Reviewerly/exHarmony huggingface dataset:
Integrating Collaboration and Leadership in Conversational Group Recommender Systems

Recent observational studies highlight the importance of considering the interactions between users in the group recommendation process, but to date their integration has been marginal. In this article, we propose a collaborative model based on the social interactions that take place in a web-based conversational group recommender system. The collaborative model allows the group recommender to implicitly infer the different roles within the group, namely, collaborative and leader user(s). Moreover, it serves as the basis of several novel collaboration-based consensus strategies that integrate both individual and social interactions in the group recommendation process. A live-user evaluation confirms that our approach accurately identifies the collaborative and leader users in a group and produces more effective recommendations.
Deep Features Fusion with Mutual Attention Transformer for Skin Lesion Diagnosis

Early skin lesion diagnosis is crucial to prevent skin cancer, and deep learning (DL) based methods are well exploited to support dermatologists’ diagnosis. The data for the diagnosis tasks include dermoscopic lesion images and textual information. It is a challenge to learn features from the multimodal data to improve diagnostic quality. Inspired by the vision and language integration models in Visual Question Answer (VQA), we present an end-to-end neural network model for skin lesion diagnosis using both images and textual information simultaneously. Specifically, we fine-grained features from the two modalities (image and text) of the dataset by the pre-trained DL models. We propose a novel approach named Mutual Attention Transformer (MAT), which consists of self-attention blocks and guided-attention blocks, to enable the interactions between the features from both modalities concurrently. We then develop a fusion mechanism to integrate the represented features before the final classification output layer. The experimental results on the HAM10000 dataset demonstrate that the proposed method outperforms the state-of-art methods for skin lesion diagnosis.
Score-based likelihood ratios for linguistic text evidence with a bag-of-words model

The likelihood ratio paradigm for quantifying the strength of evidence has been researched in many fields of forensic science. Within this paradigm, score-based approaches for estimating likelihood ratios are becoming more prevalent in the forensic science literature. In this study, a score-based approach for estimating likelihood ratios is implemented for linguistic text evidence. Text data are represented via a bag-of-words model with the Z-score normalised relative frequencies of selected most-frequent words (the number of the most-frequent words = N), and the Euclidean, Manhattan and Cosine distance measures are trialled as the score-generating functions for comparing paired text samples. The score-to-likelihood-ratio conversion model was built using a common source method, and the best fitting model was selected from the parametric models of the Normal, Log-normal, Gamma and Weibull distributions. With the Amazon Product Data Authorship Verification Corpus, two groups of documents (each group including documents of approximately 700, 1400 and 2100 words) were synthesised for each author, allowing 720 same-author comparisons and 517,680 different-author comparisons to test the validity of the system. A series of experiments was conducted using combinations of the following conditions: the three score functions, the different values of N for the feature vector and the different document lengths. The validity of the system was assessed using the log-likelihood-ratio cost (Cllr), and the strength of the derived likelihood ratios was charted in the form of Tippett plots. It was demonstrated that 1) the Cosine measure consistently outperforms the other measures—the best performance is achieved with N = 260, regardless of the document length (e.g., Cllr values of 0.70640, 0.45314 and 0.30692, respectively, for 700, 1400 and 2100 words)—and 2) the derived likelihood ratios are very well calibrated irrespective of the distance measures and document lengths. A follow-up experiment showed that the described score-based approach is relatively robust and stable for a limited quantity of background data. The derived likelihood ratios that were estimated separately to the three distance measures were logistic regression fused; and the fusion achieved a further improvement in performance—for example, a Cllr of 0.23494 for 2100 words. This study demonstrates the possibility of designing likelihood ratio–based systems that discriminate between same-author and different-author documents.
Agora: A Privacy-Aware Data Marketplace

We propose Agora, the first blockchain-based data marketplace that enables multiple privacy-concerned parties to get compensated for contributing and exchanging data, without relying on a trusted third party during the exchange. Agora achieves <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data privacy</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">output verifiability</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">atomicity of payments</i> by leveraging cryptographic techniques, and is designed as a decentralized application via smart contracts. Particularly, data generators provide encrypted data to data brokers who use a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">functional secret key</i> to learn nothing but the output of a specific, agreed upon, function over the raw data. Data consumers can purchase decrypted outputs from the brokers, accompanied by corresponding <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">proofs of correctness</i> . We implement a working prototype of Agora on Ethereum and experimentally evaluate its performance and deployment costs. As a core building block of Agora, we propose a new <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">functional encryption</i> scheme with additional public parameters that operate as a trust anchor for verifying decrypted results.
SATDBailiff-mining and tracking self-admitted technical debt

Self-Admitted Technical Debt (SATD) is a metaphorical concept to describe the self-documented addition of technical debt to a software project in the form of source code comments. SATD can linger in projects and degrade source-code quality, but it can also be more visible than unintentionally added or undocumented technical debt. Understanding the implications of adding SATD to a software project is important because developers can benefit from a better understanding of the quality trade-offs they are making. However, empirical studies, analyzing the survivability and removal of SATD comments, are challenged by potential code changes or SATD comment updates that may interfere with properly tracking their appearance, existence, and removal. In this paper, we propose SATDBailiff, a tool that uses an existing state-of-the-art SATD detection tool, to identify SATD in method comments, then properly track their lifespan. SATDBailiff is given as input links to open source projects, and its output is a list of all identified SATDs, and for each detected SATD, SATDBailiff reports all its associated changes, including any updates to its text, all the way to reporting its removal. The goal of SATDBailiff is to aid researchers and practitioners in better tracking SATDs instances, and providing them with a reliable tool that can be easily extended. SATDBailiff was validated using a dataset of previously detected and manually validated SATD instances. SATDBailiff is publicly available as an open source, along with the manual analysis of SATD instances associated with its validation, on the project website.1
Captioning Transformer With Scene Graph Guiding

Image captioning is a challenging task which aims to generate descriptions of images. Most existing approaches adopt the encoder-decoder architecture, where encoder takes the image as input and decoder predicts corresponding word sequence. However, a common defect of these methods is that the abundant semantic relationships between relevant regions are ignored, leading the decoder to give a misled caption. To alleviate this issue, we propose a novel model, which utilizes sufficient semantic relationships provided by scene graph to guide the word generation process. To some extent, the scene graph narrows the semantic gap between images and descriptions, and hence improves the quality of generated sentences. Extensive experimental results demonstrate that our model achieves superior performance on various quantitative metrics.
Application of Intelligent Fuzzy Decision Tree Algorithm in English Teaching Model Improvement

As the number of students in universities continues to grow, the university academic management system has a large amount of data on student performance. However, the utilization of these data is only limited to simple query and statistical work, and there is no precedent of using these data for improving English teaching mode. With the application of fuzzy theory in machine learning and artificial intelligence, the fuzzy decision tree algorithm was born by integrating fuzzy set theory with decision tree algorithm. In this paper, we propose a way to obtain the centroids of continuous attribute clustering by K-means algorithm and combine the triangular fuzzy number to fuzzy the continuous data. In addition, this paper analyzes the influence of nearest neighbor distance on classification, introduces Gaussian weight function, gives different voting weights to the neighborhood according to the distance, and establishes a weighted K-nearest neighbor classification algorithm. To address the problem of low classification efficiency of K-nearest neighbor algorithm when the dataset is large, this paper further improves the algorithm and establishes the partitioned weighted K-nearest neighbor algorithm. The classification time was shortened from 11.39 seconds to 5.22 seconds, and the classification efficiency greatly improved.
Productivity Analysis Method based on Manufacturing Big-data using the FOM System in the FOMs Package

The key to transforming the manufacturing industry is the appropriate application of information technology and the utilization of data. Therefore, a method for efficient factory operation management, as well as IT system introduction, is important. From this, it is possible to create data-driven value and strengthen the competitiveness of enterprises. In this paper, we propose a FOMs (smart-Factory Operation Managements) Package method based on 4M data of manufacturing sites to improve productivity and competitiveness. In addition, we performed a case study by applying part of the FOMs Package to the manufacturer. We performed a detailed and multi-dimensional analysis of the MES/POP data that the manufacturer managed, and were able to derive not only the products that needed improvement but also related facilities, workers, downtimes, and defects. As a result, we found that the proposed method was effective in analyzing 4M manufacturing data and making decisions for factory operation management.
HRCP : High-ratio channel pruning for real-time object detection on resource-limited platform

Recently, deep learning algorithms have demonstrated remarkable performance in object detection tasks and have applied to many fields such as intelligent networked vehicles and drones. Different from other fields like video surveillance, the fields of intelligent networked vehicles and drones only have resource-limited platform and have high requirements in model storage and real-time inference. To satisfy these requirements, model compression approaches are developed to reduce model parameters and computation. As channel pruning is a coarse-grained hardware-friendly weight pruning method, it is widely used in model compression. Most studies have only focused on utilizing the scale factor of Batch Normalization (BN) layers to determine whether to prune a certain channel. However, the shift factor turns to very important when its value is large. The channel with large shift factor has a great influence on subsequent layers. In our paper, we take shift factor into consideration to protect these channels. We first introduce network building blocks to analyze YOLOv3-SPP3 model and demonstrate the importance of shift factor on the channel saliency in the pruning process. Then we present a new pruning method called high-ratio channel pruning (HRCP) to improve the performance of the pruned model based on definition of the channel saliency with scale factor and shift factor of BN layer. Our experimental results show that HRCP performs better than SlimYOLOv3 when model volumes are reduced by 50%, 88%, and 92% accordingly. In high-ratio pruning, our HRCP can be pruned more 9.7% volumes than SlimYOLOv3 under the same performance condition.
Local2Global: Unsupervised multi-view deep graph representation learning with Nearest Neighbor Constraint

Multi-view feature fusion is a vital phase for multi-view representation learning. Recently, most Graph Auto-Encoders (GAEs) and their variants focus on multi-view learning. However, most of them ignore deep representation fusion of features of each multi-view. Furthermore, there are scarcely unsupervised constraints guiding to enhance the graph representation capability in training process. In this paper, we propose a novel unsupervised Multi-view Deep Graph Representation Learning (MDGRL) framework on multi-view data which is based on the Graph Auto-Encoders (GAEs) for local feature leaning, a feature fusion module for producing global representation and a valid variant of Variational Graph Auto-Encoder (VGAE) for global deep graph representation learning. To fuse Nearest Neighbor Constraint (NNC) between the maximal degree nodes which represents the most close joining node and their adjacent nodes into VGAE, we propose a new Nearest Neighbor Constraint Variational Graph Auto-Encoder (NNC-VGAE) to enhance the global deep graph representation capability for multi-view data. In the training process of NNC-VGAE, NNC makes the adjacent nodes gradually close to the maximal degree node. Hence, the proposed MDGRL has excellent deep graph representation capability for multi-view data. Experiments on eight non-medical benchmark multi-view data sets and four medical data sets confirm the effectiveness of our MDGRL compared with other state-of-the-art methods for unsupervised clustering.
On the scalability of building binary decision diagrams for current feature models

Binary decision diagrams (BDD) have been proposed for numerous product-line analyses. These analyses typically exploit properties unique to decision diagrams, such as negation in constant time and space. Furthermore, the existence of a BDD representing the configuration space of a product line removes the need to employ SAT or #SAT solvers for their analysis. Recent work has shown that the performance of state-of-the-art BDD libraries is significantly lower than previously reported and hypothesized. In this work, we provide an assessment of the state-of-the-art of BDD scalability in this domain and explain why previous results on the scalability of BDDs do not apply to more recent product-line instances.
ScaffoldSketch: Accurate Industrial Design Drawing in VR

We present an approach to in-air design drawing based on the two-stage approach common in 2D design drawing practice. The primary challenge to 3D drawing in-air is the accuracy of users' strokes. Beautifying or auto-correcting an arbitrary drawing in 2D or 3D is challenging due to ambiguities stemming from many possible interpretations of a stroke. A similar challenge appears when drawing freehand on paper in the real world. 2D design drawing practice (as taught in industrial design school) addresses this by decomposing the process of creating realistic 2D projections of 3D shapes. Designers first create scaffold or construction lines. When drawing shape or structure curves, designers are guided by the scaffolds. Our key insight is that accurate industrial design drawing in 3D becomes tractable when decomposed into auto-correcting scaffold strokes, which have simple relationships with one another, followed by auto-correcting shape strokes with respect to the scaffold strokes. We demonstrate our approach's effectiveness with an expert study involving industrial designers.
PiSLTRc: Position-Informed Sign Language Transformer With Content-Aware Convolution

Since the superiority of Transformer in learning long-term dependency, the sign language Transformer model achieves remarkable progress in Sign Language Recognition (SLR) and Translation (SLT). However, there are several issues with the Transformer that prevent it from better sign language understanding. The first issue is that the self-attention mechanism learns sign video representation in a frame-wise manner, neglecting the temporal semantic structure of sign gestures. Secondly, the attention mechanism with absolute position encoding is direction and distance unaware, thus limiting its ability. To address these issues, we propose a new model architecture, namely PiSLTRc, with two distinctive characteristics: (i) content-aware and position-aware convolution layers. Specifically, we explicitly select relevant features using a novel content-aware neighborhood gathering method. Then we aggregate these features with position-informed temporal convolution layers, thus generating robust neighborhood-enhanced sign representation. (ii) injecting the relative position information to the attention mechanism in the encoder, decoder, and even encoder-decoder cross attention. Compared with the vanilla Transformer model, our model performs consistently better on three large-scale sign language benchmarks: PHOENIX-2014, PHOENIX-2014-T and CSL. Furthermore, extensive experiments demonstrate that the proposed method achieves state-of-the-art performance on translation quality with <inline-formula><tex-math notation="LaTeX">$+1.6$</tex-math></inline-formula> BLEU improvements.
Blind Reconstruction of BCH and RS Codes Using Single-Error Correction

In this paper, a blind reconstruction method of Bose-Chaudhuri-Hocquenghem (BCH) and Reed-Solomon (RS) codes is proposed, which aims to reconstruct the generator polynomials of BCH and RS codes from the intercepted noisy bitstream in non-cooperative environments.The previous blind reconstruction methods try to select correct (or noiseless) codewords or extract code information from the received bitstream. However, they need to have sufficiently correct codewords for successful reconstruction. Therefore, the existing methods are highly dependent on the channel conditions because a sufficient number of correct codewords should be available for achieving good reconstruction performance.To overcome such high dependency on channel conditions, the proposed blind reconstruction method performs error correction of single-error codewords through the proposed single-error correction method (SCM).Then, both the correct codewords and the corrected single-error codewords are utilized for blind reconstruction. Specifically, the correct codewords are selected based on the algebraic property of BCH and RS codes that codeword polynomials have consecutive roots. At the same time, the codewords presumably with a single error are selected and corrected based on the fact that the most of BCH and RS codeword polynomials with either a single error or double errors do not have consecutive roots. Based on this fact, SCM is devised, which selects and corrects the codewords with a single error with high probability. Finally, it is verified that the proposed blind reconstruction method shows much better reconstruction performance by using only one-tenth of the received codewords compared to the existing best methods even under harsh channel conditions.
CANNON: Reliable and Stealthy Remote Shutdown Attacks via Unaltered Automotive Microcontrollers

Electronic Control Units (ECUs) in modern vehicles have recently been targets for shutdown attacks, which can disable safety-critical vehicle functions and be used as means to launch more dangerous attacks. Existing attacks operate either by physical manipulation of the bus signals or message injection. However, we argue that these cannot simultaneously be remote, stealthy, and reliable. For instance, message injection is detected by modern Intrusion Detection System (IDS) proposals and requires strict synchronization that cannot be realized remotely. In this work, we introduce a new class of attacks that leverage the peripheral clock gating feature in modern automotive microcontroller units (MCUs). By using this capability, a remote adversary with purely software control can reliably "freeze" the output of a compromised ECU to insert arbitrary bits at any time instance. Utilizing on this insight, we develop the CANnon attack for remote shutdown. Since the CANnon attack produces error patterns indistinguishable from natural errors and does not require message insertion, detecting it with current techniques is difficult. We demonstrate this attack on two automotive MCUs used in modern passenger vehicle ECUs. We discuss potential mitigation strategies and countermeasures for such attacks.
Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge

Answer selection, which is involved in many natural language processing applications, such as dialog systems and question answering (QA), is an important yet challenging task in practice, since conventional methods typically suffer from the issues of ignoring diverse real-world background knowledge. In this article, we extensively investigate approaches to enhancing the answer selection model with external knowledge from knowledge graph (KG). First, we present a context-knowledge interaction learning framework, Knowledge-aware Neural Network, which learns the QA sentence representations by considering a tight interaction with the external knowledge from KG and the textual information. Then, we develop two kinds of knowledge-aware attention mechanism to summarize both the context-based and knowledge-based interactions between questions and answers. To handle the diversity and complexity of KG information, we further propose a Contextualized Knowledge-aware Attentive Neural Network, which improves the knowledge representation learning with structure information via a customized Graph Convolutional Network and comprehensively learns context-based and knowledge-based sentence representation via the multi-view knowledge-aware attention mechanism. We evaluate our method on four widely used benchmark QA datasets, including WikiQA, TREC QA, InsuranceQA, and Yahoo QA. Results verify the benefits of incorporating external knowledge from KG and show the robust superiority and extensive applicability of our method.