Reviewerly/exHarmony huggingface dataset:
Training Code-Switching Language Model with Monolingual Data

Lack of code-switching data is an issue of training codeswitching language model. In this paper, we propose an approach to train code-switching language models with monolingual data only. By constraining and normalizing output projection matrix in RNN based language model, we make the embeddings of different languages close to each other. With the numerical and visualized results, we show that the proposed approaches remarkably improve the code-switching language modeling trained from monolingual data. The proposed approaches are comparable or even better than training code-switching language model with artificially generated code-switching data. Furthermore, we use unsupervised bilingual word translation to analyze if semantically equivalent words in different languages are mapped together.
Optimal Resource Allocation of Cloud-Based Spark Applications

Nowadays, the big data paradigm is consolidating its central position in the industry, as well as in society at large. Lots of applications, across disparate domains, operate on huge amounts of data and offer great advantages both for business and research. According to analysts, cloud computing adoption is steadily increasing to support big data analyses and Spark is expected to take a prominent market position for the next decade. As big data applications gain more and more importance over time and given the dynamic nature of cloud resources, it is fundamental to develop an intelligent resource management system to provide Quality of Service guarantees to end-users. This article presents a set of run-time optimization-based resource management policies for advanced big data analytics. Users submit Spark applications characterized by a priority and by a hard or soft deadline. Optimization policies address two scenarios: i) identification of the minimum capacity to run a Spark application within the deadline; ii) re-balance of the cloud resources in case of heavy load, minimising the weighted soft deadline application tardiness. The solution relies on an initial non-linear programming model formulation and a search space exploration based on simulation-optimization procedures. Spark application execution times are estimated by relying on a gamut of techniques, including machine learning, approximated analyses, and simulation. The benefits of the approach are evaluated on Microsoft Azure HDInsight and on a private cloud cluster based on POWER8 by considering the TPC-DS industry benchmark and SparkBench. The results obtained in the first scenario demonstrate that the percentage error of the prediction of the optimal resource usage with respect to system measurement and exhaustive search is in the range 4&#x2013;29 percent while literature-based techniques present an average error in the range 6&#x2013;63 percent. Moreover, in the second scenario, the proposed algorithms can address complex problems like computing the optimal redistribution of resources among tens of applications in less than a minute with an error of 8 percent on average. On the same considered tests, literature-based approaches obtain an average error of about 57 percent.
A Connected Auto-Encoders Based Approach for Image Separation with Side Information: With Applications to Art Investigation

X-radiography is a widely used imaging technique in art investigation, whether to investigate the condition of a painting or provide insights into artists' techniques and working methods. In this paper, we propose a new architecture based on the use of `connected' auto-encoders in order to separate mixed X-ray images acquired from double-sided paintings, where in addition to the mixed X-ray image one can also exploit the two RGB images associated with the front and back of the painting. This proposed architecture uses convolutional auto-encoders that extract features from the RGB images that can be employed to (1) reproduce both of the original RGB images, (2) reconstruct the associated separated X-ray images, and (3) regenerate the mixed X-ray image. It operates in a totally self-supervised fashion without the need for examples containing both the mixed X-ray images and the separated ones. Based on images from the double-sided wing panels from the famous Ghent Altarpiece, painted in 1432 by the brothers Hubert and Jan Van Eyck, the proposed algorithm has been experimentally verified to outperform state-of-the-art X-ray separation methods in art investigation applications.
DNA Sequences Compression by GP² R and Selective Encryption Using Modified RSA Technique

Humans, by nature, have always been fascinated by the possibility of being able to acquire more information in minimum possible time and space. The effective lossless compression method, effective data structure, and DNA (Deoxyribonucleic Acid) data searching are quite essential as they provide a stimulus to easy accessibility and communication. The proposed algorithm is a new Lossless Compression algorithm, which compresses data, based on two tiers. Firstly, it searches for the exact Genetic Palindrome(GP), Palindrome(P) and Reverse(R)[GP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> R] and the substring is reported, which is replaced by the corresponding ASCII character creating a Library file. By using the ASCII code, the Library file acts as a signature as well as provides the security of data. Secondly, modified RSA technique is proposed for the selection encryption purpose. This selection encryption of the modified RSA technique is an approach to lessen computational resources for greatly sized DNA facts. The experimental work shows 44% to 45% original sequence is encrypted where above 95% of the original file is damaged by using this method. This technique can find out the 3.851273 bits per base of the compression rate. The O(n) is the complexity of this algorithm. The running time is a few seconds of this algorithm. This is a hybrid approach to the compression & encryption process. For reducing the compression rate, the first pass output is again compressed by the second pass but it is lossy, This experiment is performed on benchmark DNA order.
Comparative Study on Approaches of Recommendation Systems

In the current context, characterized by an information overload, also known as infobesity, it has become essential to design mechanisms that allow users to access what interests them as quickly as possible. Hence, recommender systems have emerged. This article then consists of examining and comparing the different existing recommendation approaches: those content-based filtering, those collaborative filtering, and finally the demographic and social approaches, while indicating, for each approach, the fields of application, some interesting examples, as well as its advantages and limitations. Then, we will indicate the hybridization techniques available to overcome these limitations.
The network approach to assess the structure of knowledge: Storage, distribution and retrieval as three measures in analysing concept maps

Abstract We present three new standardised network concept map (CM) measures that can provide unique information about learning‐related progress, which cannot be determined from previously known measures. Grounded in cognitive development theory on the one hand, and network theory on the other hand, our measures reveal how knowledge is stored, distributed and retrieved. We validated the new measures by testing their ability to discriminate between CMs of respondents with different levels of competency in statistics (students before and after taking an introductory statistics course and experts in the field of statistics). We also validated our measures against the most commonly used traditional and network measures. Based on a small sample of respondents, we show that two of the newly proposed compound measures reveal significant differences between experts and novices in the field, with higher values for experts, showing that expert knowledge is better distributed, more connected and balanced. More importantly, our measures were sensitive enough to show learning‐related progress for students, albeit statistically non‐significant, while common indicators from network theory did not demonstrate these small shifts. The validity of our new measures can be inferred from the consistency of the results from different sets of measures. Practitioner Notes What is already known about this topic A concept map (CM) is a knowledge assessment tool that can be used to evaluate student understanding of a topic and learning‐related progress. However, there has been a lack of standardised, complex and domain‐general CM metrics. Network theory has been shown to be useful in developing a standardised approach to CM evaluation. What this paper adds Based on the theory of cognitive development, we derived three standardised network measures of a CM: knowledge storage capacity, knowledge distributivity and knowledge retrieval. The new measures were empirically found to discriminate between groups of students with different levels of competency. Implications for practice and/or policy The new measures provide unique information about learning‐related progress. The newly developed measures enable educational researchers to make more objective evaluations of individual map elements, while simultaneously allowing for a holistic view of the quality of the knowledge structure of students.
Minimizing state access delay for cloud-native network functions

In the era of cloud services, there is a strong desire to improve the elasticity and reliability of applications in the cloud. The standard way of achieving these goals is to decouple the life-cycle of important application states from the life-cycle of individual application instances: states, and data in general, are written to and read from cloud databases, deployed close to the application code. The high performance requirements on the application impose strict latency limits on these storage solutions for state access. Cloud database instances are therefore distributed on multiple hosts in order to strive to ensure data locality for all functions. However, the shared nature of certain states, and the inevitable dynamics of the application workload necessarily lead to inter-host data access within the data center (or even across data centers, if the application requires a multi-data center setup). In order to minimize the inter-host communication due to state externalization, we propose an advanced cloud scheduling algorithm that places functions' states across the hosts of a data center. We create a model for the state placement with the aim of minimizing state access latency, and we prove that it is a complex problem. We therefore propose heuristics for fast and efficient placement methods and we evaluate those across realistic scenarios. We show that our approximations are close to the optimal placement, and in large-scale settings the algorithms take only a few minutes to yield good placement results.
An optimal trajectory planning algorithm for autonomous trucks: Architecture, algorithm, and experiment

Safe lane changing of the dynamic industrial park and port scenarios with autonomous trucks involves the problem of planning an effective and smooth trajectory. To solve this problem, we propose a new trajectory planning method based on the Dijkstra algorithm, which combines the Dijkstra algorithm with a cost function model and the Bezier curve. The cost function model is established to filter target trajectories to obtain the optimal target trajectory. The third-order Bezier curve is employed to smooth the optimal target trajectory. Road experiments are carried out with an autonomous truck to illustrate the effectiveness and smoothness of the proposed method. Compared with other conventional methods, the improved method can generate a more effective and smoother trajectory in the truck lane change.
Decentralized Distributed Blockchain Ledger for Financial Transaction Backup Data

Due to the fact that financial data stands a great risk of attack, several schemes are being deployed to ensure security and integrity of financial data. In considering the security of the financial transaction data, it is essential to consider a network that ensure that processing nodes would have copies of the financial accounting ledger so as to prevent complete when a section of the network fails. In this article we propose an application of a blockchain to financial transaction data backup mechanism over a decentralized network. By using a decentralized distributed blockchain ledger, each node can have a copy of the transaction data such that, failure in one node does not engender a total failure in transaction data. The system have been described and simulated using the element of account ledger which consist of credit and debit transaction accompanied by timestamps and transaction ID. To secure this information in the chain each block contains in addition to the account ledger information a hash of the previous block as well as the hash of the current block.
Easing Legal News Monitoring with Learning to Rank and BERT

While ranking approaches have made rapid advances in the Web search, systems that cater to the complex information needs in professional search tasks are not widely developed, common issues and solutions typically rely on dedicated search strategies backed by ad-hoc retrieval models. In this paper we present a legal search problem where professionals monitor news articles with constant queries on a periodic basis. Firstly, we demonstrate the effectiveness of using traditional retrieval models against the Boolean search of documents in chronological order. In an attempt to capture the complex information needs of users, a learning to rank approach is adopted with user specified relevance criteria as features. This approach, however, only achieves mediocre results compared to the traditional models. However, we find that by fine-tuning a contextualised language model (e.g. BERT), significantly improved retrieval performance can be achieved, providing a flexible solution to satisfying complex information needs without explicit feature engineering.
Privacy-Preserving Image Sharing Via Sparsifying Layers on Convolutional Groups

We propose a practical framework to address the problem of privacy-aware image sharing in large-scale setups. We argue that, while compactness is always desired at scale, this need is more severe when trying to furthermore protect the privacy-sensitive content. We therefore encode images, such that, from one hand, representations are stored in the public domain without paying the huge cost of privacy protection, but ambiguated and hence leaking no discernible content from the images, unless a combinatorially-expensive guessing mechanism is available for the attacker. From the other hand, authorized users are provided with very compact keys that can easily be kept secure. This can be used to disambiguate and reconstruct faithfully the corresponding access-granted images. We achieve this with a convolutional autoencoder of our design, where feature maps are passed independently through sparsifying transformations, providing multiple compact codes, each responsible for reconstructing different attributes of the image. The framework is tested on a large-scale database of images with public implementation available.
Towards High-Performance Object Detection: Task-Specific Design Considering Classification and Localization Separation

Object detection performs two tasks (classification and localization) simultaneously. Two tasks share a similarity: they need robust features that effectively represent the visual appearance of the objects. However, two tasks also have different properties. First, classification mainly requires features from discriminative parts of an object to determine the object category, whereas localization mainly requires features from the entire object regions for localizing by drawing a bounding box. Second, classification has a translation invariant property, whereas localization has a translation variant property. In order to increase the efficiency of object detection, it is necessary to design a network in consideration of the commonalities and differences of two tasks. In this work, we simply modified layers of the existing object detection networks into three parts by considering such characteristics: lower-layer feature sharing part, layer separation part, and feature fusion part. As a result, the performance of the proposed method was noticeably improved by properly sharing, separating, and fusing layers of the existing object detection networks.
Fast Clustering With Co-Clustering Via Discrete Non-Negative Matrix Factorization for Image Identification

How to effectively cluster large-scale image data sets is a challenge and is receiving more and more attention. To address this problem, a novel clustering method called fast clustering with co-clustering via discrete non-negative matrix factorization, is proposed. Inspired by co-clustering, our algorithm reduces computational complexity by transforming clustering tasks into co-clustering tasks. Although our model has the same form of objective function as normalized cut, we relax it to a matrix decomposition problem, which is different from most graph-based approaches. In addition, an efficient optimization algorithm is proposed to solve the relaxed problem, where a discrete solution corresponding to the clustering result can be directly obtained. Extensive experiments have been conducted on several synthetic data sets and real word data sets. Compared with the state-of-the-art clustering methods, the proposed algorithm achieves very promising performance.
Multi-MotifGAN (MMGAN): Motif-Targeted Graph Generation And Prediction

Generative graph models create instances of graphs that mimic the properties of real-world networks. Generative models are successful at retaining pairwise associations in the underlying networks but often fail to capture higher-order connectivity patterns known as network motifs. Different types of graphs contain different network motifs, an example of which are triangles that often arise in social and biological networks. It is hence vital to capture these higher-order structures to simulate real-world networks accurately. We propose Multi-MotifGAN (MMGAN), a motif-targeted Generative Adversarial Network (GAN) that generalizes the benchmark NetGAN approach. The generalization consists of combining multiple biased random walks, each of which captures a different motif structure. MMGAN outperforms NetGAN at creating new graphs that accurately reflect the network motif statistics of input graphs such as Citeseer, Cora and Facebook.
Adaptive learning of aggregate analytics under dynamic workloads

Large organizations have seamlessly incorporated data-driven decision making in their operations. However, as data volumes increase, expensive big data infrastructures are called to rescue. In this setting, analytics tasks become very costly in terms of query response time, resource consumption, and money in cloud deployments, especially when base data are stored across geographically distributed data centers. Therefore, we introduce an adaptive, reciprocity-based Machine Learning mechanism which is light-weight, stored client-side, can estimate the answers of a variety of aggregate queries and can avoid the big data back-end. The estimations are performed in milliseconds are inexpensive and accurate as the mechanism learns from past analytical-query patterns. However, as analytic queries are ad hoc and analysts' interests change over time we develop solutions that can swiftly and accurately detect such changes and adapt to new query patterns. The capabilities of our approach are demonstrated using extensive evaluation with real and synthetic datasets.
A Complete Study of Methodology of Hand Gesture Recognition System for Smart Homes

Gesture Recognition is the technique that permits human to bond with the system and interconnect naturally without the use of any mechanical amenities. Gestures can be procured from movement or state but ordinarily emanates from the face or hand. Current field includes hand gesture recognition technique which is useful for processing human information which is not fetched through speech. Computer vision and image processing can be used to regulate gesture recognition. Real-time vision-based hand gesture recognition is considered to be more attainable for Human Computer Interaction (HCI) with the use of latest evolutions in the area of technology vision and pattern determination. The incorporation of the technique of Hand Motion Acknowledgment in the control of Smart Home appliances is genuinely effective. This paper introduces a Hand Gesture Recognition System used to control shrewd home machines utilizing hand developments utilizing Hidden Markov Model Algorithm and Microsoft Kinect Sensor.
Small Energy Masking for Improved Neural Network Training for End-To-End Speech Recognition

In this paper, we present a Small Energy Masking (SEM) algorithm, which masks inputs having values below a certain threshold. More specifically, a time-frequency bin is masked if the filterbank energy in this bin is less than a certain energy threshold. A uniform distribution is employed to randomly generate the ratio of this energy threshold to the peak filterbank energy of each utterance in decibels. The unmasked feature elements are scaled so that the total sum of the feature values remain the same through this masking procedure. This very simple algorithm shows relatively 11.2% and 13.5% Word Error Rate (WER) improvements on the standard LibriSpeech test-clean and test-other sets over the baseline end-to-end speech recognition system. Additionally, compared to the input dropout algorithm, SEM algorithm shows relatively 7.7% and 11.6% improvements on the same LibriSpeech test-clean and test-other sets. With a modified shallow-fusion technique with a Transformer LM, we obtained a 2.62% WER on the Lib-riSpeech test-clean set and a 7.87% WER on the LibriSpeech test-other set.
Attentional Fused Temporal Transformation Network for Video Action Recognition

Effective spatiotemporal feature representation is crucial to the video-based action recognition task. Focusing on discriminate spatiotemporal feature learning, we propose Attentional Fused Temporal Transformation Network (AttnTTN) for action recognition on top of popular Temporal Segment Network (TSN) framework. In the network, Attentional Fusion Module (AttnFM) is designed to fuse the appearance and motion features at multiple ConvNet levels for each video snippet, forming a short-term video descriptor. With fused features as inputs, Temporal Transformation Networks (TTN) are employed to model middle-term temporal transformation between the neighboring temporal snippets following a sequential order. AttnTTN achieves the state-of-the-art results on two most popular action recognition datasets: UCF101 and HMDB51.
Attention-Based Curiosity in Multi-Agent Reinforcement Learning Environments

Several paradigms exist in Reinforcement Learning to improve the exploration capabilities of agents, among which the curiosity-driven approach is followed in this work. Extending previous work that utilizes attention to make curiosity state-and action-selective, we expand the range of experiments by introducing two multi-agent environments. The first one is for robot soccer, the second one features a driving scenario in urban settings. Moreover, as during training the different number of observations must be matched between multiple time-steps, we propose an attention-based approach, called Recurrent Temporal Attention (RTA) to do this. The corresponding implementation can be found at https://github.com/szemenyeim/DynEnv.