Reviewerly/exHarmony huggingface dataset:
Explanation of Machine Learning Classification Models with Fuzzy Measures: An Approach to Individual Classification

In the field of Machine Learning, there is a common point in almost all methodologies about measuring the importance of features in a model: estimating the value of a collection of them in several situations where different information sources (features) are available. To establish the value of the response feature, these techniques need to know the predictive ability of some features over others. We can distinguish two ways of performing this allocation. The first does not pay attention to the available information of known characteristics, assigning a random allocation value. The other option is to assume that the feasible values for the unknown features have to be any of the values observed in the sample (in the known part of the database), assuming that the values of the known features are correct. Despite its interest, there is a serious problem of overfitting in this approach, in situations in which there is a continuous feature: the values of a continuous feature are not likely to occur in any other, so there is a large loss of randomization (there will surely be an insignificant number of records for each possible value). In this scenario, it is probably unrealistic to assume a perfect estimation. Then, in this paper we propose a new methodology based on fuzzy measures which allows the analysis and consideration of the available information in known features, avoiding the problem of overfitting in the presence of continuous features.
Equitable Access to Intelligent Tutoring Systems Through Paper-Digital Integration

Intelligent Tutoring Systems (ITS) can only respond adaptively to the digital learning activities of the students. If students are learning offline without any digital devices, they have little or no means to receive personalized learning materials with the help of intelligent systems. This paper proposes a Paper-Digital Integration System that can provide offline learners equitable access to ITS capabilities by looking at their work on paper and giving personalized printable feedback. We analyzed data from a paper algebra assessment of N = 17 students and found mistakes that may generalize and help us offer adaptive paper-based recommendations to students. Our analysis showed us some specific algebra mistakes that may help in providing intelligent feedback.
Research on the Development of Ship Target Detection Based on Deep Learning Technology

The rapid development of inland shipping and marine shipping has promoted the economic growth, but it also makes the frequency of ship collision, ship Landing and other accidents more and more high, illegal fishing, illegal ship parking and other phenomena often occur. Therefore, the realization of automatic detection of ship targets is of great significance to the management of surface ships. Combined with the advantages of deep learning in big data feature learning, this paper proposes a fast ship target detection method based on deep learning.
The Karlsruhe Java Verification Suite

Thanks to the deductive verifier KeY, the formal verification of Java programs has a long-standing tradition at Karlsruhe. The design of KeY implies some properties that can restrict its use in real-world application cases: (1) Verifying long, code-intensive methods with many instructions or bit-wise operations is difficult even if their behaviour is not overly complex, and (2) tracking formal guarantees through unverified code is difficult if not impossible using KeY . To mitigate these weak spots, we introduce the Karlsruhe Java Verification Suite, a collection of formal Java verification tools that work with the Java Modeling Language (JML). Complementing KeY, the suite comprises JJBMC, a bounded model checker for Java and JML and the Property Checker, a type checker for user-defined property types. In this paper, we first discuss formally how tools sharing a common specification language can share distributed obligations in a general setting, and then specialise this to the case of Java and our tool suite. In a case study, we show that the Karlsruhe Java Verification Suite can verify a program that none of the three components could have proved alone.
Contextual Attention Network for Emotional Video Captioning

This paper investigates an emerging and challenging task—emotional video captioning. Formally, given a video, the task aims to not only describe the factual content of the video, but also discover the emotional clues in the video. We propose a novel Contextual Attention Network (CANet), which recognizes and describes the fact and emotion in the video by semantic-rich context learning. To be specific, at each time step, we first extract visual and textual features from both input video and previously generated words. Then, we apply the attention mechanism to these features to capture informative contexts for captioning. We train the CANet model with the joint optimization of cross-entropy loss <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {L}_{CE}$</tex-math></inline-formula> and contrastive loss <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {L}_{CL}$</tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {L}_{CE}$</tex-math></inline-formula> constrains the semantics of the generated sentence to be close to human annotation and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {L}_{CL}$</tex-math></inline-formula> encourages discriminative representation learning from positive and negative pairs of video and caption. Experiments on two emotional video captioning datasets (i.e., EmVidCap and EmVidCap-S) demonstrate the superiority of CANet compared to the state-of-the-art approaches.
Multi-vendor Software Ecosystem: Challenges from Company’ Perspective

Software ecosystem is the term used for the distributed development of software when a company outsources a whole or a part of their product to single or multiple vendors. Along with the various benefits, outsourcing comes up with various challenges for companies as well as vendors. As there are many studies conducted to identify issues faced by vendors, this study intends to summarize the challenges faced by companies who outsource and then extends the challenges list by defining new challenges which can be observed in the multivendor ecosystem. Defined new challenges are preliminary in nature, thus open for discussion and validation through case studies or other means.
MASSTagger: Metadata Aware Semantic Strategy for Automatic Image Tagging

As the social media usage is expanding and people are using more and more platforms to share their stories through pictures, the use of image tags is becoming more relevant in the social media world. On social media. It is often seen that the option of tagging the images that are uploaded. Since image tagging is optional to the users, many images are posted untagged. Now the problem which arises is without image tags these images are hard to find. To overcome this issue many automatic tagging tools have been proposed. But image tagging requires precision and accuracy so that the images are tagged automatically based on the content in them. The proposed methodology uses Structural Topic Modelling and semantic similarity to tag the images and classify them into their respective categories.
From Verified Scala to STIX File System Embedded Code Using Stainless

We present an approach for using formal methods in embedded systems and its evaluation on a case study. In our approach, the developers describe the system in a restricted subset of the high-level programming language Scala. We then use 1) a verification system to formally prove properties of such Scala program, and 2) a source-to-source translator to map Scala to C code. We have adapted the Stainless verification system to support constructs for describing embedded software (more machine integer types and early returns) and to support verification patterns needed for embedded systems code (array swap operation, pre-allocated and initialized memory, constant-length arrays). The implemented C code translator generates code that can be compiled with compilers such as GCC and integrated into larger C applications. We evaluate our approach on a case study of a file system of an instrument on the Solar Orbiter satellite. We have ported around a thousand lines of C code to Scala. We wrote specification and proof hints to make the code verify. Stainless verified the absence of run-time errors, as well as function preconditions, postconditions, and data structure invariants. The generated C code was integrated into the existing code base and exhibits very similar code size, memory use, and performance. In this process we identified multiple bugs in the well-tested code base, which were fixed in-orbit.
Developing Personas for Designing Health Interventions

As more human needs are addressed with technology, designing positive user experiences becomes increasingly important in developing effective health interventions. Designing successful user experiences for digital health interventions requires a deep understanding of patient challenges. In this paper, we attempted to identify challenges that diabetic patients face adhering to guideline-recommended care through persona development. Previous user experience research suggests that such an approach can be particularly beneficial in designing digital health interventions. We explain how we developed data personas from Electronic Health Records (EHR) and combined them with proto personas that were generated by a group of medical experts for the same patient population. Our results support previous research that suggests combining data and proto personas is beneficial for intervention design. Additionally, our results reveal that combining data and proto personas is likely to improve intervention design by addressing fairness issues that may result from the underrepresentation of certain populations in EHR datasets.
Vulnerability of Feature Extractors in 2D Image-Based 3D Object Retrieval

Recent advances in 3D modeling software and 3D capture devices contribute to the availability of large-scale 3D objects. Together with the prevalence of deep neural networks (DNNs), DNN-based 3D object retrieval systems are widely applied, especially by inputting 2D images to retrieve 3D objects. Although DNNs have shown vulnerable to adversarial attacks in classification, the vulnerability of DNN-based 3D object retrieval system remains under-explored. In this paper, we formulate the problem of attacking against DNN-based feature extractors in the 2D image-based 3D object retrieval system. Specifically, we consider the attack happens under a reasonable scenario that the candidate 3D object database is unknown to the adversary, which challenges adversarial example generation. To tackle this difficulty, we set up a reasonable hypothesis on the information which the adversary can be accessible, and then propose two effective perturbation generation methods: one is to corrupt domain-level alignment (CDA) and the other one is to corrupt class-level alignment (CCA). In converse, we propose a novel progressive adversarial training (PAT) method to improve the feature extractor robustness, which can effectively and stably mitigate both CDA and CCA attacks. Experimental results demonstrate that a typical feature extractor can be effectively compromised by attacks. Moreover, the transferability of the adversarial query illustrates the possibility of realistic black-box attacks. The successful defense against both CDA and CCA attacks by PAT can validate the superiority of the proposed defense method.
Homomorphic Method Additive Using Pailler and Multiplicative Based on RSA in Integers Numbers

When we use conventional encryption, we are confronted with a situation we are used to and don’t even consider it a problem. To work with encrypted data, we have to decipher it, and then it can become the property of the attackers. Homomorphic encryption implies that you can perform operations on encrypted text and get a perfect result without decrypting the text. For example, such a scheme can be used in e-elections (counting votes while preserving voters’ anonymity), in cloud computing, insecure search (delivering the result without analyzing its real content), or feedback systems. This document aims to create a crypto-system that allows making calculations on encrypted data, more particularly on integers, and combining two encryption algorithms, additive homomorphic following paillier method, and multiplicative homomorphic applying RSA. All this to guarantee the security of calculations in cloud storage.
Incorporating Prior Knowledge in Local Differentially Private Data Collection for Frequency Estimation

Local differential privacy (LDP) is a prevalent measure of privacy protection as it provides rigorous privacy guarantees and has been widely studied for statistical analysis, especially in frequency estimation. As a representative LDP-enabled frequency estimation algorithm, Google's <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Randomized Aggregation Privacy-Preserving Ordinal Response</i> (RAPPOR) has been put into practice. However, it achieves sub-optimal utility due to the following limitations. Firstly, the adoption of the MD5 hash function inevitably results in the hash collision. Secondly, the application of the randomized response technique leads to randomness. To improve the practical effectiveness of RAPPOR and the utility of frequency-based services, we propose an LDP-enabled frequency estimation method called PK-RAPPOR, in which we devise an effective re-encoding hash function (RE-HF) incorporating prior knowledge (PK) about the rough frequency ranking of items. RE-HF divides items into several cohorts based on the PK and generates a unique hash value set for each item. Compared with the original RAPPOR, the hash collision can be eliminated for items from different cohorts, and the effect of randomness can be decreased by the overlapping of items from the same cohorts. We validate our proposed method with theoretical analysis and demonstrate its effectiveness with experiments on both synthetic and real-world datasets.
AES S-Box Hardware With Efficiency Improvement Based on Linear Mapping Optimization

This brief presents a new Advanced Encryption Standard (AES) S-Box hardware design based on linear mappings optimized by combining multiplicative and exponential offsets. Generally, the performance of S-Box with composite field representations depends on the structure of linear mappings (i.e., transformation matrices) between the polynomial field and composite field before and after the S-Box. So far, multiplicative and exponential offsets have been applied only to Boyar-Peralta type S-Box variants for optimizing the transformation matrix. In this brief, we apply the offset methods to another S-Box based on the redundant Galois field arithmetic and evaluate the performance by logic synthesis. Specifically, we design and evaluate two new types of S-Box hardware: one for encryption only (ENC) and the other for both encryption and decryption (ENC/DEC). The evaluation result confirms that the proposed ENC and ENC/DEC S-Boxes achieve a performance up to 8.7% and 28.8% higher, respectively, than the highest-performing conventional ones in terms of the area timing (AT) product.
Unpaired Image-to-Image Translation Using Negative Learning for Noisy Patches

Unpaired image-to-image translation finds a mapping between two domains that do not have paired data. One approach is patchwise contrastive learning, a one-sided translation that maximizes mutual information between corresponding input and output patches. Noncorresponding patches are treated as negatives. Previous approaches randomly select noncorresponding patches, resulting in semantically similar patches incorrectly labeled as negatives. Inspired by negative learning, we propose the novel patchwise negative learning loss to address this issue. We do not naively minimize mutual information between all noncorresponding ones, unlike prior methods. Instead, we choose one noncorresponding patch and maximize dissimilarity with the query patch. The selected noncorresponding patch reduces the chance of choosing false negatives that contain high mutual information. By further maximizing dissimilarity with that single negative, we discourage our model from fitting on noisy negative patches. We demonstrate the capabilities of our model against other prominent image translation methods on the selfie2anime, horse2zebra, and cat2dog datasets.
An Efficient High-Throughput Structured-Light Depth Engine

In this article, an efficient high-throughput depth engine is proposed to generate high-quality 3-D depth maps for speckle-pattern structured-light depth cameras. A dynamic-binarization (DB) method is introduced with a significant reduction of computational complexity in contrast to the sum-of-absolute-distance (SAD) method. The depth map evaluation shows good robustness compared with other window-based correlation methods. Parallel architecture and reuse of intermediate results are employed for efficient hardware implementation. Our design is verified on a field-programmable gate array (FPGA) and implemented in the SMIC 55-nm CMOS technology, achieving a frame rate of 1731.77 fps ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$640\times480$ </tex-math></inline-formula> ) with an area efficiency of 3.75 fps/KGE. The proposed engine shows a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.71\times $ </tex-math></inline-formula> promotion of area efficiency in contrast to the SAD-based implementation. In addition, the subpixel estimation algorithm deployed in postprocessing is optimized for efficient hardware implementation, reducing the gate count by 69.2% without significant performance loss.
A Novel Data Reutilization Strategy for Real-Time Hyperspectral Image Compression

The HyperLCA compressor is a transform based algorithm specifically designed for the real-time compression of hyperspectral images captured by pushbroom scanners, using limited computational resources. It is based on the HyperLCA Transform, which follows an unmixing-like strategy to independently compress each hyperspectral frame in a causal manner. A novel approach with respect to the original HyperLCA Transform is introduced in this work. By reusing the information used to compress one frame in the subsequent frames, it has been possible to increase the HyperLCA Transform compression performance and to reduce its computational burden. Additionally, the proposed approach is applicable not only to the targeted compressor but also to other causal hyperspectral analysis algorithms based on orthogonal projections and/or unmixing like strategies. The proposed solution has been tested in a real UAV-based acquisition platform, demonstrating the ability of our proposal to compress and transmit the captured hyperspectral data to a ground station in real-time.
SusRec: An Approach to Sustainable Developer Recommendation for Bug Resolution Using Multimodal Ensemble Learning

The sustainability of an open source project is essential for the long-term and reliable development of software. Most existing studies focus on the recommendation accuracy of bug report assignment while ignoring inexperienced developers in the open source community. This gives inexperienced developers less opportunity to resolve bugs and can cause them to gradually lose interest in the development of open source software (OSS). To address this problem, this article proposes a novel approach called sustainable recommender (SusRec) to make sustainable report assignments without sacrificing accuracy. The SusRec approach is based on multimodal learning and ensemble learning, and it consists of two stages: the preprocessing stage and the developer scoring stage. In the preprocessing stage, the approach selects candidate developers who have participated in the resolution of bugs under the product of a new bug report. It then divides the candidate developers into three types—core developers, active developers, and peripheral developers—according to their experience. In the developer scoring stage, multimodal learning is adopted to score the three types of bug report–developer pairs, and ensemble learning is adopted to weight the scores of the three types of bug report–developer pairs and recommend developers for bug reports. We conduct extensive experiments using the bug repositories of the Eclipse and Mozilla projects to compare the proposed SusRec approach with the baseline methods in bug report assignment. The results demonstrate that the proposed SusRec approach cannot only improve the accuracy of developer recommendations for bug reports, but also the sustainability of OSS projects by providing more opportunities for active developers and peripheral developers to participate in bug resolution.
Relation and Entropy Weight-Aware Knowledge Graph Embedding for Cloud Manufacturing

Cloud manufacturing has emerged as a service-oriented paradigm, in which knowledge graphs (KGs) play a crucial role in enabling modularization and on-demand servitization by converting unstructured resources into a structured graph representation and factual knowledge for manufacturing tasks. KG embedding converts entities and relations into a low-dimensional space while expressing rich semantics of high-dimensional KGs. Existing works just focus on translating relations surrounding the nodes of graphs, while ignoring the applicability for modeling and inferring multiple relation patterns in the manufacturing context. Furthermore, the relative importance of complex manufacturing relations among different dimensions in embedded procedures has been ignored, leading to unclear representation learning of complex relations and entities in KGs. To overcome this issue, in this article, a novel relation and entropy weight-aware embedding model is proposed, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TransCE</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TransCE</i> performs coordinated transformations in vector space by normalizing the distance to integrate coordination. An entropy-based weighting method is also proposed to represent complex relations and entities surrounding the edges of graph embedding and assign the weighted value of relations to support the score function. Extended experiments are performed on several datasets, and a manufacturing indicates that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TransCE</i> shows remarkable improvement relative to baseline models.