Reviewerly/exHarmony huggingface dataset:
DVKCM: Knowledge-guided Conversation Generation with Dynamic Vocabulary

Knowledge-guided conversation models, whose inputs are current input sentence with its background knowledge, make the generation of responses more informative and meaningful. Existing methods assume that words in responses come from the vocabulary of the whole corpus. However, for specific input and knowledge, only a small vocabulary is useful in prediction and other words lead to uncorrelated noise. In this paper, we propose a Dynamic Vocabulary based Knowledge-guided Conversation Model (DVKCM). Inspired by dynamic vocabulary mechanism, DVKCM adopts the vocabulary construction module to allocate the sentence-level vocabulary which relates to the input sentence and background knowledge, and then only uses the small vocabulary to execute the decoding part. Through the sentence-level vocabulary mechanism, we reduce the generation of noise effectively. Experiments on both automatic and human evaluation verify the performance of our model compared with previous models. Moreover, we find that dynamic vocabulary can be applied to other conversation models to improve their performance.
An Estimation Method of Attributed Network Representation Spatial Dimension

Network representation learning has been demonstrated to be helpful in network analysis and achieves great success in recent years. However, the spatial dimension, which is a crucial parameter of representation space, rely on expert experience for manual configuration, lacking of theoretical model support. To address this issue, we propose a novel method called EMANRD for estimating the dimension of representation space that meets the goal of network representation learning based on the original network data. Specifically, EMANRD mixedly integrate topological structure and attribute information into a metrizable space and estimate the ideal representation spatial dimension by designing a ideal representation space generating model. Experimental results on three real-world datasets demonstrate that EMANRD can effectively estimate the ideal dimension of representation space.
Blind Quality Assessment Method to Evaluate Cloud Removal Performance of Aerial Image

People often use image-inpainting-based methods to remove cloud from aerial images, but it lacks a targeted quantitative evaluator to assess the removal result. In order to solve this issue to some extent, we propose an assessment method that combines ranking learning and regression learning. The main framework consists of several CNN down-sampling steps layer-by-layer. Firstly, to learn the distortion features of the inpainted image, an image classification task is conducted to train the network. Secondly, we use the proposed joint loss function to regress the features into the FR-IQA evaluator SSIM by retraining the whole network. Through end-to-end training, the proposed model learns a priori of the aerial image and realizes the approximation of SSIM. Experimental results demonstrate that our method has achieved better performance on SROCC, RMSE and PLCC in comparison with other blind image quality assessment methods.
Quick Projection Mapping on Moving Object in the Manual Assembly Guidance

In the modern assembly, manual assembly is one of the essential works in the factory. By the help of the robots and any other automatic equipments, modern assembly is quite important to improve the efficiency of the necessary manual assembly. Projection is one of the most significant assisted method to solve the assemble problem. This paper presents a quick projection method for the slightly moving object in the manual assembly guidance. By using the closed-loop alignment approach, the proposed method is low-latency and auto tracking to the target object. We designed a tracking and matching system to realize the projecting on the target object. The result showed that the precious of the projecting information can satisfy the needs which the workers followed the projecting guidance to do the assembly. This system also released workers pressure because of the wrong operation and made them comfortable to do their works in the factory.
GOOSE: An Object-oriented Search Algorithm with Graph-based Database

NoSQL database especially key-value store is employed in most search engine. Being one of many NoSQL databases, Graph Database is tailored to store objects and bidirectional relationships with attributes as node and graph so that it can better represent the real world scenario. On the other hand, Object-oriented paradigm sparks as a convenient approach to programming as it reflects the reality as a model of classes. However, both graph database and object-oriented paradigm are seldom addressed and utilized in search algorithms. This signifies an opportunity to discover an alternative for search engine using graph database and object-oriented search algorithm that imitate search capability of a human brain. In this paper, we present a graph-based object-oriented search algorithm named GOOSE that is designed for internet search purpose.
Performance Analysis of the KMM Digital Image Compression Process

This paper analyses the effect of the K-Modulus-Method (KMM), especially the 5-Modulus-Method (5MM) on the performance of the lossy digital image compression process. Prior to any compression operation, the digital image is transformed in a fashion that all its pixels become divisible by K. This is followed by dividing each pixel value by K, thus reducing the possible digital image values by a factor of K. After that, a modified version of the standard JPEG process is performed on the image. The quality of the method is evaluated using standard measures, such as the Compression Ratio (CR), Peak signal to Noise ratio (PSNR) and the Structural similarity index (SSIM).
Semantic Segmentation of Street Scenes Using a Hybridisation between 3D Features and CNNs

Semantic segmentation (SS) provides pixel-wise label maps explaining the content of the images. To alleviate the accuracy of the labels, this paper proposes SS schemes based on CNNs and 3D features. Mainly, the CNNs receive multidimensional hybrid input arrays of 3D and color features, from which valuable context-aware information could be extracted. The suggested SS method accepts a single CNN working with all the features concomitantly, or an ensemble of CNNs driven by subsets of 3D features. The role of CNNs is discussed both for feature extraction and classification. Thus, the final label map is directly generated by CNNs or is produced by Random Forest (RF) using features provided by the last neural layers. The approach is exemplified for an application assisting persons with visual deficiencies. The experiments show increased performance for the CNNs processing the whole set of 3D features. Also, the CNNs driven by 3D features (in block or subsets) offer refined context-aware features for RF.
Construction of Classification Model for Product Quality Incidents Based on Dual Membership Fuzzy Support Vector Machine

The incidents causing deaths and injuries only account for a small part of all the information related to product quality and safety. The vast majority of such information is about the damage and loss of property. Given unbalanced data distribution, a classification model based on dual membership fuzzy support vector machine (D-FSVM) was first established, followed by the steps for using D-FSVM to classify the product quality incidents. The cases regarding children's toys were selected as the experimental subject. The validity and feasibility of the model were finally verified by experimental simulation.
Prime Numbers and Its Applications in Security: Case Study

Prime Numbers are the major building blocks in integer universe. Prime numbers play an important role in number theory and cryptography. With this unique nature of prime number, it is mainly used in security. Many security algorithms have used prime numbers because of their uniqueness. In this paper, we have discussed the importance of prime numbers and their application.
Model-Based Design of A Real-time Context-Aware Speech Enhancement System on an FPGA-SoC

Modern Implementation platforms are suitable for realizing complex adaptive systems that require the system to adjust its behavior to satisfy variable design goals. A prominent example is context-aware systems that are capable of automatically adapting their operations according to the dynamic system context. This work introduces a model-based design flow for a context-aware FPGA-SoC-based system in the speech enhancement domain. The proposed design flow implements the adaptive context-awareness by exploiting the runtime reconfiguration feature of the state-of-the-art FPGA-SoC devices. By employing the proposed design flow and architecture, a context-aware speech enhancement application can be implemented in such a way that it is able to change its own processing algorithm to meet runtime goals and can lead to the area reduction of the FPGA by using one hardware-accelerated algorithm at a time.
Hybrid Meet-in-the-Middle Attacks for the Isogeny Path-Finding Problem

Isogeny-based cryptography has received attention as a candidate of post-quantum cryptography (PQC), and its security is based on the hardness of isogeny problems. The idea of meet-in-the-middle (MITM) is a bidirectional search for a collision, and it gives a powerful tool in cryptanalysis. In this paper, we propose hybrid approaches of MITM for solving the isogeny path-finding problem. Specifically, we first build part of trees of isogenies in a conventional way, and we then search a pair of isogenous curves of prime power degree by the algebraic approach using modular polynomials, proposed by Takahashi et al.¥! at MathCrypt 2019. Our hybrid approaches relax the requirements of sizes of search tables in MITM, and they also enable us to parallelize the part of algebraic search perfectly and easily. Here we show experimental results of our hybrid approaches to discuss a comparison with pure MITM approaches from a perspective of performance and sizes of search tables.
The Research on Peculiarities of the Blockchain Implementation in Global Regulatory Legislations

The presented research provides the ethical examination of the blockchain-driven systems (e.g., Social Credit System) for social engineering with the additional analysis of its impact on the Society (efficiency) and the modifications, which the System brings to it (effectiveness). SCS from the Western perspective is digital dystopia (de Freytas-Tamura 2017). For foreign parties can use SCS as the instrument of the trust gaining and vice versa- China increases its face of the open economy by the SCS. However, behind the scope of the observation of the current research focus is the ethics of the blockchain- enabled platforms for the control of the credibility of the behavior of the parties as the System, which really makes discrimination without any appeal opportunity. Moreover, the System immediately harms the reputation of the evaluated party via blackclisting regardless the potential mistakes in these conclusions. The results appeal via the manipulations, making the errors, is possible only in judiciary procedure with rare reevaluation of the rank- SCS destroys the trust in the Society- these projects were preliminary elaborated for the control of minorities (e.g., the Sinczian and Uighur autonomous regions- largest minorities centers in PRC). Is the Blockchain in these realities blessing or a curse?
Automatically Finding the Number of Clusters for Large Datasets based on Coresets

Finding the number of clusters present in a dataset is fundamental and also important in data cluster analysis. In this paper, we investigate a visual-based method for automatically determining the number of clusters. In particular, we present a modification of the ProTraS algorithm to sample datasets. Our method then applies an "efficient formulation" of the iVAT algorithm to generate a grayscale image presenting the clustering tendency of the obtained sample data. The image is then transformed into a binary one using the Otsu's threshold. Finally, the Watershed segmentation algorithm is applied to the binary image to obtain the number of clusters. Experiments are conducted on both clustering benchmarks as well as datasets generated by the HAWKS model, a new data generator that has been recently introduced for clustering. Results are given to show the effectiveness of the proposed method.
About the New Handbook on Theory, Practice and Metrology of Audiovisual Systems

The article describes the new handbook printed in 2019 and devoted to the theoretical fundamentals and practical implementations of digital audiovisual systems as well as principles and examples of its metrological support. The main parts of the book are briefly presented.
Memory Attention Neural Network for Multi-domain Dialogue State Tracking

In a task-oriented dialogue system, the dialogue state tracker aims to generate a structured summary (domain-slot-value triples) over the whole dialogue utterance. However, existing approaches generally fail to make good use of pre-defined ontologies. In this paper, we propose a novel Memory Attention State Tracker that considers ontologies as prior knowledge and utilizes Memory Network to store such information. Our model is composed of an utterance encoder, an attention-based query generator, a slot gate classifier, and ontology Memory Networks for every domain-slot pair. To make a fair comparison with previous approaches, we also conduct experiments with RNN instead of pre-trained BERT as the encoder. Empirical results show that our model achieves a compatible joint accuracy on MultiWoz 2.0 dataset and MultiWoz 2.1 dataset.
Does Supervised Learning of Sentence Candidates Produce the Best Extractive Summaries?

In this work multi-document, extractive summaries have been obtained using supervised learning algorithms in a well-known dataset (DUC 2002); the methodology has three steps: the pre-processing step, which filters irrelevant words and reduces vocabulary using stemming; the representation step, which transforms sentences into vectors; and the classification step which selects sentences for the summary. Noting that the last step is crucial because it determines the relevance of each sentence according to the information included in the embeddings. We found that the classifiers performance is not related to the summary quality mainly classifier’s goal is not aligned to summarizer’s goal, as classifier is based on selecting whole sentences, while summarization is evaluated by n-grams, for example ROUGE-n, and therefore it is relevant while comparing performances between different works in the state of the art.
Transforming Product Catalogue Relational into Graph Database: a Performance Comparison

Due to the continuous expansion of telecom operators (TOs) services and subscriber's base, a number of objects in TO product catalogue (PC) traditionally developed employing some relational database (RD) rapidly increase. This can result in degraded PC database performance in terms of reduced object visibility or slow query response time, which mandates a search for new types of databases that can cope with such challenges. A possible solution to these challenges is the transformation of RD into some graph database (GD), with visualisation and organisation capabilities that can cope with the increasing complexity of PC RDs. Hence, in this paper, a novel implementation concerning the transformation of a set of PC data and functions of the TO RD into ArangoDB GD is proposed. The proposed approach allows the upgrading of PC RD into a higher presentation layer through the implementation of the GD, which enables advanced data analysis and visualization. The developed solution shows that GD with appropriately structured queries, data and links between them, improves the visibility and simplifies the entire search process. Obtained results show that GD outperforms RD in terms of search query response time for different functions executed on an equal amount of database objects.
CASE-A Conditional Adaptive Scalable Expert Structure for Classification

It is always challenging to train the machine learning model, such as the neural network, that the overhead is too expensive to increase the training set. This paper proposes a new framework, Conditional Adaptive Scalable Expert (CASE), for the machine learning based classification problems. With a reflexive model and the novel blocking, resurrection and periodical reconstruction mechanism, multiple different types of naïve classifiers are grouped into an expert classifier. CASE has many features, such as explainable parameters, adaptive learning, inheritance from other classifiers, extensibility for the joining of new classifiers, etc. Experiments show that with 0.1% of the necessary training set for classical neural network training, our framework produces a relative good-enough (0.61% less accuracy) classifier. Such features illustrate that CASE is a practical framework with wide application potentials.
A Cross-Layer Connection Based Approach for Cross-Lingual Open Question Answering

Cross-lingual open domain question answering (Open-QA) has become an increasingly important topic. When training a monolingual model, it is often necessary to use a large number of labeled data for supervised training, which makes it difficult to real applications, especially for low-resource languages. Recently, thanks to multilingual BERT model, a new task, so called zero-shot cross-lingual QA has emerged in this field, i.e., training a model for a language rich in resources and directly testing in other languages. The existing problems in the current research include two main points. The one is in document retrieval stage, directly working multilingual pretraining model for similarity calculation will result in insufficient retrieval accuracy. The other is in the stage of answer extraction, the answers will involve different levels of abstraction related to retrieved documents, which needs deep exploration. This paper puts forward a cross-layer connection based approach for cross-lingual Open-QA. It consists of Match-Retrieval module and Connection-Extraction module. The matching network in the retrieval module makes heuristic adjustment and expansion on the learned features to improve the retrieval quality. In the answer extraction module, the reuse of deep semantic features is realized at the network structure level through cross-layer connection. Experimental results on a public cross-lingual Open-QA dataset show the superiority of our proposed approach over the state-of-the-art methods.
A Survey on Intelligence Tools for Data Analytics

The recent development in artificial intelligence is quite astounding in this decade. Especially, machine learning is one of the core subareas of AI. Also, ML field is an incessantly growing along with evolution and becomes a rise in its demand and importance. It transmogrified the way data is extracted, analyzed, and interpreted. Computers are trained to get in a self-training mode so that when new data is fed they can learn, grow, change, and develop themselves without explicit programming. It helps to make useful predictions that can guide better decisions in a real-life situation without human interference. Selection of ML tool is always a challenging task, since choosing an appropriate tool can end up saving time as well as making it faster and easier to provide any solution. This chapter provides a classification of various machine learning tools on the following aspects: for non-programmers, for model deployment, for Computer vision, natural language processing, and audio for reinforcement learning and data mining.
Semi‐supervised learning induced abnormal emotional tendency analysis in <scp>British</scp> culture

The abnormal emotional tendency analysis refers to the automatically detecting the minor negative sentiment from various emotions which are obtained from text, voice, image, or video. In this paper, we focus on detecting abnormal emotional tendency in English sentences on Twitter. In order to solve the issue that the text in Twitter is fragment and the associated emotions are imbalance, we extract proper features of Twitter to represent abnormal emotional tendency and input the extracted features into an improved self‐supervised reserved self‐training. Then, we optimize the termination condition by using a threshold method which is based on sentiment tendency analysis. The optimized termination condition cannot only preserve the merit that reserved self‐training can solve the imbalance emotions in Twitter corpus, but also can resist the overtraining. The experimental results show that the improved semi‐supervised reserved self‐training is effect.
An Efftcient Content Based Remote Sensing Image Retrieval Using Artiftcial Neural Network

Remote sensing is being used in different fields like agriculture, research etc.. Remote sensed images contains complex visual contents. This paper explains about the content based remote sensing image retrieval using ANN. In remote sensing method the sensors which will be fixed on an aircraft or satellite is used for capturing remote sensing images. Due to the increase in the use of remote sensing technology and also the number of satellites used, the volume of image dataset is increasing exponentially. Content Based Remote sensing Image Retrieval is used as to reduce the difficult in managing large volume of earth data.
UAV Collision Avoidance with Varying Trigger Time

This paper examines the effect that the selection of the different collision avoidance trigger time has on UAV safety and avoidance efficiency and finds the optimal trigger time for different cases. The trigger time indicates when a collision avoidance maneuver begins. An earlier trigger time is safer for the UAV to avoid all the obstacles but causes the UAV to deviate away from its intended path, while a delayed avoidance trigger action reduces overall path deviation but entails a higher risk of collision. Thus, the balance between safety and avoidance efficiency is important. Simulations for different mission scenarios show that by selecting specific avoidance trigger times, missed waypoints which are a result of the avoidance maneuver, could be reduced by over 40%. In addition, avoidance time and space required by the avoidance maneuver are also reduced, as compared to always starting the avoidance maneuver when the obstacle is first detected. Hence, selecting the avoidance trigger time can improve the performance of the UAV's avoidance maneuver, especially for real-time applications.