Reviewerly/exHarmony huggingface dataset:
Approximate leave-future-out cross-validation for time series models

One of the common goals of time series analysis is to use the observed seriesto inform predictions for future observations. In the absence of any actual newdata to predict, cross-validation can be used to estimate a model's futurepredictive accuracy, for instance, for the purpose of model comparison orselection. As exact cross-validation for Bayesian models is oftencomputationally expensive, approximate cross-validation methods have beendeveloped; most notably methods for leave-one-out cross-validation (LOO-CV). Ifthe actual prediction task is to predict the future given the past, LOO-CVprovides an overly optimistic estimate as the information from futureobservations is available to influence predictions of the past. To tackle theprediction task properly and account for the time series structure, we can useleave-future-out cross-validation (LFO-CV). Like exact LOO-CV, exact LFO-CVrequires refitting the model many times to different subsets of the data. UsingPareto smoothed importance sampling, we propose a method for approximatingexact LFO-CV that drastically reduces the computational costs while alsoproviding informative diagnostics about the quality of the approximation.
Multi-Perspective Analysis of News Articles using Machine Learning Algorithms

Nowadays, many machine learning algorithms are evolving.It is a very difficult task to select a particular algorithm for a specific problem.A multi-perspective analysis of the given input data has to be performed to select a particular algorithm.In this study a case study has been taken for selecting an algorithm for the classification of news articles.Multiperspective analysis is performed on the data using various machine learning algorithms namely Random Forest Classifier, Decision tree, AdaBoostClassifier, SVM with Linear SVC and SVM with NuSVC.For the multi perspective analysis, features from the dataset are extracted and standard metrics are used.The metrics used are Kappa, Accuracy, Fmeasure, Recall, and Precision.For the BBC news standard dataset, SVM Linear SVC proves to be effective because its classification rate is 96% and false positive rate is 0.75%.
ANEW for Spanish Twitter Sentiment Analysis Using Instance-Based Multi-label Learning Algorithms

In the last years, different efforts have been made to extract information that users express through online social networking services, e.g. Twitter. Despite the progress achieved, there are still open gaps to be addressed. Related to the sentiment analysis issue, we stand out the following gaps: (a) low accuracy in sentiment classification task for short texts; and, (b) lack of tools for sentiment analysis in several languages. Aiming to fill these gaps, in this paper we apply the Spanish adaptation of ANEW (Affective Norms for English Words) as resource to improve the Twitter sentiment analysis by applying a variety of multi-label classifiers in a corpus of Spanish tweets collected by us. To the best of our knowledge, this is the first work using a Spanish adaptation of ANEW for sentiment analysis.
Deep Modular Network Architecture for Depth Estimation from Single Indoor Images

We propose a novel deep modular network architecture for indoor scene depth estimation from single RGB images. The proposed architecture consists of a main depth estimation network and two auxiliary semantic segmentation networks. Our insight is that semantic and geometrical structures in a scene are strongly correlated, thus we utilize global (i.e. room layout) and mid-level (i.e. objects in a room) semantic structures to enhance depth estimation. The first auxiliary network, or layout network, is responsible for room layout estimation to infer the positions of walls, floor, and ceiling of a room. The second auxiliary network, or object network, estimates per-pixel class labels of the objects in a scene, such as furniture, to give mid-level semantic cues. Estimated semantic structures are effectively fed into the depth estimation network using newly proposed discriminator networks, which discern the reliability of the estimated structures. The evaluation result shows that our architecture achieves significant performance improvements over previous approaches on the standard NYU Depth v2 indoor scene dataset.
Recent Progress in the Quantum-to-the-Home Networks

For secure data transmission to the end users in a conventional fiber-to-the-home (FTTH) network, quantum cryptography (QC) is getting much consideration nowadays.QC or more specifically quantum key distribution (QKD) promises unconditionally secure protocol, the Holy Grail of communication and information security that is based on the fundamental laws of quantum physics.In this chapter, we discuss the design issues in a hybrid quantum-classical communication network, performance of the cost-effective offthe-shelf telecommunication equipment, our latest results on a four-state (Quadrature Phase Shift Keying, 'QPSK') RF sub-carrier assisted continuous-variable quantum key distribution (CV-QKD) multiuser network based on ultra-low loss quantum channel (pure silica core fiber, 'PSCF') and microelectromechanical systems (MEMS) based add/drop switch.The results are thoroughly compared with the commercially available high-cost encryption modules.It is expected that the discussed cost-effective and energy efficient QKD network can facilitate the practical applications of the CV-QKD protocol on the commercial scale in near future for smart access networks.
Permutation Invariant Likelihoods and Equivariant Transformations.

In this work, we fill a substantial void in machine learning and statisticalmethodology by developing extensive generative density estimation techniquesfor exchangeable non-iid data. We do so through the use of permutationinvariant likelihoods and permutation equivariant transformations of variables.These methods exploit the intradependencies within sets in ways that areindependent of ordering (for likelihoods) or order preserving (fortransformations). The proposed techniques are able to directly modelexchangeable data (such as sets) without the need to account for permutationsor assume independence of elements. We consider applications to point cloudsand provide several interesting experiments on both synthetic and real-worlddatasets.
A Simple Load-Balancing Scheme with High Scaling Efficiency

This chapter describes an image partitioning scheme that can be used to distribute the work of computing pixel values across multiple processing units. The resulting workload distribution scheme is simple to implement, yet effective.
Evaluating CryptoNote-Style Blockchains

To hide user identity, blockchain-based cryptocurrencies utilize public key based coin addresses to represent users. However, the user identity can still be identified by linking the coin addresses to the IP address of a user, through network traffic analysis. Ring Signature based protocols, such as CryptoNote and RingCT, have been designed to anonymize the payers of a transaction, and deployed in leading cryptocurrencies like Bytecoin and Monero. This paper provides a comprehensive evaluation on the performance of Bytecoin and Monero, at both the protocol level and the system level. In particular, our evaluation includes theoretical complexity analysis of the protocols and practical performance analysis of the Bytecoin and Monero implementation. In addition, we also provide an analysis on the existing Bytecoin and Monero transactions, based on the public blockchain data. Our results identify the execution bottleneck and space overhead of generating and verifying transactions, which may encourage the design of more efficient protocols. We also provide insights based on our analysis on the performance of specific cryptographic algorithms, static analysis of the ring size distribution, of the input size distribution and output size distribution, and of the transaction size distribution.
Adaptive Estimation for Approximate k-Nearest-Neighbor Computations

Algorithms often carry out equally many computations for easy and hard problem instances. In particular, algorithms for finding nearest neighbors typically have the same running time regardless of the particular problem instance. In this paper, we consider the approximate k-nearest-neighbor problem, which is the problem of finding a subset of O(k) points in a given set of points that contains the set of k nearest neighbors of a given query point. We propose an algorithm based on adaptively estimating the distances, and show that it is essentially optimal out of algorithms that are only allowed to adaptively estimate distances. We then demonstrate both theoretically and experimentally that the algorithm can achieve significant speedups relative to the naive method.
QUESTIONNAIRE SURVEY ON REMOVAL METHODS OF SHORING UNDER SLABS AND BEAMS IN THE CONVENTIONAL FORMWORK

There have been formwork methods which make it possible to remove the bottom forms for slabs and beams before the strength of concrete reaches design strength. In order to collect information of those methods in real site, we carried out some questionnaires targeting the engineers involved in selecting methods. In this report, firstly we classified formwork methods and wrapped up the questionnaire results about removal methods of shoring under slabs and beams in the conventional formwork.
Privacy-Preserving Remote User Authentication with k-Times Untraceability

Remote user authentication has found numerous real-world applications, especially in a user-server model. In this work, we introduce the notion of anonymous remote user authentication with k-times untraceability (k-RUA) for a given parameter k, where authorized users authenticate themselves to an authority (typically a server) in an anonymous and k-times untraceable manner. We define the formal security models for a generic k-RUA construction that guarantees user authenticity, anonymity and user privacy. We provide a concrete instantiation of k-RUA having the following properties: (1) a third party cannot impersonate an authorized user by producing valid transcripts for the user while conversing during a session; (2) a third party having access to the communication channel between the user and the authority cannot identify the session participants; (3) the authority can trace the real identities of dishonest users who have authenticated themselves for more than k times; (4) our k-RUA construction avoids using expensive pairing operations—which makes it efficient and suitable for devices having limited amount of computational resources.
A Discourse Construction Grammar Approach to Discourse Analysis: Microblog Parody and Instant Messaging

On the basis of Goldberg’s (1995) Construction Grammar (CxG) and Östman’s (2005) Construction Discourse perspectives, and by incorporating the theories of genre, register and cohesion from Systemic Functional Grammar, this research attempts to set up a construction grammar framework for discourse analysis, namely the discourse construction grammar ( dcg ) model. With dcg , we see a discourse first as an overarching abstract discourse construction, which consists of and integrates a number of ever smaller schematic constructions. Moreover, in order to account for the nexion of clauses into sentences and sentences into cross-sentential discourse chunks from a dcg perspective, this paper also resorts to clause conjunct construction and inter-sentential conjunction construction conceptions. Alongside establishing our dcg model, we have analyzed a trendy microblog templatic parody as well as a piece of dialogic instant messaging to exemplify our multi-layered and multi-faceted construction treatment of a piece of discourse.
Student Debugging Practices and Their Relationships with Project Outcomes

Debugging is an important part of the software development process, studied by both the CS education and software engineering communities. Most prior work has focused either on novice or professional programmers. Intermediate-to-advanced students (such as those enrolled in post-CS2 Data Structures courses) who are working on large and complex projects have largely been ignored. We present results from an empirical observational study that examined junior-level undergraduate students' debugging practices on relatively large (4-week lifecycle) projects, using IDE clickstream data collected by a custom Eclipse plugin. Specifically, we hypothesize that there are differing debugging behaviors exhibited, and that differing behaviors lead to differing project out-comes. For example, how often do students use the symbolic debugger available in modern IDEs, versus how often do they use diagnostic print statements, or both? What triggers a debugging session? What follows a debugging session? Does it matter when in the project lifecycle that debugging takes place? We have a number of interesting preliminary results. When using the debugger, there was a negative relationship between step-over and step-into actions versus final course grades, indicating that when students "spin their wheels" while debugging, they tend to perform more poorly. Students also tend to perform better on the project when debugging takes place earlier in the overall project life-cycle. We developed an algorithm to identify diagnostic print statements in the students' projects. We found that over 90% used at least one diagnostic print statement, and about 75% used the symbolic debugger, at least once in any given project.
Influence of Knowledge Acquisition on Organizational Performance Based on Return on Equity and Return on Assets of State Owned Commercial Enterprises in Kenya

Purpose of this study was to analyze the influence of knowledge acquisition on organizational performance based on return on equity and return on assets of state owned commercial enterprises in Kenya.This study was based on explanatory and descriptive research designs since they were more appropriate to test the hypotheses.The target population comprised of 275 members of top management team of 55 commercial state-owned enterprises in Kenya, as at 31 st October 2016.The study utilized simple random sampling to select a sample of 268 members of top management team in the 55 commercial state-owned enterprises.Response rate of 71% was obtained and analytical tests conducted were Pearson correlation coefficients, One Way ANOVA, and Multiple linear regression.The correlation coefficients for return on equity was (r = -0.052,p = 0.481) and return on asset was (r = 0.075, p = 0.309) on knowledge acquisition respectively.Result for One-Way ANOVA on two segments of state owned commercial enterprises was, F (14, 173) = 3.104, p=0.000.The regression coefficients on return on equity were: unmoderated model; β= -0.957, t (-1.392), p>0.05 and moderated model; β= -4.957, t (-2.567), p<0.05 respectively.While the regression coefficients on return on assets were: unmoderated model; β=0.0002, t (-0.001), p=0.999 and moderated models; β= -0.088, t (-0.118), p=0.906 respectively.The study concludes there is significant influence of knowledge acquisition and organizational performance based on return on equity of state owned commercial enterprises but there is no significant influence of knowledge acquisition on organizational performance based on return on assets of state owned commercial enterprises in Kenya.
Rocks and the Geological Record

For geologists, rocks are the key to the past. They also offer the possibility to find out something about the inside of the Earth, how it is built up, and to which temperatures and pressure the rocks have been subjected to at different depths.
An Interactive Teaching Tool for Introducing Novices to Machine Translation

The first step in the research process is developing an understanding of the problem at hand. Novices may be interested in learning about machine translation (MT), but often lack experience and intuition about the task of translation (either by human or machine) and its challenges. The goal of this work is to allow students to interactively discover why MT is an open problem, and encourage them to ask questions, propose solutions, and test intuitions. We present a hands-on activity in which students build and evaluate their own MT systems using curated parallel texts. By having students hand-engineer MT system rules in a simple user interface, which they can then run on real data, they gain intuition about why early MT research took this approach, where it fails, and what features of language make MT a challenging problem even today. Developing translation rules typically strikes novices as an obvious approach that should succeed, but the idea quickly struggles in the face of natural language complexity. This interactive, intuition-building exercise can be augmented by a discussion of state-of-the-art MT techniques and challenges, focusing on areas or aspects of linguistic complexity that the students found difficult. We envision this lesson plan being used in the framework of a larger AI or natural language processing course (where only a small amount of time can be dedicated to MT) or as a standalone activity. We describe and release the tool that supports this lesson, as well as accompanying data.
Optimized attacks on twin-field quantum key distribution

In twin-field quantum key distribution the two communicating parties, Alice and Bob, each send a weak coherent pulse to a third party stationed between them. The key bits are generated by interference between these pulses, with the results communicated to Alice and Bob. We consider optimized strategies for eavesdropping on the communication built upon state discrimination and quantum non-demolition measurements. We find that the best strategy comprises a two-step process but that even this does not compromise the security of the protocol.
The Role of Total Quality Management in the Face of Challenges: A Study in Lafarge-Jordan

This paper presents the findings of a study investigating how the application of Total Quality Management (TQM) can help organizations to tackle the different business challenges brought about as a consequence of the recent global financial crisis including those associated with planning, cost reduction and increasing competition. The study focuses on the experiences of employees based within the leading concrete and cement producer ‘Lafarge-Jordan’. Using a qualitative approach and the use of convenience sampling, a questionnaire was developed to gather the views of the company’s employees on the perceived impact of TQM in their organization on its ability to meet different business challenges. 73 employees completed the survey. Partial Least Squares (PLS) were chosen using SmartPLS 3 software with a two-stage approach in measurement and structural model testing. The results showed that customer focus and senior management support are the two most important variables that enhance the ability to face different types of challenges. Integrative relationships with suppliers and continuous improvement were found also to have a significant impact on reducing quality costs to help overcome competition. Based on the previous literature and results, this research recommends the orientation towards greater employee involvement because they are the cornerstone to successful TQM; further, it is more advantageous to analyze both the soft and hard effects of TQM to overcome challenges.
ANALISIS WAWASAN KEJURUAN MAHASISWA JURUSAN PENDIDIKAN TEKNIK OTOMOTIF UNIVERSITAS NEGERI MAKASSAR

Abstrak Penelitian ini bertujuan untuk mengetahui wawasan kejuruan bagi mahasiswa Jurusan Pendidikan Teknik Otomotif Universitas Negeri Makassar dengan aspek: (a) Teknik Kendaraan Ringan, (b) Teknik Sepeda Motor, (c) Teknik Alat Berat, (d) Teknik Perbaikan Bodi, dan (e) Teknik Ototronik. Sampel penelitian terdiri atas 59 orang mahasiswa yang diambil secara random dengan menggunakan teknik stratified random sampling. Analisis data hasil penelitian menggunakan teknik analisis deskriptif yang meliputi: rata-rata, standar deviasi, dan kategori distribusi frekuensi. Hasil penelitian menunjukkan bahwa wawasan kejuruan mahasiswa Jurusan Pendidikan Teknik Otomotif Universitas Negeri Makassar belum optimal. Hal ini terlihat dari rata-rata sub variabel penelitian diperoleh hasil masing-masing Teknik kendaraan Ringan 3,29, Teknik Sepeda Motor 3,24, Teknik Alat Berat 3,15, Teknik Perbaikan Bodi 3,03, dan Teknik Ototronik 2,64. Begitu pula hasil tes pembelajaran praktik, hanya 47,45% yang memiliki hasil tes termasuk dalam kategori tinggi. Kata Kunci: Wawasan, Kejuruan, Mahasiswa. Abstract This study aims to determine vocational insights for students of Department of Education of Automotive Engineering, State University of Makassar with aspects: (a) Light Vehicle Engineering, (b) Motorcycle Engineering, (c) Mechanical Equipment, (d) Mechanical Repair Body, and (e) Ototronik technique. The study sample consisted of 59 students drawn randomly using stratified random sampling technique. Analysis of survey data using descriptive analysis techniques which include: average, standard deviation, and frequency distribution category. The results showed that the students of Department of Education vocational insight of Automotive Engineering, State University of Makassar is not optimal. This can be seen from the average sub-variables were obtained the results of each technique Lightweight vehicles 3.29, Motorcycle Engineering of 3.24, Mechanical Equipment of 3.15, Body Repair Techniques of 3.03, and Ototronics Engineering of 2.64. Similarly, the practice of learning test results only 47.45% had test results included in the high category. Keywords: Insights, Vocational, Students.
Data observatories: decentralised data and interdisciplinary research

Sharing data and observations has been at the centre of academic discourse for centuries. The Internet has enabled new promising methods of sharing, introducing a number of challenges. This paper discusses the concept of data observatories as decentralised platforms for sharing data and observations, and outlines a reference architecture for their deployment. It argues that decentralisation has been a necessity for interdisciplinary research and that it will benefit data-driven innovation beyond academia. It concludes with discussion on the necessary conditions of decentralisation and literacy for innovation and wider engagement in academic discourse.
Visual and Thermal Data for Pedestrian and Cyclist Detection

With the continued advancement of autonomous vehicles and their implementation in public roads, accurate detection of vulnerable road users (VRUs) is vital for ensuring safety. To provide higher levels of safety for these VRUs, an effective detection system should be employed that can correctly identify VRUs in all types of environments (e.g. VRU appearance, crowded scenes) and conditions (e.g. fog, rain, night-time). This paper presents optimal methods of sensor fusion for pedestrian and cyclist detection using Deep Neural Networks (DNNs) for higher levels of feature abstraction. Typically, visible sensors have been utilized for this purpose. Recently, thermal sensors system or combination of visual and thermal sensors have been employed for pedestrian detection with advanced detection algorithm. DNNs have provided promising results for improving the accuracy of pedestrian and cyclist detection. This is because they are able to extract features at higher levels than typical hand-crafted detectors. Previous studies have shown that amongst the several sensor fusion techniques that exist, Halfway Fusion has provided the best results in terms of accuracy and robustness. Although sensor fusion and DNN implementation have been used for pedestrian detection, there is considerably less research undertaken for cyclist detection.