Reviewerly/exHarmony huggingface dataset:
Mapping the potentials of blockchain in improving supply chain performance

This paper aims at reviewing and systematically mapping research on blockchain potentials in improving supply chain performance. Articles were retrieved from several prominent databases, selected, reviewed, grouped into several themes and synthesized. This paper suggests that applying blockchain in the supply chain could improve its performance in terms of transparency, traceability, sustainability, trust, and cost-efficiency. As a cutting-edge technology, blockchain has not been widely implemented in supply chain industries. Research on blockchain application in the supply chain is also relatively limited. This paper contributes to the literature by offering a comprehensive map of research on blockchain potentials in improving supply chain performance. The findings of this study will also be beneficial for managers who seek for a comprehensive understanding of how blockchain technology affects their companies particularly in supply chain management.
A Deep Learning-Based Blockchain Mechanism for Secure Internet of Drones Environment

Drones are equipped with high-vision cameras, advanced sensors, and GPS receivers to deliver diverse services from high altitude thereby creating an airborne network. In this environment, physical things (drones, sensors, etc.,) are controlled using computational algorithms to form a cyber-physical system for the Internet of drones. Although the drones provide manifold benefits still there are many issues (security, privacy, and data integrity) which must be resolved before the usage of drones in smart cyber-physical systems. So, in this paper, a blockchain-based security mechanism for cyber-physical systems is proposed to ensure secure transfer of information among drones. In this mechanism, the miner node is selected using a deep learning-based approach, i.e., a deep Boltzmann machine, using features like computational resources, the available battery power, and flight time of the drone. The proposed mechanism is evaluated based on different performance metrics and the results obtained show the potential benefits of the proposed scheme.
Guided Transformer

Asking clarifying questions in response to ambiguous or faceted queries has been recognized as a useful technique for various information retrieval systems, especially conversational search systems with limited bandwidth interfaces. Analyzing and generating clarifying questions have been studied recently but the accurate utilization of user responses to clarifying questions has been relatively less explored. In this paper, we enrich the representations learned by Transformer networks using a novel attention mechanism from external information sources that weights each term in the conversation. We evaluate this Guided Transformer model in a conversational search scenario that includes clarifying questions. In our experiments, we use two separate external sources, including the top retrieved documents and a set of different possible clarifying questions for the query. We implement the proposed representation learning model for two downstream tasks in conversational search; document retrieval and next clarifying question selection. Our experiments use a public dataset for search clarification and demonstrate significant improvements compared to competitive baselines.
Temporal analysis of multimodal data to predict collaborative learning outcomes

Abstract The analysis of multiple data streams is a long‐standing practice within educational research. Both multimodal data analysis and temporal analysis have been applied successfully, but in the area of collaborative learning, very few studies have investigated specific advantages of multiple modalities versus a single modality, especially combined with temporal analysis. In this paper, we investigate how both the use of multimodal data and moving from averages and counts to temporal aspects in a collaborative setting provides a better prediction of learning gains. To address these questions, we analyze multimodal data collected from 25 9–11‐year‐old dyads using a fractions intelligent tutoring system. Assessing the relation of dual gaze, tutor log, audio and dialog data to students' learning gains, we find that a combination of modalities, especially those at a smaller time scale, such as gaze and audio, provides a more accurate prediction of learning gains than models with a single modality. Our work contributes to the understanding of how analyzing multimodal data in temporal manner provides additional information around the collaborative learning process.
Socially and Contextually Aware Human Motion and Pose Forecasting

Smooth and seamless robot navigation while interacting with humans depends on predicting human movements. Forecasting such human dynamics often involves modeling human trajectories (global motion) or detailed body joint movements (local motion). Prior work typically tackled local and global human movements separately. In this letter, we propose a novel framework to tackle both tasks of human motion (or trajectory) and body skeleton pose forecasting in a unified end-to-end pipeline. To deal with this real-world problem, we consider incorporating both scene and social contexts, as critical clues for this prediction task, into our proposed framework. To this end, we first couple these two tasks by i) encoding their history using a shared Gated Recurrent Unit (GRU) encoder and ii) applying a metric as loss, which measures the source of errors in each task jointly as a single distance. Then, we incorporate the scene context by encoding a spatio-temporal representation of the video data. We also include social clues by generating a joint feature representation from motion and pose of all individuals from the scene using a social pooling layer. Finally, we use a GRU based decoder to forecast both motion and skeleton pose. We demonstrate that our proposed framework achieves a superior performance compared to several baselines on two social datasets.
An efficient blind color image watermarking algorithm in spatial domain combining discrete Fourier transform

For achieving the purpose of rapid protection of color image copyright better, a novel color image blind watermarking algorithm is proposed in this paper, which integrates discrete Fourier transform (DFT) in spatial domain. According to the unique feature of the direct current (DC) component of DFT, two procedures of embedding and blind extracting watermark are accomplished in spatial domain utilizing the correlation principle between adjacent pixel blocks and different steps in different channels of color image, without real DFT. The experiment results demonstrate that the presented algorithm has strong robustness and high real time under the premise of better watermark invisibility, and solves the puzzles of slow running-speed and weak robustness of large capacity color image digital watermarking algorithm.
Exploiting Unlabeled Data in Smart Cities using Federated Edge Learning

Privacy concerns are considered one of the main challenges in smart cities as sharing sensitive data induces threatening problems in people's lives. Federated learning has emerged as an effective technique to avoid privacy infringement as well as increase the utilization of the data. However, there is a scarcity in the amount of labeled data and an abundance of unlabeled data collected in smart cities; hence there is a necessity to utilize semi-supervised learning. In this paper, we present the primary design aspects for enabling federated learning at the edge networks taking into account the problem of unlabeled data. We propose a semi-supervised federated edge learning method called FedSem that exploits unlabeled data in real-time. FedSem algorithm is divided into two phases. The first phase trains a global model using only the labeled data. In the second phase, Fedsem injects unlabeled data into the learning process using the pseudo labeling technique and the model developed in the first phase to improve the learning performance. We carried out several experiments using the traffic signs dataset as a case study. Our results show that FedSem can achieve accuracy by up to 8% by utilizing the unlabeled data in the learning process.
Blockchain Application for Central Banks: A Systematic Mapping Study

Blockchain is a novel technology capturing the attention of Central Banks and a technology with significant disruptive potential. However, a gap in research effort between practitioners and academics seems to have emerged. This paper analyses and maps that gap by exploring trends in peer-reviewed research contributions through thematic categorisation of academic literature on Distributed Ledger Technology (DLT) use-cases for services, operations and functions performed by central banks. Furthermore, this paper provides summaries of opportunities and challenges for central banks arising from blockchain adaptation to each of those use-cases. To achieve this goal, we utilise a Systematic Mapping Study approach. The paper presents an in-depth assessment of statistical and thematic analysis of research maturity and the types of researchers, with specific emphasis on types of central bank use-cases considered for blockchain adaptation. Our work contributes to an understanding of where the most or least attention is directed, allowing for identification of gaps and opportunities for both academics, practitioners and combinations of each. Results show that the research topic is a comparatively new domain. It confirms the gap between depth and volume of the research provision from industry and academia, with industry leading the trend. Our study also found that the most research-intensive use-cases are those for: 1) Central Bank issued Digital Currency (CBDC), 2) Regulatory Compliance and 3) Payment Clearing and Settlement Systems (PCS) operated by central banks; a comparatively low engagement was found in the areas of 4) Assets Transfer/Ownership and 5) Audit Trail.
Preemptive All-reduce Scheduling for Expediting Distributed DNN Training

Data-parallel training is widely used for scaling DNN training over large datasets, using the parameter server or all-reduce architecture. Communication scheduling has been promising to accelerate distributed DNN training, which aims to overlap communication with computation by scheduling the order of communication operations. We identify two limitations of previous communication scheduling work. First, layer-wise computation graph has been a common assumption, while modern machine learning frameworks (e.g., TensorFlow) use a sophisticated directed acyclic graph (DAG) representation as the execution model. Second, the default sizes of tensors are often less than optimal for transmission scheduling and bandwidth utilization. We propose PACE, a communication scheduler that preemptively schedules (potentially fused) all-reduce tensors based on the DAG of DNN training, guaranteeing maximal overlapping of communication with computation and high bandwidth utilization. The scheduler contains two integrated modules: given a DAG, we identify the best tensor-preemptive communication schedule that minimizes the training time; exploiting the optimal communication scheduling as an oracle, a dynamic programming approach is developed for generating a good DAG, which merges small communication tensors for efficient bandwidth utilization. Experiments in a GPU testbed show that PACE accelerates training with representative system configurations, achieving up to 36% speed-up compared with state-of-the-art solutions.
Deeply supervised convolutional neural network for shadow detection based on a novel aerial shadow imagery dataset

Shadow detection is an essential work for remote sensing image analysis, as the presence of shadows in high resolution images not only degrades the radiometric information but also disturbs the image interpretation. In this paper, a convolutional neural network (CNN) based shadow detection framework for aerial remote sensing images is presented. We construct a publicly available Aerial Imagery dataset for Shadow Detection (AISD), which is the first aerial shadow imagery dataset, as far as we know. Based on AISD, we propose a novel Deeply Supervised convolutional neural network for Shadow Detection (DSSDNet). To solve the insufficient feature extraction problem of shadows, the DSSDNet model is designed to include two steps: (1) an encoder-decoder residual (EDR) structure is adopted to extract multi-level and discriminative shadow features; (2) a deeply supervised progressive fusion (DSPF) process is then imposed on EDR to further boost the detection performance by directly guiding the training of the network and fuse adjacent feature maps progressively. The proposed DSSDNet is compared with several state-of-the-art methods in both qualitative and quantitative analysis. Results show that the proposed DSSDNet is more accurate, and more consistent to the shape of the objects casting shadows, with the average F-score being 91.79% on the testing images.
Multimodality Sentiment Analysis in Social Internet of Things Based on Hierarchical Attentions and CSAT-TCN With MBM Network

Multimodality sentiment analysis in the social Internet of Things is a developing field, which is basic to empathetic mechanisms, affective computing, and artificial intelligence. Current works in this domain do not explicitly consider the influence of contextual information fusion based on correlation coefficient and memory network with branch structure for sentiment analysis. Unlike present works, this article presents a hierarchical self-attention fusion (H-SATF) model for capturing contextual information better among utterances, a contextual self-attention temporal convolutional network (CSAT-TCN) for sentiment recognition in the social Internet of Things, and a multibranch memory (MBM) network that stores self-speaker and interspeaker sentimental states into global memories. For MOSI data sets, the hybrid H-SATF-CSAT-TCN-MBM model outperforms the state-of-the-art networks and shows 0.31%-9.93% improvement.
Encoder-Decoder With Cascaded CRFs for Semantic Segmentation

When dealing with semantic segmentation, how to locate the object boundary information more accurately is a key problem to distinguish different objects better. The existing methods lose some image information more or less in the process of feature extraction, which also includes the boundary and context information. At present, some semantic segmentation methods use CRFs (conditional random fields) to obtain boundary information, but they usually only deal with the final output of the model. In this article, inspired by the skip connection of FCN (Fully convolution network) and the good boundary refinement ability of CRFs, a cascaded CRFs is designed and introduced into the decoder of semantic segmentation model to learn boundary information from multi-layers and enhance the ability of the model in object boundary location. Furthermore, in order to supplement the semantic information of images, the output of the cascaded CRFs is fused with the output of the last decoder, so that the model can enhance the ability of locating the object boundary and get more accurate semantic segmentation results. Finally, a number of experiments on different datasets illustrate the feasibility and efficiency of our method, showing that our method enhances the model’s ability to locate target boundary information.
Policy-based reinforcement learning for time series anomaly detection

Time series anomaly detection has become a crucial and challenging task driven by the rapid increase of streaming data with the arrival of the Internet of Things. Existing methods are either domain-specific or require strong assumptions that cannot be met in realistic datasets. Reinforcement learning (RL), as an incremental self-learning approach, could avoid the two issues well. However, the current investigation is far from comprehensive. In this paper, we propose a generic policy-based RL framework to address the time series anomaly detection problem. The policy-based time series anomaly detector (PTAD) is progressively learned from the interactions with time-series data in the absence of constraints. Experimental results show that it outperforms the value-based temporal anomaly detector and other state-of-the-art detection methods whether training and test datasets come from the same source or not. Furthermore, the tradeoff between precision and recall is well respected by the PTAD, which is beneficial to fulfill various industrial requirements.
When adaptive learning is effective learning: comparison of an adaptive learning system to teacher-led instruction

Adaptive learning systems personalize instruction to students’ individual learning needs and abilities. Such systems have shown positive impacts on learning. Many schools in the United States have adopted adaptive learning systems, and the rate of adoption in China is accelerating, reaching almost 2 million unique users for one product alone in the past 3 years. Given such rapid adoption in China, it is useful to examine the efficacy of adaptive learning within that country’s educational system. This study aimed to compare the learning impacts of individualized adaptive learning courseware to two common instructional approaches in China: large-group and small-group classroom instruction. This paper describes the results of two efficacy studies of one of China’s first adaptive learning systems, Squirrel AI Learning. One study compares classroom-based individualized adaptive learning instruction to large-group instruction, and another to small-group instruction. Chinese eighth-grade students from two provinces randomly assigned to use Squirrel AI Learning showed greater gains on a mathematics test than those randomly assigned to whole-class or small-group instruction led by expert teachers. Findings provide a basis for further research into the selection, use, and impact of adaptive learning systems in Chinese education.
Email Spam Detection Using Machine Learning Algorithms

Email Spam has become a major problem nowadays, with Rapid growth of internet users, Email spams is also increasing. People are using them for illegal and unethical conducts, phishing and fraud. Sending malicious link through spam emails which can harm our system and can also seek in into your system. Creating a fake profile and email account is much easy for the spammers, they pretend like a genuine person in their spam emails, these spammers target those peoples who are not aware about these frauds. So, it is needed to Identify those spam mails which are fraud, this project will identify those spam by using techniques of machine learning, this paper will discuss the machine learning algorithms and apply all these algorithm on our data sets and best algorithm is selected for the email spam detection having best precision and accuracy.
A Survey of Mental Modeling Techniques in Human–Robot Teaming

As robots become increasingly prevalent and capable, the complexity of roles and responsibilities assigned to them as well as our expectations for them will increase in kind. For these autonomous systems to operate safely and efficiently in human-populated environments, they will need to cooperate and coordinate with human teammates. Mental models provide a formal mechanism for achieving fluent and effective teamwork during human–robot interaction by enabling awareness between teammates and allowing for coordinated action. Much recent research in human–robot interaction has made use of standardized and formalized mental modeling techniques to great effect, allowing for a wider breadth of scenarios in which a robotic agent can act as an effective and trustworthy teammate. This paper provides a structured overview of mental model theory and methodology as applied to human–robot teaming. Also discussed are evaluation methods and metrics for various aspects of mental modeling during human–robot interaction, as well as recent emerging applications and open challenges in the field.
Imbalanced regression and extreme value prediction

Research in imbalanced domain learning has almost exclusively focused on solving classification tasks for accurate prediction of cases labelled with a rare class. Approaches for addressing such problems in regression tasks are still scarce due to two main factors. First, standard regression tasks assume each domain value as equally important. Second, standard evaluation metrics focus on assessing the performance of models on the most common values of data distributions. In this paper, we present an approach to tackle imbalanced regression tasks where the objective is to predict extreme (rare) values. We propose an approach to formalise such tasks and to optimise/evaluate predictive models, overcoming the factors mentioned and issues in related work. We present an automatic and non-parametric method to obtain relevance functions, building on the concept of relevance as the mapping of target values into non-uniform domain preferences. Then, we propose SERA, a new evaluation metric capable of assessing the effectiveness and of optimising models towards the prediction of extreme values while penalising severe model bias. An experimental study demonstrates how SERA provides valid and useful insights into the performance of models in imbalanced regression tasks.
Enhancing Federated Learning in Fog-Aided IoT by CPU Frequency and Wireless Power Control

Machine learning models have been built in fog nodes in fog-aided Internet-of-Things (IoT) networks to provision future events prediction and image classification by training data collected from IoT devices. However, sending massive data from all devices to a fog node incurs huge network traffic in wireless links in between. Federated learning is proposed to address the challenge by training models locally in IoT devices and only sharing model parameters in the fog node. In this article, we investigate both the CPU frequency control and wireless transmission power control of all IoT devices to balance the tradeoff between the device energy consumption and federated learning time (consisting of both the computation and communication latencies) in fog-aided IoT networks. We formulate the joint optimization of CPU and power control as a nonlinear programming (NLP) problem with the objective to minimize the energy consumption of all IoT devices constrained by the federated learning time requirement. An alternative direction algorithm, which alternatively optimizes the CPU frequency and wireless transmission power until convergence, is hence designed to solve this problem and its performance is demonstrated via extensive simulations.