Reviewerly/exHarmony huggingface dataset:
Towards Linking Camouflaged Descriptions to Implicit Products in E-commerce

As the emergence of E-commerce services, billions of products are sold online everyday. How to detect illegal products from the large-scale online products has become an important and practical research problem. In order to evade detection, malicious sellers usually utilize camouflaged text to describe their illegal products implicitly. Thus brings great challenges to the current detection systems since newly camouflaged text can hardly be learned from historical data and the distribution of illegal and normal products is extremely unbalanced. Rather than solving this problem as a classification task in most previous efforts, we reformulate the problem from a perspective of implicit entity linking, which targets at linking a camouflaged description to a known product. In this paper, we introduce three types of context that could help to infer implicit entity from camouflaged descriptions and propose an end-to-end contextual representation model to capture the effect of different context. Furthermore, we involve a symmetric metric to model the matching score of the input title to the product by learning the mutual effect among the context. The experimental results on the datasets collected from a real-world E-commerce site demonstrate the advantage of the proposed model against the state-of-the-art methods.
3D Motion Recovery via Low Rank Matrix Restoration with Hankel-Like Augmentation

This paper proposes a 3D skeleton recovery model equipped with a joint augmented low-rank and sparse prior and an articulation-graph-based isometric constraint to exploit temporal and spatial correlation, respectively. The corrupted 3D skeleton sequence is represented as a matrix and constrained by several priors in the proposed model. A Hankel-like augmentation is adopted to strengthen the low-rankness and we integrate a decoupling technique to reduce the internal interferences of the data. We solve our model via an alternating direction method under the augmented Lagrangian multiplier framework with a Gauss-Newton solver for the subproblem of isometric optimization. Experimental results on two skeleton datasets demonstrate the effectiveness and superiority of the proposed model in motion reconstruction and skeleton recovery, compared with state-of-the-art methods.
Occluded Object Tracking System (OOTS)

Visual object tracking remains a challenge facing an intelligent control system. A variety of applications serve many purposes such as surveillance. The developed technology faces plenty of obstacles that should be addressed including occlusion. In visual tracking, online learning techniques are most common due to their efficiency for most video sequences. Many object tracking techniques have emerged. However, the drifting problem in the case of noisy updates has been a stumbling block for the majority of relevant techniques. Such a problem can now be surmounted through updating the classifiers. The proposed system is called the Occluded Object Tracking System (OOTS) It is a hybrid system constructed from two algorithms: a fast technique Circulant Structure Kernels with Color Names (CSK-CN) and an efficient algorithm occlusion-aware Real-time Object Tracking (ROT). The proposed OOTS is evaluated with standard visual tracking benchmark databases. The experimental results proved that the proposed OOTS system is more reliable and provides efficient tracking results than other compared methods.
Multi-Branch Convolutional Network for Context-Aware Recommendation

Factorization Machine (FM)-based models can only reveal the relationship between a pair of features. With all feature embeddings fed to a MLP, DNN-based factorization models which combine FM with multi-layer perceptron (MLP) can only reveal the relationship among some features implicitly. Some other DNN-based methods apply CNN to generate feature interactions. However, (1) they model feature interactions at the bit-wise (where only part of an embedding is utilized to generate feature interactions), which can not express the semantics of features comprehensively, (2) they can only model the interactions among the neighboring features. To deal with aforementioned problems, this paper proposes a Multi-Branch Convolutional Network (MBCN) which includes three branches: the standard convolutional layer, the dilated convolutional layer and the bias layer. MBCN is able to explicitly model feature interactions with arbitrary orders at the vector-wise, which fully express context-aware feature semantics. Extensive experiments on three public benchmark datasets are conducted to demonstrate the superiority of MBCN, compared to the state-of-the-art baselines for context-aware top-k recommendation.
Secure Secret Sharing With Adaptive Bandwidth in Distributed Cloud Storage Systems

The development of cloud storage technology has brought us great convenience that we can store data in remote servers and access it on any connected device.However, the frequent leakage of private data has brought more and more attention to the protection of private data.To solve related problems, the distributed storage schemes have been proposed.Considering security and fault tolerance, many of those schemes adopt threshold secret sharing techniques which are wildly used in distributed storage systems with disaster tolerance function.Nevertheless, in practical situations, the bandwidth between users and different servers may be unbalanced or even unfixed, which leads to low communication efficiency of the schemes when adopting original secret sharing.To obtain higher communication efficiency under different communication loads, we proposed a novel adaptive bandwidth secret sharing scheme in distribution cloud storage systems.In addition, we consider the scenario in which we use Shamir's secret sharing scheme and Staircase codes respectively in order to improve the applicability of the adaptive bandwidth scheme.We make the comparative of performance analysis to show the advantages and disadvantages of these two schemes.In general, compared with the non-adaptive schemes, the proposed adaptive bandwidth scheme can make full use of unbalanced bandwidth and achieve higher average communication rate when the upper bound of bandwidth is large enough.
Novel Analytical Models of Face Recognition Accuracy in Terms of Video Capturing and Encoding Parameters

To fit the tight resource constraints, including network bandwidth, the video streams in Computer Vision systems are adapted dynamically by changing the video capturing and encoding parameters. We propose two novel analytical models that characterize the face recognition accuracy in terms of these parameters, specifically resolution, quantization, and actual bitrate. We find that the accuracy is a logistic function of the video quantization parameter, with the value of the Sigmoid's midpoint being a function of the resolution. Alternatively, we find that the accuracy is equal to the sum of two exponentials of the actual video bitrate, with the resolution as a multiplicative factor with one exponential. We develop an evaluation framework to validate the models using two distinct video datasets with 99 videos and the widely used Labeled Faces in the Wild (LFW) dataset with 13, 233 images. We conduct 1,668 experiments that involve varying combinations of encoding parameters. We show that both models hold true for the deep-learning and statistical-based face recognition. The developed models achieve an average coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) of 98.7% to 99.8%.
Sed-Net: Detecting Multi-Type Edits Of Images

As the editing of digital images is greatly facilitated by the powerful and convenient tools, detecting and tracing edit operations on images to deal with rampant pirate copies has recently attracted more and more attention. Especially, it is important to classify different types of image edits between an original image and an edited image. As there has been no work considering complex edit types like adding logos and it is possible to make multiple types of edits to an image, we propose a deep Siamese network model trained by a weighted sigmoid cross-entropy loss to increase recall while keeping high accuracy. We also construct a flexible dataset involving ten types of edits for this task to train and test the model. A detailed ablation analysis shows the effectiveness of each part of the model. The results of the experiments demonstrate that the model achieves high accuracy on ten types and can handle unseen types.
Data Visualization for Making Sense of Scientific Literature

In order to solve the problem that it is difficult for beginners to obtain information effectively when facing the visual design of scientific literature, this paper puts forward a visual design and analysis framework of scientific literature. By analyzing the attributes of existing scientific literature visualization design, such as chart type, interactivity, layout and label, the framework obtains the potential impact of scientific literature visualization design on audience understanding. At the same time, this method also puts forward the corresponding design strategy by making use of the properties of visual design tools, combined with the current situation of literature co-citation network visualization design. The research results show that the existing visual design of scientific literature can improve the readability and interpretability of visual design through interactive visualization and adaptive layout adjustment.
S-Semantics–an Example

The s-semantics makes it possible to explicitly deal with variables in program answers. So it seems suitable for programs using nonground data structures, like open lists. However it is difficult to find published examples of using the s-semantics to reason about particular programs. Here we apply s-semantics to prove correctness and completeness of Frühwirth’s n queens program. This is compared with a proof, published elsewhere, based on the standard semantics and Herbrand interpretations.
Rider-Rank Algorithm-Based Feature Extraction for Re-ranking the Webpages in the Search Engine

Abstract The webpage re-ranking is a challenging task while retrieving the webpages based on the query of the user. Even though the webpages in the search engines are ordered depends on the importance of the content, retrieving the necessary documents based on the input query is quite difficult. Hence, it is required to re-rank the webpages available in the websites based on the features of the pages in the search engines, like Google and Bing. Thus, an effective Rider-Rank algorithm is proposed to re-rank the webpages based on the Rider Optimization Algorithm (ROA). The input queries are forwarded to different search engines, and the webpages generated from the search engines with respect to the input query are gathered. Initially, the keywords are generated for the webpages. Then, the top keyword is selected, and the features are extracted from the top keyword using factor-based, text-based and rank-based features of the webpage. Finally, the webpages are re-ranked using the Rider-Rank algorithm. The performance of the proposed approach is analyzed based on the metrics, such as F-measure, recall and precision. From the analysis, it can be shown that the proposed algorithm obtains the F-measure, recall and precision of 0.90, 0.98 and 0.84, respectively.
Enhanced Tokenizer for Sinhala Language

Tokenization process plays a prominent role in natural language processing (NLP) applications. It chops the content into the smallest meaningful units. However, there is a limited number of tokenization approaches for Sinhala language. Standard analyzer in apache software library and natural language toolkit (NLTK) are the main existing approaches to tokenize Sinhala language content. Since these are language independent, there are some limitations when it applies to Sinhala. Our proposed Sinhala tokenizer is mainly focusing on punctuation-based tokenization. It precisely tokenizes the content by identifying the use case of punctuation mark. In our research, we have proved that our punctuation-based tokenization approach outperforms the word tokenization in existing approaches.
Navigation and Estimation Improvement by Environmental-Driven Noise Mode Detection

This paper deals with the state estimation of nonlinear stochastic dynamic systems, where the measurement noise is modelled by the multimodal Gaussian sum probability density function. The multimodal density is able to sufficiently capture various environmental features and phenomena affecting sensor readings. The design particularly focuses on the environmental-driven detector of the measurement noise mode for the terrain-aided navigation using a point-mass filter, which allows reducing the overall measurement noise variance used by the estimator and, consequently, decreasing the estimator or navigator error. Throughout the paper, the detector design and validation are illustrated with the help of a terrain-aided navigation system.
Adaptive Cooperative Navigation Strategies for Complex Environments

Multi-UAV Cooperation is a promising strategy to enhance navigation performance. In this framework, a UAV, termed chief, can exploit cooperative vehicles, deemed deputies, who share positioning information and enable relative measurements by ranging and/or bearing sensors, to improve its state estimation accuracy. Different cooperative strategies are available depending on the status of the formation. Specifically, based on the GNSS coverage conditions, cooperative aiding can be used to support position and/or attitude estimation. This paper introduces an adaptive navigation filter that automatically selects the relevant strategy to be used in each phase of flight. In addition, within a multi-UAV scenario, the effect of simultaneous exploitation of different cooperative navigation strategies onboard different UAVs is investigated. The adaptive filter is tested both on simulated and experimental flight data, showing the benefit of adaptively using shared data within the cooperative multi-vehicle system.
BAMSProd: A Step towards Generalizing the Adaptive Optimization Methods to Deep Binary Model

Recent methods have significantly reduced the performance degradation of Binary Neural Networks (BNNs), but guaranteeing the effective and efficient training of BNNs is an unsolved problem. The main reason is that the estimated gradients produced by the Straight-Through-Estimator (STE) mismatches with the gradients of the real derivatives. In this paper, we provide an explicit convex optimization example where training the BNNs with the traditionally adaptive optimization methods still faces the risk of non-convergence, and identify that constraining the range of gradients is critical for optimizing the deep binary model to avoid highly suboptimal solutions. Besides, we propose a BAMSProd algorithm with a key observation that the convergence property of optimizing deep binary model is strongly related to the quantization errors. In brief, it employs an adaptive range constraint via an errors measurement for smoothing the gradients transition while follows the exponential moving strategy from AMSGrad to avoid errors accumulation during the optimization. The experiments verify the corollary of theoretical convergence analysis, and further demonstrate that our optimization method can speed up the convergence about 1.2× and boost the performance of BNNs to a significant level than the specific binary optimizer about 3.7%, even in a highly non-convex optimization problem.
Rgbd-Fg: A Large-Scale Rgb-D Dataset For Fine-Grained Categorization

Fine-grained visual categorization (FGVC) has received a great deal of attention in recent years. Currently, several public datasets are available for FGVC. However, all these datasets were created with RGB images. RGB-D sensors can provide high-quality synchronized video in terms of both color and depth. In this paper, we introduce a multi-view, large-scale RGB-D dataset called RGBD-FG to establish a novel benchmark for FGVC in RGB-D images. RGBD-FG contains RGB data and the corresponding depth data of vegetables and fruits. Our dataset was captured by a depth sensor and contained 50 categories and a total of 93,051 RGBD images with labels, and organized in a hierarchical manner. Additionally, we used several strategies on our dataset for FGVC, including a multi-modal deep CNN. We present extensive experimental results to create state-of-the-art baselines for the dataset. We hope that this dataset can fill the gap of FGVC among the RGB-D datasets.
Joint motion and residual information latent representation for P-frame coding

This paper proposes an inter-frame prediction frame encoding for the P-frame video compression challenge of the Workshop and Challenge on Learned Image Compression (CLIC). For this challenge, we use an uncompressed reference (previous) frame to compress the current frame. So, this is not a complete solution for learning-based video compression. The main goal is to represent a set of frames with an average of 0.075 bpp (bits per pixel), which is a very low bitrate. A restriction on the model size is also requested to avoid overfitting. Here we propose an autoencoder architecture that jointly represents the motion and residue information at the latent space. Three trained models were used to achieve the target bpp and a bit allocation algorithm is also proposed to optimize the quality performance of the encoded dataset.
Neural Network Based Evil Waveforms Detection

Distortions of GNSS signals can lead to unacceptable pseudo-range errors. The object of study is a certain type of distortion - evil waveforms (EWF) - which is a rare perturbation occuring at the stage of signal generation. Detecting those distortions post-correlation traditionally involve designing hand crafted structure tests on a densely sampled autocorrelation function (ACF). However, traditional hand crafted tests have to be designed for specific scenarios hence lack flexibility compared to data-based methods. A neural network architecture capable of processing the structure of the ACF is proposed, implicitly learning structure tests, in order to tackle the evil waveforms detection problem.
A Study of Methods for the Generation of Domain-Aware Word Embeddings

Word embeddings are essential components for many text data applications. In most work, "out-of-the-box" embeddings trained on general text corpora are used, but they can be less effective when applied to domain-specific settings. Thus, how to create "domain-aware" word embeddings is an interesting open research question. In this paper, we study three methods for creating domain-aware word embeddings based on both general and domain-specific text corpora, including concatenation of embedding vectors, weighted fusion of text data, and interpolation of aligned embedding vectors. Even though the investigated strategies are tailored for domain-specific tasks, they are general enough to be applied to any domain and are not specific to a single task. Experimental results show that all three methods can work well, however, the interpolation method consistently works best.
New soft biometrics for limited resource in keystroke dynamics authentication

In recent years, some researches on free text authentication by keystroke dynamics have been proposed. The main problem of these proposed researches is the requirement of a very long training time. To increase users’ willingness to use the proposed authentication system, one simple and feasible way is shortening the training time. In this case, the training data is limited and is known as the limited resource problem. In this paper, we propose new soft biometrics and a new classifier for limited resources in free text authentication in English. Our new soft biometrics combines the idea of data mining and statistical prediction. Because our soft biometric is a mining result of limited resources, it is sensitive to outliers and a traditional statistical classifier cannot be applied. To solve this problem, the proposed classifiers considered the problem of an outlier and calculated the difference of cluster distribution. There are 114 participants in our experiments. Experimental results show that our approach can improve the accuracy of free text authentication in the case of limited resources.
HateSense: Tackling Ambiguity in Hate Speech Detection

Hate speech propagated online has been a long-trailing issue which induces several negative effects on society. The current efforts for the automated detection of hate speech online have utilized machine learning techniques in order to try and solve the issue as a classification problem. However, the significant drawback that has been identified in existing literature is that the inability of existing systems to tackle the ambiguity when it comes to hate speech detection, more specifically differentiating between hateful and offensive content. This research aims to tackle this issue of ambiguity in hopes of improving hate speech detection in general. The proposed system will utilize human reasoning techniques such as ontologies and fuzzy logics along with sentiment analysis in order to detect hate speech and deconstruct the ambiguity present. The results of the proposed approach show that the system can perform well when it comes to differentiating between hateful and offensive content and it is able to outperform existing systems in crucial factors. Yet, the deconstruction of ambiguity becomes difficult when there are a smaller number of hateful keywords present although the fuzzy control system was able to compensate in most cases. Thereby this research stresses the need for considering the disambiguation between hateful and offensive content when it comes to hate speech detection and utilization of human reasoning techniques to further facilitate this process.
Quantifying the similarity of 2D images using edge pixels: an application to the forensic comparison of footwear impressions

We propose a novel method to quantify the similarity between an impression (Q) from an unknown source and a test impression (K) from a known source. Using the property of geometrical congruence in the impressions, the degree of correspondence is quantified using ideas from graph theory and maximum clique (MC). The algorithm uses the x and y coordinates of the edges in the images as the data. We focus on local areas in Q and the corresponding regions in K and extract features for comparison. Using pairs of images with known origin, we train a random forest to classify pairs into mates and non-mates. We collected impressions from 60 pairs of shoes of the same brand and model, worn over six months. Using a different set of very similar shoes, we evaluated the performance of the algorithm in terms of the accuracy with which it correctly classified images into source classes. Using classification error rates and ROC curves, we compare the proposed method to other algorithms in the literature and show that for these data, our method shows good classification performance relative to other methods. The algorithm can be implemented with the R package shoeprintr.
METHODOLOGICAL FOUNDATIONS OF USER INVOLVEMENT RESEARCH: A CONTRIBUTION TO USER-CENTRED DESIGN THEORY

Abstract The concept of involving user perspectives into product development processes has its roots in the early 1960s. Although this seems to be following a quite long tradition, as a design research field, it did not improve substantially and, so far, no consistent perception or even definition of the concept can be found. The paper points out where design research on user involvement still lacks methodological and theoretical foundation and makes the attempt of providing impulses for systemizing the existing body of knowledge within the Design Society as a research community.