Reviewerly/exHarmony huggingface dataset:
First-order stable model semantics with intensional functions

In classical logic, nonBoolean fluents, such as the location of an object, can be naturally described by functions. However, this is not the case in answer set programs, where the values of functions are pre-defined, and nonmonotonicity of the semantics is related to minimizing the extents of predicates but has nothing to do with functions. We extend the first-order stable model semantics by Ferraris, Lee, and Lifschitz to allow intensional functions—functions that are specified by a logic program just like predicates are specified. We show that many known properties of the stable model semantics are naturally extended to this formalism and compare it with other related approaches to incorporating intensional functions. Furthermore, we use this extension as a basis for defining Answer Set Programming Modulo Theories (ASPMT), analogous to the way that Satisfiability Modulo Theories (SMT) is defined, allowing for SMT-like effective first-order reasoning in the context of Answer Set Programming (ASP). Using SMT solving techniques involving functions, ASPMT can be applied to domains containing real numbers and alleviates the grounding problem. We show that other approaches to integrating ASP and CSP/SMT can be related to special cases of ASPMT in which functions are limited to non-intensional ones.
Clustering mixed numeric and categorical data with artificial bee colony strategy

Data objects with both numeric and categorical attributes are prevalent in many real-world applications. However, most of the partitional clustering algorithms dealing with such data may trap into local optima. To further promote the performance, a novel clustering algorithm, called ABC-K-Prototype s (Artificial Bee Colony clustering based on K-Prototypes), is presented on the basis of the K-Prototypes algorithm, the search strategy of the artificial bee colony, and the chaos theory. In the presented approach, the one-step k-prototypes procedure is first given, and then this procedure is combined with the search strategy of the artificial bee colony to address the mixed numeric and categorical data. In the search process of scout bees, the chaotic map is utilized to generate chaotic sequences for substituting the random numbers. To accelerate the convergence of the ABC-K-Prototypes algorithm, the multi-source search is adopted in the search process of scout bees. Finally, the performance of the ABC-K-Prototypes algorithm is demonstrated by a series of experiments on mixed numeric and categorical data in comparison with that of the other popular algorithms.
Optimizing graph processing on GPUs using approximate computing

Parallelizing graph algorithms on GPUs is challenging due to the irregular memory accesses and control-flow involved in graph traversals. In this work, we tame these challenges by injecting approximations. In particular, we improve memory coalescing by renumbering and replicating nodes, memory latency by adding edges among specific nodes brought into shared memory, and thread-divergence by normalizing degrees across nodes assigned to a warp. Using a suite of graphs with varied characteristics and five popular algorithms, we demonstrate the effectiveness of our proposed techniques. Our approximations for coalescing, memory latency and thread-divergence lead to mean speedups of 1.3×, 1.41× and 1.06× achieving accuracies of 83%, 78% and 84%, respectively.
The Social Interaction Experiences of Older People in a 3D Virtual Environment

Virtual worlds offer much potential in supporting social interaction for older adults, particularly as a platform which can provide an interactive and immersive social experience. Yet, there has not been much work carried out to study the use, interaction and behavior of older people in 3D virtual world systems, especially studies which investigate their interactions in a fully functional virtual world. Most focus on issues related to usability such as cognitive difficulties when navigation in a 3D space and we know little about their perceptions and preferences when socializing in a virtual space. In this chapter, we report an experimental study examining the various factors which affected the social experience of older users in virtual worlds. The study involved 38 older participants engaging with a 3D and non-3D virtual grocery store. A mixed method of questionnaire and contextual interview was used for data collection and analysis. Overall, we found that physical presence was a significant predictor of many measures defining the quality of social interaction, yet participants often reported a sense of artificiality in their virtual experience. Interestingly, avatars were not considered directly important for social interaction and instead were only seen as a “place holder” to complete the tasks. Two factors contributed to this, the lack of non-verbal communication and the perceived difficulty in embodying physical people with virtual avatars.
Deep Learning Applications to Cytopathology: A Study on the Detection of Malaria and on the Classification of Leukaemia Cell-Lines

This chapter discusses a few applications of deep learning networks in cytopathology. Specifically, the detection of malaria from slide images of blood smear and classification of leukaemia cell-lines are addressed. The chapter starts with relevant theory for traditional (deep) multi-layer neural networks with back-propagation, followed by motivation, theory and training in Convolutional Neural Networks (CNN), the trending deep-learning based classifier. The detection of malaria from blood smear slide images using CNN is addressed followed by a discussion on the transfer learning capability of CNN by taking the classification of leukaemia cell-lines: K562, MOLT & HL60 as an example. The transfer learning capability of CNN is of particular interest especially when there are only very limited number of training samples to come up with a stand alone deep CNN classifier.
Creativity in Trusted Data: Research on Application of Blockchain in Supply Chain

Creative computing is very popular in recent years and has great potential.The introduction of systematic creative mechanisms and principles can provide solutions to practical problems, such as potential forgery and tampering of trade data in traditional supply chains.Guided by being creative, this paper proposes a new trusted data management scheme based on blockchain.First, a smart contract suitable for supply chain is designed to code trade rules in order to prevent performance risks and improve the credibility of trade data processing.Then, a ZSS04 scheme and sampling techniques are used to complete transaction data integrity check.Furthermore, an optimized PBFT consensus mechanism is proposed to achieve efficient, low cost, and scalable transaction data management.Finally, the trusted data management in untrusted environment is implemented by using the native characteristics of blockchain.Results show that the scheme can provide new ideas and technical support for transaction data management in supply chain.
Speedy

Deep convolutional neural networks (CNNs) have achieved remarkable performance with the cost of huge computation. Moreover, the current trend of CNNs is towards more complex and deeper topology. Compressing CNNs to sparse have emerged as the most attractive approach to reduce the amount of computation and memory requirement. This compression is achieved by pruning the redundant connection in networks. FPGAs have been an effective solution to accelerate CNN inference for its high parallel computing, flexibility and energy-efficiency. Although existing FPGA architectures are able to excellently process dense CNN models, they cannot benefit from the computation reduction when accelerating the sparse CNN models. Because most of the arithmetic operations involve addition and multiplication with zero operands, meanwhile accelerating sparse CNN models incurs significant data encoding and decoding overhead. In this paper, we propose a FPGA accelerator Speedy that can efficiently exploit sparsity in CNN models. We first investigate the dataflow design space to explore the available performance with different parallelization strategies. The result of exploration is Speedy dataflow which provides enough parallel multiplications and maximizes the weight reuse. Then, we propose a novel data representation combined with memory partition technique to increase the on-chip bandwidth. Finally, we propose Speedy FPGA architecture in which we apply line buffer design and high-throughput PE. In the experiments, we evaluate Speedy on contemporary neural networks. Speedy provides flexible parameters for different FPGA scale. First, we evaluate the resources utilization and hardware efficiency with different design configurations. Then we compare our design with previous FPGA implementations. Overall, Speedy achieves 11.3x-20.8x and 1.5x-6.8x speed up for Alexnet and VGGnet with 90% weight sparsity.
Professional Development Model for ESP Teachers Working at Engineering Universities

The article describes application of pedagogical modelling in designing professional development (PD) syllabus for Language for Specific Purposes (LSP) teachers working at Russian engineering universities. The authors consider meaningful factors defining professional requirements for LSP tertiary teachers and view the problems of LSP teachers’ professional development from engineering pedagogy perspective. They substantiate the necessity of developing the Profile of LSP tertiary teachers’ professional competencies as a standard description of qualification requirements for this specialty. The authors analyze the results of LSP teachers’ diagnostics that revealed methodo-logical deficit of the respondents in theory and practice of cross-disciplinary approach to teaching Language for Specific (Engineering) Purposes. The organizational and pedagogical model based on qualification requirements of the above Profile is proposed as the basis of professional development syllabus design, selection of teaching methods and assessment of learning outcomes. The addressees of the above model are tertiary LSP teachers willing to improve their professional competences and cross-disciplinary integrative skills in order to develop foreign language professional communicative competence of engineering students.
Cloud Computing Architecture for High-volume ML-based Solutions

A large number of modern projects use machine learning technology to perform a variety of business calculations. There are two main ways to integrate machine-learning models into the logic of industrial applications. The first way is to rewrite models from the data analysis language (for example R or Python) to the industrial development language (for example Java, Go or Scala). The second way is to equip models with a web-interface and integrate it into the calculation. In this article, we explore the second method. A deployment architecture for machine learning in the clouds is proposed. The possibilities of the proposed scheme for scaling are described. Examples of practical use of the proposed architecture for organizing data storage with compression are also given.
Holocaust and World War Two Linked Open Data Developments in the Netherlands

NIOD, Network War Collections (Netwerk Oorlogsbronnen) and EHRI all work on connecting and making war and Holocaust collections findable and (re-)usable. And both use new technology and Linked Open Data for these goals. This paper gives an overview of the latest developments of the work done in the Netherlands. It is organized around the axis of What, Where, Who & When.
Robots for Social Good: Exploring Critical Design for HRI

Robots are being increasingly developed as social actors, entering public and personal spaces such as airports, shopping malls, care centres, and even homes, and using human or animal-like social techniques to work with people. Some even aim to engineer social situations, or are designed specifically for an emotional response (e.g., comforting a person). However, if we consider these robots as social interventions, then it is important to recognize that the robots design - its behavior, its application, its appearance, even its marketing image - will have an impact on the society and in the spaces it enters. While in some cases this may be a positive effect, social robots can also contribute negatively, e.g., reinforcing gender stereotypes or promoting ageist views. This full-day workshop aims to offer a forum for Human-Robot Interaction (HRI) researchers to explore this issue, and to work toward potential opportunities for the field. Ultimately, we want to promote robots for social good that can contribute to positive social changes for socio-political issues (e.g., ageism, feminism, homelessness, environmental issues). The political aspects of technologies have long been scrutinized in related areas such as Science and Technology Studies (STS) and Human-Computer Interaction (HCI). In particular, critical design explicitly targets the design of technologies that can contribute to our understanding of how technology can impact society. This workshop aims to strengthen this discussion in the HRI community, with the goal of working toward initial recommendations for how HRI designers can include elements of critical design in their work.
Determinants impacting diffusion of knowledge in higher learning institutes in India: an empirical study

This study explores to identify the factors and barriers influencing the diffusion of knowledge in higher learning institutes in India. Several adoption theories and models have been explored. Relevant literature has been studied in the Indian context. This has helped to conceptualize the essence of diffusion of knowledge in educational organizations. This is helpful to upgrade the intellectual capital of the society. A conceptual model has been developed. Some hypotheses have been formulated. The proposed model has been validated through regression analysis by conducting a sample survey considering 318 useable responses. The results highlight that competence of concerned stakeholders, institutional support and word of mouth positively impact on the diffusion of knowledge mechanisms. The proposed model bears 87% explanative power.
Exploiting languages proximity for part-of-speech tagging of three French regional languages

This paper presents experiments in part-of-speech tagging of low-resource languages. It addresses the case when no labeled data in the targeted language and no parallel corpus are available. We only rely on the proximity of the targeted language to a better-resourced language. We conduct experiments on three French regional languages. We try to exploit this proximity with two main strategies: delexicalization and transposition. The general idea is to learn a model on the (better-resourced) source language, which will then be applied to the (regional) target language. Delexicalization is used to deal with the difference in vocabulary, by creating abstract representations of the data. Transposition consists in modifying the target corpus to be able to use the source models. We compare several methods and propose different strategies to combine them and improve the state-of-the-art of part-of-speech tagging in this difficult scenario.
Cross-view Identical Part Area Alignment for Person Re-identification

Person re-identification aims to associate images captured by non-overlapping cameras. It is a challenging task because images are often in different conditions such as background clutter, illumination variation, viewpoint changes and different camera settings. Viewpoint changes and pose variations often cause body part self-occlusion and misalignment. To deal with the problem, local features from human body parts are extracted. However, with viewpoint changes, the body parts also rotate horizontally. It is inappropriate to extract feature from entire area of body parts directly because the visible surface of body parts would turn away if viewpoint changes. Comparing identical areas provides a new way to pay attention to the details of person images. In this paper, we propose a Rotation Invariant Network to find the identical areas in cross-view images to extract robust local features. Extensive experiment show the effectiveness of our method on public datasets including CUHK03, Market1501 and DukeMTMC.
A hybrid weed optimized coverage path planning technique for autonomous harvesting in cashew orchards

A coverage path planning algorithm is proposed for discrete harvesting in cashew orchards. The main challenge in such an orchard is the collection of fruits and nuts lying on the floor. The manual collection of fruits and nuts is both time consuming and labour intensive. The scenario begs for automated collection of fruits and nuts. There are methods developed in research for continuous crop fields, but none for discrete coverage. The problem is visualized as a graph traversal problem and paths for autonomous maneuvering are generated. A novel Mahalanobis distance based partitioning approach for performing coverage is introduced. The proposed path planner was able to achieve a mean coverage of 52.78 percentage with a deviation of 18.95 percentage between the best and worst solutions. Optimization of the generated paths is achieved through a combination of local and global search techniques. This was implemented by combining a discrete invasive weed optimization technique with an improved 2-Opt operator. A case study is formulated for the fruit picking operations in the orchards of Puducherry. The performance of the proposed algorithm is benchmarked against existing methods and also with performance metrics such as convergence rate, convergence diversity and deviation ratio. The convergence rate was observed to be 99.97 percent and 97.83 percent for a dataset with 48 and 442 nodes respectively. The deviation ratio was 0.02 percent and 2.16 percent, with a convergence diversity of 1.18 percent and 30.14 percent for datasets with 48 and 442 nodes. The achieved solutions was on par with the global best solutions achieved so far for the test datasets.
Adaptively Weighted Multi-task Learning Using Inverse Validation Loss

Multi-task learning aims to enhance the performance of a model by inductive transfer of information among tasks. However, joint optimization of multiple tasks is challenging due to unbalanced data ranges and variations in task difficulties which can cause the model to converge only for a single task which has large values. To address these problems, we propose a novel weighting scheme based on validation loss. The proposed weighted scheme is evaluated on three datasets, including publicly available Comma.ai and Udacity benchmark dataset and GTA-V dataset. Our experiments demonstrate the superior performance of the proposed approach compared to the current state-of-the-art methods.
Low-Rank Based Image Analyses for Pathological MR Image Segmentation and Recovery

The presence of pathologies in magnetic resonance (MR) brain images causes challenges in various image analysis areas, such as registration, atlas construction and atlas-based segmentation. We propose a novel method for the simultaneous recovery and segmentation of pathological MR brain images. Low-rank and sparse decomposition (LSD) approaches have been widely used in this field, decomposing pathological images into (1) low-rank components as recovered images, and (2) sparse components as pathological segmentation. However, conventional LSD approaches often fail to produce recovered images reliably, due to the lack of constraint between low-rank and sparse components. To tackle this problem, we propose a transformed low-rank and structured sparse decomposition (TLS2D) method. The proposed TLS2D integrates the structured sparse constraint, LSD and image alignment into a unified scheme, which is robust for distinguishing pathological regions. Furthermore, the well recovered images can be obtained using TLS2D with the combined structured sparse and computed image saliency as the adaptive sparsity constraint. The efficacy of the proposed method is verified on synthetic and real MR brain tumor images. Experimental results demonstrate that our method can effectively provide satisfactory image recovery and tumor segmentation.
Medical Image Segmentation with Adjustable Computational Complexity Using Data Density Functionals

Techniques of automatic medical image segmentation are the most important methods for clinical investigation, anatomic research, and modern medicine. Various image structures constructed from imaging apparatus achieve a diversity of medical applications. However, the diversified structures are also a burden of contemporary techniques. Performing an image segmentation with a tremendously small size (&lt;25 pixels by 25 pixels) or tremendously large size (&gt;1024 pixels by 1024 pixels) becomes a challenge in perspectives of both technical feasibility and theoretical development. Noise and pixel pollution caused by the imaging apparatus even aggravate the difficulty of image segmentation. To simultaneously overcome the mentioned predicaments, we propose a new method of medical image segmentation with adjustable computational complexity by introducing data density functionals. Under this theoretical framework, several kernels can be assigned to conquer specific predicaments. A square-root potential kernel is used to smoothen the featured components of employed images, while a Yukawa potential kernel is applied to enhance local featured properties. Besides, the characteristic of global density functional estimation also allows image compression without losing the main image feature structures. Experiments on image segmentation showed successful results with various compression ratios. The computational complexity was significantly improved, and the score of accuracy estimated by the Jaccard index had a great outcome. Moreover, noise and regions of light pollution were mostly filtered out in the procedure of image compression.
Context-Aware Data Mining vs Classical Data Mining: Case Study on Predicting Soil Moisture

The purpose of this article is to investigate whether or not including context data (context-awareness) in a classical data mining process would enhance the overall results. For that, the efficiency of the predictions was analyzed and compared: in a classical data mining process versus a context-aware data mining process. The two processes were applied on existing data collected from more weather stations to predict the future soil moisture. The classic data mining process considers historical data on soil moisture and temperature in a time interval, while the context-aware process also adds collected context information on air temperature for that location. The obtained results show advantages of CADM over classical DM.
HCI of Arabia

column Share on HCI of Arabia: the challenges of HCI research in Egypt Authors: Galal H. Galal-Edeen Cairo University, UCL Institute of Digital Health, and UCL Interaction Centre Cairo University, UCL Institute of Digital Health, and UCL Interaction CentreView Profile , Yasmeen Abdrabou The German University in Cairo and Bundeswehr University Munich The German University in Cairo and Bundeswehr University MunichView Profile , Maha Elgarf KTH Royal Institute of Technology KTH Royal Institute of TechnologyView Profile , Hala M. Hassan Cairo University Cairo UniversityView Profile Authors Info & Claims InteractionsVolume 26Issue 3May - June 2019 pp 55–59https://doi.org/10.1145/3318215Published:23 April 2019Publication History 4citation596DownloadsMetricsTotal Citations4Total Downloads596Last 12 Months140Last 6 weeks21 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access