Reviewerly/exHarmony huggingface dataset:
A novel chaos map based medical image encryption scheme

In the medical industry, digital images are frequently used to diagnose a variety of diseases. These images comprise of lot of secret information regarding health of the patient. Image encryption is used to protect this secret information by changing the image into unrecognizable form. This research paper proposes a novel chaos map and a pseudo random key. The method comprises of three phases to encrypt the medical images. Firstly, a novel pseudorandom key is generated using the XOR operations and left rotate operations. Secondly, a novel chaos map is created using the cosine and sine function. Finally, pseudorandom key and chaos map are used to construct the cipher image. The novel chaotic map's robustness has been assessed according to the Shannon Entropy, Lyapunov Exponent, and Bifurcation Diagram. The proposed mathematically transformed hybrid chaos map provides a wider chaotic range and shows more complex behavior than the various existing chaotic maps.
Virtual label guided multi-view non-negative matrix factorization for data clustering

Non-negative matrix factorization (NMF) has attracted widespread attention due to its good performance and physical interpretation. However, it remains challenging when handling multi-view data for clustering. On one hand, the current multi-view NMF methods do not fully utilize the virtual label information that can be learned in the clustering process. On the other hand, they usually perform the procedures of learning latent representation and clustering individually. To solve these problems, we develop a novel multi-view clustering model, named virtual label guided multi-view non-negative matrix factorization (VLMNMF). Specifically, we learn the virtual label information of each view, which is used to guide the learning of the latent representation of data. Then, we integrate the latent representation learning and clustering process of the data into a joint framework. A multi-view graph Laplacian is further imposed on the learned low-dimensional representation, which can well preserve the local geometric structure of multi-view data. Experiments on several benchmark datasets illustrate the efficacy of the proposed method.
DeepEmotionNet: Emotion mining for corporate performance analysis and prediction

Since previous studies in cognitive psychology show that individuals' affective states can help analyze and predict their future behaviors, researchers have explored emotion mining for predicting online activities, firm profitability, and so on. Existing emotion mining methods are divided into two categories: feature-based approaches that rely on handcrafted annotations and deep learning-based methods that thrive on computational resources and big data. However, neither category can effectively detect emotional expressions captured in text (e.g., social media postings). In addition, the utilization of these methods in downstream explanatory and predictive applications is also rare. To fill the aforementioned research gaps, we develop a novel deep learning-based emotion detector named DeepEmotionNet that can simultaneously leverage contextual, syntactic, semantic, and document-level features and lexicon-based linguistic knowledge to bootstrap the overall emotion detection performance. Based on three emotion detection benchmark corpora, our experimental results confirm that DeepEmotionNet outperforms state-of-the-art baseline methods by 4.9% to 29.8% in macro-averaged F-score. For the downstream application of DeepEmotionNet to a real-world financial application, our econometric analysis highlights that top executives' emotions of fear and anger embedded in their social media postings are significantly associated with corporate financial performance. Furthermore, these two emotions can significantly improve the predictive power of corporate financial performance when compared to sentiments. To the best of our knowledge, this is the first study to develop a deep learning-based emotion detection method and successfully apply it to enhance corporate performance prediction.
Neural reactive path planning with Riemannian motion policies for robotic silicone sealing

Due to its excellent chemical and mechanical properties, silicone sealing has been widely used in many industries. Currently, the majority of these sealing tasks are performed by human workers. Hence, they are susceptible to labor shortage problems. The use of vision-guided robotic systems is a feasible alternative to automate these types of repetitive and tedious manipulation tasks. In this paper, we present the development of a new method to automate silicone sealing with robotic manipulators. To this end, we propose a novel neural path planning framework that leverages fractional-order differentiation for robust seam detection with vision and a Riemannian motion policy for effectively learning the manipulation of a sealing gun. Optimal control commands can be computed analytically by designing a deep neural network that predicts the acceleration and associated Riemannian metric of the sealing gun from feedback signals. The performance of our new methodology is experimentally validated with a robotic platform conducting multiple silicone sealing tasks in unstructured situations. The reported results demonstrate that compared with directly predicting the control commands, our neural path planner achieves a more generalizable performance on unseen workpieces and is more robust to human/environment disturbances.
High-performance reversible data hiding in encrypted images with adaptive Huffman code

With the development of cloud computing, reversible data hiding in encrypted images (RDHEI) has attracted increasing attention. In this paper, a high-performance RDHEI algorithm with Adaptive Huffman Code (RDHEI-AHC) is proposed for cloud computing. First, multi-MSB (Most Significant Bit) of each pixel is predicted by geometric correction, which takes full advantage of spatial correlation. Next, the image is encrypted by stream cipher. Adaptive Huffman code is then used to insert the auxiliary information and secret message into the encrypted image. Finally, to resist ciphertext-only attacks, the marked encrypted image must be scrambled. In comparison to most state-of-the-art RDHEI approaches, experimental results show that the RDHEI-AHC method delivers a greater embedding rate and improved security.
Entrepreneurial ecosystems in conflict regions: evidence from Ukraine

Abstract Entrepreneurship is a productive force of innovation and economic development. However, in post-conflict regions, there is a greater challenge in allocating entrepreneurial talent to productive entrepreneurship. In this study, we examine the entrepreneurship ecosystem, which is built on the “bottom-up” principles to understand whether its pillars can facilitate productive entrepreneurship in two Ukrainian regions shaken by multiple revolutions and regime change. We introduce a model that puts entrepreneurial conditions in cities and formal institutional changes to a competitive test. Building on the regional entrepreneurship literature, we perform an empirical study in a developing country to reveal what drives productive entrepreneurship in post-conflict regions with entrepreneurship culture, formal networks, debt and equity financing emerging as important determinants of productive entrepreneurship. The effect of formal institutions is significant but highly correlated with rent-seeking behavior of government and corruption. Our analysis suggests that the entrepreneurial conditions in regions focusing on the bottom-up processes of supporting entrepreneurship should work better to enhance productive entrepreneurship activity in a post-conflict region.
Feasibility of using AI to auto-catch responsible frames in ultrasound screening for breast cancer diagnosis

The research of AI-assisted breast diagnosis has primarily been based on static images. It is unclear whether it represents the best diagnosis image.To explore the method of capturing complementary responsible frames from breast ultrasound screening by using artificial intelligence. We used feature entropy breast network (FEBrNet) to select responsible frames from breast ultrasound screenings and compared the diagnostic performance of AI models based on FEBrNet-recommended frames, physician-selected frames, 5-frame interval-selected frames, all frames of video, as well as that of ultrasound and mammography specialists. The AUROC of AI model based on FEBrNet-recommended frames outperformed other frame set based AI models, as well as ultrasound and mammography physicians, indicating that FEBrNet can reach level of medical specialists in frame selection.FEBrNet model can extract video responsible frames for breast nodule diagnosis, whose performance is equivalent to the doctors selected responsible frames.
Core loss: Mining core samples efficiently for robust machine anomaly detection against data pollution

Machine anomaly detection is the task of detecting machine abnormal condition via the collected monitoring data. Recently, increasing attention has focused on autoencoder (AE) based unsupervised anomaly detection (UAD) for mechanical equipment. Typically, the UAD is based on the assumption that all of the training data are normal. In real scenarios, however, the raw monitoring data may be polluted by abnormal data due to machine failures, environmental noise, sensor failures, etc. Without effective regularization, AE-based methods would overfit these polluted data. To address this issue, we design an effective loss, called core loss, which can perform AE-based UAD in a both model-agnostic and end-to-end manner under data pollution. First, we experimentally observe that the AE shows the self-clean characteristic (SCC) under data pollution, i.e., the network will prioritize learning the normal data from the polluted training data. Next, we focus on the core samples, which make up a clean and representative subset of the unlabeled data. Based on the SCC, we propose the core prior that reconstruction errors in the middle with high density correspond to the core samples. Finally, to enhance the SCC, the core loss exploits the core prior to effectively mine and learn the core samples. The experimental results show that the core loss can effectively improve the performance of different network structures on both clean and polluted data. The corresponding Python codes are available at https://github.com/albertszg/Coreloss.
Modeling and optimizing micro-service based cloud elastic management system

Cloud elasticity is a comprehensive solution that takes into account all running applications and resources, including the cloud management system. Cloud resources and applications are constantly changing in terms of capacity and behavior, implying a dynamic change in the architecture and characteristics of the cloud management system. Following the micro-service architecture pattern, the new trend in application modeling is to decompose it into components and make them standalone cooperated modules. By customizing the application operation modules to match new tasks, this design provides the application with rapid adaptation agility to changes in requirements. This paper proposes a full-stack micro-service-based elastic cloud management system that elastically scales and manages cloud resources. The proposed model focuses on the elastic scaling performance of micro-service management modules by analyzing cloud management in three areas: interactions, end-to-end delay, and communication. In addition, optimizing the decoupling and orchestration scheduling of micro-service cloud management system components to achieve elastic management scaling. To the best of our knowledge, no work has investigated cloud management as a workload required for elastic resource provisioning. In comparison to a monolithic architecture, the proposed micro-service-based model achieves 45% to 65% enhancement in elastic scaling performance.
A new multi-source Transfer Learning method based on Two-stage Weighted Fusion

Transfer Learning (TL) as an effective tool to solve the classification problem with few or no labeled training data usually confronts with the multiple source domains issue. Fusion of complementary knowledge in different source domains is expected to improve the classification accuracy in the target domain. For this purpose, we present a new Multi-source transfer learning method via Two-stage Weighted Fusion (MTWF). In MTWF, a credal classification rule is first developed to preserve the local imprecise information as much as possible because it could be difficult to commit some patterns to one precise class when using TL techniques. Then, multiple credal classification results characterized by mass function values produced by patterns in each source domain under different feature spaces are locally integrated to extract useful information in each source domain. The locally integrated credal classification results in different source domains are regarded as multiple sources of evidence for global combination using belief functions to make the class decision, and they are first discounted via the weights estimated by domain-consistency. Whereas, there exist some conflicts among these discounted credal classification results, so we proposed to further discount them via Belief Jensen–Shannon (BJS) divergence for the small conflicts. MTWF was compared with a variety of advanced methods, and the experiment results demonstrate that MTWF can significantly improve the classification accuracy.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1070" altimg="si2.svg"><mml:mi>d</mml:mi></mml:math>-EMR: Secure and distributed Electronic Medical Record management

As more and more data is produced, finding a secure and efficient data access structure has become a major research issue. The centralized systems used by medical institutions for the management and transfer of Electronic Medical Records (EMRs) can be vulnerable to security and privacy threats, often lack interoperability, and give patients limited or no access to their own EMRs. In this paper, we first propose a privilege-based data access structure and incorporates it into an attribute-based encryption mechanism to handle the management and sharing of big data sets. Our proposed privilege-based data access structure makes managing healthcare records using mobile healthcare devices efficient and feasible for large numbers of users. We then propose a novel distributed multilevel EMR (d-EMR) management scheme, which uses blockchain to address security concerns and enables selective sharing of medical records among staff members that belong to different levels of a hierarchical institution. We deploy smart contracts on Ethereum blockchain and utilize a distributed storage system to alleviate the dependence on the record-generating institutions to manage and share patient records. To preserve privacy of patient records, our smart contract is designed to allow patients to verify attributes prior to granting access rights. We provide extensive security, privacy, and evaluation analyses to show that our proposed scheme is both efficient and practical.
Renewable energy communities or ecosystems: An analysis of selected cases

The rapid proliferation of renewable energy communities/ecosystems is an indication of their potential contribution to the ongoing energy transition. A common characteristic of these ecosystems is their complex composition, which often involves the interaction of multiple actors. Currently, the notions of "networking", "collaboration", "coordination", and "cooperation", although having different meanings, are often loosely used to describe these interactions, which creates a sense of ambiguity and confusion. To better characterize the nature of interactions in current and emerging ecosystems, this article uses the systematic literature review method to analyse 34 emerging cases. The objective is threefold (a) to study the interactions and engagements between the involved actors, aiming at identifying elements of collaboration. (b) Identify the adopted technological enablers, and (c) ascertain how the composition and functions of these ecosystems compare to virtual power plants. The outcome revealed that the interactions between the members of these ecosystems can be described as cooperation and not necessarily as collaboration, except in a few cases. Regarding technological enablers, a vast panoply of technologies, such as IoT devices, smart meters, intelligent software agents, peer-to-peer networks, distributed ledger systems/blockchain technology (including smart contracts, blockchain as a platform service, and cryptocurrencies) were found. In comparison with virtual power plants, these ecosystems have similar composition, thus, having multiple actors, comprised of decentralized and heterogeneous technologies, and are formed by aggregating various distributed energy resources. They are also supported by ICT and are characterized by the simultaneous flow of information and energy.
Implementation of Artificial Intelligence Chatbot in Optimizing Customer Service in Financial Technology Company PT. FinAccel Finance Indonesia

Chatbots are increasingly being used as a representative agent for big companies as their implementation opens up promising opportunities to improve customer service. The present paper examines chatbots in this context, elaborating on their impacts in service quality. The purpose of this research is to understand the form of implementation of an artificial intelligence chatbot in the field of customer service in PT. FinAccel Finance Indonesia. A descriptive qualitative method is used in this research by collecting data through interviews and literature studies. This study uses SWOT analysis (strength, weakness, opportunity, threat) as a strategy planning technique and problem solving that emphasizes the importance of the role of internal and external factors. The authors informed the solution obtained by PT. FinAccel Finance Indonesia after knowing the advantages and disadvantages of chatbots.
Classical versus reinforcement learning algorithms for unmanned aerial vehicle network communication and coverage path planning: A systematic literature review

Summary The unmanned aerial vehicle network communication includes all points of interest during the coverage path planning. Coverage path planning in such networks is crucial for many applications, such as surveying, monitoring, and disaster management. Since the coverage path planning belongs to NP‐hard issues, researchers in this domain are constantly looking for optimal solutions for this task. The speed, direction, altitude, environmental variations, and obstacles make coverage path planning more difficult. Researchers have proposed numerous algorithms regarding coverage path planning. In this study, we examined and discussed existing state‐of‐the‐art coverage path planning algorithms. We divided the existing techniques into two core categories: Classical and reinforcement learning. The classical algorithms are further divided into subcategories due to the availability of considerable variations in this category. For each algorithm in both types, we examined the issues of mobility, altitude, and characteristics of known and unknown environments. We also discuss the optimality of different algorithms. At the end of each section, we discuss the existing research gaps and provide future insights to overcome those gaps.
A New Approach to Optimize SVM for Insulator State Identification Based on Improved PSO Algorithm

The failure of insulators may seriously threaten the safe operation of the power system, where the state detection of high-voltage insulators is a must for the normal and safe operation of the power system. Based on the data of insulators in aerial images, this work explored an enhanced particle swarm algorithm to optimize the parameters of the support vector machine. A support vector machine model was therefore established for the identification of the normal and defective states of the insulators. This methodology works with the structure minimization principle of SVM and the characteristics of particle swarm fast optimization. First, the aerial insulator image was segmented as a target by way of the seed region growth based on double-layer cascade morphological improvements, and then, HOG features plus GLCM features were extracted as sample data. Finally, an ameliorated PSO-SVM classifier was designed to realize insulator state identification. Comparisons were made between PSO-SVM and conventional machine learning algorithms, SVM and Random Forest, and an optimization algorithm, Gray Wolf Optimization Support Vector Machine (GWO-SVM), and advanced neural network CNN. The experimental results showed that the performance of the algorithm proposed in this paper touched the top level, where the recognition accuracy rate was 92.11%, the precision rate 90%, the recall rate 94.74%, and the F1-score 92.31%.
Influence of Transfer Learning on Machine Learning Systems Robustness to Data Quality Degradation

The evolution of the Machine Learning (ML) has led to the emergence of Transfer Learning (TL) approach, which allows reusing pretrained industrial ML systems after their input data values or even an application domain has changed. In this paper, we investigate the TL process and its impact on the performance of ML-end systems with integrated network facilities. Especially, we focus on ML-systems designed for the classification of image media-files, transmitted over a network. Packet loss in a network is considered as a major input Data Quality (DQ) deterioration factor that can result in ML system classification performance degradation after pretraining on good inputs. To investigate the typical industrial TL process, we study the relationships between the ML model's last layer weights, hyperparameters, and classification performance throughout the retraining process. In addition, we conduct an empirical study to evaluate how the TL affects ML model performance in real application scenarios. For our experiments, we employ real image media-files, and transmit them over a real wireless network with inherent data losses for a classification on a remote ML-end system. According to our results, retraining ML models on corrupted data allows to enhance their robustness to a DQ degradation in the considered image classification scenarios. However, DQ influence on the ML system performance may vary depending on the data and system types.
Contributions of Shape, Texture, and Color in Visual Recognition

We investigate the contributions of three important features of the human visual system (HVS)—shape, texture, and color—to object classification. We build a humanoid vision engine (HVE) that explicitly and separately computes shape, texture, and color features from images. The resulting feature vectors are then concatenated to support the final classification. We show that HVE can summarize and rank-order the contributions of the three features to object recognition. We use human experiments to confirm that both HVE and humans predominantly use some specific features to support the classification of specific classes (e.g., texture is the dominant feature to distinguish a zebra from other quadrupeds, both for humans and HVE). With the help of HVE, given any environment (dataset), we can summarize the most important features for the whole task (task-specific; e.g., color is the most important feature overall for classification with the CUB dataset), and for each class (class-specific; e.g., shape is the most important feature to recognize boats in the iLab-20M dataset). To demonstrate more usefulness of HVE, we use it to simulate the open-world zero-shot learning ability of humans with no attribute labeling. Finally, we show that HVE can also simulate human imagination ability with the combination of different features.