Reviewerly/exHarmony huggingface dataset:
Robust Selective Search

Selective search is a modern distributed search architecture designed to reduce the computational cost of large-scale search. Selective search creates topical shards that are deliberately content-skewed, placing highly similar documents together in the same shard. During query time, rather than searching the entire corpus, a resource selection algorithm selects a subset of the topic shards likely to contain documents relevant to the query and search is only performed on these shards. This substantially reduces total computational costs of search while maintaining accuracy comparable to exhaustive distributed search.
An integrated study of the electrical parameters of the samples of magnetite ores in relation to their genesis and mineral composition (on the example of the Goroblagodatskoe skarn-magnetite deposit)

The temperature dependences of electrical resistance (at DC and AC voltage at 1 kHz frequency) of the samples of the magnetite ore and magnetite from the contact of the syenite-porphyry with the volcanic-sedimentary rocks from the Goroblagodatskoye iron-ore deposit in the temperature range 20—800 °C have been studied. The frequency dependences of the active electrical resistance and dielectric losses in the range 0,01—100 kHz have been obtained at 20 °C. For the magnetite ore and magnetite from the syenite-porphyry contact with volcanogenic sedimentary rocks in the studied temperature and frequency ranges, the relationship between electrical resistance (lgR) and dielectric losses (lgtgδ) has been revealed. The character of the relations is different, that allows to separate uniquely the magnetite ore and magnetite. The parameters of high-temperature conductivity (activation energy E o and electrical resistance coefficient lgR o )nave been obtained. The parameters of the studied samples of pyroxene-orthoclase-magnetite, garnet-magnetite, epidote-chlorite-magnetite ores form a straight line, as if forming its different parts. The correlation between the parameters of E 0 and lgR o , samples of these ores has the following form: lgR o , — 2,2—6,6 E o . The parameters of magnetite samples from the contact of syenite porphyry with volcanogenic-sedimentary rocks also form a straight line, as if forming its different parts, with the correlation form as following: lgR o — 2,1—6,6 E 0 . It has been found that with the increasing distance to the syenite intrusion, the electrical parameters of magnetite ore change: EQ increases, lgR o , decreases. For magnetite from the contact of the syenite-porphyry and volcanic-sedimentary rocks there is another picture — the further you are from the syenite-porphyries, the smaller E 0 is, and the bigger the lgR o , is. The T 0 temperatures, at which the electrical resistance at the constant voltage becomes equal to the active resistance at the alternating voltage for the samples of garnet-magnetite ore with the different magnetite content, P,%, have been revealed. The correlation between the T0 parameter and the magnetite content in the ore, P(Fe 3 0 4 , %) = 323,4 — 47,4 ln( T 0 ), R 2 = = 0,93, has been established.
English language and history

The use of computational approaches in history is not new (Boonstra et al 2004). However, until fairly recently, their use has been restricted to a relatively small number of fields such as economic history and historical demography. These are fields that make extensive use of quantitative sources, typically in tabular form, that have long been wellsuited to analysis using a computer. Despite the potential benefits of quantitative sources, the overwhelming majority of historians use textual sources and thus, computational approaches have traditionally had little penetration into mainstream history. Over the past decade or so this has changed rapidly. The mass digitisation and dissemination of historical source material such as Early English Books Online (EEBO), Eighteenth Century Collections Online (ECCO), the British Library Newspaper Collections, The Times Digital Archive, Project Gutenberg, and many others have meant that large amounts of historical texts are now available in digital form (Hitchcock 2013). Thus, historians now have access to an unprecedented volume of digitised source material. However, the computational techniques used to analyse such materials them remain rooted in traditional close reading with the digital simply providing ease of access and the use of keyword searching. There is thus a requirement to adopt and adapt the digital approaches available from other fields, and develop new techniques where appropriate, such that scale of the resources available to us can be exploited in a way that is impossible for close reading alone. This challenge is not unique to history; it has occurred across humanities disciples, providing a major impetus to the field of digital humanities (Kirschenbaum 2010; Schreibman et al. 2014; Terras 2016) where a major theme is the use of digital approaches to the analysis of texts in disciplines including English Literature (Moretti 2013; Jockers 2013) and History (Atkinson & Gregory, 2017; Pumfrey et al.2012).
A Case Study of ETD Metadata Remediation at the University of Houston Libraries

This paper provides a case study on remediating electronic theses and dissertations (ETD) metadata at the University of Houston Libraries. The authors provide an overview of the team’s efforts to revise existing ETD metadata in its institutional repository as part of their commitment to aligning ETD records with the Texas Digital Library Descriptive Metadata Guidelines for Electronic Theses and Dissertations, Version 2.0 (TDL guidelines, version 2). The paper reviews the existing literature on metadata quality and ETD metadata practices, noting how their case study adds one of the first documented cases of ETD metadata remediation. The metadata upgrade process is described, with close attention to the tools and workflows developed to complete the remediation. The authors conclude the paper with a discussion of lessons learned, the project’s limitations, future plans, and the emerging needs of metadata remediation work.
A Multi-Camera Rig with Non-Overlapping Views for Dynamic Six-Degree-of-Freedom Measurement

Large-scale measurement plays an increasingly important role in intelligent manufacturing. However, existing instruments have problems with immersive experiences. In this paper, an immersive positioning and measuring method based on augmented reality is introduced. An inside-out vision measurement approach using a multi-camera rig with non-overlapping views is presented for dynamic six-degree-of-freedom measurement. By using active LED markers, a flexible and robust solution is delivered to deal with complex manufacturing sites. The space resection adjustment principle is addressed and measurement errors are simulated. The improved Nearest Neighbor method is employed for feature correspondence. The proposed tracking method is verified by experiments and results with good performance are obtained.
Distant Vehicle Detection: How Well Can Region Proposal Networks Cope with Tiny Objects at Low Resolution?

High-performance faster R-CNN has been applied to many detection tasks. Detecting tiny objects at very low resolution remains a challenge, however, and a few studies addressed explicitly the detection of such objects yet. Focusing on distant object detection at very low resolution images for driver assistance systems, we introduce post-trained net surgery to (1) analyze the network activation patterns, (2) study the potential of prior information to improve localization and binary classification performance, and (3) to support the development of priors for improving the network performance. We use post-trained net surgery to analyze the feature maps used for bounding box regression and classification for RPNs in detail, and to discuss the complexity of the network activation patterns. Using these findings, we show that incorporating prior maps into the network architecture improves the performance of bounding box regression and binary classification for small object detection in low resolution images.
Inverted Index Based Ranked Keyword Search in Multi-user Searchable Encryption

Cloud storage is a promising technology for large scale data storage. Searchable encryption protocols are developed to securely store and efficiently retrieve encrypted data stored in cloud without revealing any sensitive information. As data grows faster, even search schemes requiring linear time are not feasible to handle big data. We propose a sub-linear search scheme using inverted index supporting fine grained search control to multiple users. The documents are ranked to return the most relevant results and to reduce the communication cost. The scheme hides the document-ids and access patten from the cloud provider and proves the security.
Increasing the Robustness of CNN-Based Human Body Segmentation in Range Images by Modeling Sensor-Specific Artifacts

This paper addresses the problem of human body parts segmentation in range images acquired using a structured-light imaging system. We propose a solution based on a fully convolutional neural network trained on realistic synthetic data that were simulated in a way that closely emulates our structured-light imaging system with its inherent artifacts such as occlusions, noise and missing data. The results on synthetic test data demonstrate quantitatively the performance of our method in identifying 33 body parts, with negligible confusion between the front and back sides of the body and between the left and right limbs. Our experiments highlight the importance of sensor-specific data augmentation in the training set to improve the robustness of the segmentation. Most importantly, when applied to range data actually acquired by our system, the method was capable of accurately segmenting the different body parts with inter-frame consistency in real-time.
An open Jackson Network Model for Heterogeneous Infrastructure as a Service on Cloud Computing

Cloud computing is an environment which provides services for user demand such as software, platform, infrastructure.Applications which are deployed on cloud computing have become more varied and complex to adapt to increase end-user quantity and fluctuating workload.One popular characteristic of cloud computing is the heterogeneity of network, hosts and virtual machines (VM).There were many studies on cloud computing modeling based on queuing theory, but most studies have focused on homogeneity characteristic.In this study, we propose a cloud computing model based on open Jackson network for multi-tier application systems which are deployed on heterogeneous VMs of IaaS cloud computing.The important metrics are analyzed in our experiments such as mean waiting time; mean request quantity, the throughput of the system.Besides that, metrics in model is used to modify number VMs allocated for applications.Result of experiments shows that open queue network provides high efficiency.
Realtime Semantic Enrichment of Video Streams in the Age of Big Data

This paper describes AgileRAI, a framework for searching, organizing, and accessing multimedia data in a fast and semantic-driven way. AgileRAI supports realtime ingestion of video streams on which different machine-learning techniques (such as global and local visual features extraction and matching) are applied in a parallel and scalable way. Extracted features are matched to a reference database of visual patterns (e.g., faces, logos, and monuments) in order to produce a set of metatags describing the ingested contents. Furthermore, these tags are semantically enriched using open semantic data repositories. The system is designed with a scale-out pattern architecture based on Apache Spark, ensuring high performance in Big Data management environments.
How to infer possibilities: A reply to Oaksford et al. (2018).

Individuals draw conclusions about possibilities from assertions that make no explicit reference to them. The model theory postulates that assertions such as disjunctions refer to possibilities. Hence, a disjunction of the sort, A or B or both, where A and B are sensible clauses, yields mental models of an exhaustive conjunction of possibly A, possibly B, and possibly both A and B, which each hold in default of information to the contrary. Oaksford, Over, and Cruz (this issue) are critical of the model theory and defend a probabilistic approach to reasoning. In this reply, we deal with their three main claims: (a) Our results concern only the periphery of their probabilistic theory. We show that they refute their theory insofar as it applies to possibilities. (b) The model theory leads to logical absurdities. We rebut this criticism as it applies to the model theory in Hinterecker, Knauff, and Johnson-Laird (2016), and explain why standard modal logics, which concern possibilities, do not set appropriate norms for inferences about them. (c) The algorithm for reasoning based on models needs a normative theory. In fact, it has such a theory, but the demand for "a specification of a sound, complete, and decidable normative system" is chimerical for everyday reasoning. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Correction of errors due to uneven intensity distribution and inaccurate phase shifts in prism-based simultaneous phase-shifting interferometry

Simultaneous phase-shifting interferometry (SPSI) is a suitable technique for dynamic surface measurement due to its rapid measurement capability. Based on whether there are obvious multiple phase-shifting channels in the setup, it can be divided into the multi-channel type and the pixelated phase-mask type. For the former type, two important error sources are the uneven intensity distribution among different phase-shifting channels and inaccurate phase shifts in each phase-shifting channel. In order to diminish the influence of these two error sources, several methods have been proposed. However, in some of these methods, only one error source of the two was considered and corrected. In the methods where the two error sources were both considered, some assumptions such as the uniform background intensity and fringe contrast are needed, so a complete and universal suppression method for both error sources is still needed in actual applications. In this paper, for a prism-based SPSI, we proposed such an error-correction method that mainly contains the following contributions. First, the intrinsic parameters of the system, including intensity distribution coefficients and relative phase shifts among phase-shifting channels, are calibrated in advance. We also checked the uniformity of phase shifts in each phase-shifting channel. Second, based on the obtained parameters, a corrected four-step phase-shifting algorithm is deduced to recover the more accurate measured phase. Results of both simulation and experiment verify the effectiveness of the proposed method.
The impact of anemia in prolong culture conversion amongs MDR-TB patients in persahabatan hospital Indonesia

Purpose: Anemia was known to be the complication of Tuberculosis (TB). Low hemoglobin concentration was associated with prolonged time of culture sputum conversion in TB but the association in MDR TB is still unknown. Sputum culture conversion in MDR-TB was the main predictor of successful therapy outcome. This study aims to understand whether anemia amongs MDR-TB patients could prolong the time for sputum conversion. Methods & Materials: The method of this study was cohort retrospective, with total 363 patients from MDR-TB patients treated in Persahabatan Hospital from January until December 2016. The time of culture conversion was obtained from Indonesian online database e-TB Manager under supervision of Persahabatan Hospital authorized staffs. Anemia level was further analyzed into 3 categories of anemic status (anemia and non anemia), anemic type (normocytic normochrom and microcytic hypochrome), and anemic classification (mild, moderate, severe) along with their correlation towards the length of culture conversion using the survival analysis rate Kaplan-Meier curve. Results: Of the total 363 medical records, only 201 datas fitted into inclusion criteria in which 83 of 201 MDR-TB patients (41.3%) have anemia with the average of age 38.3 ± 12.9 and the average of Haemoglobin level 11.4 ± 1.2. The length of culture conversion time in both anemia and non-anemia patients were mostly occurred in 2ndmonth of treatment with 31 (37%) and 41 (34.8%) incidence respectively. Survival analysis rate showed a significant rate difference in prolong conversion time based on the anemic status (anemia and non-anemia). However, there is no significant relation of classification and types of anemia towards the prolong conversion time. In this study we also evaluated BMI level since it could be confounding factor. The result showed a consistant relation with previous study in this setting. Conclusion: Anemia increased the risk of prolonged time in culture conversion of MDR-TB patients. There should be an effort in improving the nutritional status and hematological profile in MDR-TB patients with anemia.
Applying Game Mechanics to Networked Music HCI Systems

We discuss the use of game mechanics as a means to facilitate collaboration in networked music performances. We first look at core concepts of gaming and how these relate to musical creativity and performance. We offer an overview of various perspectives towards game mechanics and rule systems with examples from video games and musical practices. We then describe audiovisual software that we developed in order to study the integration of game-like elements and mechanics into networked music performance. Finally, we offer the results of a preliminary user study conducted using this system in private and public performance contexts. We observe that imposing various game mechanics upon a networked performance affects the nature of the musical collaboration as well as the audience’s attentiveness towards the performance.
Ant Colony Optimization for Cryptanalysis of Simplified-DES

Ant Colony Optimization is a search meta-heuristic inspired by the foraging behavior of real ant, having a very wide applicability. Especially, it can be applied to different combinatorial optimization problem. In this paper, we present a novel Ant Colony Optimization (ACO) based attack for cryptanalysis of Simplified Data Standard Encryption (S-DES). A known Plaintext attack is used to recover the secret key requiring only two Plaintext-Ciphertext pairs. Moreover, our approach allows us to break S-DES encryption system in a minimum search space when compared with other techniques. Experimental results prove that ACO can be considered as a convincing tool to attack the key used in S-DES.
Convolutional Neural Networks with Layer Reuse

A convolutional layer in a Convolutional Neural Network (CNN) consists of many filters which apply convolution operation to the input, capture some special patterns and pass the result to the next layer. If the same patterns also occur at the deeper layers of the network, why wouldn't the same convolutional filters be used also in those layers? In this paper, we propose a CNN architecture, Layer Reuse Network (LruNet), where the convolutional layers are used repeatedly without the need of introducing new layers to get a better performance. This approach introduces several advantages: (i) Considerable amount of parameters are saved since we are reusing the layers instead of introducing new layers, (ii) the Memory Access Cost (MAC) can be reduced since reused layer parameters can be fetched only once, (iii) the number of nonlinearities increases with layer reuse, and (iv) reused layers get gradient updates from multiple parts of the network. The proposed approach is evaluated on CIFAR-10, CIFAR-100 and Fashion-MNIST datasets for image classification task, and layer reuse improves the performance by 5.14%, 5.85% and 2.29%, respectively. The source code and pretrained models are publicly available.
Study of Awareness of Cyber-Security among Medical Students

Cyber-crimes, in simpler terms the name implies the crime committed using any computing device has been present since advent of computers, but now has become common threat as smaller and compact devices like mobile and free network provisions are present. Among various population groups, young students are frequently involved in this technological advance and are exposed to cyber-crimes. A questionnaire-based survey method was applied to analyze the medical student's awareness in the area of cyber security and some suggestions are set to overcome these issues.
Reading Comprehension in University Texts: The Metrics of Lexical Complexity in Corpus Analysis in Spanish

The article focuses on the practical field of the development and implementation of a software application developed for the automatic processing of eight metrics to calculate the lexical complexity in a corpus that contains the transcriptions of university educational videos in Spanish called VYTEDU, prepared by teachers from the University of Guayaquil, Ecuador. The obtained result allowed to demonstrate the different indexes of lexical complexity that the texts have in terms of the comprehensibility of their content. One of the main characteristics of the texts lies in the difference in size and content. It should be noted that although some texts had greater content, the index of lexical complexity was lower than other texts whose content was smaller in size. The diffusion of the software supposes the use of it as a tool to continue researching in the field of Natural Language Processing. The application developed using free software tools facilitated the use of libraries in the field of Natural Language Processing contributing to the analysis of the complexity of text comprehension, making this research a second step to build an automatic simplification tool for text in Spanish in the higher academic field that is proposed as future work, since the first step was the construction of the VYTEDU corpus together with its publication.
Promoting Students’ Conceptual Knowledge using Video Analysis on Tablet Computers

The common idea of video analysis in physics education so far is that students record the motion of an object using a video camera, transfer the video on a computer and analyze it with a suitable software.In our approach students perform video analysis time-efficiently using only tablet computers.We present the results of a study which examines learning effects of tablet PC-supported video analysis in high school physics courses in a pre-post-test design with treatment and control groups for two essential topics of mechanics, the uniform motion (N = 109 matched samples) and the accelerated motion (N = 70 matched samples).The results show that the treatment leads to significantly higher learning gain regarding conceptual knowledge in comparison to traditional teaching without video-based motion analysis, especially for the cognitively more demanding topic.We discuss the results in the context of learning theories and reveal implications for future research.