Reviewerly/exHarmony huggingface dataset:
Uncertainty-Aware Policy Optimization: A Robust, Adaptive Trust Region Approach

In order for reinforcement learning techniques to be useful in real-world decision making processes, they must be able to produce robust performance from limited data. Deep policy optimization methods have achieved impressive results on complex tasks, but their real-world adoption remains limited because they often require significant amounts of data to succeed. When combined with small sample sizes, these methods can result in unstable learning due to their reliance on high-dimensional sample-based estimates. In this work, we develop techniques to control the uncertainty introduced by these estimates. We leverage these techniques to propose a deep policy optimization approach designed to produce stable performance even when data is scarce. The resulting algorithm, Uncertainty-Aware Trust Region Policy Optimization, generates robust policy updates that adapt to the level of uncertainty present throughout the learning process.
Semi-Supervised Data Programming with Subset Selection

The paradigm of data programming, which uses weak supervision in the form of rules/labelling functions, and semi-supervised learning, which augments small amounts of labelled data with a large unlabelled dataset, have shown great promise in several text classification scenarios.In this work, we argue that by not using any labelled data, data programming based approaches can yield sub-optimal performances, particularly when the labelling functions are noisy.The first contribution of this work is an introduction of a framework, SPEAR which is a semi-supervised data programming paradigm that learns a joint model that effectively uses the rules/labelling functions along with semi-supervised loss functions on the feature space.Next, we also study SPEAR-SS which additionally does subset selection on top of the joint semi-supervised data programming objective and selects a set of examples that can be used as the labelled set by SPEAR.The goal of SPEAR-SS is to ensure that the labelled data can complement the labelling functions, thereby benefiting from both data-programming as well as appropriately selected data for human labelling.We demonstrate that by effectively combining semi-supervision, data-programming, and subset selection paradigms, we significantly outperform the current state-of-the-art on seven publicly available datasets. 1
Study on Image Classification Algorithm Based on Improved DenseNet

Abstract In order to perform fast analysis and recognition of power equipment images. A residual attention mechanism based image classification model is proposed. First, the power equipment dataset is preprocessed and the power equipment dataset is randomly divided into a training set and a test set. Based on the improved DenseNet, the image features of the training set are extracted. The training set images are used for training, and the classification test is implemented on the test set. The experimental results show that the accuracy of the improved DenseNet algorithm is improved by 8.89%. It can be seen that the classification accuracy of the improved DenseNet algorithm is effectively improved.
Novel Slot Detection: A Benchmark for Discovering Unknown Slot Types in the Task-Oriented Dialogue System

Existing slot filling models can only recognize pre-defined in-domain slot types from a limited slot set.In the practical application, a reliable dialogue system should know what it does not know.In this paper, we introduce a new task, Novel Slot Detection (NSD), in the task-oriented dialogue system.NSD aims to discover unknown or out-of-domain slot types to strengthen the capability of a dialogue system based on in-domain training data.Besides, we construct two public NSD datasets, propose several strong NSD baselines, and establish a benchmark for future work.Finally, we conduct exhaustive experiments and qualitative analysis to comprehend key challenges and provide new guidance for future directions 1 .* The first three authors contribute equally.Weiran Xu
Local search pivoting rules and the landscape global structure

In local search algorithms, the pivoting rule determines which neighboring solution to select and thus strongly influences the behavior of the algorithm and its capacity to sample good-quality local optima. The classical pivoting rules are first and best improvement, with alternative rules such as worst improvement and maximum expansion recently studied on hill-climbing algorithms. This article conducts a thorough empirical comparison of five pivoting rules (best, first, worst, approximated worst and maximum expansion) on two benchmark combinatorial problems, NK landscapes and the unconstrained binary quadratic problem (UBQP), with varied sizes and ruggedness. We present both a performance analysis of the alternative pivoting rules within an iterated local search (ILS) framework and a fitness landscape analysis and visualization using local optima networks. Our results reveal that the performance of the pivoting rules within an ILS framework may differ from their performance as single climbers and that worst improvement and maximum expansion can outperform classical pivoting rules.
AutoFormer: Searching Transformers for Visual Recognition

Recently, pure transformer-based models have shown great potentials for vision tasks such as image classification and detection. However, the design of transformer networks is challenging. It has been observed that the depth, embedding dimension, and number of heads can largely affect the performance of vision transformers. Previous models configure these dimensions based upon manual crafting. In this work, we propose a new one-shot architecture search framework, namely AutoFormer, dedicated to vision transformer search. AutoFormer entangles the weights of different blocks in the same layers during supernet training. Benefiting from the strategy, the trained supernet allows thousands of subnets to be very well-trained. Specifically, the performance of these subnets with weights inherited from the supernet is comparable to those retrained from scratch. Besides, the searched models, which we refer to AutoFormers, surpass the recent state-of-the-arts such as ViT and DeiT. In particular, AutoFormer-tiny/small/base achieve 74.7%/81.7%/82.4% top-1 accuracy on ImageNet with 5.7M/22.9M/53.7M parameters, respectively. Lastly, we verify the transferability of AutoFormer by providing the performance on downstream benchmarks and distillation experiments. Code and models are available at https://github.com/microsoft/AutoML.
THE STUDY OF THE DYNAMICS OF THE COGNITIVE COMPONENT OF TEACHERS’ DIGITAL COMPETENCE IN THE CONTEXT OF DISTANCE EDUCATION (BASED ON THE MATERIAL OF THE CITY OF OMSK AND THE OMSK REGION)

The article considers the results of the authors’ own research on the dynamics of the cognitive component of digital competence among teachers of the city of Omsk and the Omsk region in the context of distance education. The study was conducted from February 2020 to March 2021 and captured the period of active introduction of digital technologies in the general education system in connection with the pandemic. The analysis of the obtained data shows the dynamics of the cognitive component of the formation of digital competence among teachers, due to the experience of professional activity
Complaint Management System for Sarawak Rural Area Water Supply Department

This Complaint Management System is developed to manage the complaints received by the Corporate Communication Unit of the Sarawak Rural Area Water Supply Department. This system was developed to solve the current problems faced in managing, retrieving and updating data using the manual method by the Department. Users of the system are the administrative staff and division water engineers. Users are able to add, update and delete complaint records, as well as view information in graphical format such as pie charts and graphs. Rapid Application Development (RAD) was used to develop this system, consisting of four phases: analysing user requirement, develop a prototype, construction and cut over. The feedback received from the Department’s staff is positive and showed that the users are satisfied with the system developed.
Phase Demodulation Method for Fringe Projection Measurement Based on Improved Variable-Frequency Coded Patterns

The phase-to-height imaging model, as a three-dimensional (3D) measurement technology, has been commonly applied in fringe projection to assist surface profile measurement, where the efficient and accurate calculation of phase plays a critical role in precise imaging. To deal with multiple extra coded patterns and 2π jump error caused to the existing absolute phase demodulation methods, a novel method of phase demodulation is proposed based on dual variable-frequency (VF) coded patterns. In this paper, the frequency of coded fringe is defined as the number of coded fringes within a single sinusoidal fringe period. First, the effective wrapped phase (EWP) as calculated using the four-step phase shifting method was split into the wrapped phase region with complete period and the wrapped phase region without complete period. Second, the fringe orders in wrapped phase region with complete period were decoded according to the frequency of the VF coded fringes and the continuous characteristic of the fringe order. Notably, the sampling frequency of fast Fourier transform (FFT) was determined by the length of the decoding interval and can be adjusted automatically with the variation in height of the object. Third, the fringe orders in wrapped phase region without complete period were decoded depending on the consistency of fringe orders in the connected region of wrapped phase. Last, phase demodulation was performed. The experimental results were obtained to confirm the effectiveness of the proposed method in the phase demodulation of both discontinuous objects and highly abrupt objects.
Developing English job interview skill using artificial intelligence technology

Vocational high schools (VHS) mainly prepare the students to engage in employment with a number of specific skills even though some students may continue to study in university/ college level. Various job vacancy advertisements require English skill in completing either the documents or interview. In competing the documents such as CV and cover letter, the students may adopt and adapt some templates even consult to the experts to get the best quality. However, in the interview, the students should perform themselves in live interaction. If the students are not well prepared, they may pass the documents selection but fail the interview step. Therefore, English job interview preparation is needed to equip the students ready in facing the job interview. Nowadays, artificial intelligence (AI) technology can help to improve the students’ speaking skill including job interview. The use of AI becomes higher due to the advantages such as flexibility, accurateness, and direct feedback. This program applied AI technology to develop English job interview skill in of the students in SMK AAG Penerbangan Adisutjipto Yogyakarta. The result was the selected AI successfully improved the students’ job interview skills indicated on the increased mark after applying the technology.
Source code patches from dynamic analysis

Dynamic analysis can identify improvements to programs that cannot feasibly be identified by static analysis; concurrency improvements are a motivating example. However, mapping these dynamic-analysis-based improvements back to patch-like source-code changes is non-trivial. We describe a system, Scopda, for generating source-code patches for improvements identified by execution-trace-based dynamic analysis. Scopda uses a graph-based static program representation (abstract program graph, APG), containing inter-procedural control flow and local data flow information, to analyse and transform static source-code. We demonstrate Scopda's ability to generate sensible source code patches for Java programs, though it is fundamentally language agnostic.
Building Dictionaries for Low Resource Languages: Challenges of Unsupervised Learning

The development of natural language processing resources for Albanian has grown steadily in recent years. This paper presents research conducted on unsupervised learning-the challenges associated with building a dictionary for the Albanian language and creating part-of-speech tagging models. The majority of languages have their own dictionary, but languages with low resources suffer from a lack of resources. It facilitates the sharing of information and services for users and whole communities through natural language processing. The experimentation corpora for the Albanian language includes 250K sentences from different disciplines, with a proposal for a part-of-speech tagging tag set that can adequately represent the underlying linguistic phenomena. Contributing to the development of Albanian is the purpose of this paper. The results of experiments with the Albanian language corpus revealed that its use of articles and pronouns resembles that of more high-resource languages. According to this study, the total expected frequency as a means for correctly tagging words has been proven effective for populating the Albanian language dictionary.
Detecting Deepfake Videos Using Euler Video Magnification

Recent advances in artificial intelligence make it progressively hard to distinguish between genuine and counterfeit media, especially images and videos. One recent development is the rise of deepfake videos, based on manipulating videos using advanced machine learning techniques. This involves replacing the face of an individual from a source video with the face of a second person, in the destination video. This idea is becoming progressively refined as deepfakes are getting progressively seamless and simpler to compute. Combined with the outreach and speed of social media, deepfakes could easily fool individuals when depicting someone saying things that never happened and thus could persuade people in believing fictional scenarios, creating distress, and spreading fake news. In this paper, we examine a technique for possible identification of deepfake videos. We use Euler video magnification which applies spatial decomposition and temporal filtering on video data to highlight and magnify hidden features like skin pulsation and subtle motions. Our approach uses features extracted from the Euler technique to train three models to classify counterfeit and unaltered videos and compare the results with existing techniques.
Implementation of E-Learning for ESP in Tourism During the Covid-19 Pandemic

Covid-19 has been declared a global pandemic by WHO. This affects all aspects of human life, including education. The entire teaching and learning process during the Covid-19 pandemic was carried out online or by implementing e-learning. The online system of study intends to avoid human contact. This study aims to look at the media used to implement e-learning, considerations in choosing e-learning media, as well as the advantages and disadvantages found in implementing e-learning. There are six English lecturers who teach Tourism English at Tourism Department of Bali State Polytechnic. The data used in this study were obtained from questionnaires distributed online. The data is then analyzed by looking for a percentage and describing it.
Research Data Analysis Methods in Addressing the K-12 Learning Competency on Data Analysis Procedures Among Senior High School Research Courses

The purpose of the research process is to benefit human life in various ways. It is important to consider how research methods are taught; equipping students with these research literacy skills leading to increased research output by an individual, particularly when it comes to the preliminary steps in planning data analysis procedures. It also provides the initiative in K-12 learning on research methods by assisting students in developing and retaining research skills and addressing the K-12 learning competency on data analysis procedures. As data analysis is viewed as the most complex and mysterious phase of a research project, scientific analysis requires an analytical systematic method that collects a large number of data points and is then evaluated to draw a reliable and relevant conclusion. To obtain valid results, data must be handled correctly from the time of collection through to the analysis stage. In this case, this article aimed to provide preliminary research data analysis methods, both in quantitative and qualitative researches for senior high school students. There are a variety of methods that may be utilized to analyze data. Data analysis needs carefully planning, regardless of whether the approach is to be based on qualitative or quantitative techniques.
Data Stream Classification Algorithms for Workload Orchestration in Vehicular Edge Computing: A Comparative Evaluation

This paper reports on the use of online data stream classification algorithms to support workload orchestration in vehicular edge computing environments.These algorithms can be used to predict the ability of available computational nodes to successfully handle computational tasks generated from vehicular applications.Several online data stream classification algorithms have been evaluated based on synthetic datasets generated from simulated vehicular edge computing environments.In addition, a multi-criteria decision analysis technique was utilized to rank the different algorithms based on their performance metrics.The evaluation results demonstrate that the considered algorithms can handle online classification operations with various trade-offs and dominance relations with respect to their obtained performance.In addition, the utilized multi-criteria decision analysis technique can efficiently rank various algorithms and identify the most appropriate algorithms to augment workload orchestration.Furthermore, the evaluation results show that the leveraging bagging algorithm, with an extremely fast decision tree base estimator, is able to maintain marked online classification performance and persistent competitive ranking among its counterparts for all datasets.Hence, it can be considered a promising choice to reinforce workload orchestration in vehicular edge computing environments.
Separable Reversible Data Hiding in Encryption Image with Two-Tuples Coding

Separable Reversible Data Hiding in Encryption Image (RDH-EI) has become widely used in clinical and military applications, social cloud and security surveillance in recent years, contributing significantly to preserving the privacy of digital images. Aiming to address the shortcomings of recent works that directed to achieve high embedding rate by compensating image quality, security, reversible and separable properties, we propose a two-tuples coding method by considering the intrinsic adjacent pixels characteristics of the carrier image, which have a high redundancy between high-order bits. Subsequently, we construct RDH-EI scheme by using high-order bits compression, low-order bits combination, vacancy filling, data embedding and pixel diffusion. Unlike the conventional RDH-EI practices, which have suffered from the deterioration of the original image while embedding additional data, the content owner in our scheme generates the embeddable space in advance, thus lessening the risk of image destruction on the data hider side. The experimental results indicate the effectiveness of our scheme. A ratio of 28.91% effectively compressed the carrier images, and the embedding rate increased to 1.753 bpp with a higher image quality, measured in the PSNR of 45.76 dB.
A Digital Image Watermarking Using Dwt and Lshaped Tromino Fractal Encryption

This paper presents a digital image watermarking scheme in the frequency domain for colour images.The algorithm was developed using the digital wavelet transform together with fractal encryption.A host image was first transformed into a frequency domain using the discrete wavelet transform after which a binary watermark was permuted and encrypted with a fractal generated from the watermark and a random key.The encrypted watermark is then embedded into the host image in the frequency domain to form a watermarked image.The algorithm's performance was examined based on the image quality of the watermarked image using peak signal to noise ratio.A perceptual metrics called the structural similarity index metric was further used to examine the structural similarity of the watermarked image and the extracted watermark.Again, the normalised cross-correlation was introduced to further assess the robustness of the algorithm.Our algorithm produced a peak signal to noise ratio of 51.1382dB and a structural similarity index of 0.9999 when tested on colour images of Lena, baboon and pepper indicating the quality of the watermarked images produced and hence indicates a higher imperceptibility of the proposed algorithm.The extracted watermark also had a structural similarity of 1 and a normalised cross correlation of 1 indicating a perfect similarity between the original watermark and the extracted watermark hence shows a higher performance of the proposed algorithm.The algorithm also showed a very good level of robustness when various attacks such as Gaussian noise, Poisson noise, salt and pepper noise, speckle noise and filtering were applied.
An MMCM-based high-speed true random number generator for Xilinx FPGA

For a true random number generator (TRNG) on an FPGA, the use of a pair of clocking elements has an advantage of minimal usage of its logic elements. This paper presents a novel high-speed TRNG for recent Xilinx FPGAs using their clocking elements called mixed-mode clock managers (MMCMs). By following the proposed parameter selection methods, both better randomness and higher throughput of generated bitstrings can be achieved. According to our evaluation on an Artix-7 FPGA with the most promising sets of parameters, 38.2% (42 out of 110) of the sets passed AIS-31 Procedure B, which means that an appropriate parameter set can be found by ten or less trials with more than 99% probability. The average throughput of them was 2.44 Mbit/s, which was comparable to recent FPGA-based TRNGs. An initial prototype of dynamic reconfiguration of the parameters is also presented in this paper.
Investigating Clues for Estimating ICAP States Based on Learners’ Behavioural Data During Collaborative Learning

Interactions based on the learners’ state of understanding and their attitudes toward tasks are considered important for realising a support system for collaborative learning. In this study, as a first step, we tried to detect whether the learner’s state is Passive in the ICAP theory from the data obtained during collaborative learning. We actually conducted an experiment of collaborative learning between participants and obtained data on facial features, gaze directions, and speech state during the experiment. Based on these data, we investigated clues to classify the status of ICAP as either Passive or not. As a result, we were able to find several candidates. On the other hand, in the state classification of participants’ states using these independent variables, it was not possible to show high accuracy. In future experiments, we plan to simultaneously measure physiological indices as a clue to estimate participants’ internal state.