Reviewerly/exHarmony huggingface dataset:
Automated Data Labeling and Anomaly Detection Using Airborne Sound Analysis

Anomaly detection is facing the challenge of generating a maximum of information with a limited number of sensors in production machines and minimal effort of data analysis. Machine operators are often able to perceive changes acoustically and based on experience. In order to imitate this intelligent human capability, a systematic methodology has been developed in this work. Firstly, an introduction to this topic will be given and the acoustic sensor set up as well as the data preprocessing will be described. Secondly, an approach for automatic data labeling as an input for the intelligent anomaly detection will be presented. Using a model-based approach, anomaly detection is performed in the next step. This allows not only the detection of gradual and sudden process changes but also systematic problem solving. Finally, the anomaly detection will be validated for cutting processes.
Semantic Segmentation via Enhancing Context Information by Fusing Multiple High-Level Features

Semantic segmentation has been one of the most important research areas in computer vision. In semantic segmentation, both the context information and spatial information are important in semantic segmentation performance of a convolutional neural network (CNN) model. This study focuses on methods to optimize the context information. We proposed a structure consisting of multiple upsampling blocks and concatenated Dense Block(DB), enhancing the context information on the decoder by the fusion of multiple high-level features. Furthermore, we use Blur Pooling [1], which is a better downsampling method proposed by Richard Zhang, making the CNN more shift-invariant to an input image. Finally, we determined the drawback of using the cross entropy, which can result in poor small-object-detection performance, on semantic segmentation tasks. Furthermore, we boosted the performance of our model by applying the soft-IoU loss function. Finally, we test our model's performance using the CamVid dataset established. The experimental results show that our proposed approach can provide a better ability of integrating the context feature in the upsampling path. The mIoU result of our proposed model was 70.534% on the Camvid dataset, outperforming the 65.8% mIoU result of FC-DenseNet67, which is our baseline model, obtained from the study in [2].
Automatic Critical Mechanic Discovery Using Playtraces in Video Games

We present a new method of automatic critical mechanic discovery for video games using a combination of game description parsing and playtrace information. This method is applied to several games within the General Video Game Artificial Intelligence (GVG-AI) framework. In a user study, human-identified mechanics are compared against system-identified critical mechanics to verify alignment between humans and the system. The results of the study demonstrate that the new method is able to match humans with higher consistency than baseline. Our system is further validated by comparing MCTS agents augmented with critical mechanics and vanilla MCTS agents on 4 games from GVG-AI. Our new playtrace method shows a significant performance improvement over the baseline for all 4 tested games. The proposed method also shows either matched or improved performance over the old method, demonstrating that playtrace information is responsible for more complete critical mechanic discovery.
Wheeled Robot Path Planning in Natural Environment

In this work we present a method to obtain the obstacle-free path optimized under shortest travel time condition in a a priori known map, with velocity profile generation. The method is in fact a set of three submethods: the pathfinding algorithm (PA), the optimization algorithm (OA) and the velocity profile generation algorithm (VPGA). The PA is used as a subfunction for the VPGA genetic algorithm that searches for the PA parameters which give the time-optimal path. As the last step the VPGA algorithm is used to obtain the velocity profile for the robot. The velocity profile takes into consideration areas where the path is close to the obstacle. In those areas the robot velocity is reduced to make it less sensitive for errors that can cause a collision. The velocity profile is not computed in the GA fitness function due to computation cost. The solution fitness is approximated using extracted path characteristics, which approach is much more effective.
Stochastic One-Step Training for Feedforward Artificial Neural Networks

This paper studies the use and application of a fast method (non-iterative and instantaneous) for Feedforward Neural Networks training in which the weights of the hidden layer are assigned randomly, and the weights of the output layer are trained through a linear regression adjustment. The method solves two of the problems that are present in traditional training: training time and optimal structure. While traditional iterative training methods require long periods to train a single structure, the proposed method allows training a structure in a single step (not iterative). In this way, by scanning the number of neurons in the hidden layer, many structures are trained in a short time, and it is possible to obtain an optimal topology. A quality control criterion of the predictions is proposed based on the coefficient of determination that guarantees short times and an optimal number of hidden neurons to characterize a specific problem. The feasibility of the proposed method is tested by comparing its performance against building functions of the artificial neural networks toolbox in Matlab®, resulting superior in both approximation quality and training time. A rigorous study and analysis are performed for the regression of simulated data on two different surfaces with a specific noise and different topologies of the neural network. The resulting process time is at least 150 times shorter for proposed training than with the iterative training that Matlab uses, thus obtaining well-founded learning rules. A novel way of an amputated matrix is proposed that breaks the paradigm of the way multiple-output systems are trained and improves the quality of predictions with no detriment to training times.
Consistency and asymptotic normality of wavelet estimator in a nonparametric regression model

In this paper, the nonparametric regression model with repeated measurements based on ϕ-mixing errors is considered. The rth mean consistency, strong consistency, strong convergence rate, complete consistency and the asymptotic normality of the wavelet estimator are established under some mild conditions on moments and mixing coefficients.
Isomap Based on Cuckoo Search Algorithm with t-distribution Parameters

Isomap borrows the idea of geodesics which is from differential geometry, hoping data can keep geodesic distance on the manifold after transforming into low dimensional space mapping. Isomap takes advantage of Gradient descent or Stochastic gradient descent to solve the problem in the end. However, Isomap is prone to get trapped in local minimum, and the result obtained is always a local optimal solution. In order to solving such kind of problem, this article comes up with Cuckoo search to optimize the solutions about figuring out manifold distance.
A Trustworthy Evaluation System Based on Blockchain

With the development of the Internet, online shopping, online movies, online music and other online services are greatly convenient for people's life. However, due to the widespread false information in these websites, the interests of users and privacy are threatened. To address this problem, we designed a trustworthy evaluation system based on blockchain named TESB2. TESB2 uses blockchain technology to effectively protect the data and privacy of users. It effectively guides the evaluation behavior of users and ensures the fairness and credibility of evaluation information by combining the evaluation behavior of users with reputation rewards and punishments. And we innovatively propose a malicious user detection method by combining blockchain technology and machine learning algorithm. Finally, we showed TESB2 through the movie scenario, and its performance was satisfactory.
Ensure the Validity of Forensic Evidence by Using a Hash Function

A hash function is a mathematical function used to identify and authenticate the file data. Data corruption in forensic evidence will tamper the digital evidence and could mislead the investigation. To ensure information security, various identity and access management tools are necessary. Hashing plays a key role in such scenarios and can be used to check the integrity of the data. Ensuring the validity of such evidence becomes crucial. Validity can be performed at various stages, viz. collection, preservation, and at the time of analysis.
Fast Dynamic Density Outlier Detection Algorithm for Power Quality Disturbance Data

The existing anomaly detection methods have the problems of low accuracy and slow identification speed for outliers detection of high-dimensional power quality disturbance data with time sequence characteristics and large fluctuations. In order to solve the above problems, a fast dynamic density anomaly detection method for power quality disturbance data is proposed. The data set is divided into different time slices according to time, and only the changed data in different time slices are dynamically clustered, so that the data on the next time slice can get accurate clustering results with less time cost. The experimental results show that the proposed method not only ensures the accuracy of anomaly detection results, but also improves the time efficiency.
Quantum Enabled Private Recognition of Composite Signals in Genome and Proteins

We present a quantum enabled secure multiparty computation service able to perform private recognition of composite signals. This system is based on the generation and distribution of quantum oblivious keys. The engineered system is going to be tested in the Madrid Quantum Network.
Tile-o-Scope AR: An Augmented Reality Tabletop Image Labeling Game Toolkit

Crowdsourcing games involving image labeling tasks are commonly digital, played online, and have rules set by designers. In this work we explore the potential of tabletop image labeling games, incorporating physical elements, in-person community-based gameplay, and support for customizable rules. We developed an augmented reality game toolkit called Tile-o-Scope AR and conducted two studies. The first study demonstrates how the toolkit can facilitate in-person discussions through collaborative image labeling, and the toolkit’s potential adaptability to other games and applications. The second study, using three different activities designed for the toolkit, demonstrates the toolkit’s flexibility for creating customized experiences for audiences of different backgrounds.
Recommending Domain Specific Keywords for Twitter

Twitter has become the most popular social media in today’s world. More than 284 million users are online monthly, and 80% user accesses their twitter account through mobile. A tweet is limited to 140 characters, so it contains concise information about particulars. Due to its popularity and usage, near about 500 million tweets are sent per day that relates to different domains. This work focuses on recommending domain specific keywords for twitter. For this purpose, 10 domains are chosen as a sample. Then we apply Term Frequency-Inverse Document Frequency (TF-IDF) and Log likelihood methods and compared the keywords extracted from both against each domain to make our result much valuable. Furthermore, the categorization of keywords is made as noun and verb, and also finds out the sentiment words. At the end, a relevancy test is performed from five users. These keywords can be great value in clustering tweets data and can be used for identifying a user’s interest in any specific domain. Furthermore, these keywords are of the great asset for advertisement purpose.
A Fast Automated Model Selection Approach Based on Collaborative Knowledge

Great attention has been paid to data science in recent years. Besides data science experts, plenty of researchers from other domains are conducting data analysis as well because big data is becoming more easily accessible. However, for those non-expert researchers, it can be quite difficult to find suitable models to conduct their analysis tasks because of their lack of expertise and the existence of excessive models. In the meantime, existing model selection approaches rely too much on the content of data sets and take quite long time to make the selection, which makes these approaches inadequate to recommend models to non-experts online. In this paper, we present an efficient approach to conducting automated model selection efficiently based on analysis history and knowledge graph embeddings. Moreover, we introduce exterior features of data sets to enhance our approach as well as address the cold start issue. We conduct several experiments on competition data from Kaggle, a well-known online community of data researchers. Experimental results show that our approach can improve model selection efficiency dramatically and retain high accuracy as well.
Training Data Poisoning in ML-CAD: Backdooring DL-Based Lithographic Hotspot Detectors

Recent efforts to enhance computer-aided design (CAD) flows have seen the proliferation of machine learning (ML)-based techniques. However, despite achieving state-of-the-art performance in many domains, techniques, such as deep learning (DL) are susceptible to various adversarial attacks. In this work, we explore the threat posed by training data poisoning attacks where a malicious insider can try to insert backdoors into a deep neural network (DNN) used as part of the CAD flow. Using a case study on lithographic hotspot detection, we explore how an adversary can contaminate training data with specially crafted, yet meaningful, genuinely labeled, and design rule compliant poisoned clips. Our experiments show that very low poisoned/clean data ratio in training data is sufficient to backdoor the DNN; an adversary can “hide” specific hotspot clips at inference time by including a backdoor trigger shape in the input with ~100% success. This attack provides a novel way for adversaries to sabotage and disrupt the distributed design process. After finding that training data poisoning attacks are feasible and stealthy, we explore a potential ensemble defense against possible data contamination, showing promising attack success reduction. Our results raise fundamental questions about the robustness of DL-based systems in CAD, and we provide insights into the implications of these.
Detection of Change of Users in SNS by Two Dimensional CNN

In this paper, we proposed a method for detecting hacked accounts in SNS without predetermined features since trend of topics and slang expressions always change and hackers can make messages which are matched with the predetermined features. There are some researches in which a hacked account or impersonation in SNS is detected. However, they have problems that predetermined features were used in their method or evaluation procedure was not appropriate. On the other hand, in our method, a feature named 'category' is automatically extracted among recent tweets by machine learning. We evaluated the categories with 1,000 test accounts. As a result, 74.4% of the test accounts can be detected with the rate up to 96.0% when they are hacked and only one new message is posted. Moreover, 73.4% of the test accounts can be detected with the rate up to 99.2% by one new posted message. Furthermore, other hacked accounts can also be detected with the same rate when several messages are sequentially posted.
Intelligent computing methods used in acoustic emission and magnetic flux leakage detection of tank bottom

As the main equipment for petroleum and petrochemical raw materials and products storage, storage tanks are widely used in petroleum and petrochemical companies. The safe operation of storage tank is the precondition of normal production. The tank bottom is the highest risk for it is always used under the composite medium, some of them is very corrosive and often causing tank bottom failure. For the tank bottom belongs to the concealed work which always runs under the corrosive composite medium circumstance, and the bottom defect couldn't find by daily inspection. So, the newly developed acoustic emission detection technology is emerging, which has advantages of online detection and avoiding the expense and risk of opening the tank. But the acoustic emission detection has disadvantages of give qualitative results, which couldn't give the accurate remaining thickness of the tank bottom, so the magnetic flux leakage detection is developed which can give the quantitative results. These two methods have their own merits and demerits, so the two methods are often used together in the practice. Recently, our institute in accordance with the requirements of the group company safety and environmental protection department to carry out a large number of acoustic emission testing work online in order to avoid the tank perforation, two hundred tanks of DAQING oil field, LIAOHE oil field and other oilfield belong to CNPC were tested by acoustic emission detection technology and forty tanks of these tanks were testified by magnetic flux leakage detection technology when opening the tanks. The results show that the two detection methods can reflect the corrosion of the bottom of the tank well, and can provide decision support for the daily management and maintenance of the tank.
Semantic Analysis and Evaluation of Translation Based on Abstract Meaning Representation

Meaning Representation (AMR) offers a novel scheme and perspective on the relation between linguistic form and semantic of various sentences. In order to explore the semantic equivalence among homologous translations, the paper analyzes the scenarios of same semantic structures covered by AMR and proposes a framework of variation in homologous translations. In addition, the framework is mapped into the annotation of AMR and used for common semantic characteristics mining in homologous translations. Accordingly AMR semantic structure matching (Smatch) score is applied to machine translation quality evaluation task. Experiments on small scale dataset preliminarily prove the effectiveness of AMR in translation quality evaluation.
Toward a secure QR code system by fingerprinting screens

Quick response (QR) codes have been widely used in mobile applications, due to its convenience and the pervasive built-in cameras on smartphones. Recently, however, QR codes have been reported suffering attacks for being sniffed just before the QR code is scanned, which lead to financial loss. In this study, we propose ScreenID, for enhancing the QR code security by identifying its authenticity, which embeds a QR code with information of unique screen fingerprint - PWM frequency. PWM frequencies are adjusted to different values by screen manufacturers, therefore can successfully differentiate screens. To improve the estimation accuracy of PWM frequency, ScreenID incorporates a model for the interaction between the camera and screen in the temporal and spatial domains. Extensive experiments demonstrate that ScreenID can differentiate screens of different models, types and manufacturers and thus improve the security of QR codes.
A two-dimensional code security authentication method based on digital watermarking

A security authentication method of QR code based on digital watermarking is proposed to improve the security of two-dimensional code. This method can be applied to verify the authenticity of QR code. Firstly, the host image is partitioned into blocks and then the Two-dimensional Discrete Cosine Transform(2D-DCT) is applied for each block. Secondly, a smaller block is selected from each block in the process above, and then the Singular Value Decomposition (SVD) is applied for the smaller block. At last, the 2D-DCT and SVD transforms are applied for the image of watermark, after that the watermark is embedded by modifying the characteristic value of the SVD. The process of extracting watermark image is opposite to the process of embedding watermark. Computer simulation and experimental results prove this method has the characteristics of strong robustness, invisibility, high security and low computational cost.
New Technology Architecture and Research Hotspot of Blockchain in 2020

Blockchain technology is bringing new changes to the global financial landscape and immeasurable potential power for economic development in this new era. We believe that the development of blockchain technology has huge prospects, and it is necessary to sort out the current status of blockchain technology. Based on the synthesis of domestic and foreign research, this paper combines the development of blockchain in China in recent years, summarizes the current status of the architecture of blockchain technology, reviews its development and predicts its future development.
An abstract form of the first epsilon theorem

Abstract We present a new method of computing Herbrand disjunctions. The up-to-date most direct approach to calculate Herbrand disjunctions is based on Hilbert’s epsilon formalism (which is in fact also the oldest framework for proof theory). The algorithm to calculate Herbrand disjunctions is an integral part of the proof of the extended first epsilon theorem. This paper introduces a more abstract form of epsilon proofs, the function variable proofs. This leads to a computational improved version of the extended first epsilon theorem, which allows a nonelementary speed up of the computation of Herbrand disjunctions. As an application, sequent calculus proofs are translated into function variable proofs and a variant of the axiom of global choice is shown to be removable from proofs in Neumann–Bernays–Gödel set theory.
An Information Theoretic Learning for Causal Direction Identification

Causal inference has been one of the central problems in many data science research and one of the most important problem in causal inference is to draw causal conclusions using observation data. In this paper, we focus on learning causal relationships between variables using observation data. We proposed novel scoring method based on mutual information and corresponding learning algorithms. We then showed the consistency of our models under mild conditions and also validated the effectiveness of our approach in benchmarking experiments.
No false grounds and topology of argumentation

Abstract We integrate Dung’s argumentation framework with a topological space to formalize Clark’s no false lemmas theory for solving the Gettier problem and study its logic. Our formalization shows that one of the two notions of knowledge proposed by Clark, justified belief with true grounds, satisfies Stalnaker’s axiom system of belief and knowledge except for the axiom of closure under conjunction. We propose a new notion of knowledge, justified belief with a well-founded chain of true grounds, which further improves on Clark’s two notions of knowledge. We pinpoint a seemingly reasonable condition which makes these three notions of knowledge collapse into the same one and explain why this result looks counter-intuitive. From a technical point of view, our formal analysis driven by the philosophical issues reveals the logical structure of the grounded semantics in Dung’s argumentation theory.