Reviewerly/exHarmony huggingface dataset:
DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations

Top-performing Model-Based Reinforcement Learning (MBRL) agents, such as Dreamer, learn the world model by reconstructing the image observations. Hence, they often fail to discard task-irrelevant details and struggle to handle visual distractions. To address this issue, previous work has proposed to contrastively learn the world model, but the performance tends to be inferior in the absence of distractions. In this paper, we seek to enhance robustness to distractions for MBRL agents. Specifically, we consider incorporating prototypical representations, which have yielded more accurate and robust results than contrastive approaches in computer vision. However, it remains elusive how prototypical representations can benefit temporal dynamics learning in MBRL, since they treat each image independently without capturing temporal structures. To this end, we propose to learn the prototypes from the recurrent states of the world model, thereby distilling temporal structures from past observations and actions into the prototypes. The resulting model, DreamerPro, successfully combines Dreamer with prototypes, making large performance gains on the DeepMind Control suite both in the standard setting and when there are complex background distractions. Code available at https://github.com/fdeng18/dreamer-pro .
THE EFFECTIVENESS OF MYOFASCIAL RELEASE IN THE CERVICAL REGION FOR TENSION HEADACHE RELIEF

Myofascial release is a manual thrust applied to the body face to treat the facial system. Myofascial release restores the flexibility and elasticity of the fascia, thus having a broad ability to alter the body's range of motion and dissolve restrictions in the fascial tissues. Sedentary lifestyle and positions that cause pain can gradually cause muscle and fascial restrictions and shortening. This study aims to identify the effectiveness of myofascial release in the cervical region for tension headache relief. This study is characterized as a bibliographical research of qualitative descriptive character, where the researcher is the one who describes the research object, assuming the role of observer and explorer, directly collecting the data in the place where the study phenomena occurred or appeared. It is concluded that through the use of this technique, it is possible to deactivate trigger points, promote a correct alignment of the body structure, minimize movement restriction and pain without compromising the myofascial structures.
Best Computer Lock

It is a good thing that you’re security conscious about the data and other confidential information on your computer. You undertake great measures to ...
Quantum optimisation via maximally amplified states.

This paper presents the Maximum Amplification Optimisation Algorithm (MAOA), a highly efficient quantum algorithm designed for combinatorial optimisation in the restricted circuit depth context of near-term quantum computing. The algorithm is shown to produce maximum possible amplification of optimal solutions, which grows quadratically in achievable circuit depth, and does so without the computationally demanding variational procedure required by other near-term quantum algorithms, such as QAOA or QWOA. A simple but novel modification to the existing Grover adaptive search in the context of similar restricted circuit depths is also introduced, referred to as the restricted Grover adaptive search (RGAS). The MAOA and RGAS are simulated on a practical vehicle routing problem, a computationally demanding portfolio optimisation problem, and an arbitrarily large problem with normally distributed solution qualities. In all cases, the MAOA and RGAS are shown to provide substantial speedup over classical-sampling in finding optimal solutions, where the MAOA consistently outperforms the RGAS. More importantly, the MAOA is shown to provide a speedup which is extraordinarily close to the upper bound of what is possible within the QWOA framework, which includes QAOA.
Evaluation of wild position of material messaging for the ring shock on the sound changing-state awakening system

AbstractRing changing-state technology is associated the shock status for black-white dot pattern of coruscateawakening rate (COAR) and classification awakening rate (CLAR) on the ring awakening imagery. Theawakening rate condition by the ring awakening imagery is associated with the sound shock system. Asto come up with a black-white dot pattern of the wild changing-state, we are associated of the ringvalue with ring layer position by the sound-down structure. The concept of awakening rate is heck outthe reference of coruscate rate and classification rate for changing-state signal by the ring shockimagery. Moreover to offer a wild changing-state of the COAR-CLAR of the average-minimum in terms ofthe ring-shock imagery, and ring position shock that is found the a ring value of the far changing-state ofthe Ri-ai-FA-φAVG-MIN with 11.65±12.76 units, that was the a ring value of the convenient changing-stateof the Ri-ai-CO-φAVG-MIN with 4.52±3.54 units, that was the a ring value of the flank changing-state ofthe Ri-ai-FL-φAVG-MIN with 0.98±0.90 units, that was the a ring value of the vicinage changing-state ofthe Ri-ai-VI-φAVG-MIN with 0.22±0.18 units. The sound shock will be to look into at the ability of the ringshock imagery for the restrain degree awakening rate on the COAR-CLAR that is to be stick-out the wildcoruscate and classification imagery by the awakening rate system. We will be to look into of imagery bythe classification signal and to work out a ring data of sound shock rate by the sound awakening system.
English Culture Introduction Teaching Based on Comprehensive Competence Assessment from the Perspective of Applied Linguistics

Under the trend of globalization, English is widely used all over the world. English ability has become an important indicator to measure comprehensive talents. Due to the fundamental differences between Chinese culture and English culture, many students will encounter great difficulties in learning. In the teaching practice, the reasonable introduction of English culture is the key to improve the teaching level and quality. This paper first explains the concepts of Applied Linguistics and culture introduction teaching. This paper focuses on the influence of cultural introduction on English teaching from the perspective of Applied Linguistics, starting from listening, speaking, reading, writing, translation and vocabulary. This study shows that the introduction of English culture into English curriculum should be based on the theory of Applied Linguistics in order to enhance the effectiveness of the introduction. At the same time, by analyzing the influence of English culture introduction teaching, this paper explores the implementation strategies of English culture introduction teaching from the perspective of Applied Linguistics. The experimental data show that the proposed method can improve the classroom efficiency of English learning and promote teaching reform and innovation.
Communication-Efficient ADMM-based Federated Learning

Federated learning has shown its advances over the last few years but is facing many challenges, such as how algorithms save communication resources, how they reduce computational costs, and whether they converge. To address these issues, this paper proposes exact and inexact ADMM-based federated learning. They are not only communication-efficient but also converge linearly under very mild conditions, such as convexity-free and irrelevance to data distributions. Moreover, the inexact version has low computational complexity, thereby alleviating the computational burdens significantly.
Off-policy Reinforcement Learning with Optimistic Exploration and Distribution Correction

Improving the sample efficiency of reinforcement learning algorithms requires effective exploration. Following the principle of $\textit{optimism in the face of uncertainty}$ (OFU), we train a separate exploration policy to maximize the approximate upper confidence bound of the critics in an off-policy actor-critic framework. However, this introduces extra differences between the replay buffer and the target policy regarding their stationary state-action distributions. To mitigate the off-policy-ness, we adapt the recently introduced DICE framework to learn a distribution correction ratio for off-policy RL training. In particular, we correct the training distribution for both policies and critics. Empirically, we evaluate our proposed method in several challenging continuous control tasks and show superior performance compared to state-of-the-art methods. We also conduct extensive ablation studies to demonstrate the effectiveness and rationality of the proposed method.
AI-based voice assistants for digital marketing: preparing for voice marketing and commerce

Artificial intelligence-based voice assistants (VAs) may alter the decision-making process throughout the entire consumer journey. With the distinction between humans and machines becoming increasingly blurred, VAs introduce biases and heuristics in consumer behaviour with consequences for digital marketing and e-commerce practices. The authors explain ‘why’ and ‘how’ marketing managers need to explore the challenges and opportunities posed by voice technologies and prepare to employ a combination of marketing and commerce initiatives. This chapter defines voice marketing and voice commerce practices through relevant cases while collocating voice-based activities within the broader communication mix strategy. Furthermore, a conceptual framework shed light on the disruptive impact of AI-based VAs across product categories.
The Comparison of Grayscale Image Enhancement Techniques for Improving the Quality of Marker in Augmented Reality

<p class='IJASEITAbtract'>Natural Feature Tracking (NFT) in Augmented Reality (AR) applications use feature detection and a feature matching approach to aligning virtual objects in a real environment. Thus, this tracking detects and compares features that are naturally found in the image (query of image) with the visible feature in the real environment. Therefore, the query of an image must contain good features to track. One of the representing natural features that is easily found in the image is in the corner, and a feature from Accelerated Segment Test (FAST) is one of the fastest corner detectors. However, the FAST corner uses the intensity of the grayscale pixel to determine the candidate corner. Hence, the intensity greatly affects the detection result. Therefore, FAST corner uses the grayscale conversion process to changes the color image into a grayscale image. Thus, the conversion process can lose some details of the images, such as sharpness, shadow, and color image structure. Hence, this process will affect the result of FAST corner to find the feature corner. Besides, Contrast Enhancement also can improve the quality of low contrast grayscale image. In this paper, there are three techniques of the Contrast Enhancement (CE) method were compared, which are Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Colormap. As a result, Colormap is better than HE and CLAHE to extract conner and others feature accurately.
Multi-target tracking for video surveillance using deep affinity network: A brief review

Deep learning models are known to function like the human brain. Due to their functional mechanism, they are frequently utilized to accomplish tasks that require human intelligence. Multi-target tracking (MTT) for video surveillance is one of the important and challenging tasks, which has attracted the researchers' attention due to its potential applications in various domains. Multi-target tracking tasks require locating the objects individually in each frame, which remains a huge challenge as there are immediate changes in appearances and extreme occlusions of objects. In addition to that, the Multi-target tracking framework requires multiple tasks to perform i.e. target detection, estimating trajectory, associations between frame, and re-identification. Various methods have been suggested, and some assumptions are made to constrain the problem in the context of a particular problem. In this paper, the state-of-the-art MTT models, which leverage from deep learning representational power are reviewed.
Remote Patient Monitoring System

The main objective of the project is to develop a model that allows pill intake monitoring of a patient or an elderly person without any physical presence.
A Systematic Approach for Malay Language Dialect Identification by Using CNN

As Malaysia moves forward towards the Industrial Revolution (IR 4. 0), computer systems have become part of everyday life, leading to increased man-machine interactions. Verbal communication is a convenient means to interact with computers. Speech recognition systems need to be robust to cater for various languages and dialects in order to interact better with humans. Dialects within a spoken language present a challenge for computers require a speech recognition system to translate these verbal commands to computer understanding of the underlying meaning from spoken words. In this paper, works on Malay language dialect identification are presented using Convolution Neural Network (CNN) trained on Mel Frequency Cepstral Coefficient (MFCC) features. Data was collected from 12 native speakers. Each speaker was instructed to utter 10 carefully selected words to emphasize the dialect nuances of the eastern, northern and central (standard) Malay dialect. The MFCC features were then extracted from the recorded audio samples and converted to graphical form. The images were then used to train a custom CNN neural network to differentiate between the various spoken words and their dialects. Results demonstrate that CNN was able to effectively differentiate between the spoken words with excellent accuracy (between 85% and 100%).
Characteristic Of Extreme Rainfall In Province Of South Sulawesi

The effect of climate change on extreme rainfall is important to note due to its damaging impact on the environment and humanity. This study aims to analyze the characteristics of extreme rainfall in the Province of South Sulawesi. The characteristics investigated are trends and models of extreme rainfall distribution. Trend analysis of extreme rainfall used the Mann-Kendall Test. Modeling of extreme rainfall distribution used the Annual Maximum approach with Generalized Extreme Value (GEV) distribution. The extreme rainfall distribution model is approached with non-stationary models. The method of parameter estimation used the maximum likelihood method coupled with the BFGS Quasi-Newton methods. Model selection performed based on the smallest Akaike’s Information Criterion (AIC) value. Using rainfall data in Makassar rain gauge for a period from 1983 to 2015, the results show that there is a decreasing trend of extreme rainfall in Makassar Municipality and the appropriate distribution model for extreme rainfall data is a non-stationary model.
Simple data balancing achieves competitive worst-group-accuracy

We study the problem of learning classifiers that perform well across (known or unknown) groups of data. After observing that common worst-group-accuracy datasets suffer from substantial imbalances, we set out to compare state-of-the-art methods to simple balancing of classes and groups by either subsampling or reweighting data. Our results show that these data balancing baselines achieve state-of-the-art-accuracy, while being faster to train and requiring no additional hyper-parameters. In addition, we highlight that access to group information is most critical for model selection purposes, and not so much during training. All in all, our findings beg closer examination of benchmarks and methods for research in worst-group-accuracy optimization.
Fitness Landscape Footprint: A Framework to Compare Neural Architecture Search Problems.

Neural architecture search is a promising area of research dedicated to automating the design of neural network models. This field is rapidly growing, with a surge of methodologies ranging from Bayesian optimization,neuroevoltion, to differentiable search, and applications in various contexts. However, despite all great advances, few studies have presented insights on the difficulty of the problem itself, thus the success (or fail) of these methodologies remains unexplained. In this sense, the field of optimization has developed methods that highlight key aspects to describe optimization problems. The fitness landscape analysis stands out when it comes to characterize reliably and quantitatively search algorithms. In this paper, we propose to use fitness landscape analysis to study a neural architecture search problem. Particularly, we introduce the fitness landscape footprint, an aggregation of eight (8)general-purpose metrics to synthesize the landscape of an architecture search problem. We studied two problems, the classical image classification benchmark CIFAR-10, and the Remote-Sensing problem So2Sat LCZ42. The results present a quantitative appraisal of the problems, allowing to characterize the relative difficulty and other characteristics, such as the ruggedness or the persistence, that helps to tailor a search strategy to the problem. Also, the footprint is a tool that enables the comparison of multiple problems.
PE-DCA: Penalty elimination based data center allocation technique using guided local search for IaaS cloud

In Cloud computing the user requests are passaged to data centers (DCs) to accommodate resources. It is essential to select the suitable DCs as per the user requests so that other requests should not be penalized in terms of time and cost. The searching strategies consider the execution time rather than the related penalties while searching DCs. In this work, we discuss Penalty Elimination-based DC Allocation (PE-DCA) using Guided Local Search (GLS) mechanism to locate suitable DCs with reduced cost, response time, and processing time. The PE-DCA addresses, computes, and eliminates the penalties involved in the cost and time through iterative technique using the defined objective and guide functions. The PE-DCA is implemented using CloudAnalyst with various configurations of user requests and DCs. We examine the PE-DCA and the execution after-effects of various costs and time parameters to eliminate the penalties and observe that the proposed mechanism performs best.
Economic Globalization and Organizational Performance in Nigeria: A Conceptual Review

Global changes in economic and business activities have propelled strategic alliances among countries aimed at expanding markets across the globe. This requires catalysts such as Foreign Direct Investment, international trade, and diffusion of technology which are driven by emerging investment policy to serve as predominant factors for improved business performance in Nigeria. Qualitative research was adopted to elicit relevant information which reveals that globalization is a measure to open Nigeria’s economy through a structural adjustment programme for international markets. International trade breeds the exchange of raw materials and products between nations as a result of the interchange of products and services. Foreign trade impacts the GDP growth of the country. Diffusion of technology aids in the spreading of innovation across economies for improves business performance through disseminating technical information, know-how, and adopting new techniques and technologies by diverse users of the product and processes. Privatization promotes FDI which involves the transfer of state-owned enterprises either partly or completely managed by companies or individuals. Nigeria is a market for FDI based on economic liberation policy, Public-Private-Partnership (PPP), and other strategic alliances with multinational companies. It concludes that economic globalization connects different parts of the world to take advantage of lower labour costs in developing economies; leverage technical expertise and experience from the developed world.
DEDUCTIVE AND INDUCTIVE INSTRUCTIONS FOR TEACHING ENGLISH GRAMMAR IN ONLINE CLASSROOM

Realizing the importance of teaching grammar to young learners, pre service teachers, as prospective teachers, need to reflect their own grammar teaching performance. Moreover, during the covid-19 pandemic outbreak where all learning processes are conducted online, teaching strategy needs to be considered. Therefore, this study aimed to find out whether the pre-service teachers apply the deductive or inductive teaching instructions in teaching grammar to young learners during the online class and to investigate how pre-service teachers develop the instructions. To reach the objectives, qualitative research was employed by observing and analysing the teaching videos of thirteen pre service teachers. The pre service teachers were the students of English Education Department in a private university in Jakarta who enrolled the Teaching Internship program. All videos were transcribed and then the analysed data were put in a table and coded to ease the identification of deductive and inductive instructions. The results revealed that more students adopted deductive grammar instructions in teaching grammar for young learners rather than inductive instruction. Furthermore, the way the pre service teachers developed both instructions followed the PPP and TTT models with different emphasis on the Presentation and Teach parts. The tendency done in the Presentation part for inductive teaching was the pre service teacher acted as instructor whereas the Teach part in inductive teaching was as facilitator. Eventually, from this study it can be concluded that pre service teachers adopted deductive and inductive grammar instructions because both are suitable for teaching grammar to young learners in online classroom setting within consideration of meaningful learning activities.
The Impact of Socio-Economic Factors on Human Capital Investments

The MENA region needs to diversify and move towards building knowledge-based economies so that their participation in the human capital of the world can be improved. At present, despite an above average allocation of public spending on the education sector, the returns in terms of jobs and educational quality are not seen. Several challenges like unemployment, poor quality of education, lack of private sector jobs, inability to leverage on technology for increasing the scope of education, war and immigrant crises, and centralized control over education are acting as barriers in the improvement of efficiency of educational systems and hence, the human capital. This paper provides an overview of the current status of key indicators in the region regarding the subject and examines possible relationships between the proxies. This study aims at examining critically how education systems have contributed to downturn in socioeconomic development in the MENA region. It also suggestsa framework based on possible solutions that can allow the region to finally realize the returns on its investments in the education sector. The dependent variable of this study was Human Quality Investment in MENA region while the explanatory variables were; use of technology in learning, centralized control, regional threat, common language, information technology in education, unemployment rate and quality of education.