Reviewerly/exHarmony huggingface dataset:
The Evaluation of 5G technology from Sentiment Analysis Perspective in Twitter

Sentiment analysis is the action of explaining the meaning of emotions such as positive, negative, or neutral through various text mining methods and materials. Thus, companies can both focus on target customers and prevent customer churn by taking into account their current customers' opinions about their products with the help of sentiment analysis. In this work, we concentrate on to observe the perception of 5G technology using various machine learning and deep learning algorithms. For this purpose, naive Bayes, support vector machine, and k-nearest neighbour methods are employed as conventional machine learning algorithms. In addition, Recurrent Neural Networks and Long Short-Term Memory Networks as deep learning methodologies are utilized to observe the effect of deep learning models. In order to show the performance of proposed model, 58,965 released tweets with 5G hashtag are gathered from Twitter as English through customized crawler. Experiment results demonstrate the superior classification success of recurrent neural networks in terms of the perception of sentiments of user comments.
Generative adversarial networks and faster-region convolutional neural networks based object detection in X-ray baggage security imagery

X-ray images have now been widely used in public transportation security. However, the ever-increasing number of dangerous object types, along with the complexity of the baggage's contents, makes it unsuitable for clearly displaying various baggages and hazards on X-ray images obtained directly from the inspection system. This article aims to improve the detection of dangerous objects by proposing a data augmentation method for enriching the X-ray prohibited item images using a Generative Adversarial Networks(GANs) based approach. Using the improved GANs model, a new X-ray images were generated with better quality and diversity, and the performance of the proposed method was evaluated using FID(Fréchet Inception Distance) score and Faster R-CNN (Region-based Convolutional Neural Network). Experimental results show that the new augmented X-ray security inspection image dataset can improve the detector performance. If other representative training datasets are utilized, we believe that our methodology could aid in the detection of other kinds of threat objects.
D-PRESENT: A Lightweight Block Cipher with Dynamic Key-Dependent Substitution Boxes

The need of appropriate cryptographic solution into the embedded applications is one of the major concerns of IoT developers. The small devices used in this application have low computation power, small memory, and limited power supply. Hence, the conventional cryptographic primitives are not suited. Among many lightweight algorithms, PRESENT is one of the two recognized by regulatory body of cryptography worldwide fitted for small devices. PRESENT is an example of a Substitution-Permutation Network (SPN) that consists 31 rounds. In each round it performs three operations; add round key, substitution and permutation. For substitution operation, the S-box used in both Substitution layer and Key scheduling of PRESENT is static in nature. Despite of its recognition, PRESENT is susceptible for offline analysis of an attacker due to a static S-box used in key scheduling and substitution layer. In the current study, D-PRESENT was developed to improve performance of PRESENT algorithm in terms of avalanche effect, execution time and throughput and eventually reduced susceptibility from any key cryptanalysis. The results showed that there is a performance improvement in D-PRESENT over PRESENT with 2.53% in terms of avalanche effect, 31.76 in terms of execution time and 47.06% in terms of throughput.
Navigation and Obstacle Avoidance System in Unknown Environment

Recently, drones have been used in many different applications such as search and rescue operations, extinguishing fires, and environment mapping. As the number of moving drones increases in the sky, the collisions risk increases. In this paper, we present a system model, prototype, and preliminary evaluation for UAV obstacle avoidance. The obstacle avoidance system prototype uses ultrasonic sensors for obstacle detection, S-BUS communication protocol for drone control, and Savitzky-Golay filter for data smoothing.
Question Answering system based on Knowledge Graph of Film Culture

The research and development of intelligent question answering system in today's society is more and more fierce, and it has more and more extensive application prospects. Different from the traditional question answering system which is more biased towards document retrieval, the question answering system based on knowledge graph can accurately identify the user's intention and give accurate answers. This article builds an intelligent Chinese question and answering system for the field of film culture, which can help users quickly and accurately query related issues in film culture. First of all, the knowledge graph of film culture is constructed, and Neo4j graph database is used to store data. Next, the naive Bayes model is used to classify user's problems. Finally, according to the user's intention and keywords, the questions are converted into knowledge graph query statements and the answers are returned after the database query.
Efficient Secure Neural Network Prediction Protocol Reducing Accuracy Degradation

Machine learning models inherently memorize significant amounts of information, and thus hiding not only prediction processes but also trained models, i.e., model obliviousness, is desirable in the cloud setting. Several works achieved model obliviousness with the MNIST dataset, but datasets that include complicated samples, e.g., CIFAR-10 and CIFAR-100, are also used in actual applications, such as face recognition. Secret sharing-based secure prediction for CIFAR-10 is difficult to achieve. When a deep layer architecture such as CNN is used, the calculation error when performing secret calculation becomes large and the accuracy deteriorates. In addition, if detailed calculations are performed to improve accuracy, a large amount of calculation is required. Therefore, even if the conventional method is applied to CNN as it is, good results as described in the paper cannot be obtained. In this paper, we propose two approaches to solve this problem. Firstly, we propose a new protocol named Batch-normalizedActivation that combines BatchNormalization and Activation. Since BatchNormalization includes real number operations, when performing secret calculation, parameters must be converted into integers, which causes a calculation error and decrease accuracy. By using our protocol, calculation errors can be eliminated, and accuracy degradation can be eliminated. Further, the processing is simplified, and the amount of calculation is reduced. Secondly, we explore a secret computation friendly and high accuracy architecture. Related works use a low-accuracy, simple architecture, but in reality, a high accuracy architecture should be used. Therefore, we also explored a high accuracy architecture for the CIFAR10 dataset. Our proposed protocol can compute prediction of CIFAR-10 within 15.05 seconds with 87.36% accuracy while providing model obliviousness.
Word Semantic Similarity Based on CiLin and Word2vec

This paper presents a method to calculate the semantic similarity with TongyiciCiLin and Word2vec. In the part of CiLin, the semantic similarity of words is calculated by using the distance of words as the main factor, the number of branches and the distance between branches as the fine-tuning parameters. In the part of Word2vec, this paper constructs a special Corpus based on movie review, and uses Word2vec model to calculate the semantic similarity of Chinese words. Then, the final semantic similarity is calculated by using the dynamic weighting strategy to fuse CiLin and Word2vec. The method makes full use of the semantic information of words in the knowledge base and Corpus. The experimental results show that the algorithm has better accuracy and more robust to domain sensitivity.
Domain-Specific Contextualized Embedding: A Systematic Literature Review

Word embedding has successfully resolved various Natural Language Processing (NLP) problems. Unfortunately, the method has a weakness in detecting polysemy and homonym. Those issues led to the emergence of a new approach, which is named contextualized embedding. Many researchers examined such embedding to resolve problems in various particular areas. However, the studies are published disparate and complex. To provide a more comprehensive overview of contextualized embedding research in specific domains, a Systematic Literature Review (SLR) was conducted. The SLR results show that research on domain-specific contextualized embedding pays more attention to solving NLP problems in the Healthcare domain, by the percentage of more than 65%, followed by the Academic & Research field and other areas. The popularity of the Healthcare domain is associated with the availability of abundant datasets, mostly in English. BERT is the most contextualized embedding models used for domain-specific tasks, followed by ELMo, and finally GPT-1, as well as XLNET. Almost all reviewed papers reported performance improvements by using domain-specific contextualized embedding in their proposed model. Contextualized embedding can resolve polysemy problems and reduces overfitting. Besides, many downstream tasks have proved the ease implementation of the embedding. The shortcomings of this embedding are the high requirements of computation resources, the long execution time, and the computation complexity. Domain-specific contextualized embedding has resolved many problems, mostly classification tasks (e.g., Question and Answering) and tagging tasks (e.g., Named Entity Recognition). The two evaluation methods for measuring the performance of domain-specific contextualized embedding are Intrinsic evaluation and Extrinsic evaluation.
Identifying and Tackling Key Challenges in Semantic Word Spotting

Semantic word spotting is an extension of the traditional word spotting approach that uses not only visual but also semantic information to determine the similarity between a word image and a given query. Current approaches in this area achieve a semantic retrieval by embedding word images into a textually trained semantic space. The related literature presents remarkable results regarding established metrics indicating that the task of semantic word image retrieval is solved. A closer look at the results reveals, however, that this is only partially the case. In this work, we identify and solve current key challenges for semantic word spotting. We analyze the published works in this field towards these challenges and show why they do not solve them. For this purpose, we demonstrate that the used embedding space from current methods contains strong artifacts influencing the retrieval task. Furthermore, we evaluate a more suitable and established embedding approach from Natural Language Processing for semantic word spotting. We also explain the challenges of mapping word images into a semantic embedding space and evaluate different architectures for this task. Thereby, we present a new architecture that outperforms current approaches in this area. In addition, we show that commonly used metrics are not suitable for evaluating a semantic retrieval and present a new evaluation metric for this task.
Formal Verification of Neural Networks?

Machine learning is a popular tool for building state of the art software systems. It is more and more used also in safety critical areas. This demands for verification techniques ensuring the safety and security of machine learning based solutions. However, we argue that the popularity of machine learning comes from the fact that no formal specification exists which renders traditional verification inappropriate. Instead, validation is typically demanded, but formalization of so far informal requirements is necessary to give formal evidence that the right system is build. Moreover, we present a recent technique that allows to check certain properties for an underlying recurrent neural network and which may be uses as a tool to identify whether the system learned is right.
Enhancing Code Based Zero-Knowledge Proofs Using Rank Metric

The advent of quantum computers is a threat to most currently deployed cryptographic primitives. Among these, zero-knowledge proofs play an important role, due to their numerous applications. The primitives and protocols presented in this work base their security on the difficulty of solving the Rank Syndrome Decoding (RSD) problem. This problem is believed to be hard even in the quantum model. We first present a perfectly binding commitment scheme. Using this scheme, we are able to build an interactive zero-knowledge proof to prove: the knowledge of a valid opening of a committed value, and that the valid openings of three committed values satisfy a given linear relation, and, more generally, any bitwise relation. With the above protocols it becomes possible to prove the relation of two committed values for an arbitrary circuit, with quasi-linear communication complexity and a soundness error of 2/3. To our knowledge, this is the first quantum resistant zero-knowledge protocol for arbitrary circuits based on the RSD problem. An important contribution of this work is the selection of a set of parameters, and an a full implementation, both for our proposal in the rank metric and for the original LPN based one by Jain et al. in the Hamming metric, from which we took the inspiration. Beside demonstrating the practicality of both constructions, we provide evidence of the convenience of rank metric, by reporting performance benchmarks and a detailed comparison.
Identifying Semantic Outliers of Source Code Artifacts and Their Application to Software Architecture Recovery

Understanding software architecture is essential to software maintenance. There has been much effort to derive software architecture views from source code artifacts. Typically, along with structural information, the semantic information derived from an identifier name and comments are helpful. However, because code vocabulary choice depends on a developer's subjective decision, some source code may have semantically low text quality, leading to an inaccurate architecture recovery. This paper aims to improve the architecture recovery of a software system by identifying and removing the semantic outliers of source code artifacts. Accordingly, we propose a novel measure Conceptual Conformity (CC), which computes the similarity between two latent topic distributions obtained from both the source code and its package. We use CC to identify source code that is not relevant to the package's semantic context and define it as a semantic outlier. Because the semantic outliers may cause inaccurate architecture recovery, we remove them during the recovery process. We apply our approach to three open-source projects. The results demonstrate that, for projects with low recovery performance, removing outliers leads to higher recovery accuracy.
Creating Hardware Component Knowledge Bases with Training Data Generation and Multi-task Learning

Hardware component databases are vital resources in designing embedded systems. Since creating these databases requires hundreds of thousands of hours of manual data entry, they are proprietary, limited in the data they provide, and have random data entry errors. We present a machine learning based approach for creating hardware component databases directly from datasheets. Extracting data directly from datasheets is challenging because: (1) the data is relational in nature and relies on non-local context, (2) the documents are filled with technical jargon, and (3) the datasheets are PDFs, a format that decouples visual locality from locality in the document. Addressing this complexity has traditionally relied on human input, making it costly to scale. Our approach uses a rich data model, weak supervision, data augmentation, and multi-task learning to create these knowledge bases in a matter of days. We evaluate the approach on datasheets of three types of components and achieve an average quality of 77 F1 points—quality comparable to existing human-curated knowledge bases. We perform application studies that demonstrate the extraction of multiple data modalities including numerical properties and images. We show how different sources of supervision such as heuristics and human labels have distinct advantages that can be utilized together to improve knowledge base quality. Finally, we present a case study to show how this approach changes the way practitioners create hardware component knowledge bases.
The change of teachers’ role in teaching under the environment of “Artificial Intelligence +”

Artificial Intelligence enabled classroom teaching is the core driving force of classroom reform in the era of information 2.0. In this process, how to understand the conflict between Artificial Intelligence and teacher’s role and position the teacher’s role in the new technology environment is an important basis for leading the deep integration of Artificial Intelligence and education and teaching. In this context, “man-machine collaboration” will be an important trend of future education. Artificial intelligence technology, with its application in the field of education of intelligent platform, intelligent tools, and intelligent services, has brought challenges and dilemmas to teachers’ professional roles. As an assistant teacher and analyst in classroom teaching, artificial intelligence has greatly contributed to the high efficiency, accuracy, and diversity of teaching. Teachers should be the designers, decision makers and educators of classroom teaching, so as to promote the all-round development of students and make them the talents needed by the contemporary society.
Application of geographic information system and remote sensing in heavy metal assessment

Remote sensing techniques are useful in the detection of the sources of pollution and avoid possible environmental problems. They are used to detect heavy metal (HM) pollution from natural sources as well as from poor agricultural practices. Detection of HM pollution by remote sensing helps us to modify agricultural and other practices to avoid the transfer and mobilization of HMs to other parts of the environment. Remote sensing can successfully map the background level of soil HMs and accurately represent polluted areas. HMs are the cause of critical environmental problems worldwide. Traditional methods to determine HM concentration are time consuming and expensive as they rely on field sampling.
Particle Filter based Single Shot MultiBox Detector for Human Moving Prediction

Moving object tracking has become a center of attention for computer vision researchers. It is quite challenging to track a moving object correctly, especially when the object has occlusion, changes in illumination, unexpected movements, and arbitrary poses. To enhance the accuracy of the moving object detector, we proposed SSD (Single Shot Multibox Detector) in addition to PF (Particle Filter) to provide prediction of moving human. Performance evaluation was done with the comparison to the previously proposed HOG-SVM as a detector. The proposed system has been successfully tested in two videos. PF based SSD with 100 particles performs well, with RMSE 7.44 and 91.24 effective particle. The results show that the addition of SSD in measurement process could enhance the PF's performance to track moving human. The results have also shown that the proposed method was successfully implemented in combination with a specific color detection to track a specific human object.
Generalized Isolation Forest: Some Theory and More Applications Extended Abstract

Isolation Forest is a popular outlier detection algorithm that isolates outlier observations from regular observations by building multiple random decision trees. Multiple extensions enhance the original Isolation Forest algorithm including the Extended Isolation Forest which allows for non-rectangular splits and the SCiForest which improves the fitting of individual trees. All these approaches rate the outlierness of an observation by its average path-length. However, we find a lack of theoretical explanation on why these isolation-based algorithms offer such good practical performance. In this paper, we present a theoretical framework that describes the effectiveness of isolation-based approaches from a distributional viewpoint. We show that these algorithms fit a mixture of distributions, where the average path length of an observation can be viewed as a (somewhat crude) approximation of the mixture coefficient. Using this framework, we derive the Generalized Isolation Forest (GIF) which also trains random trees, but combining them moves beyond using the average path-length. In an extensive evaluation of over 350, 000 experiments, we show that GIF outperforms the other methods on a variety of datasets while having comparable runtime.
CPPM

We present a novel chi-squared progressive photon mapping algorithm (CPPM) that constructs an estimator by controlling the bandwidth to obtain superior image quality. Our estimator has parametric statistical advantages over prior nonparametric methods. First, we show that when a probability density function of the photon distribution is subject to uniform distribution, the radiance estimation is unbiased under certain assumptions. Next, the local photon distribution is evaluated via a chi-squared test to determine whether the photons follow the hypothesized distribution (uniform distribution) or not. If the statistical test deems that the photons inside the bandwidth are uniformly distributed, bandwidth reduction should be suspended. Finally, we present a pipeline with a bandwidth retention and conditional reduction scheme according to the test results. This pipeline not only accumulates sufficient photons for a reliable chi-squared test, but also guarantees that the estimate converges to the correct solution under our assumptions. We evaluate our method on various benchmarks and observe significant improvement in the running time and rendering quality in terms of mean squared error over prior progressive photon mapping methods.
Using Machine Learning to Predict Short-Term Movements of the Bitcoin Market

We analyze the predictability of the bitcoin market across prediction horizons ranging from 1 to 60 min. In doing so, we test various machine learning models and find that, while all models outperform a random classifier, recurrent neural networks and gradient boosting classifiers are especially well-suited for the examined prediction tasks. We use a comprehensive feature set, including technical, blockchain-based, sentiment-/interest-based, and asset-based features. Our results show that technical features remain most relevant for most methods, followed by selected blockchain-based and interest-based features. Additionally, we find that predictability increases for longer prediction horizons. Although a quantile-based long-short trading strategy generates monthly returns of up to 31% before transaction costs, it leads to negative returns after taking transaction costs into account due to the particularly short holding periods.
Stability Analysis of Smooth Positive Fuzzy Systems

The paper addresses the stability problem of positive systems represented by smooth fuzzy models. It provides the sufficient conditions of stability for such systems through the Banach Contraction Theorem. The Illustrative example shows that through the employment of the smooth compositions, the fuzzy model will have higher tolerance to the noises and disturbances for returning back to the stability, rather than the classical fuzzy models.