Reviewerly/exHarmony huggingface dataset:
On Interpretable Algorithm of Artificial Intelligence based on Information Network Analysis

The high-speed development and wide application of artificial intelligence technology have brought great convenience to human beings and strongly promoted the degree of social intelligence. However, due to the complexity and huge structure of artificial intelligence algorithm model, its algorithm operation process and calculation method have some shortcomings such as opacity. The lack of strict interpretable results will inevitably bring a certain degree of hidden dangers to production and life. In this paper, different interpretable artificial intelligence algorithms are used to analyze and mine information networks. Through designing multiple groups of experiments, the performance of different algorithm models is verified and analyzed, and the interpretable process mechanism of artificial intelligence algorithms and the future research prospects and directions are explored.
A Hardware-Assisted Security Monitoring Method for Jump Instruction and Jump Address in Embedded Systems

With the development of embedded systems towards networking and intelligence, the security threats they face are becoming more difficult to prevent. Existing protection methods make it difficult to monitor jump instructions and their target addresses for tampering by attackers at the low hardware implementation overhead and performance overhead. In this paper, a hardware-assisted security monitoring module is designed to monitor the integrity of jump instructions and jump addresses when executing programs. The proposed method has been implemented on the Xilinx Kintex-7 FPGA platform. Experiments show that this method is able to effectively monitor tampering attacks on jump instructions as well as target addresses while the embedded system is executing programs.
An Identification Method for Indication Value of Digital Display Instruments Based on Machine Vision

Based on machine vision, an useful solution are provided in this paper for indication value identification of digital display instruments. In particular, for such key procedures as ROI extracting, grayscale processing and template matching, new methods are invented to achieve a good result.
Research on UAV Target Detection Algorithm Based on Improved Air-Borne-YOLOv3

While the rapid development of multi-rotor UAVs brings wide applications, it also brings harm to social life and personal safety due to problems such as “indiscriminate flying” and “black flying”. Therefore, there is an urgent need to carry out research work on anti-VAV systems. The most critical technology in the countermeasure system is the real-time detection of the target UAV. The traditional YOLOv3 detection algorithm cannot achieve a balance between real-time performance and detection accuracy on airborne embedded devices with limited computing power. Therefore, this paper first improves the end structure of MobileNetV3-Small and replaces the backbone network of the original YOLOv3 with it, which greatly reduces the amount of calculation, but brings about the loss of detection accuracy; Next, an improved receptive field module(RFB+) is added, which strengthens the detection ability of targets in complex backgrounds; Finally, the improved YOLOv3 algorithm is named Air-Borne-YOLOv3. The feasibility and superiority of the improved algorithm are proved by the actual flight test results.
The Differences and Advantages between Cloud Services and Traditional Services

In recent years, cloud services in the Internet field has been a hot topic, after several years of explosive technology development and several years of market precipitation, the cloud platform finally achieved universal unification in 2017, and Kubernetes (often referred to as k8s), an open source container cluster management system from Google Cloud Platform, has become the de facto standard in the industry. Google, Amazon, IBM, Microsoft, Alibaba, Tencent, Huawei and a series of well-known Internet manufacturers at home and abroad have built their own cloud services and cloud platforms. This article discusses the differences and advantages of cloud services compared with traditional services.
Domestic Violence Crisis Recognition Method based on Bi-LSTM+Attention

Domestic violence (DV) is getting more and more attention due to the critical trouble and danger that pose great challenge to human rights and health. The popularity of various social medias can help these victims to share their experiences and obtain more possible support from numerous open communities. However, it is not only inefficient but also laborious and time-consuming to manually access and browse through a great deal of available posts on the Internet and social media space. Therefore, considering the advantages of Deep Learning (DL) technology in Natural Language Processing, adopting DL as an efficient strategy for automatic recognition and evaluation of DV victims is in the urgent requirements. This paper takes social media Facebook posts about domestic violence as the research object and uses Word2Vec to build word vector model. The paper uses Bi-LSTM+self-Attention deep learning models to accomplish DV crisis recognition task and compares it with CNN, RNN and LSTM. The assessment of experimental results show that the accuracy of CNN and LSTM are both above 90% which is better than RNN; And the accuracy of Bi-LSTM is the highest after using the Attention mechanism. The research results of this article will provide reference and help for public DVCS to rescue DV victims.
Image Deblurring Based on Normalized-weighted Total Variation

Image deblurring is an important and challenging problem in imaging processing. It aims to restore clear images from degenerated ones caused by camera shake or target motion. The total variation (TV) regularization has been widely used in image deblurring, which needs to be carefully modified because of over-smoothing issue and solution bias caused by the homogeneous penalization. In this paper, a non-blind image deblurring method based on normalized-weighted TV (NWTV) is proposed. The method utilizes a weight vector to indicate the importance of image gradients, and the weight vector is normalized in a range between 0 and 1. The NWTV method can perform well in images with sparse or dense gradients. The performance of NWTV has been compared with several state-of-the-art image deblurring methods including MPTV, TV-ADMM, LO-IG and INSR. Experimental results demonstrate that the proposed method achieves comparative or better performance.
Global Feature Extraction Graph Neural Networks for Session Recommendation

The recommender system based on session sequence is mainly used for the recommendation task when the user information is scarce. Usually, the user session is expressed as an ordered sequence of items. However, learning user preferences from ordered sequences is a complex problem in deep learning based recommender system. This paper proposes a novel model, the Global Feature Extraction Graph Neural Network for Session Recommendation (GFE-GNN), which transforms the session sequence from sequential representation to graphical representation. While obtaining the sequence order, the global graph feature extraction is used to obtain the potential sequence of items, so as to learn the complex preferences of users. Finally, the problem of predicting the next item of the session is transformed into a graph classification problem through the aggregation layer. Experimental results on real datasets show that GFE-GNN is superior to the most advanced session recommendation methods.
Exploring the “Trinity” Model of Training Artificial Intelligence Talents

With the deepening globalization of science and technology, the importance of artificial intelligence technology has become increasingly prominent, the competition for talents has become increasingly fierce, and many countries have made the cultivation of talents a vital force in promoting the development of artificial intelligence. However, the current model of cultivating intelligent talents is outdated, with problems such as weak discipline construction, single-course training, and lack of practical experience. In the pursuit of cultivating talents with the overall development of knowledge, ability, and quality, the “trinity” training model based on artificial intelligence is built to provide high-level, open, and complex, intelligent talents for economic and social development.
Research on Unmanned helicopter Path Panning Based on Improved Wolf Pack Algorithm

Given the problem of unmanned helicopter (UH) path planning in complex mountain environment, an improved wolf pack algorithm has been proposed. Firstly, the mathematical model of three-dimensional environment is established and the track length, flight altitude and collision index are introduced into the fitness function. Then the traditional wolf pack algorithm is described, and the threat heuristic factor is introduced as the heuristic information to form part of the particle position update, which is used to guide the artificial wolf search and enhance the effectiveness and pertinence of particle search process. Then the adaptive step size is added to method to adjust global and local search capability of the algorithm. Finally, the smooth optimal path is fitted by B-spline interpolation. The simulation results proves that the improved algorithm can avoid falling into local optimization, shorten the search time and get the global optimal path faster comparing with the traditional wolf pack algorithm, which show the effectiveness of the improved algorithm
The Use of AI in the Elderly Care Industry in China

The population aging in China has deepened rapidly in recent years. The most critical factor affecting elderly care is the labor problem with the population aging. Lack of labor poses a severe challenge to government governance. The overall labor force is insufficient, rural labor flows to cities, and the elderly become “intellectual blindness” to a certain extent, making the government face difficulties in population policy, Rural Revitalization, and data application. “Artificial intelligence” is closely related to human social life. The emergence of new things such as AI robots, AI terminals, and AI intelligent networks profoundly affects our daily lives. In such a background, intelligent elderly care entered the public view and attracted attention from all walks of life. Applying artificial intelligence to the elderly care industry can alleviate the shortage of elderly service human resources. The Chinese government is expected to encourage the R&D of artificial intelligence in elderly care services and build an artificial intelligence network to make the application of artificial intelligence in the elderly care industry more rapid and sustainable.
Static Fuzzy Bag-of-Words: Exploring Static Universe Matrices for Sentence Embeddings

Vector semantics has slightly become a key tool for natural language processing, especially concerning text analysis. This kind of vector representation is usually encoded through embeddings that can be used to encode semantic information at different levels of granularity. In fact, through the years, not only models for word embeddings have been developed but also for sentence and documents. With this work, we address sentence embeddings, in particular the non-parametric ones, which offer a good trade-off between performance and inference speed. We present Static Fuzzy Bag-of-Word (SFBoW) model, a refinement of the Fuzzy Bag-of-Words approach yielding fixed-dimension sentence embeddings. We targeted fixed-size embeddings to promote caching a re-usability, speeding the inference of a system that relies on our model. In this paper, we explore various approaches for the construction of a static universe matrix, fundamental to make the sentence embeddings of fixed size. To show the validity of our approach, we benchmarked our model on a semantic similarity task, obtaining competitive performances.
Arabic Anaphora Resolution System Using New Features: Pronominal and Verbal Cases

Anaphoric resolution is one of the major problems in natural language processing (NLP), since this linguistic phenomenon is very answered in natural languages, especially in Arabic; therefore, a resolution mechanism is needed in several applications of NLP. The resolution process consists in establishing the link between anaphoric entities and their referents in the text. This research has two main objectives—the first one is to implement a resolution system dedicated to pronominal and verbal anaphoras in the Arabic language (called A 3T) based on a rule-based approach combining rules and statistical features to identify the referent. The second objective is to create a specialized corpus for Arabic anaphora resolution which we’ve named A 3C, in order to fill the gap of lack of resources. Using the AnATAr as a test corpus, our A 3T system obtains an accuracy of 83.19% for pronominal anaphora and 57.23% for verbal anaphora.
A Commonsense-Enhanced Document-Grounded Conversational Agent: A Case Study on Task-Based Dialogue

This paper argues that future dialogue systems must retrieve relevant information from multiple structured and unstructured data sources in order to generate natural and informative responses as well as exhibit commonsense capabilities and flexibility in dialogue management. To this end, we firstly review recent methods in document-grounded dialogue systems (DGDS) and commonsense-enhanced dialogue systems and then demonstrate how these techniques can be combined in a unified, commonsense-enhanced document-grounded dialogue system (CDGDS). As a case study, we use the Task2Dial dataset, a newly collected dataset which contains instructional conversations between an information giver (IG) and information follower (IF) in the cooking domain. We then propose a novel architecture for commonsense-enhanced document-grounded conversational agents, demonstrating how to incorporate various sources to synergistically achieve new capabilities in dialogue systems. Finally, we discuss the implications of our work for future research in this area.
ITAcotron 2: The Power of Transfer Learning in Expressive TTS Synthesis

A text-to-speech (TTS) synthesiser has to generate intelligible and natural speech while modelling linguistic and paralinguistic components characterising human voice. In this work, we present ITAcotron 2, an Italian TTS synthesiser able to generate speech in several voices. In its development, we explored the power of transfer learning by iteratively fine-tuning an English Tacotron 2 spectrogram predictor on different Italian data sets. Moreover, we introduced a conditioning strategy to enable ITAcotron 2 to generate new speech in the voice of a variety of speakers. To do so, we examined the zero-shot behaviour of a speaker encoder architecture, previously trained to accomplish a speaker verification task with English speakers, to represent Italian speakers’ voiceprints. We asked 70 volunteers to evaluate intelligibility, naturalness, and similarity between synthesised voices and real speech from target speakers. Our model achieved a MOS score of 4.15 in intelligibility, 3.32 in naturalness, and 3.45 in speaker similarity. These results showed the successful adaptation of the refined system to the new language and its ability to synthesise novel speech in the voice of several speakers.
BloomQDE: Leveraging Bloom’s Taxonomy for Question Difficulty Estimation

Current question answering systems often focus on providing a simple entity or short sentence as an answer. By gaining confidence in information retrieval systems, users start to ask more complex questions that require sophisticated answers, such as reasoning chains. However, no research has been carried out yet to determine how exhaustive an answer should be. We combine Bloom’s learner’s levels of understanding with question difficulty classification. Therefore, we heuristically determine the threshold within Bloom’s taxonomy that separates question types into simple and complex. Moreover, we extract keywords from the taxonomy within question datasets and categorize questions accordingly. Then, we train a word n-gram multi-layer perceptron (MLP) and an LSTM model with syntactic features. The results are further improved by applying a genetic algorithm for parameter tuning.
A Comparative Study on Language Models for Dravidian Languages

We train embeddings for four Dravidian languages, a family of languages spoken by the people of South India. The embeddings are trained using the latest deep learning language models, to successfully encode semantic properties of words. We demonstrate the effect of vocabulary size on word similarity and model performance. We evaluate our models on the downstream task of text classification and small custom similarity tasks. Our best model attains accuracy on par with the current state of the art while being only a fraction of its size. Our models are released on the popular open-source platform HuggingFace. We hope that by publicly releasing our trained models, we will help in accelerating research and easing the effort involved in training embeddings for downstream tasks.
The Mechanism of Cloud Computing Influencing Accounting Informatization

Cloud computing is a modern information analysis and processing model born with the overall level of the world economy and the rapid development of Internet information technology. Cloud computing is the integration and commercialization of data information distributed processing, parallel processing and grid computing. The existence of cloud computing is not a new network foundation, but a network application concept. The role of cloud computing is actually a combination of computing, services and applications for public use of software. The application of cloud computing to accounting information system will further promote the openness and sharing of information resources. Enterprises promote the development of accounting informatization, actively explore and build accounting informatization and management platform with cloud computing technology as the core, and promote the construction of accounting informatization into a new stage of development. This paper analyzes the mechanism of cloud computing affecting accounting information, constructs the cloud computing accounting information system, establishes the cloud computing accounting information construction mode, analyzes the mechanism of cloud computing affecting accounting information disclosure, and puts forward the strategy of accounting information construction under cloud computing, in order to provide useful reference for accounting information construction.
Fusion of Dual Neighborhood Information of Knowledge Graph for Recommendation

In actual recommendation scenarios, researchers usually use auxiliary information to improve the performance of the recommendation system. The knowledge graph represents the current trend of recording information in the form of entity relationships, and the comprehensive degree of information extraction is directly proportional to the recommendation performance. In order to enhance the ability to extract information from the knowledge graph and reduce the noise introduced, this paper proposes a recommendation algorithm based on fusion of dual neighborhood information of knowledge graph. The algorithm uses knowledge graphs to disseminate and extract potential features of items, and characterize users' preferences for items. When extracting neighborhood information, by combining low-order neighborhoods and multi-hop neighborhoods, the divergence of the recommendation effect can be ensured, and the neighborhood information can be extracted more accurately while reducing noise. At the same time, by introducing edge weight regularization, additional supervision signals are provided for learning the edge scoring function, which can help the recommender system to obtain better generalization ability. A large number of experiments have been conducted on three real data sets, and the method proposed in this paper is significantly better than existing methods in recommendation performance. Especially on the Book-Crossing data set, all evaluation indicators exceeded the comparison algorithm, and Recall@10, Recall@50, Recall@100 even increased by more than 20.0%. These results prove the correctness and effectiveness of the proposed method for the knowledge graph recommendation algorithm.
A Strategy to Lead with Multiple Dependencies in a Coding Branch Structure: A Case Study with Mobile Device Production

The productions of smartphones increased in recent years, and software manufacturers need to increase their production management capacity in code versioning in the same proportion. The models explored in the literature use product derivation methods with the highest occurrence of using a software line model, combining it with only one technique called clone-and-own. This article presents the uTrack, a strategy to lead with multiple dependencies in a coding branch structure: a case study with mobile device production. The uTrack check if a set of CLs from the base Product Branch is already propagated to the derived Product branch. We compare our solution with a previous manual method. Comparing the performance time of both approaches, it appears that 90% of the participants were faster when using uTrack. Regarding usability, 70% of the participants considered the UTrack very easy, while the manual method was considered very easy by only 20% of the participants. uTrack is a promising tool for tracking CLs at different branch levels in software mobile products lines.
Amplifying Human Content Expertise with Real-World Machine-Learning Workflows

Human-led content classification and enrichment have long been the most impactful yet most expensive form of content workflow operations. Large content library owners often find that they have irreplaceable content expertise concentrated in only a few contributors. They become one of the critical gating factors in effective library content utilization. — Recent advancements and productization of innovative AI technology empowers accumulated human expertise and liberate teams from tedious manual activities, enabling much higher productivity and creativity. By applying the latest AI Computer Vision, Natural Language Processing, Machine Learning, and Video Analytics, a content library can be quickly transformed from a pile of ingested media and tape files to rich content that is fully annotated, searchable, and enhanced for efficient user consumption. — Augmenting the content processing workflows in commonly used tools such as a Media Asset Management system with AI functionality brings an immediate benefit to the end user and abstracts the AI technology complexity. — This paper provides an overview of the AI capabilities applicable to the Media and Entertainment industry. It outlines challenges introduced by the new technology capabilities and best practices to overcome them. — In this paper, we take the user through the approach, steps, and processes of taking a timeconsuming, novel content application task usually performed by a human expert, adapt that task to an AI challenge, build and deploy the necessary ML model, then apply it to content as a part of ongoing content operations using familiar asset management systems.