Reviewerly/exHarmony huggingface dataset:
Automatic Image Colorization based on Multi-Discriminators Generative Adversarial Networks

This paper presents a deep automatic colorization approach which avoids any manual intervention. Recently Generative Adversarial Network (GANs) approaches have proven their effectiveness for image colorization tasks. Inspired by GANs methods, we propose a novel colorization model that produces more realistic quality results. The model employs an additional discriminator which works in the feature domain. Using a feature discriminator, our generator produces structural high-frequency features instead of noisy artifacts. To achieve the required level of details in the colorization process, we incorporate non-adversarial losses from recent image style transfer techniques. Besides, the generator architecture follows the general shape of U-Net, to transfer information more effectively between distant layers. The performance of the proposed model was evaluated quantitatively as well as qualitatively with places365 dataset. Results show that the proposed model achieves more realistic colors with less artifacts compared to the state-of-the-art approaches.
Validation of Whole Slide Imaging for primary surgical pathology diagnosis of prostate biopsies.

Whole slide imaging (WSI) is an important component of digital pathology which includes digitization of glass slides and their storage as digital images. Implementation of WSI for primary surgical pathology diagnosis is evolving, following various studies which have evaluated the feasibility of WSI technology for primary diagnosis.The present study was a single-center, observational study which included evaluation by three pathologists and aimed at assessing concordance on specialty-specific diagnosis and comparison of time taken for diagnosis on WSI and conventional light microscopy (CLM).Seventy prostate core biopsy slides (reported between January 2016 and December 2016) were scanned using Pannoramic MIDI II scanner, 3DHISTECH, Budapest, Hungary, at 20× and 40×. Sixty slides were used for validation study following training with 10 slides.Intraobserver concordance for diagnosis between the two platforms of evaluation was analyzed using Cohen's κ statistics and intraclass correlation coefficient (ICC); observation time for diagnosis was compared by Wilcoxon signed-rank test.Interpretation on WSI using 20× and 40× was comparable with no major discordance. A high level of intraobserver agreement was observed between CLM and WSI for all three observers, both for primary diagnosis (κ = 0.9) and Grade group (κ = 0.7-0.8) in cases of prostatic adenocarcinoma. The major discordance rate between CLM and WSI was 3.3%-8.3%, which reflected the expertise of the observers. The time spent for diagnosis using WSI was variable for the three pathologists.WSI is comparable to CLM and can be safely incorporated for primary histological diagnosis of prostate core biopsies.
Comparison of Term Weighting Techniques in Spam SMS Detection

Short message services are one of the most widely used communication services. The increased use of mobile devices and the lowering of SMS costs by operators enable short message services to remain popular. However, this popularity causes tens of users to be exposed to spam SMS every day. The term spam can simply be referred to as unwanted messages by users. Although organizations take measures against spam SMS and there are widely used spam SMS filtering systems, the problem of spam SMS is becoming widespread. There are many studies in the literature for the detection of spam SMS, but new and efficient methods are still needed. In this study, TF-IDF and RF term weighting methods which are frequently used in text mining applications were compared in order to classify spam SMS and to use the limited content of SMSs more meaningfully. The vectors obtained from the data set were weighted by TF-IDF and RF term weighting methods and classified with 5 different classifiers popular in this field.
Blockchain-Based Privacy Preserving Trust Management Model in VANET

With the development of the intelligent traffic system (ITS), vehicular ad hoc network (VANET) has been widely used as an important part of ITS. Location based service (LBS) is such a significant application of VANET. But how to protect vehicles’ data and privacy security has not been well solved. We propose a trust management model based on blockchain. In our scheme, we use blockchain to realize the data security of vehicles, and we give a trust management algorithm to construct anonymous cloaking region to protect the privacy security. We use various datasets to simulate the model. And the experimental results show that our model can resist most common trust attacks and greatly reduce the possibility of vehicle privacy disclosure.
Interdependencies between Development and Migration

This chapter discusses macro-economic theories on migration and development in order to contribute towards a multidisciplinary understanding of migration. It has two guiding questions: does migration trigger development and/or does development trigger migration? Classic economic theory of the so-called 'convergence school' expects sending areas to obtain major benefits from out-migration for their development process. According to the most simple economic explanation of migration, people move in order to improve their life situation. In short, low- as well as high-skilled international migration will increase while internal migration will slowly decrease. The chapter argues that migration flows are largely specific to different forms of macro-economic organisation and the corresponding differences in per capita income. Macro-economic theories analyse (economic) development within spatial economic entities. In some situations migration contributes to a diverging development while in others it enforces convergence. The empirical studies reviewed indicate that migration has had positive short-term effects for sending and receiving countries.
PROMT Systems for WMT 2020 Shared News Translation Task.

This paper describes the PROMT submissions for the WMT 2020 Shared News Translation Task. This year we participated in four language pairs and six directions: English-Russian, Russian-English, English-German, German-English, Polish-English and Czech-English. All our submissions are MarianNMT-based neural systems. We use more data compared to last year and update our back-translations with better models from the previous year. We show competitive results in terms of BLEU in most directions.
Human Activity Recognition Using Positioning Sensor and Deep Learning Technique

In this paper, Long Short Term Memory (LSTM) deep learning model is used to identify human activities using sequential data obtained from cameras, wearable sensors, or other modalities. The proposed method recognizes human activities by optimizing hyper-parameters for the chosen deep learning model. The proposed approach is validated using public domain UTD MHAD dataset and found to be more accurate outperforming the state-of-the-art in terms of accuracy of activity recognition. Datasets categorized as the depth, skeleton, and inertial modalities have been analyzed for all available features. The dependency of the proposed deep learning model on the hyper-parameters is investigated extensively and discussed in detail. Experimental results depicted in this paper demonstrate the fact that the proposed method is a better choice for indoor activity recognition.
Data librarianship as a field study

Abstract Data are generated during all human activities related to digital technology. In recent times, scientific research has increasingly opted for digital data as its primary source of data; data definition changes for different disciplines and researchers. In this context, we study the main characteristics of data librarianship as a specialized field of traditional librarianship concerned with data use in libraries. Our work is organized as follows: First, we present a proposed Venn diagram on the theoretical foundations of data librarianship; then, we point out the core skills needed by data librarians. Based on a non-exhaustive literature review, we point out the main topics of research in data librarianship. We describe the significance of research data, data management, data curatorship, and data repositories. Finally, we list a few certification courses in data librarianship. We conclude that data librarianship plays a dynamic role in the practical application of data technologies in libraries, and that professional development, certification, and training in data librarianship are interdisciplinary tasks linked to digital technologies.
Enhanced Tree-Seed Algorithm Solving Real-World Problems

In this paper, an enhanced variant of the efficient nature-inspired Tree-seed optimisation algorithm (TSA) is designed. The original TSA lacks memory of old good solutions and mechanism for rotated objective functions. Two new mechanisms are used to increase the efficiency of the original TS algorithm. At first, archive A for successful seeds is used to help unsuccessful trees (TSrA). Secondly, rotationally invariant Eigenvector transformation of seeds is employed to cope with rotated problems (TSrAeig). Newly proposed TS variants are applied on a set of 22 real-world optimisation problems from CEC 2011. These problems enable to evaluate methods properly for future using in real applications. Results achieved by TSrA and TSrAeig are compared with results of the original TSA algorithm. Newly proposed mechanisms increase the performance of the original TSA algorithm significantly.
Inspection of fiber waviness in carbon fiber laminates by Talbot-Lau X-ray grating interferometry

In this work, we explore the possibilities of Talbot-Lau grating interferometer (TLGI) X-ray computed tomography (XCT) and radiographic testing (RT), to investigate and define fiber waviness in carbon fiber reinforced polymers (CFRP). The additional X-ray contrast mechanisms gained by TLGI provide high sensitivity to inhomogeneities and fiber misalignment in laminate CFRP. Particularly, dark-field contrast (DFC) caused by small angle scattering of X-rays allows the investigation of relatively large specimens, since the scattered signal provides information on a sub-pixel level. We investigated an aerospace grade vacuum infusion CFRP specimen by TLGI-XCT to visualize and quantify fiber waviness using DFC imaging. However, because of long TLGI-XCT scanning durations, that are often a multitude higher than standard XCT in combination with very limited specimen dimensions, this method is mostly not applicable at industrial standards. Therefore, the reduction to single radiographic images for the inspection of out-of-plane fiber waviness is a potential way to tackle both of these limitations at once. By applying an increasing tilt angle to the specimen, fiber waviness will cause a distinct peak in the scatter signal once fiber bundles are aligned horizontally and thus parallel to the optical axis of the X-ray tube. We show that by exploiting this characteristic behavior information about out-of-plane fiber waviness can be extracted from radiographic TLGI images.
Fall detection system with 3-axis accelerometer

Abstract In this work, the designed and developed process of a fall detection system using a NodeMCU microcontroller is described. A 3-axis acceleration sensor is used to obtain the data of the movement of humans and changes in acceleration are used to determine trends in falling. The ESP8266 microcontroller is used to the receive the data in real time and alert the family through the LINE application. Experimental results show that the system is capable of precisely producing alarms for falling actions and has a high detection precision and effectiveness.
Deep Neural Network for Automatic Speech Recognition from Indonesian Audio using Several Lexicon Types

Recently, automatic speech recognition has benefited from advances in deep neural networks (DNNs) to train and deploy speech recognition models. Speech recognition models enable computers to recognize and translate spoken language into text. In this paper, we present an approach to creating an Indonesian voice-to-text dataset using audio collected from YouTube channels and to evaluating the speech recognition models using several lexicon types. The lexicons are created from unique words of the speech corpus. We compared the performance of Time Delay Neural Network Factorization (TDNNF) and Gaussian Mixture Model - Hidden Markov Model (GMM-HMM) models for mono phone, DELTA+DELTA-DELTA, and Speaker Adaptive Training (SAT) based on the %WER when trained using unvalidated and validated datasets. The results showed that there was no significant difference in the %WER among the lexicon types. Moreover, the results revealed that the models trained using a validated dataset perform better than an unvalidated dataset. Additionally, lexicon enmap_kv_vocab_full returned the best result with 29.41% WER when trained using the TDNNF model on an unvalidated dataset. However, lexicon enmap_vocab_l_char provided the best result, with 11.35% WER, when trained using the TDNNF model on a validated dataset.
Distributed Algorithm for Parallel Edit Distance Computation

The edit distance is the measure that quantifies the difference between two strings. It is an important concept because it has its usage in many domains such as natural language processing, spell checking, genome matching, and pattern recognition. Edit distance is also known as Levenshtein distance. Sequentially, the edit distance is computed by using dynamic programming based strategy that may not provide results in reasonable time when input strings are large. In this work, a distributed algorithm is presented for parallel edit distance computation. The proposed algorithm is both time and space efficient. It is evaluated on a hybrid setup of distributed and shared memory systems. Results suggest that the proposed algorithm achieves significant performance gain over the existing parallel approach.
Augmented Reality Towards Facilitating Abstract Concepts Learning

Chemistry is often regarded as a complex and demanding subject for youth to learn, partly because of abstract concepts that are challenging to depict. These areas require spatial reasoning, defined as the ability to retain, generate and manipulate abstract visual images in space, either physically or mentally. By allowing the superimposition of virtual objects in the real world, Augmented Reality (AR) facilitates students’ understanding of difficult concepts through spatial reasoning by making them visible and allowing for multidimensional perspectives. “Periodic Fable” is an AR serious game targeting 8 to 11-year-old children, designed to teach them basic chemistry concepts of the Periodic Table of Elements in non-formal settings. After designing and implementing the game, we conducted an exploratory study with 36 young participants, using a mixed-method approach. A comparative study between pre- and post-intervention questionnaires and observation results shows a positive learning outcome, demonstrating the potential of this tool in a non-formal context.
Case study of geochemical clustering as a tool for tracing sources of clays for archaeological and modern bricks

The study was conducted using 14 hierarchical clustering ways and combining them with 4 inter-related sets of elements, i.e. the contents of Al, Ba, Ca, Fe, Ga, K, Mg, Mn, Na, Nb, P, Rb, Si, Sr, Th and Ti determined by EDXRF in 44 splits of 10 archaeological and 4 modern bricks, as well as in 38 clay samples from two quarries. Empirical scoring of tree dendrograms of archaeological samples helped to identify Complete Linkage, Weighted Pair Group Average and Ward’s methods as the most suitable for sourcing. Successful identification of geochemical clustering methods for fingerprinting sources of bricks is determined by the intrinsic features of the geochemical composition of stonework or quarries: their similarity, determined by the geochemical peculiarities of clay indicators, such as Al, Rb, Ga, K, Th, Fe, Ti and Nb, and differences, expressed by the elements that are more abundant in sands and silts (Si, Na), carbonates (Ca, Sr, Mg), organic matter (P) and other lithological-mineralogical tracers. It has been found that the mean values of the geochemical composition of the allied objects have much more useful fingerprinting properties. It is strongly recommended for source fingerprinting to select not only typical lithological-mineralogical samples, but also homogeneous sampling sets excluding possible outliers. It has been shown that each raw clay material has its own specific geochemical features. This is an essential useful feature for source fingerprinting using clustering of the objects of interest.
The University of Maryland’s Submissions to the WMT20 Chat Translation Task: Searching for More Data to Adapt Discourse-Aware Neural Machine Translation

This paper describes the University of Maryland’s submissions to the WMT20 Shared Task on Chat Translation. We focus on translating agent-side utterances from English to German. We started from an off-the-shelf BPE-based standard transformer model trained with WMT17 news and fine-tuned it with the provided in-domain training data. In addition, we augment the training set with its best matches in the WMT19 news dataset. Our primary submission uses a standard Transformer, while our contrastive submissions use multi-encoder Transformers to attend to previous utterances. Our primary submission achieves 56.7 BLEU on the agent side (en→de), outperforming a baseline system provided by the task organizers by more than 13 BLEU points. Moreover, according to an evaluation on a set of carefully-designed examples, the multi-encoder architecture is able to generate more coherent translations.
Centralized vs. decentralized ledgers in the money supply process: a SWOT analysis

<abstract> This study aims to better understand the role of centralized and decentralized ledgers in the money supply process. The aim is to highlight the strengths, weaknesses, opportunities, and threats of these tools in the context of finance and banking. A thorough investigation of the prior literature was carried out using sources extracted from various academic databases. A SWOT analysis based on an integrative literature review methodology was conducted to synthesize various research contributions and analyze relevant information related to centralized and decentralized ledgers. The findings reveal that centralized ledgers are still critical in the record-keeping of financial transactions, despite the strengths and opportunities of decentralized ledgers outweighing those of centralized ledgers. This study helps to increase the understanding of financial and banking sector managers concerning the importance of decentralized ledgers in delivering more value to customers. </abstract>
The synergic relationship between e-commerce and Sentiment Analysis: A content analysis of published articles between 2007 and 2020

Electronic Commerce or e-commerce a trend with greater evolution. It has achieved unstoppable growth and is still in the transformation phase, which is why, in time, it will position itself as the most competitive and complex market. It is expected that there will be more than 2 billion digital buyers in the world in 2020, by 2021 it will reach $4.88 billion in global retail e-commerce sales alone and by 2040, it is expected that around 95% of all purchases are made through e-commerce and 85% of consumers do research online before making a purchase. In this way, the organizations need to know if users speak positively or negatively about their products or services, causing changes in how their brand is perceived. For this reason, organizations have developed strategies and tools to manage their reputation; One of them is the Sentiment Analysis, which is defined as an automated technique to extract significant information from customers, related to their attitudes, emotions and opinions. This complex data analysis is carried out with the implementation of automated algorithms. The approach of these methodologies assumes the interest of this research for the extraction of information that allows its later use in the decision-making of organizations. Finally, to learn more about its evolution, trends, research areas, authors, among others, a bibliometric analysis was applied from 2007 to date on synergy between Electronic Commerce and Sentiment Analysis.
Explicit List-Decodable Codes with Optimal Rate for Computationally Bounded Channels

A stochastic code is a pair of encoding and decoding procedures (Enc, Dec) where $${{\rm Enc} : \{0, 1\}^{k} \times \{0, 1\}^{d} \rightarrow \{0, 1\}^{n}}$$ . The code is (p, L)-list decodable against a class $$\mathcal{C}$$ of “channel functions” $$C : \{0,1\}^{n} \rightarrow \{0,1\}^{n}$$ if for every message $$m \in \{0,1\}^{k}$$ and every channel $$C \in \mathcal{C}$$ that induces at most pn errors, applying Dec on the “received word” C(Enc(m,S)) produces a list of at most L messages that contain m with high probability over the choice of uniform $$S \leftarrow \{0, 1\}^{d}$$ . Note that both the channel C and the decoding algorithm Dec do not receive the random variable S, when attempting to decode. The rate of a code is $$R = k/n$$ , and a code is explicit if Enc, Dec run in time poly(n). Guruswami and Smith (Journal of the ACM, 2016) showed that for every constants $$0 < p < \frac{1}{2}, \epsilon > 0$$ and $$c > 1$$ there exist a constant L and a Monte Carlo explicit constructions of stochastic codes with rate $$R \geq 1-H(p) - \epsilon$$ that are (p, L)-list decodable for size $$n^c$$ channels. Here, Monte Carlo means that the encoding and decoding need to share a public uniformly chosen $${\rm poly}(n^c)$$ bit string Y, and the constructed stochastic code is (p, L)-list decodable with high probability over the choice of Y. Guruswami and Smith pose an open problem to give fully explicit (that is not Monte Carlo) explicit codes with the same parameters, under hardness assumptions. In this paper, we resolve this open problem, using a minimal assumption: the existence of poly-time computable pseudorandom generators for small circuits, which follows from standard complexity assumptions by Impagliazzo and Wigderson (STOC 97). Guruswami and Smith also asked to give a fully explicit unconditional constructions with the same parameters against $$O(\log n)$$ -space online channels. (These are channels that have space $$O(\log n)$$ and are allowed to read the input codeword in one pass.) We also resolve this open problem. Finally, we consider a tighter notion of explicitness, in which the running time of encoding and list-decoding algorithms does not increase, when increasing the complexity of the channel. We give explicit constructions (with rate approaching $$1 - H(p)$$ for every $$p \leq p_{0}$$ for some $$p_{0} >0$$ ) for channels that are circuits of size $$2^{n^{\Omega(1/d)}}$$ and depth d. Here, the running time of encoding and decoding is a polynomial that does not depend on the depth of the circuit. Our approach builds on the machinery developed by Guruswami and Smith, replacing some probabilistic arguments with explicit constructions. We also present a simplified and general approach that makes the reductions in the proof more efficient, so that we can handle weak classes of channels.