Reviewerly/exHarmony huggingface dataset:
A-MnasNet: Augmented MnasNet for Computer Vision

Convolutional Neural Networks (CNNs) play an essential role in Deep Learning. They are extensively used in Computer Vision. They are complicated but very effective in extracting features from an image or a video stream. After AlexNet [5] won the ILSVRC [8] in 2012, there was a drastic increase in research related with CNNs. Many state-of-the-art architectures like VGG Net [12], GoogleNet [13], ResNet [18], Inception-v4 [14], Inception-Resnet-v2 [14], ShuffleNet [23], Xception [24], MobileNet [6], MobileNetV2 [7], SqueezeNet [16], SqueezeNext [17] and many more were introduced. The trend behind the research depicts an increase in the number of layers of CNN to make them more efficient but with that the size of the model increased as well. This problem was fixed with the advent of new algorithms which resulted in a decrease in model size. As a result, today we have CNN models which are implemented on mobile devices. These mobile models are small and fast which in turn reduce the computational cost of the embedded system. This paper resembles similar idea, it proposes a new model Augmented MnasNet (A-MnasNet) which has been derived from MnasNet [1]. The model is trained with CIFAR-10 [4] dataset and has a validation accuracy of 96.89% and a model size of 11.6 MB. It outperforms its baseline architecture MnasNet which has a validation accuracy of 80.8% and a model size of 12.7 MB when trained with CIFAR-10.
What do we know about AR Storytelling?

In the last years, there has been an abundance of AR applications developed, across multiple sectors. However, AR still remains in many domains just a buzzword, with very little evaluations carried out to study its impact on users in terms of exploration, navigation, learning and enjoyment.
Cross hyperledger fabric transactions

Hyperledger Fabric is a private block-chain based business logic solution that can safely accomodate transactions between untrusted or partially trusted participants. Moreover its scalable and modular structure makes Hyperledger Fabric capable of complex logical functions to handle transactions between different organizations. Hyperledger Fabric offers the possibility to modify user relationships, data structures, and business logic in a single Hyperledger Fabric environment. In this article, we study the interoperability between different Hyperledger Fabrics characterized for instance by different logics, currencies or use cases. We propose a cross-Hyperledger Fabric transaction solution which enables users to complete transactions across multiple Hyperledger Fabrics safely and efficiently. We conduct a feasibility and security analysis of the proposed scheme and we propose performance analysis based on a real deployed platform. We measure the performance of our interchain communication scheme on two Hyperledger Fabrics deployed in two different cloud-based platforms (one in France and one in Germany). We evaluate the performance of the proposed inter-communication scheme in terms of end-to-end transaction delay, the intra-Hyperledger delay and the system throughput. Our experiments show that all these parameters are dependent on the network traffic and therefore open an interesting research direction related to optimal scheduling of Hyperledger Fabric intercommunication. Moreover, we measure the impact of the rate of successfully received transactions function of transaction rate. That is, the inordinate small interval between two continuous transactions reduces the rate of successful transactions. This opens another interesting research direction related to optimal tuning the interval between two transactions to eradicate transactions losses.
TKU-CE: Cross-Entropy Method for Mining Top-K High Utility Itemsets

Mining high utility itemsets (HUIs) is one of the most important research topics in data mining because HUIs consider non-binary frequency values of items in transactions and different profit values for each item. However, setting appropriate minimum utility thresholds by trial and error is a tedious process for users. Thus, mining the top-k high utility itemsets (top-k HUIs) without setting a utility threshold is becoming an alternative to determining all of the HUIs. In this paper, we propose a novel algorithm, named TKU-CE (Top-K high Utility mining based on Cross-Entropy method), for mining top-k HUIs. The TKU-CE algorithm follows the roadmap of cross entropy and tackles top-k HUI mining using combinatorial optimization. The main idea of TKU-CE is to generate the top-k HUIs by gradually updating the probabilities of itemsets with high utility values. Compared with the state-of-the-art algorithms, TKU-CE is not only easy to implement, but also saves computational costs incurred by additional data structures, threshold raising strategies, and pruning strategies. Extensive experimental results show that the TKU-CE algorithm is efficient, memory-saving, and can discover most actual top-k HUIs.
Wavelet–Fourier CORSING techniques for multidimensional advection–diffusion–reaction equations

Abstract We present and analyze a novel wavelet–Fourier technique for the numerical treatment of multidimensional advection–diffusion–reaction equations based on the COmpRessed SolvING (CORSING) paradigm. Combining the Petrov–Galerkin technique with the compressed sensing approach the proposed method is able to approximate the largest coefficients of the solution with respect to a biorthogonal wavelet basis. Namely, we assemble a compressed discretization based on randomized subsampling of the Fourier test space and we employ sparse recovery techniques to approximate the solution to the partial differential equation (PDE). In this paper we provide the first rigorous recovery error bounds and effective recipes for the implementation of the CORSING technique in the multidimensional setting. Our theoretical analysis relies on new estimates for the local $a$-coherence, which measures interferences between wavelet and Fourier basis functions with respect to the metric induced by the PDE operator. The stability and robustness of the proposed scheme are shown by numerical illustrations in the one-, two- and three-dimensional cases.
Digital content of regional central libraries in the Russian Federation as a potentiality for user access growth

The government requirements toward digital content acquisition and use in libraries have been changing. The standards and regulations comprising the indicators of library activities in the digital environment are characterized. The findings of the study of the official websites of the central libraries of 85 subjects of the Russian Federation (22 republics, 46 regions, 9 territories, 4 autonomous districts, 1 autonomous region, and three cities of federal status), are cited. The goals of the study were: to examine the structure of local history digital content on the websites of central libraries of the RF subjects; to identify typical errors in acquiring the local history digital content that actually breaks down the barriers for remote users. The Russian libraries’ experience in generating local lore electronic information resources available to remote users, is discussed. The most popular types of electronic information resources are analyzed. The lack of unified terminology for digital content and the polysemy of the type names are emphasized. The presentation of local history digital content in the headings of websites of the RF central libraries of various federal status, is considered. Prospects for research of local history digital content optimization on the websites of RF central libraries are outlined.
Pre-analytics, a national survey of Senonetwork Italia breast centers: Much still to do ahead

Introduction Pre-analytics involves handling and processing of microbiopsy and surgical specimen. It is critical for the preservation of morphology and the integrity of molecular markers, which are paramount as prognostic and predictive factors in breast cancer. Although pre-analytical variables in breast cancer are codified by national and international guidelines, there is currently no data on their actual endorsement in clinical practice among Breast Units (BU). Materials and methods An anonymous questionnaire was sent by e-mail by Senonetwork Italia, a no-profit organization representing the multidisciplinary network of BU in Italy. The questionnaire involved twenty-four questions concerning critical issues related to the average time and transport temperature of the samples, monitoring of warm and cold ischemia, average fixation time for biopsies and surgical specimens, inking of the margins, and radiography of the operating sample. Results Forty-nine of 113 affiliated BU (43%), involved in the management of 44% of all breast cancer treated every year in Italy, answered the questionnaire. More than 90% of the BU reported a biopsy/VABB fixation time between 6 and 24 h. Only 41% of the Centers received the fresh operative sample to be sectioned immediately, 20% used the vacuum method and the sample arrived in the laboratory within 24–72 h. Delay in sectioning the sample was reported in as many as 40% of BU, while hot and cold ischemia time was monitored in only 4.2% and 6.2% of BU, respectively. Conclusion Critical issues on pre-analytics are reported by the majority of dedicated BU in Italy. This represents a major challenge regarding quality of care, and improvements are needed in order to obtain valid and reproducible results of prognostic and predictive factors.
An Efficient Technique for Image Captioning Using Deep Neural Network

With the huge expansion of Internet and trillions of gigabytes of data generated every single day, the needs for the development of various tools have become mandatory in order to maintain system adaptability to rapid changes. One of these tools is known as image captioning. Every entity in Internet must be properly identified and managed, and therefore in the case of image data, automatic captioning for identification is required. Similarly, content generation for missing labels, image classification and artificial languages all requires the process of image captioning. This paper discusses an efficient and unique way to perform automatic image captioning on individual image and discusses strategies to improve its performances and functionalities.
User-Defined Mid-Air Haptic Sensations for Interacting with an AR Menu Environment

Abstract Interfaces that allow users to interact with a computing system by using free-hand mid-air gestures are becoming increasingly prevalent. A typical shortcoming of such gesture-based interfaces, however, is their lack of a haptic component. One technology with the potential to address this issue, is ultrasound mid-air haptic feedback. At the moment, haptic sensations are typically designed by system engineers and experts. In the case of gestural interfaces, researchers started involving non-expert users to define suitable gestures for specific interactions. To our knowledge, no studies have involved – from a similar participatory design perspective – laymen to generate mid-air haptic sensations. We present the results of an end-user elicitation study yielding a user-defined set of mid-air haptic sensations to match gestures used for interacting with an Augmented Reality menu environment. In addition, we discuss the suitability of the end-user elicitation method to that end.
Quantum Key Agreement Via Non-maximally Entangled Cluster States

In this paper, we present a quantum key agreement (QKA) protocol with non-maximally entangled four-qubit cluster states. In our scheme, each participant carries out two unitary operations on the same photons of each cluster state according to its own encryption key. Then, all participants generate identical four-bit shared keys by positive operator-valued measurement (POVM). Since POVM is probabilistic, it is necessary to prepare a longer raw key, and we only select sub-keys in public locations of qubit sequences where all participants succeed in POVMs as the final negotiation key. The advantage of our protocol is that non-maximally entangled cluster states are firstly utilized as the carrier of quantum information in quantum key agreement protocol. It also ensures that the shared key is decided by all participants, not by someone alone. In addition, it is proved that our protocol can resist external attacks and participant attacks, which demonstrates a high security.
A Novel Sentence-Level Agreement Architecture for Neural Machine Translation

In neural machine translation (NMT), there is a natural correspondence between source and target sentences. The traditional NMT method does not explicitly model the translation agreement on sentence-level. In this article, we propose a comprehensive and novel sentence-level agreement architecture to alleviate this problem. It directly minimizes the difference between the representations of the source-side and target-side sentence on sentence-level. First, we compare a variety of sentence representation strategies and propose a “Gated Sum” sentence representation to achieve better sentence semantic information. Then, rather than a single-layer sentence-level agreement architecture, we further propose a multi-layer sentence agreement architecture to make the source and target semantic spaces closer layer by layer. The proposed agreement module can be integrated into NMT as an additional training objective function, and can also be used to enhance the representation of the source-side sentences. Experiments on the NIST Chinese-to-English and the WMT English-to-German translation tasks show that the proposed agreement architecture achieves significant improvements over state-of-the-art baselines, demonstrating the effectiveness and necessity of exploiting sentence-level agreement for NMT.
Improved human-object interaction detection through skeleton-object relations

Current methods for human-object interaction detection often use the spatial relation between a human and an object as an interaction pattern. However, this strategy is relatively simple and has low discrimination in similar interactions. To solve this drawback, the spatial relation between skeletons and objects is proposed to model the interaction pattern and improve the detection accuracy. First, the skeleton-object interaction pattern image is extracted for each interaction proposal. Second, a deep neural network is applied to learn the interaction features from these images. Finally, the interaction feature is added to the human-object interaction detection network by a multistream structure. In the experiments, we evaluate the proposed method on the HICO-DET and V-COCO datasets. Experimental results show that the proposed method can achieve the best performance compared with state-of-art methods.
AutoChef: Automated Generation of Cooking Recipes

Cooking is an endeavour unique to humans. It is mainly considered an art requiring culinary intuition acquired through practice. The preparation of food is a complex and subjective process that makes it challenging to determine underlying rules for automation. In this paper, we present AutoChef, the first open-source autonomous recipe generator. AutoChef extracts the data from existing recipes using natural language processing, learns the combination of ingredients, preparation actions and cooking instructions, and autonomously generates the recipes. Furthermore, AutoChef uses Genetic Programming to represent and evolve the recipes. The fitness of recipes is designed to evaluate the combination of ingredients, actions and cooking-processes learned from the existing recipe data. Finally, the resulting recipes are translated back into text format and evaluated by human experts.
VacuumCleanAR

Consumer purchase decisions are not only determined by the quality or price of a product. Customers also want an innovative product that they can identify with in something more than just a functional way. Much of this appeal is often bound up with the innovative character of a product. However, the global market and the huge variety of products available make it challenging for companies to help customers understand the particular innovations in their products, especially in terms of technical "hidden" innovations. Augmented reality (AR) offers interactive experiences in real-world environments through digitalized information. In this paper, we present a design case study about an AR-based approach to reveal the hidden innovations to potential users in an engaging and "emotional" way by using the example of a vacuum cleaner. Based on an empirical study, we designed and implemented the fully functional HoloLens application VacuumCleanAR, which allows users to discover the hidden innovations of a vacuum cleaner in a less functional and more consumer-centric way. This reveals the scope for augmenting other physical artifacts in a similar fashion.
Application of an Improved Hough Transform and Image Correction Algorithm in ACC

Abstract Numerous studies have shown that ACC (Augmentative and Alternative Communication), as a tool in language and communication training for children with autism, has effectively improved and played a positive role. ACC’s Picture Exchange System (PECS) provides an important support for the correction and feature recognition of language and training teaching cards. When designing the picture exchange function of ACC, this paper proposes a modified Hough transform and image correction algorithm suitable for teaching cards to solve the problems of large data calculation, time-consuming calculation, large space occupation and false peaks caused by traditional Hough transform algorithm in detecting straight lines, which also optimizes the interactive experience of the teaching scene. Finally, we design two experiments to compare and analyse the classic Hough transform, random Hough transform and improved Hough transform. Experimental results show that the improved Hough transform algorithm has superior performance in terms of execution efficiency and accuracy of line detection.
An Image Hashing Algorithm for Authentication with Multi-Attack Reference Generation and Adaptive Thresholding

Image hashing-based authentication methods have been widely studied with continuous advancements owing to the speed and memory efficiency. However, reference hash generation and threshold setting, which are used for similarity measures between original images and corresponding distorted version, are important but less considered by most of existing models. In this paper, we propose an image hashing method based on multi-attack reference generation and adaptive thresholding for image authentication. We propose to build the prior information set based on the help of multiple virtual prior attacks, and present a multi-attack reference generation method based on hashing clusters. The perceptual hashing algorithm was applied to the reference/queried image to obtain the hashing codes for authentication. Furthermore, we introduce the concept of adaptive thresholding to account for variations in hashing distance. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.
Communication-Efficient Distributed Deep Learning with GPU-FPGA Heterogeneous Computing

Distributed deep learning has an inevitable bottleneck in communication between GPUs. This bottleneck is due to the difference in the data transfer latency of internode and intra-node communication. Here, we propose a GPU-FPGA (field programmable gate array) heterogeneous computing system and a hardware-specific communication module with parameter-based computation/communication overlap. In our system, we configure a network interface card (NIC) with a data aggregation function. To reduce the load on CPUs, we developed a device driver for remote direct memory access between GPUs and FPGAs. The hardware and software can be run with TensorFlow and Horovod. For comparison, we compared our system with the conventional GPUDirect RDMA system. Results of the measurement show Allreduce latency is reduced to 1/4 by offloading Allreduce to FPGA NICs. Our hardware/software co-design can also conceal about 90% of the communication overhead and improve scalability by 20%. The end-to-end time consumed for training in distributed deep learning with ResNet-50 and ImageNet is reduced to 87.3% without any degradation in validation accuracy.
Using Bayesian networks to clarify interpretation of exposure–response regression coefficients: blood lead–mortality association as an example

We examine how Bayesian network (BN) learning and analysis methods can help to meet several methodological challenges that arise in interpreting significant regression coefficients in exposure–response regression modeling. As a motivating example, we consider the challenge of interpreting positive regression coefficients for blood lead level (BLL) as a predictor of mortality risk for nonsmoking men. We first note that practices such as dichotomizing or categorizing continuous confounders (e.g. income), omitting potentially important socioeconomic confounders (e.g. education), and assuming specific parametric regression model forms leave unclear to what extent a positive regression coefficient reflects these modeling choices, rather than a direct dependence of mortality risk on exposure. Therefore, significant exposure–response coefficients in parametric regression models do not necessarily reveal the extent to which reducing exposure-related variables (e.g. BLL) alone, while leaving fixed other correlates of exposure and mortality risks (e.g. education, income, etc.) would reduce adverse outcome risks (e.g. mortality risks). We then consider how BN structure-learning and inference algorithms and nonparametric estimation methods (partial dependence plots) can be used to clarify dependencies between variables, variable selection, confounding, and quantification of joint effects of multiple factors on risk, including possible high-order interactions and nonlinearities. We conclude that these details must be carefully modeled to determine whether a data set provides evidence that exposure itself directly affects risks; and that BN and nonparametric effect estimation and uncertainty quantification methods can complement regression modeling and help to improve the scientific basis for risk management decisions and policy-making by addressing these issues.
Interactive Question-Posing System for Robot-Assisted Reminiscence From Personal Photographs

Reminiscence is a lifelong activity that happens throughout our lifespan. While memories can serve as topics in people's everyday conversations, recalling the past can also help us build self-esteem and increase our level of happiness. In this paper, we aim to develop a robot companion that helps people to recollect their memories from personal photographs. We focus on how a robot can associate concepts relevant to the content in the photographs and evoke people's memories by asking questions that are both relatable and engaging. To understand the content of a picture, we applied deep learning techniques in order to recognize events, objects, and scenes in it. Then, these observations and any user utterances are considered in a Markov random field (MRF)-based algorithm that contains common sense knowledge of a number of events, with loopy belief propagation being used to infer possible associated concepts and topics. Afterward, the robot poses appropriate questions about the selected topics, guiding the user to reminisce through conversation. Our results show that the proposed system can pose related and appropriate questions to interact with the user, and has the potential guide the user to recall the past in an organized way.