Reviewerly/exHarmony huggingface dataset:
Profiling US Restaurants from Billions of Payment Card Transactions

A payment card (such as debit or credit) is one of the most convenient payment methods for purchasing goods and services. Hundreds of millions of card transactions take place across the globe every day, generating a massive volume of transaction data. The data render a holistic view of cardholder-merchant interactions, containing insights that can benefit various applications, such as payment fraud detection and merchant recommendation. However, utilizing these insights often requires additional information about merchants missing from the data owner's (i.e., payment company's) perspective. For example, payment companies do not know the exact type of product a merchant serves. Collecting merchant attributes from external sources for commercial purposes can be expensive. Motivated by this limitation, we aim to infer latent merchant attributes from transaction data. As proof of concept, we concentrate on restaurants and infer the cuisine types of restaurants from transactions. To this end, we present a framework for inferring the cuisine types of restaurants from transaction data. Our proposed framework consists of three steps. In the first step, we generate cuisine labels for a limited number of restaurants via weak supervision. In the second step, we extract a wide variety of statistical features and neural embeddings from the restaurant transactions. In the third step, we use deep neural networks (DNNs) to infer the remaining restaurants' cuisine types. The proposed framework achieved a 76.2% accuracy in classifying the US restaurants. To the best of our knowledge, this is the first framework to infer the cuisine types of restaurants by analyzing transaction data as the only source.
Providing Semi-private Feedback on a Shared Public Screen by Controlling Presentation Onset

We describe a novel technique to provide semi-private feedback on a shared public screen. The technique uses a no-onset presentation that takes advantage of perceptual limitations in human vision to avoid alerting other users to feedback directed at one individual user by suppressing the sudden onset of the feedback. Three experiments evaluated the effectiveness of the technique and appropriate timing parameters and alternatives for presentation onset. Our experiments indicated that an 80 ms no-onset presentation allows participants to interpret information directed to them with over 90% accuracy, but their ability to interpret simultaneously presented information intended for others will be close to random chance. The technique initially camouflages the information being presented by overlaying additional visual elements and then removes those elements to reveal only the elements encoding the information being presented. We discuss applications for the technique, including classroom clicker usage, which was our original motivation for the study.
A Task Allocation Method based on Blockchain Node Hash Power

Recently, the shape rise of digital encryption currency, making the block chain technology has gradually become a hot topic in academia and industry. However, most scholars' research focuses on the blockchain foundation and application, and rarely involves the task allocation method in the blockchain. For this, this paper proposes a task allocation method based on block chain-bus power. When as-signing a task, the method considers the computing power of each node in the blockchain and the processing time of each task to be assigned to each node, and calculates the processing benefit of the task at the node. Then, the task is allocated to the node with the most efficient treatment for maximum benefit. The innovation of this paper is that not only considers the task processing time, but also considers the computing power of each node in the blockchain, and achieves resources that can save the task processing time and make full use of the node computing power. Finally, through the study of the example, the ideal task allocation scheme is obtained, which verifies the effectiveness of the method.
A Novel Maneuvering Target Tracking Algorithm Using Polynomial Filter

In order to improve the performance of maneuvering target tracking in complex observation scenarios, a composite expanding memory polynomial (EMP)/fading memory polynomial (FMP) filtering algorithm based on the polynomial filter is proposed. According to the analysis of the advantages and disadvantages of four forms of polynomial filters, the composite EMP/FMP filter is constructed by combining the self-initialization of EMP filter with the indefinite tracking of FMP filter. Additionally, there is no transient phenomenon when switching two filters. Then, the filter which applies the EMP/FMP filter as the pre-filter, and the unscented Kalman filter (UKF) as the main filter is simulated. The experimental results indicate that the EMP/FMP pre-filter enhances the quality of measurements, and effectively improves the target tracking performance of UKF for maneuvering target.
Mask Guided Pedestrian Tracking from Single Point Initialization

Abstract Pedestrian tracking has a wide range of applications, such as intelligent monitoring and unmanned driving. In pedestrian tracking system, object detection algorithms are generally used to provide the initial bounding boxes for object tracking algorithms. If the bounding box provided by detection algorithm is not accurate, it will seriously affect the performance of object tracking. To solve this problem, this paper proposes a mask guided pedestrian tracking algorithm based on Siamese network. Firstly, Mask R-CNN is introduced to acquire exemplar image and its mask. Secondly, a light-weight convolutional neural network (CNN) is applied as the backbone of proposed algorithm. Thirdly, a channel attention module is introduced to our proposed algorithm and integrated with the light-weight CNN. The feature maps of the exemplar image and its mask are adjusted by a channel attention model and fused to enhance the discrimination ability of exemplar branch. Finally, our proposed tracker is trained GOT-10k dataset and evaluated on object tracking benchmark (OTB), experimental results show the excellent performance of proposed algorithm compared with state-of-the-art trackers.
Virtual Reality Simulations in Science Education

This study examines the role of virtual reality (VR) in the promotion of writing with greater complexity and lexical density. Using a combination of neuroimaging and traditional measures, the author characterizes differences in writing complexity and lexical density scores across four different pedagogical modalities: VR alone, VR followed by a textbook reading, textbook reading followed by VR, and textbook reading alone. Middle school students recruited from a rural middle school in the Mid-Atlantic Region of the United States responded to two prompts related to science content found in VR environments and a textbook. The authors hypothesized that exposure to a virtual environment prior to responding to the writing prompts would enhance both argumentative and summative writing products, when compared to participants who only had access to the textbook experiences. Participants who were exposed to the VR environment then had access to a textbook demonstrated significantly greater writing complexity and lexical density scores than those who had VR alone, or access to the text alone.
Automatic Detection and Classification of Road Conditions Using Statistical Model for Autonomous Driving

Autonomous car is driverless; it creates lots of new possibilities, but also imposes new challenges. A major challenge for autonomous cars is safety. The previous studies showed that road condition is one of the main factors of traffic accidents. This paper proposes a method to automatically collect the status of road conditions from a group of cars, and uses a statistical model to quantitatively classify the road conditions. The processed information is then shared with peer cars to prevent from potential dangers. The performance study showed that the proposed algorithm could effectively classify road conditions. The algorithm is particularly useful for uncertain road conditions in remote and developing areas.
Fitness Function Design for Neuroevolution in Goal-Finding Game Environments

Recently, games like Pac-Man have been hotbeds for neuroevolution research and NEAT has emerged as one of the leading techniques in the game playing domain [14] . In the context of the snake game, the goal of this paper is to enhance neuroevolution strategies with better fitness functions for effective goal finding. We develop greedy and non-greedy fitness functions, and demonstrate the effectiveness of these functions in both environments with and without dynamic obstacles. We then present an alternate implementation using the NEAT algorithm combined with Novelty Search to increase the genetic diversity of the agent population and explore the problem space without specifying direct objectives. These conclusions suggest that even with a low number of simple inputs, and simple fitness functions, agents are quickly able to achieve a novice amount of expertise in the Snake game using NEAT.
Statistical outlier labelling – a comparative study

Outliers always exist in real industrial data. They originate from different, often unknown sources. They are considered with respect in statistical analysis, robust regression and in data mining. One may find a lot of interesting approaches and consideration. Their detection, labelling, identification, isolation, filtering and interpretation is subject of many research activities. On the other hand effect of the outliers on control systems analysis has not been sufficiently investigated. Their effect is often neglected or considered contemptuously. This work addresses the subject of outlier detection from the perspective of control system performance analysis. The work focuses on statistical data-driven approaches. Selected statistical outlier detection approaches are proposed and compared on real industrial control loop data originating from process industry. Obtained results form a starting point for potential application of automatic outlier detection methods.
Task Scheduling algorithm in cloud computing: A Dynamic Variable Quantum Round robin

Abstract In recent years a Cloud computing became an attentional topic in IT. In cloud computing a Task allocation is a well-known problem. in the heterogeneous environments like CC task scheduling could be either static or dynamic manner to make the cloud computing more efficient and thus it rallies the users satisfaction. Round-robin fashion is most task scheduling algorithm suitable because it is fair but it had a static time slice problem, so in this paper we proposed dynamic quantum to allocate tasks to appropriate resource which will reduce turnaround time, waiting time, responds time and increase throughput attending to achieve high system performance.
Locating Texts in Images by Fully Convolutional Network with Bounding Text Labeling

Texts in images are often viewed as the regions of interest. Locating such areas for further analysis may help to extract image-related information and facilitate many interesting applications. Considering that pixel-based image segmentation is effective in identifying the areas containing texts, we propose a text detection scheme based on the Fully Convolutional Network (FCN), which employs Feature Pyramid Network (FPN) and Atrous Spatial Pyramid Pooling (ASPP) to effectively mark the pixels related to texts in an image. A comparatively efficient labeling approach is adopted to group related letters/characters and separate different words by oval-shaped text labeling masks with boundaries. The experimental results demonstrate feasibility of the proposed text-detection scheme when compared with the state-of-the-art text detection approaches.
Reliable Classification with Ensemble Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are widely used in computer vision and natural language processing. Due to large computational requirements, implementation of CNNs on FPGAs becomes an popular option. As CNNs being used in safety critical applications, reliability become a priority. This poses challenges as FPGAs are prone to suffer soft errors. Traditional fault tolerant techniques based on modular redundancy introduce a large overhead, which may not be acceptable for many resources-limited embedded system. This paper explores the use of an ensemble of CNNs to build reliable classifiers. The idea is to combine several “weak” classifiers to obtain a “strong” one, so that the classifier can still work reliably if one of its members fails. Differently from traditional ensemble learning that looks for the classifiers to complement each other, in our case similarity is also important to achieve fault tolerance. To evaluate the potential of using ensembles to implement fault tolerant CNNs, an initial study is done on ResNets. The results show that, relative to a single deep ResNet, an ensemble of shallow ResNets could provide similar classification results while providing an effective protection against errors with limited overhead.
Manipulating GUI structures declaratively

GUIs often contain structures that are incidental, not properly manipulatable through well-defined APIs. For example, modifying a list of items in a GUI's model may require extraneous bookkeeping operations in the view, such as adding and removing event handlers, and updating the menu structure. Observing GUIs in practice gives an indication that programmers may find it difficult or tedious to implement complete and convenient sets of operations for manipulating various structures: useful operations for adding, inserting, swapping, or reordering elements are often missing, inconsistent, or limited. This paper introduces a DSL for programming operations that manipulate such incidental structures. The programmer specifies structures via relations between elements, concretely by defining methods that unestablish and establish a relation. This gives the programmer an ability to describe structural transformations via rules that control which relations should hold before and after a rule is applied. The API for structure manipulation is generated from these rules. Our DSL can give an abstract view on ad-hoc structures, making it easier to provide the necessary set of operations for their convenient manipulation.
Extending the $$\rho $$Log Calculus with Proximity Relations

$$\rho $$ Log-prox is a calculus for rule-based programming with strategies, which supports both exact and approximate computations. Rules are represented as conditional transformations of sequences of expressions, which are built from variadic function symbols and four kinds of variables: for terms, hedges, function symbols, and contexts. $$\rho $$ Log-prox extends $$\rho $$ Log by permitting in its programs fuzzy proximity relations, which are reflexive and symmetric, but not transitive. We introduce syntax and operational semantics of $$\rho $$ Log-prox, illustrate its work by examples, and present a terminating, sound, and complete algorithm for the $$\rho $$ Log-prox expression matching problem.
Metadata Interoperability for Institutional Repositories: A Case Study in Malang City Academic Libraries

The aim of this study is to understand, describe, and analyze metadata interoperability in Universitas Brawijaya Library that used Brawijaya Knowledge Garden (BKG) and Eprints software, University of Muhammadiyah Malang Library that used Ganesha Digital Library (GDL) and Eprints software, and Malang State Library that used Muatan Lokal (Mulok) software. This study also discussed supporting and inhibiting factors for interoperability metadata. This study employed a case study-qualitative approach. The finding indicates that the metadata interoperability can be performed by using metadata crosswalks. Implementation metadata crosswalks by mapping BKG fields and GDL Fields to the Dublin Core Metadata Element Set (DCMES). The results in the mapping of appropriate metadata schemes without removing the existing metadata scheme element and demonstrating technical specifications for standard metadata. Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) features can use for metadata interoperability in the union catalog that have been being developed by the National Library of Indonesia, called Indonesia OneSearch. The supporting factors in metadata interoperability are standard metadata and a standard protocol for interoperability; whereas, the inhibiting factor is the minimum human resources having metadata capability and the open access policy that has not applied to each academic libraries. All of those academic libraries need to make an effort to external interoperability to union catalogs to improve visibility digital content and applied open access policies.
Platform Utilizing Others' Behavior Data to Detect Anomalous Operation Hiding Private Information

As the number of IoT consumer electronics is increasing, cyberattacks to IoT devices are increasing. In particular, operating IoT devices by attackers make users feeling unsafe and may harm users physically. Therefore, we have proposed a method to detect anomalous operations by learning users' behavior. However, this method misdetects many legitimate operations if a sufficient amount of data on the users' behavior. One approach to avoid misdetections even if a sufficient amount of data cannot be obtained from each user is to use the data collected from the others. But users do not want to share their private information with others. In this paper, we proposed an anomaly detection platform that utilizes the dataset of similar users without sharing their private information.
Swamp, Sound, Sign: Reflections on interspecies difference in compositional practice

Soundscape composition and environmental sound art already imply critiques and negotiations of nature/culture divide and human/non-human difference. This article, along with the composition it frames, thinks through a vision of environmental sound art that completes a link between sonic practice and its object. As a project, it navigates human/animal difference through a sonic knowing which is founded on life’s shared constitution in signs. Sounds beyond spoken words, like the signs that dominate non-human life, are foundationally non-symbolic, and the ability of environmental sound art to resemble and evoke networks of icons and indices is in some respects a privileged position of electroacoustic music. The article presents a non-dualistic sonic thinking within the decentred perspective of the environment, which emerges as a plural product of its engagements and participants. A vision for soundscape composition is presented, along with a frame for its interpretation as sonic thought, or phonosophy.
OPPORTUNITIES FOR INCREASING STUDENT’S DIGITAL LITERACY FROM PRIMARY SCHOOL AGE THROUGH ADDITIONAL FORMS OF EDUCATION

The formation of digital literacy among primary school students is an important problem of the modern society and those involved in this process - educational institutions and parents. Digital literacy is a set of competencies necessary for the successful participation of the individual in the informed society. Digital literacy refers to the availability of knowledge and skills in the use of digital devices, behavior in expression and cooperation in an electronic environment.Nowadays, children grow up among technology, but they use it mostly for fun and communication, not to organize effective educational activities. This leads to the conclusion that despite the frequent use of technology and electronic devices by children, it does not mean that they are digitally literate. The formation of habits for the use of certain technologies to solve specific problems leads to the construction of digital literacy.The use of digital tasks and activities in primary school to increase digital literacy must follow successfully created and established activities to expand and upgrade. Building digital literacy is closely linked to the development of digital learning habits. An important factor for the successful formation of digital literacy is to define skills that will help students in the future digital world.The formation of digital literacy is a complex and long process that has different dimensions over the years. Regulated school hours are insufficient to carry out systematic digital training. Additional forms of training prove to be a successful tool for the formation and improvement of skills for working with digital and programmable devices.This article presents the results of a survey among teachers working with primary school students on opportunities to work with children to develop digital skills. The additional forms of education used by them are described and good pedagogical practices are derived.
Pocolo: Power Optimized Colocation in Power Constrained Environments

There is a considerable amount of prior effort on co-locating applications on datacenter servers for boosting resource utilization. However, we note that it is equally important to take power into consideration from the co-location viewpoint. Applications can still interfere on power in stringent power constrained infrastructures, despite no direct resource contention between the coexisting applications. This becomes particularly important with dynamic load variations, where even if the power capacity is tuned for the peak load of an application, co-locating another application with it during its off-period can lead to overshooting of the power capacity. Therefore, to extract maximum returns on datacenter infrastructure investments one needs to jointly handle power and server resources. We explore this problem in the context of a private-cloud cluster which is provisioned for a primary latency-critical application, but also admits secondary best-effort applications to improve utilization during off-peak periods. Our solution, Pocolo, draws on principles from economics to reason about resource demands in power constrained environments and provides answers to the when/where/what questions pertaining to co-location. We implement Pocolo on a Linux cluster to demonstrate its performance and cost benefits over a number of latency-sensitive and best-effort datacenter workloads.
HiPAES: A Novel High Performance Parallel AES

The Advanced Encryption Standard (AES) is an iterative, symmetric, key-size based, multiple-round block cipher in the Rijndael cipher family. Earlier efforts to parallelise AES in software have focused on single-stage parallelisation. We developed HiPAES, a novel, sophisticated, multistage and high performance pipelined parallel AES, simultaneously exploiting both in-round parallelism as well as across-round parallelism. Our implementation and a thorough hard-nosed experimental evaluation on GPU reveals a significantly high speedup of 3.6x.