Reviewerly/exHarmony huggingface dataset:
Sequential Bayesian Optimization for Adaptive Informative Path Planning with Multimodal Sensing

Adaptive Informative Path Planning with Multi-modal Sensing (AIPPMS) considers the problem of an agent equipped with multiple sensors, each with different sensing accuracy and energy costs. The agent's goal is to explore the environment and gather information subject to its resource constraints in unknown, partially observable environments. Previous work has focused on the less general Adaptive Informative Path Planning (AIPP) problem, which considers only the effect of the agent's movement on received observations. The AIPPMS problem adds additional complexity by requiring that the agent reasons jointly about the effects of sensing and movement while balancing resource constraints with information objectives. We formulate the AIPPMS problem as a belief Markov decision process with Gaussian process beliefs and solve it using a sequential Bayesian optimization approach with online planning. Our approach consistently outperforms previous AIPPMS solutions by more than doubling the average reward received in almost every experiment while also reducing the root-mean-square error in the environment belief by 50%. We completely open-source our implementation to aid in further development and comparison. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/sisl/SBO_AIPPMS
Design Patterns for AI-based Systems: A Multivocal Literature Review and Pattern Repository

Systems with artificial intelligence components, so-called AI-based systems, have gained considerable attention recently. However, many organizations have issues with achieving production readiness with such systems. As a means to improve certain software quality attributes and to address frequently occurring problems, design patterns represent proven solution blueprints. While new patterns for AI-based systems are emerging, existing patterns have also been adapted to this new context.The goal of this study is to provide an overview of design patterns for AI-based systems, both new and adapted ones. We want to collect and categorize patterns, and make them accessible for researchers and practitioners. To this end, we first performed a multivocal literature review (MLR) to collect design patterns used with AI-based systems. We then integrated the created pattern collection into a web-based pattern repository to make the patterns browsable and easy to find.As a result, we selected 51 resources (35 white and 16 gray ones), from which we extracted 70 unique patterns used for AI-based systems. Among these are 34 new patterns and 36 traditional ones that have been adapted to this context. Popular pattern categories include architecture (25 patterns), deployment (16), implementation (9), or security & safety (9). While some patterns with four or more mentions already seem established, the majority of patterns have only been mentioned once or twice (51 patterns). Our results in this emerging field can be used by researchers as a foundation for follow-up studies and by practitioners to discover relevant patterns for informing the design of AI-based systems.
Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models

Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper, we study the benefits and challenges of using a learned dynamics model when performing PbRL. In particular, we provide evidence that a learned dynamics model offers the following benefits when performing PbRL: (1) preference elicitation and policy optimization require significantly fewer environment interactions than model-free PbRL, (2) diverse preference queries can be synthesized safely and efficiently as a byproduct of standard model-based RL, and (3) reward pre-training based on suboptimal demonstrations can be performed without any environmental interaction. Our paper provides empirical ev-idence that learned dynamics models enable robots to learn customized policies based on user preferences in ways that are safer and more sample efficient than prior preference learning approaches. Supplementary materials and code are available at https://sites.google.com/berkeley.edu/mop-rl.
Double-suppressed possibilistic fuzzy Gustafson–Kessel clustering algorithm

Possibilistic fuzzy c-means clustering (PFCM) is an unsupervised hybrid clustering algorithm, which can partly inherit the stability of fuzzy c-means clustering (FCM) algorithm and the noise robustness of possibilistic c-means clustering (PCM) algorithm. However, there are still several limitations in the PFCM when clustering complex data with multiple characteristics: sensitivity to strong noise, coincident clustering problem in multi-class clustering, and disability to deal with strong correlations of feature components. Therefore, a double-suppressed possibilistic fuzzy Gustafson–Kessel​ clustering algorithm (DS-PFGK) is presented. Firstly, we design a new objective function with a modified Mahalanobis distance and an improved weight parameter setting method based on the stability of fuzzy memberships and the anti-noise ability of possibilistic memberships. Moreover, combined with the absolute attribute of possibilistic memberships and the covariance matrix in the modified Mahalanobis distance, an ellipsoidal cluster core which can depict the distribution of sample data is generated to divide each cluster into different regions for selecting appropriate learning objects and identifying noisy data adaptively. Then, a “double-suppressed competitive learning” strategy is designed for the selected learning objects to solve the coincident clustering problem in multi-class clustering by suppressing the nonwinner possibilistic memberships, and to reduce the iteration number by suppressing nonwinner fuzzy memberships and rewarding winner fuzzy memberships. Finally, a segmentation algorithm on noisy color images based on the DS-PFGK is proposed based on the generated cluster cores. Experiments on synthetic datasets, real datasets, and color images show the good performance of the proposed methods compared with several classical and state-of-the-art clustering algorithms.
Bert-CK: A study of user profile classification based on Bert and CK-means+ fusion

In traditional user portrait construction methods, static word vectors can extract only shallow semantic representations, which cannot manage word polysemy. Moreover, the common clustering algorithm K-means has the problems of initial K values and unstable initial centroid selection. A Bert-CK model based on Bert and CK-means+ is proposed. First, Bert is used to extract semantic and syntactic text features at various levels, and word vectors and sentence vectors are obtained according to the context. Then, the CK-means+ algorithm is improved based on canopy and mean calculation. Next, the K value and initial centroid are determined. The sentence vectors are input to CK-means+ to obtain user classification and topic features. Finally, semantic features and topic features are fused and classified. CK-means+ is evaluated on the Sogou user portrait dataset. The experimental results verify that Bert-CK is better than the baseline model.
Gender and food security in Nigeria: the role of corporate social responsibility in the oil-producing communities

Purpose The purpose of this paper is to critically examine the multinational oil companies’ (MOCs) corporate social responsibility (CSR) initiatives in Nigeria. Its special focus is to investigate the impact of the global memorandum of understanding (GMoU) on gender and food security in the Niger Delta region of Nigeria. Design/methodology/approach This paper adopts a survey research technique aimed at gathering information from a representative sample of the population. A total of 800 women respondents were sampled across the rural areas of the Niger Delta region. It is essentially cross-sectional, describing and interpreting the current situation. Findings The results from the use of a combined propensity score matching (PSM) and logit model indicate that the CSR interventions of the MOCs using GMoUs have contributed to empowering women to effectively discharge their role in food and nutritional security. This is achieved by enhancing coherence in policies on gender, agriculture, nutrition, health, trade and other relevant areas in the Niger Delta. The findings also show that the CSR intervention of MOCs supported ecologically sound approaches to food production, such as agro-ecology that promotes sustainable farming and women’s empowerment in the region. Practical implications This suggests that recognizing and respecting the local knowledge of farmers, including women farmers, will help develop locally relevant food and nutrition security in sub-Saharan Africa. Social implications This implies that promoting the implementation of everybody’s right to food, particularly that of women, as well as giving women rights to other resources such as land, in addition to engaging women and men in challenging the inequitable distribution of food within the household, will help strengthen food security in Africa. Originality/value This research contributes to the gender debate in agriculture from a CSR perspective in developing countries and serves as a basis for the host communities to demand social projects. It concludes that corporate establishments have an obligation to help solve problems of public concern.
A Systematic Review of Automated Query Reformulations in Source Code Search

Fixing software bugs and adding new features are two of the major maintenance tasks. Software bugs and features are reported as change requests. Developers consult these requests and often choose a few keywords from them as an ad hoc query. Then they execute the query with a search engine to find the exact locations within software code that need to be changed. Unfortunately, even experienced developers often fail to choose appropriate queries, which leads to costly trials and errors during a code search. Over the years, many studies have attempted to reformulate the ad hoc queries from developers to support them. In this systematic literature review, we carefully select 70 primary studies on query reformulations from 2,970 candidate studies, perform an in-depth qualitative analysis (e.g., Grounded Theory), and then answer seven research questions with major findings. First, to date, eight major methodologies (e.g., term weighting, term co-occurrence analysis, thesaurus lookup) have been adopted to reformulate queries. Second, the existing studies suffer from several major limitations (e.g., lack of generalizability, the vocabulary mismatch problem, subjective bias) that might prevent their wide adoption. Finally, we discuss the best practices and future opportunities to advance the state of research in search query reformulations.
A Reliable Prediction Algorithm Based on Genre2Vec for Item-Side Cold-Start Problems in Recommender Systems with Smart Contracts

Personalized recommender systems are used not only in e-commerce companies but also in various web applications. These systems conventionally use collaborative filtering (CF) and content-based filtering approaches. CF operates using memory-based or model-based methods; both methods use a user-item matrix that considers user preferences as items. This matrix denotes information on user preferences, which refers to the user ratings for items. The model-based method exploits the fact that the input matrix is factorized. CF approaches can effectively provide personalized recommendation results to users; however, cold-start problems arise because both these methods depend on the users’ ratings for items to predict users’ preferences. We proposed an approach to alleviate the cold-start problem along with a methodology for utilizing blockchain that can enhance the reliability of the processes of the recommendations. We attempted to predict an average rating for a new item to alleviate item-side cold-start problems. First, we applied the concept of word2vec, treating each user’s item-selection history as a sentence. Then, we derived genre2Vec based on the skip-gram technique and predicted an average rating for a new item by utilizing the vectors and category ratings. We experimentally demonstrated that our approach could generate more accurate results than conventional CF approaches could. We also designed the processes of the recommendation based on the concept of blockchain addressing the smart contract. Based on our approach, we proposed a system that can secure reliability as well as alleviate the cold-start problems in recommender systems.
Axiomatization of Blockchain Theory

The increasing use of artificial intelligence algorithms, smart contracts, the internet of things, cryptocurrencies, and digital money highlights the need for secure and sustainable decentralized solutions. Currently, the blockchain technology serves as the backbone for most decentralized systems. However, the question of axiomatization of the blockchain theory in the first-order logic has been open until today, despite the efficient computational implementations of these systems. This did not allow one to formalize the blockchain structure, as well as to model and verify it using logical methods. This work introduces a finitely axiomatizable blockchain theory T that defines a class of blockchain structures K using the axioms of the first-order logic. The models of the theory T are well-known blockchain implementations with the proof of work consensus algorithm, including Bitcoin, Ethereum (PoW version), Ethereum Classic, and some others. By utilizing mathematical logic, we can study these models and derive new theorems of the theory T through automatic proofs. Also, the axiomatization of blockchain opens up new opportunities to develop blockchain-based systems that can help solve some of the open problems in the fields of artificial intelligence, robotics, cryptocurrencies, etc.
A Knowledge Graph Embedding Based Service Recommendation Method for Service-Based System Development

Web API is an efficient way for Service-based Software (SBS) development, and mashup is a key technology which merges several web services to deal with the increasing complexity of software requirements and expedite the service-based system development. The efficient service recommendation method is vital for the software development. However, the existing methods often suffer from data sparsity or cold start issues, which should lead to bad effects. Currently, this paper starts with SBS development, and proposes a service recommendation method based on knowledge graph embedding and collaborative filtering (CF) technology. In our model, we first construct a refined knowledge graph using SBS-service co-invocation record and SBS and service related information to mine the potential semantics relationship between SBS and service. Then, we learn the SBS and service entities in the knowledge graph. These heterogeneous entities (SBS and service, etc.) are embedded into the low-dimensional space through the representation learning algorithms of Word2vec and TransR, and the distances between SBS and service vectors are calculated. The input of recommendation model is SBS requirement (target SBS), the similarities functional SBS set is extracted from knowledge graph, which can relieve the cold start problem. Meanwhile, the recommendation model uses CF to recommend service to target SBS. Finally, this paper verifies the effectiveness of method on the real-word dataset. Compared with the several state-of-the-art methods, our method has the best service hit rate and ranking quality.
An Efficient Encryption Scheme with Fully Hidden Access Policy for Medical Data

With the development of informatization, patient medical data are generally stored in the form of electronic medical records. Hospitals store a large amount of medical data on cloud servers, which can ensure the integrity and sharing of medical data. There are many problems when sharing medical data, the most important of which is the security of private data. The ciphertext-policy attribute-based encryption (CP-ABE) algorithm not only supports data encryption, but also supports data access control. The CP-ABE algorithm is applied to medical databases. Through the encryption of medical data and the fine-grained control of data owner access to medical data, the privacy security and sharing security of medical data are realized. However, the traditional CP-ABE algorithm also has problems for the data user regarding access policy privacy leakage and the low efficiency of medical data encryption and decryption. This paper proposes a secure and efficient encryption technique. Medical data are encrypted using a symmetric encryption algorithm, and the CP-ABE algorithm with a fully hidden access policy encrypts the symmetric key. To implement access policy hiding, it uses an attribute bloom filter (ABF) for all access structures. The solution also supports the outsourced decryption of medical data, which can improve the encryption and decryption efficiency of the technique. Finally, in this paper, it is demonstrated that the scheme is selectively chosen-plaintext attack secure (CPA-secure). The experimental results show that users have less computational consumption to obtain medical data.
Civil Aviation Travel Question and Answer Method Using Knowledge Graphs and Deep Learning

In this paper, a civil aviation travel question and answer (Q&amp;A) method based on integrating knowledge graphs and deep learning technology is proposed to establish a highly efficient travel information Q&amp;A platform and quickly and accurately obtain question information and give corresponding answers to passengers. In the proposed method, a rule-based approach is employed to extract triads from the acquired civil aviation travel dataset to construct a civil aviation travel knowledge graph. Then, the ELECTRA-BiLSTM-CRF model is constructed to recognize the entity, and an improved ALBERT-TextCNN model is used for intent classification. Finally, Cypher query templates are transformed into Cypher query statements and retrieved in the Neo4j database, and the query returns the result, which realizes a new civil aviation travel Q&amp;A method. A self-built civil aviation dataset is selected to prove the effectiveness of the proposed method. The experimental results show that the proposed method based on integrating knowledge graphs and deep learning technology can achieve better Q&amp;A results, and it has better generalization and high accuracy.
Convolutional neural networks combined with feature selection for radio‐frequency fingerprinting

Abstract Radio‐frequency fingerprinting is a technique for the authentication and identification of wireless devices using their intrinsic physical features and an analysis of the digitized signal collected during transmission. The technique is based on the fact that the unique physical features of the devices generate discriminating features in the transmitted signal, which can then be analyzed using signal‐processing and machine‐learning algorithms. Deep learning and more specifically convolutional neural networks (CNNs) have been successfully applied to the problem of radio‐frequency fingerprinting using a spectral domain representation of the signal. A potential problem is the large size of the data to be processed, because this size impacts on the processing time during the application of the CNN. We propose an approach to addressing this problem, based on dimensionality reduction using feature‐selection algorithms before the spectrum domain representation is given as an input to the CNN. The approach is applied to two public data sets of radio‐frequency devices using different feature‐selection algorithms for different values of the signal‐to‐noise ratio. The results show that the approach is able to achieve not only a shorter processing time; it also provides a superior classification performance in comparison to the direct application of CNNs.
Neural architecture search for resource constrained hardware devices: A survey

Abstract With the emergence of powerful and low‐energy Internet of Things devices, deep learning computing is increasingly applied to resource‐constrained edge devices. However, the mismatch between hardware devices with low computing capacity and the increasing complexity of Deep Neural Network models, as well as the growing real‐time requirements, bring challenges to the design and deployment of deep learning models. For example, autonomous driving technologies rely on real‐time object detection of the environment, which cannot tolerate the extra latency of sending data to the cloud, processing and then sending the results back to edge devices. Many studies aim to find innovative ways to reduce the size of deep learning models, the number of Floating‐point Operations per Second, and the time overhead of inference. Neural Architecture Search (NAS) makes it possible to automatically generate efficient neural network models. The authors summarise the existing NAS methods on resource‐constrained devices and categorise them according to single‐objective or multi‐objective optimisation. We review the search space, the search algorithm and the constraints of NAS on hardware devices. We also explore the challenges and open problems of hardware NAS.
Motion Context guided Edge-preserving network for video salient object detection

Video salient object detection targets at extracting the most conspicuous objects in a video sequence, which facilitate various video processing tasks, e.g., video compression, video recognition, etc. Although remarkable progress has been made for video salient object detection, most existing methods still suffer from coarse edge boundaries which may hinder their usage in real-world applications. To alleviate this problem, in this paper, we propose a Motion Context guided Edge-preserving network (MCE-Net) model for video salient object detection. MCE-Net can generate temporally consistent salient edges, which are then leveraged to refine the salient object regions completely and uniformly. The core innovation in MCE-Net is an Asymmetric Cross-Reference Module (ACRM), which is designed to exploit the cross-modal complementarity between spatial structure and motion flow, facilitating robust salient object edge extraction. To leverage the extracted edge features for salient object refinement, we fuse them with multi-level spatial–temporal embeddings in a paralleled guidance manner, generating the final saliency results. The proposed method is end-to-end trainable and the edge annotations are generated automatically from ground truth saliency maps. Experimental evaluations on five widely-used benchmarks demonstrate that our proposed method can achieve superior performance to other outstanding methods. Moreover, the experimental results indicate that our method can preserve salient objects with clear boundary structures in video sequences.