Reviewerly/exHarmony huggingface dataset:
English with Lucy YouTube Channel to Teach Students’ Speaking Skill

The research aimed to analyze speaking skill through “English with Lucy” YouTube channel. The research was conduct in the class 11 of SMK Kesehatan Letris Indonesia 2. The method of the research was Quantitative with Pre-experimental study. Writer choose one class as Experimental class. The used purposive sampling technique by choosing 33 participant and conducted pre-test and post-test to gather the writer data. The experimental class was using “English with Lucy” YouTube channel on learning English. The writer lasted for four months and writer compared the results of pre-test and post-test through SPSS 22 to conclude the results of research. The experimental class results would be the conclusion of research as this class used Vlogger Video during the lesson. The highest score of the pre-test was 60. And the highest score of the post-test was 92. There were the differences between pre-test score and post-test score. The value of the df t-value is searched based on n=32 with a range of 0,025. Its df t-value is 25,14. Because the t-test value is higher than the df t-value (25.14 &gt; 1.6956) h1 is accepted and h0 is rejected, so the learning method is successful in improving students’ speaking skill. It means that teaching speaking skill through “English with Lucy” YouTube channel was significant.
Learning trivializing flows

The recent introduction of machine learning techniques, especially normalizing flows, for the sampling of lattice gauge theories has shed some hope on improving the sampling efficiency of the traditional HMC algorithm.Naive use of normalizing flows has been shown to lead to bad scaling with the volume.In this talk we propose using local normalizing flows at a scale given by the correlation length.Even if naively these transformations have a small acceptance, when combined with the HMC algorithm lead to algorithms with high acceptance, and also with reduced autocorrelation times compared with HMC.Several scaling tests are performed in the 4 theory in 2D.
High-order masking of NTRU

The main protection against side-channel attacks consists in computing every function with multiple shares via the masking countermeasure. While the masking countermeasure was originally developed for securing block-ciphers such as AES, the protection of lattice-based cryptosystems is often more challenging, because of the diversity of the underlying algorithms. In this paper, we introduce new gadgets for the high-order masking of the NTRU cryptosystem, with security proofs in the classical ISW probing model. We then describe the first fully masked implementation of the NTRU Key Encapsulation Mechanism submitted to NIST, including the key generation. To assess the practicality of our countermeasures, we provide a concrete implementation on ARM Cortex-M3 architecture, and eventually a t-test leakage evaluation.
Refining Noisy Labels with Label Reliability Perception for Person Re-identification

Most person re-identification (Re-ID) approaches rely excessively on a great quantity of annotated training data. However, due to sampling errors or annotated errors, the label noise is unavoidable, which usually causes a dramatic decrease in the performance of existing Re-ID methods. To address this problem, we propose the label reliability perception (LRP) for person Re-ID by refining noisy labels. Specifically, a feature-fusion block (FFB) is proposed to enhance the discrimen- ability of pedestrians' features by expanding the network's attention span due to the fused feature, which is generated by overlapping the coarse-grained feature obtained by global average pooling and fine-grained features obtained by evenly dividing the feature map in the height dimension and performing global max pooling. In addition, the label dual perception (LDP) is proposed to refine noisy labels instead of filtering samples by evaluating the reliability of each training sample's label. Specifically, we meticulously design five evaluation modes for each sample to perceive the reliability of the labels of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor images. Finally, we utilize the most reliable label to replace the noisy label and optimize the network. Extensive experiments prove the superiority of the proposed model over the competing methods; for instance, on Market1501, our method achieves 88.8% rank-1 accuracy and 70.5% mAP (4.7% and 4.3% improvements over the state-of-the-arts) under noise ratio 20%, and similarly on DukeMTMC-ReID, our method achieves 77.7% and 60.3%.
Pendidikan Inklusi: Gangguan Keterlambatan Bicara (Speech Delay) pada Siswa SDN Sukasari 5

Children's language gradually develops according to the stimulation given by parents, teachers and society. The language development of children aged 6-12 years can be seen through speaking fluently, mastery of language and delivery of words is more complex. This study aims to identify cases of children who are late to speak, to find out the efforts of teachers and parents in overcoming children who are late to speak and to design a concept model for developing language skills of children who are late speaking. The approach used in this study is a qualitative descriptive approach. Data sources consist of: subjects (children, teachers, parents and speech therapists). Data was collected through observation, and interviews. The results showed that there were children who were late in speaking at the age of 6-12 years at the Sukasari 5 State Elementary School with a type of speech and language expressive disorder. Factors that affect children's speech delay consist of: intelligence, use of a second language, speech style/model that is imitated, health, and family relationships. The concept of the model that the researcher offers in this study is in the form of strategies/techniques to overcome speech delays in children, namely: training children to speak correctly, slowly and repeatedly, when speaking always pay attention to the grammar spoken, always involving children in speaking in every situation by improving children's pronunciation is still wrong, and using several systems such as regular consultations to find out the child's development to doctors and child psychologists.
PLAME: Piecewise-Linear Approximate Measure for Additive Kernel SVM

Additive Kernel SVM has been extensively used in many applications, including human activity detection and pedestrian detection. Since training an additive kernel SVM model is very time-consuming, which is not scalable to large-scale datasets, many efficient solutions have been developed in the past few years. However, most of the existing methods normally fail to achieve one of these three important conditions which are (1) low classification error, (2) low memory space, and (3) low training time. In order to simultaneously fulfill these three conditions, we develop the new piecewise-linear approximate measure (PLAME) for additive kernels. By incorporating PLAME with the well-known dual coordinate descent method, we theoretically show that this approach can achieve the above three conditions. Experimental results on twelve real datasets show that our approach can achieve the best trade-off between the accuracy, memory space, and training time compared with different types of state-of-the-art methods.
SoK: Differentially Private Publication of Trajectory Data

Trajectory analysis holds many promises, from improvements in traffic management to routing advice or infrastructure development. However, learning users' paths is extremely privacy-invasive. Therefore, there is a necessity to protect trajectories such that we preserve the global properties, useful for analysis, while specific and private information of individuals remains inaccessible. Trajectories, however, are difficult to protect, since they are sequential, highly dimensional, correlated, bound to geophysical restrictions, and easily mapped to semantic points of interest. This paper aims to establish a systematic framework on protective masking and synthetic-generation measures for trajectory databases with syntactic and differentially private (DP) guarantees, including also utility properties, derived from ideas and limitations of existing proposals. To reach this goal, we systematize the utility metrics used throughout the literature, deeply analyze the DP granularity notions, explore and elaborate on the state of the art on privacy-enhancing mechanisms and their problems, and expose the main limitations of DP notions in the context of trajectories.
Novel private cloud architecture: A three tier approach to deploy private cloud using virtual machine manager

In Present days, one of the most appealing technologies is cloud computing in the current time. Now a day, different organizations are offering various cloud services concerning their platforms. These organizations are profitable in cost-efficiency to the end-users in various factors that include process, storage, and applications instance. Most of the end-users choose the services from the cloud service provider, whereas some medium-scale organizations are also depending on the service providers even though they have enough resources. This causes the problem due to a lack of knowledge on design and deployment issues. In this paper, we proposed a private cloud that offers cloud services with limited resources so that small and medium scale organizations can be benefited. In this work, we proposed a novel three tier architecture that contains Domain Controller (DC), Virtual Machine Manager (VMM), and Member Servers (MSs) at a different level of functioning and also have their responsibilities. In this Novel three tier architecture was designed in a hierarchal fashion in which the upper layer accesses the lower layer users. The top layer, which deals with the domain controller whose responsibility, is to control the domain-specific issues like adding the member in the network, granting permissions, creating network groups, etc. The middle layer has various administrative users like Virtual Machine Manager (VMM), Storage Area Network (SAN), Disaster Recovery (D/R), and Operational Manager (SCOM), which deals with their responsibilities in cloud services. In the bottom layer, the physical servers exist and are configured by DC and get access by the middle layer users. This architecture was tested with various applications with different instances concerning storage and process utilities.
An efficient calibration model for fringe projection profilometry using inclined Lambertian plane based on photometric stereo

System calibration is essential step for fringe projection profilometry (FPP) that determines how phase could be transformed into 3D surface. In this work, an efficient calibration model using inclined Lambertian plane for FPP is proposed, which effectively simplify the calibration process compared with the conventional methods and avoid extensive nonlinear fitting in establishing the relationship between phase and height. The principle for recovering the shape of the Lambertian plane based on photometric stereo (PS) is firstly introduced according to our previous work. With phase retrieved through 4-step phase shifting algorithm, the generation of surface height with respect to phase map and the shape of the Lambertian plane are discussed in theory, which allows the directly establishment of the calibration model. With the proposed 3D measurement system, our method is evaluated by contrast experiment with the polynomial phase-to-height model. In experiments, a plaster model and a compressor blade are measured according to the calibrations utilizing the new method, demonstrates the excellent measurement accuracy and the efficiency improvement. The parallel measurement using commercial 3D scanner is also provided, where most of the measured blade profile deviations lays within ±0.2 mm.
IMPLEMENTATION OF SOBEL EDGE DETECTION

Tremendous growth of VLSI and reduced chip size always stands as an added advantage to the automobile field.Advanced driver assistance system(ADAS) is seen as the keystone to next generation of technological revolution.Existing lane detection method uses graphics processor which lags in time compared to Field programmable Gate array (FPGA) processor.We propose a system that uses FPGA in replacement of Graphic processor to enhance the speed of operation.ADAS assist the driver in driving action.They are implemented to increase the safety and also to provide driving comfort for the users.In this paper we proposed a method where preprocessing is done by taking an image as an input and converting it into matrix and the output is generated as coe file format using MATLAB.Later this file is given as an input to Vivado and sobel edge detection is performed.There are various method that can be used to detect lane but this paper focuses on sobel edge detection since it is simple and it is not time consuming.The output is generated as coe file format which can be used in post processing in MATLAB and can be converted it back into the original image.We have completed synthesis and generated utilization report which help us to analyze the utilization of the design with different resources.This can be later implemented using Zynq 7000 series board.With this we can provide an effective driving direction for modern cars and provide the exact position of the vehicle in the lane.It can also alert the driver when the vehicle is found to be moved away from the lane.So our proposed system increases efficiency and also the driving safety of automatic driving.
INVESTIGATING MEMORY LOSS: HOW DEPRESSION AFFECTS STUDENTS’ MEMORY ENDURANCE

Depression is a disorder that affects feelings and thoughts in the form of feelings of continuous sadness and a sense of loss of interest before doing an activity. One of the effects of depression on adolescents is related to their decreased memory ability sometimes they do not remember the subject matter and even seem to daydream a lot. Depression that affects students' memory endurance is influenced by the amount of pressure they receive during school, at home, and even unfinished tasks. This causes a weakening of the memory system, especially short-term memory. The aim of this study was to investigate how depression affects students' memory endurance. The researcher uses a descriptive as a research method with exposure generated by a questionnaire. By generating and analyzing the result, some methods are suggested to help the students improve their memory endurance in learning some materials and lessons at school.
Parakeet: Practical Key Transparency for End-to-End Encrypted Messaging

Encryption alone is not enough for secure end-toend encrypted messaging: a server must also honestly serve public keys to users.Key transparency has been presented as an efficient solution for detecting (and hence deterring) a server that attempts to dishonestly serve keys.Key transparency involves two major components: (1) a username to public key mapping, stored and cryptographically committed to by the server, and, (2) an outof-band consistency protocol for serving short commitments to users.In the setting of real-world deployments and supporting production scale, new challenges must be considered for both of these components.We enumerate these challenges and provide solutions to address them.In particular, we design and implement a memory-optimized and privacy-preserving verifiable data structure for committing to the username to public key store.To make this implementation viable for production, we also integrate support for persistent and distributed storage.We also propose a future-facing solution, termed "compaction", as a mechanism for mitigating practical issues that arise from dealing with infinitely growing server data structures.Finally, we implement a consensusless solution that achieves the minimum requirements for a service that consistently distributes commitments for a transparency application, providing a much more efficient protocol for distributing small and consistent commitments to users.This culminates in our production-grade implementation of a key transparency system (Parakeet) which we have open-sourced, along with a demonstration of feasibility through our benchmarks.
Spatial awareness application using mixed reality for 3D X-ray CT examination

Abstract In the medical field, X-ray computed tomography (CT) is used to determine the size of defects and damage in an examined object, and to diagnose infectious diseases. Generally, data captured by 3D X-ray CT are viewed as images in three directions (sagittal, axial, and coronal) on a computer. However, augmented reality, virtual reality, and mixed reality are emerging as alternatives for imaging captured data for 3D X-ray CT and are used for medical simulations and educational purposes. Although these techniques are capable of 3D expression, they are limited to the representation of the surface structure of the object. The original function required of these technologies is to check the tomographic image of a specific part of the object by specifying any direction and position in three dimensions. This study proposes a method of representation in which a 2D digital imaging communication image is superimposed on a surface-rendered cross-section of an object. In addition, it proposes a pointing system using motion capture and a spatial reality display. The observer can confirm the object from any direction and understand the structure spatially. It can be moved and rotated with movements similar to actually holding, grabbing, moving, and rotating with hands. Moreover, the cross-section can be observed in any direction. In addition, by matching the respective scales of the device and application, the object can be represented with an error of less than 1 mm in the horizontal, vertical, and depth directions with respect to the actual object. Therefore, the proposed method is an effective 3D representation method in 3D X-rays, which are voxel data containing internal information. Furthermore, this method can easily indicate the desired location where the cross-section image can be viewed in a CT image. This study will help improve the efficiency of 3D X-ray inspection and surgery in the medical field.
Visual inspection with augmented reality head‐mounted display: An Australian usability case study

Abstract Augmented reality (AR) is an Industry 4.0 technology. For more than a decade, advancements in AR technology and their applications have been expected to revolutionise the manufacturing industry and deliver quality and productivity gains. However, due to factors such as equipment costs, skills shortages and technological limitations of AR devices, operational deployment beyond prototypes has been constrained. Real‐world, usability studies can explore barriers to implementation and improve system design. This paper details a mixed method usability case study of an AR head‐mounted display (HMD) to perform a short, simple visual inspection task. Twenty‐two participants from South Australian manufacturing businesses inspected a pump and pipe skid while working at height. Overall, workload demands for the task were considered acceptable and just below the “low” workload threshold (NASA Task Load Index, mean = 29.3) and the system usability was rated “average” (system usability scale, mean = 68.5). The results suggest the task did not place too high a burden on users and was an appropriate initial exposure to AR HMDs, but further refinement to the interface would be desirable before implementation to minimise frustration and promote learning. Users were enthusiastic and open‐minded about the AR HMD although results indicate that even with recent advancements in AR HMD technology, interactions between the task, technology and environment continue to cause human and technical challenges—some of which are relatively straightforward to address but others are dependent on larger‐scale efforts.
Active learning and novel model calibration measurements for automated visual inspection in manufacturing

Abstract Quality control is a crucial activity performed by manufacturing enterprises to ensure that their products meet quality standards and avoid potential damage to the brand’s reputation. The decreased cost of sensors and connectivity enabled increasing digitalization of manufacturing. In addition, artificial intelligence enables higher degrees of automation, reducing overall costs and time required for defect inspection. This research compares three active learning approaches, having single and multiple oracles, to visual inspection. Six new metrics are proposed to assess the quality of calibration without the need for ground truth. Furthermore, this research explores whether existing calibrators can improve performance by leveraging an approximate ground truth to enlarge the calibration set. The experiments were performed on real-world data provided by Philips Consumer Lifestyle BV . Our results show that the explored active learning settings can reduce the data labeling effort by between three and four percent without detriment to the overall quality goals, considering a threshold of p = 0.95. Furthermore, the results show that the proposed calibration metrics successfully capture relevant information otherwise available to metrics used up to date only through ground truth data. Therefore, the proposed metrics can be used to estimate the quality of models’ probability calibration without committing to a labeling effort to obtain ground truth data.
Copycat Crime and Copycat Criminals

How prevalent is copycat crime? Can we accurately identify it? What role does the media play in encouraging it? These are among the questions that Ray Surette addresses in his comprehensive study of the nature of copycat crime, both past and present, and the forces that drive it. Surette goes beyond prevalent myths and anecdotal evidence to rigorously define copycat crime and to place it in theoretical context. He also thoroughly explores how the changing nature of the media, especially the evolution of social media, has had an impact on copycat crime and criminals. His book brings together insights from a range of disciplines and a wealth of examples, successfully countering misconceptions and making evidence-based recommendations for social policies and practices.
Comparison of algorithms for the removal of impulsive noise from an image

Image pre-processing is an important operation that is used to redefine an image to improve human visual perception and information extraction. To de-noise an image tainted with impulsive noise, several state-of-the-art methods have been presented. This work examines and compares several de-noising methods based on the median filter and its advanced non-linear techniques. The research also focuses on the recommended implementation methodologies for using deep learning to de-noise impulsive noise. The paper focuses on one approach's limitations and possible remedies, as well as other techniques that have been offered. The study also identifies several other difficulties that have yet to be resolved.
Monitoring Health of IoT Equipped 3-Phase Induction Motor using Interactive Dashboard

Induction motors, one of the most crucial industrial parts, are highly expensive and need regular maintenance. Lack of maintenance knowledge could lead to malfunctions that are unworkable for industries. The motor's continuous operation causes an increase in temperature. It is impossible to manually gauge the temperature of a 3-Phase induction AC motor every single second. If the temperature is higher than the typical or average value, the motor may become flawed, which could affect how well it works. Because of this, it's imperative to constantly monitor the temperature. To resolve this issue the proposed system creates an interactive dashboard, from which defects in temperature variance can be found and examined for preventive maintenance. The motor's maximum, average, and minimum temperatures are displayed on an interactive dashboard. The suggested system's goal is to provide real-time aspect monitoring from any location.