Reviewerly/exHarmony huggingface dataset:
TextMix: using NLP and APIs to generate chunked sentence scramble tasks

A Computer-Assisted Language Learning (CALL) application, TextMix, was developed as a proof-of-concept for applying Natural Language Processing (NLP) sentence chunking techniques to creating ‘sentence scramble’ learning tasks. TextMix addresses limitations of existing applications for creating sentence scrambles by using NLP to parse and scramble syntactic components of sentences, while connecting with Application Programming Interfaces (APIs) to provide repeated exposure to authentic sentences in the context of texts such as Wikipedia articles. In addition to identifying a novel application of NLP and APIs in CALL, this project highlights the need for teacher-friendly interfaces that prioritize pedagogically useful ways of chunking text.
Towards Non-Ambiguous Reverse Dictionary

Reverse dictionary(RD) is to map a description or definition to a name of a concept. Most of the existing reverse dictionary methods are word-based, failing to handle the polysemy of words. In this paper, we propose Non-Ambiguous Reverse Dictionary(NARD), a synset-based model using synset embeddings to replace word embeddings. NARD provides different and effective embeddings for different senses in multi-sense words, allowing for a more convergent mapping of words and descriptive sentences at the semantic level. With a cross-dictionary sense tagging, NARD can utilize all word-based dictionaries for training. The pre-trained synset embeddings and the multi-task learning methods are adopted to improve the generalization performance of the model. NARD are trained on one-fifth of the synset-labeled English datasets, and achieves the new state-of-the-art results.
ALAPI: An Automated Voice Chat System in Bangla Language

Automated chat systems or chatbots are quite popular nowadays. Automated chat systems can be voice based chat system or text-based chat system. Voice based chat systems, to recognize the speech or instruction from the speaker and to give a suitable answer or to follow the given instructions is quite developed in English and some other widely used languages. Though Bangla is the primary language of 210 million people, Bangla language-based voice chatbots are not that common. In this paper we are proposing an open domain Bangla chat system “Alapi(conversational)”. The target of this proposal is to develop a simple voice chat system in Bangla language by using speech recognition technology and Artificial Intelligence (AI), which can take an audio input, and give a relevant audio output for that.
Decision Support System to Measure Child Development Based on Denver II using Naïve Bayes Classifier

This study aims to develop a decision support system to measure child development. The test was based on Denver II. Naïve Bayes Classifier (NBC) was used to classify the test result. The Software Development Life Cycle (SDLC) used is incremental development. This study used two increments. The first increment was used to build the classification model using NBC. The model’s accuracy, precision, recall, and f-measure were 95.1%, 95.4%, 95.1%, and 94.8%, respectively. The second increment was used for developing the application. Based on the black box testing, all functionality run as expected.
Implementation of Intelligent Wheelchair Control Based on Voice Commands and Medical Parameters

The wheelchair is one of the assistive device's movements for people with disabilities. In this day and age, many patients have difficulty operating manual wheelchairs, so they experience difficulties in terms of mobility. Efforts to facilitate user mobility can be done by applying technology to wheelchairs. The design of this wheelchair uses Google Assistant as the main control in operation with the method wall following with PID controller. The system also uses the MAX30100 sensor as a medical parameter for heart rate as a speed controller for wheelchairs and health monitoring that will be sent to ThinkSpeak. In this study, the wheelchair can be operated in light intensity of 1190 lux with suitable distance for wall is 20–35 cm. The wheelchair also can identify three voice command with five different dialects.
Enterprise Security

As we increasingly depend on technology, cyber threats and vulnerabilities are creating trust issues for businesses and enterprises, and cybersecurity is being considered as the number one threat to the global economy over the next 5-10 years. In this chapter, the authors explain this phenomenon by first describing the changing cyber ecosystem due to extreme digitalization and then its ramifications that are plainly visible in the latest trends in cyber-attacks. In the process, they arrive at five key implications that any modern enterprise needs to be cognizant of and discuss eight emerging measures that may help address consequences of those implications substantially. It is hoped that these measures will play a critical role in making enterprise security more proactive, cognitive, automated, connected, invisible, and risk aware.
Penalty Prediction in Discipline Reinforcement Based on Multi-Task Learning

Penalty Prediction in Discipline Reinforcement aims to automatically determine how to punish a party member who violates the party's discipline. The task is challenged by two main issues. The first is the party affiliation of the accused cannot be effectively learned by conventional models, which has a significant impact on the result. The second is the case number of various penalties is highly imbalanced, which reduces the accuracy of the model due to insufficient training data. To handle these issues, we formalize the predicting task as a problem of multi-label classification and define the prediction of each label to be a single task. Thus, we introduce a multi-task learning framework. Besides, we take party affiliation into consideration and define an auxiliary task which predicts party affiliation. More specifically, we propose a model which integrates the contextual representations of fact descriptions and the party affiliation to predict penalties to be given. To verify the proposed approach, we conduct experiments on a dataset relevant to party discipline reinforcement. Empirical results demonstrate that the proposed model outperforms some prior models significantly.
Impact of Mobile Money Transfer on the Education Sector in Sub-Saharan Africa

Digitalization of payments related to education has played a significant role in driving the fourth agenda of the Sustainable Development Goals (SDG) aimed at providing free, equitable, and quality primary and secondary education to children by 2030. Since the launch of mobile money transfer (MMT) technologies by Safaricom in Kenya in 2007, many providers have developed a range of services to ensure efficient, transparent, and sustainable means of paying for school and college fees. This has led to enhanced teacher-student interaction times, reduced absenteeism, improved security in handling money, and made it easier for families to save, plan, and educate their children. This chapter reviews key success cases of countries and institutions that have digitalized payments and other education services to empower disadvantaged communities.
Simultaneous augmentation of textures and deformation based on dynamic projection mapping

In this demonstration, we exploit human perception characteristics and dynamic projection mapping techniques and realize overwriting of both textures and deformation of a real object. To keep the projection following a moving object and induce deformation illusion, we developed a 1000 fps projector-camera system and demonstrated augmentation of the real world. In the demonstration, the audience will see a plaster figure turning into a colorful and flabby object.
A Novel Mobile Device-Based Approach to Quantitative Mobility Measurements for Power Wheelchair Users

This study is motivated by the fact that there are currently no widely used applications available to quantitatively measure a power wheelchair user's mobility, which is an important indicator of quality of life. To address this issue, we propose an approach that allows power wheelchair users to use their own mobile devices, e.g., a smartphone or smartwatch, to non-intrusively collect mobility data in their daily life. However, the convenience of data collection brings substantial challenges in data analysis because the data patterns associated with wheelchair maneuvers are not as strong as other activities, e.g., walking, running, etc. In addition, the built-in sensors in different mobile devices create significant heterogeneity in terms of sensitivity, noise patterns, sampling settings, etc. To address the aforementioned challenges, we developed a novel approach composed of algorithms that work collaboratively to reduce noise, identify patterns intrinsic to wheelchair maneuvers, and finalize mobility analysis by removing spikes and dips caused by abrupt maneuver changes. We conducted a series of experiments to evaluate the proposed approach. Experimental results showed that our approach could accurately determine wheelchair maneuvers regardless of the models and placements of the mobile devices.
Гетероструктура квантово-каскадного детектора частотного диапазона 2.5 ТГц

The design of the heterostructure of a 2.5 THz range quantum-cascade detector is proposed and heterostructure is grown by molecular-beam epitaxy technique. To optimize the thicknesses of the layers of the heterostructure cascades, a numerical method for iterative solution of the Schrodinger ¨ −Poisson equation in the k · p formalism was used. The grown heterostructure of the quantum-cascade detector showed a high structural perfection, confirmed by the small values of the average FWHM of the high-order satellite peaks on the X-ray diffraction rocking curves, which were (8.3 ± 0.5)′′. Analysis of dark-field images obtained by transmission electron microscopy showed that the total thickness of the layers in the cascade is (137.3 ± 6.9) nm, which corresponds to the calculated thickness of the layers in the cascade of the heterostructure of the quantum-cascade detector.
PLIO: Physical Layer Identification using One-shot Learning

The Internet of Things (IoT) is connecting a massive scale of everyday objects to the internet. We need to ensure the secure connectivity and authentication of these devices. Physical (PHY)-layer identification methods can distinguish between different devices by leveraging their unique hardware imperfections. But these methods typically require large quantities of training data which makes them impractical for large deployment scenarios. Also, these methods do not address the PHY-layer identification of new devices joining an IoT network. In this paper, we propose a PHY-layer identification method using one-shot learning that can identify new devices using the network solicitation packet of the devices as reference packets. We show that our method can accurately identify new devices without training, achieving a precision and recall over 80% even in the presence of 10 dBm noise. Furthermore, we show that with minimal retraining using only three packets from each device, we can accurately identify all devices in the IoT network with a precision and recall of 93%.
Cryptanalysis of Fuzzy-Based Mobile Lightweight Protocol Scheme

With the advancement of technology evolvement in this twenty-first century, the use of mobile devices also increased with a rapid growth. Due to pluses of mobile devices like lightweight, mobility, features on day-to-day routines makes it a handy choice for today’s society. With the increasing use of the mobile technology, need of securing the data which were communicated among the devices becomes prevalent factor for research. Among various technologies for achieving the data security, the authentication protocol is more suitable because it provides authentication in addition to data security. The authentication protocol is broadly classified into three categories, i.e., one factor (identity), two factor (identity and text password), and three factor (identity, text password, and fuzzy biometric password). Among these, the three factor is widely used due to more secure as to others. Recently, Qiu et al. in 2020 proposed the authentication scheme using three factor for the mobile lightweight devices and proved safe against attacks. However, we prove that the Qiu et al.’s scheme is yet susceptible to the attacks like replay, session-specific temporary information (SSTI), denial of service, and stolen verifier. In addition, the Qiu et al.’s scheme yields overhead as to other conventional three-factor schemes.
An Efficient CannyRaw Edge Detection Algorithm for Raw Images

With the considerable increase in popularity of mobile photography, a large portion of efforts has been made to optimize the image signal processing (ISP) pipeline that transfers the raw images obtained directly from mobile camera sensors into color images. In this work, we demonstrate that the raw images are quite informative by showing the edges extracted from raw images and color images across a bunch of edge detection algorithms. Given that the characteristics and distribution of raw and color images are different, we present CannyRaw, an efficient edge detection algorithm designed for raw images, requiring no prior knowledge of the sensor and optics. Experimental results of the edge images under different illumination levels illustrate the robustness of CannyRaw to illumination and noise. Besides, the system power and resources can be reduced by the elimination of ISP.
OPay: an Orientation-based Contactless Payment Solution Against Passive Attacks

The usage of contactless payment has surged in recent years, especially during the Covid19 pandemic. A Passive relay (PR) attack against a contactless card is a well-known threat, which has been extensively studied in the past with many solutions available. However, with the mass deployment of mobile point-of-sale (mPoS) devices, there emerges a new threat, which we call mPoS-based passive (MP) attacks. In an MP attack, the various components required in a PR attack, including an NFC reader, a wireless link, a remote card emulator, and a remote payment terminal, are conveniently combined into one compact device, hence the attack becomes much easier. Since the attacker and the victim are in the same location, the previous distance bounding or ambient sensor-based solutions are no longer effective. In this paper, we propose a new orientation-based payment solution called OPay. OPay builds on the observation that when a user makes a legitimate contactless payment, the card and the terminal surface are naturally aligned, but in an attack scenario, this situation is less likely to occur. This allows us to distinguish the legitimate payments from passive attacks based on measuring the alignment of orientations. We build a concrete prototype using two Arduino boards embedded with NFC and motion sensors to act as a card and a payment terminal respectively. To evaluate the feasibility, we recruited twenty volunteers in a user study. Participants generally find OPay easy to use, fast and reliable. Experiments show that OPay can substantially reduce the attack success rate by 85-99% with little inconvenience to real users. To our best knowledge, OPay is the first solution that can prevent both the PR and MP attacks, while preserving the existing usage model in contactless payment.
Evaluation of an Actor Model-based Consensus Algorithm on Neo Blockchain

Neo Blockchain is a permissioned blockchain that adopts Practical Byzantine Fault Tolerance variant consensus algorithm called delegated Byzantine Fault Tolerance (dBFT). This algorithm is implemented using an Actor Model-based consensus algorithm, namely Akka.NET framework.In this paper, dBFT consensus algorithm among four nodes is simulated -- one primary and three backup nodes, that communicates using Akka.NET framework on a private chain on Neo Blockchain. The framework is used to evaluate the inner workings of the algorithm.The simulation resulted in the Akka.NET framework supporting multi-phases of dBFT consensus algorithm and parallel execution of tasks. In addition, it offers the asynchronous feature. Thus, it reduces the number of locks used and reduces deadlocks to potentially improve blockchain performance and scalability.
Hand Segmentation based on Geometry

Nowadays, gesture recognition accounts for the main part in human computer interaction (HCI), which is convenient for human life, work, entertainment and etc. However, the gesture recognition results are vulnerable to the environment, this paper tries to extract more complete hand information in traditional static gesture pictures or dynamic videos. According to the geometry, the angles between adjacent fingers can be calculated for identifying fingers and wrist. In fact, the sum of fingers' angle in hand is not more than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pi$</tex> , which can be used to judge that the specific location of the fingers and wrist. Thus, a better effect of cutting, ensuring the integrity of the hand can be achieved, there is also a robustness of gesture identification and rotation invariance.
To the question of realization of machine vision technology based on FPGA-architectures

Abstract In the last few years, machine learning and machine vision technologies have started to gain more and more popularity. This industry occupies one of the leading positions in the field of information technology. The paper is devoted to the development of a machine vision algorithm based on new generations of FPGAs for recognizing handwritten Cyrillic characters in images and video streams, in particular. The article raises the issues of using FPGA as an image segmentation accelerator and organizing work with the video stream, choosing the most suitable FPGA platform, creating training samples of handwritten characters, and working with the convolutional neural network AlexNet.
Classification of minority class in imbalanced data sets

This paper focuses on imbalanced data sets and uses different methodologies for the part of classification process related to building train and test subsets. Popular classification methods are then applied and evaluated based on the recall result for the minority class. On the basis of the results from our experiments we suggest that for imbalanced data sets when the minority class presents noticeably higher interest, we should use alternative methodology for building the train subset and not the standard random allocation of observations, in order to improve the predictive power of the classifier for the minority class.
Social Distancing Through Image Processing, Video Analysis, and CNN

The well-being emergency brought about by the episode of the COVID-19 has driven numerous nations to actualize intense social separating. Social removing is a bunch of non-drug intercessions or measures proposed to forestall the spread of a COVID-19 by keeping an actual distance among individuals and decreasing the occasions individuals come into close contact with one another. It normally includes staying away from others and trying not to assemble in huge gatherings. In any case, as spoken it isn’t as simple to screen the group at any area. Because of the group, the requirement for crowd examination framework for dealing with thick group is at a raise. We can execute this utilizing different calculations and advances, however we’ll become more acquainted with not many basic yet exquisite methods to dominate this issue. Utilizing the ideas IMAGE PROCESSING, VIDEO ANALYSIS AND CONVOLUTION NEURAL NETWORK (CNN), we can undoubtedly recognize a group and get frightened when the conventions of social distancing get abused at any open or private spots. This can be an extraordinary method to screen the public exercises in the coordinated and private areas, public spots and all the public help places. The framework when detects a group or assembling movement at any spot then it sends the quick insinuation to the director or the overseeing board and afterward, they can make a move over the action. This can be a great initiative to reduce the direct human supervision in any scene. Being fully automatic and functional, this advancement will be encouraged by private and government organizations.
SLBNet: Shallow and Lightweight Bilateral Network for Pose Estimation

Human pose estimation from images is an important task in many real-life applications. However, most existing methods focus on improving the effectiveness without considering efficiency, making the networks computationally expensive with a huge size. As depthwise separable convolution can help compress the model size and floating point operations (FLOPs), some methods combined it to make human pose estimation affordable on resource-constrained devices. However, depthwise separable convolution also slows down the inference speed, especially on GPU devices. In this paper, we introduce a shallow and lightweight bilateral network (SLBNet). Our network inferences much faster than the existing methods while achieves competitive performance. We evaluate our networks on the MPII and COCO datasets. Specially, our SLBNet yields 67.8 Average Precision (AP) on COCO test set with only 3.6M parameters and 4.5G FLOPs at 253 FPS on a single 2080Ti GPU, and 25 FPS on an Intel i7-8700K CPU machine.
Single and Ensemble CNN Models with Out-Category Penalty in Cifar 10

In recent years, CNN have been used in many image recognition tasks. However, most of these CNN models learn only the features of the image, and do not learn the meta-information of the image. In this study, we proposed CNN models that can learn not only image features but also meta-information such as animals and vehicles by imposing an out-category penalty on the cifar10 dataset. As a result, our proposed model was found to be able to learn with meta-information and produce higher accuracy than existing CNN models.
Discrete Wavelet Transform Based Non-blind Video Authentication Using Principal Component Analysis

This article introduces a non-blind video watermarking scheme embedded with QR code using Principal Component Analysis on Wavelet Transform Coefficients. The proposed method segregates the color video into individual frames. Each frame in video sequence is converted into YUV color space. Luminance component of each frame is decomposed using Discrete Wavelet Transform. Block based Principal Component Analysis (PCA) is applied to selective band of coefficients to get high invisibility. The binary image is chosen to be inserted in principal components of selected coefficients. In this proposal, embedding takes place in mid-frequency coefficients of DWT with least degradation of the perceptual quality of video. Hidden watermark is detected from the marked video by following the inverse processing steps. Strength of the algorithm is tested against various attacks like median filtering, Gaussian noise, salt and pepper noise and rotation, etc. Experimental results exhibit the imperceptibility of video and robustness of watermark image from the proposed scheme.