Reviewerly/exHarmony huggingface dataset:
Performance Monitoring for End-to-End Speech Recognition

Measuring performance of an automatic speech recognition (ASR) system without ground-truth could be beneficial in many scenarios, especially with data from unseen domains, where performance can be highly inconsistent. In conventional ASR systems, several performance monitoring (PM) techniques have been well-developed to monitor performance by looking at tri-phone posteriors or pre-softmax activations from neural network acoustic modeling. However, strategies for monitoring more recently developed end-to-end ASR systems have not yet been explored, and so that is the focus of this paper. We adapt previous PM measures (Entropy, M-measure and Auto-encoder) and apply our proposed RNN predictor in the end-to-end setting. These measures utilize the decoder output layer and attention probability vectors, and their predictive power is measured with simple linear models. Our findings suggest that decoder-level features are more feasible and informative than attention-level probabilities for PM measures, and that M-measure on the decoder posteriors achieves the best overall predictive performance with an average prediction error 8.8%. Entropy measures and RNN-based prediction also show competitive predictability, especially for unseen conditions.
Modeling Method for Performance Prediction of Centrifugal Compressor Based on Broad Learning System

An accurate model is very important for the design and control of centrifugal compressor. This paper proposes a rapid modeling method for centrifugal compressor based on broad learning system. The method can be used for off-line modeling of compressor performance prediction. When new data samples enter into the model, this paper uses incremental learning algorithm to realize fast online modeling of compressor. Furthermore, simulation results show that the proposed method can achieve a good prediction precision and can be used in compressor performance prediction online.
Cost minimization of scheduling scientific workflow applications on clouds

Summary Workflow scheduling with minimum cost is one of the most challenging problems for the users who need to execute a large‐scale scientific application on a cloud platform. However, traditional methods are hard to cover the highly complex applications and ignore the billing model of the public clouds. In this paper, we address the problem of scheduling a scientific application on cloud platform from the perspective of users. First, we propose a Satisfiability Modulo Theories (SMT) based algorithm to schedule a scientific application on cloud platform, the SMT algorithm constructs the scheduling problem to first‐order logic expressions and checks the expressions by solvers, which minimizes the number of Virtual Machine instances (VMs) allocated to the application. Furthermore, due to the hourly payment of cloud, we develop a heuristic algorithm called Multiple Strategies Algorithm (MSA) which determines the minimum instance hours of a scientific application deployed on VMs. At last, we combine the proposed SMT based algorithm and the MSA to a framework named SMT‐MSA, and compare it with other outstanding algorithms in experiments, the results show that, in most of cases, our algorithms reduce more cost than the other three methods which are HEFT, MSMD and IC‐PCPD2.
A Data-Driven Approach to the Fragile Families Challenge: Prediction through Principal-Components Analysis and Random Forests

Sociological research typically involves exploring theoretical relationships, but the emergence of “big data” enables alternative approaches. This work shows the promise of data-driven machine-learning techniques involving feature engineering and predictive model optimization to address a sociological data challenge. The author’s group develops improved generalizable models to identify at-risk families. Principal-components analysis and decision tree modeling are used to predict six main dependent variables in the Fragile Families Challenge, successfully modeling one binary variable but no continuous dependent variables in the diagnostic data set. This indicates that some binary dependent variables are more predictable using a reduced set of uncorrelated independent variables, and continuous dependent variables demand more complexity.
Improved Ranking Based Collaborative Filtering Using SVD and Borda Algorithm

Ranking based collaborative filtering (CF) as opposed to the well-known memory based collaborative filtering has better performance in accommodating user preferences. However, ranking based collaborative filtering still suffers both sparsity and scalability issues. This work incorporates the Singular Value Decomposition (SVD) technique to reduce the user-item rating matrix dimension to solve the scalability and sparsity of many unrated items faced by ranking based collaborative filtering. From the evaluation, the proposed method acquires 4 times higher coverage but 33.3% lower F1-score compared to BordaRank method. The proposed method also gets 6 times higher F1-score and 17.8% higher coverage compared to pure SVD method. The running time of the proposed method shows that on lower neighborhood size, the proposed method gets the fastest and the best performance. Thus, incorporation of SVD to ranking based CF can be a good approach to overcome sparsity and scalability problems.
Random point patterns and bags of visual words for remote sensing imagery

We exploit a bag of visual words approach with random point patterns to remote sensing image classification. The image is divided into patches of equal size, and the local maximums of its histogram are determined. For every intensity in the image, a point pattern is formed based on patch centers and according to local histogram maximums. Further, we investigate the type of the generated random point pattern and mark corresponding fragments with a label. The created point patterns can be of three types: cluster, regular, or random. Therefore, image patches are labeled as a cluster, regular, random, cluster-regular, cluster-random, regular-random, cluster-regular-random, or none when patch’s center does not belong to any point pattern. We form a representation in which the frequencies of patch types are used to differentiate images between the classes. Also, a spatial extension, which considers the relative arrangement of patch types based on their co-occurrences, is used. In addition, a median frequencies vector of the detected patch intensity maximums as the image spectral feature description is added. The introduction of such features significantly reduces the size of a codebook while delivering a classification accuracy comparable to matching the known methods.
Summarization in the Financial and Regulatory Domain

This chapter presents an introduction to automatic summarization techniques with special consideration of the financial and regulatory domains. It aims to provide an entry point to the field for readers interested in natural language processing (NLP) who are experts in the finance and/or regulatory domain, or to NLP researchers who would like to learn more about financial and regulatory applications. After introducing some core summarization concepts and the two domains are considered, some key methods and systems are described. Evaluation and quality concerns are also summarized. To conclude, some pointers for future reading are provided.
Impact of exponential technologies on global supply chain management

Technology has been rapidly evolving over the last several decades, revolutionizing all industries, such as communication, transportation, education, health care, life sciences, energy, retail, among many others. This momentum has created many opportunities for companies in their supply chain to expand their business globally to gain competitive advantages and as a result to increase their profit. Several leading technologies including blockchain, Internet of Things, virtual/augmented/extended reality, machine learning, and 3D printing, among others, have been identified as having a significant influence on global supply chain operations. In this study, we survey the literature on the implications of technologies in terms of their potential value creation, such as increasing profit, improving efficiency and agility, reducing cost and lead times, increasing visibility, and eliminating waste on global supply chain management. Based on the findings, we identify the challenges and limitations of these technologies within global supply chain operations.
Perspective Games

We introduce and study perspective games, which model multi-agent systems in which agents can view only the parts of the system that they own. As in standard multi-player turn-based games, the vertices of the game graph are partitioned among the players. Starting from an initial vertex, the players jointly generate a computation, with each player deciding the successor vertex whenever the generated computation reaches a vertex she owns. A perspective strategy for a player depends only on the history of visits in her vertices. Thus, unlike observation-based models of partial visibility, where uncertainty is longitudinal - players partially observe all vertices in the history, uncertainty in the perspective model is transverse - players fully observe part of the vertices in the history. Perspective games are not determined, and we study the problem of deciding whether a player has a winning perspective strategy. In the pure setting, we show that the problem is EXPTIME-complete for objectives given by a deterministic or universal parity automaton over an alphabet that labels the vertices of the game, and is 2EXPTIME-complete for LTL objectives. Accordingly, so is the model-checking complexity of Perspective-ATL*- an extension of ATL* with path quantification that captures perspective strategies. In all cases, the complexity in the size of the graph is polynomial - exponentially easier than games with observation-based partial visibility. In the probabilistic setting, we show that deciding whether a player has an almost-winning randomized perspective strategy is undecidable. Finally, we study perspective games with objectives given by ω-regular conditions over the set of vertices. In particular, we compare the power of perspective and memoryless strategies and show, for example, that while the generalized-Büchi and the Streett objectives do not admit memoryless strategies, generalized Büehl admits perspective strategies, and Streett does not. We also describe a fragment of LTL that admits perspective strategies.
Expressive Wearables

Self-expression is a vital practice for a functioning social life. Wearables have become expressive everyday products, while studies showed how physical collocation can be an opportunity for social technology. This article identifies a perspective for future design of wearables as an extension of the body in its social context: designing for diversity in expression with respect to social boundaries. The collected literature demonstrates the development of new forms of expressive wearables that challenges norms of dress and three groups of participatory methods enable re-search into everyday life practices. The two initial studies—inquiring into everyday life and exploring the wearable design for new practices—exemplify these methods and point a way forward with a focus for design on distinct practices of self-expression.
A Comparison of the Use of Several Different Resources on Lexicon Based Indonesian Sentiment Analysis on App Review Dataset

The mobile application (or usually known as mobile app) market has grown drastically over the last 15 years. Based on this fact, it is important for app developer to keep their engagement with their user by understanding their user's needs. Lexicon based sentiment analysis is a method that can be used for determining the sentiment polarization of a review or a comment in the App Store. There are two resources needed in lexicon based sentiment analysis for Indonesian language: machine translation and lexicon resource. In this study, we compare the performance of several different combinations of machine translations and lexicon resources in order to know the best resource combination that can be used in lexicon based sentiment analysis on App Review. The result shows that the combination of Google Translate and SentiWordNet can reach the highest overall accuracy by getting the score 0.72.
Reconnaissance assessment of the Stenian-Tonian granitoids of southern Tanzania for metal resources by using geological remote sensing and geochemical techniques

This study presents the use of geological remote sensing and geochemistry to determine spatial extents, geochemical characteristics and petrogenetic origin of Ndanda-Masasi metagranitoids in order to explore for important metals (Sn, W, Ta and Nb) in the metagranitoids. Geological remote sensing was done using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, and major and trace elements were analysed using a combination of Inductively Coupled Plasma Mass Spectrometer (ICP-MS) and Inductively Coupled Plasma Emission Spectrometer (ICP-ES). It has been revealed that the metagranitoids and other rocks in the Ndanda-Masasi area crop out in a larger spatial coverage than how it is depicted in available geological maps. Moreover, the meta-granitoids have variable SiO2 contents, low Mg, Ni and Cr concentrations and moderate to large Fe-numbers. They classify mainly as magnesian- and ferroan-type granites and minor granodiorite and quartz monzonite. The chondrite normalized Rare Earth Elements (REE) patterns of the rocks show slight to strong enrichment of light REE over middle REE and heavy REE ((La/Yb)N = 4.2–289.1), due to their variable contents of MREE and HREE. The rocks show flat patterns of middle REE and heavy REE and have negative Eu-anomalies (mean Eu/Eu* = 0.7), with the exception few rocks in some localities yielding positive Eu-anomalies. They are characterised by variable Sr/Y (4.7–356.7), Nb/Ta (2.6–44.5) and well correlated trends with negative slopes of SiO2 vs MgO, TiO2, CaO, and Al2O3 diagrams. These geochemical features reflects a most likely magmatic formation of the rocks in the presence of plagioclase, hornblende, garnet and rutile, or/and fractionation of the phases at variable crustal depths. Numerous myrmekites forming convex grain boundaries around K-feldspar in the rocks indicate Ca- and Na-metasomatism as an important process affecting the rocks in their evolution. The omnipresent retrograde assemblage of titanite, chlorite, epidote and muscovite forming at the expense of plagioclase, hornblende, biotite and opaque (Fe-Ti-oxides?) reflects their formation during uplifting history of the rocks. Contrary to granitoids of similar age such as those of the Karagwe-Ankolean Belt, which are metalliferous, our reconnaissance survey found that the Ndanda-Masasi meta-granitoids have uneconomic concentration of most important metals.
An IoT and blockchain-based approach for ensuring transparency and accountability in regulatory compliance

Regulatory compliance is an essential exercise in the modern societies confirming safety and prevention of harm to consumers. Despite many efforts from international and national quality control authorities, transparency and accountability in regulatory compliance remain a challenging technical-legal problem sitting atop a heavy reliance on trust. This paper presents a theoretical model of regulatory compliance aiming at improving accountability for systems and data audit and introduces a higher degree of transparency in management and quality control. It explores the technical aspects of two emerging technologies the Internet of Things (IoT) and Blockchain, and using a common use-case in practice shows how to better align these technologies with legal concerns and trust in regulatory compliance.
THE EFFECT OF BINARY DATA TRANSFORMATION IN CATEGORICAL DATA CLUSTERING

Abstract This paper focuses on hierarchical clustering of categorical data and compares two approaches which can be used for this task. The first one, an extremely common approach, is to perform a binary transformation of the categorical variables into sets of dummy variables and then use the similarity measures suited for binary data. These similarity measures are well examined, and they occur in both commercial and non-commercial software. However, a binary transformation can possibly cause a loss of information in the data or decrease the speed of the computations. The second approach uses similarity measures developed for the categorical data. But these measures are not so well examined as the binary ones and they are not implemented in commercial software. The comparison of these two approaches is performed on generated data sets with categorical variables and the evaluation is done using both the internal and the external evaluation criteria. The purpose of this paper is to show that the binary transformation is not necessary in the process of clustering categorical data since the second approach leads to at least comparably good clustering results as the first approach.
A Study on the Readability of Scientific Publications

Several works have used traditional readability measures to investigate the readability of scientific texts and its association with scientific impact . However, these works are limited in terms of dataset size, range of domains, and examined readability and impact measures. Our study addresses these limitations, investigating the readability of paper abstracts on a very large multidisciplinary corpus, the association of expert judgments on abstract readability with traditional readability measures, and the association of abstract readability with the scientific impact of the corresponding publication.
Reversible Data Hiding in Encrypted Images Using Public Cloud and Cellular Automata

Reversible data hiding in encrypted images using reversible cellular automata is proposed in this article where the content owner encrypts an image using a stream cipher algorithm with an encryption key and uploads the encrypted image into the server. Later, the server embeds additional messages into the encrypted image using a three-stage selection method with an embedding key. On the user side, using both keys, an original image can be recovered. The proposed method has been observed to perform better in embedding payloads with lower computation complexity and higher security in comparison with the existing methods.
Personal Big Data, GDPR and Anonymization

Big data are analyzed to reveal patterns, trends and associations, especially relating to human behavior and interactions. However, according to the European General Data Protection Regulation (GDPR), which is becoming a de facto global data protection standard, any intended uses of personally identifiable information (PII) must be clearly specified and explicitly accepted by the data subjects. Furthermore, PII cannot be accumulated for secondary use. Thus, can exploratory data uses on PII be GDPR-compliant? Hardly so. Resorting to anonymized data sets instead of PII is a natural way around, for anonymized data fall outside the scope of GDPR. The problem is that anonymization techniques, based on statistical disclosure control and privacy models, use algorithms and assumptions from the time of small data that must be thoroughly revised, updated or even replaced to deal with big data. Upgrading big data anonymization to address the previous challenge needs to empower users (by giving them useful anonymized data), subjects (by giving them control on anonymization) and controllers (by simplifying anonymization and making it more flexible).
Robust Vehicle Detection in Aerial Images Based on Image Spatial Pyramid Detection Model

Vehicle detection in high resolution aerial images obtained by unmanned aerial vehicles (UAV) has a wide application in traffic surveillance. Recently, many detectors based on convolutional neural network (CNN) have achieved great success in object detection. However, it would be difficult for them to perform efficiently on aerial images because the significant difference in target size caused by the altitude change of the UAV platform brings great challenge for these detectors to conduct precise localization. To improve the detection performance on aerial images, we propose an Image Spatial Pyramid Detection Model (ISPDM) which mainly consists of two stages. In the first stage, we divide the image into several patches and select some of them with an image patch selection progress. In the second stage, we utilize YOLOv3 to detect vehicles the original image along with the selected patches and obtain the final result with an integrated decision-making algorithm. Finally, the superiority of the proposed algorithm is well demonstrated by comparison with other solutions for vehicle detection in high resolution aerial images through extensive experiments.
Agile Methods and Maturity Models Assessments: What’s Next?

The adoption of Software Process Improvement (SPI) initiatives, based on reference models, such as CMMI-DEV, is largely described in the literature. Challenges and benefits are already known, especially when these models are combined with agile methods. However, little is known on what happens after the appraisal. Do companies continue to use maturity models-based processes? Do they sacrifice agile practices? The objective of this study is thus to identify how software processes evolve in companies that use agile methods and have been assessed against maturity-based reference models. We performed a case study in four companies. Our results show that companies that use agile methods struggle to use, maintain and evolve their processes along with maturity models after the official assessment.
Architecture-Aware Bayesian Optimization for Neural Network Tuning

Hyperparameter optimization of a neural network is a non-trivial task. It is time-consuming to evaluate a hyperparameter setting, no analytical expression of the impact of the hyperparameters are available, and the evaluations are noisy in the sense that the result is dependent on the training process and weight initialization. Bayesian optimization is a powerful tool to handle these problems. However, hyperparameter optimization of neural networks poses additional challenges, since the hyperparameters can be integer-valued, categorical, and/or conditional, whereas Bayesian optimization often assumes variables to be real-valued. In this paper we present an architecture-aware transformation of neural networks applied in the kernel of a Gaussian process to boost the performance of hyperparameter optimization. The empirical experiment in this paper demonstrates that by introducing an architecture-aware transformation of the kernel, the performance of the Bayesian optimizer shows a clear improvement over a naïve implementation and that the results are comparable to other state-of-the-art methods.