Reviewerly/exHarmony huggingface dataset:
Learning to Style-Aware Bayesian Personalized Ranking for Visual Recommendation

Recently, product images have been gaining the attention of recommender system researchers in the field of visual recommendation.This is because the visual appearance of products has a significant impact on consumers' decisions.Extensive studies have been done to integrate the features extracted by convolutional neural networks directly into recommendations.This improves the performance of recommender systems.Style features, an important type of features, are rarely considered.Style features play a vital role in the visual recommendation as a user's decision depends largely on whether the product fits his/her style.However, the representation of the conventional image features fails in capturing the styles of a product.To bridge this gap, we propose introducing style feature modeling, which is highly relevant with user preference, into the visual recommendation model.Furthermore, we propose incorporating the style features into collaborative learning to create awareness pertaining to the preferences of users.The experiments conducted on two public implicit feedback datasets demonstrate the effectiveness of our approach for the visual recommendation.
Improving Recommender Systems Using Co-Appearing and Semantically Correlated User Interests

Background: Recommender Systems use user interests to provide more accurate recommendations according to user actual interests and behavior. Methods: This work aims at improving recommender systems by discovering hidden user interests from the existing interests. User interest expansion would contribute in improving the accuracy of recommender systems by finding more user interests using the given ones. Two methods are proposed to perform the expansion: Expanding interests using correlated interests’ extractor and Expanding interests using word embeddings. Results: Experimental work shows that such expanding is efficient in terms of accuracy and execution time. Conclusion: Therefore, expanding user interests proved to be a promising step in the improvement of the recommender systems performance.
A Comprehensive Evaluation Model of Power Market Operation Efficiency Based on Extension Cloud Theory

Aiming at the fuzziness and randomness existed in the electric power market operation efficiency evaluation, and evaluation index selection in different regional electricity market such as subjectivity and uncertainty problems, put forward reasonable and effective method of electric power market operation efficiency evaluation. Firstly, on the basis of the electric power market operation efficiency level boundary information, the subjective weighting method of the analytic hierarchy process and the objective weighting method of entropy weight method are combined to integrate by additive integration to evaluate the comprehensive weight of all indexes of the operation efficiency of the power market. Secondly, the analysis method of the uncertainty of the cloud model is combined with the qualitative and quantitative analysis methods of the matter-element theory in the extension theory, and it establishes an evaluation model of power market operation efficiency based on extension cloud theory. On this basis, the credibility factor and risk factors are introduced to reflect the credibility and risk information of the assessment results. Finally, an example is given to verify the rationality and validity of this method.
BlackMarks: Blackbox Multibit Watermarking for Deep Neural Networks

Deep Neural Networks have created a paradigm shift in our ability to comprehend raw data in various important fields ranging from computer vision and natural language processing to intelligence warfare and healthcare. While DNNs are increasingly deployed either in a white-box setting where the model internal is publicly known, or a black-box setting where only the model outputs are known, a practical concern is protecting the models against Intellectual Property (IP) infringement. We propose BlackMarks, the first end-to-end multi-bit watermarking framework that is applicable in the black-box scenario. BlackMarks takes the pre-trained unmarked model and the owner's binary signature as inputs and outputs the corresponding marked model with a set of watermark keys. To do so, BlackMarks first designs a model-dependent encoding scheme that maps all possible classes in the task to bit '0' and bit '1' by clustering the output activations into two groups. Given the owner's watermark signature (a binary string), a set of key image and label pairs are designed using targeted adversarial attacks. The watermark (WM) is then embedded in the prediction behavior of the target DNN by fine-tuning the model with generated WM key set. To extract the WM, the remote model is queried by the WM key images and the owner's signature is decoded from the corresponding predictions according to the designed encoding scheme. We perform a comprehensive evaluation of BlackMarks's performance on MNIST, CIFAR10, ImageNet datasets and corroborate its effectiveness and robustness. BlackMarks preserves the functionality of the original DNN and incurs negligible WM embedding runtime overhead as low as 2.054%.
Pcard: Personalized Restaurants Recommendation from Card Payment Transaction Records

Personalized Point of Interest (POI) recommendation that incorporates users' personal preferences is an important subject of research. However, challenges exist such as dealing with sparse rating data and spatial location factors. As one of the biggest card payment organizations in the United States, our company holds abundant card payment transaction records with numerous features.
Anomaly Detection With Multiple-Hypotheses Predictions

In one-class-learning tasks, only the normal case (foreground) can be modeled with data, whereas the variation of all possible anomalies is too erratic to be described by samples. Thus, due to the lack of representative data, the wide-spread discriminative approaches cannot cover such learning tasks, and rather generative models, which attempt to learn the input density of the foreground, are used. However, generative models suffer from a large input dimensionality (as in images) and are typically inefficient learners. We propose to learn the data distribution of the foreground more efficiently with a multi-hypotheses autoencoder. Moreover, the model is criticized by a discriminator, which prevents artificial data modes not supported by data, and enforces diversity across hypotheses. Our multiple-hypothesesbased anomaly detection framework allows the reliable identification of out-of-distribution samples. For anomaly detection on CIFAR-10, it yields up to 3.9% points improvement over previously reported results. On a real anomaly detection task, the approach reduces the error of the baseline models from 6.8% to 1.5%.
A Stream/Block Combination Image Encryption Algorithm Using Logistic Matrix to Scramble

Abstract In this paper, a new chaotic image encryption scheme is proposed, which uses the combination of stream cipher and block cipher to spread and then uses the Logistic mapping matrix to perform the block sort transformation to complete the confusion. The specific method is as follows: First, perform the triple XOR operation, non-linear S-box transformation and linear cyclic shift transformation on the key. Second, the triple XOR operation of the transformed key and the plaintext and Logistic mapping sequence are used to obtain the semi-ciphertext. And set the obtained semi-ciphertext and plaintext as key components of the next round of encryption. Finally, the transformation of sorting and partition of the Logistic mapping matrix are used to confuse the semi-ciphertext. The experimental results show that the proposed algorithm improves the encryption efficiency, has good security and can resist common attacks.
Quantum state engineering by nondeterministic noiseless linear amplification

We address quantum state engineering of single- and two-mode states by means of non-deterministic noiseless linear amplifiers (NLAs) acting on Gaussian states. In particular, we show that NLAs provide an effective scheme to generate highly non-Gaussian and non-classical states. Additionally, we show that the amplification of a two-mode squeezed vacuum state (twin-beam) may highly increase entanglement.
A Comprehensive Framework for the Security Risk Management of Cyber-Physical Systems

Cyber Physical Systems are facing huge and diverse set of security risks, especially cyber-attacks that can cause disruption to physical services or create a national disaster. Information and communication technology (ICT) has made a remarkable impact on the society. As a Cyber Physical System (CPS) relies basically on information and communication technology, this puts the system’s assets under certain risks especially cyber ones, and hence they must be kept under control by means of security countermeasures that generate confidence in the use of these assets. And so there is a critical need to give a great attention on the cybersecurity of these systems, which consequently leads to the safety of the physical world. This goal is achieved by adopting a solution that applies processes, plans and actions to prevent or reduce the effects of threats. Traditional IT risk assessment methods can do the job, however, and because of the characteristics of a CPS, it is more efficient to adopt a solution that is wider than a method, and addresses the type, functionalities and complexity of a CPS. This chapter proposes a framework that breaks the restriction to a traditional risk assessment method and encompasses wider set of procedures to achieve a high level strategy that could be adopted in the risk management process, in particular the cybersecurity of cyber-physical systems.
A SURVEY OF THE MOST CURRENT IMAGE ENCRYPTION AND DECRYPTION TECHNIQUES

: Nowadays, the internet plays a key role in almost every aspect of life. This article presents a new set of data storage and protection challenges. It shows a general introduction to cryptography and discusses most of the available articles of image encryption techniques that increase the levels of data security. However, different encryption techniques can be used to protect the digital data confidentiality from unauthorized access. Furthermore, the cryptographic techniques discussed in this article can be used to support researchers to develop new techniques to provide high levels of security and thus reduce the risk of security. With the advancement of science and knowledge, the protection techniques lead to increase versatility and security of data transfer in the future .
Analysing the Investigation and Prosecution of Cryptocurrency Crime as Provided for by the South African Cybercrimes Bill

Abstract The purpose of this article is to determine the effectiveness of the Cybercrimes Bill of 2018 in investigating and prosecuting cryptocurrency crime. The method used to determine this enquiry is based on the analysis of certain criminal, procedural, and investigatory support provisions of the bill, accompanied by recommendations where necessary. An analysis of the Cybercrimes Bill of 2018 in its entirety falls outside the scope of this article. The significance of this enquiry rests on the increasing use of cryptocurrencies in criminal activity (including money laundering, investment scams, fraud, hacking, and cyber extortion). The investigation and prosecution of such criminal activity may be exacerbated by the unique characteristics inherent in a cryptocurrency; a cryptocurrency is an unregulated, online, encrypted (and thus pseudonymous) ‘currency’ denominated in its own units of value. Cryptocurrencies are thus an international online ‘currency’ with multijurisdictional presence. Any criminal activity associated with its use will therefore invariably possess a cyber-element. Such criminal activity will thus need to be addressed from a legal basis that caters for the criminal and procedural processes necessary in the investigation and subsequent prosecution of such crime.
Wearable Devices for Health and Wellbeing: Design Insights from Twitter

Wearable devices are increasingly recognized for their potential to improve health and wellbeing.However, challenges remain for wide-scale adoption and use.This paper explores perception and reactions towards wearable devices with a particular emphasis on factors that influence the adoption and use to improve health and well-being and which can also inform their design as components of a behavioral change system.We use social media analytics to analyze and categorize tweets related to major manufacturers of consumer wearable devices from June 1, 2017 -May 31, 2018.We used extant literature on the design of persuasive systems to inform the definition of pertinent categories.The findings confirmed the relevance of persuasive design features such as Dialog, credibility, and social support, though to various degrees.The analysis sheds light on other user priorities pertaining to device characteristics, integration with other systems, issues surrounding actually wearing these devices on a regular basis.
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation

Current methods to interpret deep learning models by generating saliency maps generally rely on two key assumptions. First, they use first-order approximations of the loss function neglecting higher-order terms such as the loss curvatures. Second, they evaluate each feature's importance in isolation, ignoring their inter-dependencies. In this work, we study the effect of relaxing these two assumptions. First, by characterizing a closed-form formula for the Hessian matrix of a deep ReLU network, we prove that, for a classification problem with a large number of classes, if an input has a high confidence classification score, the inclusion of the Hessian term has small impacts in the final solution. We prove this result by showing that in this case the Hessian matrix is approximately of rank one and its leading eigenvector is almost parallel to the gradient of the loss function. Our empirical experiments on ImageNet samples are consistent with our theory. This result can have implications in other related problems such as adversarial examples as well. Second, we compute the importance of group-features in deep learning interpretation by introducing a sparsity regularization term. We use the $L_0-L_1$ relaxation technique along with the proximal gradient descent to have an efficient computation of group feature importance scores. Our empirical results indicate that considering group features can improve deep learning interpretation significantly.
Towards fast and scalable trapped-ion quantum logic with integrated photonics

Trapped-ion qubits promise certain fundamental advantages for quantum information processing (QIP), owing to their indistinguishability and relatively high isolation from noisy environments. Though these qualities have allowed demonstrations of the necessary primitives for quantum computation, the complexity of the optical apparatus required is a major impediment to implementation at scales where quantum systems offer a clear advantage over classical computers. Here, we build on previous work with trap-integrated waveguide optics, describing designs and simulations for commercial foundry-fabricated ion trap chips with integrated Si3N4 waveguides and grating couplers to implement multi-qubit operations. We detail a design intended to address and implement quantum logic gates between 5 ions in a single register, and a configuration which utilizes the stable on-chip path lengths of waveguide devices to enact a novel fast entangling two-qubit gate. The devices and approaches presented here could form elements of a scalable architecture for trapped-ion QIP.
Obfuscating Simple Functionalities from Knowledge Assumptions

This paper shows how to obfuscate several simple functionalities from a new Knowledge of OrthogonALity Assumption (KOALA) in cyclic groups which is shown to hold in the Generic Group Model. Specifically, we give simpler and stronger security proofs for obfuscation schemes for point functions, general-output point functions and pattern matching with wildcards. We also revisit the work of Bishop et al. (CRYPTO 2018) on obfuscating the pattern matching with wildcards functionality. We improve upon the construction and the analysis in several ways: We obtain our results by first obfuscating a simpler "big subset functionality", for which we establish full virtual black-box security; this yields a simpler and more modular analysis for pattern matching. Finally, we extend our distinguishing attacks to a large class of simple linear-in-the-exponent schemes.
Video Generation from Single Semantic Label Map

This paper proposes the novel task of video generation conditioned on a SINGLE semantic label map, which provides a good balance between flexibility and quality in the generation process. Different from typical end-to-end approaches, which model both scene content and dynamics in a single step, we propose to decompose this difficult task into two sub-problems. As current image generation methods do better than video generation in terms of detail, we synthesize high quality content by only generating the first frame. Then we animate the scene based on its semantic meaning to obtain the temporally coherent video, giving us excellent results overall. We employ a cVAE for predicting optical flow as a beneficial intermediate step to generate a video sequence conditioned on the initial single frame. A semantic label map is integrated into the flow prediction module to achieve major improvements in the image-to-video generation process. Extensive experiments on the Cityscapes dataset show that our method outperforms all competing methods.
Improving Prostate Cancer Detection with Breast Histopathology Images

Deep neural networks have introduced significant advancements in the field of machine learning-based analysis of digital pathology images including prostate tissue images. With the help of transfer learning, classification and segmentation performance of neural network models have been further increased. However, due to the absence of large, extensively annotated, publicly available prostate histopathology datasets, several previous studies employ datasets from well-studied computer vision tasks such as ImageNet dataset. In this work, we propose a transfer learning scheme from breast histopathology images to improve prostate cancer detection performance. We validate our approach on annotated prostate whole slide images by using a publicly available breast histopathology dataset as pre-training. We show that the proposed cross-cancer approach outperforms transfer learning from ImageNet dataset.
Quantum speedup, non-Markovianity and formation of bound state

Abstract In this paper, we investigate the relationship between the quantum speedup, non-Markovianity and formation of a system-environment bound state. Previous results show a monotonic relation between these three such that providing bound states with more negative energy can lead to a higher degree of non-Markovianity, and hence to a greater speed of quantum evolution. By studying dynamics of a dissipative two-level system or a V-type three-level system, when similar and additional systems are present, we reveal that the quantum speedup is exclusively related to the formation of the system-environment bound state, while the non-Markovian effect of the system dynamics is neither necessary nor sufficient to speed up the quantum evolution. On the other hand, it is shown that only the formation of the system-environment bound state plays a decisive role in the acceleration of the quantum evolution.
Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset

Large-scale public datasets have been shown to benefit research in multiple areas of modern artificial intelligence. For decision-making research that requires human data, high-quality datasets serve as important benchmarks to facilitate the development of new methods by providing a common reproducible standard. Many human decision-making tasks require visual attention to obtain high levels of performance. Therefore, measuring eye movements can provide a rich source of information about the strategies that humans use to solve decision-making tasks. Here, we provide a large-scale, high-quality dataset of human actions with simultaneously recorded eye movements while humans play Atari video games. The dataset consists of 117 hours of gameplay data from a diverse set of 20 games, with 8 million action demonstrations and 328 million gaze samples. We introduce a novel form of gameplay, in which the human plays in a semi-frame-by-frame manner. This leads to near-optimal game decisions and game scores that are comparable or better than known human records. We demonstrate the usefulness of the dataset through two simple applications: predicting human gaze and imitating human demonstrated actions. The quality of the data leads to promising results in both tasks. Moreover, using a learned human gaze model to inform imitation learning leads to an 115\% increase in game performance. We interpret these results as highlighting the importance of incorporating human visual attention in models of decision making and demonstrating the value of the current dataset to the research community. We hope that the scale and quality of this dataset can provide more opportunities to researchers in the areas of visual attention, imitation learning, and reinforcement learning.
An Analysis on Recent Mobile Application Trend in Bangladesh

Development in the technology sector, making the world closer and easier day by day. It is very difficult to stay aside without the touch of the technology. From the invention of computer, the daily life of smart people has changed. Now we are seeing smart phone is ruling the world. It has replaced the great invention of computer. Almost every work of a computer and many features are now enabled in a small smart phone. The apps inside a smart phone are one of the main factors that is helping the people to make their life simpler. Because of the immense usage of smart phone, app developers are increasing day by day. In this paper, we have tried to find out the recent app using trend in Bangladesh. We have operated a survey. From there we have figured out the most demanding app types and also the specification in Bangladesh. Our work will help the app developers beyond any platform. This will help them to build a demanding app and be in a good position in the market place.
An End-to-End Network for Panoptic Segmentation

Panoptic segmentation, which needs to assign a category label to each pixel and segment each object instance simultaneously, is a challenging topic. Traditionally, the existing approaches utilize two independent models without sharing features, which makes the pipeline inefficient to implement. In addition, a heuristic method is usually employed to merge the results. However, the overlapping relationship between object instances is difficult to determine without sufficient context information during the merging process. To address the problems, we propose a novel end-to-end network for panoptic segmentation, which can efficiently and effectively predict both the instance and stuff segmentation in a single network. Moreover, we introduce a novel spatial ranking module to deal with the occlusion problem between the predicted instances. Extensive experiments have been done to validate the performance of our proposed method and promising results have been achieved on the COCO Panoptic benchmark.
Consumer protection requires artificial intelligence

Technology companies have quickly become powerful with their access to large amounts of data and machine learning technologies, but consumers could be empowered too with automated tools to protect their rights.