Reviewerly/exHarmony huggingface dataset:
The methodology for creating an information and analytical system for control, planning and management of the educational process in HEIS and its functional structure

The aim of the study is to establish the level of efficiency and feasibility of automated information and analytical management system, as well as to identify ways to organize cooperation between individual departments of the university and consider the level of IAS components evaluation by teachers in the educational process, which are key elements of the transition to a digital management model of HEIs. The hypothesis is that information technologies of educational process management increase the involvement of participants in the educational sector in the model of university administration, increase the efficiency of planning and ensure the objectivity of the control system. The result of the study is to establish that the information and analytical system of education process management in the university is user-friendly, positively perceived by students, and contributes to the simplicity and efficiency of assessment in HEIs.
Adaptive Multi-Resolution Attention with Linear Complexity

Transformers have improved the state-of-the-art across numerous tasks in sequence modeling. Besides the quadratic computational and memory complexity with respect to the sequence length, the self-attention mechanism only processes information at the same scale, i.e., all attention heads are in the same resolution, resulting in the limited power of the Transformer. To remedy this, we propose a novel and efficient structure named Adaptive Multi-Resolution Attention (AdaMRA for short), which scales linearly to sequence length in terms of time and space. Specifically, we leverage a multi-resolution multi-head attention mechanism, enabling attention heads to capture long-range contextual information in a coarse-to-fine fashion. Moreover, to capture the potential relations between query representation and clues of different attention granularities, we leave the decision of which resolution of attention to use to query, which further improves the model's capacity compared to the vanilla Transformer. In an effort to reduce complexity, we adopt kernel attention without degrading the performance. Extensive experiments demonstrate the effectiveness and efficiency of our model by achieving state-of-the-art speed-memory-accuracy trade-off. To facilitate AdaMRA utilization by the scientific community, the implementation will be made publicly available.
Dynamic multi-scale loss optimization for object detection

With the continuous improvement of deep object detectors via advanced model architectures, imbalance problems in the training process have received more attention. It is a common paradigm in object detection frameworks to perform multi-scale detection. However, each scale is treated equally during training. In this paper, we carefully study the objective imbalance of multi-scale detector training. We argue that the loss in each scale level is neither equally important nor independent. Different from the existing solutions of setting multi-task weights, we dynamically optimize the loss weight of each scale level in the training process. Specifically, we propose an Adaptive Variance Weighting (AVW) to balance multi-scale loss according to the statistical variance. Then we develop a novel Reinforcement Learning Optimization (RLO) to decide the weighting scheme probabilistically during training. It makes better utilization of multi-scale training loss without extra computational complexity and learnable parameters for backpropagation. Without bells and whistles, the proposed method improves ATSS by 0.9 AP on the MS COCO benchmark. And it achieves 82.1 mAP on Pascal VOC 2007 test set, which outperforms other reinforcement-learning-based methods.
Cross-Domain Recommendation with Cross-Graph Knowledge Transfer Network

The cross-domain recommender systems aim to alleviate the data sparsity problem in a target domain by transferring knowledge from a source domain. However, existing works ignore the latent information underlying the user-item interactions. In addition, they don’t explicitly model the intra-domain and cross-domain interactions. To address these concerns, we propose a novel model named cross-graph knowledge transfer network to improve the recommendation performance. To explicitly model intra-domain and cross-domain interactions, we utilize the graph structure to transfer knowledge across domains. Firstly, we design a neighbor sampling method to extract useful intra-domain and cross-domain interactions. Secondly, we aggregate multiple interactive information in each domain and generate intra-domain embeddings by using intra-domain attention mechanism. Thirdly, we fuse the information from two domains to generate effective user and item embeddings by using cross-domain attention mechanism. Finally, we feed user and item embeddings into the domain-specific prediction layers for personalized recommendation. We conduct experiments on real-world datasets. The results show that our model outperforms five state-of-art methods.
A Tight Analysis of Slim Heaps and Smooth Heaps

The smooth heap and the closely related slim heap are recently invented self-adjusting implementations of the heap (priority queue) data structure. They are simple to describe and efficient in practice. For both slim and smooth heaps, we derive the following tight bounds on the amortized time per operation: O(log n) for delete-min and delete; O(log log n) for decrease-key; and O(1) for make-heap, find-min, insert, and meld, where n is the current number of items in the heap. These bounds are tight not only for slim and smooth heaps, but for any heap in Iacono and Özkan's pure heap model, intended to capture all “self-adjusting” heap implementations. Slim and smooth heaps are the first known data structures to match Iacono and Özkan's lower bounds while satisying the constraints of their model.
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability

Conventional saliency maps highlight input features to which neural network predictions are highly sensitive. We take a different approach to saliency, in which we identify and analyze the network parameters, rather than inputs, which are responsible for erroneous decisions. We find that samples which cause similar parameters to malfunction are semantically similar. We also show that pruning the most salient parameters for a wrongly classified sample often improves model behavior. Furthermore, fine-tuning a small number of the most salient parameters on a single sample results in error correction on other samples that are misclassified for similar reasons. Based on our parameter saliency method, we also introduce an input-space saliency technique that reveals how image features cause specific network components to malfunction. Further, we rigorously validate the meaningfulness of our saliency maps on both the dataset and case-study levels.
Instance-Based Zero-Shot learning for semi-Automatic MeSH indexing

Zero-shot learning constitutes a variant of the broader category of weakly supervised learning algorithms. Its main asset is the possibility of identifying entities for which no training data are provided in advance. Under this extreme scenario, conventional supervised learning methods cannot operate properly, while consumption of human resources for obtaining even limited instances may be highly restricted, especially when the label space is quite complex because of its cardinality and the underlying semantic dependencies. However, removing the human factor from the learning loop under complicated tasks cannot guarantee robust performance. Thus, semi-automated solutions are widely accepted by both the research and industrial communities, favoring cooperation of human and machine, mainly for alleviating the spent effort of the former, and for acquiring safer predictions. In contrast with the majority of existing Zero-shot learning approaches, we propose a generalized instance-based method oriented towards tackling the Multi-label classification task without performing any transductive operations over the test instances. Instead, we aim to provide a label ranking of the unseen classes exploiting sentence-based semantic embeddings and label similarities, through a dedicated fine-tuned language representational model. We also use a pattern matching rule to further boost the ranking of our method. Some realistic assumptions are made in order for our approach to work correctly and provide said ranking. Results on a biomedical database with a semantically rich fine-grained label space are really promising, rendering its utilization as a helpful and computationally inexpensive tool for facilitating semi-automated indexing.
A Greedy Algorithm for Quantizing Neural Networks

We propose a new computationally efficient method for quantizing the weights of pre- trained neural networks that is general enough to handle both multi-layer perceptrons and convolutional neural networks. Our method deterministically quantizes layers in an iterative fashion with no complicated re-training required. Specifically, we quantize each neuron, or hidden unit, using a greedy path-following algorithm. This simple algorithm is equivalent to running a dynamical system, which we prove is stable for quantizing a single-layer neural network (or, alternatively, for quantizing the first layer of a multi-layer network) when the training data are Gaussian. We show that under these assumptions, the quantization error decays with the width of the layer, i.e., its level of over-parametrization. We provide numerical experiments, on multi-layer networks, to illustrate the performance of our methods on MNIST and CIFAR10 data, as well as for quantizing the VGG16 network using ImageNet data.
Synthetic Completeness for a Terminating Seligman-Style Tableau System

Hybrid logic extends modal logic with nominals that name worlds. Seligman-style tableau systems for hybrid logic divide branches into blocks named by nominals to achieve a local proof style. We present a Seligman-style tableau system with a formalization in the proof assistant Isabelle/HOL. Our system refines an existing system to simplify formalization and we claim termination from this relationship. Existing completeness proofs that account for termination are either analytic or based on translation, but synthetic proofs have been shown to generalize to richer logics and languages. Our main result is the first synthetic completeness proof for a terminating hybrid logic tableau system. It is also the first formalized completeness proof for any hybrid logic proof system.
RaftMLP: Do MLP-based Models Dream of Winning Over Computer Vision?

For the past ten years, CNN has reigned supreme in the world of computer vision, but recently, Transformer is on the rise. However, the quadratic computational cost of self-attention has become a severe problem of practice. There has been much research on architectures without CNN and self-attention in this context. In particular, MLP-Mixer is a simple idea designed using MLPs and hit an accuracy comparable to the Vision Transformer. However, the only inductive bias in this architecture is the embedding of tokens. Thus, there is still a possibility to build a non-convolutional inductive bias into the architecture itself, and we built in an inductive bias using two simple ideas. A way is to divide the token-mixing block vertically and horizontally. Another way is to make spatial correlations denser among some channels of token-mixing. With this approach, we were able to improve the accuracy of the MLP-Mixer while reducing its parameters and computational complexity. Compared to other MLP-based models, the proposed model, named RaftMLP has a good balance of computational complexity, the number of parameters, and actual memory usage. In addition, our work indicates that MLP-based models have the potential to replace CNNs by adopting inductive bias. The source code in PyTorch version is available at \url{this https URL}.
A Silicon Nitride Reconfigurable Linear Optical Processor

The characterization of a broadband Si 3 N 4 integrated linear optical processor operating in the C-band is reported. The impact of losses on the processor accuracy is discussed towards the photonic implementation of state-of-the-art neural networks.
Graphic Design Training Using Photoshop Software as a Business Opportunity

Graphic Design is currently one of the few software that has been widely used for the development of knowledge because it is very easy to use in various aspects of work. Jobs by utilizing graphic design have a very large opportunity to be used by students with the current pandemic conditions, because apart from being easy to use in basic work for students, many social media users such as Instagram and Facebook want good Instagram and Facebook feeds. For this reason, it is inevitable that many social media users need photo editing services such as graduation photo editing and travel photo editing so that it can be used as a business opportunity among students to take the job because the work can be done anywhere with easily accessible tools. In today's world of work, if human resources (HR) do not have additional abilities or skills, it will be difficult to compete in obtaining work. Through this introduction, students are also expected to get to know the tools that exist in Photoshop software. With the introduction of Photoshop, it is hoped that it can provide new insights and knowledge to the students of Budi Agung Medan Private High School about making graphic design as a business opportunity.
Managerial Overconfidence and Access to Funding: Do Banks Help Managers to Avoid Investment Mistakes?

Using unique data on German small and medium-sized enterprises (SMEs), we show that banks’ lending decisions help their corporate clients to correct investment distortions associated with managerial overconfidence. We find a strong positive relation between CEO overconfidence measure inferred directly from the SME managers’ sales forecasts and the banks' propensity to reject a loan application. We show that SMEs with overconfident managers performed better when their loan applications were rejected than comparable firms with overconfident managers who were granted a loan. We conclude that banks’ information incorporated in lending decisions provide valuable guidance to their corporate borrowers' investment policy. These findings complement studies examining the merits of banks as financial consultants as well as the literature highlighting the role of banks in overcoming informational asymmetries between lenders and corporate borrowers.
Quantization-Error-Robust Deep Neural Network for Embedded Accelerators

Quantization with low precision has become an essential technique for adopting deep neural networks in energy- and memory-constrained devices. However, there is a limit to the reducing precision by the inevitable loss of accuracy due to the quantization error. To overcome this obstacle, we propose methods reforming and quantizing a network that achieves high accuracy even at low precision without any runtime overhead in embedded accelerators. Our proposition consists of two analytical approaches: 1) network optimization to find the most error-resilient equivalent network in the precision-constrained environment and 2) quantization exploiting adaptive rounding offset control. The experimental results show accuracies of up to 98.31% and 99.96% of floating-point results in 6-bit and 8-bit quantization networks, respectively. Besides, our methods allow the lower precision accelerator design, reducing the energy consumption by 8.5%.
Locally Differentially-Private Randomized Response for Discrete Distribution Learning

We consider a setup in which confidential i.i.d. samples $X_1,\dotsc,X_n$ from an unknown finite-support distribution $\boldsymbol{p}$ are passed through $n$ copies of a discrete privatization channel (a.k.a. mechanism) producing outputs $Y_1,\dotsc,Y_n$. The channel law guarantees a local differential privacy of $\epsilon$. Subject to a prescribed privacy level $\epsilon$, the optimal channel should be designed such that an estimate of the source distribution based on the channel outputs $Y_1,\dotsc,Y_n$ converges as fast as possible to the exact value $\boldsymbol{p}$. For this purpose we study the convergence to zero of three distribution distance metrics: $f$-divergence, mean-squared error and total variation. We derive the respective normalized first-order terms of convergence (as $n\to\infty$), which for a given target privacy $\epsilon$ represent a rule-of-thumb factor by which the sample size must be augmented so as to achieve the same estimation accuracy as that of a non-randomizing channel. We formulate the privacy-fidelity trade-off problem as being that of minimizing said first-order term under a privacy constraint $\epsilon$. We further identify a scalar quantity that captures the essence of this trade-off, and prove bounds and data-processing inequalities on this quantity. For some specific instances of the privacy-fidelity trade-off problem, we derive inner and outer bounds on the optimal trade-off curve.
A novel feature selection algorithm based on damping oscillation theory

Feature selection is an important task in big data analysis and information retrieval processing. It reduces the number of features by removing noise, extraneous data. In this paper, one feature subset selection algorithm based on damping oscillation theory and support vector machine classifier is proposed. This algorithm is called the Maximum Kendall coefficient Maximum Euclidean Distance Improved Gray Wolf Optimization algorithm (MKMDIGWO). In MKMDIGWO, first, a filter model based on Kendall coefficient and Euclidean distance is proposed, which is used to measure the correlation and redundancy of the candidate feature subset. Second, the wrapper model is an improved grey wolf optimization algorithm, in which its position update formula has been improved in order to achieve optimal results. Third, the filter model and the wrapper model are dynamically adjusted by the damping oscillation theory to achieve the effect of finding an optimal feature subset. Therefore, MKMDIGWO achieves both the efficiency of the filter model and the high precision of the wrapper model. Experimental results on five UCI public data sets and two microarray data sets have demonstrated the higher classification accuracy of the MKMDIGWO algorithm than that of other four state-of-the-art algorithms. The maximum ACC value of the MKMDIGWO algorithm is at least 0.5% higher than other algorithms on 10 data sets.
Three-Dimensional Robot Motion Design by Combining Interactive and Non-Interactive Evolutionary Computation for an Intelligent Transformable Phone Robot: BaBi

This paper presents an interactive method for designing robot facial expressions and motions of an intelligent transformable phone robot BaBi. Designing an objective function to evaluate facial expressions and motions suitable for BaBi with an original structure is quite hard although users can easily imagine favorite motions. Therefore, the proposed method employs Interactive Evolutionary Computation (IEC) in addition to general optimization. The method allows users to add candidates to a case base when finding satisfying candidates and evaluates candidates' scores according to the case base. It applies a Different Evolution (DE) method to generate new candidates. Users may alternately push the buttons to generate face and body motion without making any selection of candidates with a selection interface. The method will make selection with the scores when users do not make any selection of candidates. Users can select or not select. It is an IEC process when users make selections, while it is a non-Interactive Evolutionary Computation (non-IEC) process when users do not make any selection, resulting in being a fusion of IEC and non-IEC. Adding more candidates to the case base can speed up generating the desired motion and promote the non-IEC progress and reduce the burden on users. Experimental results have shown that the proposed method produces the robot motions effectively.
Comparison of prices depending on factors in the secondary housing market

The article considers the issue of pricing for the secondary real estate market regarding local causes (physical properties of housing). The aim of the study was to verify the following hypotheses: the influence of the pricing factors of residential real estate on its value is determined by its price segment and the influence of infrastructure on the value of apartments in different cities is the same. In the hypothesis test, data were used on the secondary housing market of the cities of Novosibirsk and Krasnoyarsk, taken from the site of «CIAN» apartment sale announcements and from various open data sources. During the study, non-parametric methods of machine learning, model-agnostic methods for the interpretation of predictive models, hierarchical clustering are involved. As a result of the work, the first hypothesis was confirmed and the second hypothesis was refuted, the high accuracy of forecasting the cost of an apartment was achieved, and the peculiarities of price formation for secondary housing objects were revealed and described.
Asymptotic Convergence Rates of the Length of the Longest Run(s) in an Inflating Bernoulli Net

In image detection, one problem is to test whether the set, though mainly consisting of uniformly scattered points, also contains a small fraction of points sampled from some (a priori unknown) curve, for example, a curve with C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α</sup> -norm bounded by β. One approach is to analyze the data by counting membership in multiscale multianisotropic strips, which involves an algorithm that delves into the length of the path connecting many consecutive “significant” nodes. In this paper, we develop the mathematical formalism of this algorithm and analyze the statistical property of the length of the longest significant run. The rate of convergence is derived. Using percolation theory and random graph theory, we present a novel probabilistic model named, pseudo-tree model. Based on the asymptotic results for the pseudo-tree model, we further study the length of the longest significant run in an “inflating” Bernoulli net. We find that the probability parameter p of significant node plays an important role: there is a threshold p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> , such that in the cases of p <; p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> and p > p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> , very different asymptotic behaviors of the length of the significant runs are observed. We apply our results to the detection of an underlying curvilinear feature and prove that the test based on our proposed longest run theory is asymptotically powerful.
Multimodal analysis of the predictability of hand-gesture properties.

Embodied conversational agents benefit from being able to accompany their speech with gestures. Although many data-driven approaches to gesture generation have been proposed in recent years, it is still unclear whether such systems can consistently generate gestures that convey meaning. We investigate which gesture properties (phase, category, and semantics) can be predicted from speech text and/or audio using contemporary deep learning. In extensive experiments, we show that gesture properties related to gesture meaning (semantics and category) are predictable from text features (time-aligned BERT embeddings) alone, but not from prosodic audio features, while rhythm-related gesture properties (phase) on the other hand can be predicted from either audio, text (with word-level timing information), or both. These results are encouraging as they indicate that it is possible to equip an embodied agent with content-wise meaningful co-speech gestures using a machine-learning model.