Reviewerly/exHarmony huggingface dataset:
A Study of Written Errors of Iranian Learners: A Systematic Review

The collection of this systematic article was an attempt to show a survey of current activities in the field of error analysis in English language from 2012 to 2018 in Iran. In the country, English is being taught as Foreign Language from middle school. Khansir and Gholami Dashti (2014) mentioned that English accepted as language to pass in the examination. However, English system in this country often put on formal accuracy and teachers focus on the teaching of English grammar in their classes. Error analysis approach has been a widely discussed topic for more than 60 years over the world. However, all the findings of this paper from previous research works on error analysis can be good reasons in order to guide and help reduce such errors in the written language of Iranian students in EFL setting and the results of this work can also be used to as a guide for English learners over the world. This article identifies and presents the major reasons leading to the English written errors among the Iranian learners. Finally, it reports the effective strategies and suggestions for helping the students to improve their English language. The researchers of this study studied 178 papers in this process and 37 of the papers were relevant.
Suppression of noise in modulation frequency range of interferometer using spectral subtraction method

The envelope of interference fringes, whose peak position must be estimated in wideband light-source-based interferometry systems, is reconstructed using discrete Fourier transform pairs. When using a band pass filter in the frequency domain, it is difficult to remove the noise spectrum buried in the spectral region of the fringe signal for the reconstruction of the envelope. This study proposes a spectral subtraction method (SSM)-based algorithm that efficiently removes this fringe-spectral buried noise. An optical experiment was conducted to evaluate the performance of the proposed method. The results indicate that the SSM-based algorithm is reliable for fringe-mixed noise removal. Therefore, the proposed method is of great significance in fringe-spectral buried noise processing in interferometry.
Where does biometrics sit in today's security ecosystem?

Not so long ago, biometric technology was a thing of science fiction. But as costs fell and more innovations became commercially viable, biometric security systems have been rolled out widely, first in areas like defence and law enforcement, then in more day-to-day environments like our workplaces and libraries. Today the technologies are vastly different – but almost ubiquitous: most modern smartphones incorporate at least one form of biometric ID technology, whether fingerprint or facial recognition.
Social recommendation algorithms with user feedback information

Summary Social media information can effectively improve the performance of personalized recommendation model. However, the feedback information in the social media which can accurately reflect users' implicit preferences is often ignored by most existing methods. To improve the users' experience and reduce the push of unwelcome information, in this article, we propose a new social recommendation algorithm with user feedback information. Different from the existing recommendation methods based on probability matrix decomposition, we incorporate the user implicit feedback information into the user rating prediction function. To reduce the data sparsity of implicit feedback information, we also adopt social network trust calculation in our algorithm. As a result, we can not only optimize the recommendation list but also filter out most of disgusting content. Compared with PMF, UserCF, CUNE, and TrustSVD, but slightly lower than RSTE, the experimental results of our model on real‐world datasets demonstrate the effectiveness of our proposed method, and further verify that the user experience is significantly improved without obviously reducing the accuracy of the recommendation.
Continuously Non-malleable Codes in the Split-State Model

Abstract Non-malleable codes (Dziembowski et al., ICS’10 and J. ACM’18) are a natural relaxation of error correcting/detecting codes with useful applications in cryptography. Informally, a code is non-malleable if an adversary trying to tamper with an encoding of a message can only leave it unchanged or modify it to the encoding of an unrelated value. This paper introduces continuous non-malleability, a generalization of standard non-malleability where the adversary is allowed to tamper continuously with the same encoding. This is in contrast to the standard definition of non-malleable codes, where the adversary can only tamper a single time. The only restriction is that after the first invalid codeword is ever generated, a special self-destruct mechanism is triggered and no further tampering is allowed; this restriction can easily be shown to be necessary. We focus on the split-state model, where an encoding consists of two parts and the tampering functions can be arbitrary as long as they act independently on each part. Our main contributions are outlined below. We show that continuous non-malleability in the split-state model is impossible without relying on computational assumptions. We construct a computationally secure split-state code satisfying continuous non-malleability in the common reference string (CRS) model. Our scheme can be instantiated assuming the existence of collision-resistant hash functions and (doubly enhanced) trapdoor permutations, but we also give concrete instantiations based on standard number-theoretic assumptions. We revisit the application of non-malleable codes to protecting arbitrary cryptographic primitives against related-key attacks. Previous applications of non-malleable codes in this setting required perfect erasures and the adversary to be restricted in memory. We show that continuously non-malleable codes allow to avoid these restrictions.
Visual Feedback System for Manipulating Objects Using Hand Motions in Virtual Reality Environment

With the recent development of various kinds of virtual reality devices, there has been an active research effort to increase the sense of reality by recognizing the physical behavior of users rather than by classical user input methods.Among such devices, the Leap Motion controller recognizes the user's hand gestures and can realistically trace the user's hand in a virtual reality environment.However, manipulating an object in virtual reality using a recognized user's hand often causes the hand to pass through the object, which should not occur in the real world.This study presents a way to build a visual feedback system for enhancing the user's sense of interaction between hands and objects in virtual reality.In virtual reality, the user's hands are examined precisely by using a ray tracing method to see if the virtual object collides with the user's hand, and when any collision occurs, visual feedback is given through the process of reconstructing the user's hand by moving the position of the end of the user's fingers that enter the object through sign distance field and reverse mechanics.This enables realistic interaction in virtual reality in real time.
Development Strategy of Educational Robot Industry Based on Big Data Analysis

With the development of Internet technology, the influx of massive data has enabled research in the development of related fields such as the education robot industry. The era of big data has knocked on the door of artificial intelligence that has been silent for many years, combining statistical methodology, information theory, probability theory, etc. Disciplines, a new Internet era is coming. Educational robots, as a segmented industry of service robots, are closely related to artificial intelligence, and have been at the core of capital in recent years. In this context, using big data analysis to promote the healthy development of the education robot industry is of great significance to the development of the domestic education robot industry. This article uses big data technology to analyze the human resource management and debt structure and solvency of Shanghai Future Partners Robot Co., Ltd., analyzes the macroeconomic environment and industry environment of Shanghai Future Partners Robot Co., Ltd., and discusses the opportunities and challenge. Study the internal resources and capabilities of Shanghai Future Partners Robotics Co., Ltd., and analyze the company's strengths and weaknesses in terms of production management, technology research and development capabilities, financial management, marketing management, and human resource management. This article adopts the method of combining theory with practice and big data analysis, combining the learned economic theory, corporate strategy theory, financial management science, and human resources science with the actual situation of the development of Shanghai Future Partner Robot Co., Ltd., and On this basis, the strategic goals and strategic positioning of the company's future development are determined. Finally, it discusses how to control and implement corporate development strategies. The study found that the company's engineering and technical personnel ratio is as high as 61.8%, and the employees are of high quality, which is in line with the high-tech and composite characteristics of the industry.
Cheetah: An Accurate Assessment Mechanism and a High-Throughput Acceleration Architecture Oriented Toward Resource Efficiency

Convolutional neural network (CNN) is widely used in artificial intelligence for its excellent recognition accuracy. With its scale increasing rapidly and architecture becoming complicated, it is much difficult to implement CNN in hardware platform efficiently. Many FPGA-based CNN accelerators are proposed in previous work. However, when evaluating resource efficiency, their assessment methods are: 1) device related; 2) frequency related; or 3) they confuse resource efficiency with resource occupancy. There is an insistent demand for intuitive and fair assessment criteria. When implementing CNNs, they still have improvement room in computing resource efficiency, especially for layers with large feature size and few feature maps. In this work, we propose R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> score</sub> and C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> score</sub> , which compose a comprehensive and accurate resource efficiency assessment mechanism for evaluation and design guidance, respectively. Under the guidance, we introduce Cheetah, an FPGA-based high-throughput acceleration architecture. Its computing part can optimize the use of available resources in both time and space aspects, resulting in better throughput improvement. An auxiliary storage system and a pipeline stage compression method are designed for less storage overhead and shorter inference latency. We implement AlexNet and ResNet18 on KCU1500 at 230 and 240 MHz, respectively, with a throughput of 2411.01GOP/s and 2435.05GOP/s for 16-bit quantification. Cheetah achieves an excellent average R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> score</sub> of (0.9441, 0.9456) on different FPGA devices, while the others' mainly distribute between 0.3 and 0.8. Finally, Cheetah has 6.78X speed improvement and 1.87X power-efficiency improvement than that of Nvidia Jetson TX2, which is the fastest, most power-efficient embedded AI computing device.
Multi-value private information retrieval with colluding databases via trace functions

A classic t-private PIR (private information retrieval) scheme allows a user to retrieve one out of n values from k communicating replicated databases while any t of the k databases cannot identify the value being retrieved. However, in reality, the user may be more interested in retrieving multiple values simultaneously. This paper is devoted to the PIR problem with m retrieved values and k databases against t colluding databases (t-private MPIR) in the context of information-theoretic security. The relationship m⩽w(k-t) is derived, where w is the largest average amount of information got by the user from each of the k databases. When w=1, via trace functions in finite fields, a t-private MPIR scheme retrieving m=k-t values is presented with the complexity O(logn). Our scheme provides a lower complexity than those in the literature (the best known complexity for general t and k is a fractional power function of n).
Network Security Applications Using the Port Knocking Method

Abstract The most important point in network services is security of access in the port. However, the problem that occurs is an open port or access that cannot be accessed with authentication that can facilitate unauthorized users to be accessed by the server. This is the basis for increasing access rights to the server that is built without having to close the port used by the user. Therefore a port knocking method is needed. Port knocking is a security system that can perform a function that is blocking unwanted access. In principle, port knocking successfully closes all ports on the server. If the user needs access to the server, the user does a “knock” to use the service, then if the user has finished accessing the port is closed again. The system built in this study uses three ports, namely port 22 (SSH), port 23 (Telnet), and port 80 (Web). Port Access time is 10 seconds each. Based on the results of the analysis and testing of the system implementation carried out, the results of the system can be run properly and can improve the security of network systems that are built compared to networks that do not use Port Knocking security. In research assessing on public and local networks. According to the results analysis, carrying out a remote server via local time requires faster time from a remote server through the public. In the SSH protocol there is a difference in time of 2.42 seconds, Telnet 2.14 seconds, and Web 2.19 seconds.
Automated Smartification of Notices to Airmen

Notices to airmen (NOTAMs) are text messages targeted to pilots to make them aware of short term events and obstacles. Typical examples are military actions in airspaces or closed runways. NOTAMs are written in a strongly compressed format and vocabulary, making the information hard to access for non-trained readers. Smart NOTAM is a commercial service offered by Skyguide that filters irrelevant messages for a specific route or period. Additionally, it converts the messages into a standardized phraseology and removes irrelevant information. Having access to thousands of NOTAMs together with their smartified version, a transformer model was trained and evaluated to automate the currently manual smartification process. NOTAMs contain a large number of factual details such as coordinates or radio frequencies, making the decoding process vulnerable to the inaccurate generation of such details. Therefore, the model was extended by a pointer-network that allows copying words directly from the source text. It achieves a BLEU score above 86% on unseen test data. Visual inspection of fails reveals many ground truth messages written in other languages than English or containing factual details added from other sources.
Internet-of-Things Marketplaces: State-of-the-Art and the Role of Distributed Ledger Technology

The advent of the Internet-of-Things (IoT) generates increasing data with the majority being gathered for a single purpose and staying unused after serving this purpose. With IoT platforms, cross-domain use cases, combining data from different sources, become possible. Accordingly, the need for marketplaces to trade data arises. This paper examines existing IoT platforms to frame the current opportunities for an IoT marketplace. In a second step, it analyzes the potentials of Distributed Ledger Technology (DLT) regarding transaction costs and efficiencies. In doing so, a classification regarding the functional distribution of IoT marketplaces is developed.
Digital Forensic Investigation of Internet of Thing Devices: A Proposed Model and Case Studies

Internet of Things (IoT) forensics is challenging, partly due to constant and rapid developments in the hardware and supporting software, as well as the underpinning infrastructure. This necessitates the development of a model that can be used to guide digital forensic investigations of IoT devices, while allowing flexibility to incorporate potential differences in (legal) requirements between jurisdictions. In this paper, we present one such forensic model, and describe how to set up an IoT testbed/lab to train new or inexperienced forensic investigators to examine devices and potential evidential sources. Finally, we evaluate the utility of our model using two case studies.
Content-based Propagation of User Markings for Interactive Segmentation of Patterned Images

Efficient and easy segmentation of images and volumes is of great practical importance. Segmentation problems that motivate our approach originate from microscopy imaging commonly used in materials science, medicine, and biology. We formulate image segmentation as a probabilistic pixel classification problem, and we apply segmentation as a step towards characterising image content. Our method allows the user to define structures of interest by interactively marking a subset of pixels. Thanks to the real-time feedback, the user can place new markings strategically, depending on the current outcome. The final pixel classification may be obtained from a very modest user input. An important ingredient of our method is a graph that encodes image content. This graph is built in an unsupervised manner during initialisation and is based on clustering of image features. Since we combine a limited amount of userlabelled data with the clustering information obtained from the unlabelled parts of the image, our method fits in the general framework of semi-supervised learning. We demonstrate how this can be a very efficient approach to segmentation through pixel classification.
Human Annotation and Emotion Recognition for Counseling System with Cloud Environment Using Deep Learning

As we progress in making computers understand human, we as human beings have made a great advance in human–computer interaction. As a part of this research work, we have leveraged the existing deep learning architecture to develop an artificially intelligent system that helps understand the facial emotion of a person undergoing a feedback session with carefully picked questions that help in identifying the deviation of the observed facial emotion from the expected facial emotion. This knowledge is used to filter out people who are in real need of counseling. In this project, we have made a comparison study on CNNs with differing depths[22] to find out the best suitable architecture to recognize human facial emotion that helps with the counseling system. The workflow starts with the detection of a face, proceeds with face recognition and annotation using FaceNet architecture, and moves on to recognizing the facial emotion of the person using a trained convolutional neural network model. The models are trained on a FER2013 for emotion recognition and trained on manually collected images for face recognition (FaceNet). The trained models are hosted on the server side and are connected to the client workstation through an intermediate proxy server to share connectivity over the LAN.
Safe Optimal Control Under Parametric Uncertainties

We address the issue of safe optimal path planning under parametric uncertainties using a novel regularizer that allows trading off optimality with safety. The proposed regularizer leverages the notion that collisions may be modeled as constraint violations in an optimal control setting in order to produce open-loop trajectories with reduced risk of collisions. The risk of constraint violation is evaluated using a state-dependent relevance function and first-order variations in the constraint function with respect to parametric variations. The approach is generic and can be adapted to any optimal control formulation that deals with constraints under parametric uncertainty. Simulations using a holonomic robot avoiding multiple dynamic obstacles with uncertain velocities are used to demonstrate the effectiveness of the proposed approach. Finally, we introduce the car vs. train problem to emphasize the dependence of the resultant risk aversion behavior on the form of the constraint function used to derive the regularizer.
Fingerprint and Keystroke Dynamics Fusion in Multimodal Biometrics System

Recently biometrics traits are the most popular way by which we can secure our data and devices. In the case of them, we do not need any specific passwords or cards, simply measurable human traits become a key. However, it was proven that sometimes one trait is not enough to assure high accuracy level of biometrics system. In the proposed algorithm, we concentrate on two measurable traits—fingerprint and keystroke dynamics. The first of them is commonly used in diversified approaches. The second one is less used due to its low distinguishability. Methods to extract feature vector from fingerprint and keystroke dynamics along with the way to use it in multimodal biometrics system are presented in this paper. We divided experiments into three main parts: accuracy of fingerprint-based method, precision of keystroke dynamics-based algorithm, and at last the concept of multimodal system. During the experiments we have used diversified approaches, like k-nearest neighbors, k-means, Naive Bayes classifier, and decision trees. All experiments were realized on the sample sets collected by the authors. Performed analysis has shown that it is clearly possible to recognize human identity on the basis of keystroke dynamics with satisfactory accuracy level. Moreover, it showed that it is a huge impact of machine learning algorithm selection on the quality of classification and recognition.
Optimizing data center network throughput by solving TCP Incast problem using k‐means algorithm

Summary TCP in casting referred to as one of the complex problems in the data center, which receives a huge amount of traffic volume from n number of senders. There are many methods proposed to solve the TCP Incast problem. In this paper, there are two steps proposed: (i) grouping sender nodes using the k‐means algorithm and (ii) handling incoming packets from the senders after grouping. Grouping senders make it easy to handle the incoming packets effectively as the complexity reduces reasonably. This is achieved through the k‐means algorithm that groups the senders and makes a priority queue for each group. The second step can be achieved through Pro‐Ack (Pro‐Acknowledgement) control in one of the methods for handling a huge volume of data at the receiver side. We demonstrated how the incoming packets can be processed and handled effectively through Pro‐Ack control with the help of the k‐means algorithm. Along with the above concepts, the robust algorithm is proposed to make priority queues. The input throughput also out performs with the existing algorithms.