Reviewerly/exHarmony huggingface dataset:
Chatbots in Logistics: A Field Experiment on Intelligent Freight Dispatching

Fueled by the widespread adoption of algorithms and artificial intelligence (AI), the use of chatbots has become increasingly popular in various business contexts. In this paper, we study how to effectively and appropriately use chatbots in logistics, particularly in dispatching freights automatically. Specifically, this paper seeks to understand the effects of two voice chatbot design features (i.e., identity disclosure and anthropomorphism) on the operational performance of freight dispatching. In collaboration with a large truck-sharing platform, we conducted a field experiment that randomly assigned over 10,000 truck drivers to receive outbound calls from the voice chatbot dispatcher of our focal platform. Our empirical results suggest that chatbot identity disclosure at the beginning of the conversation significantly reduces the operational performance in terms of response rate. However, humanizing the voice chatbot by adding our proposed anthropomorphism features (i.e., interjections and filler words) significantly improves response rate, conversation duration, and order acceptance intention. Moreover, interestingly, humanizing the voice chatbot along with its identity disclosure can still improve operational outcomes, and its magnitude is similar to the improvement brought about by humanizing the chatbot without identity disclosure. This finding indicates that improving anthropomorphism may potentially mitigate the negative effects of chatbot identity disclosure. Finally, we propose one plausible explanation related to the enhanced trust between humans and algorithms for the performance improvement and empirically show that drivers are more likely to disclose information to chatbot dispatchers with anthropomorphism features. Our proposed anthropomorphism improvement solutions are now being implemented and used by our collaborator platform.
Sentiment Analysis Using Deep Learning: A Comparative Study

Sentiment Analysis is a technique associated with Natural Language Processing in understanding the sentiments of the people. In today’s environment where shopping, watching movies, expressing thoughts or emotions is done via social platforms. The social platforms have become a stage where people can express their sentiments freely. Such behaviour has made it important for companies to provide the best of the platforms for their customers while using their services based on the polarity of their sentiments. In other cases where the manner of the sentiments is to be understood for security reasons or regarding any social issues, sentiment analysis has been the most important tool. In this work we have studied NLP, different deep learning techniques such as LSTM, BiLSTM and CNN along with Word Embeddings and their types: Glove and Word2Vec and performed a comparative study. We found improvement in BiLSTM and LSTM models using Glove(50d) word embeddings. [1]The accuracy received using LSTM and BiLSTM are: 90.36% using batch size 100 and 91.68% using batch size 128. We also tested the best model which was BiLSTM with batch size of 128 on an unlabelled testing data and also projected the results of it. The results were very impressive and promising and at the end the word clouds were created for all the reviews, positive and negative reviews respectively in the unlabelled dataset.
QuatCNNEx: A Knowledge Graph Embedding Model based on Quaternion Convolutional Neural Network

We can infer the missing links in the Knowledge Graph from the known knowledge when the existing Knowledge Graph is incomplete. Knowledge Graph Embedding is a common method to solve this kind of problem, that is, using low dimensional embedding of entities and relations to infer the missing links. The existing Knowledge Graph Embedding methods either do not make full use of the interactive feature information of entities and relations or have weak modeling ability for composition relation. Therefore, the combination of quaternion and convolution neural network is explored to construct QuatCNNEx which is a new Knowledge Graph Embedding model. The quaternion used in the model encodes the triple of Knowledge Graph, the quaternion embedding of entities and relations are used to the input of convolution neural network, and the model combines the Hadamard product of quaternion to predict the missing links in Knowledge Graph. The results of the experiment show that QuatCNNEx can model the composition relations and predict the missing triples in the Knowledge Graph using the interactive feature information of entities and relations.
Automatic Exploration of Domain Knowledge in Healthcare

Throughout the years, healthcare has been one of the privileged areas to apply the information discovery process, empowering and supporting medical staff on their daily activities. One of the main reasons for its success is the availability of medical expertise, which can be incorporated in training models to reach higher levels of performance. While this has been done painfully and manually, during the preparation step, it has become hindered with the advent of AutoML. In this paper, we present the automation of data preparation and feature engineering, while exploring domain knowledge represented through extended entity-relationship (EER) diagrams. A COVID-19 case study shows that our automation outperforms existing AutoML tools, such as auto-sklearn [4], both in quality of the models and processing times.
Face Forgery Detection by Multi-dimensional Image Decomposition

With the development of face manipulation techniques and the potential harms of fake videos and images to public safety, the field of face forgery detection has received a lot of attention. However, existing face forgery detection methods mostly rely on specific datasets, and the generalization performance appears to be degraded when testing across datasets. In this paper, we use multi-dimensional image decomposition, i.e., spatial-frequency decomposition and computer graphics decomposition, to provide additional forgery clues. On the one hand, we decompose the face image into three components: high-frequency component, medium frequency component, and low-frequency component using spatial-frequency decomposition; on the other hand, we use computer graphics algorithms to simulate the face generation process, separate the 3D model and decompose the face image into five components. For the decomposed components, we introduce a three-stream neural network to fuse multi-modality information. Extensive experiments have shown that our method achieves state-of-the-art performance.
Collaborative Clustering Based on Adaptive Laplace Modeling for Neuroimaging Data Analysis

Aging subjects with neurodegenerative conditions have multiple contributors and pathology progression patterns that result in heterogeneous disease biology and different disease phenotypes. Clinical data play a crucial role in disentangling such disease heterogeneity, but they are usually by noise, which can result in errors in clustering leading to spurious non-clinically relevant clusters. A limitation of conventional neuroimaging clustering methods is neglecting the potential bias caused by noise. To remove noise, we introduce adaptive regularization based on coefficient distribution modeling in transform domain. Different from traditional sparsity techniques that assume zero expectation of the coefficients, we use the data of interest to form the Laplace distributions so that they can depict the statistical characteristics more accurately. Furthermore, we use feature clusters to provide weak supervision for enhanced clustering of subjects. To this end, we employ nonnegative matrix tri-factorization to collaboratively cluster subjects and features. Experimental results on synthetic data and the real-life clinical dataset PRVENT-AD demonstrate superior effectiveness of the proposed approach.
Impact of Morphological Segmentation on Pre-trained Language Models

Pre-trained Language Models are the current state-of-the-art in many natural language processing tasks. These models rely on subword-based tokenization to solve the problem of out-of-vocabulary words. However, commonly used subword segmentation methods have no linguistic foundation. In this paper, we investigate the hypothesis that the study of internal word structure (i.e., morphology) can offer informed priors to these models, such that they perform better in common tasks. We employ an unsupervised morpheme discovery method in a new word segmentation approach, which we call Morphologically Informed Segmentation (MIS), to test our hypothesis. Experiments with MIS on several natural language understanding tasks (text classification, recognizing textual entailment, and question-answering), in Portuguese, yielded promising results compared to a WordPiece baseline.
Natural Language Query for Technical Knowledge Graph Navigation

Technical knowledge graphs are difficult to navigate. To support users with no coding experience, one can use traditional structured HTML form controls, such as drop-down lists and check-boxes, to construct queries. However, this requires multiple clicks and selections. Natural language queries, on the other hand, are more convenient and less restrictive for knowledge graphs navigation. In this paper, we propose a system that enables natural language queries against technical knowledge graphs. Given an input utterance (i.e., a query in human language), we first perform Named Entity Recognition (NER) to identify domain specific entity mentions as node names, entity types as node labels, and question words (e.g., what, how many and list) as keywords of a structured query language before the rule-based formal query constructions. Three rules are exploited to generate a valid structured formal query. The web-based interactive application is developed to help maintainers access industrial maintenance knowledge graph which is constructed from text data.
Sequential Multi-view Fusion Network for Fast LiDAR Point Motion Estimation

The LiDAR point motion estimation, including motion state prediction and velocity estimation, is crucial for understanding a dynamic scene in autonomous driving. Recent 2D projection-based methods run in real-time by applying the well-optimized 2D convolution networks on either the bird’s-eye view (BEV) or the range view (RV) but suffer from lower accuracy due to information loss during the 2D projection. Thus, we propose a novel sequential multi-view fusion network (SMVF), composed of a BEV branch and an RV branch, in charge of encoding the motion information and spatial information, respectively. By looking from distinct views and integrating with the original LiDAR point features, the SMVF produces a comprehensive motion prediction, while keeping its efficiency. Moreover, to generalize the motion estimation well to the objects with fewer training samples, we propose a sequential instance copy-paste (SICP) for generating realistic LiDAR sequences for these objects. The experiments on the SemanticKITTI moving object segmentation (MOS) and Waymo scene flow benchmarks demonstrate that our SMVF outperforms all existing methods by a large margin.
Event-Processing Model for Smart Contracts with Oracle and EEG

In blockchain (BC) systems, smart contracts (SCs) often use a transaction-driven execution model. However, real-world financial applications are complex, and often driven by events instead. This paper presents an intelligent event-driven framework to automatically trigger SC execution by collecting event information, and classifying these events, and analyzing various relationship among these events, and use a service-model for SCs to register interested events so that when the concerned event happened, they can be automatically triggered. This paper uses oracle machines (OMs) to collect event information, EEG (Event Evolution Graph) to analyze relationship among events. This paper then uses multi-layer perceptron model to evaluate the design. This paper proposes a new method to solve the scalability and performance challenges in the SC ecosystem, which shows that the event-driven execution model would be more intelligent.
Contrastive Self-Supervised Learning as a Strong Baseline for Unsupervised Hashing

Contrastive self-supervised learning has shown to learn representations transferable to a variety of downstream applications, e.g., object detection and classification. While utilizing a contrastive self-supervised objective to learn generalizable features has been much explored, employing it to directly learn binary representations for image search is yet to be studied. This paper presents Contrastive Self-supervised deep Hashing (CSHash), a simple yet effective unsupervised hashing framework aimed at producing compact binary hash codes that preserve semantic similarity between data without relying on human annotations. CSHash is trained on the basis of a contrastive learning objective, pulling together the augmentations of the same sample and keep apart those of different samples in the hash code space. Evaluation on three datasets shows that simply trained by a contrastive self-supervised loss, CSHash is a strong baseline for unsupervised hashing. It yields discriminative, high-quality binary codes and performs comparably to other unsupervised hashing methods. Additionally, we perform thorough analyses on the main components of CSHash to provide a better insight into the framework.
Deep Integration System of English Information Education(EIE) Based on RBF Algorithm

A perfect EIE and teaching system is composed of an efficient, sensitive, dynamic and innovative information technology platform, hard conditions and optimized soft conditions such as high-quality manpower, intelligence and perseverance. In the information network environment, in order to effectively promote the deep integration of EIE, a new teaching development path is needed to carry and lead. Therefore, this paper proposes the research on the deep fusion system of EIE based on RBF algorithm, discusses and analyzes the RBF neural network learning algorithm, information fusion technology and the fusion point of English education in information teaching, and analyzes the RBF neural network algorithm. In order to verify the teaching effect of the deep integration system of EIE based on RBF algorithm, this paper compares the EIE proposed in this paper with the traditional education mode, investigates the students' evaluation of learning English under the two modes, and tests the students' autonomous learning ability under English information teaching; The results showed that only 59% of the students said that under the traditional English education, their self-reflection ability was improved, while the EIE reached 88%. 84% of the students said that the EIE was helpful to improve their English reading ability, while the traditional education was only 66%; 45.72% of students like to explore independently, but their ability is poor; There are different ways to solve difficulties in learning. Therefore, the deep integration of EIE based on RBF algorithm is helpful to improve students' autonomous learning ability. The improvement of learning ability still needs time to prove. However, it can also prove that the teaching effect of the deep integration system of EIE based on RBF algorithm is worthy of application.
Blockchain - And Secure Architecture from Cyber Threat

In the digital world, blockchain technology is a relatively new concept. It is among the newest technologies of the contemporary day. The foundation for bitcoins and other cryptocurrencies is blockchain. Blockchains have an unalterable digital record that is serving as the foundation for all future digital transactions. Additionally, middlemen and third-party trustworthiness are not included in the blockchain. Additionally, it is assisting in resolving the Internet privacy issue. The technology has a lot of potential and the ability to drastically alter our way of life. As a result, there is a significant investment being made in blockchain technology. This essay aims to provide a succinct introduction to the fundamental elements of blockchain technology and cryptocurrencies. The publication also tries to highlight the cryptocurrency market.
PIP: Physical Interaction Prediction via Mental Simulation with Span Selection

Accurate prediction of physical interaction outcomes is a crucial component of human intelligence and is important for safe and efficient deployments of robots in the real world. While there are existing vision-based intuitive physics models that learn to predict physical interaction outcomes, they mostly focus on generating short sequences of future frames based on physical properties (e.g. mass, friction and velocity) extracted from visual inputs or a latent space. However, there is a lack of intuitive physics models that are tested on long physical interaction sequences with multiple interactions among different objects. We hypothesize that selective temporal attention during approximate mental simulations helps humans in physical interaction outcome prediction. With these motivations, we propose a novel scheme: Physical Interaction Prediction via Mental Simulation with Span Selection (PIP). It utilizes a deep generative model to model approximate mental simulations by generating future frames of physical interactions before employing selective temporal attention in the form of span selection for predicting physical interaction outcomes. To the best of our knowledge, attention has not been used with deep learning to tackle intuitive physics. For model evaluation, we further propose the large-scale SPACE+ dataset of synthetic videos with long sequences of three prime physical interactions in a 3D environment. Our experiments show that PIP outperforms human, baseline, and related intuitive physics models that utilize mental simulation. Furthermore, PIP’s span selection module effectively identifies the frames indicating key physical interactions among objects, allowing for added interpretability, and does not require labor-intensive frame annotations. PIP is available on https://sites.google.com/view/piphysics .
Models and Methods of Data Mining in Project Management

This article discusses Data Mining in the field of Project Management in terms of identifying hidden patterns or relationships between objects in large data sets. Theoretical foundations and tasks that are solved during the application of data mining are considered. The properties and features of existing methods of data mining are analyzed and described - from the construction of decision trees to neural networks. Tasks and problems in the field of project management are formulated, which can be solved using the methods of data mining.
Aggregating Layers for Deepfake Detection

The increasing popularity of facial manipulation (Deepfakes) and synthetic face creation raises the need to develop robust forgery detection solutions. Crucially, most work in this domain assume that the Deepfakes in the test set come from the same Deepfake algorithms that were used for training the network. This is not how things work in practice. Instead, we consider the case where the network is trained on one Deepfake algorithm, and tested on Deepfakes generated by another algorithm. Typically, supervised techniques follow a pipeline of visual feature extraction from a deep backbone, followed by a binary classification head. Instead, our algorithm aggregates features extracted across all layers of one backbone network to detect a fake. We evaluate our approach on two domains of interest Deepfake detection and Synthetic image detection, and find that we achieve SOTA results.
An Electroglottographic Method for Assessing the Emotional State of the Speaker

The purpose of the study is to determine the shape of the electroglottographic (EGG) wave, the values of the closed quotient (CQ) coefficient and the pitch values (F0) of speech uttered in emotional states of joy, sadness, fear, anger and in a neutral (calm) state. Two methods were used: EGG and spectrographic analysis. The object of the study was electroglottograms and speech (F0 and duration of phrases) of 12 healthy speakers (6 men and 6 women, age 25.3 ± 4.5 and 24.5 ± 4.8 years, respectively). It was found that the values of the CQ coefficient in men are maximal in the neutral (calm) state – 0.98; in women – in a state of sadness – 0.97. The minimal values of the CQ coefficient for men are shown for the state of fear – 0.81, for women – for the state of joy – 0.69. In men, the states of anger and joy are characterized by significantly higher pitch values than the neutral (calm) state; in women, the pitch values are significantly higher in the states of fear, anger, and joy vs the state of sadness and the neutral state. Significant differences in the pitch values between the states of fear, anger, joy; a neutral state and a state of sadness were not revealed.
Multicore Federated Learning for Mobile-Edge Computing Platforms

With increasingly strict data privacy regulations, federated learning (FL) has become one of the most often heard machine learning techniques due to its privacy-preserving trait. To efficiently implement the FL intelligence, researchers recently resort to a newly emerged computing paradigm, mobile-edge computing (MEC), and bring about a burst of works. However, most existing works neglect practical issues in MEC systems, e.g., device heterogeneity, unstable channel conditions, and unknown user mobility. Any of them, if not handled properly, can cause fatal failures to FL. This article proposed a novel FL framework, called multicore FL (MC-FL), to help FL intelligence land successfully on realistic MEC systems. A distinct feature of MC-FL is maintaining and training multiple global models (GMs) that exhibit different tradeoffs between learning performances and computational complexity. While this modification seems simple, it can effectively handle the device heterogeneity and device status variations, and improve the compatibility and robustness of FL. Furthermore, MC-FL employs a partial client participation scheme that allows participating clients to vary across time. This enables MC-FL to function under uncertain mobile environments. We rigorously prove the convergence of the designed MC-FL framework. In particular, we propose an online client scheduling scheme for MC-FL to judiciously schedule clients for training multiple GMs in a manner that minimizes the completion time of MC-FL. We also provide a service provisioning scenario with MC-FL to show how service subscribers could benefit from multiple GMs and improve their Quality of Experience (QoE). We evaluate our method on real-world data sets, and the results show that MC-FL outperforms state-of-the-art benchmarks.
A Study of Visitor Interaction with Virtual Museum

The development and opening of Virtual Museums have accelerated with the rapid development of human computer interaction and virtual reality technology. The paradigm of interaction between visitor and museum has shown changes. New technologies have significantly transformed the museum visitor experience and the meaning-making process of museum collections. It is proposed that “the interaction behavior of Virtual Museums and visitors is a whole process and space interaction behavior” and “Virtual Museum narrative is a collaborative narrative”. This paper summarizes the current status of Virtual Museum research and the four main research directions through literature review; studies the interaction behavior between Virtual Museum and visitors, analyzes the museum interaction object and museum narrative process; deeply explores the similarities and differences between Virtual Museum visitor interaction and traditional museum visitor interaction, and proposes the essential changes brought by virtual reality technology to museum visitors. It is a good indication to improve the visitor experience in Virtual Museums.