Reviewerly/exHarmony huggingface dataset:
Urns Filled with Bitcoins: New Perspectives on Proof-of-Work Mining

It is widely assumed that the selection process in a blockchain is based on proportional winning probabilities. The reliability and security of any blockchain is based upon this assumption. However, making an analogy between the Bitcoin protocol and the classical statistical urn problem, we argue that, at least on a theoretical level, the selection process in several blockchains is based on nonproportional winning probabilities. This reveals a misconception regarding the incentive structure of many blockchain protocols. We develop an empirical approach to testing for nonproportional winning probabilities in any blockchain, and offer a solution to this problem.
HMC: A Hybrid Reinforcement Learning Based Model Compression for Healthcare Applications

Artificial intelligence (AI) healthcare applications to optimize workflows, reduce costs while focusing on patient care are on the rise. While deeper and wider neural networks are designed for complex healthcare applications, model compression is poised to be an effective way to deploy networks on medical devices that often have hardware and speed constraints. Most state-of-the-art model compression techniques require a resource centric manual process that explores a large space to find a trade-off solution between model size and accuracy. Recently, reinforcement learning (RL) approaches are proposed to automate such hand-crafted process. However, most RL model compression algorithms are model-free, meaning a very long time to train such RL agents due to the huge state space. In this work, we develop a hybridRL model compression (HMC) method that integrates model-based and model-free RL approaches. We demonstrate our method on a wide range of imaging data on healthcare related model architectures. Compared to model-free RL approaches, our results show that HMC method reduces the training time significantly, exhibits better generalization capabilities across different data sets, and preserves comparable model compression performance.
Deep spectral feature pyramid in the frequency domain for long-term action recognition

In this paper, we propose a novel Deep Spectral Feature Pyramid in the Frequency domain (DSFP) to share the merits of deep features and spectral approaches for long-term action recognition. More specifically, in the spatial domain, deep features of sparse sampled frames are extracted by Convolutional Neural Networks (CNNs) to cover long-term temporal structure. In the frequency domain, appearance features of sampled frames are partitioned recursively along the time dimension and spectral transform is applied to each partitioned feature respectively. All coefficients of partitioned features are then concatenated into a video-level feature to better model the spatio-temporal structure of actions in the form of a pyramid. So DSFP could model actions from both microcosmic and macroscopic aspects. Extensive experiments conducted on two challenging action benchmarks UCF101 and HMDB51 show that our proposed DSFP is effective for spatio-temporal representation of actions and achieves comparable performance with the state-of-the-arts.
Real-time Screen Sharing Using Web Socket for Presenting Without Projector

The presentation is generally done using a laptop device or Personal Computer (PC) connected to a projector. Participants see a presentation slide show on a particular screen or white painted the wall. However, sometimes, problems occur when the projectors' equipment is not available to the presentation process does not run optimally. Other problems such as projector damage or dead pixel conditions and rainbow effects on the projector often interfere with the display permanently. A presentation without using a projector is one solution to overcome problems that have been proposed in previous research. However, there is still a particular server that is used by presenters and PCs to access those used by clients. The solution to overcome this problem, in this study, will be developed a system that can carry out the activity of sharing presentation between presenter and audience without using a projector. The presenter only requires an ordinary laptop or PC connected to a wireless network. Participants as clients can access the presentation display via a smartphone that is connected to the same wireless network as the server. The system built consists of two main applications: Screen Sharing Server and Screen Sharing Client, which allows sharing of views by implementing a socket web on its data communication model. Test results with 20 smartphone audiences can see the presentation results from the presenter's laptop. The presentation was successfully carried out with the concept of screen sharing in real-time without using additional projectors and computers, no need for cables and without the need for an internet connection.
Use of LIME for Human interpretability in Sinhala document classification

With advancement of technology in Sri Lanka, use of Sinhala text usage has grown rapidly over the time where automatic categorization is helpful for efficient content management. As a result, experts tend to use machine learning application to categorize this large volume of data in an efficient and accurate manner. Most of these learning models are operating in a black-box where there is no way to understand how the model has decided which category an instance is assigned. Understanding the reason behind why learning model makes these predictions is very important to trust such models and to provide reasonable justifications in real world application. Intention of this research is to present the work carried on related to document classification model prediction interpretation where a set of text classifiers has been studied with use of SinNG5, freely available Sinhala Document corpus.
Information Relaxation Bounds for Partially Observed Markov Decision Processes

Partially observed Markov decision processes (POMDPs) are an important class of control problems that are ubiquitous in a wide range of fields. Unfortunately, these problems are generally intractable, so, in general, we must be satisfied with suboptimal policies. But how do we evaluate the quality of these policies? This question has been addressed in recent years in the Markov decision process (MDP) literature through the use of information-relaxation-based duality, where the nonanticipativity constraints are relaxed, but a penalty is imposed for violations of these constraints. In this paper, we extend the information relaxation approach to POMDPs. It is of course well known that the belief-state formulation of a POMDP is an MDP, and therefore, the previously developed results for MDPs also apply to POMDPs. Under the belief-state formulation, we use recently developed change-of-measure arguments to solve the so-called inner problems, and we use standard filtering arguments to identify the appropriate Radon-Nikodym derivatives. We also show, however, that dual bounds can also be constructed without resorting to the belief-state formulation. In this case, change-of-measure arguments are required for the evaluation of the so-called dual feasible penalties rather than for the solution of the inner problems. We compare dual bounds for both formulations and argue that, in general, the belief-state formulation provides tighter bounds. The second main contribution of this paper is to show that several value function approximations for POMDPs are in fact supersolutions. This is of interest because it can be particularly advantageous to construct penalties from supersolutions, since absolute continuity (of the change of measure) is no longer required, and therefore, significant variance reduction can be achieved when estimating the duality gap directly. Dual bounds constructed from supersolution-based penalties are also guaranteed to provide tighter bounds than the bounds provided by the supersolutions themselves. We use applications from robotic navigation and telecommunication to demonstrate our results.
Construction Research and Application of Poverty Alleviation Knowledge Graph

Based on the integration of multi-source data, an approach of domain-specific knowledge graph construction is proposed to guide the construction of a "people-centered" poverty alleviation knowledge graph, and to achieve cross-functional and cross-regional sharing and integration of national basic data resources and public services. Focusing on "precise governance and benefit people service", poverty alleviation ontology is constructed to solve semantic heterogeneity in multiple data sources integration, and provide an upper data schema for poverty alleviation knowledge graph construction. Karma modeling is used to implement semantic mapping between ontology concepts and data, and integrate multi-source heterogeneous data into RDF data. The RDF2Neo4j interpreter is developed to parse RDF data and store RDF data schema based on the graph database Neo4j. Based on visualization technology and natural language processing technology, Poverty Alleviation Knowledge Graph Application System is designed to achieve knowledge graph query and knowledge question answering function, which improved the application value of government data.
Investigating Query Reformulation Behavior of Search Users

Search engine users usually strive to reformulate their queries in the search process to gain useful information. It is hard for search engines to understand users' search intents and return appropriate results if they submit improper or ambiguous queries. Therefore, query reformulation is a bottleneck issue in the usability of search engines. Modern search engines normally provide users with some query suggestions for references. To help users to better learn their information needs, it is of vital importance to investigate users' reformulation behaviors thoroughly. In this paper, we conduct a detailed investigation of users' session-level reformulation behavior on a large-scale session dataset and discover some interesting findings that previous work may not notice before: (1) Intent ambiguity may be the direct cause of long sessions rather than the complexity of users' information needs; (2) Both the added and the deleted terms in a reformulation step can be influenced by the clicked results to a greater extent than the skipped ones; (3) Users' specification actions are more likely to be inspired by the result snippets or the landing pages, while the generalization behaviors are impacted largely by the result titles. We further discuss some concerns about the existing query suggestion task and give some suggestions on the potential research questions for future work. We hope that this work could provide assistance for the researchers who are interested in the relative domain.
Delta-oriented development of model-based software product lines with DeltaEcore and SiPL: A comparison

Model-based development has become a widely used approach to implement software, especially for embedded systems. Such systems must often be delivered to customers in a large number of variants exposing slightly different functionality. This need is addressed by model-based software product line (MBSPL) engineering. Implementation methodologies for MBSPLs have to be able to specify variability in models and to generate models as instances of an MBSPL. Delta modeling is a transformational approach to implement variability of an MBSPL. Variants are generated by applying one or several delta modules onto a core model. While the basic concepts of delta modeling for MBSPLs are well understood, essential development tasks such as implementation and analysis of delta modules as well as managing entire networks of delta modules are barely supported by many tools. Both our tool suites, DeltaEcore and SiPL, constitute the realization of state of the art practices in delta-oriented development of MBSPLs to address these challenges. In this chapter, we demonstrate the capabilities of DeltaEcore and SiPL, highlight their individual strengths, and compare them regarding their capabilities. With these contributions, for academics, we illustrate the state of practice, e.g., as basis for implementing results of their ongoing research and, for practitioners, we provide means to select and apply the most adequate tool in a delta-oriented MBSPL.
Multimodal user interaction framework for e-commerce

E-commerce has grown to be a major user of e-services and online purchases through e-commerce are largely preferred over the traditional brick and mortar purchasing. Yet it is challenging for the consumers to fully experience the products or services with limited senses, lack of tangibility and sense of presence. Therefore, the objective of this study is to assess how multimodal interactions can be used in e-commerce, to improve the consumer experience. This research describes the design and implementation of multimodal interactions enabled prototype framework for e-commerce developed to address that issue. The framework supports three interaction modes: speech inputs, gesture inputs and interactive 3D product presentations. A case study and a usability study conducted on this framework showed satisfactory results and also revealed some interesting social and cultural barriers to multimodal interactions that involve language and psychological factors.
Facial Expression Recognition Using Local Composition Pattern

One or more motions or positions of the muscles under the skin of the face is a facial expression. Here, a new image feature descriptor is proposed which is more efficient than Local Binary Pattern (LBP) and Local Derivative Pattern (LDP). In the state of the artwork by thresholding the neighboring pixel values with the central pixel is leveled in each pixel of an image. In our proposed method, edge response is used to detect edges in a radial direction. The directional derivative is computed in the horizontal and vertical direction which is used to extract more discriminatory features. Using neighboring pixels, a new smart threshold is determined. Experimental results show that our method gives a better recognition rate than the existing method. The result proves that it is better than the state of the art descriptors.
A Unified Relational Storage Scheme for RDF and Property Graphs

With the advance of Semantic Web and development of Linked Data, the scale of knowledge graphs has surged dramatically. On the one hand, RDF graph is a mainstream data model of the knowledge graph. On the other hand, property graphs are widely accepted in graph databases. How to manage large-scale RDF and property graphs in an interchangeable way has become popular in both academic and industrial communities. Thus we present an effective unified relational storage scheme, that can seamlessly accommodate both RDF and property graphs. Furthermore, we have implemented the storage schema on an open-source graph database to verify its effectiveness. Ultimately, our experimental results show that the proposed unified storage schema for both RDF and property graphs can effectively manage large-scale knowledge graphs, efficiently avoid data redundancy, and achieve high-performance queries.
Design of Finite Impulse Response Filter with Controlled Ripple Using Cuckoo Search Algorithm

In this paper, an efficient design of finite impulse response (FIR) filter is presented with improved fitness function using cuckoo search algorithm (CSA). CSA is recently proposed evolutionary technique (ET), which has efficient ability of exploration and therefore used in FIR filter design. The fitness function is constructed in the frequency domain as a mean squared error (MSE) between the designed and desired response. In this fitness function, tolerable limits for magnitude response in passband and stopband region have been embedded, which helps in gaining the controlled ripple in irrespective bands. The designed filters are realized on general-purpose microcontroller using Arduino platform and filter performance is tested using fidelity parameters, which are; passband error (Erpb), stopband error (Ersb), and minimum stopband attenuation (As). The exhaustive experimental analysis confirms that proposed methodology is statically stable and obtains improved fidelity parameters when compared with previous state of the art.
A Systematic Literature Review on Factors Impacting Agile Adaptation in Global Software Development

Agile practices are considered as a major attraction for global software development (GSD) projects owing to its flexible nature. Beside the major benefits it offers to GSD, there are few challenges that hinders its implementation across the global software industry. This study contributes in constructing a systematic literature review for exploring the major factors impacting the agile adaptation at global level. We have identified and analyzed 28 research studies (2015-2019). These selected studies have revealed Scrum and Extreme Programming (XP) as the most popular agile practices that are adapted irrespective of the software type and organizational structure. Furthermore 5 tool categories are also presented i.e. modeling, requirement elicitation, data tracking tools etc. that are commonly used while practicing agile. The major findings of this study conclude that these agile methodologies are heavily adapted due to their iterative model and quick code delivery but basic challenges like poor customer involvement and lack of documentation are badly affecting its growth at global level.
Convolutional Deep Feedforward Network for Image Classification

Deep feedforward network (DFN) is a conceptual stepping stone of many well-known deep neural networks (DNN) in image classification and natural language application. The development on the standard DFN can rarely be found in the literature recently due to the popularity in convolutional networks. The recent trend of research focuses on the increment of the convolutional layers in a deeper and wider network architecture for achieving higher accuracy and lower misclassification rate. However, stacking the convolutional layers may not result in better accuracy due to the sparsity of interconnected of hidden nodes. In this paper, a convolutional deep feedforward network (C-DFN) is proposed to anlayse the performance of deep neural networks by increasing the number of fully-connected layers. C-DFN contains a Gabor-convolutional layer as a trainable feature extractor and followed by the four fully-connected layers. Experiments are conducted to evaluate the performance of proposed network with three other structures, i.e. deep belief network, deep feedforward network and convolutional deep belief network. The experimental results showed that C-DFN obtained the lowest average misclassfication rate of 9.41% in the image classification.
Introduction to New Zealand hard rock gold deposits and their exploration models

Since the first official discovery of gold in 1852, gold has been mined in New Zealand from three main types of deposits: placer (c. 19 Moz Au), orogenic (mesothermal; c. 8 Moz Au) and epithermal (c. 11 Moz Au). Currently, gold production in New Zealand is from more than 30 placer mines (>50,000 oz Au per annum) on the West Coast, and in Otago and Southland, and from hard-rock mines at the Macraes orogenic deposits in northeast Otago (202,990 oz Au produced in 2018), and the epithermal deposits of the Waihi area (83,492 oz Au produced in 2018) in the Hauraki Goldfield. The discovery of the Sams Creek intrusion-hosted gold deposit in west Nelson in 1974 has also encouraged exploration for this type of gold deposit. Exploration models for orogenic, intrusion-hosted and epithermal gold deposits have been developed addressing general deposit characteristics, mineral system models and key features for use in exploration. Orogenic deposits are hosted in Western Province Greenland Group (e.g. Reefton Goldfield) and Eastern Province Haast Schist Group (e.g. Macraes), whereas epithermal deposits are associated with Cenozoic volcanic rocks in Northland (e.g. Puhipuhi), and the Coromandel (e.g. Waihi) and Taupo (e.g. Ohakuri) volcanic zones.
Indexing Name in Hadith Translation Using Hidden Markov Model (HMM)

Hadith is one of Islamic's source beside the Qur'an which contains words and actions narrated by the Prophet SAW. There are so Prophet SAW's friends who followed his journey and had an important role in spreading Islam. There are still many people who do not know and have difficult time to finding the names of the friends who participated in the Prophet SAW's journey. Therefore, this research will make an indexing of the names that appear in the nine narrators' hadith collections. This indexing names uses Named Entity Recognition (NER) because indexing names only need entities in the form of people names. To make indexing names in hadith collections, this research will be use Hidden Markov Model (HMM) as a method. HMM is very often used in previous research and often gets quite good performance scores. Using the HMM's method and using several combinations of features, the system has a pretty good performance by counting recall, precision and F-1 for by calculating this performance. The values of performance were obtained using HMM's method are 86%. But by using cross validation based on the parameters, the performance values increase 2%, which means that the performance in this research is quite good for 38.102 data hadith.
Signed Network Embedding Based on Noise Contrastive Estimation and Deep Learning

Network embedding is a representation learning method to learn low-dimensional vectors for vertices of a given network, aiming to capture and preserve the network structure. Signed networks are a kind of networks with both positive and negative edges, which have been widely used in real life. Presently, the mainstream signed network embedding algorithms mainly focus on the difference between positive and negative edges, but ignore the role of empty edges. Considering the sparsity of signed networks, a signed network embedding algorithm NDSE is proposed based on noise contrastive estimation model and deep learning framework. The algorithm emphasizes the role of empty edges and optimizes a carefully designed objective function that preserves both local and global network structures. Empirical experiments prove the effectiveness of the NDSE on three real data sets and one signed network task.
Towards Efficient On-Board Deployment of DNNs on Intelligent Autonomous Systems

With their unprecedented performance in major AI tasks, deep neural networks (DNNs) have emerged as a primary building block in modern autonomous systems. Intelligent systems such as drones, mobile robots and driverless cars largely base their perception, planning and application-specific tasks on DNN models. Nevertheless, due to the nature of these applications, such systems require on-board local processing in order to retain their autonomy and meet latency and throughput constraints. In this respect, the large computational and memory demands of DNN workloads pose a significant barrier on their deployment on the resource-and power-constrained compute platforms that are available on-board. This paper presents an overview of recent methods and hardware architectures that address the system-level challenges of modern DNN-enabled autonomous systems at both the algorithmic and hardware design level. Spanning from latency-driven approximate computing techniques to high-throughput mixed-precision cascaded classifiers, the presented set of works paves the way for the on-board deployment of sophisticated DNN models on robots and autonomous systems.