Reviewerly/exHarmony huggingface dataset:
From seen to unseen: Designing keyboard-less interfaces for text entry on the constrained screen real estate of Augmented Reality headsets

Text input is a very challenging task in the constrained screen real-estate of Augmented Reality headsets. Typical keyboards spread over multiple lines and occupy a significant portion of the screen. In this article, we explore the feasibility of single-line text entry systems for smartglasses. We first design FITE, a dynamic keyboard where the characters are positioned depending on their probability within the current input. However, the dynamic layout leads to mediocre text input and low accuracy. We then introduce HIBEY, a fixed 1-line solution that further decreases the screen real-estate usage by hiding the layout. Despite its hidden layout, HIBEY surprisingly performs much better than FITE, and achieves a mean text entry rate of 9.95 words per minute (WPM) with 96.06% accuracy, which is comparable to other state-of-the-art approaches. After 8 days, participants achieve an average of 13.19 WPM. In addition, HIBEY only occupies 13.14% of the screen real estate at the edge region, which is 62.80% smaller than the default keyboard layout on Microsoft Hololens.
Credibility score based multi-criteria recommender system

Recommender system has been emerged as a personalization tool to solve the issue of information overload in an e-commerce environment. Traditional collaborative filtering (CF) based recommender systems (RSs) suggest items to users based on their overall ratings which are used to find out similar users. Multi-criteria ratings are used to capture user preferences efficiently in multi-criteria recommender systems (MCRS), and incorporation of various criteria ratings can lead to higher performance in MCRS. Usually, user relies on the credibility of an item provided through his/her social circle or similar users, which is called a personal view on items from their close ones. However, it is not generally sufficient to depend exclusively on the personal view of the user. Therefore, public view that includes whole community can play a key role in the credibility of an item. In this paper, we propose a MCRS based on the credibility score of an item, which is an aggregated value of credibility scores on various criteria of an item. These credibility scores are computed based on personal and public views. However, different users have different priorities to various criteria of an item. Therefore, we use genetic algorithm (GA) to learn appropriate weights in the aggregation task of credibility score. The experiment results on Yahoo! Movies and modified MovieLens dataset demonstrate the effectiveness of proposed credibility score based MCRS in terms of coverage, recall, precision, and f-measure.
A systematic density-based clustering method using anchor points

Clustering is an important unsupervised learning method in machine learning and data mining. Many existing clustering methods may still face the challenge in self-identifying clusters with varying shapes, sizes and densities. To devise a more generic clustering method that considers all the aforementioned properties of the natural clusters, we propose a novel clustering algorithm named Anchor Points based Clustering (APC). The anchor points in APC are characterized by having a relatively large distance from data points with higher densities. We take anchor points as centers to obtain intermediate clusters, which can divide the whole dataset more appropriately so as to better facilitate further grouping. In essence, based on the analysis of the identified anchor points, the relationship among the corresponding intermediate clusters can be better revealed. In short, the difference in local densities (densities within neighboring data points) of the anchor points characterizes their different properties, that is to say, all the intermediate clusters may fall into one or multiple identified levels with different densities. Finally, based on the properties of anchor points, APC spontaneously chooses the appropriate clustering strategies and reports the final clustering results. To evaluate the performances of APC, we conduct experiments on twelve two-dimensional synthetic datasets and twelve multi-dimensional real-world datasets. Moreover, we also apply APC to the Olivetti Face dataset to further assess its effectiveness in terms of face recognition. All experimental results indicate that APC outperforms four classical methods and two state-of-the-art methods in most cases.
Assessing complementarity and optimising the combination of intermittent renewable energy sources using ground measurements

The use of intermittent renewable energy sources (IRES) in traditional energy systems must be suitably planned, as their variability affects the reliability and costs of electricity generation. Assessing the complementarity among multiple IRES and determining their optimal mix can mitigate the impacts of variability from independent sources. Although complementarity studies have considered a single time series per source to evaluate complementarity, this approach is not suitable for large areas, because the spatial variability may be lost when multiple time series are merged. In contrast, if data are not merged, optimisation might lead to impractical results. We propose the use of factor analysis for grouping similar data and present two optimisation models that are easily reproducible and not restricted by the number of decision variables. To this end, we considered the south-eastern and central-western areas of Brazil, where the share of IRES remains small but is expected to increase, and includes global solar radiation, wind speed, and river inflow IRES. The river inflow data were clustered into 11 groups. As solar radiation is evenly distributed in the region, rendering models with two or more factors unsuitable, all measurement stations were grouped. In contrast, as wind speeds vary significantly, the corresponding data were not merged but treated separately. The complementarity between data groups was assessed using the Pearson correlation coefficient. Overall, the different IRES exhibited low correlation, suggesting their complementarity. Despite their different objectives, both optimisation models indicate that the photovoltaic generation share in the IRES mix should be around 50%. Obtaining such optimal mixes can guide the expansion of IRES adoption for electricity generation in the region.
An Automated System for Regional Nativity Identification of Indian speakers from English Speech

This paper proposes an automated system to identify speaker's regional nativity by analysing their English speech utterances. A database of English speech of native speakers of three South Indian languages: Kannada (KAN), Tamil (TAM) and Telugu (TEL), is especially collected for this study, in text-independent mode. Mel Frequency Cepstral Coefficients (MFCCs) features are used with three different classifiers, namely, Gaussian Mixture Model (GMM), GMM-Universal Background Model (GMM-UBM) and i-vector. The i-vector classifier gave accuracies of 93.9%. Nativity identification from English speech is observed to be relatively easier for native speakers of Kannada language, than for Tamil and Telugu speakers.
The Establishment and Dynamic Properties of a New 4D Hyperchaotic System with Its Application and Statistical Tests in Gray Images

In this paper, a new 4D hyperchaotic system is generated. The dynamic properties of attractor phase space, local stability, poincare section, periodic attractor, quasi-periodic attractor, chaotic attractor, bifurcation diagram, and Lyapunov index are analyzed. The hyperchaotic system is normalized and binary serialized, and the binary hyperchaotic stream generated by the system is statistically tested and entropy analyzed. Finally, the hyperchaotic binary stream is applied to the gray image encryption. The histogram, correlation coefficient, entropy test, and security analysis show that the hyperchaotic system has good random characteristics and can be applied to the gray image encryption.
Collaborative Deep Learning for Super-Resolving Blurry Text Images

Text image, the one with its content dominated by text, is a common type of images seen in many applications. In practice, text images are often degraded by many factors mixed together. This article aims at recovering degraded text images that suffer from the mixture of multiple degradations, including low resolution, uniform blurring and noise. It is observed that such mixed degradations treat the low-frequency components and high-frequency components of an image in different manners, and the emphasis of recovery is on predicting high-frequency information. Motivated by such an observation, we proposed a neural network that collaboratively works on the prediction of high-frequency information and the recovery of text image with both low and high frequencies. The experiments are conducted on one existing benchmark dataset of document images and one new dataset covering a wide range of text images. The results show that the proposed method noticeably outperformed the existing ones.
A sigmoid attractiveness based improved firefly algorithm and its applications in IIR filter design

Recently, a novel population-based optimisation algorithm, namely firefly algorithm (FA), which mimics the flashing and attraction behaviour of fireflies, has shown promising performance in solving global optimisation problems. However, the preliminary studies have shown that FA often gets stuck in local optima. In this paper, we investigate the reasons why the FA suffers from getting stuck in local optima; and then propose an improved firefly algorithm (IFA). These improvements are twofold: first, a sigmoid-based attractiveness is employed to reformulate its definition and strengthen its local refinement ability; second, a dynamic step parameter tuning strategy is designed to adjust the random search intensity and narrow the search space iteratively to strengthen its global search ability. The empirical results indicate IFA can well balance between the global exploration and the local exploitation, and provides the best solutions, at least the competitive results, for most of 12 global optimisation problems over other FA variants. Besides, by employing IFA to solve well-known infinite impulse response filter design problems, we evaluate the effectiveness and efficiency of IFA. The experimental results and comparisons show that IFA performs better than, at least as competent again, other meta-heuristics in terms of the solution accuracy, solution robustness, and convergence rate.
A high-efficiency energy and storage approach for IoT applications of facial recognition

This work introduces a high-efficiency approach for face recognition applications based on features using a recent algorithm called Floor of Log (FoL). The advantage of this method is the reduction of storage and energy, maintaining accuracy. K-Nearest Neighbors and Support Vector Machine algorithm was applied to learn the better parameter of the FoL algorithm using cross-validation. Accuracy and the size after the compression process were adopted to evaluate the proposed algorithm. The FoL was tested in CelebA, Extended YaleB, AR, and LFW face datasets obtaining the same or better results when compared with the approach using the same classifiers with uncompressed features, but with a reduction of 86 to 91% compared to the original data size. The proposed method of this work presents a robust and straightforward algorithm of compression of features for face recognition applications. The FoL is a new supervised compression algorithm that can be adapted to achieve great results and integrated with edge computing systems.
Optimal Fuzzy Logic Control for Temperature Control Based on Social Spider Optimization

Abstract Fuzzy Logic Controller (FLC) is useful for many applications like industrial, home, military and medical applications, so one of main application is temperature control of A proposed controller combines the advantages of controller structure and fuzzy inference for air heater temperature control applications. The input variables of the PDFLC-SSO are error and change of error, while its output is the control signal. PDFLC-SSO has seven membership functions. These functions are triangular shaped. Centroid method (CM) is selected for the defuzzification process. The universe of discourse range is used from (-1 to 1) for each input and output. Temperature heater model was utilized to test the controller; the gain factors was tuned using trial and error method and SSO algorithm to reach the optimum response with minimum settling time, minimum peak overshoot and minimum Integral Time Absolute Error (ITAE). Carried out simulations showed an average improvement in measurements using ITAE. ITAE is 7.46% for a constant set-value using PDFLC-SSO, while it is 9.55 for a constant set-value using PDFLC.
Privacy Preserving Data Publishing for Multiple Sensitive Attributes Based on Security Level

Privacy preserving data publishing has received considerable attention for publishing useful information while preserving data privacy. The existing privacy preserving data publishing methods for multiple sensitive attributes do not consider the situation that different values of a sensitive attribute may have different sensitivity requirements. To solve this problem, we defined three security levels for different sensitive attribute values that have different sensitivity requirements, and given an L s l -diversity model for multiple sensitive attributes. Following this, we proposed three specific greed algorithms based on the maximal-bucket first (MBF), maximal single-dimension-capacity first (MSDCF) and maximal multi-dimension-capacity first (MMDCF) algorithms and the maximal security-level first (MSLF) greed policy, named as MBF based on MSLF (MBF-MSLF), MSDCF based on MSLF (MSDCF-MSLF) and MMDCF based on MSLF (MMDCF-MSLF), to implement the L s l -diversity model for multiple sensitive attributes. The experimental results show that the three algorithms can greatly reduce the information loss of the published microdata, but their runtime is only a small increase, and their information loss tends to be stable with the increasing of data volume. And they can solve the problem that the information loss of MBF, MSDCF and MMDCF increases greatly with the increasing of sensitive attribute number.
Deep multi-person kinship matching and recognition for family photos

In this paper, we propose a novel Deep Kinship Matching and Recognition (DKMR) framework for multi-person kinship matching and recognition, which is a complicated and challenging task with little previous literature. Compared with most existing kinship understanding methods that mainly work on matching kinship in pairwise face images, we target at recognizing the exact kinship in nuclear family photos consisting of multiple persons. The proposed DKMR framework contains three modules. Firstly, we design a deep kinship matching model (termed DKM-TRL) to predict kin-or-not scores by integrating the triple ranking loss into a Siamese CNN model. Secondly, we develop a deep kinship recognition model (named DKR-GA) to predict the exact kinship categories, in which gender and relative age attributes are utilized to learn more discriminative representations. Thirdly, based on the outputs of DKM-TRL and DKR-GA, we propose a reasoning conditional random field (R-CRF) model to infer the corresponding optimal family tree by exploiting the common kinship knowledge of a nuclear family. To evaluate the effectiveness of our DKMR framework, we conduct extensive experiments and the results show that it can gain superior performance on Group-Face dataset, TSKinFace dataset and FIW dataset over state-of-the-arts.
An Asynchronous Data Fusion Algorithm for Target Detection Based on Multi-Sensor Networks

The time interval of the observational data changes irregularly because of the difference of sensors' sampling rate, the communication delay and the target leaving observation region of the sensor sometimes. These problems of asynchronous observation data greatly reduce the tracking accuracy of the multi-sensors system. Therefore, asynchronous data fusion system is more practical than synchronous data fusion system, and worthier of study. By establishing an asynchronous track fusion model with irregular time interval of observation data and combining with the Track Quality with Multiple Model (TQMM), an asynchronous track fusion algorithm with information feedback is proposed, and the TQMM is used for weight allocation to improve the performance of the asynchronous multi-sensor fusion system. The simulation result shows that the algorithm has better tracking performance compared with other algorithms, so that this kind of problem of track-to-track fusion for asynchronous sensors is solved effectively.
Blockchain-Based Reputation System in Agri-Food Supply Chain

Supply chains are evolving into automated and highly complex networks and are becoming an important source of potential benefits in the modern world. However, it is challenging to track the provenance of data and maintain traceability throughout the network. The traditional supply chains are centralized and dependent on third party for trading. Centralized systems lack transparency, accountability and auditability. In our proposed solution, we have presented a blockchain-based reputation system in Agriculture and Food (Agri-Food) supply chain. It leverages the key features of blockchain and smart contract deployed over ethereum blockchain network. Although blockchain provides an immutable network for supply chain events, it still fails to solve the problem of trust among entities. Therefore, a reputation system is required that logs reviews of sellers and maintains the trust between trading entities.
CrowdPrivacy

Location-based services (LBSs) typically crowdsource geo-tagged data from mobile users. Collecting more data will generally improve the utility for LBS providers; however, it also leads to more privacy exposure of users’ mobility patterns. Although the tension between data utility and user privacy has been recognized, there lacks a solution that determines how much data to collect—in both spatial and temporal domains—is the “best” for both mobile users and the service provider. This article proposes a strategy toward making an optimal tradeoff such that a user submits data only if her mobility privacy will not be compromised and the data utility of the LBS provider will be sufficiently improved. To this end, we first define and formulate a concept called privacy exposure , which incorporates both the spatial distribution and the temporal transition of a user’s activity points . Second, we define and quantify data utility in terms of spatial repetitions and temporal closeness among data based on an economic principle. Then, we propose a PRivacy-preserving and UTility-Enhancing Crowdsourcing (PRUTEC) algorithm to determine, on behalf of each mobile user, whether a newly sensed piece of data should be submitted to the LBS provider. Our simulation demonstrates that PRUTEC improves the data utility of the service provider with a much less amount of data to collect and reduces privacy exposure for mobile users while collecting useful data continuously.
Find Out A New Method to Study Abductive Reasoning in Empirical Research

This article presents a new tool that provides a methodological context to observe and analyze, both qualitatively and quantitatively, manifestations of abductive reasoning in empirical research. Abduction is a form of a complex reasoning carried out to make sense of surprising or ambiguous phenomena or fill the gaps in our beliefs. Despite the ubiquity of abduction in professional and everyday problem-solving processes, little empirical research was dedicated to investigate this type of reasoning, and most of them focused on products of abduction—abductive hypotheses. Our instrument, Find Out, catches abduction as a real-life form of reasoning consisting of two phases—generation and evaluation of hypotheses. It offers the possibility to account on abduction from both product and process perspective and enables both qualitative and quantitative analyses on gathered data to be conducted. In this article, the task and examples of qualitative analyses of the data are presented.
On-Site Inspecting Zombie Lending

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An Overview of Popular Digital Image Processing Filtering Operations

Digital image processing (DIP) is carried out to produce an altered image that is more suitable for the intended application. Image filtering and image warping are considered to be the most common methods of image processing. Within image filtering, these operations are carried out to aid the altering or enhancing an image to either remove specific features or highlight features of interest within the image. These operations are generally carried out in the pre-processing stage and can have very positive results on the quality of feature extraction and the results of image analysis. This paper aims to give a brief overview of some popular image filtering operations, focusing on edge detection filters, smoothing filters and the advantages of greyscale over colour images.
Information Leakage in Encrypted Deduplication via Frequency Analysis

Encrypted deduplication combines encryption and deduplication to simultaneously achieve both data security and storage efficiency. State-of-the-art encrypted deduplication systems mainly build on deterministic encryption to preserve deduplication effectiveness. However, such deterministic encryption reveals the underlying frequency distribution of the original plaintext chunks. This allows an adversary to launch frequency analysis against the ciphertext chunks and infer the content of the original plaintext chunks. In this article, we study how frequency analysis affects information leakage in encrypted deduplication, from both attack and defense perspectives. Specifically, we target backup workloads and propose a new inference attack that exploits chunk locality to increase the coverage of inferred chunks. We further combine the new inference attack with the knowledge of chunk sizes and show its attack effectiveness against variable-size chunks. We conduct trace-driven evaluation on both real-world and synthetic datasets and show that our proposed attacks infer a significant fraction of plaintext chunks under backup workloads. To defend against frequency analysis, we present two defense approaches, namely MinHash encryption and scrambling. Our trace-driven evaluation shows that our combined MinHash encryption and scrambling scheme effectively mitigates the severity of the inference attacks, while maintaining high storage efficiency and incurring limited metadata access overhead.
A SMOOTHING METHOD THAT LOOKS LIKE THE HODRICK–PRESCOTT FILTER

In recent decades, in the research community of macroeconometric time series analysis, we have observed growing interest in the smoothing method known as the Hodrick–Prescott (HP) filter. This article examines the properties of an alternative smoothing method that looks like the HP filter, but is much less well known. We show that this is actually more like the exponential smoothing filter than the HP filter although it is obtainable through a slight modification of the HP filter. In addition, we also show that it is also like the low-frequency projection of Müller and Watson (2018, Econometrica 86, 775–804). We point out that these results derive from the fact that all three similar smoothing methods can be regarded as a type of graph spectral filter whose graph Fourier transform is discrete cosine transform. We then theoretically reveal the relationship between the similar smoothing methods and provide a way of specifying the smoothing parameter that is necessary for its application. An empirical examination illustrates the results.