Reviewerly/exHarmony huggingface dataset:
A Methodological Review of Machine Learning in Applied Linguistics

The traditional linear regression in applied linguistics (AL) suffers from the drawbacks arising from the strict assumptions namely: linearity, and normality, etc. More advanced methods are needed to overcome the shortcomings of the traditional method and grapple with intricate linguistic problems. However, there is no previous review on the applications of machine learning (ML) in AL, the introduction of interpretable ML, and related practical software. This paper addresses these gaps by reviewing the representative algorithms of ML in AL. The result shows that ML is applicable in AL and enjoys a promising future. It goes further to discuss the applications of interpretable ML for reporting the results in AL. Finally, it ends with the recommendations of the practical programming languages, software, and platforms to implement ML for researchers in AL to foster the interdisciplinary studies between AL and ML.
Better Robustness by More Coverage: Adversarial Training with Mixup Augmentation for Robust Fine-tuning

Pretrained language models (PLMs) perform poorly under adversarial attacks. To improve the adversarial robustness, adversarial data augmentation (ADA) has been widely adopted to cover more search space of adversarial attacks by adding textual adversarial examples during training. However, the number of adversarial examples for text augmentation is still extremely insufficient due to the exponentially large attack search space. In this work, we propose a simple and effective method to cover a much larger proportion of the attack search space, called Adversarial and Mixup Data Augmentation (AMDA). Specifically, AMDA linearly interpolates the representations of pairs of training samples to form new virtual samples, which are more abundant and diverse than the discrete text adversarial examples in conventional ADA. Moreover, to fairly evaluate the robustness of different models, we adopt a challenging evaluation setup, which generates a new set of adversarial examples targeting each model. In text classification experiments of BERT and RoBERTa, AMDA achieves significant robustness gains under two strong adversarial attacks and alleviates the performance degradation of ADA on the clean data. Our code is available at: https://github.com/thunlp/MixADA .
AraBench: Benchmarking Dialectal Arabic-English Machine Translation

Low-resource machine translation suffers from the scarcity of training data and the unavailability of standard evaluation sets. While a number of research efforts target the former, the unavailability of evaluation benchmarks remain a major hindrance in tracking the progress in low-resource machine translation. In this paper, we introduce AraBench, an evaluation suite for dialectal Arabic to English machine translation. Compared to Modern Standard Arabic, Arabic dialects are challenging due to their spoken nature, non-standard orthography, and a large variation in dialectness. To this end, we pool together already available Dialectal Arabic-English resources and additionally build novel test sets. AraBench offers 4 coarse, 15 fine-grained and 25 city-level dialect categories, belonging to diverse genres, such as media, chat, religion and travel with varying level of dialectness. We report strong baselines using several training settings: fine-tuning, back-translation and data augmentation. The evaluation suite opens a wide range of research frontiers to push efforts in low-resource machine translation, particularly Arabic dialect translation. The evaluation suite and the dialectal system are publicly available for research purposes.
Quantum tomography of noisy ion-based qudits

Quantum tomography makes it possible to obtain comprehensive information about certain logical elements of a quantum computer. In this regard, it is a promising tool for debugging quantum computers. The practical application of tomography, however, is still limited by systematic measurement errors. Their main source are errors in the quantum state preparation and measurement procedures. In this work, we investigate the possibility of suppressing these errors in the case of ion-based qudits. First, we will show that one can construct a quantum measurement protocol that contains no more than a single quantum operation in each measurement circuit. Such a protocol is more robust to errors than the measurements in mutually unbiased bases, where the number of operations increases in proportion to the square of the qudit dimension. After that, we will demonstrate the possibility of determining and accounting for the state initialization and readout errors. Together, the measures described can significantly improve the accuracy of quantum tomography of real ion-based qudits.
Origin-Aware Next Destination Recommendation with Personalized Preference Attention

Next destination recommendation is an important task in the transportation domain of taxi and ride-hailing services, where users are recommended with personalized destinations given their current origin location. However, recent recommendation works do not satisfy this origin-awareness property, and only consider learning from historical destination locations, without origin information. Thus, the resulting approaches are unable to learn and predict origin-aware recommendations based on the user's current location, leading to sub-optimal performance and poor real-world practicality. Hence, in this work, we study the origin-aware next destination recommendation task. We propose the Spatial-Temporal Origin-Destination Personalized Preference Attention (STOD-PPA) encoder-decoder model to learn origin-origin (OO), destination-destination (DD), and origin-destination (OD) relationships by first encoding both origin and destination sequences with spatial and temporal factors in local and global views, then decoding them through personalized preference attention to predict the next destination. Experimental results on seven real-world user trajectory taxi datasets show that our model significantly outperforms baseline and state-of-the-art methods.
PHD Filter for Object Tracking in Road Traffic Applications Considering Varying Detectability

This paper considers the object detection and tracking problem in a road traffic situation from a traffic participant’s perspective. The information source is an automotive radar which is attached to the ego vehicle. The scenario characteristics are varying object visibility due to occlusion and multiple detections of a vehicle during a scanning interval. The goal is to maintain and report the state of undetected though possibly present objects. The proposed algorithm is based on the multi-object Probability Hypothesis Density filter. Because the PHD filter has no memory, the estimate of the number of objects present can change abruptly due to erroneous detections. To reduce this effect, we model the occlusion of the object to calculate the state-dependent detection probability. Thus, the filter can maintain unnoticed but probably valid hypotheses for a more extended period. We use the sequential Monte Carlo method with clustering for implementing the filter. We distinguish between detected, undetected, and hidden particles within our framework, whose purpose is to track hidden but likely present objects. The performance of the algorithm is demonstrated using highway radar measurements.
Automatically Generating Natural Language Descriptions of Images by a Deep Hierarchical Framework

Automatically generating an accurate and meaningful description of an image is very challenging. However, the recent scheme of generating an image caption by maximizing the likelihood of target sentences lacks the capacity of recognizing the human-object interaction (HOI) and semantic relationship between HOIs and scenes, which are the essential parts of an image caption. This article proposes a novel two-phase framework to generate an image caption by addressing the above challenges: 1) a hybrid deep learning and 2) an image description generation. In the hybrid deep-learning phase, a novel factored three-way interaction machine was proposed to learn the relational features of the human-object pairs hierarchically. In this way, the image recognition problem is transformed into a latent structured labeling task. In the image description generation phase, a lexicalized probabilistic context-free tree growing scheme is innovatively integrated with a description generator to transform the descriptions generation task into a syntactic-tree generation process. Extensively comparing state-of-the-art image captioning methods on benchmark datasets, we demonstrated that our proposed framework outperformed the existing captioning methods in different ways, such as significantly improving the performance of the HOI and relationships between HOIs and scenes (RHIS) predictions, and quality of generated image captions in a semantically and structurally coherent manner.
Design and Analysis of High-Accuracy Telecentric Surface Reconstruction System Based on Line Laser

Non-contact measurement technology based on triangulation with cameras is extensively applied to the development of computer vision. However, the accuracy of the technology is generally not satisfactory enough. The application of telecentric lenses can significantly improve the accuracy, but the view of telecentric lenses is limited due to their structure. To address these challenges, a telecentric surface reconstruction system is designed for surface detection, which consists of a single camera with a telecentric lens, line laser generator and one-dimensional displacement platform. The designed system can reconstruct the surface with high accuracy. The measured region is expanded with the used of the displacement platform. To achieve high-accuracy surface reconstruction, we propose a method based on a checkerboard to calibrate the designed system, including line laser plane and motor direction of the displacement platform. Based on the calibrated system, the object under the line laser is measured, and the results of lines are assembled to make the final surface reconstruction. The results show that the designed system can reconstruct a region of 20×40 mm2, up to the accuracy of micron order.
Improved key of RSA Algorithm Generated from The image based on SHA256 Algorithm

At the present time, information security has become very necessary and is considered a very difficult problem in the event that it is exposed by the attackers and becomes easy and available to them.Therefore, data protection and non-disclosure to unauthorized persons are required to do so.The solution is to encrypt it and there are a large number of encryption algorithms in use.In this paper, both the RSA generic encryption algorithm and the hash algorithm known as SHA256 were used, in which the RSA algorithm was improved by generating the algorithm keys from an image generated with the SHA256 hashing algorithm.Where two keys are formed, one of which is public for encryption and is available and the other is a private key that is not disclosed only to the person who has access to the message, and effective encryption of the data was produced in addition to the retrieval of the original text.The proposed method has been programmed in VisualBaic.Net 2012 language.
Contextual BERT: Conditioning the Language Model Using a Global State

BERT is a popular language model whose main pre-training task is to fill in the blank, i.e., predicting a word that was masked out of a sentence, based on the remaining words.In some applications, however, having an additional context can help the model make the right prediction, e.g., by taking the domain or the time of writing into account.This motivates us to advance the BERT architecture by adding a global state for conditioning on a fixed-sized context.We present our two novel approaches and apply them to an industry use-case, where we complete fashion outfits with missing articles, conditioned on a specific customer.An experimental comparison to other methods from the literature shows that our methods improve personalization significantly.
A Review of Energy-related Cost Issues and Prediction Models in Cloud Computing Environments

With the expansion of cloud computing, optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important, since it directly affects providers' revenue and customers' payment.Thus, providing prediction information of the cloud services can be very beneficial for the service providers, as they need to carefully predict their business growths and efficiently manage their resources.To optimize the use of cloud services, predictive mechanisms can be applied to improve resource utilization and reduce energy-related costs.However, such mechanisms need to be provided with energy awareness not only at the level of the Physical Machine (PM) but also at the level of the Virtual Machine (VM) in order to make improved cost decisions.Therefore, this paper presents a comprehensive literature review on the subject of energy-related cost issues and prediction models in cloud computing environments, along with an overall discussion of the closely related works.The outcomes of this research can be used and incorporated by predictive resource management techniques to make improved cost decisions assisted with energy awareness and leverage cloud resources efficiently.
ON-OFF Privacy Against Correlation Over Time

We consider the problem of ON-OFF privacy in which a user is interested in the latest message generated by one of n sources available at a server. The user has the choice to turn privacy ON or OFF depending on whether he wants to hide his interest at the time or not. The challenge of allowing the privacy to be toggled between ON and OFF is that the user's online behavior is correlated over time. Therefore, the user cannot simply ignore the privacy requirement when privacy is OFF. We represent the user's correlated requests by an n-state Markov chain. Our goal is to design ON-OFF privacy schemes with optimal download rate that ensure privacy for past and future requests. We devise a polynomial-time algorithm to construct an ON-OFF privacy scheme. Moreover, we present an upper bound on the achievable rate. We show that the proposed scheme is optimal and the upper bound is tight for some special families of Markov chains. We also give an implicit characterization of the optimal achievable rate as a linear programming (LP).
Graph-based temporal action co-localization from an untrimmed video

We present an efficient approach for temporal action co-localization (TACL), which means to simultaneously localize all action instances in an untrimmed video. Compared with the conventional instance-by-instance action localization, TACL can exploit the contextual and temporal relationships among action instances to reduce the localization ambiguities. Motivated by the strong relational modeling capability of graph neural networks, we propose a Graph-based Temporal Action Co-Localization (G-TACL) method. By considering each action proposal as a node, G-TACL effectively aggregates contextual and temporal features from related action proposals to jointly recognize and localize all action instances in a single shot. Moreover, we introduce a novel multi-level consistency evaluator to measure the relatedness between any two action proposals. This is achieved by considering their high-level contextual similarities, low-level temporal coincidences and feature correlations. We exploit the Gated Recurrent Units (GRUs) to iteratively update the features of each node, which are then used to regress the temporal boundaries of action proposals and finally achieve action co-localization. Experimental results on three datasets, i.e., THUMOS14, MEXaction2 and ActivityNet v1.3 datasets demonstrate that our G-TACL is superior or comparable to the state-of-the-arts.
Implementation of Cosine Modulated Digital Filter Bank on Processor with ARM Architecture

This paper presents a class of multichannel cosine-modulated filter banks (CMFB) of analysis based on the modulation effect with a fast discrete-cosine transformation of the fourth type (DCT-IV), which is calculated using the fast Fourier transform. As a prototype filter, a low-frequency filter with a finite pulse characteristic was used, frequency-shifted copies of which were made using an effective technology for polyphase representation of the filter Bank. The comparison of the number of arithmetic operations performed by digital down converter (DDC) based on cascade integral-comb (CIC) and CMFB based on different number of channels is given. A software description of the CMFB algorithm is presented in the form of block diagrams describing the capabilities of the Opencl and clfft software libraries for implementing the DCT-IV filter Bank and modulation algorithm on a GPU. The obtained algorithm was tested on an ARM family processor and a mali GPU with a table with sample rate for different number of channels with the maximum load of the graphics processor (GPU) and the minimum load of the Central processor (CPU).
Distributed Ledger Technology and the Future of Money and Banking

Abstract Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. https://bitcoin.org/bitcoin.pdf outlined an alternative to the current monetary system in which banks are replaced by a peer-to-peer system to issue and transfer digital money: the Bitcoin. While Bitcoin has attracted a substantial investment volume, the system has not achieved the status of a viable alternative monetary system. However, the distributed ledger technology (DLT) underlying the payment system is being applied successfully by financial institutions and is likely to have important implications for the future of money and banking. In this paper we therefore focus on the most advanced distributed ledger application in the financial industry: R3 Corda. This paper is structured as follows. In the first section, we relate the debate about systems of money creation to the rise of Bitcoin. Next, the development of R3 Corda is discussed and the lessons learned for monetary reform. We conclude with an assessment of the scope and likelihood of monetary reform as a consequence of DLT applications by central banks.
PFP Compressed Suffix Trees

Prefix-free parsing (PFP) was introduced by Boucher et al. (2019) as a preprocessing step to ease the computation of Burrows-Wheeler Transforms (BWTs) of genomic databases. Given a string S, it produces a dictionary D and a parse P of overlapping phrases such that BWT(S) can be computed from D and P in time and workspace bounded in terms of their combined size |PFP(S)|. In practice D and P are significantly smaller than S and computing BWT(S) from them is more efficient than computing it from S directly, at least when S is the concatenation of many genomes. In this paper, we consider PFP(S) as a data structure and show how it can be augmented to support full suffix tree functionality, still built and fitting within O(|PFP(S)|) space. This entails the efficient computation of various primitives to simulate the suffix tree: computing a longest common extension (LCE) of two positions in S; reading any cell of its suffix array (SA), of its inverse (ISA), of its BWT, and of its longest common prefix array (LCP); and computing minima over ranges and next/previous smaller value queries over the LCP. Our experimental results show that the PFP suffix tree can be efficiently constructed for very large repetitive datasets and that its operations perform competitively with other compressed suffix trees that can only handle much smaller datasets.
GIS-based multi-criteria analysis for nuclear power plant site selection in West Kalimantan

Abstract Nuclear power is set as the highest priority to slow global warming, so the construction of a nuclear power plant (NPP) using nuclear reactors as a source of heat energy is initiated to produce pollution-free electricity. Bengkayang and Mempawah Regencies in West Kalimantan provinces are selected as prospective NPP locations based on the exclusion and discretionary criteria. The exclusion criteria consist of the geological structure, types of bedrock, the existence and sufficiency of the cooling water, proximity to the population center, and terrain shape. Meanwhile, the discretionary criterion is proximity to the road network. This study aims to map and analyze the exclusion and discretionary criteria for potential NPP. The multi-criteria analysis in this study includes screening analysis, regional suitability analysis, and descriptive spatial analysis. It is performed using the Geographic Information Systems (GIS) technique on spatial data information. The screening process produced six potential locations to be used as NPP. Five locations are in Bengkayang Regency, precisely in Sungai Raya Kepulauan District and Sungai Raya District. Another location is in Mempawah Regency, precisely in Sungai Kunyit District.
Technological and Operational Mobile Learning Readiness of Secondary Teachers

Mobile learning is one of the novel directions in delivering the lesson due to the interest of the students towards mobile devices. Before it must be implemented in basic education, the readiness of internal stakeholders must be established first especially the teachers because they act as a middle component between the curriculum and the learners. This study focused on the technological and operational mobile learning readiness of secondary teachers, particularly English, Science, and Mathematics selected using stratified sampling. This was a descriptive design where the survey questionnaire was the research tool for collecting data. It was found out that secondary teachers were much ready in terms of accessibility and affordability of mobile learning. In terms of operational skills, secondary teachers were skilled in operating mobile devices. They were very much ready in reading documents and using mobile as calculators. They believed that mobile learning has a direct effect on learning and they were looking forward to immersing in mobile learning. However, they much need training and support from the institution both help desk and online.
Beyond Frequency

Utility-oriented mining which integrates utility theory and data mining is a useful tool for understanding economic consumer behavior. Traditional algorithms for mining high-utility patterns (HUPs) applies a single/uniform minimum high-utility threshold (minutil) to obtain the set of HUPs, but in some real-life circumstances, some specific products may bring lower utilities compared with others, but their profit may offer some vital information. However, if minutil is set high, the patterns with low minutil are missed; if minutil is set low, the number of patterns becomes unmanageable. In this paper, an efficient one-phase utility-oriented pattern mining algorithm, called HIMU, is proposed for mining HUPs with varied item-specific minimum utility. A novel tree structure called a multiple item utility set-enumeration tree (MIU-tree), the global sorted and the conditional downward closure properties are introduced in HIMU. In addition, we extended the compact utility-list structure to keep the necessary information, and thus this one-phase HIMU model greatly reduces the computational costs and memory requirements. Moreover, two pruning strategies are then extended to enhance the performance. We conducted extensive experiments in several synthetic and real-world datasets; the results indicates that the designed one-phase HIMU algorithm can address the "rare item problem" and has better performance than the state-of-the-art algorithms in terms of runtime, memory usage, and scalability. Furthermore, the enhanced algorithms outperform the non-optimized HIMU approach.