocol an ideal prototype for non-invasive biomedical scanning or low-power short-range radar. In this work we experimentally investigate the concept of quantum illumination at microwave frequencies. We generate entangled fields using a Josephson parametric converter to illuminate a room-temperature object at a distance of 1 meter in a free-space detection setup. We implement a digital phase conjugate receiver based on linear quadrature measurements that outperforms a symmetric classical noise radar in the same conditions despite the entanglement-breaking signal path. Starting from experimental data, we also simulate the case of perfect idler photon number detection, which results in a quantum advantage compared to the relative classical benchmark. Our results highlight the opportunities and challenges on the way towards a first room-temperature application of microwave quantum circuits.
Multispectral Person Re-Identification Using GAN for Color-to-Thermal Image Translation

We propose a ThermalGAN framework for cross-modality color-thermal person re-identification (ReID). We use a stack of generative adversarial networks (GANs) to translate a single color probe image to a multimodal thermal probe set. We use thermal histograms and feature descriptors as a thermal signature. We collected a large-scale multispectral ThermalWorld dataset for extensive training of our GAN model. In total the dataset includes 20,216 color-thermal image pairs, 516 person ID, and ground truth pixel-level object annotations. We made the dataset freely available (see http://www.zefirus.org/ThermalGAN/). We evaluate our framework on the ThermalWorld dataset to show that it delivers robust matching that competes and surpasses the state of the art in cross-modality color-thermal ReID.
A New Polynomial-time Support Measure for Counting Frequent Patterns in Graphs

Frequent subgraph mining (FSM) from graphs is an active subject in computer science research. One major challenge in FSM is the development of support measures, which are basically functions that map a pattern to its frequency count in a database. Current state-of-the-art in this topic features a hypergraph-based framework for modeling pattern occurrences which unifies the two main flavors of support measures: the overlap-graph based maximum independent set measure (MIS) and minimum image/instance based (MNI) measures. For the purpose of exploring the middle ground between these two groups, we introduce a new polynomial-time support measure, called maximum independent subedge set (MISS) measure to fill the gap between MIS and MI in terms of computation complexity and support count. Bounding theorems among all relevant support measures are also presented in this paper.
Survey of fast algorithms for Euler's elastica-based image segmentation

Segmentation has been widely studied and is one of the fundamental and classical tasks of image processing. Starting from Mumford–Shah functional, we review multiphase segmentation using piecewise constant level set approach and relate to phase transition models for multiphase segmentation. In particular, we focus on Euler's elastica-based variational functionals, in the context of fast algorithms for image segmentation problem. Its relation to illusory contour is covered following the phase transition approach. Minimizing the functionals with curvature term helps to capture the geometry of the recovered shape, but introduces complexity due to their nonconvexity and high order derivatives. We review some of fast algorithms developed to efficiently compute the minimum of curvature-based segmentation functionals, such as Euler's elastica segmentation.
Object detection application and Viola Jones algorithm for the development of a database in Alzheimer's patients

Alzheimer's, as well as other neurological diseases, alters progressively the communication abilities of people who suffer from it. In these cases, facial gestures are a very important means of communication and their treatment is the central objective of this investigation. This article describes the creation of a database with the information of patients with Alzheimer's disease represented on faces' images, respecting the etic and based on the normative and respective permissions, these care centers for the elderly people are located in different cantons of Ambato, Ecuador, which will be used later in investigations related to the recognition of facial gestures. It has worked with a population of 61 people diagnosed with Alzheimer's, between 69 and 98 years old. The data obtained on the images were processed using the Viola-Jones method, making it possible to detect objects quickly and accurately. This method used the algorithm of neighbors with the canny border's technique. As a result, it was possible to create a database that includes the images of the patients' faces, in a gray scale, using high resolution. This constitutes an important input for future research on the health subjects. It is concluded that the information obtained is of quality and pressure for future analysis.
An Empirical Study of Perfect Potential Heuristics

Potential heuristics are weighted functions over state features of a planning task. A recent study defines the complexity of a task as the minimum required feature complexity for a potential heuristic that makes a search backtrack-free. This gives an indication of how complex potential heuristics need to be to achieve good results in satisficing planning. However, these results do not directly transfer to optimal planning.In this paper, we empirically study how complex potential heuristics must be to represent the perfect heuristic and how close to perfect heuristics can get with a limited number of features. We aim to identify the practical trade-offs between size, complexity and time for the quality of potential heuristics. Our results show that, even for simple planning tasks, finding perfect potential heuristics might be harder than expected.
Comparing Summaries of a Narrative Story Produced under Different Conditions

The purpose of the present study was to investigate whether Japanese university EFL students produce summaries of a narrative story differently under two different conditions: When they refer to the original text and when they do not do so. Specifically, the study examined the students’ use of selection and deletion rules as well as paraphrasing strategies. Both quantitative and qualitative analyses were conducted. The results of the analysis revealed that, when they referred to the original text, the students produced summaries that included more idea units with details, longer and syntactically more complex sentences, and a variety of transitions. On the other hand, without the original passage, the students wrote summaries that were concise, with the focus on main idea units, and that used syntactically less complex sentences, but extensive paraphrases. These results suggest that EFL teachers need to carefully examine the use of the original text in a summarizing task. Producing a summary without an original text is seldom practiced in regular EFL classrooms in Japan. However, EFL teachers need to consider the potential pedagogical effectiveness of producing summaries without an original text if they want to develop their students’ spontaneous paraphrasing skills.
Counterexample-Guided Abstraction Refinement for Pattern Selection in Optimal Classical Planning

We describe a new algorithm for generating pattern collections for pattern database heuristics in optimal classical planning. The algorithm uses the counterexample-guided abstraction refinement (CEGAR) principle to guide the pattern selection process. Our experimental evaluation shows that a single run of the CEGAR algorithm can compute informative pattern collections in a fairly short time. Using multiple CEGAR algorithm runs, we can compute much larger pattern collections, still in shorter time than existing approaches, which leads to a planner that outperforms the state-of-the-art pattern selection methods by a significant margin.
Classification of Cervical Lesion Images Based on CNN and Transfer Learning

With the development of artificial intelligence and the large-scale application of computer aided diagnosis (CAD) system in clinical diagnosis, more and more disease screening technologies [1]–[2] can be realized without professional intervention. Because of the difficulties in obtaining images of labeled cervical lesions, there are few examples of application of vaginal endoscopic images in disease screening. In this paper, focusing on the imbalance of labelled image data, we consider the classification performance from the data level and algorithm level respectively. We use VGG19 network and transfer learning to classify cervical lesion images into cancer (ca), cervical intraepithelial neoplasia (cin), cervicitis (cv), HPV virus infection (hpv), normal (ok) and cervical polyp (polyp). At the data level, we use the SMOTE algorithm to oversample the minority classes. At the algorithm level, we use the weights balance of loss function to transform the network structure. These two methods are applied to VGG network respectively. Finally, the classification performance is evaluated by accuracy, confusion matrix, G-mean and F-score. Compared with directly feeding data into neural network, the SMOTE-TL method and DA-WB-TL method we proposed shows great superiority.
Prediction of solutions on the basis of the mathematical model of cognitive digital automata

An approach to the problem of solution prediction on the basis of the mathematical model of cognitive digital automata (CDA) is proposed. A particular advantage of the proposed mathematical model of CDA is that the training procedure can be performed on a limited (minimal) number of training sets. Prediction or generation of solutions, in its turn, is performed on the basis of the mathematical model of CDA obtained in the process of training. The mathematical model of CDA was tested on an example of modeling an n-bit adder. As a result of training, the mathematical model of CDA was formed, which made it possible to reproduce the entire set of the truth table of the n-bit parallel adder with a specified accuracy.
Symbolic Planning with Axioms

Axioms are an extension for classical planning models that allow for modeling complex preconditions and goals exponentially more compactly. Although axioms were introduced in planning more than a decade ago, modern planning techniques rarely support axioms, especially in cost-optimal planning. Symbolic search is a popular and competitive optimal planning technique based on the manipulation of sets of states. In this work, we extend symbolic search algorithms to support axioms natively. We analyze different ways of encoding derived variables and axiom rules to evaluate them in a symbolic representation. We prove that all encodings are sound and complete, and empirically show that the presented approach outperforms the previous state of the art in costoptimal classical planning with axioms.
Principles of Building a Mathematical Model for the Influence of Mineral Fertilizers on Grain Yield

Approach to the building of grain yield model is considered, depending on the intensity of application of mineral fertilizers. In order to improve the accuracy of the simulation, the system of ordinary Mono differential equations as a general model of the growth of processes’ intensity in complex systems is used as the basis. The method of identification of the basic model, the efficiency of which is confirmed experimentally, is proposed. A comparison of the accuracy of the proposed model with known regression analogues is carried out.