Reviewerly/exHarmony huggingface dataset:
Architectural Design Decisions for Machine Learning Deployment

Deploying machine learning models to production is challenging, partially due to the misalignment between software engineering and machine learning disciplines but also due to potential practitioner knowledge gaps. To reduce this gap and guide decision-making, we conducted a qualitative investigation into the technical challenges faced by practitioners based on studying the grey literature and applying the Straussian Grounded Theory research method. We modelled current practices in machine learning, resulting in a UML-based architectural design decision model based on current practitioner understanding of the domain and a subset of the decision space and identified seven architectural design decisions, various relations between them, twenty-six decision options and forty-four decision drivers in thirty-five sources. Our results intend to help bridge the gap between science and practice, increase understanding of how practitioners approach deployment of their solutions, and support practitioners in their decision-making.
A Secure Container Placement Strategy Using Deep Reinforcement Learning in Cloud

Lightweight virtualization technology represented by the container is developing rapidly and plays an increasingly important role in cloud computing. Container technology is efficient and flexible, but the kernel-sharing property also means its isolation is incomplete, making multi-tenancy container clouds vulnerable to co-resident attacks. The existing placement strategy either only considers the resource allocation problem or only considers the co-resident security problem of the virtualization technology on the same host. In this paper, we present SecCPS, a Secure Container Placement Strategy using deep reinforcement learning in a typical cloud scene. SecCPS learns to find the optimal placement to mitigate co-resident threats between containers running on virtual machines. In the design of SecCPS, we (1) present the challenges of securely placing containers running on virtual machines and establish metrics to describe co-residence in container clouds; (2) develop a secure container placement strategy using deep reinforcement learning as a preventive measure for co-residence; (3) implement the strategy and evaluate its effectiveness and scalability. The results show that SecCPS can effectively reduce co-residence compared with existing strategies and is scalable and flexible to large-scale container deployment scenarios.
POI Recommendation Algorithm based on Region Transfer Collaborative Filtering

With the development of mobile internet and social platforms, the lifestyle of check-in has become popular in people’s daily life. With the support of highly accurate positioning technology, the social platform has accumulated a large amount of user check-in data. Based on these data, the platform can provide point-of-initerest recommendation services for users.However, existing recommendation algorithms often suffer from some limitations: (1) Recommendation systems using deep learning algorithms require extremely high hardware computing power and can not be deployed on edge devices; (2) General recommendation algorithms do not fully exploit the potential of users to go to new points of interest when making recommendations. Based on the above existing problems, we propose our own collaborative filtering recommendation model: (1) The model contains simple operations and requires very little device computing power; (2) It models users’ activity areas by using a Gaussian distribution model, and then calculates the similarity between users to establish similar user groups. The similarity can be used as the probability of regional transfer of group members. Based on this method, the points-of-interest of similar users are added to the recommendation list for the purpose of exploring new interest points. The experimental results on the well-known dataset Gowalla demonstrate that the proposed approach outperforms other state-of-the-art POI recommendation methods.
Malicious Domain Names Detection Algorithm Based on Statistical Features of URLs

Malicious domains often employ a number of illegal activities to threaten people's lives, including their privacy and property. As a result, the task of detecting malicious domain names has become a focus of attention. To solve the problem, we propose a novel malicious domain name detection algorithm which is statistically characterized by Uniform Resource Locator URL) characters. Firstly, statistical features of the given URL are extracted based on prior knowledge of domain name. Secondly, a decision tree is constructed based on URL characters to count various features. In addition, this decision tree is also used to detect the already generated domain names. Finally, the legal domain names and the domain names we generated are composed as a kind of dataset. The test results show that the proposed detection algorithm has the average accuracy rate of 90.31% and precision rate of 90.03%, which means a better performance on malicious domain name detection.
Deep Reinforcement Learning with Long-Time Memory Capability for Robot Mapless Navigation

Achieving autonomous navigation of indoor robots in a mapless environment is a long-standing research problem. Deep Reinforcement Learning (DRL) is widely used for robot navigation by virtue of learning through interaction with the environment. However, the large number of trials for training need lengthy computation times. To address this issue, we propose an innovative DRL model with long-time memory capability for mobile robot’s mapless navigation, which can achieve end-to-end navigation based only on laser-ranging data and target location. The long-time memory capability is realized by introducing a memory module based on the special structure of the Long Short-Term Memory (LSTM). The memory module ensures that the model derives information from previous navigation experiences and thus optimizes the model’s decision-making. In addition, to enable the model to explore the environment more effectively, we design a novel dual-noise mechanism consisting of Gaussian noise and Ornstein-Uhlenbeck noise. Extensive experiments are conducted on the Gazebo simulation platform and validate that the proposed approach can generate a smoother navigation path and exceed the state-of-the-art performance with less computation cost.
A Network Security Situation Prediction Method Based On Attention-CNN-BiGRU

To improve the accuracy and efficiency of network security situation prediction, a network security situation prediction method based on Attention-CNN-BiGRU is proposed. It combined Convolutional Neural Network(CNN) and bidirectional Gated Recurrent Unit(BiGRU), The structure of the proposed model is more accurate for extraction and understanding of situation time series; besides, to improve the performance of proposed model, the Attention mechanism is utilized to optimize the model; particle swarm optimization(PSO) is applied to optimize varieties of hyperparameters of the model. The optimal combination of hyperparameters selected by PSO is used for training of the model. Finally, the method proposed in this paper was verified in experiment and compared with other models. The results shows that the method can acomplish the task of situation prediction with better capability.
A Novel Keyword Generation Model Based on Topic-Aware and Title-Guide

Keywords are usually one or more words or phrases that describe the subject information of the document. The traditional automatic keywords extraction methods cannot obtain the keywords which do not appear in the document, and the semantic information is not considered in the extraction process. In this paper, we introduce a novel Keyword Generation Model based on Topic-aware and Title-guide (KGM-TT). In the KGM-TT, the neural topic model is used to identify the latent topic words, and a hierarchical encoder technology with attention mechanism is able to encode the title and its content, respectively. The keywords are generated by the recurrent neural network with attention and replication mechanism in our model. This model can not only generate the keywords which do not appeared in the source document but also use the topic information and the highly summative word meaning in the title to assist the generation of keywords. The experimental results show that the F1 value of this model is about 10% higher than that of CopyRNN and CopyCNN.
Popularity prediction for marketer-generated content: A text-guided attention neural network for multi-modal feature fusion

In this paper, we focus on the popularity prediction for marketer-generated content (MGC), which has not been investigated by current studies. To address this problem, we propose a novel deep learning approach, namely the text-guided attention neural network (TGANN) model, to make full use of heterogeneous and multi-modal data related to MGCs (e.g., text descriptions and images). In the proposed model, we first design a filter-based topic model to filter out the noise words and extract topic features from textual descriptions. To lessen the influence of irrelevant information in images, we then propose a text-guided attention mechanism to use the text's topic features to guide the image region representations. Lastly, to determine the contribution of each topic and each image, the TGANN model introduces the attention computation for each visual modality and textual modality. Our experiments are conducted on two real-world datasets of MGC. The quantitative results show that the proposed model outperforms several state-of-the-art methods. The qualitative experiments demonstrate that our model can accurately capture topic attentions, image attentions, and image region attentions. The proposed model provides important practical implications for marketers and online platforms, e.g., estimating the success of online advertising campaigns, creating more attractive marketing contents, and improving recommendation systems.
Hierarchical Identity-based Puncturable Encryption from Lattices with Application to Forward Security

Puncturable encryption (PE), introduced by Green and Miers at IEEE S$&$P 2015, allows recipients to update their decryption keys to revoke decryption capability for selected messages without communicating with senders. In general, it allows users to control which ciphertexts their keys may decrypt. The notion of PE has been found very useful in many applications, such as asynchronous messaging systems, group messaging systems, public-key watermarking schemes, secure cloud emails, and many more. In this paper, we introduce a new primitive called hierarchical identity-based puncturable encryption (HIBPE) that enhances the concept of PE by allowing more general key delegation and flexible key puncture. It enhances the capability of the data owner for multi-level encrypted data sharing within a group of users by delegating the decryption keys of the users in higher-levels to generate decryption keys for the users in lower-levels. Moreover, it allows users to puncture (update) their decryption keys on tags so that a decryption key punctured on a tag can no longer decrypt ciphertexts under this tag. In addition, to control access to the users' data, the higher-level users can further puncture the delegated keys (for lower-level users) with some tags such that the part of the owner's data is labeled by the punctured tags will no longer be accessible by the lower-level users. These features offer an efficient and flexible solution for encrypted data sharing as well as data-access control mechanisms in a hierarchical setting. We propose the formal definition and security model for HIBPE schemes and provide a concrete HIBPE scheme based on the hardness of the learning with errors problem in the standard model. Further, we provide a generic construction of forward secure hierarchical identity-based encryption (fs-HIBE) from HIBPE, which enables the first quantum-safe construction of fs-HIBE in the standard model. Moreover, this is the first fs-HIBE construction by exploring the concept of PE. The proposed fs-HIBE provides quantum-safe protection for secret keys from exposure in multi-level encrypted data sharing by evolving the keys with time.
The USV Path Planning of Dueling DQN Algorithm Based on Tree Sampling Mechanism

The path planning and obstacle avoidance of USV (unmanned surface vessel) has become a research hotspot in recent years. Among them, the DQN algorithm has achieved good results in the obstacle avoidance and path planning problems of unmanned surface vessel. However, the algorithm suffers from the problems that the sampling method does not make full use of the stored information and the randomness of action selection during the training process is too large and the convergence is too slow. In this paper, we propose a Dueling DQN algorithm to optimize obstacle avoidance and path planning, which based on tree sampling mechanism. The Dueling DQN algorithm will decomposes the value function Q into a state-value function (V) and a dominance function (A). Meanwhile, the absolute value of TD-error is directly used as a priority indicator for priority sampling in the sampling process. Subsequently, the network model is built and experiments are conducted on each of the four maps. As a result, the convergence steps and loss values of the proposed algorithm on the four paths are better than those of the DQN algorithm. It shows that the dueling DQN algorithm can effectively use the stored information for optimal path planning.
Economic Analysis of Loot Box Market in Blockchain Games

As an emerging blockchain technique, the blockchain-based loot box has received significant attention recently due to its promising characters in transparency and decentralization. Because all virtual assets are bound to the players' own address, which allows the players to control and manage everything, players can trade virtual items via the 2nd exChange Market (2CM) directly. To understand the players' optimal strategies and the game provider's optimal pricing, we conduct an economic analysis of both the game provider's and players' behaviors. In addition, gas fee, a unique factor in blockchain, is taken into consideration. Specifically, we model the interactions between the game provider and players as a two-stage Stackelberg game. In Stage I, we model the game provider's optimal pricing problem to maximize his utility using prospect theory (PT) due to the intrinsic demand uncertainty. In Stage II, the players choose the market which can maximize their utility to derive their preferred items. Moreover, our analysis and numerical results show that a game provider who considers the PT modeling should adopt a conservative pricing mechanism to increase his utility. Besides, the primary market is more susceptible to gas fees than the 2CM.
Convolutional 3D Embedding for Knowledge Graph Completion

Link prediction is to predict missing relations between entities for Knowledge Graph Completion (KGC). Convolution neural network has been used in much previous work on link prediction to capture fundamental data pattern of knowledge graph. However, because these models use low-dimensional convolution operation, which limits their performance, they learn fewer expressive features. Further more, they do not have the the capability of keeping the translation property of knowledge triplet, which is an important property for knowledge reasoning. Focusing on these problems, we propose Conv3D (3D Convolution Embedding), a neural network model for link prediction that uses 3D convolution. To capture deeper feature interactions in the knowledge graph, we employ 3D convolution instead of shallow 1D or 2D convolution for generating triplet scores. We conduct link prediction experiments on four general datasets (WN18, WN18RR, FB15k, FB15k-237) and get state-of-the-art (SOTA) results on WN18 and WN18RR. We also explore the influence of convolution parameters (reshaping dimension, number of filters, kernel size) on FB15k-237 and obtain quantitative analytical findings.
Earth-Fixed Books Reduce Motion Sickness When Reading With a Head-Mounted Display

There are concerns that viewing two-dimensional (2D) content such as web pages on a head-mounted display (HMD) in the car may aggravate motion sickness. This is because when 2D content is fixed to a head-fixed coordinate system, the appearance of the content does not change even when the body moves; therefore, it is impossible to visually perceive the movement of one’s body, resulting in a sensory conflict between the visual and vestibular senses. A method for reducing motion sickness when displaying 3D content on an HMD has been investigated; however, when displaying 2D content, no such method has been investigated. Therefore, this study aims to verify to the possibility of reducing motion sickness from the change of appearance caused by fixing 2D content to the earth-fixed coordinate system when viewing it with an HMD in a moving environment. Participants sat on a seat that was mounted on a vibrating device and moved in the pitch direction while reading a book on the HMD. Consequently, the severity of motion sickness was significantly lower when the book was fixed to the earth-fixed coordinate system than when fixed to the head-fixed coordinate system. This result suggests that by fixing the content to the earth-fixed coordinate system, motion sickness can be reduced because the movement of one’s body can be perceived through changes in the appearance of the content, and the sensory conflict between visual and vestibular sensations can be resolved.
How Do Developers Search for Architectural Information? An Industrial Survey

Building software systems often requires knowledge and skills beyond what developers already possess. In such cases, developers have to leverage different sources of information to seek help. A growing number of researchers and practitioners have started investigating what programming-related information developers seek during software development. However, being a high level and a type of the most important development-related information, architectural information search activity is seldom explored. To fill this gap, we conducted an industrial survey completed by 103 participants to understand how developers search for architectural information to solve their architectural problems in development. Our main findings are: (1) searching for architectural information to learn about the pros and cons of certain architectural solutions (e.g., patterns, tactics) and to make an architecture decision among multiple choices are the most frequent purposes or tasks; (2) developers find difficulties mostly in getting relevant architectural information for addressing quality concerns and making design decisions among multiple choices when seeking architectural information; (3) taking too much time to go through architectural information retrieved from various sources and feeling overwhelmed due to the dispersion and abundance of architectural information in various sources are the top two major challenges developers face when searching for architectural information. Our findings (1) provide researchers with future directions, such as the design and development of approaches and tools for searching architectural information from multiple sources, and (2) can be used to provide guidelines for practitioners to refer to when seeking architectural information and providing architectural information that could be considered useful.
Towards improving speech recognition model with post-processing spell correction using BERT

Speech recognition has now become ubiquitous and plays an inevitable role in almost all sectors. Numerous works have been proposed on speech recognition; however, more accurate transcriptions are not possible. Exploration of various studies related to spell correction implies that several kinds of research have been carried out in this field but still it is a very challenging problem. This led to the need for a new spell corrector framework capable of leveraging the performance of the automatic speech recognition (ASR) system. The proposed work unveils state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) based spell correction module developed on top of the deep recurrent neural network (RNN) based ASR system. The impact of BERT-based spell correction on the ASR system is evaluated on three different accent datasets in the perspective of word error rate (WER), character error rate (CER), and Bilingual evaluation understudy (BLEU) score. The experimental results inferred that the enhanced spell correction module is efficacious in detecting and correcting spell errors, by achieving the WER of 5.025% on librispeech corpus, 6.35% on voxforge, and 7.05% on NPTEL corpus.
Research on Obstacle Avoidance Algorithm of Multi-UAV Consistent Formation Based on Improved Dynamic Window Approach

The artificial potential field method is widely used in UAV formation and obstacle avoidance due to its concise algorithm and easy implementation, but it is easy to fall into a local optimal solution in a large-scale environment, and the algorithm does not consider the kinematics of the controlled object; this paper proposes An improved dynamic window approach, considering the UAV kinematics model, realizes the effective obstacle avoidance of UAV formations under the consistency algorithm. Firstly, the target distance correction azimuth angle evaluation function is introduced, and the A star algorithm is integrated to replace the fixed weight of the azimuth angle evaluation function, which improves the search ability of the UAV to navigate to the target point in the unknown environment. Secondly, a new rotation cost is added to the evaluation function, and a penalty is imposed on the large rotation angle to ensure the smoothness of the trajectory. Then, the improved obstacle avoidance strategy is applied to the leading-following consistent formation algorithm, which can effectively achieve formation keeping, obstacle avoidance and collision avoidance between aircraft in unknown environments. Finally, the simulation verification based on Matlab shows that the proposed improved dynamic window strategy can significantly improve the UAV path planning and obstacle avoidance ability in the location environment, and it can be applied to the consistent formation algorithm while maintaining the formation.