Reviewerly/exHarmony huggingface dataset:
Separating invariants over finite fields

We determine the minimal number of separating invariants for the invariant ring of a matrix group $G < \mathrm{GL}_n(\mathbb{F}_q)$ over the finite field $\mathbb{F}_q$. We show that this minimal number can be obtained with invariants of degree at most $|G|n(q-1)$. In the non-modular case this construction can be improved to give invariants of degree at most $n(q-1)$. As examples we study separating invariants over the field $\mathbb{F}_2$ for two important representations of the symmetric group
DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs

Image-to-image translation has recently achieved remarkable results. But despite current success, it suffers from inferior performance when translations between classes require large shape changes. We attribute this to the high-resolution bottlenecks which are used by current state-of-the-art image-to-image methods. Therefore, in this work, we propose a novel deep hierarchical Image-to-Image Translation method, called DeepI2I. We learn a model by leveraging hierarchical features: (a) structural information contained in the shallow layers and (b) semantic information extracted from the deep layers. To enable the training of deep I2I models on small datasets, we propose a novel transfer learning method, that transfers knowledge from pre-trained GANs. Specifically, we leverage the discriminator of a pre-trained GANs (i.e. BigGAN or StyleGAN) to initialize both the encoder and the discriminator and the pre-trained generator to initialize the generator of our model. Applying knowledge transfer leads to an alignment problem between the encoder and generator. We introduce an adaptor network to address this. On many-class image-to-image translation on three datasets (Animal faces, Birds, and Foods) we decrease mFID by at least 35% when compared to the state-of-the-art. Furthermore, we qualitatively and quantitatively demonstrate that transfer learning significantly improves the performance of I2I systems, especially for small datasets. Finally, we are the first to perform I2I translations for domains with over 100 classes.
Superquadrics Augmented Rapidly-exploring Random Trees.

This thesis work investigated the advantages and disadvantages of using superquadrics (SQ) to do the collision-checking part of the Rapidly-exploring Random Trees (RRT) motion planning algorithm fo ...
CHALLENGES IN FORENSIC DETECTION OF FORGERY BY DISAPPEARING INK

Common People have used ink and paper for making notes for a long time. Due to public trust on permanency and authenticity of records written in ink, handwritten entries on different types of printed banking instruments has enjoyed confidence of both banks and customers for a long time. But, ink writings could be manipulated to achieve intentions of fraudsters without knowledge of the writer. Fraudsters first erase the ink writing by different methods and cover the area with new writings. Common methods to erase writings are physical and chemical erasures. In the cases where physical erasures are done, either traces of ink left on the paper or the signs of abrasion on the paper attracts the attention of examiner. Chemical erasures mostly lead to change in tint of the surface in the affected area. Both chemical and physical erasure leads to loss of micro printing done as a security feature in the area. Recently, we came across a case where earlier writings were changed without any easily detectable changes in normal light. Market survey reveals that disappearing ink pens are commercially available which can disappear after writing on paper in matter of hours. Since no physical abrasion is required or chemicals were used to erase the same, no tell tale signs of either kind of erasure is readily detected. This paper discusses how one such case could be detected and suggests directions to design a module so that these types of cases do not go undetected.
A Syntactic Approach to Studying Strongly Equivalent Logic Programs

In the field of Answer Set Programming (ASP), two logic programs are strongly equivalent if they are ordinarily equivalent under any extensions. This property provides a theoretical foundation for studying many aspects of logic programs such as program simplification and transformation etc. Therefore, strong equivalence has been investigated extensively for ASP and its extensions such as LPMLN. In this paper, we present a syntactic approach to studying the strong equivalence of logic programs, which provides several interesting results and would help us understand the strong equivalence from a new perspective. Firstly, we present the notions of independent sets and five kinds of syntactic transformations (S-* transformations) for logic programs. And we investigate the strong equivalence (SE) and non-strong equivalence (NSE) preserving properties of the S-* transformations in the contexts of ASP and LPMLN. Secondly, based on the properties of the S-* transformations, we present a fully automatic algorithm to discover syntactic conditions that preserve strong equivalences (SE-conditions) of ASP and LPMLN programs. To discover the SE-conditions efficiently, we present four kinds of approaches to improve the algorithm. Thirdly, we present a preliminary method to simplify the discovered SE-conditions and report the simplified SE-conditions of several kinds of LPMLN programs. After that, we present a discussion on the discovered SE-conditions and some existing problems. Finally, we present a comparison between SE-conditions discovering approaches in this paper and in the related work.
'Cannot keep up:' Contact tracing suffers amid COVID-19 surge

Contact tracing is one of the most critical jobs in helping slow the spread of COVID-19. With cases surging, that strategy is starting to struggle.
Dense U-net for super-resolution with shuffle pooling layer

Recent researches have achieved great progress on single image super-resolution(SISR) due to the development of deep learning in the field of computer vision. In these method, the high resolution input image is down-scaled to low resolution space using a single filter, commonly max-pooling, before feature extraction. This means that the feature extraction is performed in biased filtered feature space. We demonstrate that this is sub-optimal and causes information loss. In this work, we proposed a state-of-the-art convolutional neural network method called Dense U-net with shuffle pooling. To achieve this, a modified U-net with dense blocks, called dense U-net, is proposed for SISR. Then, a new pooling strategy called shuffle pooling is designed, which is aimed to replace the dense U-Net for down-scale operation. By doing so, we effectively replace the handcrafted filter in the SISR pipeline with more lossy down-sampling filters specifically trained for each feature map, whilst also reducing the information loss of the overall SISR operation. In addition, a mix loss function, which combined with Mean Square Error(MSE), Structural Similarity Index(SSIM) and Mean Gradient Error (MGE), comes up to reduce the perception loss and high-level information loss. Our proposed method achieves superior accuracy over previous state-of-the-art on the three benchmark datasets: SET14, BSD300, ICDAR2003. Code is available online.
EVALUASI KEKUATAN KOLEKSI DI PERPUSTAKAAN UNIVERSITAS ISLAM NEGERI SUNAN KALIJAGA YOGYAKARTA

In terms of information and collection dissemination in order to realize the service for users, tailor service-a service to work on a patchwork of any lack of information necessity by library users-can be used to fulfill the need. In reality, no matter how big or how many amount of collection a library, it is still unable to fulfill the need of their users for their necessity diversity of information. Hence, mutual sharing and cooperation in a library network will help and complete each other for any lack of information or collection in one of library within the network. The collection capacity will be able to support the success of University Tri Dharma especially in order to elevate each study program of every faculty or in every department of the university. The library last paradigm is service, whether the library has successfully providing the necessity service or not is uncertain if the pressure point and its collection capacity are veiled. Therefore, it is important to plot and evaluate collection capacity for the sharing purpose. This knowledge of the library collection capacity will also help to prepare for further cooperation with another library which has collection capacity too. However, the question is whether the library of Sunan Kalijaga Islamic State University has known its collection capacity? Thus, this is the importance of this research.Keywords: collection capacity, information, library
Distributed Differentially Private Mutual Information Ranking and Its Applications

Computation of Mutual Information (MI) helps understand the amount of information shared between a pair of random variables. Automated feature selection techniques based on MI ranking are regularly used to extract information from sensitive datasets exceeding petabytes in size, over millions of features and classes. Series of one-vs-all MI computations can be cascaded to produce n-fold MI results, rapidly pinpointing informative relationships. This ability to quickly pinpoint the most informative relationships from datasets of billions of users creates privacy concerns. In this paper, we present Distributed Differentially Private Mutual Information (DDP-MI), a privacy-safe fast batch MI, across various scenarios such as feature selection, segmentation, ranking, and query expansion. This distributed implementation is protected with global model differential privacy to provide strong assurances against a wide range of privacy attacks. We also show that our DDP-MI can substantially improve the efficiency of MI calculations compared to standard implementations on a large-scale public dataset.
Imparting Gestalt Essence in Identifying User Preferences towards Interior Design

We believe that interior design has becoming more popular in this century nowadays. The purpose of this study is to design and develop multimedia application that can identify human behavior and preferences in interior design. Alessi and Trollip Instructional Design Model has been utilized as a methodology in this study which consist of planning, design and development. Heuristic Evaluation and User Acceptance Test has been applied in completing this experiment. Three multimedia experts selected randomly to identify usability problems that occur in the user interface (UI) design. After the refinement was made to the application, the User Acceptance Test was conducted to the user. A total of 60 participants at random selected from certain district of Perlis and Langkawi as a target user to participate in this study. The results demonstrate this multimedia application is effective in satisfying the user needs and demand of the decoration of their dream house. Thus, the researcher was able to identify user behavior and preferences in interior design. With three dimensional (3D) features that were applied in this multimedia application, it helps the user to feel more self-assured with their interior design. At the end of this research, the development of this application bring numerous benefits for both parties either the users or the developer in many aspects. Thus, the society will be disclosing to the use of technology in the interior design in this sophisticated era.
Scene Understanding in Night-Time Using SSAN Dataset

Night vision greatly affects the efficiency of our vision which we come across daily. Research work on night vision is very essential to solve the social problems in the present scenario, but there is still a lack of database to do research on night vision using deep-learning technique. Due to poor light the object detection is a very tedious process. To overcome such hardships, we collected the night vision datasets under various conditions. This work is about scene understanding during night-time with IR-cameras. The feature extraction from night videos is mainly affected by wavelength or the intensity of IR, illumination and distance factor. We proposed a novel algorithm exclusively for object detection during night time and we compare our algorithm with various yolo versions and we found that our night vision yolo performs better in detecting various objects like Male, Female, Car, bike, Van, Cycle during night time.
The concept of competence in the musical training of afuture teacher

The article examines the concept of competence, identifies the features of the formation of professional competence of a music teacher. The purpose of our article is to outline the concept of “competence” in the pedagogical activity and musical training of future specialists. Research methods. The following research methods were used in the study of this concept: analysis and synthesis of scientific, psychological, pedagogical and methodological literature, generalization and systematization of the material.
Inverse Constrained Reinforcement Learning

In real world settings, numerous constraints are present which are hard to specify mathematically. However, for the real world deployment of reinforcement learning (RL), it is critical that RL agents are aware of these constraints, so that they can act safely. In this work, we consider the problem of learning constraints from demonstrations of a constraint-abiding agent's behavior. We experimentally validate our approach and show that our framework can successfully learn the most likely constraints that the agent respects. We further show that these learned constraints are \textit{transferable} to new agents that may have different morphologies and/or reward functions. Previous works in this regard have either mainly been restricted to tabular (discrete) settings, specific types of constraints or assume the environment's transition dynamics. In contrast, our framework is able to learn arbitrary \textit{Markovian} constraints in high-dimensions in a completely model-free setting. The code can be found it: \url{https://github.com/shehryar-malik/icrl}.
Research on Anti-collision Method of Trackless Equipment in Underground Mine

Abstract In trackless transport of underground mines, accidents are much more frequent and serious due to the lack of effective safety warning measures. In this paper, a method of anti-collision for trackless equipment based on lidar was described. The background of the technology was recommended and the characteristics of roadway and data processing method were introduced. The method of processing radar data was proposed and a distance model was built, so the distance judgment method was provided. Finally, the method was applied on a Load-Haul-Dump (LHD) and it was proved that this method had obvious effectiveness and wide application for unmanned LHD in deep mining.
Zgodovinska vloga operacijskega sistema VMS pri razvoju COBISS-a

The paper reviews, with some historical distance, the circumstances leading to the VMS operating system becoming the basis for developing and implementing the COBISS national shared bibliographic system.Following a description of the basic characteristics of VMS, the paper shows the path of its transformation in its supportive role to COBISS, as well as its omission later on.
Mathematical model of random substitution

Approaches to the selection of random substitutions based on the application of a system of criteria constructed using estimates of the proximity of distribution laws of XOR tables and tables of displacements of linear approximations of substitutions to theoretical laws inherent in random substitutions are discussed. Their non-constructiveness is noted. It is not clear which selection rates are preferred.The essence of our refined methodology for determining the laws of distribution of maxima for large samples of independent identically distributed random variables is stated. It is noted that the distribution of the maxima of large samples of independent identically distributed random variables is well studied in probability theory and is described by the distribution of Fisher-Tippett or log-Weibull extreme values. The technique is used to determine the laws of distribution of the maximums of XOR tables and the maximum of displacements of tables of linear approximations of a sample from byte random substitutions. The calculation results are compared with the experimental results. The results of calculations and experiments indicate that, in both cases, the distributions are concentrated around sufficiently pronounced maxima with quite definite and the same most probable values, which make it possible to assume that randomly generated substitutions with a high probability will differ little from each other in the values of the maxima.Based on the results obtained, a refined definition of random substitution is proposed, which is based on the properties of a sample of random substitutions.It is noted that the use of random substitutions leads to an increase in the number of cycles of arrival of ciphers to the state of random substitution by one cycle.It is concluded that random substitutions taken from the output of a random substitution generator without any restrictions can compete with the best known S-box constructions used in modern ciphers. The maxima that are increased in comparison with the limiting values, which the authors of most works on the search for S-boxes with improved indicators strive for, can be compensated for by using cyclic functions in ciphers with an increased number of activated S-boxes in the first cycles.
A Parthood Approach for Modeling Tangible Objects’ Composition TOC - An Application on Cultural Heritage

Several semantic web approaches tackle the problem of integrating multidisciplinary rich content using Linked Open Data. Cultural heritage (CH) is a multidisciplinary domain that contains a massive heterogeneous content that varies distinctly by types and properties. Various semantic web approaches have been proposed in the context of CH, and at multiple integration levels (local, national, international). These approaches focus on metadata schemata integration but give no significant importance to the representation of a tangible cultural heritage object, as a whole entity, and the different parts that compose it. Targeting the goal of the preservation and restoration of CH artifacts, we aim at modeling the CH content focusing on the composition of a CH object. We thus illustrate here an approach of using a part-whole and spatial relations to model the composition of a tangible object in general, and a CH object in particular. To do this, we introduce parthood concepts and properties for representing the composition mechanism, and 7 cases of parthood/spatial relations in tangible objects, with their corresponding logical/ontological relation(s). We implement our approach using OWL2 as the ontological language for our linked open data approach.
PERBEDAAN KETERAMPILAN BERBAHASA PRODUKTIF MAHASISWA DITINJAU DARI MODEL PEMBELAJARAN BRAIN DAN INVESTIGASI KELOMPOK

Coourse learning outcome (CPMK) provide clear direction for the learning process. In addition, it provides a real picture of actions and teaching and learning behavior for univercity students and lecturs.The learning processes and events that are interesting, fun, innovative, and creative will have a positive effec on the echievement of learning objectives. The statement indirectly requires lectures to design and implement intesting learning models. Facts in the fieldshow that there are many learning models applied by lecturers that are not in accordance with the specified learning outcomes. The learning achievement of produktive language skills (KBP) requires univercity students to be able to produce scientific papers are published in the form of academic seminars. This demand has implications for the teacher's obligation to prepare an interesting, effective, and fun learning model. The purpose of this study was to determine the effect of the BRAIN learning model on Productive Language Skills. This research was conducted at the Nusantara PGRI Kediri University in the odd semester of the 2019/2020 academic year with a sample of 62 students. The group was divided into 2 namely experiment and control. The experimental group used the BRAIN model and the control class used the Group Investigation model. The results showed that there were differences in the average language skills of students between the experimental class and the control class. If viewed from the KBP, the experimental class has an average value of 65.29 which is better than the control class of 63.88.
On the hardness of code equivalence problems in rank metric

In the recent years, the notion of rank metric in the context of coding theory has known many interesting developments in terms of applications such as space time coding, network coding or public key cryptography. These applications raised the interest of the community for theoretical properties of this type of codes, such as the hardness of decoding in rank metric. Among classical problems associated to codes for a given metric, the notion of code equivalence (to decide if two codes are isometric) has always been of the greatest interest, for its cryptographic applications or its deep connexions to the graph isomorphism problem. In this article, we discuss the hardness of the code equivalence problem in rank metric for $\mathbb{F}_{q^m}$-linear and general rank metric codes. In the $\mathbb{F}_{q^m}$-linear case, we reduce the underlying problem to another one called {\em Matrix Codes Right Equivalence Problem}. We prove the latter problem to be either in $\mathcal{P}$ or in $\mathcal{ZPP}$ depending of the ground field size. This is obtained by designing an algorithm whose principal routines are linear algebra and factoring polynomials over finite fields. It turns out that the most difficult instances involve codes with non trivial {\em stabilizer algebras}. The resolution of the latter case will involve tools related to finite dimensional algebras and Wedderburn--Artin theory. It is interesting to note that 30 years ago, an important trend in theoretical computer science consisted to design algorithms making effective major results of this theory. These algorithmic results turn out to be particularly useful in the present article. Finally, for general matrix codes, we prove that the equivalence problem (both left and right) is at least as hard as the well--studied {\em Monomial Equivalence Problem} for codes endowed with the Hamming metric.
The digital notebook: a method for the rapid processing of elicited linguistic data

Many linguists have a backlog of elicited data that has never seen the light of day, because it takes too long to process. The digital notebook method addresses this issue by streamlining the elicitation session, so that the output is immediately incorporated into a database and ready for archiving. (session 2.2.5)