Reviewerly/exHarmony huggingface dataset:
Deliberate Multi-Attention Network for Image Captioning

An image captioning method is usually combined with an attention mechanism, which performs more effectively than the one without it. However, this method is still under performed compared with the artificial image captioning. For example, it may wrongly recognize the salient image objects, incorrectly or superficially understand the image objects, and mistakenly ignore the background information, which lead to the propagation and accumulation errors when generating sentences. One way that helps solve these problems is to adopt deliberate attention mechanism in image captioning. This paper proposes a deliberate multi-attention network (DMAN) model with multi-modal mechanism. This mechanism generates the image captions by combining the information from texts, visual sentinel and attention information. The textual information comes from words through LSTM, the attention information is derived by another LSTM from extracted image features. Visual sentinel is derived from the first deliberate attention loop, and then integrated into the content vector, which is fused in the multi-modal generation step. Visual sentinel mechanism can adjust the ratio of the degree between text information and visual information in the generation step. With the deliberate multi-attention, the mechanism recognizes and understands the image salient objects more accurately, and thus it solves the propagation and accumulation errors. By evaluating with the MSCOCO dataset, the experimental results show that the proposed DMAN model is overall better than Bottom-Up-Top-Down model under four quantitative indicators. Compared with the previous deliberate attention model, DMAN model can pay attention to more detailed part of the image and generate better captioning results.
From Local to Holistic: Self-supervised Single Image 3D Face Reconstruction Via Multi-level Constraints

Single image 3D face reconstruction with accurate geometric details is a critical and challenging task due to the similar appearance on the face surface and fine details in organs. In this work, we introduce a self-supervised 3D face reconstruction approach from a single image that can recover detailed textures under different camera settings. The proposed network learns high-quality disparity maps from stereo face images during the training stage, while just a single face image is required to generate the 3D model in real applications. To recover fine details of each organ and facial surface, the framework introduces facial landmark spatial consistency to constrain the face recovering learning process in local point level and segmentation scheme on facial organs to constrain the correspondences at the organ level. The face shape and textures will further be refined by establishing holistic constraints based on the varying light illumination and shading information. The proposed learning framework can recover more accurate 3D facial details both quantitatively and qualitatively compared with state-of-the-art 3DMM and geometry-based reconstruction algorithms based on a single image.
Learning Temporal Task Models from Human Bimanual Demonstrations

Learning temporal relations between actions in a bimanual manipulation task is important for capturing the constraints of actions required to achieve the task's goal. However, given several demonstrations of a bimanual manipulation task, the problem of identifying the true temporal dependencies between actions - if there are any - is very challenging due to contradictions. We propose a model-driven approach for learning temporal task models from multiple bimanual human demonstrations that represents temporal relations on two levels. First, temporal relations between sets of actions that exhibit a tight temporal coupling, and second, temporal relations between these sets of actions. We build on Allen's interval algebra as a representation to express relations between temporal intervals. Semantically defining these interval relations allows us to soften their formulation to deal with inaccuracies in real data obtained when observing humans demonstrating the task. Our temporal task models can be learned incrementally from multiple modalities, and allow us to reason about viable alternatives during task execution in case of unexpected events. We evaluated the approach quantitatively on two datasets and qualitatively on a humanoid robot. The evaluation shows how inherent properties of bimanual human manipulation tasks can be exploited to derive a model useful for the reproduction by humanoid robots.
Adversarial NLP for Social Network Applications: Attacks, Defenses, and Research Directions

The growing use of media has led to the development of several machine learning (ML) and natural language processing (NLP) tools to process the unprecedented amount of social media content to make actionable decisions. However, these ML and NLP algorithms have been widely shown to be vulnerable to adversarial attacks. These vulnerabilities allow adversaries to launch a diversified set of adversarial attacks on these algorithms in different applications of social media text processing. In this article, we provide a comprehensive review of the main approaches for adversarial attacks and defenses in the context of social media applications with a particular focus on key challenges and future research directions. In detail, we cover literature on six key applications: 1) rumors detection; 2) satires detection; 3) clickbaits and spams identification; 4) hate speech detection; 5) misinformation detection; and 6) sentiment analysis. We then highlight the concurrent and anticipated future research questions and provide recommendations and directions for future work.
Detecting Face-Mask Wearing Status Using Motion Sensors in Commercially Available Smartwatches

Wearing a mask considerably mitigates the risk of infection from droplets. Automatic detection of whether a person wears a mask in his/her daily life and the type of masks the person wears can provide useful information for various services such as infection risk assessment, just-in-time alerts, and lifelogging. However, such automatic detection is difficult without the use of video processing or specialized equipment. In this study, the motion sensor of a commercially available smartwatch was used to detect the mask-wearing status. An investigation of the acceleration characteristic and an evaluation experiment of the mask-wearing state detection model revealed an accuracy of approximately 90% when specific motions were classified using motion sensors and machine learning. Furthermore, 98% accuracy was achieved when classifying sitting and walking activities.
GlueGAN: Gluing Two Images as a Panorama with Adversarial Learning

Inspired by panorama photos and Chinese handscroll paintings, we propose an unsupervised algorithm to glue two similar independent images with a generated image as a panorama. Different from existing inpainting methods which can only be used to complete limited missing area with supervised training, the advantage of our GlueGAN lies in generating an image with almost the same size as the input image to form an overall image. To overcome mode collapse which is common in GANs, we propose a two-stage framework. In the first generation stage of HED edge, the model generates simple HED edge images. The second image generation stage aims to generate color image with the aid of HED edge image generated in the first stage. Moreover, we additionally train a doodle-to-edge generator to help users participate in the generation process. To verify our proposed method, a dataset, including 90000 Chinese landscape painting auctions dataset, is created. Experiment results on the dataset show our proposed method can solve this problem well and has greater performance than existing algorithms.
Correction of Gamma Distortion in Fringe Projection by Compensation Image

In fringe projection profilometry, affected by the gamma distortion of the projector, the reconstructed topography of the measured object has a large error compared to the actual situation. In order to eliminate the gamma distortion error, this paper innovatively proposes a mathematical model to solve the wrapping phase with 3(N)+1 images based on the principle of the N-step phase-shift method. The model adds an image modulated by half of the background illumination with gamma distortion to the three-step phase-shifted image. After iteration, the wrapping phase can be solved, and the value of the background illumination and the value of the projector gamma distortion can be obtained. The method effectively avoids the influence of gamma distortion on solving the wrapped phase. It is verified by MATLAB simulation that the method can eliminate gamma distortion. Comparing the phases solved by the simulation standard conditions, the four-step phase-shift method, and the 3(N)+1 method, the results show that the method effectively eliminates the influence of gamma distortion and has high accuracy.
Impact of CSR on Employee Engagement: Study Case of Bank Audi in Lebanon

The purpose of this case study is to investigate the relationship between corporate social responsibility and employee engagement by specifically focusing on bank Audi, in Lebanon. The main research question of this study is "how CSR increases employee engagement and what is the mechanism creating the connection between CSR and employee engagement"? We were able to interview thirty employees at Bank Audi in order to get more insights into their experiences, while conducting a review of the CSR reports for the period between 2014 and 2020. We employed qualitative research methods in order to obtain information that would allow us to create an accurate representation of what employees' experience at Bank Audi with regard to CSR practices. The results show that the CSR culture at Bank Audi created a collective caring mindset that provided employees with the support they need to grow both personally and professionally and a sense of purpose to make a positive change in everything they do based on a strong sense of responsibility and accountability. This caring mindset is the transformative power and driving force that created a strong feeling of belonging and pride which drive employee's engagement at Bank Audi to a very high level.
Improving Traceability Using Blockchain and Internet of Things (IoT) in the Food Supply Chain

In recent years, food traceability has become one of the emerging applications of Blockchain to strengthen the aspects of anticounterfeiting and quality control. This paper intends to suggest a methodology to improve traceability and transparency in food production and supply chain using Blockchain and the Internet of Things (IoT). Typical methods of the supply chain have loopholes, which are misused by antisocial groups to distribute unhygienic substandard food products. Blockchain technology guarantees the source of origin to a customer and enables traceability, tracking, and transparency which assists to increase accountability in the food production industry by confirming customer safety and protection, developing confidence, and enhancing the quality of service.
Secure-Channel Free Certificateless Searchable Public Key Authenticated Encryption with Keyword Search

Public-key Authenticated Encryption with Keyword Search (PAEKS) is a cryptographic primitive that can resist inside keyword guessing attack (KGA). However, most of the previously proposed PAEKS frameworks suffered from certificate management problem and key escrow problem. Inspired by the ideas of certificate-based cryptography, we propose a secure-channel free certificateless searchable public key authenticated encryption with keyword search (SCF-CLPAEKS) scheme which sloves the key escrow problem in identity-based cryptosystems and the cumbersome certificate problem in conventional public key cryptosystems. Our scheme achieves security against keyword guessing attacks are performed by both inside and outside adversaries. Moreover, our scheme satisfies ciphertext indistinguishability (CI), trapdoor indistinguishability (TI), and designated testability simultaneously. The comparisons indicate that our SCF-CLPAEKS scheme enjoys a better performance compared with related schemes.
A novel approach of features selection by using slime mould algorithm

Slime mould Algorithm (SMA) is modern meta–heuristic algorithm that is depended on the mechanism of swarming of slimes. SMA considers one of the good algorithms in the field of intelligence optimization. In this paper we proposed a new binary Slime mould Algorithm to depend on transformation function which converts the continuous space to discrete space. The purpose of create this algorithm is to use in the feature selections. The new algorithm used with different dataset and compared with different algorithm. The results of numerical experimental showed that the proposed algorithm have a good performance compared with other algorithm.
A Semi-Supervised Video Object Segmentation Method Based on ConvNext and Unet

Semi-supervised video object segmentation task, i.e., separating objects in a video from the background given the mask of the first frame during segmentation. Based on the One-shot Video Object Segmentation (OSVOS) method, this paper proposes a semi-supervised video object segmentation method based on ConvNext and UNet. In this method, the original VGG was first replaced with pure ConvNet models called ConvNeXt, which outperformed VGG in feature extraction and improved by 13.4% on the ImageNet dataset; then, using the UNet architecture, it Compared with FCN, a different feature extraction method is adopted, which makes it more suitable for large image segmentation and medical segmentation; finally, Focal Loss is used to dealing with the unbalanced binary classification problem, which solves the problem that the iteration of simple samples is slow and cannot achieve the best results, and balances the uneven proportion of positive and negative examples, making our method more competitive. Our experimental results on the DAVIS2016 dataset show that the feature extraction effect of ConvNeXt is better than that of VGG in this dataset, and the UNet architecture is more suitable for video object segmentation tasks than FCN. Focal Loss also improves the experiment significantly. A J&F of 91.7 was achieved, enhancing the original OSVOS by 80.2%. Experiments show that this series of modifications are very effective and substantially improves the technical level.
SynPoses: Generating Virtual Dataset for Pedestrian Detection in Corner Cases

Pedestrian detection based on deep learning methods makes a big hit during these days. The key to achieve excellent results for deep learning-based pedestrian detection methods is a high-quality dataset. There are lots of outstanding datasets for pedestrian detection like EuroCity Persons, CityPersons, Caltech, etc. However, their efforts are not dedicated to the traffic scenarios with pedestrians in various poses. When the state-of-the-art detectors trained on datasets described above encounter the scene with pedestrians in variously complicated poses, the results are mostly worse than expected. In order to alleviate this problem, we propose a framework called SynPoses to create synthetic human with complicated poses in high quality. Experimental results show that, when facing scenarios with human in diverse poses, the performance of detectors trained on augmented dataset outperforms those trained on original dataset.
Human Activity Recognition: A Step Toward Ambient Assisted Living

Human activity recognition (HAR) finds application from the field of ambient assisted living (AAL), health condition monitoring to surveillance, gaming, etc. Persuaded by the enormous application area, research activity in this field has also been escalated in last decade. Application of machine learning techniques for HAR is also famed. This work utilizes a very well-known machine learning technique random forest classifier to classify five activities of daily life (ADL). For sensing data, only one wrist worn tri-axial accelerometer is used, which makes this system cost effective and handy. To reduce time complexity of the classifier, dimensionality of training features needs to be reduced. Here to get this, dimension of the raw data has been reduced using principal component analysis. After that using a very few statistical and frequency domain features, the system offers 96% classification accuracy.
Research on the Application of Artificial Intelligence in Games

With the advent of the information age, artificial intelligence technology (AI) is widely used in various fields, and more and more applications are used in game development. At present, through artificial intelligence technology, the non-player character (NPC) has a certain degree of self-learning and self-optimization ability to improve the playability of the game, which has become a hot spot in the application research of video games. In this paper, a game artificial intelligence system is constructed through finite state machine, fuzzy state machine, artificial neural network and genetic algorithm, and the NPC that meets the requirements of the game is obtained. This paper aims to study the application of artificial intelligence technology in game development, which has certain reference significance for artificial intelligence technology in games application in the future.
Intuitive &amp; Efficient Human-robot Collaboration via Real-time Approximate Bayesian Inference

The combination of collaborative robots and end-to-end AI, promises flexible automation of human tasks in factories and warehouses. However, such promise seems a few breakthroughs away. In the meantime, humans and cobots will collaborate helping each other. For these collaborations to be effective and safe, robots need to model, predict and exploit human's intents for responsive decision making processes. Approximate Bayesian Computation (ABC) is approach to perform probabilistic predictions upon uncertain quantities. ABC includes priors conveniently, leverages sampling algorithms for inference and is flexible to benefit from complex models, e.g. via simulators. However, ABC is known to be computationally too intensive to run at interactive frame rates required for effective human-robot collaboration tasks. In this paper, we formulate human intent prediction as an ABC problem and describe two key performance innovations which allow computations at interactive rates. Our real-world experiments with a collaborative robot set-up, demonstrate the viability of our proposed approach. Experimental evaluations convey the advantages and value of human intent prediction for packing cooperative tasks. Qualitative results show how anticipating human's intent improves human-robot collaboration without compromising safety. Quantitative task fluency metrics confirm the qualitative claims.
A High Performance JPEG Decoding Algorithm based on OPENCL

It is the solution of paralleled image decoding algorithm to address the issues of the decoding slowness and long latency for large size images in various applications. Base on JPEG decoding with high compression ratio, which widely used in various applications, this paper proposed the implementation of parallelized JPEG image decoding algorithm by using the OPENCL library. Obviously, the test results against large size images with the new algorithm demonstrated 4-5 times higher JPEG decoding performance than original decoding algorithm.
Selecting the Partial State Abstractions of MDPs: A Metareasoning Approach with Deep Reinforcement Learning

Markov decision processes (MDPs) are a common general-purpose model used in robotics for representing sequential decision-making problems. Given the complexity of robotics applications, a popular approach for approximately solving MDPs relies on state aggregation to reduce the size of the state space but at the expense of policy fidelity-offering a trade-off between policy quality and computation time. Naturally, this poses a challenging metareasoning problem: how can an autonomous system dynamically select different state abstractions that optimize this trade-off as it operates online? In this paper, we formalize this metareasoning problem with a notion of time-dependent utility and solve it using deep reinforcement learning. To do this, we develop several general, cheap heuristics that summarize the reward structure and transition topology of the MDP at hand to serve as effective features. Empirically, we demonstrate that our metareasoning approach outperforms several baseline approaches and a strong heuristic approach on a standard benchmark domain.
Ultra-short-term PV Power Forecasting Based on FA-DELM Model Considering Weather Classification and VMD Noise Reduction

Photovoltaic (PV) power generation receives light flooding attention as renewable energy generation. This article proposes a firefly algorithm (FA) optimized deep extreme learning machine (DELM) power forecasting model to accurately forecast ultra-short-term photovoltaic power generation. For the set task, this article demonstrates the implementation procedure of the proposed forecasting model with operational data of No. 38 Q CELLS, a 5.9 kW PV station in 2017 in Alice Springs, Australia, as the original input data. Firstly, the article introduces the basic concepts of PV power forecasting and the challenges of implementing the forecasting technique. Next, this paper applies the K-means algorithm and similarity days for cluster analysis. Also, this article employs VMD noise reduction to enhance the PV data to give specific weather classification. Then, this article brings in the FA and DELM to build a VMD-FA-DELM hybrid forecasting model. Finally, this article validates the presented VMD-FA-DELM forecasting model by case and invokes evaluation metrics to assess it. The results indicate that this article's VMD-FA-DELM hybrid algorithm forecasting model has higher forecast accuracy for ultra-short-term PV power output than other single models.
Deep Hybrid Learning for Anomaly Detection in Behavioral Monitoring

The task of understanding human behavior through intelligent systems is crucial in various domains from medical health and well-being to financial and social platforms. In this work, we propose a complete framework that takes advantage of collected sensor accelerometer data to generate a human activity behavioral model that can be supportive in predicting future development of human movement disabilities such as Osteoarthritis or even in the individual's rehabilitation after a surgery for Osteoarthritis. More precisely, we focus on estimating uncommon behaviors within daily activities as an indication for further examination. Challenge-point of the proposed methodology is the agnostic knowledge of different behaviours of individual's movement. Based on accelerometer sensor data collected from mobile devices, the proposed framework utilizes state-of-the-art Machine Learning models for Human Activity Recognition and introduces new Deep Hybrid Models for outlier detection suggesting a solid basis for further developments and wider applicability.
Improvised Model for Blockchain in Distributed Cloud Environment

In the current era, Blockchain Technology is blooming and growing faster. Blockchain is currently used in soft computing, agriculture, cloud network, healthcare, electronics, and electrical fields. Older technologies are upgraded to increase their reach using Distributed Cloud networks and blockchain. Implementing security for heterogeneous 5G services-based technologies of the MEC is challenging. In this paper, we propose a blockchain-based distributed cloud security solution that uses technologies such as Digital Signature, RSA algorithm, AES encryption, and a Lookup table to improve the security of blockchain and decrease the traversal time of data in the blockchain. This proposed model is adept at solving the primary concern related to storage and confidentiality in a Distributed Cloud environment.