Reviewerly/exHarmony huggingface dataset:
Weapon Detection for Particular Scenarios Using Deep Learning

The development of object detection systems is normally driven to achieve both high detection and low false positive rates in a certain public dataset. However, when put into a real scenario the result is generally an unacceptable rate of false alarms. In this context we propose to add an additional step that models and filters the typical false alarms of the new scenario while roughly maintaining the ability to detect the objects of interest. We propose to use the false alarms of the new scenario to train a deep autoencoder and to model them. The latter will act as a filter that checks whether the output of the detector is one of its typical false positives or not based on the reconstruction error measured with the Mean Squared Error (MSE) and the Peak Signal-to-Noise Ratio (PSNR). We test the system using an entirely synthetic novel dataset for training and testing the autoencoder generated with Unreal Engine 4. Results show a reduction in the number of FPs of up to 37.9% in combination with the PSNR error while maintaining the same detection capability.
Mosquito Larvae Image Classification Based on DenseNet and Guided Grad-CAM

The surveillance of Aedes aegypti and Aedes albopictus mosquito to avoid the spreading of arboviruses that cause Dengue, Zika and Chikungunya becomes more important, because these diseases have greatest repercussions in public health in the significant extension of the world. Mosquito larvae identification methods require special equipment, skillful entomologists and tedious work with considerable consuming time. In comparison with the short mosquito lifecycle, which is less than 2 weeks, the time required for all surveillance process is too long. In this paper, we proposed a novel technological approach based on Deep Neural Networks (DNNs) and visualization techniques to classify mosquito larvae images using the comb-like figure appeared in the eighth segment of the larva’s abdomen. We present the DNN and the visualization technique employed in this work, and the results achieved after training the DNN to classify an input image into two classes: Aedes and Non-Aedes mosquito. Based on the proposed scheme, we obtain the accuracy, sensitivity and specificity, and compare this performance with existing technological approaches to demonstrate that the automatic identification process offered by the proposed scheme provides a better identification performance.
A Computational System for Violin: Synthesis and Dissolution in <i>Windowless</i>

This article provides an overview of a real-time, hybrid computational system for the violin, Windowless. The system uses a custom sensor glove, the alto.glove, to track the violinist’s movements and drive a panoply of unique digital sound processing effects. The author describes the operations of the system in terms of a broad notion of synthesis, consis-tency, microintervallic motions and molecular operations. A threefold approach combining dense sonic physics, “loose” computational procedures and high system responsiveness creates a rich and thick performative medium with a vapor-like, particulate level of textural and bitwise computational detail.
Development and Implementation of an Autonomously Driven Vehicle Prototype

Technology has become cheaper and more accessible, giving way to opportunities in technological areas that were previously inaccessible. Moreover, building a fully functional self-driving vehicle has become the new gold rush in the current industries. Yet, there are many issues to solve when building an autonomous vehicle. For instance, a self-driving vehicle must be reliable, consistent, predictable, and safe. There is still substantial amount of research and work that needs to be done, and more to learn, for the industry to build a truly reliable self-driving vehicle. In this paper the basic concepts of self-driving vehicles as well as some advanced concepts have been discussed. This project explores building a self-driving car using an off-the-shelf remote-controlled car, an NVIDIA Jetson TX2 GPU which has an ARM processor on board, infra-red sensors for obstacle avoidance, and a monocular CSI camera for navigation and object detection. A combination of Google's TensorFlow, OpenCV and a Robot Operating System (ROS) enables the remote-controlled car into becoming a fully self-driving car.
No-Reference Depth Map Quality Evaluation Model Based on Depth Map Edge Confidence Measurement in Immersive Video Applications

When it comes to evaluating perceptual quality of digital media for overall quality of experience assessment in immersive video applications, typically two main approaches stand out: Subjective and objective quality evaluation. On one hand, subjective quality evaluation offers the best representation of perceived video quality assessed by the real viewers. On the other hand, it consumes a significant amount of time and effort, due to the involvement of real users with lengthy and laborious assessment procedures. Thus, it is essential that an objective quality evaluation model is developed. The speed-up advantage offered by an objective quality evaluation model, which can predict the quality of rendered virtual views based on the depth maps used in the rendering process, allows for faster quality assessments for immersive video applications. This is particularly important given the lack of a suitable reference or ground truth for comparing the available depth maps, especially when live content services are offered in those applications. This paper presents a no-reference depth map quality evaluation model based on a proposed depth map edge confidence measurement technique to assist with accurately estimating the quality of rendered (virtual) views in immersive multi-view video content. The model is applied for depth image-based rendering in multi-view video format, providing comparable evaluation results to those existing in the literature, and often exceeding their performance.
ORCA: A Matlab/Octave Toolbox for Ordinal Regression

Ordinal regression, also named ordinal classification, studies classification problems where there exist a natural order between class labels. This structured order of the labels is crucial in all steps of the learning process in order to take full advantage of the data. ORCA (Ordinal Regression and Classification Algorithms) is a Matlab/Octave framework that implements and integrates different ordinal classification algorithms and specifically designed performance metrics. The framework simplifies the task of experimental comparison to a great extent, allowing the user to: (i) describe experiments by simple configuration files; (ii) automatically run different data partitions; (iii) parallelize the executions; (iv) generate a variety of performance reports and (v) include new algorithms by using its intuitive interface. Source code, binaries, documentation, descriptions and links to data sets and tutorials (including examples of educational purpose) are available at https://github.com/ayrna/orca.
Towards an Empirically-Based IDE: An Analysis of Code Size and Screen Space

Integrated development environments (IDEs) are ubiquitous in software development. Despite their popularity, much of their designs are not based on empirical findings and have been virtually unchanged since their inception. More recently, researchers have proposed alternative IDE designs based on observational studies of developers and found promising benefits. However, many design decisions are still unsupported by empirical evidence. Towards designing an empirically-driven IDE, we performed an analysis on code size and screen space. First, we analyzed the size of code from 95 projects in 4 programming languages. Second, we calculated how much code can fit onscreen using various document arrangements in VS Code with common screen resolutions. We found (1) the length of code depends on the programming language, while the width of code is similar across languages and (2) the amount of code onscreen can be substantially increased by properly configuring the IDE and by using a high-resolution monitor.
ADQuaTe: An Automated Data Quality Test Approach for Constraint Discovery and Fault Detection

Data quality tests validate the data stored in databases and data warehouses to detect violations of syntactic and semantic constraints. Domain experts grapple with the issues related to the capturing of all the important constraints and checking that they are satisfied. Domain experts often define the constraints in an ad hoc manner based on their knowledge of the application domain and needs of the stakeholders. We propose ADQuaTe, which is an automated data quality test approach that uses an unsupervised machine learning technique to discover constraints that may have been missed by experts. ADQuaTe marks records that violate the constraints as suspicious and explains the violations. We evaluate ADQuaTe on real-world applications using a health data warehouse and a plant diagnosis database to demonstrate that the approach can uncover previously detected as well as new faults in the data.
Automated design tests to check Hibernate design recommendations

Developers must follow some architectural recommendations in order to obtain the expected behavior of frameworks. For instance, Hibernate users must implement specific methods and annotate specific classes to properly store and retrieve elements in a database. If the code of the application fails to follow such recommendations, in extreme cases, the non-conformities with architectural rules can even lead to data loss. In this paper, we present an exploratory study on the conformity of 77 open-source projects regarding Hibernate architectural rules. We identified that 76 of 77 projects do not follow such rules and almost all persistent entities fail in at least one of them. We also found that architectural violations can cause abnormalities in the behavior during the use of the framework. We discuss the reasons and implications of the projects simply ignoring architectural recommendations.
Adversarial camouflage (AC) for naval vessels

The use of different types of camouflage is a longstanding technique employed by armed forces in order to avoid detection, classification or tracking of objects of military interest. Typically, the use of such camouflage is intended to fool human observers. However, in future battle theaters one must expect to face weapons that are ’artificially intelligent’ in some way, and the question then arises as to whether the same types of camouflage will be effective against such weapons. An equally important question is if it is possible to design camouflage in order to specifically confuse ’artificially intelligent’ adversaries and what such camouflage might look like. It is this latter question that is the object of the study reported here. In particular, we consider whether carefully designed patterns of camouflage will have a detrimental effect on the performance of neural networks trained to distinguish among different ship classes. We train a neural network to distinguish between different types of military and civilian vessels and specifically require the network to determine whether the vessel is military or civilian. We then use this network to train a second network, a generative adversarial network, that will generate patterns to overlay on parts of the vessels in such a way as to thwart the performance of the first network. We show that such adversarial camouflage is very effective in confusing the original classification network.
Tame Your Annotations with MetAcsl: Specifying, Testing and Proving High-Level Properties

A common way to specify software properties is to associate a contract to each function, allowing the use of various techniques to assess (e.g. to prove or to test) that the implementation is valid with respect to these contracts. However, in practice, high-level properties are not always easily expressible through function contracts. Furthermore, such properties may span across multiple functions, making the specification task tedious, and its assessment difficult and error-prone, especially on large code bases. To address these issues, we propose a new specification mechanism called meta-properties. Meta-properties are enhanced global invariants specified for a set of functions, capable of expressing predicates on values of variables as well as memory related conditions (such as separation) and read or write access constraints. This paper gives a detailed presentation of meta-properties and their support in a dedicated Frama-C plugin MetAcsl, and shows that they are automatically amenable to both deductive verification and testing. This is demonstrated by applying these techniques on two illustrative case studies.
Ensemble Methods for Word Embedding Model Based on Judicial Text

With the continuous expansion of computer applications, scenarios such as machine translation, speech recognition, and message retrieval depend on the techniques of the natural language processing. As a technique for training word vectors, Word2vec is widely used because it can train word embedding model based on corpus and represent the sentences as vectors according to the training model. However, as an unsupervised learning model, word embedding can only characterize the internal relevance of natural language in non-specific scenarios. For a specific field like judicial, the method of expanding the vector space by creating a professional judicial corpus to enhance the accuracy of similarity calculation is not obvious, and this method is unable to provide further analysis for similarity in cases belonging to the same type. Therefore, based on the original word embedding model, we extract factors such as fines and prison term to help identify the differences, and attach the label of the case to complete supervised ensemble learning. The result of the ensemble model is better than any result of single model in terms of distinguishing whether they are the same type. The experimental result also reveal that the ensemble method can effectively tell the difference between similar cases, and is less sensitive to the details of the training data, the choice of training plan and the contingency of a single inaccurate training run.
Productivity of oyster mushrooms (Pleurotus ostreatus) on media corncobs mixed with sawdust

Abstract Mushrooms live wild in nature and widely used by the community as food and medicinal. Mushroom cultivation is a way to meet the growing demand for mushroom consumption. Oyster mushroom can grow in medium containing lignin and nutrients needed for the growth of mushrooms. The purpose of the study was to measure the effect of mixture corncob with sawdust as planting media for growth and productivity of white oyster mushroom (P. ostreatus) and get the best planting media composition for growth and productivity of white oyster mushroom. This research used a Completely Randomized Design (CRD) with six treatments and five replications. Corncob are added as much 0%, 10%, 20%, 30%, 40%, and 50%. The results showed that the best composition of growing media for the growth and productivity of white oyster mushroom is the addition of 30% corncob. Mixed corncob 30% have lignin, cellulose and hemicellulose levels more optimal for growth and productivity of white oyster mushrooms.
Metric Embedding into the Hamming Space with the n-Simplex Projection

Transformations of data objects into the Hamming space are often exploited to speed-up the similarity search in metric spaces. Techniques applicable in generic metric spaces require expensive learning, e.g., selection of pivoting objects. However, when searching in common Euclidean space, the best performance is usually achieved by transformations specifically designed for this space. We propose a novel transformation technique that provides a good trade-off between the applicability and the quality of the space approximation. It uses the n-Simplex projection to transform metric objects into a low-dimensional Euclidean space, and then transform this space to the Hamming space. We compare our approach theoretically and experimentally with several techniques of the metric embedding into the Hamming space. We focus on the applicability, learning cost, and the quality of search space approximation.
Cybersecurity Framework Requirements to Quantify Vulnerabilities Based on GQM

Of particular importance for an organization in building an effective and comprehensive secure system is to addressing a mechanism to provide a standard framework that is free from vulnerabilities. Cybersecurity experts and security requirement engineers have been addressing security issues that originated from cybersecurity requirements. Many security issues can be avoided if the security requirements are configured appropriately. In this paper, we proposed a hierarchy security requirements model based on the Goal Question Metrics (GQM) and its application mapped with the security standards towards constructing vulnerability measurements at the early stage of security development of the system design.
Long-term Autonomous Mission Planning of AUV in Large-scale Complex Marine Environment

Autonomous underwater vehicles (AUVs) have been widely used to perform marine missions. Long-term autonomous mission planning is the challenge issue that restricts their applications to the large-scale complex marine environment. In this paper, we proposed one mission planning model, which considers the various complex constraints, such as task attributes and marine environment constraints. We constructed the security situation map and formulated the mission planning into a multi-objective optimization problem that considers the time cost, the quality and quantity of completed tasks. Such a model has the extensibility features that you can associate your custom objective with extra constraints. Considering that this problem is an NP optimal problem, we used the metaheuristic algorithm -ant colony and its variants- to prove the correctness and effectiveness, and the performance of different variant algorithms are compared by the experimental simulations.
Modelling Dynamic Interactions Between Relevance Dimensions

Relevance is an underlying concept in the field of Information Science and Retrieval. It is a cognitive notion consisting of several different criteria or dimensions. Theoretical models of relevance allude to interdependence between these dimensions, where their interaction and fusion leads to the final inference of relevance. We study the interaction between the relevance dimensions using the mathematical framework of Quantum Theory. It is considered a generalised framework to model decision making under uncertainty, involving multiple perspectives and influenced by context. Specifically, we conduct a user study by constructing the cognitive analogue of a famous experiment in Quantum Physics. The data is used to construct a complex-valued vector space model of the user's cognitive state, which is used to explain incompatibility and interference between relevance dimensions. The implications of our findings to inform the design of Information Retrieval systems are also discussed.
Method of Forensic Research on Image for Finding Touch up on the Basis of Noise Entropy

This article discusses the methodology for determining the installation of digital images based on the entropy of noise. The distortion of the background noise occurs in certain cases (photomontage, retouching, etc.) in the corresponding areas of the image when digital editing tools are applied to it. Image studies were considered based on noise entropy analysis in order to identify retouching based on Comparison of the results of evaluating the level of clustering of the noise map of the investigated (retouched) fragment with the corresponding indicator of the control (original) image fragment.
A practitioner view of content strategy best practices in technical communication

Content strategy is a discipline pulled in two opposite directions: toward strategic concerns like how organizations should manage all existing content, and toward practical concerns like how to effective publish content within a specific medium. In this analysis of existing literature, we draw on past literature reviews to present a meta-analysis of best practices. By best practices we mean considerations for how to effectively work with content in a way that weds strategic and practical concerns. Content strategy has amassed a wide variety of literature in the past decade. It is time to put this literature to use by identifying best practices so that they can be tested, researchers, and taught.
Estimating CEFR reading comprehension text complexity

The purpose of this article is to present qualitative and quantitative tools to assist teachers and authors of examination materials in their preliminary assessment of text complexity/difficulty according to the Common European Framework of Reference for Languages. The qualitative scales were compiled specially for this study, based on previous research and development in the field. The quantitative scales are based on existing online instruments for stylistic and lexical analysis, adjusted to CEFR levels. Using these tools, we select and analyse texts which should represent appropriate difficulty at the various CEFR levels. The validity of the instruments is tested at B1 and B2 levels by inviting students assessed at these levels to translate the corresponding texts. The majority of the tested learners demonstrated understanding of at least 90% of the vocabulary of the text corresponding to their level. This confirms the validity of the tools proposed in assessing text complexity according to CEFR levels, at least as far as the intermediate levels B1 and B2 are concerned.
Secure list decoding

In this paper, we propose a new concept of secure list decoding. While the conventional list decoding requires that the list contains the transmitted message, secure list decoding requires the following additional security conditions. The first additional security condition is the impossibility of the correct decoding, i.e., the receiver cannot uniquely identify the transmitted message even though the transmitted message is contained in the list. This condition can be trivially satisfied when the transmission rate is larger than the channel capacity. The other additional security condition is the impossibility for the sender to estimate another element of the decoded list except for the transmitted message. This protocol can be used for anonymous auction, which realizes the anonymity for bidding.
Improving Speech Recognition with Drop-in Replacements for f-Bank Features

While a number of learned feature representations have been proposed for speech recognition, employing f-bank features often leads to the best results. In this paper, we focus on two alternative methods of improving this existing representation. First, triangular filters can be replaced with Gabor filters, a compactly supported filter that better localizes events in time, or with psychoacoustically-motivated Gammatone filters. Second, by rearranging the order of operations in computing filter bank features, the resulting coefficients will have better time-frequency resolution. By merely swapping f-banks with other types of filters in modern phone recognizers, we achieved significant reductions in error rates across repeated trials.
Improved MDL Estimators Using Local Exponential Family Bundles Applied to Mixture Families

The MDL estimators for density estimation, which are defined by two-part codes for universal coding, are analyzed. We give a two-part code for mixture families whose regret is close to the minimax regret, where regret of a code with respect to a target family ℳ is the difference between the codelength of the code and the ideal codelength achieved by an element in ℳ. Our code is constructed using a probability density in an enlarged family of ℳ (a bundle of local exponential families of ℳ) for data description. This result gives a tight upper bound on the risk of the MDL estimator defined by the two-part code, based on the theory introduced by Barron and Cover in 1991.