Reviewerly/exHarmony huggingface dataset:
Implementation of social media mining for decision making in product planning based on topic modeling and sentiment analysis

Abstract The development of information and communication technology, especially on the social media field, brings a wide change in product planning in company. At this time, a company can identify the opportunity for product planning based on customers’ opinion through Twitter’s postings. This opportunity can be used to increase the competitiveness and defend from the competitors. The main method that is used in this research is social media mining based on topic modeling and sentiment analysis. The topic that is widely discussed by the customers is identified as the importance degree and the result of sentiment analysis is identified as the satisfaction degree. We successfully apply topic modeling with Latent Semantic Analysis and K-Means cluster and sentiment analysis with lexicon dictionary from Hutto and Gilbert. Using McDonald’s 2000 tweets, it can be concluded that “service in the morning” has the highest opportunity degree and it is categorized at under-served thing. It gives us a meaning that “service in the morning” is one of the service which has a great chance at the market.
Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations

Gaussian Process Factor Analysis (GPFA) has been broadly applied to the problem of identifying smooth, low-dimensional temporal structure underlying large-scale neural recordings. However, spike trains are non-Gaussian, which motivates combining GPFA with discrete observation models for binned spike count data. The drawback to this approach is that GPFA priors are not conjugate to count model likelihoods, which makes inference challenging. Here we address this obstacle by introducing a fast, approximate inference method for non-conjugate GPFA models. Our approach uses orthogonal second-order polynomials to approximate the nonlinear terms in the non-conjugate log-likelihood, resulting in a method we refer to as \textit{polynomial approximate log-likelihood} (PAL) estimators. This approximation allows for accurate closed-form evaluation of marginal likelihoods and fast numerical optimization for parameters and hyperparameters. We derive PAL estimators for GPFA models with binomial, Poisson, and negative binomial observations and find the PAL estimation is highly accurate, and achieves faster convergence times compared to existing state-of-the-art inference methods. We also find that PAL hyperparameters can provide sensible initialization for black box variational inference (BBVI), which improves BBVI accuracy. We demonstrate that PAL estimators achieve fast and accurate extraction of latent structure from multi-neuron spike train data.
Supervised Learning of Sparsity-Promoting Regularizers for Denoising

We present a method for supervised learning of sparsity-promoting regularizers for image denoising. Sparsity-promoting regularization is a key ingredient in solving modern image reconstruction problems; however, the operators underlying these regularizers are usually either designed by hand or learned from data in an unsupervised way. The recent success of supervised learning (mainly convolutional neural networks) in solving image reconstruction problems suggests that it could be a fruitful approach to designing regularizers. As a first experiment in this direction, we propose to denoise images using a variational formulation with a parametric, sparsity-promoting regularizer, where the parameters of the regularizer are learned to minimize the mean squared error of reconstructions on a training set of (ground truth image, measurement) pairs. Training involves solving a challenging bilievel optimization problem; we derive an expression for the gradient of the training loss using Karush-Kuhn-Tucker conditions and provide an accompanying gradient descent algorithm to minimize it. Our experiments on a simple synthetic, denoising problem show that the proposed method can learn an operator that outperforms well-known regularizers (total variation, DCT-sparsity, and unsupervised dictionary learning) and collaborative filtering. While the approach we present is specific to denoising, we believe that it can be adapted to the whole class of inverse problems with linear measurement models, giving it applicability to a wide range of image reconstruction problems.
Real Time Face Recognition using Raspberry Pi

Face recognition is a fast growing and challenging area in the field of computer vision and real time applications.A lot of techniques and algorithms are available with varying degrees of accuracy and speed.Face recognition has a lot of applications in the field of advertising, healthcare, security, accessibility, and even payments.Hence, there is a need for low cost, reliable and accurate face recognition systems in todays world [3].The aim is to implement a face recognition system using a Raspberry Pi device.This system is part of an assistive device created by us for visually impaired people.The setup consists of a Raspberry Pi 3 Model B device with a camera module attached to it.The Raspberry Pi has a 1.2 GHz 64-bit CPU along with 1 GB RAM and the camera module has a resolution of 5 MP.
Implementasi Dan Analisis Server Bigbluebutton Dalam Pemanfaatan Meeting Online Pada Masa Pandemic

The need for video converence is something that cannot be ignored. Amid the Covid-19 pandemic outbreak all sectors must utilize this technology. Such is the case with Semarang Islamic State University Walisongo. Where UIN Walisongo requires all students, lecturers and education personnel to remain at home in an effort to comply with government regulations as an effort to prevent this outbreak. Therefore, to find a solution for communication to stay connected, a system for online meetings was built that could be used by students and lecturers as a medium of online learning, and the education staff and leaders to be able to always be connected in the midst of the covid outbreak.
A Simple Method for Network Visualization

In this article, we present a simple method for network visualization. The proposed method is based on distmesh [P.O. Persson and G. Strang, A simple mesh generator in MATLAB, SIAM Review 46 (2004) pp. 329–345], which is a simple unstructured triangular mesh generator for geometries represented by a signed distance function. We demonstrate a good performance of the proposed algorithm through several network visualization examples.
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning

Increasing the scale of reinforcement learning experiments has allowed researchers to achieve unprecedented results in both training sophisticated agents for video games, and in sim-to-real transfer for robotics. Typically such experiments rely on large distributed systems and require expensive hardware setups, limiting wider access to this exciting area of research. In this work we aim to solve this problem by optimizing the efficiency and resource utilization of reinforcement learning algorithms instead of relying on distributed computation. We present the "Sample Factory", a high-throughput training system optimized for a single-machine setting. Our architecture combines a highly efficient, asynchronous, GPU-based sampler with off-policy correction techniques, allowing us to achieve throughput higher than $10^5$ environment frames/second on non-trivial control problems in 3D without sacrificing sample efficiency. We extend Sample Factory to support self-play and population-based training and apply these techniques to train highly capable agents for a multiplayer first-person shooter game. The source code is available at https://github.com/alex-petrenko/sample-factory
DESIGN AND APPLICATION OF SOLAR TRACKING SYSTEM USING OPTIMIZED FUZZY LOGIC CONTROLLER BY GENETIC ALGORITHM

This study describes an intelligent control algorithm for the solar tracking system (STS) providing maximum performance from the photovoltaic panel according to different sun positions. The solar tracking system is designed as dual axis to increase the efficiency of photovoltaic panels. DC motors are preferred in order to minimize cost and to control the azimuth and zenith angles in the solar tracking system. Fuzzy logic algorithms are used to adjust the speed of these motors to track the sun’s position with a high degree of accuracy. After designing a fuzzy logic controller in order to control the motors, membership functions of controller and control rules are simultaneously found by genetic algorithms which is an optimization algorithm based on natural selection and genetic mechanics. As a result in the study, the power performance analysis is compared between a photovoltaic panel positioned on the designed solar tracking system and a photovoltaic panel positioned on the static system. According to comparison results, the photovoltaic panel positioned on the solar tracking system is observed that it shows higher performance at varying rates of depending on the seasons.
Design of Short Codes for Quantum Channels with Asymmetric Pauli Errors

One of the main problems in quantum information systems is the presence of errors due to noise. Many quantum error correcting codes have been designed to deal with generic errors. In this paper we construct new stabilizer codes able to correct a given number $$e_{\mathrm g}$$ of generic Pauli $$\varvec{X}, \varvec{Y}$$ and $$\varvec{Z}$$ errors, plus a number $$e_{\mathrm Z}$$ of Pauli errors of a specified type (e.g., $$\varvec{Z}$$ errors). These codes can be of interest when the quantum channel is asymmetric, i.e., when some types of error occur more frequently than others. For example, we design a [[9, 1]] quantum error correcting code able to correct up to one generic qubit error plus one $$\varvec{Z}$$ error in arbitrary positions. According to a generalized version of the quantum Hamming bound, it is the shortest code with this error correction capability.
FedFMC: Sequential Efficient Federated Learning on Non-iid Data

As a mechanism for devices to update a global model without sharing data, federated learning bridges the tension between the need for data and respect for privacy. However, classic FL methods like Federated Averaging struggle with non-iid data, a prevalent situation in the real world. Previous solutions are sub-optimal as they either employ a small shared global subset of data or greater number of models with increased communication costs. We propose FedFMC (Fork-Merge-Consolidate), a method that dynamically forks devices into updating different global models then merges and consolidates separate models into one. We first show the soundness of FedFMC on simple datasets, then run several experiments comparing against baseline approaches. These experiments show that FedFMC substantially improves upon earlier approaches to non-iid data in the federated learning context without using a globally shared subset of data nor increase communication costs.
Hand Gesture Recognition for Emoji Prediction

Emojis are ideograms and smileys visual symbols that are used widely in wireless communication. They present rich novel possibilities of representation and interaction as a new modality. They exist in various genres, including hand gestures, human faces, figures, and signs. Hand gestures, which are the most common and intuitive non-verbal means of communication when we are using a computer, and related work, has recently sparked an interest. Hands often appear in images, videos, and their appearances and pose can give important clues about what people are doing. A combination of hand gestures and emojis can communicate and express the message very conveniently. Considering the positive outcomes of image recognition from specific deep learning methods, we suggest an emoji predictor in real-time. This project consists of a hand gesture recognition method and emoji generator using filters to detect hands and Convolutional Neural Network (CNN) for training the model.Here, a database is being created of hand gestures to train the system.The prediction will be focused on the hand movements and capture the changes by preparing for different hand movement positions to the highest degree of precision.
A NOVEL HYBRID MODEL BY USING CONVOLUTIONAL NEURAL NETWORK AND LONG SHORT-TERM MEMORY FOR TEXT SENTIMENT ANALYSIS

Machine learning has been widely applied in sentiment analysis. However, natural language has a structure-dependent relationship, relying on a single network for feature extraction, which limits the precision of text sentiment classification. To extract semantic features and perform classification effectively, the convolutional neural network and long short-term memory model (CNN–LSTM) was proposed in the study. The Word2vec method was employed to train the initial word vector. The CNN convolutional layer and the max-pooling layer were introduced to extract the local features of the text. The LSTM module was adopted to capture the long-term dependencies between word sequences. The features extracted with LSTM and CNN were fused, and efficiency was improved using dropout regularization technology. A case study was performed on the movie review dataset to analyze the classification effect utilizing the traditional and proposed methods. Results demonstrate that compared with the single CNN and LSTM models, the evaluation index F1 of this model improves by 1.93% and 0.97%, respectively. Compared with the randomly initialized feature vector, the precision of the model improves 2.74% by the word embedding.The proposed method can receive parallel input of the text information, which reduces the training time of the network model. The mechanism combining CNN and LSTM compensates for the shortcomings of relying only on word embedding for feature extraction, enabling the model to obtain in-depth sentiment feature and effectively-identified sentiment polarity without external knowledge such as dependency syntax analysis. The proposed method provides a specific reference for the sentiment classification of comment texts in social media. Keywords: Sentiment analysis, CNN, LSTM, Feature fusion
Social Taboos in Pakistani Prime Time Urdu Dramas

The occurrence of social taboos and their frequencies on Pakistani television dramas was the core objective behind the conduction of this research work. The ultimate purpose was to find out the possible types of social taboos, frequently exposed social taboo, occurrence per drama and comparison of social taboos in these dramas. The analysis was carried out by using purposive sampling technique. The results showed that there were nine types of social taboos i.e. obscene language, nudity, disrespectful attitude, violence, drug abuse, racism, divorce, extra-material relation and abortion in Pakistani primetime Urdu dramas. The finding of the study demonstrated that obscene language was exposed more than the other social taboos, whereas the drama serial Sabaz Qadam was found having more social taboos as compare to other dramas. The occurrence of frequency of these social taboos was recorded as well. Drama Qissa Chaar Darvesh was highest in occurrence of disrespectful attitude and nudity.
SpectreRewind: Leaking Secrets to Past Instructions

Transient execution attacks utilize micro-architectural covert channels to leak secrets that should not have been accessible during logical program execution. Commonly used micro-architectural covert channels are those that leave lasting footprints in the microarchitectural state, for example, a cache state change, from which the secret is recovered after the transient execution is completed. In this paper, we present SpectreRewind, a new approach to create contention based covert channels for transient execution attacks. In our approach, a covert channel is established by issuing the necessary instructions logically before the transiently executed victim code. Unlike prior contention based covert channels, which require simultaneous multi-threading (SMT), SpectreRewind supports single hardware thread based covert channels, making it viable on systems where attacker cannot utilize SMT. We show that contention on the floating point division unit on commodity processors can be used to create a high-performance (~100 KB/s), low-noise covert channel for transient execution attacks instead of commonly used flush+reload based cache covert channels. We implement a Meltdown attack utilizing the proposed covert channel showing competitive performance compared to the stateof-the-art cache based covert channel implementation. We also show that the covert channel works in the JavaScript engine of a Chrome browser.
Youtube’da Şiddetin Görünürlüğü: “Kışkırtma” Videolarına Dair Bir İnceleme

In today's new media era, with the fact that internet and social media become one of the main usage areas of daily life, there is a huge increase in the number of content that can be watched and accessible.As access to new media has become easier, violent content has also increased.On the other hand, considering that the majority of the content in the new media can be published without being subject to a control mechanism, it is seen that especially the content focused on violence becomes more visible.In this research, "Challange" videos that have become popular on Youtube recently and attracted a large audience are examined.It has been investigated how these kinds of videos which contain elements of physical and verbal violence present the content of violence.With the content analysis method, physical and verbal violence elements in the "Challange" videos determined as samples were identified and opened for interpretation.The aim of the research is to emphasize the language of violence in these videos and to obtain arguments that can lead to discussion of why such videos have become popular.In this context, the importance of research is to pioneer the development of new perspectives on the audience's interaction with the content of violence and to be an example of similar researches.In the videos analyzed within the scope of the research, both verbal and physical violence elements were frequently encountered and the way of presenting the content of these content, which has millions of views, was discussed.In the videos analyzed within the scope of the research, both verbal and physical violence elements were frequently encountered and the way of presenting the content of these content, which has millions of views, was discussed.
Multi-task Semantic Segmentation for Apolloscape based on CGAN

Abstract China’s road traffic environment has the characteristics of mutual occlusion between targets, common use of motor vehicles and non-motor vehicles, etc., causing the problem of reduced accuracy of semantic segmentation. KITTI, Cityscapes and other datasets cannot meet the specific needs of semantic segmentation of traffic environment images in China, so Apolloscape dataset is used. The traditional method uses a single evaluation standard, and lacks the consistency check of the image semantic segmentation results. It ignores the relationship between pixels and pixels, which easily causes misidentification and leads to traffic accidents. On the basis of traditional cross-comparison evaluation indicators, this paper adds a composite evaluation of traffic environment semantic segmentation, emphasizing the role between pixels, making the results more consistent. Furthermore, it is through using the algorithm in perceiving the three typical traffic environment for verifying effectiveness. As a result, multi-task learning method is effectively applied in the three environments to achieve similar performance without increasing the computational overhead.
Research on the Security Strategy and Technology of Information Resource Network of Chinese Academy Library

Abstract This article briefly analyses the Security Strategy and Technology of Information Resource Network of Chinese Academy Library. At present, every country in the world is paying very close attention to the management of library Information resource network (IRN). Library Information Resource System is an important part of the information technology category. There are many risks in the library’s information resource system. In order to provide the users with the information accurately and timely, we need to develop a reliable and stable defense strategy.
Local Differentially Private Matrix Factorization with MoG for Recommendations

Unethical data aggregation practices of many recommendation systems have raised privacy concerns among users. Local differential privacy (LDP) based recommendation systems address this problem by perturbing a user’s original data locally in their device before sending it to the data aggregator (DA). The DA performs recommendations over perturbed data which causes substantial prediction error. To tackle privacy and utility issues with untrustworthy DA in recommendation systems, we propose a novel LDP matrix factorization (MF) with mixture of Gaussian (MoG). We use a Bounded Laplace mechanism (BLP) to perturb user’s original ratings locally. BLP restricts the perturbed ratings to a predefined output domain, thus reducing the level of noise aggregated at DA. The MoG method estimates the noise added to the original ratings, which further improves the prediction accuracy without violating the principles of differential privacy (DP). With Movielens and Jester datasets, we demonstrate that our method offers a higher prediction accuracy under strong privacy protection compared to existing LDP recommendation methods.
A Lossless Linear Algebraic Secret Image Sharing Scheme

A (k, n)-threshold secret image sharing scheme is any method of distributing a secret image amongst n participants in such a way that any k participants are able to use their shares collectively to reconstruct the secret image, while fewer than k shares do not reveal any information about the secret image.In this work, we propose a lossless linear algebraic (k, n)-threshold secret image sharing scheme.The scheme associates a vector v i to the ith participant in the vector space F k 2 α , where the vectors v i satisfy some admissibility conditions.The ith share is simply a linear combination of the vectors v i with coefficients from the secret image.Simulation results demonstrate the effectiveness and robustness of the proposed scheme compared to standard statistical attacks on secret image sharing schemes.Furthermore, the proposed scheme has a high level of security, errorresilient capability, and the size of each share is 1/k the size of the secret image.In comparison with existing work, the scheme is shown to be very competitive.