Reviewerly/exHarmony huggingface dataset:
PivotE

A Web-scale knowledge graph (KG) typically contains millions of entities and thousands of entity types. Due to the lack of a pre-defined data schema such as the ER model, entities in KGs are loosely coupled based on their relationships, which brings challenges for effective accesses of the KGs in a structured manner like SPARQL. This demonstration presents an entity-oriented exploratory search prototype system that is able to support search and explore KGs in a exploratory search manner, where local structures of KGs can be dynamically discovered and utilized for guiding users. The system applies a path-based ranking method for recommending similar entities and their relevant information as exploration pointers. The interface is designed to assist users to investigate a domain (particular type) of entities, as well as to explore the knowledge graphs in various relevant domains. The queries are dynamically formulated by tracing the users' dynamic clicking (exploration) behaviors. In this demonstration, we will show how our system visualize the underlying entity structures, as well as explain the semantic correlations among them in a unified interface, which not only assist users to learn about the properties of entities in many aspects but also guide them to further explore the information space.
View-synthesis from uncalibrated cameras and parallel planes

In this paper we present a view synthesis algorithm for the generation of novel intermediate views that smoothly interpolate between two reference images of the same scene acquired from two different point of views. If the acquisition system is not calibrated, there is no way to access the Euclidean frame and therefore it is impossible to determine the position and the orientation of the cameras as well as the three dimensional reconstruction of the imaged scene. Under the assumption of equal internal calibration of the two cameras, it is proved that the knowledge of the infinite homography is a sufficient condition for generating physically valid synthetic images from a pair of uncalibrated images. The view synthesis problem is therefore brought to the simpler well-known task of the estimation of the infinite homography through a set of geometric constraints existing in the scene. The work is complemented by the introduction of two novel techniques for estimating the infinite homography. This contributes to provide a variety of possibilities for such estimation, depending on the geometric constraints that can be identified in the imaged scene. The validity of the work is supported by a set of simulations on a synthetic three-dimensional scene and experiments on real data as well.
Golden Ratio: The Attributes of Facial Attractiveness Learned By CNN

The recent success of deep learning has promoted the applications in facial attractiveness prediction and enhancement. However, what attributes have been learned to represent facial attractiveness is not well discovered yet. In this work, we find that DNN can learn both local and global shape-cues of face (Golden Ratio) that associate with facial attractiveness. This finding is concluded from a newly trained CNN model and an interpretation of visualizing activation of the category-specific neurons. The CNN model is trained on thousands extremely attractive/unattractive face images, and achieves an accuracy of 98.05%. The deconvolutional neural network generates face-like representations that depict the intuitive attributes of four face attractive categories. The results are consistent with the beauty ratios of facial attractiveness in psychological research.
Counteracting quantum decoherence with optimized disorder in discrete-time quantum walks

Decoherence and disorder are two major difficulties limiting the performance of quantum systems in practical settings. Yet they can potentially counteract each other to partially restore the systems' quantum signatures. We adopt the particle swarm optimization method to find the optimal disorder for mitigating the effects of decoherence in one- and two-dimensional quantum random walks, achieving substantial increase in the mean walking distance for a wide range of decoherence strength. This result suggests a viable approach to constructing practical quantum systems robust against decoherence and disorder.
Phoneme Aware Speech Synthesis via Fine Tune Transfer Learning with a Tacotron Spectrogram Prediction Network

The implications of realistic human speech imitation are both promising but potentially dangerous. In this work, a pre-trained Tacotron Spectrogram Feature Prediction Network is fine tuned with two 1.6 h speech datasets for 100,000 learning iterations, producing two individual models. The two Speech datasets are completely identical in content other than their textual representation, one follows the standard English language, whereas the second is an English phonetic representation in order to study the effects on the learning processes. To test imitative abilities post-training, thirty lines of speech are recorded from a human to be imitated. The models then attempt to produce these voice lines themselves, and the acoustic fingerprint of the outputs are compared to the real human speech. On average, English notation achieves 27.36%, whereas Phonetic English notation achieves 35.31% similarity to a human being. This suggests that representation of English through the International Phonetic Alphabet serves as more useful data than written English language. Thus, it is suggested from these experiments that a phonetic-aware paradigm would improve the abilities of speech synthesis similarly to its effects in the field of speech recognition.
Implementation Business System Planning for the Design of Tracer Study at Perjuangan University

Abstract Information systems in business organizations are very important related to the timeliness and correctness of information needed by users. The first step to building an information system is planning. One methodology in planning information systems is the arrangement of strategic plans carried out using Business System Planning (BSP). This methodology is related to efforts to how information systems must be structured, integrated, and implemented by the organization for a long time. Perjuangan University is a university in Tasikmalaya. Even though Perjuangan University does not have graduates in the current year but for the future processing of graduate data is very important. Graduate have an important role in the development of an educational institution. Because graduate can act as ambassadors that reflect the quality of an educational institution in the public sphere. With the empowerment of good graduate, it can indirectly improve the good image of the alma mater towards the general public. Thus a plan is needed to build an information system tracer study which will be useful to manage graduate data and facilitate the search for graduate data. Steps taken with this methodology are business planning systems that define business goals, business processes, data classes, information architecture and integration. In this apper a prototype will be produced from the Perjuangan University tracer study information system.
Simultaneous Nonlocal Self-Similarity Prior for Image Denoising

Nonlocal image representation has achieved great success in various image processing tasks such as image denoising, image deblurring and image deblocking. Particularly, by exploiting the image nonlo-cal self-similarity (NSS) prior, many nonlocal similar patches can be searched across the whole image for a given patch, which has significantly boosted the performance of image restoration. To the best of our knowledge, most existing methods only consider the NSS prior of the input degraded image, while few methods exploit the NSS prior from external clean image corpus. However, how to utilize the NSS priors of input degraded image and external clean image corpus simultaneously is still an open problem. In this paper, we propose a novel approach for image denoising, which exploits simultaneous nonlocal self-similarity (SNSS) by integrating the NSS priors of both the input degraded image and external clean image corpus. Firstly, we search and group nonlocal similar patches from a clean image corpus, and a group-based Gaussian Mixture Model (GMM) learning algorithm is developed to learn an external NSS prior. Then, an optimal group is selected from the best suitable Gaussian component for a group of the noisy image. By integrating the group of the noisy image and the corresponding group of the Gaussian component with a low-rank constraint, an iterative algorithm is developed to solve the proposed SNSS model. Experimental results demonstrate that the proposed SNSS-based denoising method produces superior results compared with many state-of-the-art denoising methods in both objective and perceptual quality.
A Model Crypto-Asset Regulatory Framework

A stylized ‘crypto-asset’ regulatory framework (‘Framework’) is presented. It is designed to capture the regulatory permutations and implications of the most prevalent and evolving forms of tradable value created through the use of evolving cryptographically-based (‘crypto’) schemes, as well as their stylized position in a financial ecosystem. The most prevalent of these value-based schemes are based on what is now known as distributed ledger technologies (DLTs), with the main type of DLT in use known as ‘blockchain’ technology.Value-based applications that can be derived or generated from use of DLTs are known in this Framework as (private) crypto-assets. They can be distinguished from (public) applications of crypto-based value such as those planned by sovereign states for use as legal tender.The main focus of the Framework is on these evolving ‘private’ use of crypto-assets and their representation as cryptographically-secure and traceable ‘tokens’ of value. These tokenized assets can be traded – that is transferred or exchanged - within regulated or unregulated exchanges, or directly, securely and independently between parties without the need for a centralized exchange. Together they form what could be termed the ‘crypto-economy.’All these developments challenge the four corners of existing legal and regulatory frameworks with, as yet, no global unanimity as to which regulators have oversight over all or some components of these crypto-assets. The overall goal of any regulation would be to fasten the activities of the most proximate regulators to particular asset classes or value transfer/exchange mechanisms so as to avoid regulatory arbitrage. Where there are no proximate regulations to do so, regulatory sandboxes by the most proximate regulators could be employed.The model Framework presented is designed to systematize the evolving asset types.
Handwritten Chinese Characters Recognition Based on QPSO-BP

Handwritten Chinese character recognition has been applied to a number of applications. In the paper, a handwritten Chinese character recognition algorithm based on QPSO-BP was proposed. In order to avoid the premature convergence and achieve higher precision, the proposed algorithm used QPSO algorithm to optimize BP neural network. In the experimentation, the texture features, morphology features and image matrix features were extracted as the features, then the handwritten Chinese characters were classified by the QPSO-BP. The experimental results show that the QPSO-BP algorithm has higher convergence speed. The proposed algorithm is effective method of handwritten Chinese character recognition.
A Novel Energy Efficient and SLA-Aware Approach for Cloud Resource Management

Server virtualization is a well-known technique for virtual machine (VM) placement and consolidation and has been studied extensively by several researchers. This article presents a novel approach called aiCloud that advocates segmentation of hosts or physical machines (PMs) into four different classes that facilitates quick selection of PMs to reduce the time required to search host machines, called host search time (HST). The framework also introduces VM_Acceptance_State, a condition that avoids host overloading, which leads to significant reduction of SLA time per active host (SLATAH) that in turn reduces SLA violation (SLAV). The performance of aiCloud has been compared with other approaches using standard workload traces. Empirical evaluation presented shows that aiCloud has least HST and outperforms other approaches in terms of SLA violations and ESV (Energy and SLA Violation) and therefore may be an attractive strategy for efficient management of cloud resources.
Updating Ontology Alignment on the Concept Level Based on Ontology Evolution

According to one of the base definitions of an ontology, this representation of knowledge can be understood as a formal specification of conceptualization. In other words - they can be treated as a set of well-defined concepts, which represent classes of objects from the real world, along with relationships that hold between them. In the context of distributed information systems, it cannot be expected that all of the interacting systems can use one, shared ontology. It entails a plethora of difficulties related to maintaining such a large knowledge structure. A solution for this problem is called an ontology alignment, sometimes it is also referred to as an ontology mapping. It is a task of designating similar fragments of ontologies, that represent the same elements of their domain. This allows different components of a distributed infrastructure to preserve its own independent ontology while asserting mutual interoperability. However, when one of the participating ontologies change over time, the designated alignment may become stale and invalid. As easily seen in a plethora of methods found in the literature, aligning ontologies is a complex task. It may become very demanding not only in terms of its computational complexity. Thus relaunching it from the beginning may not be acceptable. In this paper, we propose a set of algorithms capable of updating a pre-designated alignment of ontologies based solely on the analysis of changes applied during their evolution, without the necessity of relaunching the mapping algorithms from scratch.
A Dynamic Scheduling Framework for Multi-Tenancy Clouds

Scheduling and resource allocation in clouds is asked to harness the power of the underlying resource pool, and thus service providers can meet Quality of Service (QoS) requirements of tenants specified in Service Level agreements (SLAs). Improving resource allocation ensures that all tenants will receive fairer access to system resources, which improves overall utilisation and throughput and reduces traffic in an over-crowded system. Moreover, real-time applications and services require critical deadlines in order to guarantee QoS. A growing number of data-intensive applications also drive the optimisation of scheduling through the utilisation of data locality in which the scheduler attempts to locate a task and ensure that the task's relevant data is on the same server that the task is being deployed to. Choosing suitable scheduling mechanisms for running of various applications that support multi-tenancy has consistently been a major challenge. This work proposes a new adaptive Deadline constrained and Data locality aware Dynamic Scheduling Framework - 3DSF - that orchestrates different schedulers based on varied requirements. This framework considers users' deadline-based QoS requirements, a cloud system's performance and a method of resource allocation to improve resource utilisation, system throughput, reduce the completion time of jobs, and better meet their QoS requirements. Our 3DSF contains: (1) a real-time, preemptive, deadline constrained job scheduler, (2) an optimised data locality aware scheduler, (3) an improved Dominant Resource Fairness (DRF) greedy resource allocation approach and (4) an adaptive suite to integrate all above-mentioned schedulers together.
Automatic Motion-Blurred Hand Matting for Human Soft Segmentation in Videos

Accurate hand segmentation is important for human segmentation. However, in videos, hand regions usually have serious motion blur, which reduces segmentation performance obviously. To solve this problem, we propose an automatic matting network to deal with motion-blurred hands. Then we combine the hand alpha mattes provided by matting network and the human segmentation results provided by segmentation network to generate our final human soft segmentation results. In addition, to train the matting network, we need a huge amount of motion-blurred hand images and their groundtruth alpha mattes. However these images are very difficult to obtain. To solve this problem, we propose an efficient semi-automatic synthetic data generation method and generate 36186 synthetic motion-blurred hand images and their alpha mattes. Experiments on synthetic images and real videos show that our method achieves state-of-art matting performance and successfully solve the problem of bad hand segmentation caused by serious motion blur.
Dyn-YCSB: Benchmarking Adaptive Frameworks

We demonstrate Dyn-YCSB, a tool that builds upon YCSB (Yahoo Cloud Serving Benchmark suite) to assist users in simulating dynamic variations in workloads. Dyn-YCSB automatically varies the parameters in YCSB workloads over time according to user-specified time series functions, without requiring users to manually change the workload configuration in individual nodes each time the workload parameters needs to be modified. The dynamic workload variations simulated with Dyn-YCSB can be used to evaluate the adaptability of such frameworks to changing workload characteristics. We demonstrate the ability of Dyn-YCSB to evaluate the adaptability of OptCon, an automated framework, that tunes the consistency settings of Cassandra with respect to the latency and staleness thresholds in an SLA.
Development of Integrated Distance Authentication and Fingerprint Authorization Mechanism to Reduce Fraudulent Online Transaction

From last numerous decades, there is a problem of counterfeit online transaction and posses as a foremost challenge of online transaction. Even though the concrete initiatives have been taken by researchers and governments, fraudsters acquire canny fashion to perform counterfeit online transaction. Fundamentally, fraudster steals the credentials of client and performs counterfeit online transaction. After scrutinizing, this research has stepped forward with a plan to trim down the counterfeit online transaction. This study propounds a working by blending two different workings which are authentication of distance and authorization of fingerprint to execute a genuine transaction. The leading key aspect behind the propound working is that after stealing credentials of the client, fraudsters could not perform counterfeit online transaction. In this study, to assess the impact, propound working is also theoretically implemented in some cases. After the assessment, it is found that the propound working is suitable to prevent counterfeit online transactions.
Coding of Image Intra Prediction Residuals Using Symmetric Graphs

The Discrete Cosine Transform (DCT) is widely deployed by modern image and video coding standards such as JPEG and H.26x. In most cases, the DCT is applied in a separable manner to rows and columns, which limits its ability to represent signals with diagonal orientation. As an alternative, non-separable transforms can represent signals with different orientations, but are significantly more computationally complex. To address this problem, in this paper we propose a set of non-separable Symmetry-Based Graph Fourier Transforms (SBGFTs), whose symmetric structures lead to a faster implementation. We study a practical image coding scenario that exploits the proposed SBGFTs, where for each intra predicted image residual block the optimal graph is chosen by solving a graph-based Rate-Distortion (R-D) problem. Experimental results indicate a coding efficiency higher than JPEG and JPEG2000.
Earn Your Wings: A Novel Approach to Deployment Governance

This paper presents a model to assess team autonomy developed and deployed in a South African bank’s IT department. The bank has been deploying SAFe® for the last two years and in the process has increased significantly the number of software releases. Historically, the teams had to obtain multiple levels of authorization prior to a release but this level of governance and control was contradictory to the principle of team empowerment at the core of agile approaches. The model is inspired from the theme of a pilot’s ability to fly an aircraft using five levels. The level is determined based on team fly-ability and elevation safety described in detail in this paper. Team fly-ability includes two elements: (1) maturity of engineering practices and (2) the ability to manage traceability and risk through ease of recovery. Elevation Safety is based on two components: (1) historical data on deployment performance and severity of incidents and (2) the application dynamics and criticality. The main benefits of this program are improved accountability of teams, reduced approval time, and reduced attempts to find workarounds and loopholes.
3D Integration of CMOS-Compatible Surface Electrode Ion Trap and Silicon Photonics for Scalable Quantum Computing

In this work, we report ion trap fabrication using prevailing foundry copper back-end-of-line process on a 300mm Si platform. Surface electrodes comprising of ~3.7 µm thick Cu and ~0.2 µm thick of Au surface finish are electroplated above a ~3 µm thick of SiO2 layer on a high-resistivity Si substrate. The innovative process, which is fully compatible with CMOS back-end, enables a fine gap trench structure between the electrodes, such that the exposed dielectric area to the trapped ions is reduced. By optimizing the electroplating process, a relatively flat Cu surface is created with a thin Au layer deposited as an effective protective layer to prevent surface oxidation. The fabricated trap is wire-bonded in a CPGA package for DC and RF testing. Small size Si traps show a good RF dissipation property which is a prerequisite for ion trapping. The further integration of TSV and Si photonics shows a promising prospect in terms of electrical and optical performance enhancement of ion trap.
Probenet: Probing Deep Networks

Despite the rapid progress of deep learning research in recent years, interpreting deep network is still quite challenging. Interpreting deep networks is essential to both end-users and developers since it gives confidence in the usage of the deep network. This paper deals with a method for interpreting deep networks, especially visual interpretation. In order to get visual interpretation from a target deep network, we propose a ProbeNet that provides a decomposed visual interpretation of the target deep network. The ProbeNet decomposes the feature representations of the point of the target deep network into human interpretable units. Furthermore, the ProbeNet provides kernel-level analysis about the target deep network. In experiments, visual interpretation of two different target deep networks showed the usefulness of the ProbeNet to interpret target deep networks.
A New Saliency Object Extraction Algorithm Based on Itti’s Model and Region Growing

A new method based on Itti's model is proposed in order to extract saliency objects as complete as possible. It combines the advantages of Itti's model and region growing. Firstly, visual features of early such as intensity, color and orientation are extracted from the input static color image. Secondly, three conspicuity maps are created according to early features. Thirdly, in order to obtain the initial seed points, thresholds based on three conspicuity maps are set respectively. Fourthly, three maps after region growing are attained. Finally, three maps which are based on region growing, are combined into a saliency map. A series of experiments has demonstrated that the algorithm is effective. Compared with Itti's model, the precision, the recall rate and F-measure of the saliency object extraction by the proposed algorithm are improved obviously.