Reviewerly/exHarmony huggingface dataset:
Supervised Hebb rule based feature selection for text classification

Text documents usually contain high dimensional non-discriminative (irrelevant and noisy) terms which lead to steep computational costs and poor learning performance of text classification. One of the effective solutions for this problem is feature selection which aims to identify discriminative terms from text data. This paper proposes a method termed "Hebb rule based feature selection (HRFS)". HRFS is based on supervised Hebb rule and assumes that terms and classes are neurons and select terms under the assumption that a term is discriminative if it keeps "exciting" the corresponding classes. This assumption can be explained as "a term is highly correlated with a class if it is able to keep "exciting" the class according to the original Hebb postulate. Six benchmarking datasets are used to compare HRFS with other seven feature selection methods. Experimental results indicate that HRFS is effective to achieve better performance than the compared methods. HRFS can identify discriminative terms in the view of synapse between neurons. Moreover, HRFS is also efficient because it can be described in the view of matrix operation to decrease complexity of feature selection.
Face-Based Multiple User Active Authentication on Mobile Devices

Multiple user active authentications, in contrast with the single user active authentication, require the verification of identity of multiple subjects. Both traditional verification and identification-based solutions fail to address the specific challenges presented in this problem. We introduce Extremal Openset Rejection, a two-fold mechanism with a sparse representation-based identification step and a verification step for this purpose. In the verification step, concentration of the sparsity vector and the overlap between matched and non-matched distributions are considered for decision making. We introduce a semi-parametric model based on Extreme Value Theory for modeling the distributions, and an algorithm to estimate the parameters of extreme value distributions. Effectiveness of the proposed method is demonstrated using three publicly available face-based mobile active authentication data sets.
A recommender system for component-based applications using machine learning techniques

Software designers are striving to create software that adapts to their users’ requirements. To this end, the development of component-based interfaces that users can compound and customize according to their needs is increasing. However, the success of these applications is highly dependent on the users’ ability to locate the components useful for them, because there are often too many to choose from. We propose an approach to address the problem of suggesting the most suitable components for each user at each moment, by creating a recommender system using intelligent data analysis methods. Once we have gathered the interaction data and built a dataset, we address the problem of transforming an original dataset from a real component-based application to an optimized dataset to apply machine learning algorithms through the application of feature engineering techniques and feature selection methods. Moreover, many aspects, such as contextual information, the use of the application across several devices with many forms of interaction, or the passage of time (components are added or removed over time), are taken into consideration. Once the dataset is optimized, several machine learning algorithms are applied to create recommendation systems. A series of experiments that create recommendation models are conducted applying several machine learning algorithms to the optimized dataset (before and after applying feature selection methods) to determine which recommender model obtains a higher accuracy. Thus, through the deployment of the recommendation system that has better results, the likelihood of success of a component-based application is increased by allowing users to find the most suitable components for them, enhancing their user experience and the application engagement.
Touch gesture-based authentication on mobile devices: The effects of user posture, device size, configuration, and inter-session variability

Touch dynamics is a behavioral biometric that authenticates users by analyzing the characteristics of the touch gestures executed on mobile devices. Current research in this field has mostly focused on identifying the best algorithms and attributes to improve authentication performance. However, such systems must also be resilient against environmental variables. In this paper, we demonstrate that the user’s posture, device size and configuration have a significant impact on the performance of touch-based authentication systems. Our results indicate that authentication accuracy increases with the device size. Furthermore, we conclude that using a device’s 3-D orientation is necessary to attain better authentication performance. Our findings indicate that the features used in state-of-the-art touch-based authentication systems are insufficient to provide constant, reliable performance when any of the studied environmental variables change. With this paper, we release a new data set. Unlike the currently publicly available touch-based authentication datasets, our collection protocols control for all the studied variables. Our research study demonstrates threats to validity that noisy environmental conditions introduce to these currently available public datasets. This work is an extension of a previous publication. Presented user authentication approaches are unique and may have immediate benefits to the development of better touch-based authentication systems.
Preventing rather than punishing: An early warning model of malfeasance in public procurement

Is it possible to predict malfeasance in public procurement? With the proliferation of e-procurement systems in the public sector, anti-corruption agencies and watchdog organizations have access to valuable sources of information with which to identify transactions that are likely to become troublesome and why. In this article, we discuss the promises and challenges of using machine learning models to predict inefficiency and corruption in public procurement. We illustrate this approach with a dataset with more than two million public procurement contracts in Colombia. We trained machine learning models to predict which of them will result in corruption investigations, a breach of contract, or implementation inefficiencies. We then discuss how our models can help practitioners better understand the drivers of corruption and inefficiency in public procurement. Our approach will be useful to governments interested in exploiting large administrative datasets to improve the provision of public goods, and it highlights some of the tradeoffs and challenges that they might face throughout this process.
TruGRC: Trust-Aware Group Recommendation with Virtual Coordinators

In recent years, an increase in group activities on websites has led to greater demand for highly-functional group recommender systems. The goal of group recommendation is to capture and distill the preferences of each group member into a single recommendation list that meets the needs of all group members. Existing aggregation functions perform well in harmonious and congruent scenarios, but tend not to generate satisfactory results when group members hold conflicting preferences. Moreover, most of current studies improve group recommendation only based on a single aggregation strategy and explicit trust information is still ignored in group recommender systems. Motivated by these concerns, this paper presents TruGRC, a novel Trust-aware Group Recommendation method with virtual Coordinators, that combines two different aggregation strategies: result aggregation and profile aggregation. As each individual’s preferences are modeled, a virtual user is built as a coordinator to represent the profile aggregation strategy. This coordinator provides a global view of the preferences for all group members by interacting with each user to resolve conflicting preferences. Then, we also model the impact from group members to the virtual coordinator in accordance with personal social influence inferred by trust information on social networks. Group preferences can be easily generated by the average aggregation method under the effect of the virtual coordinator. Experimental results on two benchmark datasets with a range of different group sizes show that TruGRC method has significant improvements compared to other state-of-the-art methods.
AnswerAuth: A bimodal behavioral biometric-based user authentication scheme for smartphones

In this paper, we present a behavioral biometric-based smartphone user authentication mechanism, namely, AnswerAuth, which relies on the very common users’ behavior. Behavior, here, refers to the way a user slides the lock button on the screen, to unlock the phone, and brings the phone towards her ear. The authentication mechanism works with the biometric behavior based on the extracted features from the data recorded using the built-in smartphone sensors, i.e., accelerometer, gyroscope, gravity, magnetometer and touchscreen, while the user performed sliding and phone-lifting actions. We tested AnswerAuth on a dataset of 10,200 behavioral patterns collected from 85 users while they performed the unlocking actions, in sitting, standing, and walking postures, using six state-of-the-art conceptually different machine learning classifiers in two settings, i.e., with and without simultaneous feature selection and classification. Among all the chosen classifiers, Random Forest (RF) classifier proved to be the most consistent and accurate classifier on both full and reduced features and provided a True Acceptance Rate (TAR) as high as 99.35%. We prototype proof-of-the-concept Android app, based on our findings, and evaluate it in terms of security and usability. Security analysis of AnswerAuth confirms its robustness against the possible mimicry attacks. Similarly, the usability study based on Software Usability Scale (SUS)1 questionnaire verifies the user-friendliness of the proposed scheme (SUS Score of 75.11). Experimental results prove AnswerAuth as a secure and usable authentication mechanism.
Weakly Supervised Human Activity Recognition From Wearable Sensors by Recurrent Attention Learning

Traditional methods of human activity recognition from wearable sensors rely on good training datasets in which thousands of training sequences should be carefully labeled. However, unlike images or videos which can be easily classified by human beings, strictly labeling such sequences of sensor data needs much more manpower and computing resources. In this paper, we present a new weakly supervised human activity recognition model based on recurrent attention learning, in which an agent is trained to extract information from weakly labeled sensor data by adaptively selecting a sequence of locations. Since, the model is non-differentiable and multiple activities may occur in a sequence of sensor data, it is trained by reinforcement learning with novel reward strategies. We evaluated our model on the traditional UCI HAR dataset and our collected weakly labeled dataset. The experimental results show that our model is superior to the traditional CNN model and the DeepConvLSTM model on both datasets.
Anomaly Detection in Streaming Nonstationary Temporal Data

This article proposes a framework that provides early detection of anomalous series within a large collection of nonstationary streaming time-series data. We define an anomaly as an observation, that is, very unlikely given the recent distribution of a given system. The proposed framework first calculates a boundary for the system's typical behavior using extreme value theory. Then a sliding window is used to test for anomalous series within a newly arrived collection of series. The model uses time series features as inputs, and a density-based comparison to detect any significant changes in the distribution of the features. Using various synthetic and real world datasets, we demonstrate the wide applicability and usefulness of our proposed framework. We show that the proposed algorithm can work well in the presence of noisy nonstationarity data within multiple classes of time series. This framework is implemented in the open source R package oddstream. R code and data are available in the online supplementary materials.
Fraud detection for financial statements of business groups

Investors rely on companies' financial statements and economic data to inform their investment decisions. However, many businesses manipulate financial statements to raise more capital from investors and financial institutions, which reduces the practicality of financial statements. The modern business environment is highly information-oriented, and firms' information systems and activities are complex and dynamic. Technology for avoiding fraud detection is continually updated. Recent studies have focused on detecting financial statement fraud within a single business, but not within a business group. Development of methods for using diverse data to detect financial statement fraud in business groups is thus a high priority in the advancement of fraud detection. This study develops an approach for detecting fraud in the financial statements of business groups. The proposed approach is applied to reduce investment losses and risks and enhance investment benefits for investors and creditors. The study objectives are achieved through the following steps: (i) design of a process for detecting fraud in the financial statements of business groups, (ii) development of fraud detection techniques for use with such statements, and (iii) demonstration and evaluation of the proposed approach.
A Robust and Rapid Camera Calibration Method by One Captured Image

A robust and rapid camera calibration method is essential in vision measurement. This paper proposes a camera calibration method using a 2.5-D coding target that requires just a single captured image and is capable of identifying the intrinsic and extrinsic parameters of a camera system simultaneously. The calibration target consists of four fixed planes to which coded patterns are attached. The fixed planes of the target prevent the ill condition that arises in Zhang's method, and they provide four different homography matrices at the same time. The extrinsic parameters of the cameras are calculated using the geometric constraints of the feature circles on each plane. The coded pattern allows the four planes to be distinguished automatically. The proposed method is first verified by comparison experiments with three classical calibration methods. The results confirm that the proposed method has the greatest accuracy and smallest variance of errors. The influences on the calibration results of geometric parameters such as the dihedral angle and the area ratio of the feature circles are then investigated by systematic experiments. This proposed camera calibration method using a single captured image represents a breakthrough in a camera calibration technology and has a broad potential for application to machine vision and vision measurement.
A Service-Oriented Perspective on Blockchain Smart Contracts

Smart contracts turn blockchains into distributed computing platforms. This paper studies whether smart contracts as implemented by a state-of-the-art blockchain technology may serve as a component technology for a computing paradigm like service-oriented computing in the blockchain, in order to foster reuse and increase cost-effectiveness.
A novel statistical approach for clustering positive data based on finite inverted Beta-Liouville mixture models

Nowadays, a great number of positive data has been occurred naturally in many applications, however, it was not adequately analyzed. In this article, we propose a novel statistical approach for clustering multivariate positive data. Our approach is based on a finite mixture model of inverted Beta-Liouville (IBL) distributions, which is proper choice for modeling and analysis of positive vector data. We develop two different approaches to learn the proposed mixture model. Firstly, the maximum likelihood (ML) is utilized to estimate parameters of the finite inverted Beta-Liouville mixture model in which the right number of mixture components is determined according to the minimum message length (MML) criterion. Secondly, the variational Bayes (VB) is adopted to learn our model where the parameters and the number of mixture components can be determined simultaneously in a unified framework, without the requirement of using information criteria. We investigate the effectiveness of our model by conducting a series of experiments on both synthetic and real data sets.
Energy Efficient and Side-Channel Secure Cryptographic Hardware for IoT-Edge Nodes

Design of ultralightweight but secure encryption engine is a key challenge for Internet-of-Things edge devices. This paper explores the system level design space for an ultralow power image sensor node for secure communication and proposes an optimized datapath architecture for 128-bit SIMON (SIMON128), a lightweight block cipher, for minimal performance, power, and area overheads with increased level of side-channel security. Various datapath architectures for SIMON are explored for simultaneously increasing energy-efficiency and resistance to power-based side-channel analysis (PSCA) attacks. Alternative datapath architectures are implemented on ASIC (15 nm CMOS) and field programmable gate array (FPGA) (Spartan-6, 45 nm) to perform power, performance, and area analysis. We show that, although a bitserial datapath minimizes area and power, a round unrolled datapath provides 80× higher energy-efficiency and 143× higher performance, compared to the baseline bitserial design. Moreover, the PSCA measurements performed using Sakura-G board with Spartan-6 FPGA, demonstrate that a 6-round unrolled datapath improves minimum-traces-to-disclosure for correlation power analysis (CPA) by at least 384× over baseline bitserial design with no successful CPA even with 500000 measurements. Finally, application to the image-sensor node demonstrates that optimized unrolled SIMON128 can provide equivalent performance to AES128 at lower area, higher energy efficiency, and improved side channel security.
Background–foreground interaction for moving object detection in dynamic scenes

Both background subtraction and foreground extraction are the typical methods used to detect moving objects in video sequences. In order to flexibly represent the long-term state and the short-term changes in a scene, a new weighted Kernel Density Estimation (KDE) is proposed to build the long-term background (LTB) and short-term foreground (STF) models, respectively. A novel mechanism is proposed to support the interaction between the LTB and STF models. The interaction includes the weight transmission and the fusion between the LTB and STF models. In the weight transmission process between the LTB and STF models, the sample weight of one model (either the background model or the foreground model) in the current time step is updated under the guidance of the decision of the other model in the previous time step. In the background-foreground fusion stage, a unified Bayesian framework is proposed to detect objects and the detection result in any time step is given by the logarithm of the posterior ratio between the background and foreground models. This interactive approach proposed in this paper improves the robustness of moving object detection, preventing deadlocks and degeneration in the models. The experimental results demonstrate that our proposed approach outperforms previous ones.
Reconciling solar forecasts: Sequential reconciliation

When forecasting hierarchical photovoltaic (PV) power generation in a region and/or over several forecast horizons, reconciliation is needed to ensure the lower-level forecasts add up exactly to the upper-level forecasts. Previously in “Reconciling solar forecasts: Geographical hierarchy” [Sol. Energy 146 (2017) 276–286] and “Reconciling solar forecasts: Temporal hierarchy” [Sol. Energy 158 (2017) 332–346], forecast reconciliation has been demonstrated for geographical and temporal hierarchies, separately. This article follows such frameworks and extends the reconciliation to spatio-temporal cases. More specifically, sequential reconciliation is used for operational day-ahead forecasting of 318 PV systems in California. It is shown that by using sequential reconciliation, aggregate-consistent forecasts can be obtained across both the geographical and temporal hierarchies. In addition, the forecast accuracy can be further improved from that of the single-hierarchy cases.
Fiber echo state network analogue for high-bandwidth dual-quadrature signal processing

All-optical platforms for recurrent neural networks can offer higher computational speed and energy efficiency. To produce a major advance in comparison with currently available digital signal processing methods, the new system would need to have high bandwidth and operate both signal quadratures (power and phase). Here we propose a fiber echo state network analogue (FESNA) - the first optical technology that provides both high (beyond previous limits) bandwidth and dual-quadrature signal processing. We demonstrate applicability of the designed system for prediction tasks and for the mitigation of distortions in optical communication systems with multilevel dual-quadrature encoded signals.
Personalized and content adaptive cultural heritage path recommendation: an application to the Gournia and Çatalhöyük archaeological sites

Although abundant research work has been published in the area of path recommendation and its applications on travel and routing topics, scarce work has been reported on context-aware route recommendation systems aimed to stimulate optimal cultural heritage experiences. This paper tries to address this issue, by proposing a personalized and content adaptive cultural heritage path recommendation system, where location is modeled using mean-shift clustering trained with actual user movement patters. Additionally, topic modeling is incorporated to formalize the implicit cultural heritage content, while first order Markov models address the movement as a temporal transition aspect of the problem. The overall architecture is applied on data collected from actual visits to the archaeological sites of Gournia and Çatalhöyük and extensive analysis on visitor movement patterns follows, especially in comparison to the curated paths in the aforementioned sites. Finally, the offline evaluation results of the proposed recommendation scheme are encouraging, validating its efficiency and setting a positive paradigm for cultural heritage route recommendations.