Reviewerly/exHarmony huggingface dataset:
A Survey on Digital Image Copy-Move Forgery Localization Using Passive Techniques

Digital images can be tampered easily with simple image editing software tools. Therefore, image forensic investigation on the authenticity of digital images’ content is increasingly important. Copy-move is one of the most common types of image forgeries. Thus, an overview of the traditional and the recent copy-move forgery localization methods using passive techniques is presented in this paper. These methods are classified into three types: block-based methods, keypoint-based methods, and deep learning-based methods. In addition, the strengths and weaknesses of these methods are compared and analyzed in robustness and computational cost. Finally, further research directions are discussed.
Collaborative filtering recommendation algorithm considering users’ preferences for item attributes

In the neighborhood-based collaborative filtering recommendation algorithm, the accuracy of the similarity calculation determines the quality of the recommendation algorithm directly. The traditional similarity measure only considers influence of common rated items among users, and ignores the attribute characteristics of users’ rated items. Low-precision similarity metrics reduce performance of recommended systems, when the dataset is extremely sparse. In order to solve above problems, this paper proposes a similarity measure model considering users’ preferences for item attributes. The model fully considers the user’s preferences for item attributes and co-rated items, and the number of co-rated items. The model establishes more connections between users and items, so as to mine user interests effectively and make it more in line with the actual application. The experimental results show that the model proposed by this paper is superior to other comparison methods in accuracy and diversity, which effectively improves the performance of the recommended algorithm.
Marketing the Library using Social Media Platforms: The Experience of the University Library Bochum, Germany

Social media is becoming more and more important as a marketing outlet for libraries. Understanding its significance, complexity, yet also its changing nature, is essential in order to productively implement social media into your library’s marketing strategy. This article will not only highlight the many benefits of this, but will also give you insight on how to get started.
Designing for the Infrastructure of the Supply Chain of Malay Handwoven Songket in Terengganu

The growing HCI interest in developing contexts and cultural craft practices is ripe to focus on their under-explored homegrown sociotechnical infrastructures. This paper explores the creative infrastructural actions embedded within the practices of songket's supply chain in Terengganu, Malaysia. We report on contextual interviews with 92 participants including preparation workers, weavers, designers, merchants, and customers. Findings indicate that increased creative infrastructural actions are reflected in these actors' resourcefulness for mobilizing information, materials, and equipment, and for making creative artifacts through new technologies weaved within traditional practices. We propose two novel approaches to design in this craft-based infrastructure. First, we explore designing for the social layer of infrastructure and its mutually advantageous exploitative relationships rooted in culture and traditions. Second, we suggest designing for roaming value-creation artifacts, which blend physical and digital materializations of songket textile design. Developed through a collaborative and asynchronous process, we argue that these artifacts represent less-explored vehicles for value co-creation, and that sociotechnical infrastructures as emerging sites of innovation could benefit from HCI research.
Social Microbial Prosthesis

In this paper, I speculate on a future where our need to socialize physically is solely to exchange bacteria. With our biological data being in the hands of private companies and governments and our environments' microbiomes becoming less diverse, our social systems, social identities and social interactions are redefined and reinvented to adapt to this new reality. In this world, everyone wears a "social microbial prosthesis" that analyzes their microbial composition from their breath and reveals sensitive information on their chest. The microbial prosthesis would be able to give off information not only on the microbial composition but also on the mental and physical health of a person. This second skin plays a role in controlling communication and interaction between people, where one is able to, by inspecting surrounding people's prosthesis, take careful considerations of who to interact with and who to avoid. Social Microbial Prosthesis is a critique of the race of private companies and governments to collect our biological data, the role of commercializing such data in shaping and changing our social identities and a response to the loss of microbial diversity in our environments due to our modern lifestyles and surroundings.
Differential uniformity and the associated codes of cryptographic functions

The associated codes of almost perfect nonlinear (APN) functions have been widely studied. In this paper, we consider more generally the codes associated with functions that have differential uniformity at least $ 4 $. We emphasize, for such a function $ F $, the role of codewords of weight $ 3 $ and $ 4 $ and of some cosets of its associated code $ C_F $. We give some properties on codes associated with differential uniformity exactly $ 4 $. We obtain lower bounds and upper bounds for the numbers of codewords of weight less than $ 5 $ of the codes $ C_F $. We show that the nonlinearity of $ F $ decreases when these numbers increase. We obtain a precise expression to compute these numbers, when $ F $ is a plateaued or a differentially two-valued function. As an application, we propose a method to construct differentially $ 4 $-uniform functions, with a large number of $ 2 $-to-$ 1 $ derivatives, from APN functions.
Single Image 3D Hand Reconstruction with Mesh Convolutions

Monocular 3D reconstruction of deformable objects, such as human body parts, has been typically approached by predicting parameters of heavyweight linear models. In this paper, we demonstrate an alternative solution that is based on the idea of encoding images into a latent non-linear representation of meshes. The prior on 3D hand shapes is learned by training an autoencoder with intrinsic graph convolutions performed in the spectral domain. The pre-trained decoder acts as a non-linear statistical deformable model. The latent parameters that reconstruct the shape and articulated pose of hands in the image are predicted using an image encoder. We show that our system reconstructs plausible meshes and operates in real-time. We evaluate the quality of the mesh reconstructions produced by the decoder on a new dataset and show latent space interpolation results. Our code, data, and models will be made publicly available.
Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections

Computing optimal transport (OT) between measures in high dimensions is doomed by the curse of dimensionality. A popular approach to avoid this curse is to project input measures on lower-dimensional subspaces (1D lines in the case of sliced Wasserstein distances), solve the OT problem between these reduced measures, and settle for the Wasserstein distance between these reductions, rather than that between the original measures. This approach is however difficult to extend to the case in which one wants to compute an OT map (a Monge map) between the original measures. Since computations are carried out on lower-dimensional projections, classical map estimation techniques can only produce maps operating in these reduced dimensions. We propose in this work two methods to extrapolate, from an transport map that is optimal on a subspace, one that is nearly optimal in the entire space. We prove that the best optimal transport plan that takes such detours is a generalization of the Knothe-Rosenblatt transport. We show that these plans can be explicitly formulated when comparing Gaussian measures (between which the Wasserstein distance is commonly referred to as the Bures or Frechet distance). We provide an algorithm to select optimal subspaces given pairs of Gaussian measures, and study scenarios in which that mediating subspace can be selected using prior information. We consider applications to semantic mediation between elliptic word embeddings and domain adaptation with Gaussian mixture models.
Gamification as a winning cyber security strategy

We are more connected than ever before. Our smart TVs and refrigerators, phones and drones, online bank accounts and electronic health records, and so much more, are on the web and in the cloud – connected and ‘talking’ to each other. While creating convenience for the end user, this growing interconnected digital footprint creates a ripe surface for a cyber criminal to attack. Just like in some video games, consumers and business leaders find themselves battling the consequences of interconnectivity and are trying to keep opponents from exploiting their information and damaging their reputation. In this “game of protection’ to balance defensive and offensive security techniques, now is the time for CISOs and business leaders to reach for a new cyber security manual – one that leverages gamification. This is the process of exploiting game-like elements to improve information retention and the application of skills, explains Brad Wolfenden of Circadence.
A novel modified training of radial basis network: Prediction of conversion and selectivity in 1-hexene dimerization process

In this study, a radial basis function neural network (RBF-NN) has been designed and constructed based on coupling the orthogonal least square (OLS) method, genetic algorithm (GA) and the gradient descent algorithm. By investigation of the effective parameters of basis function spreads on the performance of the RBF-NN, the basis function spreads and centers of the proposed RBF-NN have been obtained using OLS and GA simultaneously. The proposed technique has been applied to model 1-hexene dimerization process to evaluate its performance. The effects of temperature, pressure, Weight Hourly Space Velocity (WHSV) and Time On Stream (TOS) have been investigated on 1-hexene conversion and the dimer selectivity of 1-hexene dimerization process in the proposed scheme. The results obtained from the proposed scheme have been compared with those of other three methods; namely back propagation neural network, RBF-NN based on MATLAB neural network Toolbox (newrb) and an optimized RBF-NN model designed by Bataineh et al. The comparison of the results shows that the proposed, novel scheme outperforms the other alternative models with average Root Mean Square Error (RMSE) equal to 1.06 and the correlation coefficient (R2) of greater than 0.95 for output parameters.
Multi-Attention Network for Aspect Sentiment Analysis

Aspect sentiment analysis is a fine-gained task in sentiment analysis. In this paper, we propose a novel LSTM network model, which combines multi-attention and aspect contexts, i.e. LSTM-MATT-AC. Multi-attention mechanism that integrates the factors of location, content and class could adaptively capture important information in the contexts with the supervision of aspect targets. In other words, the model is more robust against irrelevant information. Simultaneously, aspect context mechanism extends differentiate left and right contexts given aspect targets and strengthens the expressive power of the model for handling more complication by mining deeper semantic information. Experiment results on SemEval2014 Task4 and Twitter datasets show that the accuracy of sentiment classification reaches 80.6%, 75.1% and 71.1% respectively. Compared to previous neural network-based sentiment analysis models, the accuracy has been further improved.
Deep learning for fast super-resolution reconstruction from multiple images

Recent advancements in single-image super-resolution reconstruction (SRR) are attributed primarily to convolutional neural networks (CNNs), which effectively learn the relation between low and high resolution and allow for obtaining high-quality reconstruction within seconds. SRR from multiple images benefits from information fusion, which improves the reconstruction outcome compared with example-based methods. On the other hand, multiple-image SRR is computationally more demanding, mainly due to required subpixel registration of the input images. Here, we explore how to exploit CNNs in multiple-image SRR and we demonstrate that competitive reconstruction outcome can be obtained within seconds.
A wavelet-based method for MRI liver image denoising

Image denoising stays be a standout amongst the primary issues in the field of image processing. Several image denoising algorithms utilizing wavelet transforms have been presented. This paper deals with the use of wavelet transform for magnetic resonance imaging (MRI) liver image denoising using selected wavelet families and thresholding methods with appropriate decomposition levels. Denoised MRI liver images are compared with the original images to conclude the most suitable parameters (wavelet family, level of decomposition and thresholding type) for the denoising process. The performance of our algorithm is evaluated using the signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR) and mean square error (MSE). The results show that the Daubechies wavelet family of the tenth order with first and second of the levels of decomposition are the most optimal parameters for MRI liver image denoising.
GestMan

We introduce GestMan, a cloud-based GESTure MANagement tool to support the acquisition, design, and management of stroke-gesture datasets for interactive applications. GestMan stores stroke-gestures at multiple levels of representation, from individual samples to classes, clusters, and vocabularies and enables practitioners to process, analyze, classify, compile, and reconfigure sets of gesture commands according to the specific requirements of their applications, prototypes, and interactive systems. Our online tool enables acquisition of 2-D stroke-gestures via a HTML5-based user interface as well as 3-D touch+air and webcam-based gestures via dedicated mappers. GestMan implements five software quality characteristics of the ISO-25010 standard and employs a new mathematical formalization of stroke-gestures as vectors to support efficient computation of various gesture features.
Adversarial Examples for Non-Parametric Methods: Attacks, Defenses and Large Sample Limits.

Adversarially robust machine learning has received much recent attention. However, prior attacks and defenses for non-parametric classifiers have been developed in an ad-hoc or classifier-specific basis. In this work, we take a holistic look at adversarial examples for non-parametric classifiers, including nearest neighbors, decision trees, and random forests. We provide a general defense method, adversarial pruning, that works by preprocessing the dataset to become well-separated. To test our defense, we provide a novel attack that applies to a wide range of non-parametric classifiers. Theoretically, we derive an optimally robust classifier, which is analogous to the Bayes Optimal. We show that adversarial pruning can be viewed as a finite sample approximation to this optimal classifier. We empirically show that our defense and attack are either better than or competitive with prior work on non-parametric classifiers. Overall, our results provide a strong and broadly-applicable baseline for future work on robust non-parametrics. Code available at this https URL .
Deep convolutional neural network based on densely connected squeeze-and-excitation blocks

Convolutional neural networks have achieved great successes in many visual tasks, as well as a good performance in various applications. However, research has yet to solve the practical problem of how to improve the recognition rate by increasing the depth of the network. The method proposed in this paper is based on the idea of a residual network, which shows that if a fast connection is added between network layers, the network will be deeper, the accuracy will be higher, and the training will be more efficient. The proposed Densely Squeeze-and-Excitation Network (DSENet) combines densely connected networks with the structure of a Squeeze-and-Excitation Network (SENet). The proposed structure is based on layers comprising blocks of compressed networks, and DSENet enables each of these layers to connect directly to all previous layers with adaptive weights. DSENet has many advantages, such as a high utilization rate of parameters and the ability to alleviate the disappearance of a gradient. The performance of the proposed network is evaluated on the CIFAR-10 and CIFAR-100 datasets, and experiments show that DSENet achieves better results on those datasets when compared with the most popular convolutional networks in current use.
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment

In most machine learning training paradigms a fixed, often handcrafted, loss function is assumed to be a good proxy for an underlying evaluation metric. In this work we assess this assumption by meta-learning an adaptive loss function to directly optimize the evaluation metric. We propose a sample efficient reinforcement learning approach for adapting the loss dynamically during training. We empirically show how this formulation improves performance by simultaneously optimizing the evaluation metric and smoothing the loss landscape. We verify our method in metric learning and classification scenarios, showing considerable improvements over the state-of-the-art on a diverse set of tasks. Importantly, our method is applicable to a wide range of loss functions and evaluation metrics. Furthermore, the learned policies are transferable across tasks and data, demonstrating the versatility of the method.
Hand Gesture Recognition Software Based on Indian Sign Language

Hand gestures are a powerful environment for communicating with communities with intellectual disability. It is useful for connecting people and computers. The expansion potential of this system can be known in public places where deaf people are communicating with ordinary people to send messages. In this article, we have provided a system of recognizing gestures continuously with the Indian Sign Language (ISL), which both hands are used to make every gesture. Gesture recognition continues to be a daunting task. We tried to fix this problem using the key download method. These key tips are useful for breaking down the sign language gestures into the order of the characters, as well as deleting unsupported frameworks. After the splitting gear breaks each character is regarded as a single and unique gesture. Pre-processing gestures are obtained using histogram (OH) with PCA to reduce the dimensions of the traits obtained after OH. The experiments were performed on our live ISL dataset, which was created using an existing camera.
High-throughput and lightweight hardware structures of HIGHT and PRESENT block ciphers

In this paper, low-cost and high-throughput hardware implementations of the HIGHT and PRESENT lightweight block ciphers are presented. One of the most complex blocks in the HIGHT algorithm is addition modulo 28. In the proposed structure for implementation of this modular adder, we used the structure of Ladner-Fischer, Han-Carlson, Kogge-Stone, and Sklansky adders, which are parallel prefix adders with low critical path delay and suitable hardware resources. In the PRESENT block cipher, for two key lengths 80-bit and 128-bit, the S-box is implemented based on an area-optimized combinational logic circuit. In the proposed S-box structure, the number of logic gates and critical path delay are reduced by using the simplification of the Algebraic terms. Also, to reduce the latency and increase throughput, the loop unrolling technique is applied in the structures. We implemented glitch filtering on hardware structures of the HIGHT and PRESENT block ciphers based on the latch-based method to reduce the power consumption. Also, the power analysis, timing attacks and hardware-based countermeasures based on an experimental setup are performed. It is claimed that the current and power profiles are unified and there will be no power consumption difference when encryption operation. Implementation results of the proposed architectures based on Virtex-5 XC5VLX50 and Spartan-3 XC3S200 FPGAs for different unroll factors are achieved. The results show the structures are robust against attacks and improvements in terms of execution time, throughput and throughput/area compared to others related works.
Content-Preserving Tone Adjustment for Image Enhancement

We propose a novel method based on Convolutional Neural Networks for content-preserving tone adjustment. The method is at the same time fast and accurate since we decouple the inference of the parameters and the color transform: the parameters are inferred from a downsampled version of the input image and the transformation is applied to the full resolution input. The method includes two steps of image enhancement: the first one is a global color transformation, while the second one is a local transformation. Experiments conducted on the DPED - DSLR Photo Enhancement Dataset, that has been used for the NTIRE19 Image Enhancement Challenge, and on the MIT-Adobe FiveK dataset, that is widely used for image enhancement, demonstrate the effectiveness of the proposed method.
Effect of JPEG Quality on Steganographic Security

Abstract This work investigates both theoretically and experimentally the security of JPEG steganography as a function of the quality factor. For a fixed relative payload, modern embedding schemes, such as J-UNIWARD and UED-JC, exhibit surprising non-monotone trends due to rounding and clipping of quantization steps. Their security generally increases with increasing quality factor but starts decreasing for qualities above 95. In contrast, old-fashion steganography, such as Jsteg, OutGuess, and model-based steganography, exhibit complementary trends. The results of empirical detectors closely match the trends exhibited by the KL divergence computed between models of cover and stego DCT modes. In particular, our analysis shows that the main reason for the complementary trends is the way modern schemes attenuate embedding change rates with increasing spatial frequency. Our model also provides guidance on how to adjust the embedding algorithm J-UNIWARD to substantially improve its security for high quality factors.