Reviewerly/exHarmony huggingface dataset:
Constructing self-orthogonal and Hermitian self-orthogonal codes via weighing matrices and orbit matrices

We define the notion of an orbit matrix with respect to standard weighing matrices, and with respect to types of weighing matrices with entries in a finite field. In the latter case we primarily restrict our attention the fields of order 2, 3 and 4. We construct self-orthogonal and Hermitian self-orthogonal linear codes over finite fields from these types of weighing matrices and their orbit matrices respectively. We demonstrate that this approach applies to several combinatorial structures such as Hadamard matrices and balanced generalized weighing matrices. As a case study we construct self-orthogonal codes from some weighing matrices belonging to some well known infinite families, such as the Paley conference matrices, and weighing matrices constructed from ternary periodic Golay pairs.
Human expertise in the interpretation of remote sensing data: A cognitive task analysis of forest disturbance attribution

The visual analysis of remote sensing imagery is useful for the extraction of information not readily available through automated image analysis. Previous studies have shown that the replication of human reasoning about image content is difficult due to human creativity and mental flexibility. Development of automated image analysis programs continues; however, geovisual analytics suggests that it may be more beneficial to design symbiotic computer-human interpretation systems. It is imperative to understand the experiences, knowledge, and cognitive processes that image interpreters rely on. Cognitive Task Analysis (CTA) is a methodological framework developed from Cognitive Systems Engineering (CSE) where expert users are studied with the goals of explicating their needs, wants, and cognitive abilities for dealing with complex technological systems. Here we report the results of a CTA process carried out with users of a geovisual analytic tool to support forest disturbance detection and signification. These results suggest that different facets of the cognitive processes undertaken by users are not always explicit, and differences in the participant's attentiveness to their mental processes vary greatly. Despite these differences and pathways to their final interpretations, participants were able to successfully come to similar judgments as for their peers.
Signal-to-noise ratios, instrument parameters and repeatability of Itrax XRF core scan measurements of floodplain sediments

This study used signal-to-noise ratios to assess the effects of increasing Itrax XRF instrument parameters, namely tube voltage, tube current and exposure time, on XRF spectra and measurement repeatability. Tests were performed on cores from British and Irish floodplains. Seven combinations of tube voltage and current and six exposure times were compared using signal-to-noise ratios for eight target elements. Signal-to-noise ratios may be substantially improved by selecting instrument parameters, particularly tube voltage, for specific target elements. They can also be used to assess element profile suitability for interpretation by comparison with the limit of quantification. Repeatability was assessed using the standard deviation of measurements in nine repeat scans. The variation in element levels in the majority of profiles is considerably greater than the variability in individual measurements.
Exploring Genetic Programming Systems with MAP-Elites

MAP-Elites is an evolutionary computation technique that has proven valuable for exploring and illuminating the genotype-phenotype space of a computational problem. In MAP-Elites, a population is structured based on phenotypic traits of prospective solutions; each cell represents a distinct combination of traits and maintains only the most fit organism found with those traits. The resulting map of trait combinations allows the user to develop a better understanding of how each trait relates to fitness and how traits interact. While MAP-Elites has not been demonstrated to be competitive for identifying the optimal Pareto front, the insights it provides do allow users to better understand the underlying problem. In particular, MAP-Elites has provided insight into the underlying structure of problem representations, such as the value of connection cost or modularity to evolving neural networks. Here, we extend the use of MAP-Elites to examine genetic programming representations, using aspects of program architecture as traits to explore. We demonstrate that MAP-Elites can generate programs with a much wider range of architectures than other evolutionary algorithms do (even those that are highly successful at maintaining diversity), which is not surprising as this is the purpose of MAP-Elites. Ultimately, we propose that MAP-Elites is a useful tool for understanding why genetic programming representations succeed or fail and we suggest that it should be used to choose selection techniques and tune parameters.
Uninorm based residual implications satisfying the Modus Ponens property with respect to a uninorm

In any fuzzy rules based system the inference management is usually carried out by the so-called fuzzy implication functions. In this framework, the Modus Ponens property becomes essential to make forward inferences and it is well known that this inference rule is guaranteed when the conjunction and the implication function used in the process satisfy the corresponding functional inequality. Such inequality has been extensively studied for many kinds of implication functions when the conjunction is modelled by a t-norm. However, the use of conjunctive uninorms to model conjunctions is an increasingly widespread option in fuzzy systems and for this reason the study of the Modus Ponens with respect to a conjunctive uninorm U instead of a t-norm T, which we call here U-Modus Ponens or U-conditionality, becomes very important. In this paper this new property is deeply analyzed and it is shown that usual implications derived from t-norms and t-conorms do not satisfy it, but many solutions appear among those implications derived from uninorms. In particular, the U-Modus Ponens for the case of residual implications derived from uninorms, or RU-implications, is investigated in detail when the uninorm U lies in any of the four most usual classes of uninorms.
Novel orthogonal based collaborative dictionary learning for efficient face recognition

Dictionary learning (DL) methods are widely used for pattern recognition in recent years. In most DL methods, the l1 norm is employed to promote sparsity of the coding. However, the usage of the sparse coding based methods is limited since solving the l1 based sparse coding is very time-consuming. In this paper, a novel orthogonal collaborative dictionary learning (CDL) method is proposed for accurate and efficient face classification. In this method, several class-specific dictionaries and one common dictionary are learned jointly from the training data, where the class-specific dictionaries are used to model the appearance of the subjects and the common dictionary is used to model the facial variations. To learn these dictionaries, we introduce an orthogonality promoting term to encourage the facial variations to be independent of the appearance as much as possible, and introduce a scatter constraint term to remove the variations in the class-specific dictionaries. Since CDL can derive analytical solutions for both code learning and dictionary updating, it is much more efficient than many other DL methods in terms of training and classification. Experiments conducted on seven face databases show that CDL outperforms many state-of-the-art DL methods and coding methods in both accuracy and efficiency.
Diffuse fraction estimation using the BRL model and relationship of predictors under Chilean, Costa Rican and Australian climatic conditions

In this paper a model is used to estimate the diffuse radiation using the Boland-Ridley-Lauret (BRL) model, which is developed for Chile, Costa Rica, and Australia. Additionally, artificial Direct Normal Irradiance time series are determined from the diffuse fraction results estimated by the model. Both estimates achieve excellent agreement based on the bias and normalized scatter. The relationship between the predictors of the model and the climatic conditions of each case of study, according to the Köppen-Geiger climatic classification was analyzed, which to the best of the authors' knowledge, it's the first study of this kind. An analysis on correlation and statistical significance was carried out between the model predictors and four determining geographic and climatic variables: altitude, latitude, precipitation and temperature. The statistical analysis shows that two of the six predictors correlate with temperature and precipitation and one predictor is correlated with latitude. Therefore, it can be suggested that the BRL model seems largely insensitive to the different regional climatic conditions, nevertheless, evaluation of the effect of the apparent correlation of the respective predictors and microclimatic and geographical variables points to further research in this area considering a wider selection of locations.
Advanced simultaneous phase-shifting Fizeau interferometer

This study proposes an advanced simultaneous phase-shifting Fizeau interferometer. The proposed interferometer introduces a beam expander into the simultaneous phase-shifting Fizeau interferometer which employs four point sources to produce four phase shifting interferograms on a detector simutaneously. The beam expander is used to increase the aperture of the lens array and to decrease the inclination of the beam incident on the test surface such that the imaging resolution and the measurement accuracy of the interferometer can be improved. In addition, the optical zoom can be achieved by changing the magnification of the beam expander. A step plate was experimentally measured using the proposed interferometer. The phase step is clearly imaged in the detector, and the measured results are in good agreement with those obtained by the temporal phase-shifting Fizeau interferometer, which indicates that the proposed interferometer is suitable for normal apertures.
Granular filter in medical image noise suppression and edge preservation

An alternative non-linear filtering technique for medical image denoising while preserving edge is introduced. Two different variants of the approach i.e. crisp and fuzzy are developed. The solution is demonstrated based on US breast images as well as CT studies and gave promising results in comparison with commonly known and popular filtering techniques (i.e. spatial averaging and median, bilateral filter, anisotropic diffusion). Many different measures were used to evaluate the method. There are pixel-to-pixel error measures, structural information factors and edge preservation measures. The benefits are noticeable in all three categories.
Noisy Vibrational Pairing of IoT Devices

Internet of Things (IoT) is embodied by smart network-enabled devices that utilize computing power, networking, and miniaturization to enable richer and improved user experience. Due to their interconnectedness, ubiquitous nature and low computational power, trustworthy and secure communication between IoT devices has become a security concern. To authenticate the devices, “pairing” may be secured by the use of an auxiliary channel such as audio, visual and vibrations for sharing the key or keying material between the IoT devices. In this paper, we evaluate the security of vibration channel, susceptible to an acoustic eavesdropper, that can capture audio leakage from the vibrations of the transmitting IoT device. We propose a noisy vibration scheme for cloaking vibration sounds during pairing against such attacks. The scheme only requires a speaker for emitting the masking sound during key transmission. We evaluate the scheme in proximity, co-located and remote settings with an eavesdropping attacker. We also study motion sensor exploits against this scheme and compliment it with additional measures to mask vibration effects on motion sensors. Our scheme is user transparent and requires only a speaker (that may already be present on the device), so it can be readily implemented in the IoT setting, smart wearables, and other commodity gadgets.
Comparing ontology authoring workflows with Protégé: In the laboratory, in the tutorial and in the ‘wild’

The development of ontology engineering tools has traditionally lacked a user-centred perspective, instead being guided by the need to address particular gaps indicated by anecdotal evidence. This has typically resulted in prototypes that do not obtain traction beyond a narrow scope. Understanding the authoring patterns of ontology engineers is crucial to informing the development of ontology engineering tools that cater for the activity workflows of the users and, consequently, boosting the adoption of these tools. We report evidence about how Protégé is used across three different authoring settings, addressing the threats to validity of relying on a single user study. These settings address the continuum of expertise (from intermediate to expert users), the type of tasks (whether they are free-form or prescriptive) and the effect of the location (laboratory, tutorial or on their own) and how the studies are administered (whether or not there is a close supervision). While there are activity workflows that are particular to settings, the results indicate a number of core workflows that are common to all of them. We discuss actionable recommendations for ontology engineering tools in light of these results.
Comparison of Use of a 2000 Qubit D-Wave Quantum Annealer and MCMC for Sampling, Image Reconstruction, and Classification

The next generation D-Wave quantum computer (QC) having more than 2000 qubits was demonstrated to be adequate for embedding graphs of restricted Boltzmann machines (RBMs) having the connectivity between RBM units (though still limited) already sufficient for successful training by classical algorithms. As a verification of the validity and quality of the employed embedding approach, the RBM was trained by classical contrastive divergence (CD), while the D-Wave was initially used only in the recognition step for reconstruction of incomplete test images (8 × 8 bars-and-stripes), and for their classification. The label classification errors by the D-Wave compared favorably to those obtained by classical Gibbs sampling, indicating that the QC was successfully finding the most energetically favorable combinations of the unknown visible RBM units/labels for the given fixed incomplete input image. The valid RBM embedding was further applied to investigate opportunities for using QCs in the RBM training, and specifically for replacing the classical Gibbs technique in generating a representative sample from the RBM model distribution. Statistical comparison of samples obtained by the Gibbs technique versus 10000 sample states generated from the D-Wave revealed significant differences in the observed outcomes. The D-Wave samples were found insufficiently representative of the model distribution, specifically by missing many local valley found by the Gibbs sampling. On the other hand, the D-Wave possessed an ability to find local valleys of the configuration space that were consistently missed by the classical sampling algorithms, which could potentially be very interesting for sampling applications.
Reputation assessment mechanism for carpooling applications based on clustering user travel preferences

One way to ensure sustainable and environmental friendly mobility is the use of less vehicles for carrying more passengers, and carpooling is a means to achieve this goal. One major concern in carpooling services is related to trust, as carpooling users need to either share their vehicles, if they act as drivers, or travel with strangers if they act as passengers. One way to tackle trust concerns is the utilization of user reputation assessment mechanisms, whose objective is to provide ranking of users with respect to their behavior, based on feedback provided by other users. This paper presents a newly introduced reputation assessment mechanism for carpooling applications, which, in addition to feedback provided by other users, takes into account user travel preferences. Preliminary experimental results have shown that the proposed mechanism is robust against attacks by malicious users, on their attempt to jeopardize the trustworthiness of the system, as it preserves the real reputation scores of the users, when the penetration rate of malicious users increases.
Software project management in high maturity: A systematic literature mapping

High maturity in software development involves statistically controlling the performance of critical subprocesses and using the predictability thus gained to manage projects with better planning precision and monitoring control. Maturity models such as CMMI mention statistical and other quantitative methods, techniques, and tools supporting high-maturity project management, but do not provide details about them, their use or their available types. Thus, knowledge is lacking on how to support software process improvement initiatives to select and apply statistical and other quantitative methods, techniques and tools in this context. The goal of this study is to identify various methods, techniques, and tools which can assist in high-maturity software project management. By conducting a systematic literature mapping, we identified 108 papers describing 153 contributions. We describe the contributions identified, classifying them by their type, their software technology maturation phase, the method by which they were evaluated, the development methods and characteristics which they support, and the process/indicator areas to which they were applied. We hope this work can help fill the knowledge gap on the statistical and other quantitative methods, techniques and tools actually being proposed, evaluated, experimented with and adopted by organizations to support quantitative high-maturity software project management.
Quantum error correction with the semion code

Abstract We present a full quantum error correcting procedure with the semion code: an off-shell extension of the double-semion model. We construct open-string operators that recover the quantum memory from arbitrary errors and closed-string operators that implement the basic logical operations for information processing. Physically, the new open-string operators provide a detailed microscopic description of the creation of semions at their end-points. Remarkably, topological properties of the string operators are determined using fundamental properties of the Hamiltonian, namely, the fact that it is composed of commuting local terms squaring to the identity. In all, the semion code is a topological code that, unlike previously studied topological codes, it is of non-CSS type and fits into the stabilizer formalism. This is in sharp contrast with previous attempts yielding non-commutative codes.
A posterior evaluation algorithm of steganalysis accuracy inspired by residual co-occurrence probability

Steganalysis research is committed to distinguishing the steganography media from the normal one correctly. However, individual differences of carriers disturb the detection inevitably and greatly. Existing methods treat all detection results with the same confidence level, or prior accuracy, which may make the prior accuracy overestimate or underestimate the real result. This paper presents a novel performance evaluation method of steganalysis based on posterior accuracy. Adaptive Convolution Feature (ACF) is calculated by the adaptive convolution, then a quantitative value S based on the ACF is modeled to posterior testify and estimate the detection confidence of the image under test. By clustering of carrier noise, we classify the images which we believe they have a similar confidence of the same cluster. The distribution of ACF from each cluster indicates that the S value works well for confidence evaluation. The experimental results show that the S value can distinguish and identify high-confidence samples from the low-confidence, which will greatly improve the real performance of steganalysis. Besides, it improves the steganalysis accuracy of the whole image set by matching S values between training and prediction samples.
Fast Semantic Segmentation on Video Using Block Motion-Based Feature Interpolation

Convolutional networks optimized for accuracy on challenging, dense prediction tasks are often prohibitively slow to run on each frame in a video. The spatial similarity of nearby video frames, however, suggests opportunity to reuse computation. Existing work has explored basic feature reuse and feature warping based on optical flow, but has encountered limits to the speedup attainable with these techniques. In this paper, we present a new, two part approach to accelerating inference on video. First, we propose a fast feature propagation technique that utilizes the block motion vectors present in compressed video (e.g. H.264 codecs) to cheaply propagate features from frame to frame. Second, we develop a novel feature estimation scheme, termed feature interpolation, that fuses features propagated from enclosing keyframes to render accurate feature estimates, even at sparse keyframe frequencies. We evaluate our system on the Cityscapes and CamVid datasets, comparing to both a frame-by-frame baseline and related work. We find that we are able to substantially accelerate semantic segmentation on video, achieving twice the average inference speed as prior work at any target accuracy level.
A novel reverse sparse model utilizing the spatio-temporal relationship of target templates for object tracking

In the particle filter framework, the sparse representation method models object tracking by predicting the likelihood of particles with respect to target templates. However, in most tracking methods, the spatio-temporal relationships of different templates have been neglected. This method applies the reverse sparse representation in multi-task learning, thus takes advantage of the temporal information and spatial continuity of a series of templates. Adding a predesigned weight matrix can reflect the temporal information, and make the new entrant templates have a greater influence on atoms’ selection. To solve the locally weighted reverse joint sparse model (LWRJM), we design a modified smooth l0, 2 algorithm which requires only a few iterations. The obtained sparse coding coefficients are mapped to a binary indicator vector by a statistic strategy, which is helpful to eliminate the contributions of the corrupted blocks and preserve the uncorrupted ones. Comparing with other state-of-the-art tracking methods on 100 challenging benchmark image sequences, the proposed tracker (LWRJM) outperforms other methods in both qualitative and quantitative evaluations.