Reviewerly/exHarmony huggingface dataset:
Identification of coronary arteries in CT images by Bayesian analysis of geometric relations among anatomical landmarks

We propose a robust method for the identification of coronary arteries in computed tomography angiography (CTA) images. Utilizing geometric relations among the target and reference objects, which are assumed to follow a Gaussian distribution, an anatomic and geometric model is designed by Bayesian inference, which provides robust geometric priors for the target object localization. As a prerequisite process for the identification of coronary arteries, partially broken coronary artery segments found in CTA images are grouped and reconnected by geometric analysis of higher order curves connecting the broken segments. The geometric properties such as curvature and torsion represent naturalness and consistency between the vessel segments. As a problem to identify coronary arteries from CTA images, we demonstrate the robustness and accuracy of the proposed method in comparison with existing methods including commercial workstations on a variety of CTA cases.
Deep Regression Neural Network for End-to-End Person Re-Identification

Person re-identification can be seen as a process of open set recognition. Usually, the deep learning models consider the person re-identification model as a classification model with a softmax layer. However, the softmax layer cannot be extended to unknown classes because of its closed nature, so the classification model is just regarded as the feature extractor. To overcome the problem mentioned above and make the person re-identification process end-to-end, this paper cast the person re-identification into a regression process and calculates the probability that persons in the images belong to the same identity. First, this paper proposes a deep regression model, named deep regression neural network integrating adaptive multi-attribute fusion method (DRNN-AMAF), which can make the person re-identification as regression analysis. Second, attributes are taken as the basis of this model for calculating the probability of persons belonging to the same identity, and each attribute corresponds to each branch of the deep regression neural network. Finally, hard labels of multiple attributes are adaptively fused into a soft label by the proposed multi-label fusion method based on the idea of Bayesian inference, which makes the attribute labels suitable for regression tasks. The comprehensive experiments on available public databases are conducted, and the experimental results show that our model produces competitive performance compared with the state-of-the-art approaches.
Parsimony measures in multi-objective genetic programming for symbolic regression

We investigate in this paper the suitability of multi-objective algorithms for Symbolic Regression (SR), where desired properties of parsimony and diversity are explicitly stated as optimization goals. We evaluate different secondary objectives such as length, complexity and diversity on a selection of symbolic regression benchmark problems. Our experiments comparing two multi-objective evolutionary algorithms against standard GP show that multi-objective configurations combining diversity and parsimony objectives provide the best balance of numerical accuracy and model parsimony, allowing practitioners to select suitable models from a diverse set of solutions on the Pareto front.
Label-Distribution Learning-Embedded Active Contour Model for Breast Tumor Segmentation

Tumor segmentation is the foundation of breast ultrasound image analysis. However, intensity inhomogeneity occurred in ultrasound images results in the ambiguous segmentation. In order to tackle the challenge, this paper proposed label-distribution learning embedded active contour model for the breast tumor segmentation. Considering that reasonable exploitation of label ambiguity may help to improve performance, a deep pixel-wise label distribution learning model is first proposed to learn an ambiguous label map. The learned map is independent on the intensity variation, which is robust to the intensity inhomogeneity. After that, a novel label distribution learning embedded active contour model is proposed. The new energy function is developed by introducing the new label distribution fitting energy into the active contour model framework. The proposed new fitting energy can enforce the label of pixels to be similar to the learned distribution map, which improves the robustness to the intensity inhomogeneity. To demonstrate the effectiveness of the proposed method, we conduct the experiment on the breast ultrasound images database which consists of 135 benign cases and 51 malignant cases. Our experimental results demonstrated that the proposed method outperforms the state of the art.
An FPGA-oriented HW/SW Codesign of Lucy-Richardson Deconvolution Algorithm for Hyperspectral Images

Hyperspectral images acquired during remote sensing are usually corrupted by optical blur and additive noise. This article considers a method to reduce the spatial blur in hyperspectral images by applying the Lucy-Richardson deconvolution algorithm. The algorithm is based on convolution with a point-spread function, which is assumed to be known. The complete algorithm is implemented as a HW/Sw Co-Design between the central processing unit and the programmable logic. The convolution, as a time consuming operation, is accelerated in an FPGA. When compared to the software implementation, a speed-up by a factor of 2.7 is achieved when using the accelerated HW/SW Co-Design implementation. Hyperspectral images synthetically distorted by a known Gaussian blur and an additive noise are used for testing. The images are then restored by applying the Lucy-Richardson deconvolution algorithm on each spectral band.
Adapting ISO 27001 to a Public Institution

To better face the dangers and challenges of the cyberspace, a public enterprise decided to analyse and raise the security level of its information and communication systems by following good practices in this area. The adopted methodology was based on risk management and it aimed the establishment, implementation, maintenance and continuous improvement of an information security management system in accordance with the requirements of the NP ISO/IEC 27001:2013 standard. The implementation and measurement of security controls pertaining to four sections of this standard followed the PDCA continuous cycle with one iteration. The preliminary results showed relevant advances and we were able to identify the more pressing areas and which security controls needed improvement. As an additional benefit, this methodology fostered a change in the stance of the organization, which moved from having mere perceptions on its information security levels into getting an objective knowledge of its needs.
Scaling-up ESOP Synthesis for Quantum Compilation

Today's rapid advances in quantum computing hardware call for scalable synthesis methods to map combinational logic represented as multi-level Boolean networks (e.g., an and-inverter graph, AIG) to quantum circuits. Such synthesis process must yield reversible logic function since quantum circuits are reversible. Thus, logic representations using exclusive sum-of-products (ESOP) are advantageous because of their natural relation to Toffoli gates, one of the primitives in reversible logic. This motivates developing effective methods to collapse AIG logic networks into ESOPs. In this work, we present two state-of-the-art methods to collapse an AIG into an ESOP expression, describe their shortcomings and introduce a new approach based on the divide-and-conquer paradigm. We demonstrate the effectiveness of our method in collapsing IEEE-compliant half precision floating point networks. Results show that our method can collapse designs-which were previously not solvable within a week-in less than 5 minutes. We also describe a technique capable of taking advantage of this new method to generate quantum circuits with up to 50% fewer T gates compared to state-of-the-art methods.
A Systematic Mapping Study of Language Features Identification from Large Text Collection

Natural Language Processing <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> Henceforth: NLP is an emerging research area in today's era. The NLP resources are quite useful when it comes to building a machine capable of translating between linguistic pairs - a solution that strives to resolve the language barrier problems. Based on this premise, we are focusing our research on feature identification from large text collections of Albanian language. `Rule-based' or statistical Part-of-Speech <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">22</sup> Henceforth: POS (POS) taggers are sought to be utilized that would either need considerable time for rule development or a sufficient amount of manually labelled data. In light of this, the impact of this research is based on exploring numerous cases that are conducive to progress and further development of this field. One of the goals of this paper is to conduct a systematic review study; to explore and analyze existing research that seek to target low resources language such as is the case of the Albanian language. According to prior observation of published research conducted since 2015, we are focusing our research on studies that have been published in areas that are relevant to Natural Language Processing. Based on considerable load of related research on this field, it is essential to conduct a review and provide an outline of the research situation as well as current developments in this specific but important field of research.
Exploring the diversity of mental models associated with Belize’s Managed Access Fisheries Policy

An inclusive approach to natural resource management that considers diverse perspectives and experiences has been shown to contribute to policies that see greater engagement. Despite this, there remains ongoing discussion on the role of experts and the integration of local knowledge in the creation and implementation of natural resource policies. Indeed, varying types of experience will result in variations in the interpretation and adoption of policies. We conducted a cognitive mapping exercise with 54 fishers and 25 policymakers across Belize to investigate and test their perceptions around the recently implemented fisheries policy. Our findings highlight that fishers and policymakers held mental models that differed both in content and structure. Fishers’ mental models included all concepts selected by policymakers in addition to more concepts not selected by policymakers. Fishers who participated in a two-year pilot program still held different mental models than policymakers, while having similar models to fishers who did not participate in the pilot program. These variations suggest that the relevant experience of fishers and policymakers leads to unique interpretations of the policy, which can affect compliance and adaptation. Considering only one perspective in policy development without consideration for the motivations and objectives of all users may affect policy acceptance. More innovative and robust approaches to management may capture these perspectives in a holistic approach to policy creation and implementation.
Evolving convolutional neural networks through grammatical evolution

The use of Convolutional Neural Networks (CNNs) have proven to be a solid approach often used to solve many Machine Learning problems, such as image classification and natural language processing tasks. The manual design of CNNs is a hard task, due to the high number of configurable parameters and possible configurations. Recent studies around the automatic design of CNNs have shown positive results. In this work, we propose to explore the design of CNN architectures through the use of Grammatical Evolution (GE), where a BNF grammar is used to define the CNN components and structural rules. Experiments were performed using the MNIST and CIFAR-10 datasets. The obtained results show that the presented approach achieved competitive results and maintaining relatively small architectures when compared with similar state-of-the-art approaches.
Deep-AutoCoder: Learning to Complete Code Precisely with Induced Code Tokens

Code completion is an essential part of modern IDEs. It assists the developers to speed up the process of coding and reducing typos. In this paper, we exploit the deep learning technique called LSTM to learn language models over large code corpus and make predictions of code elements. Unlike natural language, the innumerable identifiers lead to the vocabulary explosion and more difficult to predict. Therefore, we propose a new approach, the Induced Token based LSTM, to deal with the massive identifiers, thus decrease the vocabulary size. In order to induce the code tokens, we present two approaches, one is a constraint character-level LSTM and the other one is encoding identifiers with various preceding context before feeding them into a token-level LSTM. Based on the two approaches, a tool named Deep-AutoCoder is developed and evaluated in two classic completion scenarios, that is, method invocation completion and random completion. The experiment results indicate that Deep-AutoCoder outperforms the state-of-the-arts on method invocation completion and random code completion. Additionally, the empirical results of Deep-AutoCoder indicate that reducing the size of vocabulary can effectively improve the precision of code completion.
Wearable Technologies in Education: A Design Space

Wearable Technologies have a tremendous potential to improve education, empowering students as well as instructors in their teaching and learning experiences. Beyond the affordances of head-mounted displays to present information and of smartwatches to passively monitor students, the variety of form factors and sensors available enable a large number of applications to be developed. Their features range from data collection and monitoring of students' behaviors and affective states, to timely delivery of personalized notifications, alerts and reminders. This paper provides an overview of how wearable technologies have been applied in educational settings in recent years. Drawing on insights from students' feedback and analysis of the literature, we discuss opportunities and challenges involved when enhancing teaching and learning using wearable applications. Based on findings from a user study, we report what students would like to have available from wearable applications. Lastly, we identify actual concerns related to wearable technologies and point out the major challenges and design implications for next-generation devices.
Improving Arabic neural machine translation via n-best list re-ranking

Even though the rise of the neural machine translation (NMT) paradigm has brought a great deal of improvement to the field of machine translation (MT), the current translation results are still not perfect. One of the main reasons for this imperfection is the decoding task complexity. Indeed, the problem of finding the one best translation from the space of all possible translations was and still is a challenging problem. One of the most successful ways to address it is via n-best list re-ranking which attempts to reorder the n-best decoder translations according to some defined features. In this paper, we propose a set of new re-ranking features that can be extracted directly from the parallel corpus without needing any external tools. The feature set that we propose takes into account lexical, syntactic, and even semantic aspects of the n-best list translations. We also present a method for feature weights optimization that uses a quantum-behaved particle swarm optimization algorithm. Our system has been evaluated on multiple English-to-Arabic and Arabic-to-English MT test sets, and the obtained re-ranking results yield noticeable improvements over the baseline NMT systems.
Franken-swarm

In the last 20 years, literally dozens of optimization algorithms based on swarm intelligence have been proposed. Particle Swarm Optimization, Artificial Bee Colony, Cuckoo Search, Firefly Optimization, and Cat Swarm Optimization are just a small sample of the exuberance of swarm-like algorithms. Although they differ in implementation details, they all share a common structure: an update rule is applied to each solution, followed by a drop rule that decides whether to keep the updated solution or not. In this poster we explore the idea of automatically generating swarm-like optimizers. Our proposal is divided in two stages: First we decompose popular, human-crafted, swarm-like optimizers such as PSO, CS, ABC (as well as DE/GA) into a list of basic rules. Second, we use Grammatical Evolution to procedurally generate variations on this base structure by recombining these operators. We generate three instances of algorithms, and observe that they have comparable performance to DE and PSO. Our framework will be useful to gain insight on the design space of meta-heuristics and the nature of swarm-like algorithms.
Purchased Fame

For many, a browsing session starts by entering relevant keywords in a popular search engine. The websites that users thereafter land on are often determined by their position in the search results. Although little is known about the proprietary ranking algorithms employed by popular search engines, it is strongly suspected that the incoming links have a significant influence on the outcome. This has lead to the inception of various black-hat SEO techniques that aim to deceive search engines to promote a specific website. In this paper, we present the first extensive study on the ecosystem of a novel type of black-hat SEO, namely the trade of artificially created backlinks through private blog networks (PBNs). Our study is three-pronged: first, we perform an exploratory analysis, through which we capture intrinsic information of the ecosystem and measure the effectiveness of backlinks. Next, we develop and present an ML-driven methodology that detects PBN sites with an accuracy of 98.7% by leveraging various content-based and linking-based features intrinsic to the operation of the ecosystem. Finally, in a large-scale experiment involving more than 50,000 websites, we expose large networks of backlink operations, finding thousands of websites engaged in PBNs.
FPGA-Based Fault-Tolerant Quadcopter with Fuzzy Obstacle Avoidance

Quadcopters have recently become a very important component in search and rescue missions. This paper proposes solutions to several problems facing quadcopters in such missions. First, it is proposed to enable the quadcopter to change its shape depending on whether it is in traveling or reconnaissance mode. Each flying mode will have a different controller architecture housed on an FPGA. Then, two specific hazards in disastrous areas, are addressed, namely radiation affecting system electronics and unclear paths. First, a fault-tolerant FPGA-based architecture is developed and it is proven through reliability modeling that it considerably extends lifetime. Second, fuzzy logic is used to design an obstacle avoidance system and its excellent performance in collision avoidance was shown through MATLAB simulations.
Efficient Hierarchical Identity-Based Encryption System for Internet of Things Infrastructure

Security is a main concern for the Internet of Things (IoT) infrastructure as large volumes of data are collected and processed in the systems. Due to the limited resources of interconnected sensors and devices in the IoT systems, efficiency is one of the key considerations when deploying security solutions (e.g., symmetric/asymmetric encryption, authentication, etc.) in IoT. In this paper, we present an efficient Hierarchical Identity-Based Encryption (HIBE) system with short parameters for protecting data confidentiality in distributed IoT infrastructure. Our proposed HIBE system has the public parameters, private key, and ciphertext, each consisting of a constant number of group elements. We prove the full security of the HIBE system in the standard model using the dual system encryption technique. We also implement the proposed scheme and compare the performance with the original Lewko–Waters HIBE. To the best of our knowledge, our construction is the first HIBE system that achieves both full security in the standard model and short parameters in terms of the public parameters, private key, and ciphertext.
Proving Multimedia Integrity using Sanitizable Signatures Recorded on Blockchain

While significant advancements have been made in the field of multimedia forensics to detect altered content, existing techniques mostly focus on enabling the content recipient to verify the content integrity without any inputs from the content creator. In many application scenarios, the creator has a strong incentive to establish the provenance and integrity of the multimedia data created and released by him. Hence, there is a strong need for mechanisms that allow the content creator to prove the authenticity of the released content. Since blockchain technology provides an immutable distributed database, it is an ideal solution for reliably time-stamping content with its creation time and storing an irrefutable signature of the content at the time of its creation. However, a simple digital signature scheme does not allow modification of the content after the initial commitment. Authorized multimedia content alteration by its creator is often necessary (e.g., redaction of faces to protect the privacy of individuals in a video, redaction of sensitive fields in a text document) before the content is distributed. The main contributions of this paper are: (i) a novel sanitizable signature scheme that enables the content creator to prove the integrity of the redacted content, while preventing the recipients from reconstructing the redacted segments based on the published commitment, and (ii) a blockchain-based solution for securely managing the sanitizable signature. The proposed solution employs a robust hashing scheme using chameleon hash function and Merkle tree to generate the initial signature, which is stored on the blockchain. The auxiliary data required for the integrity verification step is retained by the content creator and only a signature of this auxiliary data is stored on the blockchain. Any modifications to the multimedia content requires only updating the signature of the auxiliary data, which is securely recorded on the blockchain. We demonstrate that the proposed approach enables verification of integrity of redacted multimedia content without compromising the content privacy requirements.
Analysis to Heap Overflow Exploit in Linux with Symbolic Execution

Heap overflow is a common error of buffer overflow in Linux. The control flow of a program may be hijacked when the program satisfies several specific conditions. The existing automatic exploit generation technologies for buffer overflow find vulnerability trigger point and generate exploit by checking the control flow state. However, the heap overflow data rarely lead to a control flow hijacking as well as protection mechanisms limit the trigger condition. It is difficult to analyze the exploitability of heap overflow automatically through the existing analysis technology. For the heap overflow errors in Linux, we summarize the features of exploit on the basis of analyzing the instances, building the detection model of the exploitability of heap overflow, and proposing a method for analyzing the exploitability of heap overflow based on the model. The proposed method monitors the input data and insecurity functions of the program by using taint analysis; builds the path constraints and data constraints which satisfy the conditions of heap overflow exploit through selective symbolic execution; solves the abovementioned constraints and generates the test case automatically. All the steps of our method can be finished automatically by using the symbolic execution tool S2E. The experiments show that this method can automatically analyze and detect the exploitability of heap overflow errors.