Reviewerly/exHarmony huggingface dataset:
A Practical Differentially Private Support Vector Machine

Privacy preserving data analysis is currently one of the research hotspots in the field of information security. The objective of data analysis is to extract valuable information from data, while releasing data will cause privacy leaks. It seems there is a conflict between the two competing interests. However, in many practical scenarios, they must be balanced. For example, internet service providers, such as Google, Apple and Tencent, want to capture valuable information for business from the data they collected from their users. On the other hand, they must pay attention to users' privacy protection demands. One way to achieve the balance between privacy preserving and data analysis is to extra general characteristics of whole populations without disclosing the privacy information of individuals. Differential privacy, as a mechanism that provides rigorous and provable privacy guarantee, can achieve this. It has received great attention and extensive researches in both the privacy community and the data science community. This paper proposes a support vector machine model with differential privacy. The model uses the Laplace mechanism to add random noises to the classification hyperplane, so that the attacker cannot recover the training set through the model parameters. Experiments show that the model can achieve high classification accuracy while providing privacy protection.
Overlap Analysis in The Conversation of "The Graham Norton Show"

PRIA BIJAKSANA, A320090314, OVERLAP ANALYSIS IN THE CONVERSATION OF “THE GRAHAM NORTON SHOW”. research paper. School of Teacher Training and Education. Muhammadiyah University of Surakarta. 2020. Advisor: Dr. Malikatul Lila, M.Hum.In a communication, the speakers or the listeners make something we call turn taking. Turn taking is the time for the speaker taking the turn or past the floor in conversation. They make some overlapping, self initiations or back channels. Overlapping is the condition where a speaker speaks equal or at the same time to the other speaker. Overlapping is a really unique condition because they never know when overlapp hapenned.In this research, the writer finds the types of overlapping happen among the speakers in “Graham Norton Show”. Those types of overlaps are: (1) terminal Overlap; (2)Continuers; (3) Conditional acces to the turn; and (4) Chordal or Choral. Beside that, the writer finds the reasons of overlap happen in this research. they are: (1) Asking for help; (2) Breaking up; (3) completing; (4) correcting; (5) Disagreeing; (6) seeking clarification; and (7) showing agreement.Based on the research, the writer finds that the type of overlap most often found is chordal or choral. And for the reason most ofen found is breaking up.
A Brief Introduction of Face Recognition

Image, or image sequence, have been significantly accessible due to fast pace of device development. Based on that, branches of appliance in recognition especially face recognition emerged, and have been contributing a lot to human security and privacy. This paper demonstrates a brief introduction of face recognition. To begin with, main steps are represented with an overall explanation. After that, in each specific step several outstanding algorithms are offered, followed by their performance in different platform. Finally, widespread face databases are demonstrated, along with detailed information and description of each.
Estimation of Grid Operating State Based on Mixed Data from Multiple Information Sources

With the upgrading of power grid measurement devices and the increase of various types of non-electrical data, the large data of power grid can’t be fully and effectively utilized. It is impossible to describe the operation state of power grid more accurately by physical modeling based solely on electrical data. Therefore, through the analysis and utilization of electrical data and non-electrical data, this paper uses the information physical fusion model constructed by multi-information sources to replace the traditional electrical model. The improved in formation physics fusion model realizes more comprehensive and accurate monitoring and estimation of power grid operation state. The example in this paper realizes the monitoring of the operation state of the power grid through the parameter fusion model of transformer and transmission line, and identifies the small operation faults of the power grid. Besides, the dynamic parameters derived from the fusion model are used to estimate the operation state, and the overall estimation of power grid with missing measurements is realized.
Optical Character Translation Using Spectacles (OCTS)

Optical Character Recognition (OCR) is reproducing the text as a digital format that has been produced by the non-computerized system. The translation is an essential part because people have barriers in languages. Especially when people read articles and books in their native language, they can understand more clearly and can get more ideas about the content. The framework and the portable hardware system developed takes images of printed documents and converts images as OCR text by using tesseract and then translates the text by using Google Translator API. The framework implements image capturing techniques, optical character recognition and translation using an embedded system based on Raspberry Pi. Daily used sentences were selected and captured in different development stages, viewpoints, angles, and background. Then the images are classified through the proposed system. Several pre-processing mechanisms were introduced in the process of OCR to get quality output via improving the accuracy rate of the system. From the experiment carried out on one hundred and twenty-three daily used sentences, 97% of the sentences were correctly detected and 91% Tamil and 89% Sinhala sentences were correctly translated.
Domain Adaptation via Context Prediction for Engineering Diagram Search

Effective search for engineering diagram images in larger collections is challenging because most existing feature extraction models are pre-trained on natural image data rather than diagrams. Surprisingly, we observe through experiments that even in-domain training with standard unsupervised representation learning techniques leads to poor results. We argue that, because of their structured nature, diagram images require more specially-tailored learning objectives. We propose a new method for unsupervised adaptation of out-of-domain feature extractors that asks the model to reason about spatial context. Specifically, we fine-tune a pre-trained image encoder by requiring it to correctly predict the relative orientation between pairs of nearby image regions. Experiments on the recently released Ikea Diagram Dataset show that our proposed method leads to substantial improvements on a downstream search task, more than doubling recall for certain query categories in the dataset.
Collective Intelligent Decision Making Method Based on Rationality Negotiation

Abstract Collective intelligence without considering the rationality of the decision-making process often leads to unreasonable decisions, which brings immeasurable losses to intelligent system. To solve it, a collective intelligent decision-making method based on rationality negotiation is proposed. Each individual with decision-making right is endowed with wisdom by the adapted Q learning algorithm, so that each individual has the perceived ability and individual motivation. Rationality Negotiation Model algorithm is proposed to select a rational decision of group, which reflects collective motivation of group. Then the decision result generated by the collective motivation is transformed into knowledge feedback to each individual, so that the individual also learn the collective motivation. Finally with the repeated iterative optimization, individual action becomes reasonable in the long run. A case is studied based on industrial enterprises making decision in the face of unexpected situations influence production process. The actual verification shows that the proposed decision-making scheme for intelligent system is reasonable.
A Retrieval Method of Transformer Fault Case Based on Set Pair Analysis

Case-based reasoning is one of the important means of equipment fault diagnosis. When the traditional case retrieval technology is applied to the transformer failure analysis, the retrieval result is not always correct due to the lack of data. A retrieval method of transformer fault case based on set pair analysis is proposed in this paper. The method set contact number in the set pair analysis as the similarity between cases, with the case-level weight of the index, to solve this problem. On the one hand, it takes into account the changes of indicator weight under different failure modes. On the other hand, the problem that the case similarity is comparable under the insufficient data, and non-uniform weights is solved. Some cases studies are performed to verify the proposed model. The proposed method is more effective in the diagnosis of transformer fault diagnosis, which is showed in the simulation results.
Equalization Filter Algorithm of Music Signal Based on Time-Frequency Domain Analysis

In order to effectively remove the noise existing in the transmission process of music signal, this paper discusses the music signal filtering algorithm from two different angles of time domain and frequency domain, and designs two equalization filters with adjustable parameters. And through the experimental simulation algorithm, the corresponding first-order and second-order low-frequency signal filtering cases are discussed. The effectiveness of the proposed method is demonstrated. At the same time, the method can be generalized to the case of high-order equalization filtering.
Security surveys and auditing

This chapter will discuss security surveys and security audits and the difference between these two terms, how each of them might be used, when they will occur, how to prepare for them, and how to upgrade your facility’s physical security after based on the findings of the survey or audit. As with other physical security program components, security surveys and/or audits are contributory to the overall continuous improvement process that has become a central premise of the modern security program.
Image Color Watermarking within Past Seven Years

In these day and age by fast growing the knowledge and internet, producing, distributing and manipulating of digital date become an easy task to do. Based on the other researcher result, illegal reproduction of digital information reported as the main concerned and a real problem [1], [2]. There are many problems, which arise such as copyright protection, tamper detection, ownership rights and so on [3]-[6]. This has raised questions and concerns about [4]. Thus, many researchers try to find the solution to addressing these problems. Digital watermarking provides a solution which purpose to solve these problems [5].
A New Method for Identifying the Relationship of Characters in the Video

With the development of information technology, more and more videos appear on the Internet. As relationship of characters is an important part of a video content, audiences can understand storyline of the video easily through the relationship. Meanwhile, the relationship can facilitate audiences to watch videos that they are interested in efficiently. In the last years, researchers have analyzed video characters in researches of video content detection. However, they treated characters as a number of independent objects and ignore their relationships. In this paper, we present a new approach to analyze the relationship of characters in a video by extracting the semantics of the video. The semantics of the video are a description of the things that made up with subtitle, action, character expression, Video semantic features are extracted and fused to identify the relationship of characters in the video. In our experiment, images are selected from the video that can easily show the relationship of the characters as the dataset. The accuracy of in the experiment is 89%. Its performance is better than the accuracy of using only one feature. At the same time, we predicted the relationship of characters using our model in Friends. The relationship of characters is almost accurately predicted under the comparison of the facts of Friends.
Particle Filter with Rejection Control and Unbiased Estimator of the Marginal Likelihood

We consider the combined use of resampling and partial rejection control in sequential Monte Carlo methods, also known as particle filters. While the variance reducing properties of rejection control are known, there has not been (to the best of our knowledge) any work on unbiased estimation of the marginal likelihood (also known as the model evidence or the normalizing constant) in this type of particle filter. Being able to estimate the marginal likelihood without bias is highly relevant for model comparison, computation of interpretable and reliable confidence intervals, and in exact approximation methods, such as particle Markov chain Monte Carlo. In the paper we present a particle filter with rejection control that enables unbiased estimation of the marginal likelihood.
Real-Time Notification System of Moving Obstacles in Vehicle-Camera Images Using SSD and Safe Zones

Road accidents are caused by a variety of reasons. Having something cutting in front of the car is one of the causes of the accident. Although the current hardware technology may be available to detect objects to prevent the accidental cause, such technology is still expensive, comparing to relying upon only a front camera. We thus propose a real-time analysis of images captured from a front camera to detect objects cutting in front of the car and create a warning system for the purpose. Our method of detecting and tracking objects names the single shot multi box detector, used with pre-trained object detection models. We contribute situations where objects moving with x-axis acceleration and how our regions of interest are determined, based on the actual measurement. If the object has an acceleration in the x-axis and is in the regions of interest, the system will alert the driver to make the right decision in order to prevent road accidents.
Evaluation of Physical Exercise for Osteoarthritis of the Knee through Image Processing Technique

This research aims to automatically check the correctness of knee exercises for patients with knee osteoarthritis. The patients need to be treated by physical exercise for knee but cannot know the efficacy of physical exercise while doing at home. We developed the automatic system which processes images from knee exercising video of subject who does exercise. This system utilized of histogram analysis and skeletonization. The experimental result was tested by 17 subjects. The system verifies whether subjects do an exercise correctly or incorrectly. The overall accuracy of the exercise postures classification is 73.94%, the overall accuracy of the exercise postures correctness checking is 90.18, and the overall accuracy of the automatic system which verifies the exercise postures is 88.12%. The advantage of the purpose is the patient can do physical exercise spontaneously without attaching a device to the body and know their performance at home.
ccaROS: A ROS Node for Cognitive Collaborative Architecture for an Intelligent Wheelchair

For effective Human-Robot Interaction (HRI), an intelligent wheelchair (IW) need to be cognitively enhanced. Robot Operating System (ROS) has been steadily gaining popularity among robotics researchers as an open source framework for robot control. This paper presents ccaROS - a new ROS node for a Cognitive Collaborative Architecture to achieve better HRI for an IW. The design of the ROS node is presented. It provides mechanisms for obstacle avoidance, detection and adaption of user’s navigational strategy; seamless switching of driving control from machine to human and vice-versa. This would not only assist to achieve safe navigation but also allow retention of residual skills of the user. The effectiveness of ccaROS has been evaluated through simulation studies within a ROS-USARSim environment.
Research and Design of Five-Axis Balanced Camera Stabilizer Based on BP Neural Network PID Algorithm

This paper adaptively adjusts the three parameters of proportional, integral and differential for five-axis balanced camera stabilizer. Real-time adjustment of the input of the controlled object, so that the system responds quickly and stabilizes, thereby reducing the blur caused by external factors such as jitter caused by the camera's captured image. Enables a handheld camera stabilizer to take a clear picture while the photographer's arm shakes. In order to improve the stability of the triaxial hand-held camera, the wobble of the up and down motion cannot be eliminated. Two mechanical anti-jitter shaft arms are loaded under the triaxial stabilizer to make it a five-axis stabilizer, which can keep the camera picture stable in the pitching roll course and five directions of movement above and below. Through the corresponding simulation experiment, it is verified that the control of PID controller through BP neural network can make the system have high accuracy and strong stability, maintain the balance of the picture, and obtain accurate control effect for the stable position of the camera.
Paths and Countermeasures of Information Technology in Underdeveloped Areas to Improve Technological Innovation Efficiency of Enterprises

Abstract In the context of the information economy era, China’s economy has entered a new normal. Underdeveloped regions are facing industrial undertakings and industrial upgrading. Enterprise innovation capabilities are the key factors for industrial upgrading. In view of this, this article first have analyzed the problem of information technology to improve the efficiency of technological innovation in underdeveloped areas, used Eviews8.0 to empirically analyze the influencing factors of information technology to improve the efficiency of technological innovation in enterprises, and finally used Deap2.1 software to analyze the information technology improve the efficiency of technological innovation in enterprises. The two empirical results unanimously show that in China’s underdeveloped regions, information technology has not been closely integrated with local human capital, and information technology has generally been inefficient or inefficient in improving enterprise innovation efficiency. It is necessary to strengthen the construction of information technology infrastructure in underdeveloped regions, andmake some measures to improve local information technology level and communication capacity. Strengthening education investment and training is to enhance the level of human capital accumulation, and with policy guidance to enable the majority of workers to use information technology, it can improve work skills and actively engage in technological innovation activities.
Your Pedometer Tells You: Attribute Inference via Daily Walking Step Count

Knowing individual real attributes is beneficial to intelligent marking, automatic advertising as well as a new application of smart home system. In this paper, we leverage seemingly innocent user information (step count) coming from ubiquitous mobile and sensing device for attribute inference. The utilization of sensor data breaks the traditional dependence on text, social relationship and online behavior in social media, and avoids the problem of users' gender variation in linguistic style on different social networks. Existing methods on attribute inference usually ignore the temporal information of data and purely explore different types of features manually. However, loss of temporal characteristic may reduce the representation ability to infer user's attributes. Meanwhile, excessively depending on manually defined features requires a great mass of human labor and often suffers from under specification. To address these problems, we propose a novel Hybrid Multiple Representations (HMR) model that combines simple human knowledge with automated deep learning by using stacked bidirectional LSTM, Bag-of-step and Holiday Activation methods to predict gender and age from users daily step count. Experiments are conducted on a real-world pedometer dataset where gender and age are to be predicted. The empirical results show that our HMR model does a good job on the task of predicting attributes compared with state-of-the-art baselines.
Receiver Autonomous Integrity Monitoring Method Based on Modified Unscented Kalman Particle Filter

Receiver Autonomous Integrity Monitoring (RAIM) method provides an effective way for high accuracy position and navigation service. However, it does not work well in non-Gaussian noise environment. In order to improve the RAIM performance in non-Gaussian noise environment, a modified unscented Kalman particle filtering (MUKPF) RAIM method is proposed based on the particle filter (PF) research in this paper. This method retains the PF RAIM method high-performance in the non-Gaussian noise environment and improves particle degradation problem. Moreover, the MUKPF RAIM improves algorithm efficiency which may be caused by the unscented Kalman filtering RAIM. The MUKPF RAIM provides a reliable protective way for high accuracy position in non-Gaussian noise environment.
Comparative analysis of speech coders

In this paper a comparative analysis of some of the most popular speech coders is presented. Qualitatively and quantitatively are tested Linear Prediction Coding in its implementation LPC-10e and with the use of auto-correlation and covariance,
Evasion Attack in Show and Tell Model

Recently, deep learning technology has been applied to various fields with high performance and various services. Image recognition is also used in various fields with high performance by incorporating deep learning technology. However, deep learning technology is vulnerable to evasion attacks that cause the model to be misclassified by modulating the original image. In this paper, we generate an adversarial example using the forward-backward-splitting iterative procedure. Then perform an evasion attack on the show and tell model.
Abstracting Syntactic Privacy Notions via Privacy Games

It is well understood that the huge volumes of data captured in recent years have the potential to underpin significant research developments in many fields. But, to realise these benefits, all relevant parties must be comfortable with how this data is shared. At the heart of this is the notion of privacy - which is recognised as being somewhat difficult to define. Previous authors have shown how privacy notions such as anonymity, unlinkability and pseudonymity might be combined into a single formal framework. We use and extend this work by defining privacy games for individual and group privacy within distributed environments. For each privacy notion, we formulate a game that an adversary has to win in order to break the notion. Via these games, we aim to clarify understanding of, and relationships between, different privacy notions; we also aim to give an unambiguous understanding of adversarial actions. Additionally, we extend previous work via the notion of unobservability.
Research on One Encryption Technology of Video File

Based on content-associated key technology, the paper designed and implemented an encryption algorithm for MP4 files. The algorithm first parses the MP4 file format to determine the position of the video data frame in the file, and uses the PWLCM chaotic mapping to determine the key extraction position in frames (or frame sets). In order to achieve better encryption effect, the Key frame / non-key frame hybrid extraction method is used to extract the corresponding data of each frame in the file, and to synthesize the key of the entire file. According to the playback effect of encrypted MP4 video, the algorithm is effective.