Reviewerly/exHarmony huggingface dataset:
Gaze Prediction Based on Convolutional Neural Network

In this paper gaze prediction aims to acquire good performance via “Convolutional Neural Network (CNN)” based identification. Gaze prediction was proposed to localize the gaze direction of the students in classrooms using captured face images. Such systems can raise the teachers’ awareness of whether students are gazing towards him or not and alert the students to pay attention to their teachers if their gazes were scattered. In this way, the prediction of gaze was proposed firstly to achieve the proposed eyes area detection algorithm based on modeling facial gestures. The inputs of the gaze prediction are the output of the eye detection. Secondly, the localizing gaze of the students had been considered as straight, right, or left. CNN was employed to different training models for the gaze prediction phase. The dataset was obtained from a public database for neural network training.
Hyperentanglement teleportation through external momenta states

Abstract The conventional teleportation protocol requires a state entangled in only one degree of freedom (DOF), while hyperteleportation requires more than single DOF to complete the task. The hyperteleportation schematics are demonstrated only for the photonic systems, where in the present paper we extend the idea to a hyperteleportation protocol involving the atomic internal and external states. The protocol is deterministically engineered through resonant and off-resonant atomic Bragg diffraction involving two-level neutral atoms under standard cavity-QED working environment. Moreover, the longer interaction time Bragg’s regime with well separated transverse momenta states as an output of the neutral atoms guarantees the high enough engineering fidelities with reduced decoherence rates. The experimental parameters for the demonstration of the proposed scheme are also elucidated briefly describing the optimistic feasibility for the experimental execution of the proposed schematics.
JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering

Existing KG-augmented models for commonsense question answering primarily focus on designing elaborate Graph Neural Networks (GNNs) to model knowledge graphs (KGs).However, they ignore (i) the effectively fusing and reasoning over question context representations and the KG representations, and (ii) automatically selecting relevant nodes from the noisy KGs during reasoning.In this paper, we propose a novel model, JointLK, which solves the above limitations through the joint reasoning of LM and GNN and the dynamic KGs pruning mechanism.Specifically, JointLK performs joint reasoning between LM and GNN through a novel dense bidirectional attention module, in which each question token attends on KG nodes and each KG node attends on question tokens, and the two modal representations fuse and update mutually by multi-step interactions.Then, the dynamic pruning module uses the attention weights generated by joint reasoning to prune irrelevant KG nodes recursively.We evaluate JointLK on the Com-monsenseQA and OpenBookQA datasets, and demonstrate its improvements to the existing LM and LM+KG models, as well as its capability to perform interpretable reasoning 1 .
Visible-wavelength polarization-entangled photon source for quantum communication and imaging

We present a polarization-entangled photon pair source operating in the visible light range around 532 nm. Employing a collinear crossed-crystal scheme with type-I degenerate phase matching in barium borate (BBO), our source achieves a brightness of 9.5 k pairs/s/mW and a quantum state fidelity of 98.3%, making it a candidate for integration in microscopes and make use of the advantages of mid-visible optimized single-photon detection technologies. In order to study potential applications, we present a trade-off between source brightness and polarization entanglement visibility and propose use cases for different filtering configurations of the source, capable of a brightness up to 1.23 M pairs/s/mW.
TF-SOD: a novel transformer framework for salient object detection

Most of existing salient object detection models are based on fully convolutional network (FCN), which learn multi-scale/level semantic information through convolutional layers to obtain high-quality predicted saliency maps. However, convolution is locally interactive, it is difficult to capture remote dependencies, and FCN-based methods suffer from coarse object boundaries. In this paper, to solve these problems, we propose a novel transformer framework for salient object detection (named TF-SOD), which mainly consists of the encoder part of the FCN, fusion module (FM), transformer module (TM) and feature decoder module (FDM). Specifically, FM is a bridge connecting the encoder and TM and provides some foresight for the non-local interaction of TM. Besides, FDM can efficiently decode the non-local features output by TM and achieve deep fusion with local features. This architecture enables the network to achieve a close integration of local and non-local interactions, making information complementary to each other, deeply mining the associated information between features. Furthermore, we also propose a novel edge reinforcement learning strategy, which can effectively suppress edge blurring from local and global aspects by means of powerful network architecture. Extensive experiments using five datasets demonstrate that the proposed method outperforms 19 state-of-the-art methods
Secure Smart Healthcare Monitoring in Industrial Internet of Things (IIoT) Ecosystem with Cosine Function Hybrid Chaotic Map Encryption

The technological progression is raised as a hybrid ecosystem with the industrial Internet of Things (IIoT). Among them, healthcare is also broadly unified with the Internet of Things to develop an industrial forthcoming system. Utilizing this type of system can be facilitating optimum patient monitoring, competent diagnosis, intensive care, and including the appropriate operation against the existing critical diseases. Due to enormous data theft or privacy leakage, security, and privacy towards patient-based informative data, the preservation of personal patients’ informative data has now become a necessity in the digitized community. The produced article is underlined on handsomely monitoring, perceptively extracted keyframe, and further processed lightweight cosine functions using hybrid way chaotic map keyframe image encryption. Initially, a regular concept of extracted keyframe is deployed to salvage meaningful detected frames by transmitting an alert autonomously to the administration. Then, lightweight cosine function for encryption is employed. This encryption incorporates keyframe exceedingly secure and safe from the outside world or any adversary. Our proposed methodology validates effectiveness throughout the IIoT ecosystem. The produced outcome is highly acceptable and has minimum execution time, robustness, and reasonably adopted cost-effective, secure parameter than any other (keyframes) image encryption methods. Furthermore, this methodology has optimally reduced bandwidth, essential communicating price, transmission cost, storage, and immediately judicious analysis of each occurred activity from the outside world or any adversary to remain secure and confident about the real patient-based data in the smartly developed environment.
Explainable machine learning to predict long-term mortality in critically ill ventilated patients: a retrospective study in central Taiwan

Abstract Background Machine learning (ML) model is increasingly used to predict short-term outcome in critically ill patients, but the study for long-term outcome is sparse. We used explainable ML approach to establish 30-day, 90-day and 1-year mortality prediction model in critically ill ventilated patients. Methods We retrospectively included patients who were admitted to intensive care units during 2015–2018 at a tertiary hospital in central Taiwan and linked with the Taiwanese nationwide death registration data. Three ML models, including extreme gradient boosting (XGBoost), random forest (RF) and logistic regression (LR), were used to establish mortality prediction model. Furthermore, we used feature importance, Shapley Additive exPlanations (SHAP) plot, partial dependence plot (PDP), and local interpretable model-agnostic explanations (LIME) to explain the established model. Results We enrolled 6994 patients and found the accuracy was similar among the three ML models, and the area under the curve value of using XGBoost to predict 30-day, 90-day and 1-year mortality were 0.858, 0.839 and 0.816, respectively. The calibration curve and decision curve analysis further demonstrated accuracy and applicability of models. SHAP summary plot and PDP plot illustrated the discriminative point of APACHE (acute physiology and chronic health exam) II score, haemoglobin and albumin to predict 1-year mortality. The application of LIME and SHAP force plots quantified the probability of 1-year mortality and algorithm of key features at individual patient level. Conclusions We used an explainable ML approach, mainly XGBoost, SHAP and LIME plots to establish an explainable 1-year mortality prediction ML model in critically ill ventilated patients.
A New Hybrid Approach for Efficient Emotion Recognition using Deep Learning

Facial emotion recognition has been very popular area for researchers in last few decades and it is found to be very challenging and complex task due to large intra-class changes. Existing frameworks for this type of problem depends mostly on techniques like Gabor filters, principle component analysis (PCA), and independent component analysis(ICA) followed by some classification techniques trained by given videos and images. Most of these frameworks works significantly well image database acquired in limited conditions but not perform well with the dynamic images having varying faces and images. In the past years, various researches have been introduced framework for facial emotion recognition using deep learning methods. Although they work well, but there is always some gap found in their research. In this research, we introduced hybrid approach based on RNN and CNN which are able to retrieve some important parts in the given database and able to achieve very good results on the given database like EMOTIC, FER-13 and FERG. We are also able to show that our hybrid framework is able to accomplish promising accuracies with these datasets.
Software-Based Dialogue Systems: Survey, Taxonomy, and Challenges

The use of natural language interfaces in the field of human-computer interaction (HCI) is undergoing intense study through dedicated scientific and industrial research. The latest contributions in the field, including deep learning approaches like recurrent neural networks (RNNs), the potential of context-aware strategies and user-centred design approaches, have brought back the attention of the community to software-based dialogue systems, generally known as conversational agents or chatbots. Nonetheless, and given the novelty of the field, a generic, context-independent overview of the current state of research on conversational agents covering all research perspectives involved is missing. Motivated by this context, this article reports a survey of the current state of research of conversational agents through a systematic literature review of secondary studies. The conducted research is designed to develop an exhaustive perspective through a clear presentation of the aggregated knowledge published by recent literature within a variety of domains, research focuses and contexts. As a result, this research proposes a holistic taxonomy of the different dimensions involved in the conversational agents’ field, which is expected to help researchers and to lay the groundwork for future research in the field of natural language interfaces.
Load Balancing in Cloud Computing Based on Honey Bee Foraging Behavior and Load Balance Min-Min Scheduling Algorithm

Cloud computing relies on the collection and distribution of services from internet-based data centers. With the large resource pool available in internet wide range of users are accessing the cloud. Load balance is important feature involving resource allocation to prevent overloading of any system or optimal use of resources. Major load in cloud network are concerned with CPU, memory and network. This cloud computing aspect has not yet earned too much coverage. Although load balancing is an important feature for cloud computing, concurrent computing etc. In these areas, several algorithms were suggested to solve load balance problem. However, it does recommend very few cloud computing algorithms. Given that cloud storage differs considerably from all other environments, particular load balancing algorithm should will built in sort to serve its needs. This work proposes novel load-balancing algorithm based on artificial bee colony algorithm and load balancing min-min scheduling algorithm for balancing load in cloud computing network. Simulation here is carried out in clouds to generate comparative results. Improving on various parameters like power consumption, resource utilization, stability of system are some major areas focused on. This work has used algorithm that has the best efficiency of resources, optimal performance, minimal response time, scalability and durability in integrated resource planning.
A privacy-preserving model based on differential approach for sensitive data in cloud environment

A large amount of data and applications need to be shared with various parties and stakeholders in the cloud environment for storage, computation, and data utilization. Since a third party operates the cloud platform, owners cannot fully trust this environment. However, it has become a challenge to ensure privacy preservation when sharing data effectively among different parties. This paper proposes a novel model that partitions data into sensitive and non-sensitive parts, injects the noise into sensitive data, and performs classification tasks using k-anonymization, differential privacy, and machine learning approaches. It allows multiple owners to share their data in the cloud environment for various purposes. The model specifies communication protocol among involved multiple untrusted parties to process owners’ data. The proposed model preserves actual data by providing a robust mechanism. The experiments are performed over Heart Disease, Arrhythmia, Hepatitis, Indian-liver-patient, and Framingham datasets for Support Vector Machine, K-Nearest Neighbor, Random Forest, Naive Bayes, and Artificial Neural Network classifiers to compute the efficiency in terms of accuracy, precision, recall, and F1-score of the proposed model. The achieved results provide high accuracy, precision, recall, and F1-score up to 93.75%, 94.11%, 100%, and 87.99% and improvement up to 16%, 29%, 12%, and 11%, respectively, compared to previous works.
Path Planning Algorithm for Multi-Locomotion Robot Based on Multi-Objective Genetic Algorithm with Elitist Strategy

The multi-locomotion robot (MLR), including bionic insect microrobot, bionic animal robot and so on, should choose different locomotion modes according to the obstacles it faces. However, under different locomotion modes, the power consumption, moving speed, and falling risk of MLR are different, and in most cases, they are mutually exclusive. This paper proposes a path planning algorithm for MLR based on a multi-objective genetic algorithm with elitist strategy (MLRMOEGA), which has four optimization objectives: power consumption, time consumption, path falling risk, and path smoothness. We propose two operators: a map analysis operator and a population diversity expansion operator, to improve the global search ability of the algorithm and solve the problem so that it is easy to fall into the local optimal solution. We conduct simulations on MATLAB, and the results show that the proposed algorithm can effectively optimize the objective function value compared with the traditional genetic algorithm under the equal weight of the four optimization objectives, and, under alternative weights, the proposed algorithm can effectively generate the corresponding path of the decision maker's intention under the weight of preference. Compared with the traditional genetic algorithm, the global search ability is improved effectively.
Image-based decision making for reliable and proper diagnosing in NIFTI format using watermarking

Nowadays, advancement in Magnetic Resonance Imaging (MRI) and Computed Tomography Scan (CT-Scan) technologies have defined modern neuroimaging and drastically change the diagnosing of disease in the world healthcare system. These imaging technologies generate NIFTI (Neuroimaging Informatics Technology Initiative) images. Due to COVID-19 last several months CT-Scan has been performed on millions of the CORONA patients, so billions of the NIFTI images have been produced and communicate over the internet for the diagnosing purpose to detect the coronavirus. The communication of these medical images over the internet yielding the major problem of integrity, copyright protection, and other ethical issues for the world health care system. Another critical problem is that; is doctor diagnose the impeccable medical image of the patient because a large amount of COVID-19 patient's data exists. For proper diagnosing it is also necessary to identify impeccable medical image. Therefore, to address these problems a secure and robust watermarking scheme is needed for these images. Various watermarking schemes have been developed for bmp, .jpg, .png, DICOM, and other image formats but the noticeable contribution is not reported for the NIFTI images. In this paper a robust and hybrid watermarking scheme for NIFTI images based on Lifting Wavelet Transform (LWT), MSVD (Multiresolution Singular Value Decomposition) and QR factorization. The combination of LWT, QR, and MSVD helps in retaining the sensitivity of the NIFTI image and improve the robustness of the watermarking scheme. In this scheme, multiple watermarks are inserted across the first slice of the NIFTI image. The proposed watermarking scheme is sustained against various noise attacks and performance is measured in terms of PSNR, SNR, SSIM, Quality of image, and Normalized correlation. Quality of the image is much significant that lie between .99994 to .99998 and SSIM reported from .94 to .99. Whereas the PSNR of the proposed scheme lies between 56.76 to 57.28 db and NC values lie between .9993 to .9998. which shows that the results are better than the existing schemes where PSNR is lies between 32.66 to 52.02 db. Watermarking, NIFTI, MSVD, LWT, QR and Image.
Logiformer

Machine reading comprehension has aroused wide concerns, since it explores the potential of model for text understanding. To further equip the machine with the reasoning capability, the challenging task of logical reasoning is proposed. Previous works on logical reasoning have proposed some strategies to extract the logical units from different aspects. However, there still remains a challenge to model the long distance dependency among the logical units. Also, it is demanding to uncover the logical structures of the text and further fuse the discrete logic to the continuous text embedding. To tackle the above issues, we propose an end-to-end model Logiformer which utilizes a two-branch graph transformer network for logical reasoning of text. Firstly, we introduce different extraction strategies to split the text into two sets of logical units, and construct the logical graph and the syntax graph respectively. The logical graph models the causal relations for the logical branch while the syntax graph captures the co-occurrence relations for the syntax branch. Secondly, to model the long distance dependency, the node sequence from each graph is fed into the fully connected graph transformer structures. The two adjacent matrices are viewed as the attention biases for the graph transformer layers, which map the discrete logical structures to the continuous text embedding space. Thirdly, a dynamic gate mechanism and a question-aware self-attention module are introduced before the answer prediction to update the features. The reasoning process provides the interpretability by employing the logical units, which are consistent with human cognition. The experimental results show the superiority of our model, which outperforms the state-of-the-art single model on two logical reasoning benchmarks.
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents

Text semantic matching is a fundamental task that has been widely used in various scenarios, such as community question answering, information retrieval, and recommendation.Most state-of-the-art matching models, e.g., BERT, directly perform text comparison by processing each word uniformly.However, a query sentence generally comprises content that calls for different levels of matching granularity.Specifically, keywords represent factual information such as action, entity, and event that should be strictly matched, while intents convey abstract concepts and ideas that can be paraphrased into various expressions.In this work, we propose a simple yet effective training strategy for text semantic matching in a divide-and-conquer manner by disentangling keywords from intents.Our approach can be easily combined with pretrained language models (PLM) without influencing their inference efficiency, achieving stable performance improvements against a wide range of PLMs on three benchmarks.
The March of Chatbots into Recruitment: Recruiters’ Experiences, Expectations, and Design Opportunities

Abstract Organizations’ hiring processes are increasingly shaped by various digital tools and e-recruitment systems. However, there is little understanding of the recruiters’ needs for and expectations towards new systems. This paper investigates recruitment chatbots as an emergent form of e-recruitment, offering a low-threshold channel for recruiter-applicant interaction. The rapid spread of chatbots and the casual nature of their user interfaces raise questions about the perceived benefits, risks, and suitable roles in this sensitive application area. To this end, we conducted 13 semi-structured interviews, including 11 interviews with people who are utilizing recruitment chatbots and two people from companies that are developing recruitment chatbots. The findings provide a qualitative account of their expectations and motivations, early experiences, and perceived opportunities regarding the current and future use of chatbots in recruitment. While chatbots answer the need for attracting new candidates, they have also introduced new challenges and work tasks for the recruiters. The paper offers considerations that can help to redesign recruitment bots from the recruiter’s viewpoint.