Reviewerly/exHarmony huggingface dataset:
Uncertainty-aware Evaluation of Machine Learning Performance in binary Classification Tasks

Machine learning has become a standard tool in computer vision. Nowadays, neural networks are one of the most prominent representatives in this class of algorithms that usually require training and evaluation to work as desired. There exist a variety of evaluation metrics to determine the quality of a trained neural network, which are usually threshold dependent. This results in massive changes in the resulting evaluation when the threshold is changed slightly. Further, measurements of uncertainty such as resulting from Bayesian approaches, are not considered in this analysis. In this paper, we present evaluation metrics for machine learning approaches that are able to attach a probability distribution to the utilized threshold and include uncertainty measures. We demonstrate the applicability of our approach by applying the defined metrics to a real-world example where a Bayesian neural network has been used to predict stroke lesions.
Hue-indexing-based absolute phase retrieval method using a discrete hue sequence

To achieve an effective phase unwrapping for hue-based fringe projection profilometry, this paper proposes a hue-indexing-based absolute phase retrieval method using a discrete hue sequence. First, the hue component is extracted as the wrapped phase for 3D reconstruction by projecting a programmed hue fringe pattern. Afterward, a hue-indexing sequence with a random combination of six unique hues from the hue map is designed for hue unwrapping. By assigning a hue index of the fringe geometric center, the defocusing effect in the hue unwrapping is corrected, where the fringe order of the wrapped hue is then uniquely identified. The simulations show that the root mean square (RMS) of the residual error is <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>2.2185</mml:mn> </mml:mrow> <mml:mo>×<!-- × --></mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>10</mml:mn> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>−<!-- − --></mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">r</mml:mi> <mml:mi mathvariant="normal">a</mml:mi> <mml:mi mathvariant="normal">d</mml:mi> </mml:mrow> </mml:math> , and the effectiveness of the proposed method is further verified through experiments using a plaster statue and a compressor blade. The measurement comparison of the proposed method and the coordinate measuring machine is provided, where the RMS error of the measured deviation is <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>4.09</mml:mn> </mml:mrow> <mml:mo>×<!-- × --></mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>10</mml:mn> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>−<!-- − --></mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">m</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:math> .
Do partial and distributed tests enhance new learning?

Testing facilitates subsequent learning of new information, a phenomenon known as the forward testing effect. The effect is often investigated in multilist procedures, where studied lists are followed by a retrieval test, or a control task such as restudying, and learning is compared on the final list. In most studies of the effect, tests include all material from the preceding list. We report four experiments, three of which were preregistered, to determine whether tests that are partial (not including all studied items) and distributed (including retrieval of items from earlier lists) are effective in enhancing new learning. The results show that testing of all studied material is not necessary to produce beneficial effects on new learning or to reduce intrusions. The beneficial effects of testing were substantially mediated by reduced proactive interference. Importantly, there was minimal evidence that the forward learning benefits of partial and distributed tests are offset by a cost to untested items via retrieval-induced forgetting. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Modeling Persuasion Factor of User Decision for Recommendation

In online information systems, users make decisions based on factors of several specific aspects, such as brand, price, etc. Existing recommendation engines ignore the explicit modeling of these factors, leading to sub-optimal recommendation performance. In this paper, we focus on the real-world scenario where these factors can be explicitly captured (the users are exposed with decision factor-based persuasion texts, i.e., persuasion factors). Although it allows us for explicit modeling of user-decision process, there are critical challenges including the persuasion factor's representation learning and effect estimation, along with the data-sparsity problem. To address them, in this work, we present our POEM (short for Persuasion factOr Effect Modeling) system. We first propose the persuasion-factor graph convolutional layers for encoding and learning representations from the persuasion-aware interaction data. Then we develop a prediction layer that fully considers the user sensitivity to the persuasion factors. Finally, to address the data-sparsity issue, we propose a counterfactual learning-based data augmentation method to enhance the supervision signal. Real-world experiments demonstrate the effectiveness of our proposed framework of modeling the effect of persuasion factors.
Correlation of Absolute Lymphocyte Count with Severity of COVID-19 at Dr. Mohammad Hoesin General Hospital Palembang

Background: The rRT-PCR examination is very effective as a diagnostic tool for COVID-19 but cannot be a full reference as an evaluation examination because rRT-PCR is able to capture the genetic material of the virus without ensuring that the virus is still alive or only dead particles. This study aims to explore the correlation between absolute lymphocyte values and the severity of COVID-19 at Dr. Mohammad Hoesin General Hospital Palembang.&#x0D; Methods: This study is an observational study involving 413 research subjects at Dr. Mohammad Hoesin General Hospital. Data analysis was carried out univariate and bivariate.&#x0D; Results: The absolute number of lymphocytes with the severity of COVID-19 has a negative correlation, with a strong correlation strength, which is expressed by the value of r = -0.617 and the degree of significance of p = 0.000.&#x0D; Conclusion: There is a correlation between absolute lymphocyte count and the severity of COVID-19 at Dr. Mohammad Hoesin General Hospital Palembang.
Efficient Machine Learning Techniques to Predict Lung Cancer

One of the most difficult to diagnose and one of the deadliest diseases is lung cancer. A big reason for this is that it takes a long time to identify at an early stage. For treatment, a rapid and precise diagnosis of nodules is very crucial. In order to identify cancer in its early stages, a variety of techniques have been employed. Machine learning approaches were used in this work in order to identify lung cancer nodules. We used machine learning algorithms such as LightGBM, XGBoost, K-Nearest Neighbors, Support Vector Machines, Naïve Bayes, and Random Forest to discover anomalous data. We compared all of the approaches. The results of the experiments reveal that LightGBM produces the greatest outcomes with 99.91 percent accuracy, 0.001261 loss and XGBoost outcomes with 99.86 percent accuracy, 0.001446 loss.
TokenCat: Detect Flaw of Authentication on ERC20 Tokens

The development of blockchain has promoted the prosperity of the cryptocurrency ecosystem. The majority of cryptocurrencies are ERC20 tokens implemented based on Ethereum contracts. The major role of ERC20 tokens is to carry out various trades and loans in decentralized applications (DApps). To participate in DApps, users must grant the DApps permission to spend tokens on user behalf. However, if the authorization logic of token contract is flawed implementation, the holder of token will suffer tremendous financial losses. In this work, we detect the authentication implementation of the flaws in ERC20 token, which has not been done before. We find the authentication process of the token is implemented by operating the authentication data structure of the token. Therefore, we capture the operations of the authentication data structure in token contract to infer authentication behaviors and detect authentication defects. However, it’s not a simple task as most smart contracts are not open source and the bytecode of token contract lacks type information. To tackle these problems, we utilize symbolic execution on the token bytecode, then identify the authentication data structure and capture the operations by parsing the symbolic expressions, and finally detect authentication defects through the inferred authentication behavior. To best our knowledge, this is the first work to detect the flaws in the implementation of authentication in ERC20 Token. To automate the analysis, we implement our approach in a new tool named TokenCat and use it to inspect 245,822 tokens. As a result, the TokenCat found 491 ERC20 token authentication implementation flaws with 94% precision.
Dual-Anchor Metric Learning for Blind Image Quality Assessment of Screen Content Images

The natural scene statistic is destroyed by the artificial portion in the screen content images (SCIs) and is also impractical for obtaining an accurate statistical model due to the variable composition of the artificial and natural parts in SCIs. To resolve this problem, this paper presents a dual-anchor metric learning (DAML) method that is inspired by metric learning to obtain discriminative statistical features and further identify complex distortions, as well as predict SCI image quality. First, two Gaussian mixed models with prior data are constructed as the target anchors of the statistical model from natural and artificial image databases, which can effectively enhance the metrical discrimination of the mapping relation between the feature representation and quality degradation by conditional probability analysis. Then, the distances of the high-order statistics are softly aggregated to conduct metric learning between the local features and clusters of each target statistical model. Through empirical analysis and experimental verification, only variance differences are used as quality-aware features to benefit the balance of complexity and effectiveness. Finally, the mapping model between the target distances and subjective quality can be obtained by support vector regression. To validate the performance of DAML, multiple experiments are carried out on three public databases: SIQAD, SCD, and SCID. Meanwhile, PLCC, SRCC, and the RMSE are then employed to compute the correlation between subjective and objective ratings, which can estimate the prediction of accuracy, monotonicity, and consistency, respectively. The PLCC and RMSE of the method achieved 0.9136 and 0.7993. The results confirm the good performance of the proposed method.
Multi-Objective Navigation Strategy for Guide Robot Based on Machine Emotion

In recent years, the rapid development of robot technology means more kinds of robots appear in life and they are applied in different fields of society. Service robots are mainly used to provide convenience for human beings. Guide robots are a kind of service robot, which can replace manual instruction and guidance. However, most of the existing studies provide a preset guidance trajectory for the guiding robot, or they let the user choose the next target point for position guidance, which is a lack of intelligence. To solve the above problems, a robot navigation strategy based on machine emotion is proposed. Firstly, the machine emotion of the guide robot is established according to the user’s emotional state and environmental information. Then, the machine emotion and current location information are used to estimate the user’s intention, i.e., the most desired next target point. Finally, the classical indoor path planning method and obstacle avoidance method are employed to calculate a passable path between the target point and the current position. Simulation results show that the proposed strategy can execute different navigation strategies according to user emotion. The navigation strategy proposed in this paper has been tested on Pepper robot and received good feedback from the subjects.
Perfomance analysis of Naive Bayes method with data weighting

Classification using naive bayes algorithm for air quality dataset has an accuracy rate of 39.97%. This result is considered not good and by using all existing data attributes. By doing pre-processing, namely feature selection using the gain ratio algorithm, the accuracy of the Naive Bayes algorithm increases to 61.76%. This proves that the gain ratio algorithm can improve the performance of the naive bayes algorithm for air quality dataset classification. Classification using naive bayes algorithm for air quality dataset. While the Water Quality dataset has an accuracy rate of 93.18%. These results are considered good and by using all the existing data attributes. By doing pre-processing, namely feature selection using the gain ratio algorithm, the accuracy of the Naive Bayes algorithm increases to 95.73%. This proves that the gain ratio algorithm can improve the performance of the naive bayes algorithm for air quality dataset classification. Classification using Naive Bayes algorithm for Water Quality dataset. Based on the tests that have been carried out on all data, it can be seen that the Weight nave Bayes classification model can provide better accuracy values ​​because there is a change in the weighting of the attribute values ​​in the dataset used. The value of the weighted Gain ratio is used to calculate the probability in Nave Bayes, which is a parameter to see the relationship between each attribute in the data, and is used as the basis for the weighting of each attribute of the dataset. The higher the Gain ratio of an attribute, the greater the relationship to the data class. So that the accuracy value increases than the accuracy value generated by the Naïve Bayes classification model. The increase in accuracy in the Naïve Bayes classification model is due to the number of weights from the attribute selection in the Gain ratio.
Weighting comparison analysis ROC and Full consistency Method (FUCOM) on MOORA in decision making

Decision support system is a system that can assist companies in making policy. There are several methods in a decision support system, one of which is the MOORA method. The MOORA method does not have a systematic weight determination. In several studies, the determination of the weight value is determined by experts in their field so that the value is less objective. So in this study, the Rank Order Centroid (ROC) and Full Consistency Method (FUCOM) weighting methods will be used systematically and objectively which will be applied to the MOORA method and a comparison of the results of these methods will be carried out with the calculation of the accuracy of the confusion matrix. The purpose of this study was to analyze the results of the weighting comparison using Rank Order Centroid (ROC) and Full Consistency Method (FUCOM) on the MOORA method so as to produce good accuracy. Based on the results of the study, testing with ROC weights on MOORA obtained results of 77.78% accuracy, 84% precision and 84% recall. While testing with ROC weights on MOORA obtained results of 77.78% accuracy, 84% precision and 84% recall. And testing with ROC+FUCOM weights on MOORA obtained 91.67% accuracy, 94% precision and 94% recall. So it can be concluded that the ROC+FUCOM weighting on MOORA produces good accuracy.
A Robust and Lightweight Detector for Ship Target with Complex Background in SAR Image

Accurate target detection technology on ships can improve the comprehensive perception ability of weapon equipment. For SAR ship target detection in complex environments, false and missing alarms are serious. We design a new real-time ship target detection algorithm 3S-YOLO in SAR images. Firstly, reconstruct the network structure, adjust the relationship between receptive field and multiscale fusion, and realize the lightweight processing of feature extraction network and feature fusion network. Then, the network is pruned and compressed by the FPGM pruning algorithm to accelerate the reasoning speed. Finally, the Varifocal-EIoU loss function is designed to balance the positive and negative samples and overlapping losses and highlight the contribution of positive samples. To verify the effectiveness of the 3S-YOLO algorithm, verification is carried out in public datasets SSDD and HRSID. The results show that the accuracy of the model can be improved to 99.2% and 95.6%, respectively, after optimization. After pruning, the model volume decreased significantly and could be compressed to 190 KB. Model reasoning time can be reduced to less than 3 ms. Compared with the current mainstream algorithms, 3S-YOLO has achieved good results in all aspects to meet the real-time ship target detection in SAR images.
Modified LSB2 Steganography Method to Secure the Embedded Secret Message

LSB2 method of data steganography is a popular method of secret data hiding, this method is simple and capable of hiding long messages in a covering color image. LSB2 is not secure and it cannot protect the embedded secret message from being hacked. In this research paper a modified LSB2 method will be proposed, this method will solve the security issues by using a secret private key. This key will contain 8 components and will be used to rearrange the sequence of bits hiding in the covering image. The proposed method will deal with the hiding process by selecting the first bits of the message characters, the seconds and so on. An extra task will be added to LSB2 method; this task will perform the necessary substitution of the message binary matrix according to the contents pf the PK. The PK key be easily changed when the need arises without modifying the proposed algorithm. The proposed method will be implemented using various messages and various covering images. The obtained results will be analyzed using various types of data analysis to prove the efficiency of data steganography. A quality analysis will be performed to prove the quality of the stego image, and speed analysis will be performed to prove the optimized throughputs provided by the proposed method.
A Conditional Deep Framework for Automatic Layout Generation

Automatic layout generation, which means making computers enjoy creativity, is difficult yet exciting work. Up to now, how to generate reasonable and visually appealing layouts remains a complex challenge. In this paper, we propose a novel layout generation model based on Conditional Generative Adversarial Networks (L-CGAN), which can generate layouts simply and efficiently by positioning, scaling, and flipping the given primitives. To break the bottleneck of limitation of the fixed input size of Generative Adversarial Networks, we develop a pre-processing algorithm to enable the model to generate layouts with an unrestricted number of input elements. Moreover, a graph-constraint module is proposed to guide layout optimization. We demonstrate the competitive performance of our designs in diverse data domains such as handwriting digit layout generation (MNIST Layouts), scene layout generation (AbstractScene-Layouts), and document layout generation (PubLayNet).
Interactive Reinforcement Learning-Based API Recommendation

API recommendation is an effective way to improve software productivity. It has become an important research topic in recent years, and therefore attracted lots of attention. Recently, researchers have proposed a great deal of API recommendation approaches to help developers find suitable APIs from a large number of candidates during the development process. However, most of these approaches do not effectively integrate dynamic human-machine interaction information into the recommendation session. In this article, we propose an API recommendation approach, ARENA (API Recommendation using intEractive reiNforcement leArning), which leverages reinforcement learning based on markov decision process (MDP) to improve the recommendation performance. By integrating the user interaction into the loop of MDP, ARENA enables users to continuously interact with the model. In this way, the user interaction information is used for optimizing the parameter of the model. In turn, the approach can provide personalized API recommendation result to end users.
Creative Entrances to Co-Design

Design fiction, storytelling, and games provide creative entrances to co-design activities with different groups. These have emerged from a need to develop joint perspectives, a joint language and technological imagination as prerequisites for participatory approaches and co-design. For this workshop, we invite researchers, practitioners and designers who are working in settings that are socially and culturally diverse, who bring together people of a broad age range and who have tried creative approaches in their co-design activities. Working in groups to co-design and further develop creative entrances, this workshop will explore and reflect upon the potential of design fiction, storytelling, and games to foster creativity in co-design activities and to support (technological) imagination. As a result, we plan to co-host together with the participants a digital exhibition with the results of the workshop.
A Novel Fusion Perception Method of Unmanned Driving Based on Distributed Roadside Information

Multi-sensor fusion, tracking, and prediction have a long history in signal processing, control theory, and robotics. This paper proposes a trajectory tracking method based on distributed cameras. For a start, we build an innovative model that calculates the observation noise for each camera. Secondly, we can identify the abnormal data by introducing the trustworthiness function. Then we use the weighted fusion method to fuse the data to minimize the variance. We will use the improved Bayesian filtering to deal with different observation noises for the final trajectory tracking. Simulation results confirm that the Root Mean Square Error (RMSE) of trajectory tracking using our method is reduced by at least 13% compared to those of single viewpoint perception. Even if several cameras are blocked, our approach can still perform best.