Reviewerly/exHarmony huggingface dataset:
Tackling Micro-Expression Data Shortage via Dataset Alignment and Active Learning

The research on micro-expression recognition has been drawing great attention in recent years, because of its great potential in the lie detection, clinical diagnosis, and national security. Amongst many challenges, data shortage stands out as it directly prevents an accurate training of micro-expression recognition algorithm. In this work, we present our approach within a dataset alignment and active learning (DAAL) framework. DAAL effectively queries minimum examples to label, as well as transfers features from micro-expression dataset to macro-expression dataset. Specifically, the features from micro-expression dataset are mapped to the macro-expression dataset with a translator, so that the classifier trained in macro-expression dataset can be adjusted and adapted to boost the classification performance on the micro-expression dataset. Besides, the most informative examples in the micro-expression dataset are selected through active learning in an iterative way, which effectively improves the classification ability of the model. Comprehensive experiments on CASME, CASME II, SAMM and SMIC databases firmly demonstrate that the proposed DAAL outperforms previous works by a large margin on micro-expression recognition task.
Progressive Interaction-Learning Network for Lightweight Single-Image Super-Resolution in Industrial Applications

Recently, deep learning (DL)-based industrial applications have attracted broad attention due to their advanced performance. However, the limited computational resource in portable devices always makes big DL models inapplicable in the industry. DL-based single-image super-resolution also encounters this problem because of its large computations. Besides, most lightweight convolutional-neural-network-based methods utilize features insufficiently, which restricts their capability for industrial reconstruction. To alleviate this problem, we present a progress interaction-learning network (PILN) to refine features at different levels: at the global level, we employ a progressive interaction-learning strategy to integrate hierarchical features in temporal and spatial dimensions; at the mediate level, enhanced interaction-learning units, adopting the enhanced interactive study, significantly boost the reconstruction performance; at the local level, employing pixelwise learning, residual cells are raised to search for an optimal information flow by weight distribution. Extensive experiments demonstrate that the PILN outperforms other state-of-the-art methods.
Artificial Intelligence in Video Games and eSports

This chapter starts with the introduction and the evolution of AI in gaming and esports. It explores the enabling technologies for AI in gaming (big data, virtual reality, AI chips and GPUs, online gaming, and cloud platforms). AI applications in video games and esports include AI Opponents, AI NPCs, procedural content generation, player experience modeling, antisocial behavior detection, win prediction, player telemetry analytics, intelligent tutoring, and training. Case studies section consists of DeepMind Alpha Go, Alpha Star, and Microsoft HoloLens.
GMPR: A Two-Phase Heuristic Algorithm for Virtual Machine Placement in Large-Scale Cloud Data Centers

In the cloud data centers, due to the variable resource requirements of cloud tenants, designers applied the infrastructure as a service (IaaS) model to provide services for tenants with allocating in charging. The application of virtualization technology enables multiple virtual machines (VMs) to share the resources of a physical machine (PM). Meanwhile, the efficiency of the data centers greatly depends on the working efficiency of VMs. Virtual machine placement (VMP) plays a vital role in minimizing total energy consumption and resource wastage for cloud data centers (CDCs). In this article, we propose a greedy algorithm minimizing power consumption and resource wastage (GMPR) for the VMP scheme to address the abovementioned issues. GMPR prioritizes the power-efficiency of PM to reduce the number of active PMs and to minimize total energy consumption. In addition, reducing total resource wastage involves first minimizing the resource of balance and resource wastage for a novel PM hosts VM and second minimizing resource wastage for PM placed VM. Extensive simulation results are conducted on synthetic and instances of Amazon EC2 with performance metrics confirm that GMPR has superiority in reducing energy consumption and resource wastage by an average of 1.91% and 16.18% compared with a cutting-edge method.
Integrated balancing approach for hosting services with optimal efficiency - Self Hosting with Docker

With the emergence of giant corporations that provide cloud services and gaining a monopoly over the market, the increasing need for low cost and secure services for hobbyists of industry level projects can be satisfied by using self-hosting the cloud services rather than cloud-hosted services. As cloud service architecture is less flexible and less secure in terms of performance due to its underlying approach in its infrastructure, a self-hosted service looks more adequate for this manner. This paper implements the use of hosting services with an addition of Docker and Nginx platform, a way to prevent the mentioned instances with additional capabilities giving the user more freedom and flexibility. Docker in Portainer is used to contain more containers in a single Raspberry Pi, without which each container requires separate devices.
Semantic Coding for Text Transmission: An Iterative Design

We consider the wireless text transmission using joint source-channel coding (JSCC). Classical source coding only considers the syntactic information based on probabilistic models, ignoring the meaning of source messages. Neural network based joint source and channel coders handle the source semantic information more efficiently. However, existing semantic transmission using end-to-end neural networks do not generalize well under varying channel conditions. To tackle this, we propose a semi-neural framework with an iterative architecture, named iterative semantic JSCC (IS-JSCC). Specifically, at each iteration, the remaining semantics is extracted from the intermediate decoded text and is then used as a priori information for the channel decoder in the next iteration. Instead of reconstructing text explicitly, we synthesize the semantics of candidate words in the embedding space, weighted by their posterior probability. This soft semantic synthesis alleviates the error propagation and reduces the complexity of iterative decoding as well. Results show that compared to full-neural designs, the proposed framework can improve the quality of text reconstruction by joint iterative decoding and exhibit better robustness over wireless channels.
Adversarial ELF Malware Detection Method Using Model Interpretation

Recent research shows that executable and linkable format (ELF) malware detection models based on deep learning are vulnerable to adversarial attacks. The most commonly used method in previous work is adversarial training to defend adversarial examples. Nevertheless, it is inefficient and only effective for specific adversarial attacks. Given that the perturbation byte insertion positions of existing adversarial malware generation methods are relatively fixed, we propose a new method to detect adversarial ELF malware. Using model interpretation techniques, we analyze the decision-making basis of the malware detection model and extract the features of adversarial examples. We further use anomaly detection techniques to identify adversarial examples. As an add-on module of the malware detection model, the proposed method does not require modifying the original model and does not need to retrain the model. Evaluating results show that the method can effectively defend the adversarial attacks against the malware detection model.
Predicting Students’ Satisfaction Towards Online Courses Using Aspect-Based Sentiment Analysis

In recent times, quality of teaching and students' participation is monitored in most of the universities and colleges to improve academic performance. Outcome-based education system also requires experiential learning. Student opinion is one of the powerful mechanisms to evaluate academic activity and quality in education system. It helps to decide on corrective measures towards various entities of teaching and learning process. Many research studies have been carried out in classifying the sentiment polarities of opinions. In educational domain, very limited study is carried out in fetching the key aspects or entities from the students' review. Mining the opinions in a deeper level to fetch the specific aspects confined to the topic will bring productive results. In this paper, aspect-based sentiment analysis is carried out at sentence level to find the students' satisfaction with reference to the online courses using machine learning algorithm. The proposed system has attained improved accuracy than the existing model. From this study, the specific aspects are obtained using both unsupervised and semi-supervised LDA algorithms. Students' satisfaction with specific aspects of the online courses also examined.
F-CBR: An Architecture for Federated Case-Based Reasoning

Case-based reasoning (CBR) is a problem-solving methodology in artificial intelligence that attempts to solve new problems using past experiences known as cases.Experiences collected in a single case base from an institution or geographical region are seldom sufficient to solve diverse problems, especially in rare situations.Additionally, many institutions do not promote peer-to-peer (p2p) communication or encourage data sharing through such networks to retain autonomy.The paper proposes a federated CBR (F-CBR) architecture to address these challenges.F-CBR enables solving new problems based on similar cases from multiple autonomous CBR systems without p2p communication.We also designed an algorithm to minimize (irrelevant or unsolicited) data sharing in an F-CBR system.We extend the F-CBR design to support institutions with organizational or geographical hierarchies.The F-CBR architecture was implemented and evaluated on two public datasets and a private real-world (non-specific musculoskeletal disorder patient) dataset.The findings demonstrate that the retrieval quality of F-CBR systems is comparable to or better than a single CBR system that persists all the cases on a centralized case base.F-CBR systems address data privacy by incorporating the data minimization principle.We foresee F-CBR as a viable real-world design that can aid in federating legacy CBR systems with minimal or no changes.The CBR systems used in this study are shared on GitHub to support reproducibility.
Algorithms of adaptation in inductive inference

We investigate the idea that human concept inference utilizes local adaptive search within a compositional mental theory space. To explore this, we study human judgments in a challenging task that involves actively gathering evidence about a symbolic rule governing the behavior of a simulated environment. Participants learn by performing mini-experiments before making generalizations and explicit guesses about a hidden rule. They then collect additional evidence themselves (Experiment 1) or observe evidence gathered by someone else (Experiment 2) before revising their own generalizations and guesses. In each case, we focus on the relationship between participants' initial and revised guesses about the hidden rule concept. We find an order effect whereby revised guesses are anchored to idiosyncratic elements of the earlier guess. To explain this pattern, we develop a family of process accounts that combine program induction ideas with local (MCMC-like) adaptation mechanisms. A particularly local variant of this adaptive account captures participants' hypothesis revisions better than a range of alternative explanations. We take this as suggestive that people deal with the inherent complexity of concept inference partly through use of local adaptive search in a latent compositional theory space.
EIEN: Endoscopic Image Enhancement Network Based on Retinex Theory

In recent years, deep convolutional neural network (CNN)-based image enhancement has shown outstanding performance. However, due to the problems of uneven illumination and low contrast existing in endoscopic images, the implementation of medical endoscopic image enhancement using CNN is still an exploratory and challenging task. An endoscopic image enhancement network (EIEN) based on the Retinex theory is proposed in this paper to solve these problems. The structure consists of three parts: decomposition network, illumination correction network, and reflection component enhancement algorithm. First, the decomposition network model of pre-trained Retinex-Net is retrained on the endoscopic image dataset, and then the images are decomposed into illumination and reflection components by this decomposition network. Second, the illumination components are corrected by the proposed self-attention guided multi-scale pyramid structure. The pyramid structure is used to capture the multi-scale information of the image. The self-attention mechanism is based on the imaging nature of the endoscopic image, and the inverse image of the illumination component is fused with the features of the green and blue channels of the image to be enhanced to generate a weight map that reassigns weights to the spatial dimension of the feature map, to avoid the loss of details in the process of multi-scale feature fusion and image reconstruction by the network. The reflection component enhancement is achieved by sub-channel stretching and weighted fusion, which is used to enhance the vascular information and image contrast. Finally, the enhanced illumination and reflection components are multiplied to obtain the reconstructed image. We compare the results of the proposed method with six other methods on a test set. The experimental results show that EIEN enhances the brightness and contrast of endoscopic images and highlights vascular and tissue information. At the same time, the method in this paper obtained the best results in terms of visual perception and objective evaluation.
Establishing an Intelligent Emotion Analysis System for Long-Term Care Application Based on LabVIEW

In this study, the authors implemented an intelligent long-term care system based on deep learning techniques, using an AI model that can be integrated with the Lab’s Virtual Instrumentation Engineering Workbench (LabVIEW) application for sentiment analysis. The input data collected is a database of numerous facial features and environmental variables that have been processed and analyzed; the output decisions are the corresponding controls for sentiment analysis and prediction. Convolutional neural network (CNN) is used to deal with the complex process of deep learning. After the convolutional layer simplifies the processing of the image matrix, the results are computed by the fully connected layer. Furthermore, the Multilayer Perceptron (MLP) model embedded in LabVIEW is constructed for numerical transformation, analysis, and predictive control; it predicts the corresponding control of emotional and environmental variables. Moreover, LabVIEW is used to design sensor components, data displays, and control interfaces. Remote sensing and control is achieved by using LabVIEW’s built-in web publishing tools.
The essential competencies of software professionals: A unified competence framework

Developing high-quality software requires skilled software professionals equipped with a set of basic and essential software engineering competencies (SEC). These competencies and the satisfaction levels derived from them change over a project's lifecycle, or as software professionals move from one project to another. Previous studies suggest a lack of means enabling SEC stakeholders to identify and assess competencies suitable for different projects. Additionally, previous research has mainly portrayed SEC to be static and overlooked their evolution over time and across projects. We investigate how we could effectively identify and match the competencies of software professionals necessary for different projects. We follow a mixed-method approach to iteratively develop and evaluate a framework for identifying and managing SEC. In so doing, we use the results of an extensive literature review, focus group discussions with experts from academia and industry, and data collected through interviews with 138 individuals with a supervisory role in the software industry. Drawing on the Kano model and Competency Framework for Software Engineers, we propose a Unified Competence Gate for Software Professionals (UComGSP), a framework for identifying and managing SEC. The UComGSP consists of 62 hard competencies, 63 soft competencies, and 25 essential SEC competencies. Additionally, we propose three stakeholders’ satisfaction levels for SEC assessment: basic, performance, and delighter. Furthermore, based on empirical observation, we report 27 competencies not mentioned in the reviewed literature; 11 of them are considered essential competencies. Competence development involves different stakeholders, including software professionals, educators, and the software industry. The UComGSP framework enables SEC stakeholders to (i) identify SE competencies, (ii) identify the essential SEC, and (iii) assess the satisfaction levels that can be derived from different competencies. Future research is needed to evaluate the effectiveness of the proposed framework across software development projects.
3D JPS Path Optimization Algorithm and Dynamic-Obstacle Avoidance Design Based on Near-Ground Search Drone

As various fields and industries have progressed, the use of drones has grown tremendously. The problem of path planning for drones flying at low altitude in urban as well as mountainous areas will be crucial for drones performing search-and-rescue missions. In this paper, we propose a convergent approach to ensure autonomous collision-free path planning for drones in the presence of both static obstacles and dynamic threats. Firstly, this paper extends the jump point search algorithm (JPS) in three dimensions for the drone to generate collision-free paths based on static environments. Next, a parent node transfer law is proposed and used to implement the JPS algorithm for any-angle path planning, which further shortens the planning path of the drones. Furthermore, the optimized paths are smoothed by seventh-order polynomial interpolation based on minimum snap to ensure the continuity at the path nodes. Finally, this paper improves the artificial potential field (APF) method by a virtual gravitational field and 3D Bresenham’s line algorithm to achieve the autonomous obstacle avoidance of drones in a dynamic-threat conflict environment. In this paper, the performance of this convergent approach is verified by simulation experiments. The simulation results show that the proposed approach can effectively solve the path planning and autonomous-obstacle-avoidance problems of drones in low-altitude flight missions.
Improved K‐means algorithm for clustering non‐spherical data

Abstract As one of the commonly used data mining algorithms, K‐means has the advantage of fast clustering speed, but the disadvantage is that it is less effective for clustering non‐spherical data. An improved K‐means algorithm (IK‐means) is proposed to enhance clustering efficiency for non‐spherical data. The original dataset is clustered into a relatively larger number of high‐density sub‐clusters, and the final result is obtained by merging connected sub‐clusters respectively. The connectivity among sub‐clusters is evaluated by the sub‐clusters density and the nearest distance class between sub‐clusters. By testing on University of California, Irvine(UCI) datasets and several other artificial simulation datasets, the comparison of proposed IK‐means algorithm against DBSCAN, KGFCM shows its clustering capability for data of arbitrary shape. The clustering Adjusted Rand Index (ARI) value for 72,000 sizes data is 24% higher than DBSCAN, and 95.2% higher than KGFCM. For larger datasets, the IK‐means algorithm is faster than DBSCAN and KGFCM.
Dynamic Multiscale Feature Fusion Method for Underwater Target Recognition

The feature information of small-scale targets is seriously missing under the interference of complex underwater terrain and light refraction. Moreover, the unbalanced distribution of underwater target samples can also affect the accuracy of spatial semantic feature extraction. Aiming at the above problems, this paper proposes a dynamic multiscale feature fusion method for underwater target recognition. Firstly, this paper uses multiscale info noise contrastive estimation (MS-InfoNCE) loss to extract the significant features of the target at 4 scales. Secondly, the method learns the spatial semantic features of the target through a dynamic conditional probability matrix. Finally, this paper designs different feature fusion mechanisms for different scale targets, dynamically fusing multiscale significant features and spatial semantic features to recognize underwater weak targets. The experimental results show that the recognition accuracy of the proposed algorithm is 1.38&#x0025; higher than that of the existing algorithm when recognizing underwater distorted targets.
Balanced Byzantine Reliable Broadcast with Near-Optimal Communication and Improved Computation

This paper studies Byzantine reliable broadcast (BRB) under asynchronous networks, and improves the state-of-the-art protocols from the following aspects. Near-optimal communication cost: We propose two new BRB protocols for n nodes and input message M that has communication cost O(n|M|+n2 logn), which is nearoptimal due to the lower bound of Ω(n|M|+n2). The first RBC protocol assumes threshold signature but is easy to understand, while the second RBC protocol is error-free but less intuitive. Improved computation:We propose a newconstruction that improves the computation cost of the state-of-the-art BRB by avoiding the expensive online error correction on the input message, while achieving the same communication cost. Balanced communication: We propose a technique named balanced multicast that can balance the communication cost for BRB protocols where the broadcaster needs to multicast the message M while other nodes only needs to multicast coded fragments of size O(|M|/n + logn). The balanced multicast technique can be applied to many existing BRB protocols as well as all our new constructions in this paper, and can make every node incur about the same communication cost. Finally, we present a lower bound to show the near optimality of our protocol in terms of communication cost at each node.
t-SNE Dimensionality Reduction Method Based on Sobol Sequence Initialized Archerfish Hunting Optimizer Algorithm

t-distributed stochastic neighbor embedding (t-SNE), is a famous supervised, nonlinear dimensionality reduction and data visualization method in manifold learning, its core idea is to pursue the probability isomorphism of data points from high-dimensional space to low-dimensional space, that is, it requires the points in high-dimensional space to meet a certain probability distribution, and still meet a similar probability distribution after projecting them into low-dimensional space. Generally, t-SNE algorithm will be eventually transformed into a problem of solving Kullback-Leibler (KL) divergence by gradient descent (GD) method or stochastic gradient descent (SGD) method. However, gradient dependent methods are easy to fall into the trap of local optimum, and the closer they are to the optimal value, the more oscillatory sawtooth effect will occur. Therefore, in order to overcome the shortcomings, in this paper, a novel Sobol sequence initialized archerfish hunting optimizer (SSAHO) has been proposed, which can increase the randomness and robustness of the algorithm, and via numerical experiment, its performance in improving the optimization ability has been verified.