Reviewerly/exHarmony huggingface dataset:
Coiling or slab building: Potential of orientation analysis for identification of forming techniques used by Early Neolithic potters

This work explores the potential of analysis based on quantification of the orientation of components of a ceramic body to distinguish between the two basic families of hand-building techniques (coiling and slab-building). The previous application of the method to techniques related to the use of the potter's wheel showed that this approach can yield details specific for the relationship between the orientation of the components of a ceramic body and the pottery forming technique. Consequently, we explored its potential for identification of the basic hand building techniques and found that it is very limited in this case. However, the study provides important information related to the effects of the studied forming techniques on the orientation. The results show that the hypothetical effects of the basic forming forces that define these techniques can be significantly altered during the forming of a vessel. The results of the experimental study warn against a schematic view of the orientation patterns. Slab building and coiling are among techniques where the vectors of the forming force are not sufficiently constrained by the basic principles of the technique. Consequently, they can produce very variable orientations of the microstructures of the completed ceramic vessels.
Exploring the MLDA benchmark on the nevergrad platform

This work presents the integration of the recently released benchmark suite MLDA into Nevergrad, a likewise recently released platform for derivative-free optimization. Benchmarking evolutionary and other optimization methods on this collection enables us to learn how algorithms deal with problems that are often treated by means of standard methods like clustering or gradient descent. As available computation power nowadays allows for running much 'slower' methods without noticing a performance difference it is an open question which of these standard methods may be replaced by derivative-free and (in terms of quality) better performing optimization algorithms. Additionally, most MLDA problems are suitable for landscape analysis and other means of understanding problem difficulty or algorithm behavior, due to their tangible nature.
The kernel Kalman rule

Enabling robots to act in unstructured and unknown environments requires versatile state estimation techniques. While traditional state estimation methods require known models and make strong assumptions about the dynamics, such versatile techniques should be able to deal with high dimensional observations and non-linear, unknown system dynamics. The recent framework for nonparametric inference allows to perform inference on arbitrary probability distributions. High-dimensional embeddings of distributions into reproducing kernel Hilbert spaces are manipulated by kernelized inference rules, most prominently the kernel Bayes’ rule (KBR). However, the computational demands of the KBR do not scale with the number of samples. In this paper, we present two techniques to increase the computational efficiency of non-parametric inference. First, the kernel Kalman rule (KKR) is presented as an approximate alternative to the KBR that estimates the embedding of the state based on a recursive least squares objective. Based on the KKR we present the kernel Kalman filter (KKF) that updates an embedding of the belief state and learns the system and observation models from data. We further derive the kernel forward backward smoother (KFBS) based on a forward and backward KKF and a smoothing update in Hilbert space. Second, we present the subspace conditional embedding operator as a sparsification technique that still leverages from the full data set. We apply this sparsification to the KKR and derive the corresponding sparse KKF and KFBS algorithms. We show on nonlinear state estimation tasks that our approaches provide a significantly improved estimation accuracy while the computational demands are considerably decreased.
Gated Convolutional Neural Networks for Domain Adaptation

Domain Adaptation explores the idea of how to maximize performance on a target domain, distinct from source domain, upon which the model was trained. This idea has been explored for the task of sentiment analysis extensively. The training of reviews pertaining to one domain and evaluation on another domain is widely studied for modeling a domain independent algorithm. This further helps in understanding corelation of information between domains. In this paper, we show that Gated Convolutional Neural Networks (GCN) perform effectively at learning sentiment analysis in a manner where domain dependant knowledge is filtered out using its gates. We perform our experiments on multiple gate architectures: Gated Tanh ReLU Unit (GTRU), Gated Tanh Unit (GTU) and Gated Linear Unit (GLU). Extensive experimentation on two standard datasets relevant to the task, reveal that training with Gated Convolutional Neural Networks give significantly better performance on target domains than regular convolution and recurrent based architectures. While complex architectures like attention, filter domain specific knowledge as well, their complexity order is remarkably high as compared to gated architectures. GCNs rely on convolution hence gaining an upper hand through parallelization.
Review and Perspectives on Data Sharing and Privacy in Expanding Electricity Access

Increased sensing and data collection in electric power systems from utility to minigrid to individual household scale are resulting in an explosion of data collection about users and providers of electricity services. In the push to expand energy access for poor communities, the collection, use, and curation of these data have historically taken a back seat to the goal of expanding energy access but are increasingly being recognized as important issues. We review the nascent literature on this topic, characterize current data management practices, and examine how expanding access to data and data sharing are likely to provide value and pose risks to key stakeholders: end users of electricity, microutilities, macroutilities, governments, development institutions, and researchers. We identify the key opportunities and tensions and provide recommendations for the design and implementation of new data-sharing practices and platforms. Our review and analysis suggest that although a common and open platform for sharing technical data can mitigate risks and enable efficiency, fewer benefits are likely to be realized from sharing detailed financial data. We also recommend codesigning practices with each stakeholder group, increasing legal protections for end users of electricity and using deep qualitative data in addition to quantitative metrics.
Sensate Benches – A Modern Approach to Education

The current Smart Education System includes the whiteboard screens which is an electronic visual display enabled by touchscreen technologies thus being used universally. The field of Internet of Things (IoT) including a plethora of technologies is being explored to improve the education industry. To make education more effective and interactive, it becomes necessary to create new avenues for a smarter classroom. Since screen sharing systems are becoming increasingly prevalent in today's technologically advancing world, we propose a smart system which integrates technology and education. The idea is to create a sensor-based system that would be implemented on the classroom bench surface to help facilitate interactive learning, collaboration, automatic calibration, multi-touch and gesture detection. The proposed system can support various Operating Systems viz. Windows, Mac OS, Ubuntu, etc. By integrating these features in the proposed `Sensate Benches', we may help improve the quality of smart learning.
Safe Open Science for Restricted Data

Open science is prompting wide efforts to make data from research available for broader use. However, sharing data is complicated by important protections on the data (e.g., protections of privacy and intellectual property). The spectrum of options existing between data needing to be fully open access and data that simply cannot be shared at all is quite limited. This paper puts forth a generalized remote secure enclave as a socio-technical framework consisting of policies, human processes, and technologies that work hand in hand to enable controlled access and use of restricted data. Based on experience in implementing the enclave for computational, analytical access to a massive collection of in-copyright texts, we discuss the synergies and trade-offs that exist between software components and policy and process components in striking the right balance between safety for the data, ease of use, and efficiency.
Accurate Product Attribute Extraction on the Field

In this paper we present a bootstrapping approach for attribute value extraction that minimizes the need for human intervention. Our approach automatically extracts attribute names and values from semi-structured text, generates a small labelled dataset, and bootstraps it by extracting new values from unstructured text. It is domain/language-independent, relying only on existing semi-structured text to create the initial labeled dataset. We assess the impact of different machine learning approaches to increase precision of the core approach without compromising coverage. We perform an extensive evaluation using e-commerce product data across different categories in two languages and hundreds of thousands of product pages. We show that our approach provides high precision and good coverage. In addition, we study the impact of different methods that address specific sources of error. With error analysis we highlight how these methods complement each other, obtaining insights about the individual methods and the ensamble as a whole.
Biometric Systems Interaction Assessment: The State of the Art

The design and implementation of effective and efficient biometric systems presents a series of challenges to information technology (IT) designers to ensure robust performance. One of the most important factors across biometric systems, aside from algorithmic matching ability, is the human interaction influence on performance. Changes in biometric system paradigms have motivated further testing methods, especially within mobile environments, where the interaction with the device has fewer environmental constraints, which may severely affect system performance. Testing methods involve the need for reflecting on the influence of user-system interaction on the overall system performance in order to provide information for design and testing. This paper reflects on the state of the art of biometric systems interaction assessment, leading to a comprehensive document of the relevant research and standards in this area. Furthermore, the current challenges are discussed and thus we provide a roadmap for the future of biometrics systems interaction research.
A Hybrid Architecture for Secure Management of Manufacturing Data in Industry 4.0

In this paper, we analyze the suitability of the methods available today for securely managing the wide variety of data produced by the manufacturing sector. We propose a hybrid information architecture for manufacturing, based on decentralized blockchains, cloud-based WORM storage and ordinary cloud storage. We point out shortcomings in the technology available today for realizing this architecture. In particular, we identify a need for low-cost IoT-based systems to capture, identify, preprocess, encrypt and transmit factory floor data to the corresponding data storage subsystems. We describe our proof-of-concept implementation of such an IoT system, along with the factory case study that inspired it, and argue that this system is sufficiently inexpensive to be retrofitted into today's factories.
Dual Token Blockchain Economy Framework

Over the last few years, the introduction of blockchain technology has brought forth a new set of challenges concerning blockchain token economics and its underlying business needs. Many Initial Coin Offerings (ICOs) have focused on a single token that facilitated the initial project funding but complicated the actual implementation. Business arrangements can be complex and result in disputes between even the most well-intentioned parties. Supply chain transparency requires careful modeling of a specific implementation of the blockchain technology to correctly capture it. In this paper, we propose a dual token model that takes into consideration both the financial aspect and the non-fungible nature of complex, real-world industries. Furthermore, we demonstrate the efficacy of our model to accommodate the stages hidden under the umbrella term "supply chain transparency" as it comes to the costing involved in producing the firm's products internal supply chain transparency. We show how this approach increases not only the transparency of the manufacturing process but also the profit and transparency for external stakeholders.
Cloud-Based Image Retrieval Using GPU Platforms

The process of image retrieval presents an interesting tool for different domains related to computer vision such as multimedia retrieval, pattern recognition, medical imaging, video surveillance and movements analysis. Visual characteristics of images such as color, texture and shape are used to identify the content of images. However, the retrieving process becomes very challenging due to the hard management of large databases in terms of storage, computation complexity, temporal performance and similarity representation. In this paper, we propose a cloud-based platform in which we integrate several features extraction algorithms used for content-based image retrieval (CBIR) systems. Moreover, we propose an efficient combination of SIFT and SURF descriptors that allowed to extract and match image features and hence improve the process of image retrieval. The proposed algorithms have been implemented on the CPU and also adapted to fully exploit the power of GPUs. Our platform is presented with a responsive web solution that offers for users the possibility to exploit, test and evaluate image retrieval methods. The platform offers to users a simple-to-use access for different algorithms such as SIFT, SURF descriptors without the need to setup the environment or install anything while spending minimal efforts on preprocessing and configuring. On the other hand, our cloud-based CPU and GPU implementations are scalable, which means that they can be used even with large database of multimedia documents. The obtained results showed: 1. Precision improvement in terms of recall and precision; 2. Performance improvement in terms of computation time as a result of exploiting GPUs in parallel; 3. Reduction of energy consumption.
Image enhancement with preservation of brightness and details using multiscale top-hat transform

Image enhancement is a widely used technique to enhance the visual quality of images. However, this process introduces distortions and loss of detail in the images. In this article, we present a scheme that extracts the brightness and darkness features of an image in multiple scales. This extraction process is done by multiscale top-hat transform, which uses two structuring elements in the basic operations of mathematical morphology. Image enhancement consists of adding regions of brightness and removing regions of darkness from the original image. Tests were performed using 200 images from a public database. Our approach was compared with state-of-the-art algorithms to verify its relative performance. Experimental results show that our proposal improves images in terms of contrast, detail, preserves mean brightness and also introduces less distortion than the compared algorithms.
Network Traffic based Virtual Machine Migration in Cloud Computing Environment

The development of cloud computing technology is getting more and more attention. One of the most important technologies in cloud computing is virtualization, and cloud data centers process tasks by the Virtual Machines (VMs). Network traffic is generated by the communication between two VMs placed in different Physical Machines (PMs). Due to the expansion of the size of the cloud data center, the VMs are generating much more network traffic, which increases the load of the network resource and has an impact on the efficiency of the tasks. In this paper, a network traffic based VM migration algorithm (NTVMM) is proposed. According to the network traffic generated by the VMs, most appropriate VMs and PMs are selected to implement the VM migration. It is proved that the NTVMM algorithm can effectively reduce the network traffic generated by the VMs through simulation experiments under different conditions.
Diffusion induced graph representation learning

Nowadays, graph representation learning has aroused a lot of research interest, which aims to learn the latent low-dimensional representations of graph nodes, while preserving the graph structure. Based on the local smooth assumption, some existing methods have achieved significant success. However, although the structure information of data has been taken into consideration, these models fail to capture enough connectivity pattern such as high-order connections. To alleviate this issue, we propose a Graph Diffusion Network (GDN) that can dynamically preserve local and global consistency of graph. More specifically, Graph Diffusion Auto-encoder is utilized as the main framework in GDN to nonlinearly maintain global information volume. Different from simple auto-encoders, the forward propagation in our model is conducted through Graph Diffusion System which can guide the random walk of information flow to sense the high-order local relationships on graph. Furthermore, to discover a customized graph structure that reveals the similarities between nodes, the connection relationship between nodes are refined by learned metrics with the preservation of scale-free property. By the dynamically self-refining on the graph structure, it can be promoted towards learning the intrinsic node representations in a progressive way. Experimental results on node classification tasks demonstrate the effectiveness of the proposed GDN model.
Designing with, through, and for Human-Nature Interaction

Human-nature interaction, joining the agendas of sustainable interaction design and nonanthropocentric HCI, takes up the challenges presented by climate change and environmental crisis. My dissertation research focuses on studying sustainable farming practices through posthuman concepts to experiment ways of designing with, through, and for human-nature interaction. Specifically, my work aims to discover and develop alternative design paradigms and practices for sustaining both human and non-human life.
A novel web page text information extraction method

The text information of today's mainstream web pages generally has multiple features, and can be divided into a single text page and a multi-text page. In order to extract the text information of the web page, the position of the text information can be accurately located by using the multiple features of the text and the rules of the web page design. According to the above characteristics, this paper proposed a method for extracting web page text information based on multi-feature fusion. Experiments based on a large amount of data showed that the method has universality and high accuracy for the text information extraction of single text and multi-text web pages, and is very suitable for web pages with various styles.
Mostly Order Preserving Dictionaries

Dictionary encoding, or domain encoding, is an important form of compression that uses a bijective mapping to replace attributes from a large domain (i.e. strings) with a finite domain (i.e. 32 bit integers). This encoding both reduces data storage and allows for more efficient query execution. Traditional dictionary encoding only supports efficient equality queries, while range queries require that encoded values are decoded for evaluating the predicates. An order preserving dictionary allows for range queries without decoding by ensuring that encoded keys follow the same order as the values in the dictionary. While this approach enables efficient queries it requires that the full set of values is known to create the mappings. In this work we bridge this gap by introducing mostly ordered dictionaries that use a best effort dictionary generation based on sampling the input dataset. Query evaluation on a mostly ordered dictionary avoids decoding when possible and gracefully degrades performance as the ratio of ordered values decreases.
Detecting Online Counterfeit-goods Seller using Connection Discovery

With the advancement of social media and mobile technology, any smartphone user can easily become a seller on social media and e-commerce platforms, such as Instagram and Carousell in Hong Kong or Taobao in China. A seller shows images of their products and annotates their images with suitable tags that can be searched easily by others. Those images could be taken by the seller, or the seller could use images shared by other sellers. Among sellers, some sell counterfeit goods, and these sellers may use disguising tags and language, which make detecting them a difficult task. This article proposes a framework to detect counterfeit sellers by using deep learning to discover connections among sellers from their shared images. Based on 473K shared images from Taobao, Instagram, and Carousell, it is proven that the proposed framework can detect counterfeit sellers. The framework is 30% better than approaches using object recognition in detecting counterfeit sellers. To the best of our knowledge, this is the first work to detect online counterfeit sellers from their shared images.
Distributed Framework for Automating Opinion Discretization From Text Corpora on Facebook

Nowadays, the consecutive increase of the volume of text corpora datasets and the countless research directions in general classification have created a great opportunity and an unprecedented demand for a comprehensive evaluation of the current achievement in the research of natural language processing. There are unfortunately few studies that have applied the combination of convolutional neural networks (CNN) and Apache Spark to the task of automating opinion discretization. In this paper, the authors propose a new distributed structure for solving an opinion classification problem in text mining by utilizing CNN models and big data technologies on Vietnamese text sources. The proposed framework consists of implementation concepts that are needed by a researcher to perform experiments on text discretization problems. It covers all the steps and components that are usually part of a completely practical text mining pipeline: acquiring input data, processing, tokenizing it into a vectorial representation, applying machine learning algorithms, performing the trained models to unseen data, and evaluating their accuracy. The development of the framework started with a specific focus on binary text discretization, but soon expanded toward many other text-categorization-based problems, distributed language modeling and quantification. Several intensive assessments have been investigated to prove the robustness and efficiency of the proposed framework. Resulting in high accuracy (72.99% ± 3.64) from the experiments, one can conclude that it is feasible to perform our proposed distributed framework to the task of opinion discretization on Facebook.