Reviewerly/exHarmony huggingface dataset:
End-To-End Encryption on the Instant Messaging Application Based Android using AES Cryptography Algorithm to a Text Message

Instant Messaging applications have become a common thing used by people, with the increasing popularity of smartphones with the support of various application functions. One of the most popular applications is an instant messaging application with a text message feature. However, the communication media is not always safe from attack sniffers. Symantec Enterprise Security analysis, threats to eavesdropping or wiretapping ranks first in the assault case against instant messaging. For that, how to maintain the security of message data on the communication media of smartphone users from sniffer attacks? One of the solutions uses end-to-end encryption. The encryption algorithms used in E2EE implementations may vary. In this study, the Advanced Encryption Standard (AES) cryptographic algorithm is used in this study. The study results are in the form of designing and building an instant messaging application on the Android mobile platform that can encrypt and decrypt text messages to improve security services on instant messaging applications.
Tracer Study Information Systems Success in Private Higher Education Institution

The policy of requiring Higher Educations to use Information Systems for Alumni Tracking has become a major dilemma. Problems and gaps arise in the application, including for Private Higher Education Institutions, so it is necessary to conduct research on the successful use of Tracer Study Information Systems in Private Higher Educations Institutions. The purpose of this study is to determine the success of using Tracer Study Information Systems and what factors influence the use of Tracer Study Information Systems at Private Higher Education Institutions. The researcher did it at one of the Private Higher Education Institutions, where the Leaders, Structural Officers, and Head of Study Programs at the Rectorate and Faculties became the Respondents in this research. There were 99 active respondents who filled out and returned the questionnaire. The results obtained after data processing is that the Tracer Study Information Systems still cannot provide good service in providing solutions when there are problems that occur in the medium category. The recommendation, the researcher suggests to minimize errors that appear on the system. Because it can make users trust. In addition, it must be acknowledged that the Tracer Study Information Systems developers are still developing and perfecting it, perhaps it is recommended to add the Help menu, as a guide to the use of Tracer Study Information Systems. The contribution of this research is to provide input to Private Higher Education Institution in the use of Tracer Study Information Systems, so that the policies taken will be appropriately applied, and provide an overview of the application of the Success model to the Tracer Study IS object at Private Higher Education Institution for the improvement of future research by other researchers, so that it becomes improvement material for the Success model.
Implementation of Data Science Algorithm for Monthly Inflation Prediction Based on Financial Technology Awareness Levels

Digital technology has been implemented into various sectors in Indonesia, such as the education, health, tourism sectors, and one of the most important is in the financial sector. Along with the continued development of technology in the financial sector, it will surely have a good affect to economic development. Along with the development of technology in the financial sector, it will surely have a good affect to economic development. Google trends is one of the open-source tools that can represent what keywords are most often searched by the public. In this study, author conducted research related to the relationship between the development of digital technology in the financial sector and economic development using one of the indicators, namely the inflation rate. The analysis that has been conduct use several data science algorithms and inform algorithm that has the best performance, namely Lasso Regression with MAPE value of 0.16 or 16% which means it can be interpreted as Good Forecasting. And the worst algorithm is Linear Regression with a MAPE value of 0.57 or 57%, which means it can be interpreted as Inaccurate Forecasting.
Discovering Common Hidden Causes in Sequences of Events

Abstract Human cognition is marked by its ability to explain patterns in the world in terms of variables and regularities that are not directly observable, e.g., mental states, natural laws, and causal relationships. Previous research has demonstrated a capacity for inferring hidden causes from covariational evidence, as well as the use of temporal information to identify causal relationships among observed variables. Here we explore the human ability to use temporal information to make inferences about hidden causes, causal cycles, and other causal relationships, without relying on interventions. We examine two behavioral experiments and compare participants’ judgments to those of Bayesian computational-level models that use temporal order and delay information to infer the causal structure behind observed event sequences. Our results indicate that participants are able to use order and timing information to discover hidden causes, and make inferences about causal structures relating hidden and observable variables. Computational modeling indicates that most participants are best described by normative delay model predictions, but also reveals several clusters of participants who made unexpected inferences, suggesting opportunities to enrich future models of human causal reasoning.
Calibration of coherent optical fiber bundle with LCD screen for deformation measurements

• A method that can calibrate the spatial misalignment in the coherent optical fiber bundle (COFB) was proposed. • The influence of the spatial misalignment on the deformation measurement accuracy of the COFB-based digital image correlation system was analyzed. • The accuracy of the two-dimensional (2D) displacement field measurement result is improved from 0.50 pixels to 0.03 pixels after misalignment calibration. Owning to its flexibility, high temperature resistance, and freedom from electromagnetic interference, coherent optical fiber bundle (COFB) is widely used in special imaging applications. However, limited by the manufacturing process, the fiber arrangement of COFB is prone to misalignment, resulting in imaging distortions that are not easily corrected. This limits the application of COFB in the field of optical measurement, making it difficult to meet the requirements of high-precision deformation measurement. To effectively calibrate the misalignment distortion and recover the original image information, this study proposes an easy-to-implement and highly accurate misalignment distortion calibration method. The LCD screen was used as the calibration reference to display a pre-designed regular arrangement pattern. The mapping relationship between the fiber output and the screen display information was confirmed using a correlation algorithm. The proposed method requires only 36 images to achieve misalignment calibration for 450 × 450 fibers. The image reconstruction results show that the proposed method can not only achieve global misalignment calibration, but also successfully eliminate honeycomb artifacts, thus enhancing the accuracy and practicality of COFB for deformation measurement. In addition, a COFB-based digital image correlation (DIC) system was established and a set of typical DIC experiments were implemented. The experimental results show that the proposed calibration method can significantly improve the deformation measurement accuracy of the COFB-based DIC system, and the accuracy of the two-dimensional (2D) displacement field measurement result is improved from 0.50 pixels to 0.03 pixels.
Mixup Based Cross-Consistency Training for Named Entity Recognition

Named Entity Recognition (NER) is at the core of natural language understanding. The quality and amount of datasets determine the performance of deep-learning-based NER models. As datasets for NER require token-level or word-level labels to be assigned, annotating the datasets is expensive and time consuming. To alleviate efforts of manual anotation, many prior studies utilized weak supervision for NER tasks. However, using weak supervision directly would be an obstacle for training deep networks because the labels automatically annotated contain a a lot of noise. In this study, we propose a framework to better train the deep model for NER tasks using weakly labeled data. The proposed framework stems from the idea that mixup, which was recently considered as a data augmentation strategy, would be an obstacle to deep model training for NER tasks. Inspired by this idea, we used mixup as a perturbation function for consistency regularization, one of the semi-supervised learning strategies. To support our idea, we conducted several experiments for NER benchmarks. Experimental results proved that directly using mixup on NER tasks hinders deep model training while demonstrating that the proposed framework achieves improved performances compared to employing only a few human-annotated data.
Weakly Supervised Instance Segmentation via Category-aware Centerness Learning with Localization Supervision

Deep convolutional neural networks (DCNN) trained with pixel-level segmentation masks achieve high performance in the task of instance segmentation. The difficulty of acquiring such annotation limits the application and popularization of the DCNN-based approaches. To address the issue, a weakly supervised approach is proposed in the paper which performs instance segmentation only with the supervision of bounding box or coarse localization annotation. A novel DCNN model is constructed which consists of two branches: the centerness branch and the segmentation branch. The former branch is to learn the semantically spatial importance over the areas of object instances under the localization supervision. Object proposals with exact boundaries are automatically generated and are then ranked under the guidance of the output of the centerness branch. The most matched instance proposal is assigned to each object, which is then used to supervise the segmentation branch. The losses are calculated by both the outputs of the two branches and the entire DCNN model is trained end-to-end. Experiments are extensively conducted to verify the effectiveness. With the supervision of precise bounding box annotation, our approach achieves state-of-the-art (SOTA) accuracy in the comparison with recent related works. And in the case of coarse localization annotation, our approach only deduces a slight reduction in accuracy, which significantly outperforms other approaches. The excellent performance demonstrates that our approach would be helpful to further alleviate the workload of image annotation while maintaining competitive accuracy.
The Use of Negative Interface Cues to Change Perceptions of Online Retributive Harassment

Online retributive harassment refers to a range of abusive online behaviors targeted at offenders with the intent of sanctioning norm violations. Online retributive harassment is common in online interactions, can be devastating in its effect, and is hard to moderate. We examined the role that negative interface cues (i.e., Dislikes, flags) might have in changing people's harassment perceptions to mitigate this activity. We conducted a 4x2 between-subjects experiment (N = 242) to test the effects of interface cues (control with Likes only vs. Dislikes outnumbering Likes vs. Likes outnumbering Dislikes vs. a flag with Likes) and harassment severity (low vs. high) on how people perceive retributive harassment. We hypothesized that Dislikes and flags, despite the presence of Likes, signal social disapproval and descriptive norms against harassment and thus reduce bystanders' belief that retributive harassment is appropriate, deserved, or justified. We found that Dislikes can be effective when they outnumber Likes in high-severity harassment but backfire when outnumbered by Likes. A flag, contrary to popular practice, does not have a significant mitigating effect on perceptions of retributive harassment. We demonstrate the potential of negative interface cues to signal anti-harassment norms to bystanders and discuss alternatives to social media platforms' one-size-fits-all content moderation approach.
Data-Efficient Performance Modeling for Configurable Big Data Frameworks by Reducing Information Overlap Between Training Examples

To support the various analysis application of big data, big data processing frameworks are designed to be highly configurable. However, for common users, it is difficult to tailor the configurable frameworks to achieve optimal performance for every application. Recently, many automatic tuning methods are proposed to configure these frameworks. In detail, these methods firstly build a performance prediction model through sampling configurations randomly and measuring the corresponding performance. Then, they conduct heuristic search in the configuration space based on the performance prediction model. For most frameworks, it is too expensive to build the performance model since it needs to measure the performance of large amounts of configurations, which cause too much overhead on data collection. In this paper, we propose a novel data-efficient method to build the performance model with little impact on prediction accuracy. Compared to the traditional methods, the proposed method can reduce the overhead of data collection because it can train the performance model with much less training examples. Specifically, the proposed method can actively sample the important examples according to the dynamic requirement of the performance model during the iterative model updating. Hence, it can make full use of the collected informative data and train the performance model with much less training examples. To sample the important training examples, we employ several virtual performance model to estimate the importance of all candidate configurations efficiently. Experimental results show that our method needs less training examples than traditional methods with little impact on prediction accuracy.
Classification of Student Affective States in Online Learning using Neural Networks

The ongoing pandemic moved many classes online, and disrupted the classroom teaching experience and the feedback loop between teachers and students. One key challenge is to detect the engagement and other affective states exhibited by students during online learning. This paper investigates the capabilities and limitations of neural networks to distinguish between different affective states (i.e., boredom, engagement, confusion, and frustration), and their intensity level (i.e., very low, low, high, and very high). Several models are built using a hybrid ResNet+TCN neural network architecture. The models are trained using a large dataset, DAiSEE, that contains short 10 second video recordings of students as they watch educational content ‘in the wild’. A second dataset consisting of longer videos, EmotiW2020, is used to cross validate the engagement level classification model. The affective state classification model outperforms prior models. Boredom, confusion and frustration level classification models outperform or are on par with prior models. The engagement level classification model achieved similar performance with other baseline models and was outperformed by some SOTA models, but those models used 5 times more frames and 5 to 10 times more training epochs. The engagement level classification model was validated and achieved similar performance on both the DAiSEE and EmotiW2020 datasets.
Social Media and Web Sensing with Semantic Integration on the Refugee Crisis

The refugee crises have been considered as devastating humanitarian incidents throughout human history. They involve forced migrations due to war conflicts, diseases and so on, and are more relevant to nowadays than ever. What changed during the past decades and can be exploited towards greater good is the adoption of web and social media. In this paper, the main focus delves around smart retrieving of information from online sources, such as Twitter, YouTube and culturally-dedicated websites to provide cultural experts with relevant multimedia. The final scope is to build immersive experiences about migrant stories for local communities towards a more inclusive Europe. Moreover, semantic web technologies are deployed to homogenize multi-modal data and metadata into a unified knowledge graph including ontological structures for precise annotations. Additionally, this enables knowledge extraction and insights acquisition from implicit relationships. Finally, a system-wise benchmark for all utilities is showcased to evaluate each framework distinctly.
Beyond descriptive taxonomies in data analytics: a systematic evaluation approach for data-driven method pipelines

Abstract Taxonomies can serve as a valuable tool to capture dimensions and characteristics of data analytics solutions in a structured manner and thus create transparency about different design options of the technical solution space. However, previous taxonomic approaches often remain at a purely descriptive level without leveraging morphological structures to investigate the mechanisms between different combinatorial options given in data analytics pipelines. To this end, we propose a taxonomic evaluation approach to evaluate and construct the technical core of analytical information systems more systematically. Specifically, we present a rough guidance model consisting of four steps, which we subsequently instantiate with two application scenarios from the fields of industrial maintenance and predictive business process monitoring. In this way, we demonstrate how taxonomic frameworks can guide the creation of structured evaluation studies to consider the construction and assessment of data analytics pipelines in a multi-perspective and holistic manner. Our approach is sufficiently generic to be applied to various domains, scenarios, and decision support tasks.
Dress-up: deep neural framework for image-based human appearance transfer

The fashion industry is at the brink of radical transformation. The emergence of Artificial Intelligence (AI) in fashion applications creates many opportunities for this industry and make fashion a better space for everyone. Interesting to this matter, we proposed a virtual try-on interface to stimulate consumers purchase intentions and facilitate their online buying decision process. Thus, we present, in this paper, our flexible person generation system for virtual try-on that aiming to treat the task of human appearance transfer across images while preserving texture details and structural coherence of the generated outfit. This challenging task has drawn increasing attention and made huge development of intelligent fashion applications. However, it requires different challenges, especially in the case of a wide divergences between the source and target images. To solve this problem, we proposed a flexible person generation framework called Dress-up to treat the 2D virtual try-on task. Dress-up is an end-to-end generation pipeline with three modules based on the task of image-to-image translation aiming to sequentially interchange garments between images, and produce dressing effects not achievable by existing works. The core idea of our solution is to explicitly encode the body pose and the target clothes by a pre-processing module based on the semantic segmentation process. Then, a conditional adversarial network is implemented to generate target segmentation feeding respectively, to the alignment and translation networks to generate the final output results. The novelty of this work lies in realizing the appearance transfer across images with high quality by reconstructing garments on a person in different orders and looks from simlpy semantic maps and 2D images without using 3D modeling. Our system can produce dressing effects and provide significant results over the state-of-the-art methods on the widely used DeepFashion dataset. Extensive evaluations show that Dress-up outperforms other recent methods in terms of output quality, and handles a wide range of editing functions for which there is no direct supervision. Different types of results were computed to verify the performance of our proposed framework and show that the robustness and effectiveness are high by utilizing our method.
Guided Deep Reinforcement Learning based on RBF-ARX Pseudo LQR in Single Stage Inverted Pendulum

The Guided Deep Reinforcement Learning (GDRL) method is proposed to train an optimal controller to stabilize a Single Stage Inverted Pendulum (SSIP). Firstly, the RBF (Radial Basis Function)-ARX (Auto Regressive Exogenous) without offset term model is applied to model a SSIP offline, using the data sampled from a simulated SSIP. Then, a Pseudo LQR (PLQR) is designed to provide guiding signals to the agent during the training process. Together with the policy gradient in deep reinforcement learning, the policy network is trained by the softly-combined gradient from both the action-value network and the model based PLQR. To improve the policy monotonically, a searching scheme with adjusted step size is applied during the training. All of these above consist of the proposed method. The simulation results demonstrate the superiority of training speed and controlling performance of the GDRL.
DASS: a Domain Augment Supervised SimCSE framework for sentence presentation

Unsupervised/supervised SimCSE [1] achieves the SOTA performance of sentence-level semantic representation based on contrastive learning and dropout data augment. In particular, Supervised SimCSE mined positive pairs and hard-negative pairs through NLI entailment/contradiction labels, which significantly outperformed other unsupervised/supervised models. NLI data is scarce, can we construct pseudo-NLI data to improve the semantic representation of multi-domain sentences? This paper provides a Domain Augmented Supervised SimCSE approach (DASS), which includes (1) providing two pseudo-NLI data generators, "Hard/Soft NLI Data Builder", to annotate entailment/contradiction pairs through Business Rules and Neural Classifiers, respectively. (2) "Domain Augment InfoNCE" to balance the loss of multiple domains. Experiments show that DASS can achieve intra-domain enhancement and cross-domain enhancement. Moreover, with Soft NLI Builder, DASS can achieve the best results of all domains, improving 34% and 11% compared with baseline and strong baseline.