Reviewerly/exHarmony huggingface dataset:
Radiometric and mineralogical dataset of microgranite dykes and stream sediments of Ras Abda area, north Eastern Desert, Egypt

Ras Abda Area is located in the north part of the Eastern desert, and is characterized by a rugged topography and high relief. The main exposed rock units in the area comprise older granites, younger gabbros and granites as well as several types of post granite dykes (rhyolite, basic and microgranite dykes). Radiometric measurements indicated that the microgranite samples are characterized by anomalous concentrations of the radioelements which mean that the rocks originated from radioelements-bearing magma and may be subjected to epigenetic processes of leachability and migration of uranium. The stream sediments samples show low levels of radioactivity where eU ranges from 4.0 to 7.0 ppm with an average of 5.3 ppm, and eTh ranges from 6.0 to 18.0 ppm with an average of 10.65 ppm. The eTh/eU ratio ranges from 1.2 to 3.6 where the samples distal from the microgranite dykes are characterized by the highest eTh/eU ratio (3.6) while those close to the dykes have the lower eTh/eU ratios. Generally, the average eTh/eU ratio (∼2.08) is lower than the world ratio (3.5) implying that uranium probably enriched from an adjacent source may be the microgranite dykes. The calculated factors of equilibrium (P and D) indicated disequilibrium state for both rocks types (microgranite and stream sediments) and referred to incomplete U-decay series. Mineralogical studies revealed that the heavy minerals could be classified into: a) radioactive minerals comprising uranophane, kasolite, sklodowskite, thorite and uranothorite, and b) radioelements-bearing minerals comprising columbite, fergusonite, samarskite, pyrochlore, allanite, monazite, zircon and fluorite. The heavy minerals are mostly concentrated upstream rather than downstream; meandering portions of the stream may act as natural traps for the heavy minerals.
Cross Subspace Alignment Codes for Coded Distributed Batch Matrix Multiplication.

Coded distributed batch computation distributes a computation task, such as matrix multiplication, $N$-linear computation, or multivariate polynomial evaluation, across $S$ servers through a coding scheme, such that the response from any $R$ servers ($R$ is called the recovery threshold) is sufficient for the user to recover the desired computed value. Current approaches are based on either exclusively matrix-partitioning (Entangled Polynomial (EP) Codes for matrix multiplication), or exclusively batch processing (Lagrange Coded Computing (LCC)). We present three related classes of codes, based on the idea of Cross-Subspace Alignment (CSA) which was introduced originally in the context of private information retrieval. CSA codes are characterized by a Cauchy-Vandermonde matrix structure that facilitates interference alignment along Vandermonde terms, while the desired computations remain resolvable along the Cauchy terms. These codes unify, generalize and improve upon the state-of-art codes for distributed computing. First we introduce CSA codes for matrix multiplication, which yield LCC codes as a special case, and are shown to outperform LCC codes in general over strictly download-limited settings. Next, we introduce Generalized CSA (GCSA) codes for matrix multiplication that bridge the extremes of matrix-partitioning and batch processing approaches. Finally, we introduce $N$-CSA codes for $N$-linear distributed batch computations and multivariate batch polynomial evaluations. $N$-CSA codes include LCC codes as a special case, and are in general capable of achieving significantly lower downloads than LCC codes due to cross-subspace alignment. Generalizations of $N$-CSA codes to include $X$-secure data and $B$-byzantine servers are also obtained.
Cross-Granularity Attention Network for Semantic Segmentation

Despite the remarkable progress of semantic segmentation in recent years, much remains to be addressed in order to achieve better semantic coherence and boundary delineation. In this paper, we propose a novel convolutional neural network (CNN) architecture for semantic segmentation which explicitly addresses these two issues. Specifically, we propose a categorical attention mechanism to propagate consistent category-oriented information across multi-granularity contextual interpretations to close the semantic gap residing in CNN feature hierarchy. This novel design alleviates the semantic information loss during the feature combination and transformation process in decoder network. We further integrate a contour branch in our architecture to enhance the boundary awareness of the semantic feature derived in the form of a novel element-wise contour attention at each level of feature hierarchy. Additionally, we introduce a cross-granularity contour enhancement mechanism to propagate rich boundary cues from early layers to deep layers. We perform extensive quantitative evaluations in close proximity to object boundaries which confirms its superior effectiveness in boundary delineation. These novel mechanisms which boost the essentials in segmentation, i.e., region-wise semantic coherence and accurate object contour localization, allow our architecture MeshNet to obtain state-of-the-art performance on two challenging datasets, i.e., PASCAL VOC 2012 and Cityscapes.
Deep Learning for Person Re-identification in Surveillance Videos

In the recent years, Closed Circuit Television (CCTV) is viewed as the basis for providing security. One of the most important aspects of CCTV surveillanceVideo surveillance systems security mechanism is to re-identify a person captured in one of the camera across different surveillance cameras. Re-identificationRe-identification has a major role in several applicationsApplication like automated surveillance of universities, offices, malls, home and restricted environments like embassies or laboratories with strong security restrictions. Traditionally, identifying a person in a video was practiced under the set of same external conditions (like same illumination, viewpoint, back ground conditions etc.). But when it comes to automated re-identificationRe-identification in a CCTV surveillanceVideo surveillance system, several challenges emerge as the environment is uncontrolled and keeps varying, further the poses of the person and the angles of the cameras capturing the videos also incur additional challenge for the task considered. When a person disappears from one camera view for a period of time, he should be recognized in another view of camera at a different location when there are environmental disturbances like variation in illumination, crowded scene, partial occlusions, physical appearance variations, full occlusions, view point variations, background clutter, shadows and reflections, etc. In this chapter, the major focus is on the techniques of deep learningDeep learning used to develop an end-to-end re-identification system highlighting the methods to handle the uncontrolled environment challenges mentioned. An end-to-end re-identificationRe-identification task consists of sequence of steps namely pedestrian detection, person tracking followed by person re-identification. Given a video sequence or an image as an input, firstly the humans are detected from the video sequence as a process of pedestrian detection. The person tracking within the camera is conducted, to find the different poses of the probe if needed. Then the re-identification process is conducted where the deep learningDeep learning models are used to re-identify the person with the help of gallery set of videos and evaluates the similarities of gallery set and the person of interest by using deep learningDeep learning metrics. The re-identification results end as a retrieval process where all similar images of the person of interest are retrieved. Several bench mark datasets considered in literature for re-identificationRe-identification system are VIPeR, ETHZ, PRID, CAVIAR, CUHK01, CUHK02, CUHK03, i-LIDS, RAiD, MARS, etc.
Implementasi Chat Room dan Push Notification pada e-Class Berbasis Mobile

e-Class is one of the applications of e-Learning that has been applied at Duta Wacana Christian University as an educational facilities. The utilization of e-Class has help both lecturers and students to share lecture material and other supporting academic data. However, e-Class does not yet support notification for latest information and real time discussion columns. In answering the problems faced by e-Class site users, the development of mobile-based e-Class applications that are integrated with e-Class sites can be one of solution. The e-Class application provides the same features as e-Class sites and with the use of Firebase Cloud Messaging technology helps answer the latest notifications issues and the Firebase Realtime Database to create chat rooms in real time. The application that was built was tested using task scenario and UEQ as parameters for the level of efficiency, effectiveness and convenience in the use of the application. The results obtained by the application are fairly good to implement and the results of the UEQ scale average are worth excellence. Further research that is expected from this research is to increase the efficiency of the mobile-based e-Class application.
Overview of the NTCIR-14 Lifelog-3 task

Lifelog-3 was the third instance of the lifelog task at NTCIR. At NTCIR-14, the Lifelog-3 task explored three different lifelog data access related challenges, the search challenge, the annotation challenge and the insights challenge. In this paper we review the activities of participating teams who took part in the challenges and we suggest next steps for the community.
The OPUS Resource Repository: An Open Package for Creating Parallel Corpora and Machine Translation Services.

This paper presents a flexible and powerful system for creating parallel corpora and for running neural machine translation services. Our package provides a scalable data repository backend that offers transparent data pre-processing pipelines and automatic alignment procedures that facilitate the compilation of extensive parallel data sets from a variety of sources. Moreover, we develop a web-based interface that constitutes an intuitive frontend for end-users of the platform. The whole system can easily be distributed over virtual machines and implements a sophisticated permission system with secure connections and a flexible database for storing arbitrary metadata. Furthermore, we also provide an interface for neural machine translation that can run as a service on virtual machines, which also incorporates a connection to the data repository software.
Augmented Hill Cipher

Securing data over an open network is one of the most critical problems in network security. To secure data, an encryption algorithm should be used. Hill cipher is one of most famous encryption algorithms. Although the Hill cipher is not strong enough and vulnerable to many types of attacks, it is still playing a significant role in educational systems; The original Hill cipher is vulnerable to known plaintext attack. In the last decade, Hill cipher got much attention. Researchers proposed many enhances to the Hill cipher; New modifications of the Hill cipher are proposed to enhance the security of Hill cipher. In this paper we shall show that ＂A Modified Hill Cipher Based on Circulant Matrices＂ is vulnerable to both known plaintext attack and chosen plaintext attack. Moreover, we will introduce a new mode of operation which can be used with any block cipher. Then we will propose a new enhanced encryption algorithm. After that, we shall provide a security analysis and efficiency evaluation for our new encryption algorithm.
Morphosyntactic Disambiguation in an Endangered Language Setting

Endangered Uralic languages present a high variety of inflectional forms in their morphology. This results in a high number of homonyms in inflections, which introduces a lot of morphological ambiguity in sentences. Previous research has employed constraint grammars to address this problem, however CGs are often unable to fully disambiguate a sentence, and their development is labour intensive. We present an LSTM based model for automatically ranking morphological readings of sentences based on their quality. This ranking can be used to evaluate the existing CG disambiguators or to directly morphologically disambiguate sentences. Our approach works on a morphological abstraction and it can be trained with a very small dataset.
Solar radiation prediction based on random forest of feature-extraction

Abstract In order to use solar energy efficiently, the accurate prediction of solar radiation is extremely important. The aim of this paper is to discuss and build the solar radiation prediction model, and an efficient method based on improving random forest is proposed in here. The random forest regression model is created for predicting solar radiation. The nine variables are taken as input of the original model, and the solar radiation as the output. The original data is normalized using Min-Max method and is mapped to the values in the range of zero to one. The large amount of radiation data has been dealt with univariate feature extraction. The classifier of the random forest tree is constructed by characteristic factors that can influence solar radiation, and the optimal parameters of the model are selected by the OOB error analysis. The relationship between M-try and OOB can be obtained by computing of the R platform. Hence, a prediction model of improved random forest solar radiation based on feature extraction has been described. Under the specific area of solar radiation forecast, the studies have been done, and the solar radiation is analyzed and compared with the different prediction methods. The simulation results prove the validity of the improved method; the root mean square error is reduced and the precision of prediction is improved. It is significant for the prediction of the amount of solar radiation under complex environment and the effective use of photovoltaic power generation.
Everything Is Awesome! or Is It? Cyber Security Risks in Critical Infrastructure

Industrial Control Systems (ICS) play an important role in the monitoring, control and automation of critical infrastructure such as water, gas, oil and electricity. Recent years have seen a number of high profile cyber attacks on such infrastructure exemplified by Stuxnet and the Ukrainian Power Grid attacks. This naturally begs the question: how should we manage cyber security risks in such infrastructure on which the day-to-day functioning of societies rely? What are the complexities of managing security in a landscape shaped by the often competing demands of a variety of stakeholders, e.g., managers, control engineers, enterprise IT personnel and field site operators? What are the challenges posed by the convergence of Internet of Things (IoT) and critical infrastructure through the so-called Industrial Internet of Things (IIoT)? In this paper, we discuss insights from a multi-year programme of research investigating these issues and the challenges to addressing them.
Optimization of Topic Recognition Model for News Texts Based on LDA

Latent Dirichlet Allocation (LDA) is the technique most commonly used in topic modeling methods, but it requires the number of topics generated by LDA to be specified for topic recognition modeling.Except the main iterative methods based on perplexity and nonparametric methods, recent research has no simple way to select the optimal number of topics in the model.Aiming at appropriately determining the number of topics and then optimizing the LDA topic model, this paper proposes a non-iterative method for automatically determining the number of topics.The clustering method is based on fast seeking and locating density peaks.This method transforms the traditional topic cluster number selection problem into clustering problem and thus can be used to optimize the topic recognition model for news texts.It does not need iterative optimization and can simplify model development.This method uses Word2Vec for word embedding on corpus text to explore the superior performance of word-related relationships and to express the implicit semantic relationship between topic corpora.Then, using a clustering algorithm that quickly searches for and finds the cluster peaks; the word vectors after word embedding are clustered to obtain the number of word vector clusters after word embedding.The number of clusters is used as the number of topics in the text.Finally, the experimental results show that the proposed method enjoys better precision and F1 value than the perplexitybased method, and is suitable for the identification of the number of topics in corpora in different sizes.This method can effectively find the appropriate number of topics from the news text dataset and improve the accuracy of the LDA theme model.
Biometric Image Encryption Based on Chaotic Sine Map and Information Entropy

The Multi-biometric frameworks are utilized in numerous significant biometric applications and more are connected to the UIDAI framework in India. Encryption of biometric pictures is pivotal because of the dynamic increment in the utilization of powerless frameworks and systems for chronicling, transmission and confirmation. This examination proposes a tumultuous sine guide based picture encryption plan utilizing data entropy calculation with different degrees of perplexity and dissemination. The confused sine map with data entropy encryption calculation has a high key space, entropy around equivalent to eight (for dim pictures). The significant preferred position of this strategy is the best qualities for the quantity of pixels and the bound together normal pace of progress it offers. These better qualities obviously demonstrate that the scrambled pictures delivered by the riotous sine map with data entropy calculation plan are arbitrary. Exploratory recreations were performed to acquire the cryptographic parameters and approve the calculation through normal assaults, for example, compression and noise. The proposed calculation is strong because of the pressure assault which is fundamentally required during chronicling and recuperation. Hence, the proposed cryptographic calculation can be connected to the pictures of fingerprints utilized in participation observing frameworks, in biometric gets to, and so forth utilized by most surely understood open and private associations.
How capitalism abolishes itself in the digital era in favour of <i>robo-economic systems</i>: socio-economic implications of decentralized autonomous self-owned businesses

Purpose The purpose of this paper is to explore socio-economic implications of decentralized autonomous self-owned businesses. Design/methodology/approach The authors take the approach of a case study analysis. Findings Unlike dark scenarios according to which almost half of our jobs are at risk in the wake of new emerging technologies such as AI and Blockchain (Frey and Osborne, 2013), the authors envision a world of decentralized autonomous self-owned businesses, using the example of the taxi market. The authors, first, outline their characteristics and how they work. Second, they elaborate on the socio-economic implications of markets where this novel kind of business enters. Third and most important, the authors argue that so-called robo-economic systems would be tantamount to a post-capitalistic society that is enforced by applying capitalistic principles. Research limitations/implications The presented scenario of this paper is very futuristic and furthermore reduces a complex system quite drastically to be able to focus on the two implications of interest. Therefore, we are confronted with the necessity to take assumptions on socio-technical feasibilities and individual preferences. Most of these assumptions can be challenged (see Section 6.3). Social implications The scenario presented by the authors stands in contrast to the fear that increased automation inherently leads to increased inequality. There is a general debate going on whether automation and digitalization will lead to a massive loss of employment, because machines will replace humans in many fields of work. Frey and Osborne (2013) estimate that nearly every second job is at risk to disappear in the coming decades because of computerization. Hence, many people fear to be left behind because of this development and inequality will increase tremendously as only those who own the machines will benefit directly from the profit share. In the scenario, however, the profits are mostly distributed among the consumers via reduced prices. Originality/value The proposed scenario seems to reconcile socialism (less private property) with liberalism (a radicalization of markets) and shows an effective way to tackle market power, the problem that certain persons and firms would benefit almost exclusively from the increased profit margins promised by automation.
Within- and between-speaker acoustic variability: Spontaneous versus read speech

Using principal component analysis (PCA), our previous study [JASA 145(pt. 2), 1930, (2019)] of read sentences found surprisingly similar acoustic voice spaces for groups of female and male talkers and for the individuals within groups. Formant frequencies and the balance between higher harmonic amplitudes and inharmonic energy in the voice accounted for the most acoustic variance within and across talkers, but many further details varied idiosyncratically for individual talkers. In this study, we replicated this finding using a set of recorded phone conversations from 99/100 original speakers (49 F), hypothesizing that the same measures would characterize both individual and population acoustic spaces, despite greater acoustic variability for spontaneous utterances. F0, formant frequencies, spectral noise, source spectral shape, and their variability were measured every 5 ms from vowels and approximants. Individual and group PCAs revealed that the acoustic voice spaces derived from spontaneous speech are highly similar to those spaces previously identified based on read speech. One significant difference between the two speaking styles was that unlike read speech, variability in F0 emerged as one of the variables that accounted for significant acoustic variability in spontaneous speech. Implications for voice learning, recognition, and discrimination will be discussed. [Work supported by NIH/NSF.]
DC Coefficients Recovery from AC Coefficients in the JPEG Compression Scenario

With the deployment of multimedia compression techniques, contents such as images or videos are transmitted through resource-constrained networks such as Internet of Things (IoT) scenarios. Traditional multimedia compression methods based on spatial-frequency transforms and coding techniques have been progressively improving and approaching the theoretical limit of Shannon. Therefore some new approaches are proposing to transmit only a part of the compressed data and recover the missing part at the receiver’s end. In this paper, we propose to follow this idea to highly enhance the JPEG compression by transmitting all AC coefficients and only four DC coefficients of one image. On the receiver’s end, we propose two methods to rebuild the missing DC coefficients based on the remaining DCT coefficient relationships. The first method considers the pixel relationship compared with the adjacent blocks on multiple directions. The second method considers not only the pixel relationship between adjacent blocks but also the existing relationship inside the adjacent blocks. As a result, our proposed method recovers the transmitted JPEG image with more than 25 dB considering PSNR while transmitting only 40–60% of the size of the JPEG images.