Reviewerly/exHarmony huggingface dataset:
Two conjectures on the largest minimum distances of binary self-orthogonal codes with dimension 5

The purpose of this paper is to solve the two conjectures on the largest minimum distance $d_{so}(n,5)$ of a binary self-orthogonal $[n,5]$ code proposed by Kim and Choi (IEEE Trans. Inf. Theory, 2022). The determination of $d_{so}(n,k)$ has been a fundamental and difficult problem in coding theory because there are too many binary self-orthogonal codes as the dimension $k$ increases. Recently, Kim et al. (2021) considered the shortest self-orthogonal embedding of a binary linear code, and many binary optimal self-orthogonal $[n,k]$ codes were constructed for $k=4,5$. Kim and Choi (2022) improved some results of Kim et al. (2021) and made two conjectures on $d_{so}(n,5)$. In this paper, we develop a general method to determine the exact value of $d_{so}(n,k)$ for $k=5,6$ and show that the two conjectures made by Kim and Choi (2022) are true.
Edge assisted frame interpolation and super resolution for efficient 360-degree video delivery

360° videos are getting popular providing an immersive streaming experience for the user, nevertheless, demand high bandwidth in mobile networks due to their larger spherical frames. In this preliminary work, we propose to combine frame interpolation and super resolution methods to optimize tile based 360° video delivery by streaming them at low qualities in network and increasing the quality leveraging Multi Access Edge Computing. We propose a mechanism to adaptively decide this quality conversion at the client side which improves average video quality by 30% and bandwidth saving by 43.3% compared to existing tile based streaming.
Cross-client Label Propagation for Transductive and Semi-Supervised Federated Learning

We present Cross-Client Label Propagation(XCLP), a new method for transductive federated learning. XCLP estimates a data graph jointly from the data of multiple clients and computes labels for the unlabeled data by propagating label information across the graph. To avoid clients having to share their data with anyone, XCLP employs two cryptographically secure protocols: secure Hamming distance computation and secure summation. We demonstrate two distinct applications of XCLP within federated learning. In the first, we use it in a one-shot way to predict labels for unseen test points. In the second, we use it to repeatedly pseudo-label unlabeled training data in a federated semi-supervised setting. Experiments on both real federated and standard benchmark datasets show that in both applications XCLP achieves higher classification accuracy than alternative approaches.
On Verbal Construction Frequency in Certain Genres of Popular Literature

Corpora-based language studies is a widespread practice in modern linguistics. In our study, we address so-called mass literature (or paraliterature) via a corpus of texts. The standardization of mass literature allows us to describe its genres by applying literary formulas. In brief, a formula serves for the embodiment of cultural themes and stereotypes in a universal form. While mass literature is a common subject of literary and cultural studies, from a linguistic point of view, literary formulas have not been studied well enough. We suggest that differences between the microgenres of paraliterature may be in syntax as well as in the vocabulary. Our work is based on the mass literature corpora and provides analysis of verb constructions characteristic of microgenres (love story, detective story, science fiction novel, and fantasy). In order to identify the distinctive features of mass literature microgenres, we have conducted a series of machine learning experiments. As a dataset, we compiled a corpus of 1,200 texts belonging to four microgenres. In our previous studies we showed that statistical features (text length, sentence length, and parts of speech frequencies) were insufficient for successful classification. The usage of lexical features has improved the quality of machine learning, however, the classifier based on syntactic features has shown the best results. 81 constructions have been selected as the most important features of syntactic-based machine learning. A verb construction consists of a “verb + dependencies”. We consider several types of dependencies: arguments (subject, direct and indirect objects) and adjuncts (subordinate clause and adverbial classifier). The constructions are described in terms of the fullness of the verb valencies. Several types of constructions are distinguished: complete, incomplete (with omissions of direct or indirect complements), and extended (with adjuncts). Specific examples of verb constructions in each of the microgenres have been analyzed and described. Based on the similarity of the construction profile, romance novels and detective stories show the predominance of verbs and direct speech constructions, whereas science fiction novels and fantasy demonstrate the prevalence of full constructions. Possible non-textual explanations for our observations are also provided. The method proposed in the study can be used for the investigation of various kinds of syntactic features, for instance, those associated with the author’s, temporal or genre specificities.
Mixed degree cubature Kalman filter for nonlinear system

Aiming at the problem of real-time performance of the high-degree cubature Kalman filter (CKF) algorithm in nonlinear systems, the mixed degree cubature Kalman filter (MDCKF) is proposed. The Gaussian approximation filter formulas and sampling rules for third-degree cubature and fifth-degree cubature are presented. The MDCKF uses the different degree cubature rule for the time update process and measurement update process. For more efficient use of measurement information, the fifth-degree cubature sampling rule is utilized in measurement update, while the third-degree cubature sampling rule is applied to the time update process. By simulating the target tracking problem and integrated navigation system, it demonstrates that MDCKF has similar accuracy to the fifth-degree CKF but takes less time. Thus, MDCKF has superior performance in balancing accuracy and time cost.
Structural Pruning via Latency-Saliency Knapsack

Structural pruning can simplify network architecture and improve inference speed. We propose Hardware-Aware Latency Pruning (HALP) that formulates structural pruning as a global resource allocation optimization problem, aiming at maximizing the accuracy while constraining latency under a predefined budget on targeting device. For filter importance ranking, HALP leverages latency lookup table to track latency reduction potential and global saliency score to gauge accuracy drop. Both metrics can be evaluated very efficiently during pruning, allowing us to reformulate global structural pruning under a reward maximization problem given target constraint. This makes the problem solvable via our augmented knapsack solver, enabling HALP to surpass prior work in pruning efficacy and accuracy-efficiency trade-off. We examine HALP on both classification and detection tasks, over varying networks, on ImageNet and VOC datasets, on different platforms. In particular, for ResNet-50/-101 pruning on ImageNet, HALP improves network throughput by $1.60\times$/$1.90\times$ with $+0.3\%$/$-0.2\%$ top-1 accuracy changes, respectively. For SSD pruning on VOC, HALP improves throughput by $1.94\times$ with only a $0.56$ mAP drop. HALP consistently outperforms prior art, sometimes by large margins. Project page at https://halp-neurips.github.io/.
Weighted Distillation with Unlabeled Examples

Distillation with unlabeled examples is a popular and powerful method for training deep neural networks in settings where the amount of labeled data is limited: A large ''teacher'' neural network is trained on the labeled data available, and then it is used to generate labels on an unlabeled dataset (typically much larger in size). These labels are then utilized to train the smaller ''student'' model which will actually be deployed. Naturally, the success of the approach depends on the quality of the teacher's labels, since the student could be confused if trained on inaccurate data. This paper proposes a principled approach for addressing this issue based on a ''debiasing'' reweighting of the student's loss function tailored to the distillation training paradigm. Our method is hyper-parameter free, data-agnostic, and simple to implement. We demonstrate significant improvements on popular academic datasets and we accompany our results with a theoretical analysis which rigorously justifies the performance of our method in certain settings.
Local Contrastive Feature Learning for Tabular Data

Contrastive self-supervised learning has been successfully used in many domains, such as images, texts, graphs, etc., to learn features without requiring label information. In this paper, we propose a new local contrastive feature learning (LoCL) framework, and our theme is to learn local patterns/features from tabular data. In order to create a niche for local learning, we use feature correlations to create a maximum-spanning tree, and break the tree into feature subsets, with strongly correlated features being assigned next to each other. Convolutional learning of the features is used to learn latent feature space, regulated by contrastive and reconstruction losses. Experiments on public tabular datasets show the effectiveness of the proposed method versus state-of-the-art baseline methods.
Deep Learning for Search and Recommendation

In the current digital world, web search engines and recommendation systems are continuously evolving, opening up new potential challenges every day which require more sophisticated and efficient data mining and machine learning solutions to satisfy the needs of sellers and consumers as well as marketers. The quality of search and recommendation systems impacts customer retention, time on site, and sales volume. For instance, with often sparse conversion rates, highly personalized contents, heterogeneous digital sources, more rigorous and effective models are required to be developed by research engineers and data scientists. At the same time, deep learning has started to show great impact in many industrial applications which are capable of processing complicated, large-scale and real-time data. Deep learning not only provides more opportunities to increase conversion rates and improve revenue through a positive customer experience, but also provides customers with personalized contents along with their personal shopping journey. Due to this rapid growth of the digital world, there is a need to bring professionals together from both academic research and the industry to solve real-world problems. This workshop fosters the development of a strong research community focused on solving deep learning based large-scale web search, personalized search, recommendation and ranking relevance problems that provide superior digital experience to all users.
Efficient Learning with Pseudo Labels for Query Cost Estimation

Query cost estimation, which is to estimate the query plan cost and query execution cost, is of utmost importance to query optimizers. Query plan cost estimation heavily relies on accurate cardinality estimation, and query execution cost estimation gives good hints on query latency, both of which are challenging in database management systems. Despite decades of research, existing studies either over-simplify the models only using histograms and polynomial calculation that leads to inaccurate estimates, or over-complicate them by using cumbersome neural networks with the requirements for large amounts of training data hence poor computational efficiency. Besides, most of the studies ignore the diversity of query plan structures. In this work, we propose a plan-based query cost estimation framework, called Saturn, which can e<u>S</u>timate c<u>a</u>rdinality and la<u>t</u>ency acc<u>ur</u>ately and efficie<u>n</u>tly, for any query plan structures. Saturn first encodes each query plan tree into a compressed vector by using a traversal-based query plan autoencoder to cope with diverse plan structures. The compressed vectors can be leveraged to distinguish different query types, which is highly useful for downstream tasks. Then a pseudo label generator is designed to acquire all cardinality and latency labels with the execution part of the query plans in the training workload, which can significantly reduce the overhead of collecting the real cardinality and latency labels. Finally, a chain-wise transfer learning module is proposed to estimate the cardinality and latency of the query plan in a pipeline paradigm, which further enhances the efficiency. An extensive empirical study on benchmark data offers evidence that Saturn outperforms the state-of-the-art proposals in terms of accuracy, efficiency, and generalizability for query cost estimation.
Edge GPU cluster processing system for laser interference image collection

In order to realize the real-time processing and communication of multi-laser interference images, a GPU cluster processing system for laser interference images was designed. The measured target was illuminated by multiple lasers, and the image information of the multi-laser interference fringes was obtained by the CCD. The data of laser interference images from CPU was transmitted by the GPU cluster, and the image feature recognition and transmission were completed through the cluster image processing module. A multi-channel laser interference image transmission algorithm was designed by the multi-core and multi-buffer (MCMB) algorithm. In the experiment, the laser interference images were collected by the CPU, and then the real-time communication of multi-channel images data was completed by the GPU cluster. The packet loss rate experiment showed that when the data traffic reached 110, the data was lost with the traditional UDP communication algorithm, and the slope of the fitting curve was 0.6,053. When the data flow reached 160, the data was lost with MCMB algorithm, and the slope of the fitting curve was 0.2,181. In contrast, the GPU occupancy of this algorithm was improved, and it was still not saturated at 210 data streams. In a word, the system has better optimization effect in real-time processing and communication of multi-laser interference images.
Detecting the possibility of a type of photon number splitting attack in decoy-state quantum key distribution

The existing decoy-state quantum key distribution (QKD) beating photon-number-splitting (PNS) attack provides a more accurate method to estimate the secure key rate, while it still considers that only single-photon pulses can generate secure keys in any case. However, multiphoton pulses can also generate secure keys if we can detect the possibility of PNS attack in the channel. The ultimate goal of this line of research is to confirm the absence of all types of PNS attacks. In particular, the PNS attack mentioned and detected in this paper is only the weaker version of PNS attack which significantly changes the observed values of the legitimate users. In this paper, under the null hypothesis of no weaker version of PNS attack, we first determine whether there is an attack or not by retrieving the missing information of the existing decoy-state protocols, extract a Cauchy distribution statistic, and further provide a detection method and the type I error probability. If the result is judged to be an attack, we can use the existing decoy-state method and the GLLP formula to estimate the secure key rate. Otherwise, the pulses with the same basis received including both single-photon pulses and multiphoton pulses, can be used to generate the keys and we give the secure key rate in this case. Finally, the associated experiments we performed (i.e., the significance level is 5%) show the correctness of our method.
akc: A Tidy Framework for Automatic Knowledge Classification in R

Knowledge classification is an extensive and practical approach in domain knowledge management. Automatically extracting and organizing knowledge from unstructured textual data is desirable and appealing in various circumstances. In this paper, the tidy framework for automatic knowledge classification supported by the akc package is introduced. With powerful support from the R ecosystem, the akc framework can handle multiple procedures in data science workflow, including text cleaning, keyword extraction, synonyms consolidation and data presentation. While focusing on bibliometric analysis, the akc package is extensible to be used in other contexts. This paper introduces the framework and its features in detail. Specific examples are given to guide the potential users and developers to participate in open science of text mining.
Bayes risk CTC: Controllable CTC alignment in Sequence-to-Sequence tasks

Sequence-to-Sequence (seq2seq) tasks transcribe the input sequence to a target sequence. The Connectionist Temporal Classification (CTC) criterion is widely used in multiple seq2seq tasks. Besides predicting the target sequence, a side product of CTC is to predict the alignment, which is the most probable input-long sequence that specifies a hard aligning relationship between the input and target units. As there are multiple potential aligning sequences (called paths) that are equally considered in CTC formulation, the choice of which path will be most probable and become the predicted alignment is always uncertain. In addition, it is usually observed that the alignment predicted by vanilla CTC will drift compared with its reference and rarely provides practical functionalities. Thus, the motivation of this work is to make the CTC alignment prediction controllable and thus equip CTC with extra functionalities. The Bayes risk CTC (BRCTC) criterion is then proposed in this work, in which a customizable Bayes risk function is adopted to enforce the desired characteristics of the predicted alignment. With the risk function, the BRCTC is a general framework to adopt some customizable preference over the paths in order to concentrate the posterior into a particular subset of the paths. In applications, we explore one particular preference which yields models with the down-sampling ability and reduced inference costs. By using BRCTC with another preference for early emissions, we obtain an improved performance-latency trade-off for online models. Experimentally, the proposed BRCTC reduces the inference cost of offline models by up to 47% without performance degradation and cuts down the overall latency of online systems to an unseen level.
The Influence Of Social Support And Digital Literacy Ability On Students’ Self-Efficacy

The digital era brings students to various conveniences as well as challenges in the learning process. The learning mindset is no longer about the process of direct interaction between students and lecturers. Instead it has shifted to a single process of finding things out from all sources. Digital literacy ability, belief and self-confidence of students are needed in completing lecture assignments properly and on time. This study aims to analyze the effect of social support, digital literacy ability on student self-efficacy in the Depok City area. This study uses a quantitative approach with a survey method. The population is all students studying in Depok City with a sample of 410 students taken by random sampling. The results showed that the direct effect of social support on self-efficacy was indicated by the path coefficient value of 0.200 sig value. 0.000, the direct effect of digital literacy ability on self-efficacy is indicated by the path coefficient value of 0.344, the value of sig. 0.000, the simultaneous effect of social support, digital literacy abilityon self-efficacy is indicated by the coefficient value of 0.210, Sig. F change. 0.000. From the results of the study, it can be concluded that social support and digital literacy ability have a positive and significant effect on self-efficacy individually or simultaneously.
Global Memory Transformer for Processing Long Documents

Transformer variants dominate the state of the art in different natural language processing tasks such as translation, reading comprehension and summarization. Our paper is more directed to use general memory slots added to the inputs and studying the results of adding these slots. This paper is a go on study of general memory slots rule that were added to the input of the proposed model in previous work [1]. We have two main tasks;1) pretraining task using masked language modeling and b) fine tuning task using HotpotQA . This study aims to verify the ability of the proposed model to handle chunks as if they were one chunk comparing with the base model. As baseline we used T5 transformer. We studied the rule of memory slots augmented to each input chunk and studied the model performance without selector. We found that adding memory to input chunks helped the proposed model to overcome the baseline on Masked language modeling task with specific training parameters. Ablation study reveals the ability of using the compressed input chunks with a degradation in performance.
Mixing times of three-state quantum walks on cycles

In this paper, we successfully obtain an explicit expression of the limit distribution [Formula: see text] of three-state quantum walks on cycles, the total variation distance between [Formula: see text] and the average probability [Formula: see text], and lower bound on the difference between two eigenvalues, among others. Based on the above conclusions, we finally get the mixing time [Formula: see text] of the quantum walk of Grover coin on the N-cycle. [Formula: see text] is the time required to characterize [Formula: see text] approaching [Formula: see text]. Our results show that the average probability of a three-state quantum walk on a cycle can approach its limit distribution faster than that of a two-state quantum walk, which might be of significance to quantum computation.
Implementasi Algoritma K-Nearest Neighbors Pada Penentuan Jurusan Siswa

SMA Negeri 8 Palembang has two majors, namely science and social studies. Determination of majors is done when in class X to determine the majors of the student. This research was conducted because the teacher had difficulty in determining the majors at SMA Negeri 8 Palembang. In the research case, the researcher uses the classification with the k-Nearest Neighbors algorithm, and the Euclidean distance measurement method to predict the students in determining the majors that will be taken by students. The source of the data for this research is the report card scores for the X grade students of SMA Negeri 8 Palembang, and the data for this research are 335 data on the grade X students of SMA Negeri 8 Palembang. The data collection was taken from the grade X class report cards, namely mathematics, physics, biology, English, Indonesian, history, geography, economics, and psychological test scores. In determining the majors, students in science and social studies get the average score of all subjects and the psychological test scores produced by these students to enter the science department with an average score of 80, math score 78, physics 78, biology 78, and psychological test 80. If students get an average score below 80, it will be predicted to enter social studies, to enter the social studies department with a minimum score of 70 geography subjects, 70 economics, 70 history, and 70 psychological test scores. The results obtained in this study used the K method. -Nearest Neighbors based on training data obtained from 335 student data, 101 classified social studies class according to predictions, 10 data predicted social studies, but data declared natural science, 2 science prediction data and, 222 data according to natural science predictions, and the accuracy got 96% and the results of observations using the Website using K-NN show the same data results obtained through an accuracy of 96%.