Reviewerly/exHarmony huggingface dataset:
A Personalized Recommendation Method Based on Collaborative Filtering Algorithm

Collaborative filtering algorithm is a widely used recommendation algorithm.However, when applied to e-commerce personalized recommendation, it faces the following issues: firstly, how to consider the user's interest changes over time when getting similarity between the users more precise; secondly, how to use social networks to more accurately getting the nearest neighbor of users; and thirdly, how to consider the behavior of users who have the same interests and different ratings in making the predicted rating score of item more accurately; fourthly, how to use the inherent relation between product categories, such as internal relations, while recommending.In order to solve these problems, this paper improves the traditional collaborative filtering algorithm by integrating timing updates, trust relationship, optimization of predicted rating score and structured ideas.To distinguish users' past interest characteristics and their recent ones, by introducing the idea of timing update, this paper regards the user's shopping experience as a set of time periods, considering the influences of the users' interest at different time on the similarity of the users, and the influence of trust relationship between target user and similar users on the establishment of nearest neighbor set.On this basis, faced with the difference of evaluation criteria of different users on the same recommendation item, this study optimizes scoring method of similar users and gets a pre-scoring-based predicted rating score method for target user to recommend item.Furtherly, considering the relationship between the recommended item and other items, this paper also proposes an idea of relative recommending based on recommended item as a secondary recommendation.At the end of this paper, the proposed method is verified on the review dataset in MovieLens which is provided by the College of computer science and engineering of University of Minnesota.The experimental results show that the proposed method has obvious recommendation accuracy compared with the traditional collaborative filtering algorithm.
The BLCU System in the BEA 2019 Shared Task

This paper describes the BLCU Group submissions to the Building Educational Applications (BEA) 2019 Shared Task on Grammatical Error Correction (GEC).The task is to detect and correct grammatical errors that occurred in essays.We participate in 2 tracks including the Restricted Track and the Unrestricted Track.Our system is based on a Transformer model architecture.We integrate many effective methods proposed in recent years, such as Byte Pair Encoding, model ensemble, checkpoints average and spell checker.We also corrupt the public monolingual data to further improve the performance of the model.On the test data of the BEA 2019 Shared Task, our system yields F 0.5 = 58.62 for the Restricted Track and 59.50 for the Unrestricted Track, ranking twelfth and fourth respectively.
Global Research Performance on the Design and Applications of Type-2 Fuzzy Logic Systems: A Bibliometric Analysis

There has been a significant contribution to scientific literature in the design and applications of Type-2 fuzzy logic systems (T2FLS). The T2FLSs found applications in many aspects of our daily lives, such as engineering, pure science, medicine and social sciences. The online web of science was searched to identify the 100 most frequently cited papers published on the design and application of T2FLS from 1980 to 2016. The articles were analyzed based on authorship, source title, country of origin, institution, document type, web of science category, and year of publication. The correlation between the average citation per year (ACY) and the total citation (TC) was analyzed. It was found that there is a strong relationship between the ACY and TC (r = 0.91643, P&lt;0.01), based on the papers consider in this research. The “Type -2 fuzzy sets made simple” authored by Mendel and John (2002), published in IEEE Transactions on Fuzzy Systems received the highest TC as well as the ACY. The future trend in this research domain was also analyzed. The present analysis may serve as a guide for selecting qualitative literature especially to the beginners in the field of T2FLS.
Pool-Based Genetic Programming Using Evospace, Local Search and Bloat Control

This work presents a unique genetic programming (GP) approach that integrates a numerical local search method and a bloat-control mechanism within a distributed model for evolutionary algorithms known as EvoSpace. The first two elements provide a directed search operator and a way to control the growth of evolved models, while the latter is meant to exploit distributed and cloud-based computing architectures. EvoSpace is a Pool-based Evolutionary Algorithm, and this work is the first time that such a computing model has been used to perform a GP-based search. The proposal was extensively evaluated using real-world problems from diverse domains, and the behavior of the search was analyzed from several different perspectives. The results show that the proposed approach compares favorably with a standard approach, identifying promising aspects and limitations of this initial hybrid system.
Towards a Linguistic Stylometric Model for the Authorship Detection in Cybercrime Investigations

This study proposes an integrated framework that considers letter-pair frequencies/combinations along with the lexical features of documents as a means to identifying the authorship of short texts posted anonymously on social media. Taking a quantitative morpho-lexical approach, this study tests the hypothesis that letter information, or mapping, can identify unique stylistic features. As such, stable word combinations and morphological patterns can be used successfully for authorship detection in relation to very short texts. This method offers significant potential in the fight against online hate speech, which is often posted anonymously and where authorship is difficult to identify. The data analyzed is from a corpus of 12,240 tweets derived from 87 Twitter accounts. A self-organizing map (SOM) model was used to classify input patterns in the tweets that shared common features. Tweets grouped in a particular class displayed features that suggested they were written by a particular author. The results indicate that the accuracy of classification according to the proposed system was around 76%. Up to 22% of this accuracy was lost, however, when only distinctive words were used and 26% was lost when the classification procedure was based solely on letter combinations and morphological patterns. The integration of letter-pairs and morphological patterns had the advantage of improving accuracy when determining the author of a given tweet. This indicates that the integration of different linguistic variables into an integrated system leads to better performance in classifying very short texts. It is also clear that the use of a self-organizing map (SOM) led to better clustering performance because of its capacity to integrate two different linguistic levels for each author profile.
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks

Deep neural networks have yielded superior performance in many contemporary applications. However, the gradient computation in a deep model with millions of instances leads to a lengthy training process even with modern GPU/TPU hardware acceleration. In this paper, we propose AutoAssist, a simple framework to accelerate training of a deep neural network. Typically, as the training procedure evolves, the amount of improvement by a stochastic gradient update varies dynamically with the choice of instances in the mini-batch. In AutoAssist, we utilize this fact and design an instance shrinking operation that is used to filter out instances with relatively low marginal improvement to the current model; thus the computationally intensive gradient computations are performed on informative instances as much as possible. Specifically, we train a very lightweight Assistant model jointly with the original deep network, which we refer to as Boss. The Assistant model is designed to gauge the importance of a given instance with respect to the current Boss such that the shrinking operation can be applied in the batch generator. With careful design, we train the Boss and Assistant in a nonblocking and asynchronous fashion such that overhead is minimal. To demonstrate the effectiveness of AutoAssist, we conduct experiments on two contemporary applications: image classification using ResNets with varied number of layers, and neural machine translation using LSTMs, ConvS2S and Transformer models. For each application, we verify that AutoAssist leads to significant reduction in training time; in particular, 30% to 40% of the total operation count can be reduced which leads to faster convergence and a corresponding decrease in training time.
Image Encryption Algorithm Based on Sine-Logistic Cascade Chaos

In view of the fact that the dynamic characteristic of a single chaotic system is insufficient in the image encryption algorithm, the time sequence of the output is often not the theoretical completely random, but the cascade of the chaotic system can improve the defect. And cascading chaotic systems can make the real value of discrete sequence distribution more uniform, has better dynamic characteristics. Therefore, this paper proposes an image encryption algorithm based on s-l cascade chaotic system. Through the simulation experiment of key sensitivity, histogram, correlation between adjacent elements such as evaluation index to calculate and analyze, proves that the encryption algorithm has strong ability against the attack.
Language and Visual Relations Encoding for Visual Question Answering

Visual Question Answering (VQA) involves complex relations of two modalities, including the relations between words and between image regions. Thus, encoding these relations is important to accurate VQA. In this paper, we propose two modules to encode the two types of relations respectively. The language relation encoding module is proposed to encode multi-scale relations between words via a novel masked self-attention. The visual relation encoding module is proposed to encode the relations between image regions. It computes the response at a position as a weighted sum of the features at other positions in the feature maps. Extensive experiments demonstrate the effectiveness of each modules. Our model achieves state-of-the-art performance on the VQA 1.0 dataset.
Effect of Faults on Kalman Filter of State Vectors in Linear Systems

Kalman filter (KF) is composed of a set of recursion algorithms which can be used to estimate the optimal state of the linear system, and widely used in the control system, signal processing and other fields.In the practical application of the KF, it is an unavoidable problem that how faults or anomalies are infectious to the estimation value of state vectors in the linear system, which must be paid much attention to and solved down.In this paper, the effect of sensor faults and control input anomalies on the Kalman filtering values of state vectors is discussed, the transmission relationship is established to analyze the estimation deviation of state vectors which comes from pulse or step faults/anomalies, and a sufficient condition is deduced for the convergence of the estimation deviation of state vectors; Four different system models with 3-dimension state vector and 2-dimension observation vector are selected for simulation calculation and comparative analysis, simulation results show that sensor faults and control input anomalies in linear systems may cause significant deviations in the estimation value of state vectors for a long time, and there are distinct differences in the estimation value of state vectors.The research results provide a certain theoretical reference for us to analyze system fault types and to identify fault.
The introduction of criteria for assessing an aligned parallel Persian-English corpus at the sentence level

Bilingual corpora are a collection of writings that serve as an example of the relationship between two languages for linguistic and translational applications.Examining the effectiveness of the corpora is one of the essential requirements for working with them.Therefore, the validity of the work based on the corpora is to check their quality.Scientists have identified four main attributes for corpses.These four features are representativeness, limited size, machine-readable shape, standard reference.To evaluate an entity, we need to evaluate these four properties.The limited size and intelligibility of a machine in electronic compartments are certain because they are otherwise unusable.Representativeness means to put a sample set of language variations for the language in question in the corpus.In fact, the corpus has a linguistic diversity.To evaluate this property, we examine the complexity and diversity of the figure and compute the degree of compliance with Ziff's law.For the standardization of each pair, we combine several of the following characteristics: alignment, translation, command, punctuation, separation, characterization.Finally, a fuzzy system uses the final evaluation of these criteria and uses a fuzzy rule base and fuzzy inputs of the introduced evaluators to obtain a fuzzy result for the quality of the entity.
A Deep Learning Approach for Real-Time 3D Human Action Recognition from Skeletal Data

We present a new deep learning approach for real-time 3D human action recognition from skeletal data and apply it to develop a vision-based intelligent surveillance system. Given a skeleton sequence, we propose to encode skeleton poses and their motions into a single RGB image. An Adaptive Histogram Equalization (AHE) algorithm is then applied on the color images to enhance their local patterns and generate more discriminative features. For learning and classification tasks, we design Deep Neural Networks based on the Densely Connected Convolutional Architecture (DenseNet) to extract features from enhanced-color images and classify them into classes. Experimental results on two challenging datasets show that the proposed method reaches state-of-the-art accuracy, whilst requiring low computational time for training and inference. This paper also introduces CEMEST, a new RGB-D dataset depicting passenger behaviors in public transport. It consists of 203 untrimmed real-world surveillance videos of realistic normal and anomalous events. We achieve promising results on real conditions of this dataset with the support of data augmentation and transfer learning techniques. This enables the construction of real-world applications based on deep learning for enhancing monitoring and security in public transport.
ANALISIS WEBSITE WIKI VERSAILLUS DENGAN MENGGUNAKAN METODE PIECES

The development of this age in the world is characterized by information technology. For a long time, humans can not be separated from technology. The development of Internet information technology effected the creation of the internet. The Internet sends people to communicate and search for information about anyone. How to find existing information is to open websites that exist on the internet. Website is a system where information in the form of text, images, sound, and others are presented in hypertext form. One of the websites that contain information is Versaillus. Versaillus is a website that contains information about a game called Mobile Legends and the manifold of Versaillus is a wiki’s site, but the website is not crowded by people. Therefore, the author will researching the site versaillus by using PIECES method. The results of this study can change the site versaillus to be better and better from PIECES method.
Simple Construction of Mixed-Language Texts for Vocabulary Learning

We present a machine foreign-language teacher that takes documents written in a student's native language and detects situations where it can replace words with their foreign glosses such that new foreign vocabulary can be learned simply through reading the resulting mixed-language text.We show that it is possible to design such a machine teacher without any supervised data from (human) students.We accomplish this by modifying a cloze language model to incrementally learn new vocabulary items, and use this language model as a proxy for the word guessing and learning ability of real students.Our machine foreign-language teacher decides which subset of words to replace by consulting this language model.We evaluate three variants of our student proxy language models through a study on Amazon Mechanical Turk (MTurk).We find that MTurk "students" were able to guess the meanings of foreign words introduced by the machine teacher with high accuracy for both function words as well as content words in two out of the three models.In addition, we show that students are able to retain their knowledge about the foreign words after they finish reading the document.
Learning Pushing Skills Using Object Detection and Deep Reinforcement Learning

Pushing objects to the target place on a tabletop surface is a task which requires skillful interaction with the physical world. Usually, this is achieved by precisely modeling physical properties of the objects, robot, and the environment for explicit planning. In contrast, as explicitly modeling the physical environment is not always feasible and involves various uncertainties, we learn robotic pushing skills with deep reinforcement learning and based on only visual feedback. For this, we model the task with rewards and use deep deterministic policy gradient (DDPG) algorithms to update the control strategy. And we combine You Only Look Once (YOLO) which is one of state-of-the-art object detection algorithm with DDPG algorithm, the method reduces the exploring space of the robot, produces more effective learning samples, and improves the learning efficiency greatly. In simulation experiments, the robot learned pushing skills after training 400 episodes, the success rate is around 95% and the learning efficiency is much higher than using DDPG alone. In real-world experiments, the robot learned pushing skills after training 800 episodes and the success rate is around 85%.
A Framework for Digital Forensic in Joint Heterogeneous Cloud Computing Environment

The cloud computing is nowadays an embracing computing technology by many organizations, academic institutions and business centers. Resources availability, resources capacity, security are among the factors that subscriber consider while rating Cloud Service Providers when subscribing. Cloud Service Providers (CSPs) are limited in some resources, lacking some resources requested by their customers, this gave rise to the need for interconnecting multiple clouds to interoperate and share resources. The interconnected clouds can be in different features and schemes and the system can be prone to insecurity or intrusion. The architectural modeling system was used in developing framework. In this paper, a Digital Forensic Framework that can detect intrusion within heterogeneous joint clouds was developed with the architectural model and algorithm that can handle the joint clouds heterogeneity and complexity during inter-clouds resources management. This study originates a new framework and an algorithm that enable detecting crime and locating a scene of a crime for digital investigation (digital forensic) in a joined different configured cloud service providers (CSPs) platforms.
Kernelized Bayesian Softmax for Text Generation

Neural models for text generation require a softmax layer with proper token embeddings during the decoding phase. Most existing approaches adopt single point embedding for each token. However, a word may have multiple senses according to different context, some of which might be distinct. In this paper, we propose KerBS, a novel approach for learning better embeddings for text generation. KerBS embodies two advantages: (a) it employs a Bayesian composition of embeddings for words with multiple senses; (b) it is adaptive to semantic variances of words and robust to rare sentence context by imposing learned kernels to capture the closeness of words (senses) in the embedding space. Empirical studies show that KerBS significantly boosts the performance of several text generation tasks.
Efektivitas Sistem Kearsipan Elektronik Human Resource Information System (HRIS)

With the development of information technology can create an electronic filing system that is useful for storing documents in the form of soft files.Realizing the effectiveness of the electronic filing system, several aspects need to be considered, namely speed, convenience, efficiency and, safety.In this study, we want to see how effective the electronic filing system is applied to PT Konimex Solo especially in the Human Resource Department.The researcher used qualitative descriptive research.
Implementation SHA512 Hash Function And Boyer-Moore String Matching Algorithm For Jpeg/exif Message Digest Compilation

Security information method for JPEG/exif documents generally aims to prevent security attacks by protecting documents with password and watermark. Both methods cannot be used to determine the condition of data integrity at the detection stage of the information security cycle. Message Digest is the essence of a file that has a function as a digital fingerprint to represent data integrity. This study aims to compile digital fingerprints to detect changes that occurred in JPEG / exif documents in information security. The research phase consists of five stages. The first stage, identification of the JPEG / exif document structure conducted using the Boyer-Moore string matching algorithm to find JPEG/exif segments location. The Second stage is segment content acquisition, conducted based on segment location and length obtained. The Third step, computing message digest for each segment using SHA512 hash function. Fourth stage, JPEG / exif document modification experiments to identified affected segments. The fifth stage is selecting and combining the hash value of the segment into the message digest. The obtained result shows the message digest for JPEG/exif documents composed of three hash values. The SOI segment hash value used to detect modifications for JPEG to png conversion and image editing. The APP1 hash value used to detect metadata editing. The SOF0 hash values use to detect modification for image recoloring, cropping and resizing — the combination from three hash values as JPEG/exif’s message digest.