Reviewerly/exHarmony huggingface dataset:
Augmenting Anchors by the Detector Itself

Usually, it is difficult to determine the scale and aspect ratio of anchors for anchor-based object detection methods. Current state-of-the-art object detectors either determine anchor parameters according to objects' shape and scale in a dataset, or avoid this problem by utilizing anchor-free methods, however, the former scheme is dataset-specific and the latter methods could not get better performance than the former ones. In this paper, we propose a novel anchor augmentation method named AADI, which means Augmenting Anchors by the Detector Itself. AADI is not an anchor-free method, instead, it can convert the scale and aspect ratio of anchors from a continuous space to a discrete space, which greatly alleviates the problem of anchors' designation. Furthermore, AADI is a learning-based anchor augmentation method, but it does not add any parameters or hyper-parameters, which is beneficial for research and downstream tasks. Extensive experiments on COCO dataset demonstrate the effectiveness of AADI, specifically, AADI achieves significant performance boosts on many state-of-the-art object detectors (eg. at least +2.4 box AP on Faster R-CNN, +2.2 box AP on Mask R-CNN, and +0.9 box AP on Cascade Mask R-CNN). We hope that this simple and cost-efficient method can be widely used in object detection. Code and models are available at https://github.com/WanXiaopei/aadi.
Verbal Suggestion Modulates the Sense of Ownership and Heat Pain Threshold During the “Injured” Rubber Hand Illusion

The appearance of the self-body influences the feeling that one's body belongs to oneself, that is, a sense of ownership (SoO) and pain perception. This can be identified by measuring the SoO and pain thresholds after performing the rubber hand illusion (RHI) with an injured rubber hand. The generation of SoO is thought to be caused by multisensory integration of bottom-up factors (vision, proprioceptive, and touch), and by top-down factors, such as the context effect. The appearance is one of the context effects which may become more effective when used simultaneously with other context effects (e.g., verbal suggestion). However, in the RHI, when appearance and other context effects are used simultaneously, the effect is unclear. In this study, we attempted to identify the influence of verbal suggestion on the SoO and heat pain threshold (HPT). As a preliminary step, in Experiment 1, the "normal" rubber hand and "penetrated nail" as injured rubber hand were used to clarify the context effect with appearance alone during RHI (synchronous/asynchronous), which was conducted within-subjects. In Experiment 2, we only used the "penetrated nail" rubber hand to clarify the context effect with verbal suggestion and appearance during RHI. We randomly classified participants into two suggestion groups ("fear" and "no-fear"). The RHI (synchronous/asynchronous) was conducted for each group. In each experiment, the effect of each condition was assessed by subjective measures of SoO, such as questionnaire, and objective measures of SoO, such as proprioceptive drift and electrodermal activity. Following RHI in each condition, HPT was measured. The main finding was that, in the synchronous condition, the "penetrated nail" appearance with "fear" verbal suggestion modulated questionnaire and HPT, but not electrodermal activity. We conclude that the context-included multisensory integration affected the subjective factors because it contains a higher cognitive process by verbal suggestion.
Risk Awareness in HTN Planning

Actual real-world domains are characterised by uncertain situations in which acting and use of resources require embracing risk. Performing actions in such domains always entails costs of consuming some resource, such as time, money, or energy, where the knowledge about these costs can range from totally known to totally unknown and even unknowable probabilities of costs. Think of robotic domains, where actions and their costs are non-deterministic due to uncertain factors like obstacles. Choosing which action to perform considering its cost on the available resource requires taking a stance on risk. Thus, these domains call for not only planning under uncertainty but also planning while embracing risk. Taking Hierarchical Task Network (HTN) planning as a widely used planning technique in real-world applications, one can observe that existing approaches do not account for risk. That is, computing most probable or optimal plans using actions with single-valued costs is only enough to express risk neutrality. In this work, we postulate that HTN planning can become risk aware by considering expected utility theory, a representative concept of decision theory that enables choosing actions considering a probability distribution of their costs and a given risk attitude expressed using a utility function. In particular, we introduce a general framework for HTN planning that allows modelling risk and uncertainty using a probability distribution of action costs upon which we define risk-aware HTN planning as an approach that accounts for the different risk attitudes and allows computing plans that go beyond risk neutrality. In fact, we layout that computing risk-aware plans requires finding plans with the highest expected utility. Finally, we argue that it is possible for HTN planning agents to solve specialised risk-aware HTN planning problems by adapting some existing HTN planning approaches.
Gaze-based Object Detection in the Wild

In human-robot collaboration, one challenging task is to teach a robot new yet unknown objects enabling it to interact with them. Thereby, gaze can contain valuable information. We investigate if it is possible to detect objects (object or no object) merely from gaze data and determine their bounding box parameters. For this purpose, we explore different sizes of temporal windows, which serve as a basis for the computation of heatmaps, i.e., the spatial distribution of the gaze data. Additionally, we analyze different grid sizes of these heatmaps, and demonstrate the functionality in a proof of concept using different machine learning techniques. Our method is characterized by its speed and resource efficiency compared to conventional object detectors. In order to generate the required data, we conducted a study with five subjects who could move freely and thus, turn towards arbitrary objects. This way, we chose a scenario for our data collection that is as realistic as possible. Since the subjects move while facing objects, the heatmaps also contain gaze data trajectories, complicating the detection and parameter regression. We make our data set publicly available to the research community for download.
Real-Time Encrypted Traffic Classification with Deep Learning

Confidentiality requirements of individuals and companies led to the dominance of encrypted payloads in the overall Internet traffic. Hence, traffic classification on a network became increasingly difficult as it must rely on only the packet headers. Many vital tasks such as differential pricing, providing a safe Internet for children, and eliminating malicious connections require traffic classification, even if the payload contents are encrypted. Encrypted traffic is harder to classify as packet content becomes unreadable. In this work, we aim to provide an insight into traffic classification using encrypted packets in terms of both accuracy and packet processing time. LSTM (Long Short-Term Memory) architecture is a good candidate for this problem as it can handle sequences. Each flow can be modeled as a sequence and patterns of the sequences can provide valuable information. We compare the performance of LSTM with other methods in both real-time and offline experiments. Compared to a machine learning method both online and offline LSTM excelled with precision and recall differences up to 50%. Average accuracy with LSTM was measured as 97.77% offline and 91.7% in real-time. Average packet processing time in real-time was recorded as 0.593 msec which is 5 times faster than a recent work that uses LSTM method.
Some Public-key Cryptosystems Over Finite Fields

In this paper, we present two public-key cry ptosystems over finite fields. First of them is based on polynomials. The presented system also considers a digital signature algorithm. Its security is based on the difficulty of finding discrete logarithms over GF(qd+1) with sufficiently large q and d. Is is also examined along with comparison with other polynomial based public-key systems. The other public-key cryptosystem is based on linear codes. McEliece studied the first code-based public-key cryptosystem. We are inspired by McEliece system in the construction of the new system. We examine its security using linear algebra and compare it with the other code-based cryptosystems. Our new cryptosystems are too reliable in terms of security.
Multi-Scale Features and Parallel Transformers Based Image Quality Assessment

With the increase in multimedia content, the type of distortions associated with multimedia is also increasing. This problem of image quality assessment is expanded well in the PIPAL dataset, which is still an open problem to solve for researchers. Although, recently proposed transformers networks have already been used in the literature for image quality assessment. At the same time, we notice that multi-scale feature extraction has proven to be a promising approach for image quality assessment. However, the way transformer networks are used for image quality assessment until now lacks these properties of multi-scale feature extraction. We utilized this fact in our approach and proposed a new architecture by integrating these two promising quality assessment techniques of images. Our experimentation on various datasets, including the PIPAL dataset, demonstrates that the proposed integration technique outperforms existing algorithms. The source code of the proposed algorithm is available online: https://github.com/KomalPal9610/IQA
Velocity Planning via Model-Based Reinforcement Learning: Demonstrating Results on PILCO for One-Dimensional Linear Motion with Bounded Acceleration

The time-optimal control problem (TOCP) has faced new practical challenges, such as those from the deployment of agile autonomous vehicles in diverse uncertain operating conditions without accurate system calibration. In this study to meet a need to generate feasible speed profiles in the face of uncertainty, we exploit and implement probabilistic inference for learning control (PILCO), an existing sample-efficient model-based reinforcement learning (MBRL) framework for policy search, to a case study of TOCP for a vehicle that was modeled as a constant input-constrained double integrator with uncertain inertia subject to uncertain viscous friction. Our approach integrates learning, planning, and control to construct a generalizable approach that requires minimal assumptions (especially regarding external disturbances and the parametric dynamics model of the system) for solving TOCP approximately as the perturbed solutions close to time-optimality. Within PILCO, a Gaussian Radial basis functions is implemented to generate control-constrained rest-to-rest near time-optimal vehicle motion on a linear track from scratch with data-efficiency in a direct way. We briefly introduce the importance of the applications of PILCO and discuss the learning results that PILCO would actually converge to the analytical solution in this TOCP. Furthermore, we execute a simulation and a sim2real experiment to validate the suitability of PILCO for TOCP by comparing with the analytical solution.
Penerapan Media Interaktif Power Point untuk Meningkatkan Aktivitas dan Hasil Belajar Siswa Kelas VI Tema 8 SDN Prantaan Kabupaten Blora

The purpose of this study was to increase student activity and learning outcomes by using interactive media Power Point for students of Class VI Theme 8 at SDN Prantaan. The research method used is Classroom Action Research (CAR) consisting of 3 cycles with 4 stages, namely planning, action, observation, and reflection. The subjects of this study were students of class VI with a total of 30 students. Data collection techniques used are observation, interviews, tests, and documentation. While the data analysis technique used descriptive analysis. The results of this study indicate that the average percentage of student activity classically has increased. In the pre-cycle before treatment it was 65%, in the first cycle it was 69%, in the second cycle it was 74% while in the third cycle it was 87%. While the percentage of mastery learning outcomes in the pre-cycle is 33%, in the first cycle it is 33%, in the second cycle it is 73%, while in the third cycle it is 90%. Based on the results of the research that has been done, it can be concluded that learning in Theme 8 using interactive power point media can improve student activities and learning outcomes.
Unsupervised Prompt Learning for Vision-Language Models

Contrastive vision-language models like CLIP have shown great progress in transfer learning. In the inference stage, the proper text description, also known as prompt, needs to be carefully designed to correctly classify the given images. In order to avoid laborious prompt engineering, recent works such as CoOp, CLIP-Adapter and Tip-Adapter propose to adapt vision-language models for downstream image recognition tasks on a small set of labeled data. Though promising improvements are achieved, requiring labeled data from the target datasets may restrict the scalability. In this paper, we explore a different scenario, in which the labels of the target datasets are unprovided, and we present an unsupervised prompt learning (UPL) approach to avoid prompt engineering while simultaneously improving transfer performance of CLIP-like vision-language models. As far as we know, UPL is the first work to introduce unsupervised learning into prompt learning. Experimentally, our UPL outperforms original CLIP with prompt engineering on ImageNet as well as other 10 datasets. An enhanced version of UPL is even competitive with the 8-shot CoOp and the 8-shot TIP-Adapter on most datasets. Code and models are available at https://github.com/tonyhuang2022/UPL.
Big Data Fusion Method Based on Internet of Things Collection

With the development of information collection technology, the data that people need to deal with are also increasing, which brings problems such as isomerization of data types, poor data quality, and fast data generation speed. At present, as an important method of data fusion technology, data fusion method based on deep learning has become an effective way of data fusion under the background of big data, which has important research significance. There is a problem with heterogeneous data types between time series data and text data, and it is difficult to fuse them effectively by traditional data fusion methods. In order to make full use of the information contained in text data and improve the accuracy of time series prediction, this paper proposes a data fusion model based on FC-SAE. In this model, GloVe and CNN are used to extract the features of text data, FC neural network is used to extract the potential features of time series data, and then, the SEA model is used to fuse the data, which fully discovers the relationship between data and greatly improves the prediction accuracy.
THE MODEL OF DEVELOPMENT OF LEADERSHIP QUALITIES OF FUTURE TEACHERS AS A MEANS OF SUCCESSFUL PROFESSIONAL ACTIVITY

A student should have not only knowledge, but also make decisions independently, strive for success in professional activity, apply a creative approach to any business, increase the intellectual level. That is why the purpose of our research is connected with the formation of these skills and therefore involves the development of leadership qualities of students who are able to clearly formulate their own thoughts, argue their judgments, think logically, navigate a large flow of information. At the same time, all this is possible with the implementation of an educational model for the development of leadership qualities that affects the professional sphere of future specialists and includes motivational (diagnostic stage), performing (pedagogical experiment), reflexive, control stages (generalization and comparison). At the same time, we note that in the educational process of the university, the use of such a model is based on achieving the following results: a) the self-worth of the student; b) the conditionality of the professional orientation of the content of education by future pedagogical activity; c) orientation of vocational education on the student's leadership behavior. Thus, the effectiveness of the model developed by us lies in the achievement of high indicators by bachelors of formed leadership abilities and competencies in the process of solving professional tasks.
TO THE QUESTION OF VOCATIONAL TRAINING OF STUDENTS WITH SPECIAL EDUCATIONAL NEEDS

The article deals with the issues of professional training of persons with special educational needs in vocational education organizations. in recent years, there have been significant changes in the educational policy implemented in relation to persons with special educational needs. obtaining professional education for people with special educational needs is one of the main and integral conditions for their successful socialization, full participation in society, effective self-realization in various types of professional and social activities. The authors reveal various approaches and definitions of the concepts of "training", "professional training". the tasks facing professional education organizations that provide education to persons with special educational needs are defined. The purpose of the conducted scientific research, the authors determined the theoretical justification, development and experimental verification of the effectiveness of organizational and pedagogical conditions of vocational training of students with special educational needs in the conditions of the north kazakhstan college of vocational training and service. two models of education are proposed, in which persons with special educational needs are fully included in the educational process.
The Use of Interactive PowerPoints to Increase Elementary School Student’s Learning Outcomes and Motivation in Mathematics during the Pandemic

The Covid-19 pandemic has been disturbing every human activity, including education. Schools are shifted to online learning, forcing teachers and students to teach and study from their homes. Several issues that arise due to this are problems with motivations and learning outcomes. In order to increase elementary school students’ learning outcomes and motivation in Mathematics, this research uses an interactive PowerPoint as the learning media. The subjects in this study are 25 students on the grade 6th of a private elementary school in Bekasi, Indonesia. The research methodology is mixed method, the quantitative data was taken by pretest and posttest and the qualitative data by survey questionnaire and interview with the students and mathematics teacher which is also the homeroom teacher. The data was analyzed using paired samples T-Test and thematic analysis. The quantitative results show that the interactive PowerPoint media is able to improve the students' learning outcomes. However, the results had shown an insignificant value of 0.377 and considered as not significant. At the same time, there are unexpected findings from the external factors that cause the result not to be significant, in which the student’s parents and family are involved during online learning. Despite the low significance results, through the interview, both of teachers and students stated that the interactive PowerPoint learning media that was used in mathematics during this pandemic has successfully engaged the students and increased their learning motivations.
Penerapan Teknik Ice Breaking untuk Meningkatkan Motivasi Belajar Ekonomi di Kelas XII IPS SMAN 3 Kayuagung

This study aims to increase the motivation to learn the economics of class XI IPS students at SMAN 3 Kayuagung through the application of ice breaking techniques. The research method used is classroom action research conducted in two cycles. Each cycle consists of 4 stages, namely planning, implementation, observation and reflection. The data collection technique in this research used observation sheets and tests, then the data were analyzed descriptively. The results showed that the activities of students and student learning outcomes increased in each cycle, namely in the first cycle, students' learning activities increased by 60% to 85%. and learning completeness reached 62.07%. While in Cycle II the learning activities of students were 90% to 97% and learning completeness reached 89.66%. So the conclusion of this research is that the application of ice breaking technique can increase learning motivation and learning outcomes of XII IPS students of SMAN 3 Kayuagung.
Personalized Filled-pause Generation with Group-wise Prediction Models

In this paper, we propose a method to generate personalized filled pauses (FPs) with group-wise prediction models. Compared with fluent text generation, disfluent text generation has not been widely explored. To generate more human-like texts, we addressed disfluent text generation. The usage of disfluency, such as FPs, rephrases, and word fragments, differs from speaker to speaker, and thus, the generation of personalized FPs is required. However, it is difficult to predict them because of the sparsity of position and the frequency difference between more and less frequently used FPs. Moreover, it is sometimes difficult to adapt FP prediction models to each speaker because of the large variation of the tendency within each speaker. To address these issues, we propose a method to build group-dependent prediction models by grouping speakers on the basis of their tendency to use FPs. This method does not require a large amount of data and time to train each speaker model. We further introduce a loss function and a word embedding model suitable for FP prediction. Our experimental results demonstrate that group-dependent models can predict FPs with higher scores than a non-personalized one and the introduced loss function and word embedding model improve the prediction performance.