Reviewerly/exHarmony huggingface dataset:
BIOLOGIYA DARSLARIDA “DOMINO” METODIDAN FOYDALANISH

The main purpose of studying biology is to be able to think independently and creativelyis to cultivate a spiritually mature person. For the above purpose: studentsmemory along with the development of observation, comprehension and thinking skills, iespecial attention is paid to strengthening the memory. With that in mindIt is advisable to set up a domino house in the 6th grade botany course.
Progressive Transformer-Based Generation of Radiology Reports

Inspired by Curriculum Learning, we propose a consecutive (i.e., image-to-text-to-text) generation framework where we divide the problem of radiology report generation into two steps. Contrary to generating the full radiology report from the image at once, the model generates global concepts from the image in the first step and then reforms them into finer and coherent texts using transformer-based architecture. We follow the transformer-based sequence-to-sequence paradigm at each step. We improve upon the state-of-the-art on two benchmark datasets.
A practical analysis of ROP attacks.

Control Flow Hijacking attacks have posed a serious threat to the security of applications for a long time where an attacker can damage the control Flow Integrity of the program and execute arbitrary code. These attacks can be performed by injecting code in the program's memory or reusing already existing code in the program (also known as Code-Reuse Attacks). Code-Reuse Attacks in the form of Return-into-libc Attacks or Return-Oriented Programming Attacks are said to be Turing Complete, providing a guarantee that there will always exist code segments (also called ROP gadgets) within a binary allowing an attacker to perform any kind of function by building a suitable ROP chain (chain of ROP gadgets). Our goal is to study different techniques of performing ROP Attacks and find the difficulties encountered to perform such attacks. For this purpose, we have designed an automated tool which works on 64-bit systems and generates a ROP chain from ROP gadgets to execute arbitrary system calls.
Superficies Mínimas y Resultados de Separación en Variedades Riemannianas.: Dualidad y Aproximación en Espacios de Lebesgue Variable.

In this thesis, we solve four different problems of interdisciplinary nature using two different types of techniques: one of them concerning the analysis of partial differential equations and the other, functional analysis. More specifically, we address the following questions: - What are the sufficient and necessary hypotheses so that a hypersurface is the set of discontinuities of a generalized harmonic function? - What are the geometric implications of achieving equality at Modica's estimate or its generalizations in Riemannian manifolds? - What is the dual of a variable Lebesgue space whose exponent function is unbounded? - Does the property of universal approximation of neural networks hold to approximate functions belonging to variable Lebesgue spaces?
Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings

Semantic sentence embeddings are usually supervisedly built minimizing distances between pairs of embeddings of sentences labelled as semantically similar by annotators. Since big labelled datasets are rare, in particular for non-English languages, and expensive, recent studies focus on unsupervised approaches that require not-paired input sentences. We instead propose a language-independent approach to build large datasets of pairs of informal texts weakly similar, without manual human effort, exploiting Twitter’s intrinsic powerful signals of relatedness: replies and quotes of tweets. We use the collected pairs to train a Transformer model with triplet-like structures, and we test the generated embeddings on Twitter NLP similarity tasks (PIT and TURL) and STSb. We also introduce four new sentence ranking evaluation benchmarks of informal texts, carefully extracted from the initial collections of tweets, proving not only that our best model learns classical Semantic Textual Similarity, but also excels on tasks where pairs of sentences are not exact paraphrases. Ablation studies reveal how increasing the corpus size influences positively the results, even at 2M samples, suggesting that bigger collections of Tweets still do not contain redundant information about semantic similarities. Code available at https://github.com/marco-digio/Twitter4SSE
Ordinals by Abstraction1

NB: Abstract added by Editors. In his contribution, Bob Hale aims at providing a theory of ordinals on neo-logicist bases. In this respect, two main goals have to be accomplished. First and foremost, consistency is to be retained, since Burali-Forti’s paradox easily arises from a naïve ordinal abstraction principle accompanied by unrestricted second-order logic. Second, such a reconstruction should not be provided on the basis of set theory but rather in a purely neo-logicist setting. Hale achieves both goals by modifying Shapiro and Wright’s definition of indefinite extensibility in Shapiro and Wright (2006), on the one hand, and relying on Hume’s Principle, on the other.
The Development and Approach of the Appeal Trial Mechanism of China’s Intellectual Property Cases : Centering on the Establishment of a Modern Intellectual Property Appellate Court

The establishment of the Intellectual Property Court of the Supreme People’s Court is the beginning of China’s formal establishment of a national-level intellectual property appeal hearing mechanism; however, this is only a trial and not a final judicial system reform, and future development and approaches need to be further explored. From an international perspective, the establishment of an intellectual property appeal court has become an international trend and consensus; in the local thinking, the establishment of an intellectual property appeal court is more conducive to the realization of the goal of building a large judicial system of intellectual property than the existing mechanism, and it does not hinder the maintenance of public welfare and self-interest. Finally, the innovation of the intellectual property appeal hearing mechanism is far from insufficient only by the establishment of specialized courts. In the evolution of modernization, it also needs to be equipped with corresponding judicial system and trial team, so as to essentially form the centralized and professional judicial effect of intellectual property appeal trial to improve trial efficiency and unify judgment standards.
Learning Interpretable Models for Black-Box Agents

This paper develops a new approach for learning a STRIPS-like model of anon-stationary black-box autonomous agent that can plan and act. In thisapproach, the user may ask an autonomous agent a series of questions, which theagent answers truthfully. Our main contribution is an algorithm that generatesan interrogation policy in the form of a contingent sequence of questions to beposed to the agent. Answers to these questions are used to learn a minimal,functionally indistinguishable class of agent models. This approach requires aminimal query-answering capability from the agent. Empirical evaluation of ourapproach shows that despite the intractable space of possible models, ourapproach can learn interpretable agent models for a class of black-boxautonomous agents in a scalable manner.
Simulasi Proses Enkripsi dan Dekripsi Kriptografi Klasik Berbasis Multimedia dalam Bentuk 3D

Cryptography is the science of changing the form of data/information so that it cannot be understood by third parties. In cryptography, there are 2 main concepts, namelyencryption, and decryption. Encryption is the process of converting data/information into incomprehensible to third parties. Decryption is the process of returning data/information to its original form. Complex encryption and decryption processes make it difficult for students to understand the processes that occur in detail. This is because the learning process only uses text/writing. Based on the results of questionnaires from students who have studied classical cryptography, the most difficult algorithms to understand are the Vigenere Cipher and Affine Cipher. Thus, learning media is built in the form of multimedia- based simulations in 3D. Application development is carried out using the method User- Centered Design (UCD), which makes the user the center of the application development process. After the application has been built, the results show that the application is by userneeds based on validation using the LORI instrument of 90.55%, which is included in the very good category.
Repulsive Deep Ensembles are Bayesian

Deep ensembles have recently gained popularity in the deep learning community for their conceptual simplicity and efficiency. However, maintaining functional diversity between ensemble members that are independently trained with gradient descent is challenging. This can lead to pathologies when adding more ensemble members, such as a saturation of the ensemble performance, which converges to the performance of a single model. Moreover, this does not only affect the quality of its predictions, but even more so the uncertainty estimates of the ensemble, and thus its performance on out-of-distribution data. We hypothesize that this limitation can be overcome by discouraging different ensemble members from collapsing to the same function. To this end, we introduce a kernelized repulsive term in the update rule of the deep ensembles. We show that this simple modification not only enforces and maintains diversity among the members but, even more importantly, transforms the maximum a posteriori inference into proper Bayesian inference. Namely, we show that the training dynamics of our proposed repulsive ensembles follow a Wasserstein gradient flow of the KL divergence with the true posterior. We study repulsive terms in weight and function space and empirically compare their performance to standard ensembles and Bayesian baselines on synthetic and real-world prediction tasks.
Image Processing Techniques for Extraction of Serial Numbers on Currency Notes While Depositing in Cash Deposit Machine Using Python and Raspberry Pi

Nowadays, serial numbers of currency notes are collected and entered in a register manually. If any robbery occurs in banks, ATM’s or CDMA’s, the notes are tracked by using the serial numbers that are entered manually. The aim of this project is to extract the serial numbers, which are unique to a note, and storing them in a database automatically. We can retrieve the information about transactions with the notes in ATM’s or Banks. The serial numbers are extracted using Python and Raspberry Pi. Now the values that are extracted are stored in a file in the text format by linking the serial number to the corresponding value.
Detection of masked bad words in social media content

Social Media, is it a blessing or a curse? Many people will have different views on this matter, but one can never ignore the fact that it has a very big influence in people’s lives in modern times. With the increase in popularity and availability of different social media platforms, more and more people are finding it easier and easier to communicate with each other all over the world. That is just one of the highlights of social media.
코로나19 이후 내국인대학생과 외국인대학생간의 블렌디드 러닝(대면+비대면)수업의 학습만족도 및 학습몰입도 연구 -서울소재 H대학 뷰티디자인매니지먼트학과 대상으로-

This study aims to compare and analyze learning satisfaction and learning immersion among Koreans and foreign students in the Corona 19 era and to prepare the need and alternatives for blended learning classes (face-to-face + non-face) among various class methods. In this study, 208 questionnaires were distributed to every students in the Department of Beauty Design Management at H University in Seoul, and 206 questionnaires were used for the final analysis. The statistical processing was analyzed using SPSS 21.0 as follows: First, a frequency analysis was conducted to identify the general characteristics of the subjects surveyed; second, an exploratory factor analysis was conducted for the verification of the subjects surveyed; and third, a reliability test was conducted to verify the validity of the measurement tool. Fourth, we conducted t-test, ANOVA to find differences in learning satisfaction and immersion according to demographic characteristics, and conducted a Scheme-test as a post-test of ANOVA. Female students showed higher proportion than male students, and the proportion of Korean and foreign students was about the same. The Blended Learning course found that 39.8% of students take more than five courses, meaning that online classes alone are not enough to satisfy students' needs. Foreign students showed higher satisfaction in learning according to nationality than Korean students, and foreign students also showed higher immersion in learning than Korean students.
THE INFLUENCE OF DISCUSSION TECHNIQUE TO READING COMPREHENSION AT THE TENTH GRADE STUDENTS OF SMK INSAN AQILAH

The objective of this research is to examine a significant influence of discussion technique to reading comprehension at the tenth grade students SMK Insan Aqilah 6 Tigaraksa Kabupaten Tangerang – Banten. This is a quantitative research by using survey method. The data were gathered by conducting a test, and analyzed based on descriptive statistical analysis. The calculation mentioned above, writer found out that for variables of discussion technique, the average score is 66.13 and mode score is 80, and its standard deviation is 18.36. Meanwhile, for the variable of reading comprehension, the average score is 72 mode score is 80, and its standard deviation is 16.94. The calculation of correlation coefficient emerged the value of 0.52, and it lies between moderate or middle correlation criteria. Writer interprets through above descriptions that the tenth grade students of SMK Insan Aqilah 6 Tigaraksa Kabupaten Tangerang – Banten in general have positive achievement in discussion technique and therefore, they have ability in reading comprehension especially in English when they are learning English.
STUDNETS’ ABILITY IN USING RELATIVE PRONOUNS; A CASE STUDY AT THE SECOND GRADE STUDENTS OF MTs. DARRUL MUHIBBIN PRAYA

This study is aimed to find out the students’ ability in using relative pronouns at the Second Grade Students of MTs. Darrul Muhibbin Praya. The samples of this study were classes II-A and II-B consisted of 60 students where each class consist of 30 students. The sample on this study was treated in answering the test grammar test about relative pronouns. The in the form of multiple choice consisted of 50 items and for each item by 2 times. Their ability was proving by finding out the percentage of their test. This particular inference were bravely taken under the “d”(total amount) identification which indicates that the students have ability in using relative pronouns, where “d” valued was 70%, this percentage was significance since obtained from the score 3656 that divided from 60 students. This show that the second grade students of MTs. Darrul Muhibbin Praya were able in using relative pronouns such as (who, whom, whose, which and that)
Safe Policy Optimization with Local Generalized Linear Function Approximations

Safe exploration is a key to applying reinforcement learning (RL) in safety-critical systems. Existing safe exploration methods guaranteed safety under the assumption of regularity, and it has been difficult to apply them to large-scale real problems. We propose a novel algorithm, SPO-LF, that optimizes an agent's policy while learning the relation between a locally available feature obtained by sensors and environmental reward/safety using generalized linear function approximations. We provide theoretical guarantees on its safety and optimality. We experimentally show that our algorithm is 1) more efficient in terms of sample complexity and computational cost and 2) more applicable to large-scale problems than previous safe RL methods with theoretical guarantees, and 3) comparably sample-efficient and safer compared with existing advanced deep RL methods with safety constraints.
Geology and gold mineralization in the Wadi Onib area, red sea hills, Sudan

Gold mineralization of Neo-Proterozoic age in the Arabian- Nubian Shield occurring in the Red Sea Hills area in Sudan, North Africa, is controlledby NE-SW and NW-SE trending regional shears. Gold mining in this region appears to be as old as the XXth Century Egyptian Dynasties of the Middle Kingdom. The present study area in Wadi Onib in the north- east Red Sea Hills region of Sudan encloses potential auriferous zones along the first and second-generation shears. Gold is commercially extracted by several local and multi-national mining companies. The Onib - Sol Hamed suture zone comprising the Gerf and Gebeitterranes is one such structurally identified area with an Ophiolite Complex extending along a NE-SW trend. A few oldworkings of gold are reported in the southern end of this zone. The gold occurrence along the NE-SW shear(S-1) is hosted by quartz veins intruding and altering the wall rocks in metavolcanics and pyritiferous phyllites. Gold mineralization is observed over a strike length of about 500 m with 25 m width. Another occurrence associated with smoky quartz veins intruding diorite and meta-andesite and the resulting altered wall rocks is identified along the WNW-ESE fracture trend (S-2) extending over a zone of 1500 m × 30 m. Gold values in the analyzed samples range from 0.005 to 8 ppm with spike values of 40 and 65 ppm. Higher concentration of gold is observed in fractured smoky quartz veins. In the present study, an attempt is made to describe the nature of gold mineralization at these two locations associated with quartz veins and related wall-rock alteration zones along the NE-SW shear and WNW-ESE fractures.
Speechformer: Reducing Information Loss in Direct Speech Translation

Transformer-based models have gained increasing popularity achieving state-of-the-art performance in many research fields including speech translation. However, Transformer’s quadratic complexity with respect to the input sequence length prevents its adoption as is with audio signals, which are typically represented by long sequences. Current solutions resort to an initial sub-optimal compression based on a fixed sampling of raw audio features. Therefore, potentially useful linguistic information is not accessible to higher-level layers in the architecture. To solve this issue, we propose Speechformer, an architecture that, thanks to reduced memory usage in the attention layers, avoids the initial lossy compression and aggregates information only at a higher level according to more informed linguistic criteria. Experiments on three language pairs (en→de/es/nl) show the efficacy of our solution, with gains of up to 0.8 BLEU on the standard MuST-C corpus and of up to 4.0 BLEU in a low resource scenario.
Creating legal frameworks to afford human accountability for AI decisions in war

Human control over weapons is being phased out. In its place, we are seeing the development of artificial intelligence (AI), which will take over many of a weapon’s functions. Seek, engage, and destroy functions are being programmed into lethal fully autonomous weapon systems (LAWS) to remove the human from the arena of armed conflict. AI allowing superior detection, faster analysis and response times, and lack of physically limiting factors such as exhaustion, hunger, and stress makes LAWS a desirable option for military operations, despite raising ethical concerns. These developments create several legal issues, with the most pressing being how to assign accountability. This chapter will focus on the issue of human accountability under international criminal law (ICL) for breaches of law enacted by LAWS. It will look at how existing frameworks are not sufficient to regulate accountability for their use and will suggest a new framework for responsibility that extends existing law to safely regulate AI in battle.
Escaping Saddle Points with Compressed SGD

Stochastic gradient descent (SGD) is a prevalent optimization technique for large-scale distributed machine learning. While SGD computation can be efficiently divided between multiple machines, communication typically becomes a bottleneck in the distributed setting. Gradient compression methods can be used to alleviate this problem, and a recent line of work shows that SGD augmented with gradient compression converges to an $\varepsilon$-first-order stationary point. In this paper we extend these results to convergence to an $\varepsilon$-second-order stationary point ($\varepsilon$-SOSP), which is to the best of our knowledge the first result of this type. In addition, we show that, when the stochastic gradient is not Lipschitz, compressed SGD with RandomK compressor converges to an $\varepsilon$-SOSP with the same number of iterations as uncompressed SGD [Jin et al.,2021] (JACM), while improving the total communication by a factor of $\tilde \Theta(\sqrt{d} \varepsilon^{-3/4})$, where $d$ is the dimension of the optimization problem. We present additional results for the cases when the compressor is arbitrary and when the stochastic gradient is Lipschitz.
Overlapping Spaces for Compact Graph Representations

Various non-trivial spaces are becoming popular for embedding structured data such as graphs, texts, or images. Following spherical and hyperbolic spaces, more general product spaces have been proposed. However, searching for the best configuration of product space is a resource-intensive procedure, which reduces the practical applicability of the idea. We generalize the concept of product space and introduce an overlapping space that does not have the configuration search problem. The main idea is to allow subsets of coordinates to be shared between spaces of different types (Euclidean, hyperbolic, spherical). As a result, parameter optimization automatically learns the optimal configuration. Additionally, overlapping spaces allow for more compact representations since their geometry is more complex. Our experiments confirm that overlapping spaces outperform the competitors in graph embedding tasks. Here, we consider both distortion setup, where the aim is to preserve distances, and ranking setup, where the relative order should be preserved. The proposed method effectively solves the problem and outperforms the competitors in both settings. We also perform an empirical analysis in a realistic information retrieval task, where we compare all spaces by incorporating them into DSSM. In this case, the proposed overlapping space consistently achieves nearly optimal results without any configuration tuning. This allows for reducing training time, which can be significant in large-scale applications.
Quantum Advantage in Simulating Stochastic Processes

We investigate the problem of simulating classical stochastic processes through quantum dynamics and present three scenarios where memory or time quantum advantages arise. First, by introducing and analyzing a quantum version of the embeddability problem for stochastic matrices, we show that quantum memoryless dynamics can simulate classical processes that necessarily require memory. Second, by extending the notion of space-time cost of a stochastic process P to the quantum domain, we prove an advantage of the quantum cost of simulating P over the classical cost. Third, we demonstrate that the set of classical states accessible via Markovian master equations with quantum controls is larger than the set of those accessible with classical controls, leading, e.g., to a potential advantage in cooling protocols.