Reviewerly/exHarmony huggingface dataset:
Chemistry course network teaching based on key information search and big data cloud platform

At present, the theoretical and experimental teaching parts of chemistry teaching are difficult to present through online teaching, and the teaching effect is difficult to effectively improve. In order to improve the effect of online teaching of chemistry courses and discover the problems in teaching in a timely and effective manner, this paper builds the corresponding teaching system, optimizes the performance of the system with key information search and big data cloud platform, improves the data mining ability and data analysis ability, and improves the system running speed. Moreover, this article constructs functional modules according to the actual needs of chemistry course network teaching and constructs sub-modules for functional modules. The constructed model can be displayed in the form of a webpage, and the model has the ability of data mining and problem analysis of chemistry course network teaching. In addition, in order to verify the effect of the model, this paper designs experiments to analyze its performance. The research results show that the system constructed in this paper has good performance.
Performance Analysis of Non-Profiled Side Channel Attacks Based on Convolutional Neural Networks

There are emerging issues about side channel at-tacks (SCAs) on the cryptographic devices which are widely used today for securing secret information. Recently, the neural networks have been introduced as a new promising approach to perform SCA for hardware security evaluation of cryptographic algorithms. In this work, we present a non-profiled SCA using convolutional neural networks (CNNs) on an 8-bit AVR micro-controller device running the AES-128 cryptographic algorithm. We aim to point out the practical issues that occurs in CNN based SCA methods using the aligned power traces with a large number of samples. Furthermore, a method to build a suitable dataset for CNN training is introduced. Especially, practical experiment results of the CNN based SCA methods and a comprehensive investigation on the effect of noise are also presented. These experiments are performed with the original power traces and additive Gaussian noise. The results show that the CNN based SCA with our constructed dataset provides reliable results for non-profiled attacks. However, it is also shown that the Gaussian noise added on power traces becomes a serious problem.
Deep Learning Sign Language Recognition System Based on Wi-Fi CSI

Many sensing gesture recognition systems based on Wi-Fi signals are introduced because of the commercial off-the-shelf Wi-Fi devices without any need for additional equipment. In this paper, a deep learning-based sign language recognition system is proposed. Wi-Fi CSI amplitude and phase information is used as input to the proposed model. The proposed model uses three types of deep learning: CNN, LSTM, and ABLSTM with a complete study of the impact of optimizers, the use of amplitude and phase of CSI, and preprocessing phase. Accuracy, F-score, Precision, and recall are used as performance metrics to evaluate the proposed model. The proposed model achieves 99.855%, 99.674%, 99.734%, and 93.84% average recognition accuracy for the lab, home, lab + home, and 5 different users in a lab environment, respectively. Experimental results show that the proposed model can effectively detect sign gestures in complex environments compared with some deep learning recognition models.
Recommending Composite Refactorings for Smell Removal

Structural degradation is the process in which quality attributes of a system are negatively impacted. When due attention is not paid to structural degradation, the source code may also become difficult to change. Code smells are recurring structures in the source code that may represent structural degradation. Hence, there are many catalogs and techniques for supporting the removal of code smells through refactoring recommendations, which usually consist of single refactorings such as a Move Method or an Extract Method. However, single refactorings are often not enough for completely removing certain smell occurrences. Moreover, recent studies show that developers most often apply composite refactorings - i.e., sequences of two or more refactorings - for removing code smells. Despite showing the importance of performing composite refactorings, most studies do not provide information on which composite refactoring patterns are recurrent in practice. In this context, a previous study identified 35 smell removal patterns that are frequent across multiple open source systems. However, such study has not explored how the removal patterns could help developers to apply effective composite refactorings. Thus, in this work, we propose a suite of new recommendation heuristics to help developers in applying effective composite refactorings. These heuristics are intended to remove three code smell types, namely Complex Class, Feature Envy, and God Class. After designing the heuristics, we evaluated their effectiveness through a quasi-experiment. This evaluation was conducted with 12 software developers and 9 smelly Java classes. Results indicate that developers considered our heuristics effective or partially effective in more than 93% of the cases. In addition, the evaluation helped us to identify multiple factors that contribute to the acceptance or rejection of the refactoring recommendations. Based on these factors, we defined new guidelines for the effective recommendation of smell-removal composite refactorings.
Human Motion Enhancement Using Nonlinear Kalman Filter Assisted Convolutional Autoencoders

Human motion analysis is integral to many applications ranging from biomedicine to surveillance and 3D animation. The availability of consumer RGB-D sensors makes motion capture (Mocap) more prevalent and accessible in our daily lives. However, depth-based Mocap (D-Mocap) suffers significant errors and noise due to the limitation of depth sensing, self-occlusion, and many other problems. We present a novel filter-assisted deep learning approach to improve low-quality human motion data (e.g., D-Mocap) by taking advantage of the recent progress in both deep learning and nonlinear Kalman filtering. At the heart of this method is a learned motion manifold through the use of a convolutional autoencoder trained on high-quality, rich-variety CMU Mocap data which is used to recover valid human motion from corrupted input. Furthermore, the Tobit Kalman filter (TKF), proposed to handle censored measurements, is used to assist the autoencoder with more kinematic and dynamic constraints. Two structural paradigms are investigated to handle different kinds of data errors by maximizing the synergy between the two integral parts in this work. The experimental results on both simulated and real-world human motion data demonstrate the effectiveness and robustness of the proposed methods to improve the quality of noisy and erroneous Mocap data.
UML Modeling and Black Box Testing Methods in the School Payment Information System

The payment information system enables the entry of cash transaction data to SMA Teluk Panji through administrative staff (TU). The TU maintains the money payment reports that the students pay. This approach is useful and allows students to know more about the money they have to spend. This approach gives students complete knowledge in paper form. Useful reports to discover how much they charged and students who did not pay. Model UML is a visual language for modeling and designing information systems or applications. As support for the development of this information system, UML uses diagrams and text. Any significant modeling influences the design of useful applications. The system must meet its specifications and be capable of converting data into usable and efficient users. The black box evaluation method serves as a software assessment. The method operates from inside the unknown. When the system testers test this system as designed and focused on the school payment information system, the information system software is appropriately controlled by the external control mechanism. The prototyping method aims to summarise the application developed during these phases.
An Autonomous Mobile Robot Platform for Medical Purpose

An autonomous mobile robot is a mobile robot that can execute a path-plan autonomously to perform certain behaviors or tasks. This paper presents the hardware overview consist of mechanical design, motion control system, and modular subsystem of ROM20. ROM20 is an autonomous mobile robot platform that is being developed at the Research Center for Electrical Power and Mechatronics, Indonesian Institute of Sciences (RCEPM-LIPI) for medical purpose. ROM20 uses four stepper motors connected to four mecanum wheels. The motion control system is based on UART serial communication on each pin TX and RX of two microcontroller and bluetooth module. On top of the ROM20 is the modular which can be replaced with several subsystems such as Ultraviolet type C (UVC) lamp irradiation, disinfectant mist sprayer, and food delivery warmer. ROM20 can be controlled via bluetooth communication with a distance up to 22 meters. The space used by ROM20 for maneuver in any directions is around 63 cm × 63 cm of square tile.
TTFNeXt for real-time object detection

Modern object detectors rarely achieve short training time, fast inference speed, and high accuracy at the same time. To strike a balance among them, we propose single-scale TTFNet and multi-scale TTFNeXt. In this work, we use light-head, single-stage, and anchor-free designs, which enable fast inference speed. Then, we focus on reducing training time and improving accuracy. We notice that encoding more training samples from annotated boxes plays a similar role as increasing batch size, which helps enlarge the learning rate and accelerate the training process. To this end, we introduce a dense regression approach based on Gaussian kernels. We also show through experiments that deformable convolutions in our single-scale detector are not sufficient to handle the scale-variation problem. Therefore, we extend the single-scale detector to a multi-scale version. The multi-scale design will yield redundant detections from different pyramid levels, thus we introduce our cross-level NMS algorithm to efficiently eliminate redundant results. Experiments on MS COCO show that our TTFNet and TTFNeXt have great advantages in balancing training time, inference speed, and accuracy. They can reduce training time by more than three times compared to previous real-time detectors under similar detection accuracy and faster inference speed. When training 120 epochs, our TTFNeXt is able to achieve 33.7 AP/99 FPS and 41.8 AP/40 FPS with single GTX 1080Ti.
On the Security of Lattice-Based Fiat-Shamir Signatures in the Presence of Randomness Leakage

Leakages during the signing process, including partial key exposure and partial (or complete) randomness exposure, may be devastating for the security of digital signatures. In this work, we investigate the security of lattice-based Fiat-Shamir signatures in the presence of randomness leakage. To this end, we present a generic key recovery attack that relies on minimum leakage of randomness, and then theoretically connect it to a variant of Integer-LWE (ILWE) problem. The ILWE problem, introduced by Bootle et al. at Asiacrypt 2018, is to recover the secret vector s given polynomially many samples of the form (a, 〈a〉, s)+e) ϵ ℤ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n+1</sup> , and it is solvable if the error e ϵ ℤ is not superpolynomially larger than the inner product (a, s). However, in our variant (we call the variant FS-ILWE problem in this paper), a ϵ ℤ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> is a sparse vector whose coefficients are NOT independent any more, and e is related to a and s as well. We prove that the FS-ILWE problem can be solved in polynomial time, and present an efficient algorithm to solve it. Our generic key recovery method directly implies that many lattice-based Fiat-Shamir signatures will be totally broken with one (deterministic or probabilistic) bit of randomness leakage per signature. Our attack has been validated by experiments on two NIST PQC signatures Dilithium and qTESLA. For example, as to Dilithium-III of 125-bit quantum security, the secret key will be recovered within 10 seconds over an ordinary PC desktop, with about one million signatures. Similarly, key recovery attacks on Dilithium under other parameters and qTESLA will be completed within 20 seconds and 31 minutes respectively. In addition, we also present a non-profiled attack to show how to obtain the required randomness bit in practice through power analysis attacks on a proof-of-concept implementation of polynomial addition. The experimental results confirm the practical feasibility of our method.
Feasibility and Suppression of Adversarial Patch Attacks on End-to-End Vehicle Control

In an end-to-end vehicle control scenario, where a deep neural network is trained on visual input solely, adversarial vulnerability leaves a possibility to manipulate the steering predictions. Patch-based adversarial attacks present an especially serious menace, because they can be performed in the real world by printing out a generated universal pattern. However, the boundary conditions and feasibility of such attacks to compromise the security of autonomous vehicles have been only sparsely studied so far.We demonstrate and evaluate such attacks in the CARLA simulative environment under different weather and lighting settings, while conducting experiments in open and closed loop attack scenarios. Our findings reveal that attack strength is highly dependent on the surrounding location as well as on environment conditions. We also observe that attack success in an open loop scenario only partially coincides with that in a closed loop scenario. This analysis helps to set the stage for future experiments on public roads.Furthermore, we propose a defense concept to remove malignant perturbations from an input image, which does not affect its salient regions. We analyze deviations from the unattacked vehicle trajectory both on adversarial and suppressed inputs.
Location-Aware Feature Interaction Learning for Web Service Recommendation

With the increasing prevalence of web services on the World Wide Web, a large number of functionally equivalent web services are provided by different providers. Quality-of-Service (QoS), representing the nonfunctional characteristics, plays an important role in dealing with how to recommend the optimal services to users among these candidates. Many existing methods for predicting QoS values of web services show that QoS values are intensively relevant to location due to the great influence of network distance and the internet connection between users and services. In this paper, we propose a novel location-aware feature interaction learning (LAFIL) method for predicting the QoS values of the user-service matrix and then making the recommendation by learning the underlying relation, which is hidden in the features concerning with location information. LAFIL can effectively solve the problems of data sparsity and cold-start by leveraging the location features of both users and services. To evaluate the performance of our proposed method, comprehensive experiments are conducted using a real-world dataset and the results show that our method achieves better QoS prediction accuracy compared to state-of-the-art approaches.
Mapping of hydrothermal alterations related to gold mineralization within parts of the Malumfashi Schist Belt, North-Western Nigeria

Remote sensing methods are a vital alternative for regional exploration surveys. Many ore deposits (e.g., epithermal, porphyry-related, volcanogenic massive sulphides, etc.) have distinct distribution patterns of alteration zones that can be used for recognizing this mineralization. Several known goldfields are distributed within the basement rocks of western Nigeria. The area of interest, Malumfashi Schist Belt, is located in North-Western Nigeria and is characterized by gneisses and metasediments that were intruded by Pan-African granitoids. Gold mineralization occurs as veins and veinlets that are associated with hydrothermal alteration zones (i.e., argillic, phyllic, and propylitic). Hence, the discrimination of these alteration zones is one of the key indicators for new prospective zones of gold mineralization in this metallogenic belt. In the present study, Landsat Enhanced Thematic Mapper+ (Landsat-7 ETM+) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were processed and integrated with the aim to identify possible locations for gold mineralization within the Malumfashi Schist Belt. For this purpose, the band ratio techniques and Principal Component Analysis (PCA) were applied to identify, enhance and map the different alteration types, while fractal analysis was utilized to quantify the degree of alteration within each processed image. Using the multi-criteria evaluation method, the discretized images obtained from the fractal analysis were weighted and integrated into an enhanced possible location for gold occurrences. A receiver operating curve/Area under curve analysis was then used to evaluate the reliability of the predictive model. Both spatial and GIS analyses indicate that gold mineralization displays a proximal relationship to hydrothermal alteration data. We can map sets of alteration minerals which mainly represent new and good ore prospects for the investigated area. A sensitivity analysis points out a predictive accuracy of 78%, which suggests the model is capable of predicting gold occurrences within the study region. Besides, the results showed that the prospective zones of gold accumulations mainly occur within metasedimentary units. It is recommended that the studied dataset provide a potential tool for mapping alteration minerals related to gold deposits that can be applied in other regions with analogous geological setting.
OGITO, an Open Geospatial Interactive Tool to support collaborative spatial planning with a maptable

Maptables are increasingly used to support collaborative spatial planning processes. Despite the proven benefits and claimed potential of using a maptable in such processes, software applications specifically designed for this device are still scarce. Moreover, often-used applications do not fully exploit the touch functionality of a maptable, or have low usability. To address this gap, we developed and evaluated the Open Geospatial Interactive TOol (OGITO), an open-source software application designed to support collaborative spatial planning processes with a maptable. To develop such tool, we combined human-centred design and Agile software development principles in a co-design effort with intended users and stakeholders. Through iterative development cycles and feedback from users, OGITO was evolved until it satisfied user expectations. In a case study on community mapping in Sumatra, Indonesia, a sample of users evaluated OGITO's usability. Case study participants reported high satisfaction with this tool for the tasks and context given. Our research shows the added value of iterative development and user feedback for improving and further development of the tool's usability and functionality.
Auto-scaling of Web Applications in Clouds: A Tail Latency Evaluation

Mechanisms for dynamically adding and removing Virtual Machines (VMs) to reduce cost while minimizing the latency are called auto-scaling. Latency improvements are mainly fulfilled through minimizing the "average" response times while unpredictabilities and fluctuations of the Web applications, aka flash crowds, can result in very high latencies for users' requests. Requests influenced by flash crowd suffer from long latencies, known as outliers. Such outliers are inevitable to a large extent as auto-scaling solutions continue to improve the average, not the "tail" of latencies. In this paper, we study possible sources of tail latency in auto-scaling mechanisms for Web applications. Based on our extensive evaluations in a real cloud platform, we discovered sources of a tail latency as 1) large requests, i.e. those data-intensive; 2) long-term scaling intervals; 3) instant analysis of scaling parameters; 4) conservative, i.e. tight, threshold tuning; 5) load-unaware surplus VM selection policies used for executing a scale-down decision; 6) cooldown feature, although cost-effective; and 7) VM start-up delay. We also discovered that after improving the average latency by auto-scaling mechanisms, the tail may behave differently, demanding dedicated tail-aware solutions for auto-scaling mechanisms.
Universal statistics of Fisher information in deep neural networks: mean field approach<sup>*</sup>

Abstract The Fisher information matrix (FIM) is a fundamental quantity to represent the characteristics of a stochastic model, including deep neural networks (DNNs). The present study reveals novel statistics of FIM that are universal among a wide class of DNNs. To this end, we use random weights and large width limits, which enables us to utilize mean field theories. We investigate the asymptotic statistics of the FIM’s eigenvalues and reveal that most of them are close to zero while the maximum eigenvalue takes a huge value. Because the landscape of the parameter space is defined by the FIM, it is locally flat in most dimensions, but strongly distorted in others. Moreover, we demonstrate the potential usage of the derived statistics in learning strategies. First, small eigenvalues that induce flatness can be connected to a norm-based capacity measure of generalization ability. Second, the maximum eigenvalue that induces the distortion enables us to quantitatively estimate an appropriately sized learning rate for gradient methods to converge.
The negative mayfly optimization algorithm

Abstract The global best or historical best positions were involved in updating positions of individuals in swarms for almost all of the swarm-based nature-inspired algorithms with exceptions for the averages. However, literal research to the particle swarm optimization (PSO) algorithm had proved that the all of the candidates would also leave the global best and the historical best candidates and such improvement would result in a better performance. Similarly, the negative mayfly optimization (MO) algorithm was proposed based on such conditions. Simulation experiments were carried out and verified that the negative MO algorithm could perform better than the original MO algorithm, especially in optimizing the multimodal benchmark functions.
Shadow Detection and Removal Using GAN

To remove shadowed region in a single image, it is important to obtain high accuracy in both two processes, shadow detection and removal. In order to improve the results, recent methods perform these two processes simultaneously and use GAN for the training. However, since these methods do not try to maintain the luminance of non-shadowed regions, the output images tend to be faded. In this paper, to overcome fading problem, we proposed a new GAN structure based on shadow model. Since our GAN-based method focus on the variation of the illuminance, the illuminances of the shadowed regions, whose amount of change are large, are effectively estimated. In addition, non-shadowed regions remain slightly faded due to our new GAN structure and training method. Owing to our novel GAN structure and training method, our method outperforms state-of-the-art methods in PSNR and SSIM.