Reviewerly/exHarmony huggingface dataset:
NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search

Neural architecture search (NAS) is a promising method for automatically design neural architectures. NAS adopts a search strategy to explore the predefined search space to find outstanding performance architecture with the minimum searching costs. Bayesian optimization and evolutionary algorithms are two commonly used search strategies, but they suffer from computationally expensive, challenge to implement or inefficient exploration ability. In this paper, we propose a neural predictor guided evolutionary algorithm to enhance the exploration ability of EA for NAS (NPENAS) and design two kinds of neural predictors. The first predictor is defined from Bayesian optimization and we propose a graph-based uncertainty estimation network as a surrogate model that is easy to implement and computationally efficient. The second predictor is a graph-based neural network that directly outputs the performance prediction of the input neural architecture. The NPENAS using the two neural predictors are denoted as NPENAS-BO and NPENAS-NP respectively. In addition, we introduce a new random architecture sampling method to overcome the drawbacks of the existing sampling method. Extensive experiments demonstrate the superiority of NPENAS. Quantitative results on three NAS search spaces indicate that both NPENAS-BO and NPENAS-NP outperform most existing NAS algorithms, with NPENAS-BO achieving state-of-the-art performance on NASBench-201 and NPENAS-NP on NASBench-101 and DARTS, respectively.
Moving Forward in Formation: A Decentralized Hierarchical Learning Approach to Multi-Agent Moving Together

Multi-agent path finding in formation has many potential real-world applications like mobile warehouse robots. However, previous multi-agent path finding (MAPF) methods hardly take formation into consideration. Furthermore, they are usually centralized planners and require the whole state of the environment. Other decentralized partially observable approaches to MAPF are reinforcement learning (RL) methods. However, these RL methods encounter difficulties when learning path finding and formation problem at the same time. In this paper, we propose a novel decentralized partially observable RL algorithm that uses a hierarchical structure to decompose the multi objective task into unrelated ones. It also calculates a theoretical weight that makes every task reward has equal influence on the final RL value function. Additionally, we introduce a communication method that helps agents cooperate with each other. Experiments in simulation show that our method outperforms other end-to-end RL methods and our method can naturally scale to large world sizes where centralized planner struggles. We also deploy and validate our method in a real world scenario.
GAN "Steerability" without optimization

Recent research has shown remarkable success in revealing "steering" directions in the latent spaces of pre-trained GANs. These directions correspond to semantically meaningful image transformations e.g., shift, zoom, color manipulations), and have similar interpretable effects across all categories that the GAN can generate. Some methods focus on user-specified transformations, while others discover transformations in an unsupervised manner. However, all existing techniques rely on an optimization procedure to expose those directions, and offer no control over the degree of allowed interaction between different transformations. In this paper, we show that "steering" trajectories can be computed in closed form directly from the generator's weights without any form of training or optimization. This applies to user-prescribed geometric transformations, as well as to unsupervised discovery of more complex effects. Our approach allows determining both linear and nonlinear trajectories, and has many advantages over previous methods. In particular, we can control whether one transformation is allowed to come on the expense of another (e.g. zoom-in with or without allowing translation to keep the object centered). Moreover, we can determine the natural end-point of the trajectory, which corresponds to the largest extent to which a transformation can be applied without incurring degradation. Finally, we show how transferring attributes between images can be achieved without optimization, even across different categories.
Neurosymbolic hybrid approach to driver collision warning

There are two main algorithmic approaches to autonomous driving systems: (1) An end-to-end system in which a single deep neural network learns to map sensory input directly into appropriate warning and driving responses. (2) A mediated hybrid recognition system in which a system is created by combining independent modules that detect each semantic feature. While some researchers believe that deep learning can solve any problem, others believe that a more engineered and symbolic approach is needed to cope with complex environments with less data. Deep learning alone has achieved state-of-the-art results in many areas, from complex gameplay to predicting protein structures. In particular, in image classification and recognition, deep learning models have achieved accuracies as high as humans. But sometimes it can be very difficult to debug if the deep learning model doesn't work. Deep learning models can be vulnerable and are very sensitive to changes in data distribution. Generalization can be problematic. It's usually hard to prove why it works or doesn't. Deep learning models can also be vulnerable to adversarial attacks. Here, we combine deep learning-based object recognition and tracking with an adaptive neurosymbolic network agent, called the Non-Axiomatic Reasoning System (NARS), that can adapt to its environment by building concepts based on perceptual sequences. We achieved an improved intersection-over-union (IOU) object recognition performance of 0.65 in the adaptive retraining model compared to IOU 0.31 in the COCO data pre-trained model. We improved the object detection limits using RADAR sensors in a simulated environment, and demonstrated the weaving car detection capability by combining deep learning-based object detection and tracking with a neurosymbolic model.
Enumeration of extended irreducible binary Goppa codes

The family of Goppa codes is one of the most interesting subclasses of linear codes. As the McEliece cryptosystem often chooses a random Goppa code as its key,knowledge of the number of inequivalent Goppa codes for fixed parameters may facilitate in the evaluation of the security of such a cryptosystem. In this paper we present a new approach to give an upper bound on the number of inequivalent extended irreducible binary Goppa codes. To be more specific, let $n>3$ be an odd prime number and $q=2^n$; let $r\geq3$ be a positive integer satisfying $\gcd(r,n)=1$ and $\gcd\big(r,q(q^2-1)\big)=1$. We obtain an upper bound for the number of inequivalent extended irreducible binary Goppa codes of length $q+1$ and degree $r$.
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking

Transformer based re-ranking models can achieve high search relevance through context-aware soft matching of query tokens with document tokens. To alleviate runtime complexity of such inference, previous work has adopted a late interaction architecture with pre-computed contextual token representations at the cost of a large online storage. This paper proposes contextual quantization of token embeddings by decoupling document-specific and document-independent ranking contributions during codebook-based compression. This allows effective online decompression and embedding composition for better search relevance. This paper presents an evaluation of the above compact token representation model in terms of relevance and space efficiency.
Asymptotics of Sequential Composite Hypothesis Testing under Probabilistic Constraints

We consider the sequential composite binary hypothesis testing problem in which one of the hypotheses is governed by a single distribution while the other is governed by a family of distributions whose parameters belong to a known set $\Gamma$. We would like to design a test to decide which hypothesis is in effect. Under the constraints that the probabilities that the length of the test, a stopping time, exceeds $n$ are bounded by a certain threshold $\epsilon$, we obtain certain fundamental limits on the asymptotic behavior of the sequential test as $n$ tends to infinity. Assuming that $\Gamma$ is a convex and compact set, we obtain the set of all first-order error exponents for the problem. We also prove a strong converse. Additionally, we obtain the set of second-order error exponents under the assumption that $\mathcal{X}$ is a finite alphabet. In the proof of second-order asymptotics, a main technical contribution is the derivation of a central limit-type result for a maximum of an uncountable set of log-likelihood ratios under suitable conditions. This result may be of independent interest. We also show that some important statistical models satisfy the conditions.
Subjective and Objective Quality Assessment of Mobile Gaming Video

Nowadays, with the vigorous expansion and development of gaming video streaming techniques and services, the expectation of users, especially the mobile phone users, for higher quality of experience is also growing swiftly. As most of the existing research focuses on traditional video streaming, there is a clear lack of both subjective study and objective quality models that are tailored for quality assessment of mobile gaming content. To this end, in this study, we first present a brand new Tencent Gaming Video dataset containing 1293 mobile gaming sequences encoded with three different codecs. Second, we propose an objective quality framework, namely Efficient hard-RAnk Quality Estimator (ERAQUE), that is equipped with (1) a novel hard pairwise ranking loss, which forces the model to put more emphasis on differentiating similar pairs; (2) an adapted model distillation strategy, which could be utilized to compress the proposed model efficiently without causing significant performance drop. Extensive experiments demonstrate the efficiency and robustness of our model.
Log-Regularly Varying Scale Mixture of Normals for Robust Regression

Linear regression with the classical normality assumption for the error distribution may lead to an undesirable posterior inference of regression coefficients due to the potential outliers. This paper considers the finite mixture of two components with thin and heavy tails as the error distribution, which has been routinely employed in applied statistics. For the heavily-tailed component, we introduce the novel class of distributions; their densities are log-regularly varying and have heavier tails than those of Cauchy distribution, yet they are expressed as a scale mixture of normal distributions and enable the efficient posterior inference by Gibbs sampler. We prove the robustness to outliers of the posterior distributions under the proposed models with a minimal set of assumptions, which justifies the use of shrinkage priors with unbounded densities for the coefficient vector in the presence of outliers. The extensive comparison with the existing methods via simulation study shows the improved performance of our model in point and interval estimation, as well as its computational efficiency. Further, we confirm the posterior robustness of our method in the empirical study with the shrinkage priors for regression coefficients.
JAXNS: a high-performance nested sampling package based on JAX

Since its debut by John Skilling in 2004, nested sampling has proven a valuable tool to the scientist, providing hypothesis evidence calculations and parameter inference for complicated posterior distributions, particularly in the field of astronomy. Due to its computational complexity and long-running nature, in the past, nested sampling has been reserved for offline-type Bayesian inference, leaving tools such as variational inference and MCMC for online-type, time-constrained, Bayesian computations. These tools do not easily handle complicated multi-modal posteriors, discrete random variables, and posteriors lacking gradients, nor do they enable practical calculations of the Bayesian evidence. An opening thus remains for a high-performance out-of-the-box nested sampling package that can close the gap in computational time, and let nested sampling become common place in the data science toolbox. We present JAX-based nested sampling (JAXNS), a high-performance nested sampling package written in XLA-primitives using JAX, and show that it is several orders of magnitude faster than the currently available nested sampling implementations of PolyChord, MultiNEST, and dynesty, while maintaining the same accuracy of evidence calculation. The JAXNS package is publically available at \url{https://github.com/joshuaalbert/jaxns}.
High success standard quantum teleportation using entangled coherent state and two-level atoms in cavities

We propose here a new idea for quantum teleportation of superposed coherent state which is not only almost perfect, in principle, but also feasible experimentally. We use entangled resource $\sim |\alpha,\frac{\alpha}{\sqrt{2}}\rangle-|-\alpha,-\frac{\alpha}{\sqrt{2}}\rangle$ in contrast with the usual $\sim |\alpha,\alpha\rangle-|-\alpha,-\alpha \rangle$ (both states unnormalized). Bob receives state which is then superposition of the states $|\pm \frac{\alpha}{\sqrt{2}}\rangle$ . Bob mixes these with even or odd coherent states involving superposition of states $|\pm \frac{\alpha}{\sqrt{2}}\rangle$ to obtain a two-mode state which is one of $\sim |I,0\rangle \pm |0,I\rangle$, $|I\rangle$ being the information state. Bob then obtains the teleported information by using interaction of one of these modes in two cavities with resonant two-level atoms. This scheme results in average fidelity of $\simeq 0.95$ for $|\alpha|^2 \simeq 10$, which increases with $|\alpha|^2$ and tends to 1 asymptotically, varying as $1-\frac{\pi^2}{16|\alpha|^2}+\frac{\pi^2(\pi^2+8)}{256|\alpha|^4}$ for large values of $|\alpha|^2$.
INFORMATION MANAGEMENT OF PRODUCTION RAW MATERIALS INFORMATION WITH FIFO METHOD BASED ON CLIENT SERVER

Information technology that is used properly will have a good influence on the company, among others, can increase productivity, work efficiency and of course very useful for institutions such as Koperasi Kawi Mandiri Sentosa in facing competitive business competition. The determination and speed of data processing from Koperasi Kawi Mandiri Sentosa, the presentation of fast, precise and accurate information will make it easier for companies to make the right decisions as well.&#x0D; Koperasi Kawi Mandiri Sentosa has a variety of fund products which include Voluntary Deposits and Time Deposits. As well as credit products which include Monthly Loans and Term Loans, while the procedures to be processed consist of the savings section, and the credit loan section.&#x0D; If there is a deposit transaction, it can be recorded in the customer's proof of deposit and at the same time the data can be saved to the computer automatically. Likewise, if there is a loan transaction from a customer, the data on the computer can be known with certainty and accuracy. The program that will be developed by the author is a multiuser-based savings and loan accounting information system, the software used is Microsoft Visual Basic 6.0
Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning

Evaluation of deep reinforcement learning (RL) is inherently challenging. In particular, learned policies are largely opaque, and hypotheses about the behavior of deep RL agents are difficult to test in black-box environments. Considerable effort has gone into addressing opacity, but almost no effort has been devoted to producing high quality environments for experimental evaluation of agent behavior. We present TOYBOX, a new high-performance, open-source* subset of Atari environments re-designed for the experimental evaluation of deep RL. We show that TOYBOX enables a wide range of experiments and analyses that are impossible in other environments. *https://kdl-umass.github.io/Toybox/
A Trustworthy View on XAI Method Evaluation

&lt;p&gt;As the demand grows to develop end-user trust in AI models, practitioners start to build and configure customized XAI (Explainable Artificial Intelligence) methods. The challenge is the lack of systematic evaluation of the newly proposed XAI method. As a result, it limits the confidence of XAI explanation in practice. In this paper, we follow a process of XAI method development and define two metrics in terms of consistency and efficiency in guiding the evaluation of trustworthy explanations. We demonstrate the development of a new XAI method in feature interactions called Mean-Centroid Preddiff, which analyzes and explains the feature importance order by a clustering algorithm. Following the process, we perform cross-validation on Mean-Centroid Preddiff with existing XAI methods. They show comparable consistency and gain in computation efficiency. The practice helps to adopt the core activities in the trustworthy evaluation of a new XAI method with rigorous cross-validation on consistency and efficiency.&lt;/p&gt;
PERSEPSI MAHASISWA (NON – ENGLISH) TENTANG KEBIJAKAN TOEFL SEBAGAI SYARAT KELULUSAN

To realize the vision, mission, and goals of the institution, The Institute Technology &amp; Health Cendekia Utama Kudus compiles and formulates standards that regulate excellent graduate competency. One additional performance indicator in the standard graduate competency of education standard states that students are required to have a minimum Test of English as Foreign Language (TOEFL) score of 450. TOEFL is a standardized test of English as a foreign language used to test a student's ability in mastering English. This study aims to determine student perceptions of the TOEFL policy as a graduation requirement, as well as observe the challenges &amp; difficulties faced by the students in the implementation. This research is a descriptive qualitative study. Respondents were final semester students in the study program at ITEKES Cendekia Utama Kudus. A questionnaire with a Likert scale of 1-5 was used as an instrument in this study. The results of this study indicate that students' perceptions of this policy are very positive. Students become more aware of the importance of English and have high motivation to graduate on time. Positive perceptions are also shown in the English course because it helps them to pass the TOEFL, and increases students’ motivation in learning English. Some students also consider the TOEFL score set is very high, the time required for preparation is very short, the test fee is expensive, and the policy set is too difficult, but the majority of students have a neutral response and can implement this policy well. The difficulties and challenges that most students face in the TOEFL are time allocation, lack of preparation, and lack of mastery of English grammar. Therefore, the institution must prepare and provide good facilities so that students can meet and exceed the specified passing grade maximally. Intensive TOEFL courses should be given to reduce the difficulties faced by students.&#x0D;
STUDENTS' LANGUAGE ATTITUDE TOWARDS ENGLISH PASCA DIGITALIZATION LEARNING

Attitude and awareness are essential factors in learning foreign language because they are reflected to the students feeling towards it. English is placed as foreign language in Indonesia play a very important role troughout human’s life academically on non-academically. It is due to English was functioned as the main ctool of communication in the world,especially in Technology era nowdays. All technology was designed in English to operate it. The phenomena happened recently was that the development of technology and the shifting process of learning spontanously onto digital,force students to be accustomed to operate digital media in learning without any well-preparation and training. Unrealizedly, students should communicate via digital media which English got alredy settled in it. Spontanously they were accustomed to interract with English words in their gadget, on the other hands, they learned autodidactly. This study amied to investigate how the students’ language attitude towards English learning pasca digitalization era. The study was designed by using qualitative with simple quantitative data survey and applying closed questionnaire that administered to respondents (all Business Administration students grade II academic Year 2021-2022). The sample was taken at all because the population was 56 students, then all of them will be the sample of the research. Furthermore, the Questionnaire will be analysed quantitatively based on the theory proposed by Miles, Huberman and Saldana such as data displaying, classifying, analyzing, comparing and concluding. Based on the study, it was found that the students shew positive attitude toward Learning English pasca digitalization learning. There were 47 students who are intereseted in learnng English and there were 8 students who felt bored in doing the English learning. Meanwhile the researher found something unique in this study, eventhough the students are interested in it, the students got 20% decressing score than before applying digital media for learning, the researchers found that there was another thing affected their score and English achivement, such as the teahers’ teaching methodology.
Textile D forms and $D_{4d}$

D-forms have in the past been created from inflexible materials, or considered as abstract mathematical objects. This paper describes a number of realisations of D-forms, and the related pita-forms, in textiles. Examples are given in which the created 3D space is purposefully employed and others in which ornamentation of the constituent surfaces is the highlighted feature. In particular, a set of biscornu has been fashioned to provide a 3D sampler of the axial point group $D_{4d}$ and its subgroups, using hitomezashi.
PENERAPAN LITERASI TEKNOLOGI UNTUK MENINGKATKAN KREATIVITAS SISWA KELAS V PADA MATERI ORGAN-ORGAN PENCERNAAN MANUSIA

This study aims to increase students' creativity in the material of human digestive organs by applying technological literacy at SDN Sumur Wellut III Surabaya. This type of research is Classroom Action Research (CAR). The subjects of this research were students of class V B SDN Sumur Welut III Surabaya, totaling 22 students. This research consists of two cycles. Data collection techniques used are observation and product assessment. The data analysis technique used is qualitative and quantitative data analysis. The results of the research show that the process of applying technological literacy with product assessment of making posters using the PicsArt and Canva applications can increase students' creativity in the material of human digestive organs for class V SDN Sumur Welut III Surabaya. Based on the results of the product assessment in pre-cycle conditions, the average value obtained by students was 57.5. Then in the first cycle increased to 63.4 and the second cycle to 74.3. The number of students who reached KKM ≥ 75 in the pre-cycle were 9 students, in the first cycle there were 12 students, and in the second cycle there were 19 students. The percentage of completeness in the pre-cycle is (40.9%), the first cycle is (54.5%), the second cycle is (86.3%). So that in the second cycle it has exceeded the criteria of 80% of students who reach the KKM ≥ 75. The researcher concludes that the application of technological literacy media can increase students' creativity&#x0D; &#x0D;