Reviewerly/exHarmony huggingface dataset:
Diffusion of blockchain in logistics and transportation industry: an analysis through the synthesis of academic and trade literature

Purpose Blockchain technology has fascinated researchers and industry professionals. Since its birth, the attention for blockchain has been exponentially increasing, however, most of the industries are still skeptical in adoption for value creation. The purpose of this study is to analyze the actual level of implementation and diffusion of blockchain technology within the logistics and transportation industry by comparing and using the collective intelligence of academic literature and industry practices of implementation of blockchain in this domain. Design/methodology/approach This study uses the methodology of systematic literature review along with inductive reasoning. The systematic literature review of academic and industry frontiers together has brought a bigger and real picture into consideration. Findings The results highlight that, within the transportation sector, currently there is a very low diffusion of blockchain, although applications show immense promises for the future. The various application where blockchain technology can make a significant impact are also identified. Research limitations/implications Due to the early stage of experimentation with blockchain technology, high-quality data which is relevant to the optimized usage of this technology in the logistics and transportation industry is not available. Practical implications The study will help the practitioners in identifying additional avenues in which they could implement blockchain for the effectiveness, efficiency and growth of the logistics and transportation industry. Originality/value The analysis of mixed sources of information for undertaking systematic literature review by assessing academic and trade publications is a novelty of this study.
Incremental Lossless Graph Summarization

Given a fully dynamic graph, represented as a stream of edge insertions and deletions, how can we obtain and incrementally update a lossless summary of its current snapshot? As large-scale graphs are prevalent, concisely representing them is inevitable for efficient storage and analysis. Lossless graph summarization is an effective graph-compression technique with many desirable properties. It aims to compactly represent the input graph as (a) a summary graph consisting of supernodes (i.e., sets of nodes) and superedges (i.e., edges between supernodes), which provide a rough description, and (b) edge corrections which fix errors induced by the rough description. While a number of batch algorithms, suited for static graphs, have been developed for rapid and compact graph summarization, they are highly inefficient in terms of time and space for dynamic graphs, which are common in practice.
Notions of Centralized and Decentralized Opacity in Linear Systems

We formulate notions of opacity for cyberphysical systems modeled as discrete-time linear time-invariant systems. A set of secret states is k-ISO with respect to a set of nonsecret states if, starting from these sets at time 0, the outputs at time k are indistinguishable to an adversarial observer. Necessary and sufficient conditions to ensure that a secret specification is k-ISO are established in terms of sets of reachable states. We also show how to adapt techniques for computing underapproximations and overapproximations of the set of reachable states of dynamical systems in order to soundly approximate k-ISO. Furthermore, we provide a condition for output controllability, if k-ISO holds, and show that the converse holds under an additional assumption. We extend the theory of opacity for single-adversary systems to the case of multiple adversaries and develop several notions of decentralized opacity. We study the following scenarios: first, the presence or lack of a centralized coordinator, and, second, the presence or absence of collusion among adversaries. In the case of colluding adversaries, we derive a condition for nonopacity that depends on the structure of the directed graph representing the communication between adversaries. Finally, we relax the condition that the outputs be indistinguishable and define a notion of ϵ-opacity, and also provide an extension to the case of nonlinear systems.
Generalizing the Convolution Operator in Convolutional Neural Networks

Convolutional neural networks have become a main tool for solving many machine vision and machine learning problems. A major element of these networks is the convolution operator which essentially computes the inner product between a weight vector and the vectorized image patches extracted by sliding a window in the image planes of the previous layer. In this paper, we propose two classes of surrogate functions for the inner product operation inherent in the convolution operator and so attain two generalizations of the convolution operator. The first one is the class of positive definite kernel functions where their application is justified by the kernel trick. The second one is the class of similarity measures defined based on a distance function. We justify this by tracing back to the basic idea behind the neocognitron which is the ancestor of CNNs. Both methods are then further generalized by allowing a monotonically increasing function to be applied subsequently. Like any trainable parameter in a neural network, the template pattern and the parameters of the kernel/distance function are trained with the back-propagation algorithm. As an aside, we use the proposed framework to justify the use of sine activation function in CNNs. Our experiments on the MNIST dataset show that the performance of ordinary CNNs can be achieved by generalized CNNs based on weighted L1/L2 distances, proving the applicability of the proposed generalization of the convolutional neural networks.
Operational resource theory of nonclassicality via quantum metrology

This paper presents a measure of single-mode nonclassicality using a quantum resource theory. The authors show that the measure is the first resource-theoretic measure that quantifies the ability of a state to perform useful tasks, e.g., phase sensing in a Mach Zehnder interferometer, which can also be used to lower bound the nonclassicality experimentally
Interstellar

We show that DNN accelerator micro-architectures and their program mappings represent specific choices of loop order and hardware parallelism for computing the seven nested loops of DNNs, which enables us to create a formal taxonomy of all existing dense DNN accelerators. Surprisingly, the loop transformations needed to create these hardware variants can be precisely and concisely represented by Halide's scheduling language. By modifying the Halide compiler to generate hardware, we create a system that can fairly compare these prior accelerators. As long as proper loop blocking schemes are used, and the hardware can support mapping replicated loops, many different hardware dataflows yield similar energy efficiency with good performance. This is because the loop blocking can ensure that most data references stay on-chip with good locality and the processing units have high resource utilization. How resources are allocated, especially in the memory system, has a large impact on energy and performance. By optimizing hardware resource allocation while keeping throughput constant, we achieve up to 4.2X energy improvement for Convolutional Neural Networks (CNNs), 1.6X and 1.8X improvement for Long Short-Term Memories (LSTMs) and multi-layer perceptrons (MLPs), respectively.
Homogeneous Data Normalization and Deep Learning: A Case Study in Human Activity Classification

One class of applications for human activity recognition methods is found in mobile devices for monitoring older adults and people with special needs. Recently, many studies were performed to create intelligent methods for the recognition of human activities. However, the different mobile devices in the market acquire the data from sensors at different frequencies. This paper focuses on implementing four data normalization techniques, i.e., MaxAbsScaler, MinMaxScaler, RobustScaler, and Z-Score. Subsequently, we evaluate the impact of the normalization algorithms with deep neural networks (DNN) for the classification of the human activities. The impact of the data normalization was counterintuitive, resulting in a degradation of performance. Namely, when using the accelerometer data, the accuracy dropped from about 79% to only 53% for the best normalization approach. Similarly, for the gyroscope data, the accuracy without normalization was about 81.5%, whereas with the best normalization, it was only 60%. It can be concluded that data normalization techniques are not helpful in classification problems with homogeneous data.
Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis

Polarimetric thermal to visible face verification entails matching two images that contain significant domain differences. Several recent approaches have attempted to synthesize visible faces from thermal images for cross-modal matching. In this paper, we take a different approach in which rather than focusing only on synthesizing visible faces from thermal faces, we also propose to synthesize thermal faces from visible faces. Our intuition is based on the fact that thermal images also contain some discriminative information about the person for verification. Deep features from a pre-trained Convolutional Neural Network (CNN) are extracted from the original as well as the synthesized images. These features are then fused to generate a template which is then used for verification. The proposed synthesis network is based on the self-attention generative adversarial network (SAGAN) which essentially allows efficient attention-guided image synthesis. Extensive experiments on the ARL polarimetric thermal face dataset demonstrate that the proposed method achieves state-of-the-art performance.
Coverage Path Planning With Track Spacing Adaptation for Autonomous Underwater Vehicles

In this letter we address the mine countermeasures (MCM) search problem for an autonomous underwater vehicle (AUV) surveying the seabed using a side-looking sonar. We propose a coverage path planning method that adapts the AUV track spacing with the objective of collecting better data. We achieve this by shifting the coverage overlap at the tail of the sensor range where the lowest data quality is expected. To assess the algorithm, we collected data from three at-sea experiments. The adaptive survey allowed the AUV to recover from a situation where the sensor range was overestimated and resulted in reducing area coverage gaps. In another experiment, the adaptive survey showed a 4.2% improvement in data quality for nearly 30% of the `worst' data.
Corpus Augmentation for Neural Machine Translation with Chinese-Japanese Parallel Corpora

Neural Machine Translation (NMT) has been proven to achieve impressive results. The NMT system translation results depend strongly on the size and quality of parallel corpora. Nevertheless, for many language pairs, no rich-resource parallel corpora exist. As described in this paper, we propose a corpus augmentation method by segmenting long sentences in a corpus using back-translation and generating pseudo-parallel sentence pairs. The experiment results of the Japanese-Chinese and Chinese-Japanese translation with Japanese-Chinese scientific paper excerpt corpus (ASPEC-JC) show that the method improves translation performance.
WITHDRAWN: Big data analytic of contexts and cascading tourism for smart city

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking

Dialogue state tracking (DST) aims at estimating the current dialogue state given all the preceding conversation.For multi-domain DST, the data sparsity problem is a major obstacle due to increased numbers of state candidates and dialogue lengths.To encode the dialogue context efficiently, we utilize the previous dialogue state (predicted) and the current dialogue utterance as the input for DST.To consider relations among different domain-slots, the schema graph involving prior knowledge is exploited.In this paper, a novel context and schema fusion network is proposed to encode the dialogue context and schema graph by using internal and external attention mechanisms.Experiment results show that our approach can outperform strong baselines, and the previous state-of-the-art method (SOM-DST) can also be improved by our proposed schema graph.
Deep-learning natural language processing for oncological applications

Digital health data continue to accumulate exponentially. A variety of data sources are used for patient care and for research in oncology. These sources include medical health records, up-to-date literature, ongoing medical trials, and genetic data. Electronic health records have revolutionised data accessibility. Yet, most medical information is stored in a free-text format. Thus, the data are inaccessible for computer analysis and their potential remains unused.
Cold-start Sequential Recommendation via Meta Learner

This paper explores meta-learning in sequential recommendation to alleviate the item cold-start problem. Sequential recommendation aims to capture user's dynamic preferences based on historical behavior sequences and acts as a key component of most online recommendation scenarios. However, most previous methods have trouble recommending cold-start items, which are prevalent in those scenarios. As there is generally no side information in sequential recommendation task, previous cold-start methods could not be applied when only user-item interactions are available. Thus, we propose a Meta-learning-based Cold-Start Sequential Recommendation Framework, namely Mecos, to mitigate the item cold-start problem in sequential recommendation. This task is non-trivial as it targets at an important problem in a novel and challenging context. Mecos effectively extracts user preference from limited interactions and learns to match the target cold-start item with the potential user. Besides, our framework can be painlessly integrated with neural network-based models. Extensive experiments conducted on three real-world datasets verify the superiority of Mecos, with the average improvement up to 99%, 91%, and 70% in HR@10 over state-of-the-art baseline methods.
The Opinions of Technology Supported Education of University Students

The aim of this study is to determine the opinions of university students about technology-supported education. In today’s era, technological tools are used in every sector of education. Today, when technologies are rapidly developing, the opinions of students studying at university institutions about technology are very important. As per the outcome of the research, it is important to know the opinions of working people in terms of competen-cies and attitude towards the technology. The research was carried out in the fall semester of the 2019-2020 academic year with 75 students studying at the Federal University of Kazan (Volga region), the Institute of Legislation and Comparative Law under the Government of the Russian Federation, Plekhanov Russian University of Economics and Sechenov University. As the case study method was adopted in the study, researcher prepared open-ended questions as a data collection tool. In this research, qualitative meth-od was used. According to the results obtained from the research, it was concluded that they did not feel themselves competent in technology, they did not have enough equipment and their schools are not equipped for technology-supported education.
A Unified Framework for Generalized Low-Shot Medical Image Segmentation With Scarce Data

Medical image segmentation has achieved remarkable advancements using deep neural networks (DNNs). However, DNNs often need big amounts of data and annotations for training, both of which can be difficult and costly to obtain. In this work, we propose a unified framework for generalized low-shot (one- and few-shot) medical image segmentation based on distance metric learning (DML). Unlike most existing methods which only deal with the lack of annotations while assuming abundance of data, our framework works with extreme scarcity of both, which is ideal for rare diseases. Via DML, the framework learns a multimodal mixture representation for each category, and performs dense predictions based on cosine distances between the pixels' deep embeddings and the category representations. The multimodal representations effectively utilize the inter-subject similarities and intraclass variations to overcome overfitting due to extremely limited data. In addition, we propose adaptive mixing coefficients for the multimodal mixture distributions to adaptively emphasize the modes better suited to the current input. The representations are implicitly embedded as weights of the fc layer, such that the cosine distances can be computed efficiently via forward propagation. In our experiments on brain MRI and abdominal CT datasets, the proposed framework achieves superior performances for low-shot segmentation towards standard DNN-based (3D U-Net) and classical registration-based (ANTs) methods, e.g., achieving mean Dice coefficients of 81%/69% for brain tissue/abdominal multi-organ segmentation using a single training sample, as compared to 52%/31% and 72%/35% by the U-Net and ANTs, respectively.
Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets

The recent dominance of machine learning-based natural language processing methods has fostered the culture of overemphasizing model accuracies rather than studying the reasons behind their errors.Interpretability, however, is a critical requirement for many downstream AI and NLP applications, e.g., in finance, healthcare, and autonomous driving.This study, instead of proposing any "new model", investigates the error patterns of some widely acknowledged sentiment analysis methods in the finance domain.We discover that (1) those methods belonging to the same clusters are prone to similar error patterns, and (2) there are six types of linguistic features that are pervasive in the common errors.These findings provide important clues and practical considerations for improving sentiment analysis models for financial applications.
PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection

LiDAR-based 3D object detection is an important task for autonomous driving and current approaches suffer from sparse and partial point clouds caused by distant and occluded objects. In this paper, we propose a novel two-stage framework, namely PC-RGNN, which deals with these challenges by two specific solutions. On the one hand, we introduce a point cloud completion module to recover high-quality proposals of dense points and entire view with original structures preserved. On the other hand, a graph neural network module, is designed, which comprehensively captures relations among points by the local-global attention mechanism as well as the multi-scale graph based context aggregation and substantially strengthens encoded features. Extensive experiments on the KITTI benchmark show that the proposed approach outperforms the previous state-of-the-art baselines by remarkable margins, highlighting its effectiveness.
A Gray Image Encryption Technique Using the Concept of Water Waves, Chaos and Hash Function

In this article, an innovative encryption technique is developed by getting the inspiration from the natural up-down water wave's phenomena that changes a reflection on pond's surface.The plain image is considered as water pond and stones are throwing on the pond to diffuse the pixels.The direction of diffusion follows the pattern of natural water waves in 360 degrees.The distance traveled by the waves depend upon the weight of the stone i.e. heavier is the stone, longer will be the distance.Each layer of pixels in image represents Crest (Up) and Troughs (Down) around the hitting point of stone.The number of Crest and Troughs generated by a stone in radius are measured and are categorized into 1 to 6 or six levels (Layers).The concept of Crest and Trough in encryption are achieved by addition and subtraction of pseudo-random numbers in the pixel's intensity.The pseudo random numbers are generated from Logistic map and transformed into stones of different weights ranges from 0-255.Before substitution, the pixels are shuffled in 1-dimensional vector using the index values of random numbers.The hash value is used for modification of initial seeds of the Logistic map.The initial seeds of the Logistic map are modified by the output of 256-bit hash function.Finally, the simulated results are compiled which ascertains that the projected algorithm achieves the required security standard with large pool of secret keys.
Data Elimination on Repetition using a Blockchain based Cyber Threat Intelligence

Cyber threat is a major issue that has been terrorizing the computing work. A typical cyber-physical system is crucial in ensuring a safe and secure architecture of a sustainable computing ecosystem. Cyber Threat Intelligence (CTI) is a new methodology that is used to address some of the existing cyber threats and ensure a more secure environment for communication. Data credibility and reliability plays a vital role in increasing the potential of a typical CTI and the data collected for this purpose is said to be highly reliable. In this paper, we have introduced a CTI system using blockchain to tackle the issues of sustainability, scalability, privacy and reliability. This novel approach is capable of measuring organizations contributions, reducing network load, creating a reliable dataset and collecting CTI data with multiple feeds. We have testing various parameters to determine the efficiency of the proposed methodology. Experimental results show that when compared to other methodologies, we can save upto 20% of storage space using the proposed methodology.