Reviewerly/exHarmony huggingface dataset:
A Generalization of QR Factorization To Non-Euclidean Norms

I propose a way to use non-Euclidean norms to formulate a QR-like factorization which can unlock interesting and potentially useful properties of non-Euclidean norms - for example the ability of $l^1$ norm to suppresss outliers or promote sparsity. A classic QR factorization of a matrix $\mathbf{A}$ computes an upper triangular matrix $\mathbf{R}$ and orthogonal matrix $\mathbf{Q}$ such that $\mathbf{A} = \mathbf{QR}$. To generalize this factorization to a non-Euclidean norm $\| \cdot \|$ I relax the orthogonality requirement for $\mathbf{Q}$ and instead require it have condition number $κ\left ( \mathbf{Q} \right ) = \| \mathbf{Q} ^{-1} \| \| \mathbf{Q} \|$ that is bounded independently of $\mathbf{A}$. I present the algorithm for computing $\mathbf{Q}$ and $\mathbf{R}$ and prove that this algorithm results in $\mathbf{Q}$ with the desired properties. I also prove that this algorithm generalizes classic QR factorization in the sense that when the norm is chosen to be Euclidean: $\| \cdot \|=\| \cdot \|_2$ then $\mathbf{Q}$ is orthogonal. Finally I present numerical results confirming mathematical results with $l^1$ and $l^{\infty}$ norms. I supply Python code for experimentation.
Research on Maintenance and Support Technology for Offshore Based on Beidou Satellite Navigation System

Beidou satellite navigation system can provide positioning, navigation, timing and short message communication services for ship users. In this paper, according to the requirements of the maintenance and support of ship equipment in the open sea, based on the information transmission channel of Beidou navigation satellite system, the transmission data volume is compressed by using the method of equipment status and fault coding, the remote reproduction of ship equipment status and remote guidance of fault maintenance are realized, so that the equipment maintenance support field extends to the open sea, which has strong practicability.
Does Dialog Length matter for Next Response Selection task? An Empirical Study

In the last few years, the release of BERT, a multilingual transformer based model, has taken the NLP community by storm. BERT-based models have achieved state-of-the-art results on various NLP tasks, including dialog tasks. One of the limitation of BERT is the lack of ability to handle long text sequence. By default, BERT has a maximum wordpiece token sequence length of 512. Recently, there has been renewed interest to tackle the BERT limitation to handle long text sequences with the addition of new self-attention based architectures. However, there has been little to no research on the impact of this limitation with respect to dialog tasks. Dialog tasks are inherently different from other NLP tasks due to: a) the presence of multiple utterances from multiple speakers, which may be interlinked to each other across different turns and b) longer length of dialogs. In this work, we empirically evaluate the impact of dialog length on the performance of BERT model for the Next Response Selection dialog task on four publicly available and one internal multi-turn dialog datasets. We observe that there is little impact on performance with long dialogs and even the simplest approach of truncating input works really well.
Results of Geophysical Studies of an Unfortified Bronze Age Settlement in the Southern Urals

Summary The paper presents the results of geophysical studies of the unfortified Bronze Age settlement Konoplyanka-2 (Southern Urals). The settlement is revealed by 10 depressions in the modern relief. Topographic and magnetic surveys were performed in the vicinity of the settlement in the field seasons of 2017-2019. According to the results of magnetic surveys, it was possible to identify the boundaries of buildings and reconstruct the settlement plan. There were much more dwellings than fixed on the surface of housing depressions. Inside the buildings found utility pits and wells. The constructed magnetic map became the basis for the selection of archaeological sites. At one of the dwellings with the alleged four wells, a GPR survey was performed for a targeted excavation study of geomagnetic anomalies associated with hydraulic structures. According to the GPR data, the depth of the wells was determined, their shape was restored and the position of the wells in the plan and in the 3D format was built.
Lassie: HOL4 tactics by example

Proof engineering efforts using interactive theorem proving have yielded several impressive projects in software systems and mathematics. A key obstacle to such efforts is the requirement that the domain expert is also an expert in the low-level details in constructing the proof in a theorem prover. In particular, the user needs to select a sequence of tactics that lead to a successful proof, a task that in general requires knowledge of the exact names and use of a large set of tactics.
An Assessment Model for Defense Cybersecurity Capability

As cyber threats increase around the world, many countries are focusing on increasing their cybersecurity while expanding their investments in cybersecurity.Recently, the pattern of war is changing in the direction of linking military operations and cyberattacks, and North Korea is also strengthening its cyber warfare capabilities as one of the asymmetric forces.North Korea's cybersecurity threat, along with nuclear and missile provocations, is rapidly emerging as a serious security threat on the Korean Peninsula.In order to effectively respond to North Korea's cybersecurity threat, this paper examines the trends in cybersecurity competency assessment at domestic and foreign countries, and presents a defense cybersecurity competency assessment model as a way to strengthen the cybersecurity competency level.The model will contribute to establishing cyber warfare preparedness by diagnosing the level of cybersecurity capabilities of the military and supporting decision-making so that cybersecurity forces can be efficiently equipped.
DEVELOPMENT LOGICO-MATHEMATICAL REPRESENTATIONS AND SKILLS IN PRESCHOOL CHILDREN IN THE PROCESS OF USING DENESH LOGICAL BLOCKS AND THE KEYZENER STICK

Modern preschool education is based on a personality-oriented model. Mathematics occupies a leading place in solving the problem of development of logical and mathematical ideas and skills in preschool children. It sharpens the child's mind, develops flexibility of thinking, teaches logic. In the pedagogical practice of a modern preschool institution, Dienes' logical blocks and Kuizener's wands are actively used by modern educators. The teacher's work with this material significantly contributes to the development of children's logical and mathematical ideas and skills.The set of Dienes blocks consists of 48 geometric figures of different colors (red, blue, yellow), shape (round, square, triangular, rectangular), size (large, small), thickness (thick, thin). There are no two identical figures in the set, each characterized by the four properties mentionedabove. Together with the logical blocks in the work of the educator of the preschool institution, cards are used on which the properties of the blocks (color, shape, size, thickness) are conditionally determined. The use of cards allows children to develop the ability to substitute and model properties, the ability to encode and decode information about them. These properties and skills develop in the process of performing a variety of subject-game actions. In the process of working with Dienes blocks in the game, children not only consolidate ideas about geometric shapes, signs of objects, form mental actions, but also develop mental processes: thinking, memory, attention, imagination, speech.Kuizener's sticks are didactic material for the development of children's mathematical abilities. The set contains quadrangular sticks of 10 different colors and a length of 1 to 10 cm (this can usually be stripes). George Kuizener designed sticks so that sticks of the same length are made in the same color and denote a certain number. The greater the length of the stick, the greater the numerical value it expresses. Sticks allow you to translate practical external actions into the internal plan; to master spatial relations. Didactic material gives the chance to train kids in addition of number from units and two smaller numbers; learn to measure objects; learn arithmetic operations (addition, subtraction, division, multiplication); learn to divide the whole into parts; to bring them to the realization of the relations "less – more", "less by ... – more by ...".In working with Kuizener sticks, the spatial and quantitative characteristics are not as obvious to children as color, shape, size when working with Dienes blocks. But to open these characteristics will help the joint activities of an adult with a child.
Physicochemical weathering conditions of the characteristic white soil horizon at Al Firjah area and it’s vicinity in North Kordofan State, Central Sudan.

Mineral deposits and valuable materials are deposited, concentrated, and purified by natural processes during long or short time, chemical weathering reactions is a major one. Satelliteimages exhibit distinct white horizon of high reflectance. Investigation of this phenomenonreveals that these areas were associatedwith the distal reaches of the major part of the drainage system. Exposures of the basement rocks include metasediments marl, marble, and other silicate rocks(granite, granodiorite,meta andesite,….etc) at high land where the proximal reaches of the drainage system. These rock were subjected to chemical disintegration process of hydrolysis for silicateconstituent and dissolutionfor carbonate constituent and consequently these material were transportedby the drainage system as colloidal (kaolinite) and dissolved material(calcium cation and bicarbonate), eventually these material were deposited due to evaporation at flash delta to form the kaolinite carbonatesoil at Alfirjah area and it’s vicinity. Field observations in addition to rock and soil sample analysis forearm the above mentioned hypothesis.
TorchPRISM: Principal Image Sections Mapping, a novel method for Convolutional Neural Network features visualization

In this paper we introduce a tool called Principal Image Sections Mapping - PRISM, dedicated for PyTorch, but can be easily ported to other deep learning frameworks. Presented software relies on Principal Component Analysis to visualize the most significant features recognized by a given Convolutional Neural Network. Moreover, it allows to display comparative set features between images processed in the same batch, therefore PRISM can be a method well synerging with technique Explanation by Example.
Analisis Kesesuaian Lahan Untuk Permukiman Di Kecamatan Polokarto Kabupaten Sukoharjo Tahun 2019

The population in Indonesia from year to year is increasing and including the fourth largest in the world. This increase in population has resulted in more demand for residential land and this has happened in the study area, namely Polokarto District. Increasing demand for land for settlements will result in a change of function of land to be used as residential land. More and more inhabitants have resulted in increased development of accessibility and various infrastructures so that catchment areas are reduced and a lot of development for built up land causes land damage because it does not pay attention to the characteristics of the land for land suitability in the area. Therefore, evaluation of land resources is needed to avoid land damage by conducting research to find out the limiting factors of land suitability for settlements using survey and laboratory analysis. Sampling uses stratified sampling method based on strata of land units. Data were analyzed using 2 methods, namely the scoring method, and descriptive analysis. Scoring method is used to determine the land suitability class of the study area by accumulating or adding up the total value of each classification parameter that affects the land suitability class. The results of the study for land suitability class obtained land suitability class class I (Very good to good, land with conditions very suitable for settlements) has an area of 4,388 ha with a percentage of 66% of the entire study area, while in land suitability class class II (Medium, land condition has several non-permanent inhibiting factors) has an area of 2,298 ha with a percentage of 34% of the entire study area.
Developing Efficient Custom Solr Plugin for LTR Feature Extraction

LTR (Learning to Rank) has emerged as a major source of search relevancy improvement for LexisNexis Advance (LNA). Initially, feature extraction for LTR was performed in part using ElasticSearch via AWS-hosted ESS domains. Although a great deal of work was spent customizing ESS to provide the required feature information, limitations imposed by AWS effectively limited LTR to reranking the top 20 search results. Experimentation showed that greater hDCG gains could be obtained by expanding the scope of LTR to rerank the top 50 search results, but the P90 execution time for those requests was 940ms in ESS. In order to produce a system capable of extracting the LTR features for the top 50 results of every LNA search, members of Team Europa, Team Saturn, and Team Titan partnered with the SRP Team to create a Solr-based system that was more performant than the ESS-based system. This talk describes in detail the evolution of Solr custom plugins that reduced the P90 execution time from 470ms to 150ms to 50ms in Solr. This involved the development of 3 progressively more efficient techniques were explored in Solr: 1.). a custom Term Vector Component, 2.) Function Sub-queries and custom Explorer Queries, and 3.) custom Value Sources.
The Random Oracle Controversy

Over the course of the previous chapters we have seen how random oracles allow for the creation of elegant and efficient schemes which can furthermore be proven secure in the random oracle model. In this chapter we have a closer look at what it means to have a security proof in the random oracle model rather than in the standard model.
Trading Stocks on Blocks - The Quality of Decentralized Markets

The trust-free nature of blockchain-based systems challenges the role of traditional platform providers and enables the creation of new, intermediary-free markets. Despite the growing number of such markets, the impact of the blockchain's configuration on market outcomes remains unclear. In this study, we utilize order-level data from real-world financial markets to explore the impact of the blockchain parameters block size and block creation time on the quality of decentralized markets. More specifically, we find that increasing the blocks' capacity improves market activity, while higher block frequencies impose a trade-off between higher turnovers and lower trade sizes. In addition, we identify the block creation time and block size as core drivers of daily and intraday liquidity, respectively. In consequence, improving liquidity goes hand in hand with a higher activity. However, the reciprocal relationship between blockchain parameters and the increasing price impact of a block also indicate that faster and bigger blocks are no silver bullet to scale decentralized markets and may facilitate volatility. In total, we contribute an initial, technology-agnostic assessment of the quality of decentralized markets that aims to guide interdisciplinary researchers and innovative practitioners.
Semantic segmentation of remote sensing images based on dual attention and multi-scale feature fusion

We propose a remote sensing image semantic segmentation model based on dual attention and multi-scale feature fusion to solve the problems of objects scale differences and missing small objects. This model uses ResNet50 in the coding part to extract features. First of all, the output features of each stage of ResNet50 are introduced into the pyramid pooling module, making full use of the multi-scale context information of the image to cope with the change of the object scales. Secondly, the dual attention is introduced in the final output features of ResNet50 to establish the semantic relationship between the spatial and channel dimensions, which enhances the ability of feature representation and improve the condition that small targets are difficult to segment. Finally, starting from the output features of the attention module, the features of all levels are gradually integrated to complete decoding to refine the target segmentation edge. The designed comparative experiments results show the effectiveness of the proposed method.
Learning under Diverse World Views: Model-Based Inference

People reason about uncertainty with deliberately incomplete models, including only the most relevant variables. How do people hampered by diﬀerent, incomplete views of the world learn from each other?We introduce a model of “model-based inference.” Model-based reasoners partition an otherwise hopelessly complex state space into a manageable model. We ﬁnd that unless the diﬀerences in agents’ models are trivial, interactions will often not lead agents to have common beliefs, and indeed the correct-model belief will typically lie outside the convex hull of the agents’ beliefs. However, if the agents’ models have enough in common, then interacting will lead agents to similar beliefs, even if their models also exhibit some bizarre idiosyncrasies and their information is widely dispersed.
Penerapan Case-Based Reasoning untuk Perawatan dan Perbaikan Mesin di Jurusan Teknik Mesin Polman Babel

This study aims to implement a Case-Based Reasoning (CBR) for diagnosis and recommendations for the maintenance and repair of machines in the Department of Mechanical Engineering Polman Negeri Bangka Belitung. The research begins with the process of collecting data for knowledge bases related to the maintenance and repair cases of these machines. Furthermore, the data is modeled based on its attributes. The cases that have been modeled are input into the CBR system by applying the similarity function of weighted sum and Euclidean distance. The higher the similarity between a new case and cases that have been stored on a case base, the more suitable the solution on a case base can be applied in other cases. The results obtained were 5 case bases, i.e. the base case of flat grinding machines, the case base of lathe machines, the case base of milling machines, the case base of CNC machines, and the case base of the welding machines. The results of the CBR system are able to diagnose the causes of problems with machines through the symptoms of problems that occur and able to provide recommendations for the maintenance and repair of these machines.
Generating Adversarial Computer Programs using Optimized Obfuscations

Machine learning (ML) models that learn and predict properties of computer programs are increasingly being adopted and deployed. These models have demonstrated success in applications such as auto-completing code, summarizing large programs, and detecting bugs and malware in programs. In this work, we investigate principled ways to adversarially perturb a computer program to fool such learned models, and thus determine their adversarial robustness. We use program obfuscations, which have conventionally been used to avoid attempts at reverse engineering programs, as adversarial perturbations. These perturbations modify programs in ways that do not alter their functionality but can be crafted to deceive an ML model when making a decision. We provide a general formulation for an adversarial program that allows applying multiple obfuscation transformations to a program in any language. We develop first-order optimization algorithms to efficiently determine two key aspects -- which parts of the program to transform, and what transformations to use. We show that it is important to optimize both these aspects to generate the best adversarially perturbed program. Due to the discrete nature of this problem, we also propose using randomized smoothing to improve the attack loss landscape to ease optimization. We evaluate our work on Python and Java programs on the problem of program summarization. We show that our best attack proposal achieves a 52% improvement over a state-of-the-art attack generation approach for programs trained on a \textsc{seq2seq} model. We further show that our formulation is better at training models that are robust to adversarial attacks.
Incident response as a key factor of defense

According to the ancient Chinese learning our world is governed by two opposite forces being Yin and Yang. Those forces are the force of offense and force of defense and by the same doctrine our world can be in harmony only if those two forces are in balance. The modern security experts would also see a security as a balance between attack and defense. So many defense professionals worldwide would approach their everyday tasks with such an assumption. The similar situation is with the cyber defense - the cybersecurity researchers and practitioners would see cyber defense as a balance between cyber attack and its defense. Also, this doctrine would get led with the theory that there are some passive and active principles in cyber defense. For instance, the passive principals are cyber prevention and monitoring, while the active principle is the incident response. The ongoing experience would suggest that the majority of marketplace actors would invest a lot into the passive factors of cyber defense, while the incident response would get less used due to some objective and realistic parameters. The main concern with the incident response is that it requires a lot of skills and expertise in order to get applied. There is the entire flow in a cybersecurity that would suggest that all three indicators of the cyber defense passive and active principles should get equally distributed. In addition, coping with such a concept so many cyber experts would see the incident response as a key pillar in a cyber defense. In this effort, we intend to provide a deep insight into such learning as well as provide some comments and discussions coming from our own research experience.
PENGARUH PENERAPAN MODEL PEMBELAJARAN COOPERATIVE INTEGRATED READING AND COMPOSITION (CIRC) TERHADAP HASIL BELAJAR EKONOMI PESERTA DIDIK KELAS XI

The problems in this study are 1) the low student learning outcomes, 2) the lack of student activity in the teaching and learning process, 3) and the CIRC learning model in the classroom has never been applied. The purpose of this study was to determine the effect of the application of the Cooperative Integrated Reading and Composition (CIRC) learning model on the economic learning outcomes of XI graders in the even semester of Gedongtataan 1 Public High School The method used in this study is the experimental method. The population in this study were all students of class XI.IPS 1 Gedongtataan High School totaling 94 students, the sample consisted of two classes namely class XI IPS 2 and XI IPS 3, where class XI IPS 2 as an experimental class which amounted to 33 students and class XI IPS 3 as a control class with 33 students. The sampling technique uses cluster random sampling technique. The instrument in this study used multiple choice tests and hypothesis testing in this study using the formula ttes. Hypothesis testing using the statistical formula t-test obtained t hit = 6.22 and the distribution table t at a significant level of 5% is known tdaf is t (1-1 / 2 α) = 2.00 so that it is proven thit> t daf. Thus it can be concluded that there is the effect of applying the Cooperative Integrated Reading and Composition (CIRC) learning model to the economic learning outcomes of the XI graders in the even semester of Gedongtataan 1 Public High School 2018/2019.