Miracle-dz/UniFAR-FTUs huggingface dataset:
Uptake of Australia’s Health Star Rating System

In June 2014, Australia and New Zealand adopted a voluntary front-of-pack nutrition labelling scheme in the form of the Health Star Rating (HSR) system. Our aim was to assess its uptake in Australia while a formal five-year review of the system is underway. Numbers and proportions of products eligible to carry a HSR were recorded each year between 2014 and 2017 as part of an annual survey of four large Australian retail outlets. Mean HSR values were determined for products that were and were not labelled with a HSR logo, and summary data presented overall, by HSR score, by major food category, and for leading manufacturers. Results show that uptake is increasing: HSR appeared on 4348/15,767 (28%) of eligible products in 2017 and has now appeared on 7922 products since implementation. Of those products displaying a HSR logo, more than three-quarters (76.4%) displayed a HSR of ≥3.0. Products displaying a HSR logo had a higher mean HSR (3.4), compared to products not displaying a HSR logo (2.7). Uptake was highest on convenience foods (44%), cereals (36.7%), and fruit and vegetable products (35.9%). More than 100 manufacturers were using the system, but retailers Coles, Woolworths and Aldi were together responsible for 54% of uptake. For all except Coles, Woolworths and Campbell Arnott’s, the mean HSR of products displaying a logo on pack was higher than products made by that manufacturer not showing a HSR logo. We conclude that to ensure the consistent and widespread uptake required for consumers to make informed food purchases, HSR should be made mandatory at the conclusion of the five-year review.
Exploration of Assistive Technologies Used by People with Quadriplegia Caused by Degenerative Neurological Diseases

ABSTRACT Various assistive devices and interfaces to access the computer have been developed for people with severe motor impairments. This article explores how effective these technologies are for individuals with quadriplegia caused by degenerative neurological diseases. The following questions are studied: (1) What activities are performed? (2) What tools are used? (3) What are the advantages and limitations of the tools? (4) How do users learn about and choose assistive technologies? (5) Why are some technologies abandoned? Results of a qualitative study with 15 participants indicate that study participants have strong needs for efficient text entry and communication that are not met. A lack of information about technology options limits the choices of several of the study participants. The study revealed that automated interface personalization and adaptation to disease progression should be important design goals for future assistive technologies that support users with quadriplegia caused by degenerative neurological diseases.
Improving Neural Sequence Labelling Using Additional Linguistic Information

Sequence labelling is the task of assigning categorical labels to a data sequence. In Natural Language Processing, sequence labelling can be applied to various fundamental problems, such as Part of Speech (POS) tagging, Named Entity Recognition (NER), and Chunking. In this study, we propose a method to adding various linguistic features to the neural sequence framework to improve sequence labelling. Besides word level knowledge, sense embeddings are added to provide semantic information. Additionally, selective readings of character embeddings are added to capture contextual as well as morphological features for each word in a sentence. Compared to previous methods, these added linguistic features allow us to design a more concise model and perform more efficient training. Our proposed architecture achieves state of the art results on the benchmark datasets of POS, NER, and chunking. Moreover, the convergence rate of our model is significantly better than the previous state of the art models.
Recovering a hidden community beyond the Kesten–Stigum threshold in O(|E|log*|V|) time

Abstract Community detection is considered for a stochastic block model graph of n vertices, with K vertices in the planted community, edge probability p for pairs of vertices both in the community, and edge probability q for other pairs of vertices. The main focus of the paper is on weak recovery of the community based on the graph G, with o(K) misclassified vertices on average, in the sublinear regime n1-o(1) ≤ K ≤ o(n). A critical parameter is the effective signal-to-noise ratio λ = K2(p - q)2 / ((n - K)q), with λ = 1 corresponding to the Kesten–Stigum threshold. We show that a belief propagation (BP) algorithm achieves weak recovery if λ > 1 / e, beyond the Kesten–Stigum threshold by a factor of 1 / e. The BP algorithm only needs to run for log*n + O(1) iterations, with the total time complexity O(|E|log*n), where log*n is the iterated logarithm of n. Conversely, if λ ≤ 1 / e, no local algorithm can asymptotically outperform trivial random guessing. Furthermore, a linear message-passing algorithm that corresponds to applying a power iteration to the nonbacktracking matrix of the graph is shown to attain weak recovery if and only if λ > 1. In addition, the BP algorithm can be combined with a linear-time voting procedure to achieve the information limit of exact recovery (correctly classify all vertices with high probability) for all K ≥ (n / logn) (ρBP + o(1)), where ρBP is a function of p / q.
Assessment of Binding Interaction between Bovine Lactoferrin and Tetracycline Hydrochloride: Multi-Spectroscopic Analyses and Molecular Modeling

In this paper, the interaction between bovine lactoferrin (bLf) and tetracycline hydrochloride (TCH) was researched by microscale thermophoresis (MST), multi-spectroscopic methods, and molecular docking techniques. Normal fluorescence results showed that TCH effectively quenched the intrinsic fluorescence of bLf via static quenching. Moreover, MST confirmed that the combination force between bLf and TCH was very strong. Thermodynamic parameters and molecular docking further revealed that electrostatic forces, van der Waals, and hydrogen bonding forces played vital roles in the interaction between bLf and TCH. The binding distance and energy transfer efficiency between TCH and bLf were 2.81 nm and 0.053, respectively. Moreover, the results of circular dichroism spectra (CD), ultraviolet visible (UV-vis) absorption spectra, fluorescence Excitation-Emission Matrix (EEM) spectra, and molecular docking verified bLf indeed combined with TCH, and caused the changes of conformation of bLf. The influence of TCH on the functional changes of the protein was studied through the analysis of the change of the bLf surface hydrophobicity and research of the binding forces between bLf and iron ion. These results indicated that change in the structure and function of bLf were due to the interaction between bLf and TCH.
FreeDA: deploying incompatible stock dynamic analyses in production via multi-version execution

Dynamic analyses such as those implemented by compiler sanitizers and Valgrind are effective at finding and diagnosing challenging bugs and security vulnerabilities. However, most analyses cannot be combined on the same program execution, and they incur a high overhead, which typically prevents them from being used in production. This paper addresses the ambitious goal of running concurrently multiple incompatible stock dynamic analysis tools in production, without requiring any modifications to the tools themselves or adding significant runtime overhead to the deployed system. This is accomplished using multi-version execution, in which the dynamic analyses are run concurrently with the native version, all on the same program execution. We implement our approach in a system called. FreeDA and show that it is applicable to several common scenarios, involving network servers and interactive applications. In particular, we show how incompatible stock dynamic analyses implemented by Clang's sanitizers and Valgrind can be used to check high-performance servers such as Memcached, Nginx and Redis, and interactive applications such as Git, HTop and OpenSSH.
NextGen AML: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation

Most of the current anti money laundering (AML) systems, using handcrafted rules, are heavily reliant on existing structured databases, which are not capable of effectively and efficiently identifying hidden and complex ML activities, especially those with dynamic and time-varying characteristics, resulting in a high percentage of false positives. Therefore, analysts are engaged for further investigation which significantly increases human capital cost and processing time. To alleviate these issues, this paper presents a novel framework for the next generation AML by applying and visualizing deep learning-driven natural language processing (NLP) technologies in a distributed and scalable manner to augment AML monitoring and investigation. The proposed distributed framework performs news and tweet sentiment analysis, entity recognition, relation extraction, entity linking and link analysis on different data sources (e.g. news articles and tweets) to provide additional evidence to human investigators for final decision-making. Each NLP module is evaluated on a task-specific data set, and the overall experiments are performed on synthetic and real-world datasets. Feedback from AML practitioners suggests that our system can reduce approximately 30% time and cost compared to their previous manual approaches of AML investigation.
Angled local directional pattern for texture analysis with an application to facial expression recognition

Local binary pattern (LBP) is currently one of the most common feature extraction methods used for texture analysis. However, LBP suffers from random noise, because it depends on image intensity. Recently, a more stable feature method was introduced, local directional pattern (LDP) uses the gradient space instead of the pixel intensity. Typically, LDP generates a code based on the edge response values using Kirsch masks. Yet, despite the great achievement of LDP, it has two drawbacks. The first is the static choice of the number of most significant bits used for LDP code generation. Second, the original LDP method uses the 8-neighborhood to compute the LDP code, and the value of the centre pixel is ignored. This study presents angled local directional pattern (ALDP), which is an improved version of LDP, for texture analysis. Experimental results on two different texture data sets, using six different classifiers, show that ALDP substantially outperforms both LDP and LBP methods. The ALDP has been evaluated to recognise the facial expressions emotion. Results indicate a very high recognition rate for the proposed method. An added advantage is that ALDP has an adaptive approach for the selection of the number significant bits as opposed to LDP.
Unsupervised Domain Adaptation by Adversarial Learning for Robust Speech Recognition

In this paper, we investigate the use of adversarial learning for unsupervised adaptation to unseen recording conditions, more specifically, single microphone far-field speech. We adapt neural networks based acoustic models trained with close-talk clean speech to the new recording conditions using untranscribed adaptation data. Our experimental results on Italian SPEECON data set show that our proposed method achieves 19.8% relative word error rate (WER) reduction compared to the unadapted models. Furthermore, this adaptation method is beneficial even when performed on data from another language (i.e. French) giving 12.6% relative WER reduction.
Adaptive Process Migrations in Coupled Applications for Exchanging Data in Local File Cache

Many problems in science and engineering are usually emulated as a set of mutually interacting models, resulting in a coupled or multiphysics application. These component models show challenges originating from their interdisciplinary nature and from their computational and algorithmic complexities. In general, these models are independently developed and maintained, so that they commonly employ the global file system for exchanging their data in the coupled application. To effectively use the local file cache on the compute node for exchanging the data among the processes of such applications, and consequently boosting I/O performance, this article presents a novel mechanism to migrate a process from one compute node to another node on the basis of block I/O dependency. In this newly proposed mechanism, the block I/O dependency between two involved processes running on the different nodes is profiled as block access similarity by taking advantage of the Cohen’s kappa statistic. Then, the process is supposed to be dynamically migrated from its source node to the destination node, on which there is another process having heavy block I/O dependency. As a result, both processes can exchange their data by utilizing the local file cache instead of the global file system to reduce I/O time. The experimental results demonstrate that the I/O performance can be significantly improved, and the time required for executing the application can be resultantly decreased, as expected.
Real-Time Data Incentives for IoT Searches

Effectively collecting real-time data is a fundamental problem in IoT (Internet of Things) searches. In the IoT, most data are linked with the owner's private information and cannot be publicly released on the Internet. This invalidates the use of crawlers to collect data in IoT searches. As a result, effectively motivating potential data providers (PDPs) to provide real-time on demand data becomes a key requirement for the development of an IoT search service. To address this problem, we acknowledge the realistic assumption of incomplete information, and propose a buyout-auction framework, with the constraint of QoD (Quality of Data), to collect real-time data and maximize bidders' payoff. Simulation results demonstrate that our approach can drive PDPs to participate in bidding in a timely manner and provide data under the constraints of QoD to IoT search service providers.
User Interface Agents for Guiding Interaction with Augmented Virtual Mirrors

This research investigates using user interface (UI) agents for guiding gesture based interaction with Augmented Virtual Mirrors. Compared to prior work in gesture interaction, where graphical symbols are used for guiding user interaction, we propose using UI agents. We explore two approaches for using UI agents: 1) using a UI agent as a delayed cursor and 2) using a UI agent as an interactive button. We conducted two user studies to evaluate the proposed designs. The results from the user studies show that UI agents are effective for guiding user interactions in a similar way as a traditional graphical user interface providing visual cues, while they are useful in emotionally engaging with users.
Social-Aware Multicast Incentive Mechanism for Mobile Data Offloading

Date offloading is an efficient way to address the mobile traffic congestion by redirecting some traffic of a cellular network to complimentary networks. Meanwhile, multicast applications (e.g., video conference, IPTV, and the bulk software upgrade) are very popular. However, few of existing work investigates incentive mechanism design for multicast applications in mobile data offloading. In this paper, we focus on the multicast incentive mechanism design and propose a social-aware multicast incentive mechanism (SAMIM) for data offloading, where the cellular operator will offload the data of a multicast group to some WiFi access points (APs), then these APs serve mobile users (MUs) within their coverage area through multicast transmission. Numerical simulations demonstrate the good performance of SAMIM by comparing with IM-only. In specific, SAMIM improves the system social welfare by $20\%$ compared with IM-only with the number of selected APs $|\mathcal{W}| = 15$.
An Improved and More Scalable Evolutionary Approach to Multiobjective Clustering

The multiobjective realization of the data clustering problem has shown great promise in recent years, yielding clear conceptual advantages over the more conventional, single-objective approach. Evolutionary algorithms have largely contributed to the development of this increasingly active research area on multiobjective clustering. Nevertheless, the unprecedented volumes of data seen widely today pose significant challenges and highlight the need for more effective and scalable tools for exploratory data analysis. This paper proposes an improved version of the multiobjective clustering with automatic ${k}$ -determination algorithm. Our new algorithm improves its predecessor in several respects, but the key changes are related to the use of an efficient, specialized initialization routine and two alternative reduced-length representations. These design components exploit information from the minimum spanning tree and redefine the problem in terms of the most relevant subset of its edges. This paper reveals that both the new initialization routine and the new solution representations not only contribute to decrease the computational overhead, but also entail a significant reduction of the search space, enhancing therefore the convergence capabilities and overall effectiveness of the method. These results suggest that the new algorithm proposed here will offer significant advantages in the realm of “big data” analytics and applications.
Creating a secure index for distributed data on the sensor network

Wireless sensor networks (WSNs) deployed in public and untrusted environments are prone to various security vulnerabilities. In this paper, a method has been developed to reduce the security risk of a node from tampering. The sensed data is distributed using the roots of the polynomial equation, thereby reducing the security risk. We propose a security mechanism that works on a clustered heterogeneous network. The index, located on the cluster head, is used for efficient retrieval of data. The scheme provides end-to-end confidentiality. The encrypted data can be decrypted only at the base station and it offers single-value granularity. It provides data confidentiality, authentication and authorisation. We have analysed the performance of the algorithm in terms of energy consumption. The energy consumption has increased with the addition of security. Even though the energy consumption has increased, the network can fulfil the purpose of data collection and can be deployed on large-scale networks.
Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines

Asynchronous parallel stochastic gradient optimization has been playing a pivotal role to solve large-scale machine learning problems in big data applications. Zeroth-order (derivative-free) methods estimate the gradient only by two function evaluations, thus have been applied to solve the problems where the explicit gradient calculations are computationally expensive or infeasible. Recently, the first asynchronous parallel stochastic zeroth-order algorithm (AsySZO) was proposed. However, its convergence rate is O( 1 √ T ) for the smooth, possibly non-convex learning problems, which is significantly slower than O( 1 T ) the best convergence rate of (asynchronous) stochastic gradient algorithm. To fill this gap, in this paper, we first point out the fundamental reason leading to the slow convergence rate of AsySZO, and then propose a new asynchronous stochastic zerothorder algorithm (AsySZO+). We provide a faster convergence rate O( 1 bT ) (b is the mini-batch size) for AsySZO+ by the rigorous theoretical analysis, which is a significant improvement over O( 1 √ T ). The experimental results on the application of ensemble learning confirm that our AsySZO+ has a faster convergence rate than the existing (asynchronous) stochastic zeroth-order algorithms.
Congressional twitter use revisited on the platform's 10‐year anniversary

The microblogging platform, Twitter, has been an extremely valuable tool for politicians in sharing information, fostering broader communication to constituents, and promoting their political stances. This article follows up on previous research from 2009 on this subject. We reexamined tweets from the US Congress collected in early 2017. We found Congressional tweeting habits and content have changed very little in the last 8 years. Overall, they tended to use Twitter to pass along political information and links in addition to reporting on official and unofficial activities and meetings. We discuss future spaces for research that go beyond content analysis into issues of motivation, communication, and impact.
ABE-Cities: An Attribute-Based Encryption System for Smart Cities

In the near future, a technological revolution will involve our cities, where a variety of smart services based on the Internet of Things will be developed to facilitate the needs of the citizens. Sensing devices are already being deployed in urban environments, and they will generate huge amounts of data. Such data are typically outsourced to some cloud storage because this lowers capital and operating expenses and guarantees high availability. However, cloud storage may have incentives to release stored data to unauthorized entities. In this work we present ABE-Cities, an encryption scheme for urban sensing which solves the above problems while ensuring fine-grained access control on data by means of Attribute-Based Encryption (ABE). Basically, ABE-Cities encrypts data before storing it in the cloud and provides users with keys able to decrypt only those portions of data the user is authorized to access. In ABE-Cities, the sensing devices perform only lightweight symmetric cryptography operations, thus they can also be resource-constrained. ABE-Cities provides planned expiration of keys, as well as their unplanned revocation. We propose methods to make the key revocation efficient, and we show by simulations the overall efficiency of ABE-Cities.
MPPM Aided Dimmable Reverse Polarity Optical OFDM for Visible Light Communication

In this paper, we propose a multiple pulse position modulation (MPPM) aided reverse polarity optical orthogonal frequency division multiplexing (RPO- OFDM) scheme that aims to enhance the spectral efficiency (SE) for dimmable visible light communication (VLC) systems. In this scheme, we exploit the MPPM patterns to transmit the extra information bits without the increase of the signal bandwidth, and thus improving the SE. Specifically, for each frame, the binary information stream is divided into two parallel groups, where the first group is used to select the patterns of MPPM symbols, while the other is modulated by optical OFDM. Then the time-domain OFDM signal is accommodated by both the 'on' and 'off' slots of MPPM symbols for transmissions over the VLC channel. Theoretical analysis on the frame error probability (FEP) is provided and validated by simulation results, showing that the proposed system with extended dimming control achieves the highest effective spectral efficiency (ESE) in most of the scenarios compared with a few reference systems, without substantially increasing the computational complexity.