Reviewerly/exHarmony huggingface dataset:
Smart Build Targets Batching Service at Google

Google has a monolithic codebase with tens of millions build targets. Each build target specifies the information that is needed to build a software artifact or run tests. It is common to execute a subset of build targets at each revision and make sure that the change does not break the codebase. Google's build service system uses Bazel to build targets. Bazel takes as input a build that specifies the execution context, flags and build targets to run. The outputs are the build libraries, binaries or test results. To be able to support developer's daily activities, the build service system runs millions of builds per day. It is a known issue that a build with many targets could run out of the allocated memory or exceed its execution deadline. This is problematic because it reduces the developer's productivity, e.g. code submissions or binary releases. In this paper, we propose a technique that predicts the memory usage and executor occupancy of a build. The technique batches a set of targets such that the build created with those targets does not run out of memory or exceed its deadline. This approach significantly reduces the number of builds that run out of memory or exceed the deadlines, hence improving developer's productivity.
Data approach for the studies of the job market and related areas

This paper discusses some approaches to intellectual text analysis in application to automated monitoring of the labour market. The construction of an analytical system based on Big Data technologies for the labour market is describedd. Were compared the combinations of methods of extracting semantic information about objects and connections between them (for example, from job advertisements) from specialized texts. A system for monitoring the Russian labour market has been created, and the work is underway to include other countries in the analysis. The considered approaches and methods can be widely used toextract knowledge from large amounts of texts.
The Effect of Using Guessing Game Media Themed Local Tourism on Students’ Achievement in Writing Narrative Text

The research was conducted to discover the effect of using guessing game media themed local tourism on students’ achievement in writing narrative text. The research was conducted to the grade tenth students of SMA GKPI PAMEN. This research used experimental design. To collect the data 2 classes get involved. There are X-1 as the experimental class that taught by using the guessing game media and X-2 as the control class that taught using the conventional method. The students consist of 30 students. In experimental class, the mean score for the pre-test was 45,46 and the score of post-test was 81,4. In control class, the mean of pre-test score was 46,5 and the post-test was 73,06. The analysis of the t-test showed that tscore &gt; ttable or 3,53 &gt; 1,672 at the level of significance 0,05 with the degree of freedom (df) 58. The alternative hypothesisi (Ha) is accepted. It point out that using guessing media themed local tourism is effective in teaching writing narrative text for the tenth grade students of SMA GKPI PAMEN.
Cross domain meta-network for sketch face recognition

Because of the large modal difference between sketch image and optical image, and the problem that traditional deep learning methods are easy to overfit in the case of a small amount of training data, the Cross Domain Meta-Network for sketch face recognition method is proposed. This method first designs a meta-learning training strategy to solve the small sample problem, and then proposes entropy average loss and cross domain adaptive loss to reduce the modal difference between the sketch domain and the optical domain. The experimental results on UoM-SGFS and PRIP-VSGC sketch face data sets show that this method and other sketch face recognition methods.
Towards Unbiased and Accurate Deferral to Multiple Experts

Machine learning models are often implemented in cohort with humans in the pipeline, with the model having an option to defer to a domain expert in cases where it has low confidence in its inference. Our goal is to design mechanisms for ensuring accuracy and fairness in such prediction that combine machine learning model inferences and domain expert predictions. Prior work on systems in classification settings has focused on the setting of a pipeline with a single expert and aimed to accommodate the inaccuracies and biases of this expert to simultaneously learn an inference model and a deferral system. Our work extends this framework to settings where multiple experts are available, with each expert having their own domain of expertise and biases. We propose a framework that simultaneously learns a classifier and a deferral system, with the deferral system choosing to defer to one or more human experts in cases of input where the classifier has low confidence. We test our framework on a synthetic dataset and a content moderation dataset with biased synthetic experts, and show that it significantly improves the accuracy and fairness of the final predictions, compared to the baselines. We also collect crowdsourced labels for the content moderation task to construct a real-world dataset for the evaluation of hybrid machine-human frameworks and show that our proposed learning framework outperforms baselines on this real-world dataset as well.
Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection

A key challenge in the application of evolutionary algorithms in practice is the selection of an algorithm instance that best suits the problem at hand. What complicates this decision further is that different algorithms may be best suited for different stages of the optimization process. Dynamic algorithm selection and configuration are therefore well-researched topics in evolutionary computation. However, while hyper-heuristics and parameter control studies typically assume a setting in which the algorithm needs to be chosen while running the algorithms, without prior information, AutoML approaches such as hyper-parameter tuning and automated algorithm configuration assume the possibility of evaluating different configurations before making a final recommendation. In practice, however, we are often in a middle-ground between these two settings, where we need to decide on the algorithm instance before the run ("oneshot" setting), but where we have (possibly lots of) data available on which we can base an informed decision. We analyze in this work how such prior performance data can be used to infer informed dynamic algorithm selection schemes for the solution of pseudo-Boolean optimization problems. Our specific use-case considers a family of genetic algorithms.
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations

As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner. In this work, we analyze two popular post hoc interpretation techniques: SmoothGrad which is a gradient based method, and a variant of LIME which is a perturbation based method. More specifically, we derive explicit closed form expressions for the explanations output by these two methods and show that they both converge to the same explanation in expectation, i.e., when the number of perturbed samples used by these methods is large. We then leverage this connection to establish other desirable properties, such as robustness, for these techniques. We also derive finite sample complexity bounds for the number of perturbations required for these methods to converge to their expected explanation. Finally, we empirically validate our theory using extensive experimentation on both synthetic and real world datasets.
WITHDRAWN: Blockage of forbidden activities using cyber ingress

In today's era, Internet is the most important thing in our life. It performs the main role in our successful life. Meanwhile, internet also gives a way for some illegal activities. Such illegal activities are unable to find easily. Since each and every search queries are stored in search engine’s data warehouse. The filtration of illegal activities is more time consuming process. So to prevent the difficulties, the illegal activities are filtered before it leaves from the internal network. Most of the illegal activities ruling in network were pornography, malicious software, theft, hacking and so on. Especially ingress is developed to control and to avoid the child pornographic content.
Fair and Optimal Cohort Selection for Linear Utilities.

The rise of algorithmic decision-making has created an explosion of research around the fairness of those algorithms. While there are many compelling notions of individual fairness, beginning with the work of Dwork et al., these notions typically do not satisfy desirable composition properties. To this end, Dwork and Ilvento introduced the fair cohort selection problem, which captures a specific application where a single fair classifier is composed with itself to pick a group of candidates of size exactly $k$. In this work we introduce a specific instance of cohort selection where the goal is to choose a cohort maximizing a linear utility function. We give approximately optimal polynomial-time algorithms for this problem in both an offline setting where the entire fair classifier is given at once, or an online setting where candidates arrive one at a time and are classified as they arrive.
Gaussian Kernelized Self-Attention for Long Sequence Data and Its Application to CTC-based Speech Recognition

Self-attention (SA) based models have recently achieved significant performance improvements in hybrid and end-to-end automatic speech recognition (ASR) systems owing to their flexible context modeling capability. However, it is also known that the accuracy degrades when applying SA to long sequence data. This is mainly due to the length mismatch between the inference and training data because the training data are usually divided into short segments for efficient training. To mitigate this mismatch, we propose a new architecture, which is a variant of the Gaussian kernel, which itself is a shift-invariant kernel. First, we mathematically demonstrate that self-attention with shared weight parameters for queries and keys is equivalent to a normalized kernel function. By replacing this kernel function with the proposed Gaussian kernel, the architecture becomes completely shift-invariant with the relative position information embedded using a frame indexing technique. The proposed Gaussian kernelized SA was applied to connectionist temporal classification (CTC) based ASR. An experimental evaluation with the Corpus of Spontaneous Japanese (CSJ) and TEDLIUM 3 benchmarks shows that the proposed SA achieves a significant improvement in accuracy (e.g., from 24.0% WER to 6.0% in CSJ) in long sequence data without any windowing techniques.
Crowdsourced Phrase-Based Tokenization for Low-Resourced Neural Machine Translation: The Case of Fon Language

Building effective neural machine translation (NMT) models for very low-resourced and morphologically rich African indigenous languages is an open challenge. Besides the issue of finding available resources for them, a lot of work is put into preprocessing and tokenization. Recent studies have shown that standard tokenization methods do not always adequately deal with the grammatical, diacritical, and tonal properties of some African languages. That, coupled with the extremely low availability of training samples, hinders the production of reliable NMT models. In this paper, using Fon language as a case study, we revisit standard tokenization methods and introduce Word-Expressions-Based (WEB) tokenization, a human-involved super-words tokenization strategy to create a better representative vocabulary for training. Furthermore, we compare our tokenization strategy to others on the Fon-French and French-Fon translation tasks.
Media Video Company Profile Penunjang Informasi Dan Promosi PT. Harafiel Trijaya

The internet has become the life of every human being, the internet has several types such as text, images, photos, sound, video and audio-visual. Every company, whether large, medium, or small-scale companies need something called a company profile as well as PT. Harafiel Trijaya. Currently PT. Harafiel Trijaya still uses promotion by means of promotional designs which are deemed insufficient for its dissemination to the wider community, so it is necessary to make a very good profile video so that the public or potential customers know a lot about PT. Harafiel Trijaya. PT. Harafiel Trijaya is engaged in the construction service industry (Engineering and Construction) which includes Civil Works, Building and Architecture Works, Mechanical and Electrical Works. This video company profile was created using video support applications such as Adobe Premiere Pro.
High-Capacity Reversible Data Hiding in Encrypted Images using Adaptive Encoding

With the popularization of digital information technology, the reversible data hiding in encrypted images (RDHEI) has gradually become the research hotspot of privacy protection in cloud storage. As a technology which can embed additional information in encrypted domain, extract the embedded information correctly and recover the original image without loss, RDHEI has been widely paid attention by researchers. To embed sufficient additional information in the encrypted image, a high-capacity RDHEI method using adaptive encoding is proposed in this paper. Firstly, the occurrence frequency of different prediction errors of the original image is calculated and the corresponding adaptive Huffman coding is generated. Then, the original image is encrypted with stream cipher and the encrypted pixels are marked with different Huffman codewords according to the prediction errors. Finally, additional information is embedded in the reserved room of marked pixels by bit substitution. The experimental results show that the proposed algorithm can extract the embedded information correctly and recover the original image losslessly. Compared with similar algorithms, the proposed algorithm makes full use of the characteristics of the image itself and greatly improves the embedding rate of the image. On UCID, BOSSBase, and BOWS-2 datasets, the average embedding rate of the proposed algorithm reaches 3.162 bpp, 3.917 bpp, and 3.775 bpp, which is higher than the state-of-the-art algorithm of 0.263 bpp, 0.292 bpp, and 0.280 bpp, respectively.
Non-maximum Suppression Also Closes the Variational Approximation Gap of Multi-object Variational Autoencoders

Learning object-centric scene representations is crucial for scene structural understanding. However, current unsupervised scene factorization and representation learning models do not reason about scene objects' relations while making an inference. In this paper, we address the issue by introducing a differentiable correlation prior that forces the inference models to suppress duplicate object representations. The extension is evaluated by adding it to three different scene understanding approaches. The results show that the models trained with the proposed method not only outperform the original models in scene factorization and have fewer duplicate representations, but also close the approximation gap between the data evidence and the evidence lower bound.
Hero: On the Chaos When PATH Meets Modules

Ever since its first release in 2009, the Go programming language (Golang) has been well received by software communities. A major reason for its success is the powerful support of library-based development, where a Golang project can be conveniently built on top of other projects by referencing them as libraries. As Golang evolves, it recommends the use of a new library-referencing mode to overcome the limitations of the original one. While these two library modes are incompatible, both are supported by the Golang ecosystem. The heterogeneous use of library-referencing modes across Golang projects has caused numerous dependency management (DM) issues, incurring reference inconsistencies and even build failures. Motivated by the problem, we conducted an empirical study to characterize the DM issues, understand their root causes, and examine their fixing solutions. Based on our findings, we developed \textsc{Hero}, an automated technique to detect DM issues and suggest proper fixing solutions. We applied \textsc{Hero} to 19,000 popular Golang projects. The results showed that \textsc{Hero} achieved a high detection rate of 98.5\% on a DM issue benchmark and found 2,422 new DM issues in 2,356 popular Golang projects. We reported 280 issues, among which 181 (64.6\%) issues have been confirmed, and 160 of them (88.4\%) have been fixed or are under fixing. Almost all the fixes have adopted our fixing suggestions.
Compute- and Memory-Efficient Reinforcement Learning with Latent Experience Replay

Recent advances in off-policy deep reinforcement learning (RL) have led to impressive success in complex tasks from visual observations. Experience replay improves sample-efficiency by reusing experiences from the past, and convolutional neural networks (CNNs) process high-dimensional inputs effectively. However, such techniques demand high memory and computational bandwidth. In this paper, we present Latent Vector Experience Replay (LeVER), a simple modification of existing off-policy RL methods, to address these computational and memory requirements without sacrificing the performance of RL agents. To reduce the computational overhead of gradient updates in CNNs, we freeze the lower layers of CNN encoders early in training due to early convergence of their parameters. Additionally, we reduce memory requirements by storing the low-dimensional latent vectors for experience replay instead of high-dimensional images, enabling an adaptive increase in the replay buffer capacity, a useful technique in constrained-memory settings. In our experiments, we show that LeVER does not degrade the performance of RL agents while significantly saving computation and memory across a diverse set of DeepMind Control environments and Atari games. Finally, we show that LeVER is useful for computation-efficient transfer learning in RL because lower layers of CNNs extract generalizable features, which can be used for different tasks and domains.
Oriole: Thwarting Privacy against Trustworthy Deep Learning Models

Deep Neural Networks have achieved unprecedented success in the field of face recognition such that any individual can crawl the data of others from the Internet without their explicit permission for the purpose of training high-precision face recognition models, creating a serious violation of privacy. Recently, a well-known system named Fawkes (published in USENIX Security 2020) claimed this privacy threat can be neutralized by uploading cloaked user images instead of their original images. In this paper, we present Oriole, a system that combines the advantages of data poisoning attacks and evasion attacks, to thwart the protection offered by Fawkes, by training the attacker face recognition model with multi-cloaked images generated by Oriole. Consequently, the face recognition accuracy of the attack model is maintained and the weaknesses of Fawkes are revealed. Experimental results show that our proposed Oriole system is able to effectively interfere with the performance of the Fawkes system to achieve promising attacking results. Our ablation study highlights multiple principal factors that affect the performance of the Oriole system, including the DSSIM perturbation budget, the ratio of leaked clean user images, and the numbers of multi-cloaks for each uncloaked image. We also identify and discuss at length the vulnerabilities of Fawkes. We hope that the new methodology presented in this paper will inform the security community of a need to design more robust privacy-preserving deep learning models.
“The lost streetcar” by Nikolay Gumilyov: literature review

The aim of the work is to review the scientific literature on one of N. Gumilyov’s most famous poems, “The Lost Streetcar,” one hundred years from the date of writing, due to the lack of an accurate dating of its creation, is in 2019, 2020, or 2021. The availability of a large number of analytical works indicates the constant interest of researchers in this multifaceted and ambiguous poem. The work is aimed at the implementation of several tasks: to give a comprehensive picture of the assessment of the text; look at it through the prism of analytics; reveal the true validity of certain interpretations; identify the main points of application of analytical thought; to discover how a clash of different interpretations of the same facts makes it possible to characterize the truth of these interpretations. It consists of four interconnected parts that take into account a different degree of interpretation breadth. The total representation of the materials associated with each of these parts allows drawing a number of conclusions. In particular, that the very first characteristics given by the poet’s contemporaries do not lose their relevance nowadays. And that researchers’ enthusiasm for seeking intertextual links sometimes leaves out discussing the individual author style features of the text itself. It is also important that researchers literally dissolve the poem in contexts that are significant for world culture and deprive it of independence. Assessments and interpretations of the text can be both in a relationship of mutual complementarity, and can be mutually exclusive. The perception of the text as reflecting biographical facts and esoteric generalizations and preferences of the author represents the main opposition, the components of which are focused on all studies of the poem.
The 3TConv: An Intrinsic Approach to Explainable 3D CNNs

Current deep learning architectures that make use of the 3D convolution (3DConv) achieve state-of-the-art results on action recognition benchmarks. However, the 3DConv does not easily lend itself to explainable model decisions. To this end we introduce a novel and intrinsic approach, whereby all the aspects of the 3DConv are rendered explainable. Our approach proposes the temporally factorized 3D convolution (3TConv) as an interpretable alternative to the regular 3DConv. In a 3TConv the 3D convolutional filter is obtained by learning a 2D filter and a set of temporal transformation parameters, resulting in a sparse filter requiring less parameters. We demonstrate that 3TConv learns temporal transformations that afford a direct interpretation by analyzing the transformation parameter statistics on a model level. Our experiments show that in the low-data regime the 3TConv outperforms 3DConv and R(2+1)D while containing up to 77\% less parameters.
Optimally adaptive Bayesian spectral density estimation for stationary and nonstationary processes

This article improves on existing methods to estimate the spectral density of stationary and nonstationary time series assuming a Gaussian process prior. By optimising an appropriate eigendecomposition using a smoothing spline covariance structure, our method more appropriately models both smooth and rough data. We further justify the utility of this optimal eigendecomposition by investigating the performance of alternative covariance functions other than smoothing splines. We show that the optimal eigendecomposition provides a material improvement, while the other covariance functions under examination do not, all performing comparatively well as the smoothing spline. During our computational investigation, we introduce new validation metrics for the spectral density estimate, inspired from the physical sciences. We validate our models in an extensive simulation study and demonstrate superior performance with real data.