Reviewerly/exHarmony huggingface dataset:
Computational Complexity of Deciding Provability in Linear Logic and its Fragments

Linear logic was conceived in 1987 by Girard and, in contrast to classical logic, restricts the usage of the structural inference rules of weakening and contraction. With this, atoms of the logic are no longer interpreted as truth, but as information or resources. This interpretation makes linear logic a useful tool for formalisation in mathematics and computer science. Linear logic has, for example, found applications in proof theory, quantum logic, and the theory of programming languages. A central problem of the logic is the question whether a given list of formulas is provable with the calculus. In the research regarding the complexity of this problem, some results were achieved, but other questions are still open. To present these questions and give new perspectives, this thesis consists of three main parts which build on each other: We present the syntax, proof theory, and various approaches to a semantics for linear logic. Here already, we will meet some open research questions. We present the current state of the complexity-theoretic characterization of the most important fragments of linear logic. Here, further research problems are presented and it becomes apparent that until now, the results have all made use of different approaches. We prove an original complexity characterization of a fragment of the logic and present ideas for a new, structural approach to the examination of provability in linear logic.
Augmenting Depth Estimation With Geospatial Context

Modern cameras are equipped with a wide array of sensors that enable recording the geospatial context of an image. Taking advantage of this, we explore depth estimation under the assumption that the camera is geocalibrated, a problem we refer to as geo-enabled depth estimation. Our key insight is that if capture location is known, the corresponding overhead viewpoint offers a valuable resource for understanding the scale of the scene. We propose an end-to-end architecture for depth estimation that uses geospatial context to infer a synthetic ground-level depth map from a co-located overhead image, then fuses it inside of an encoder/decoder style segmentation network. To support evaluation of our methods, we extend a recently released dataset with overhead imagery and corresponding height maps. Results demonstrate that integrating geospatial context significantly reduces error compared to baselines, both at close ranges and when evaluating at much larger distances than existing benchmarks consider.
A study on the Effect of Telecollaboration through Social Networking on Reading Comprehension and Vocabulary Learning: The Case of Iranian Female High School EFL Learners

Technology enhanced instruction has been a paramount issue in recent decades leading to a shift in educational context. Moreover, telecollaboration as an electronically mediated intercultural communication has been introduced for the purpose of foreign language learning as well as the development of intercultural competence. Along this way, the present study was an attempt to probe on the effect of social networking through mobile assisted language learning (MALL) instruction (i.e., WhatsApp) on high school students’ reading comprehension and vocabulary learning. Furthermore, the difference between the effect of social networking through WhatsApp on the learners’ reading comprehension ability and vocabulary learning was compared. The design of the study was quasi-experimental with pre-test/post-test and the experimental and control groups. To do so, 60 female high school learners were homogenized using the Oxford Placement Test. Reading comprehension and vocabulary pre- and post-tests were administered among the learners to examine the effect of MALL instruction though WhatsApp on the learners’ reading comprehension and vocabulary learning. To analyze the data, independent samples t-test and MANOVA were used. Findings showed the outperformance of the experimental group over the control group in both reading comprehension and vocabulary learning. However, no statistically significant difference was found between the learners’ reading comprehension and vocabulary learning when they received social networking instruction through WhatsApp. Therefore, it can be concluded that using MALL can have beneficial effects on EFL learners’ reading comprehension and vocabulary.
Pedagogías emergentes para garantizar una educación inclusiva en tiempos de confinamiento

The use of technologies as a means of ensuring accessibility to learning and meeting the diversity of students (face-to-face or remote) has been the subject of numerous studies and educational experiences. As universities begin to mitigate the risks posed by confinement by Covid-19, online learning is becoming the only option viable. Responding to diversity is a pending issue under UNESCO's 2030 agenda at the Universidad Michoacana de San Nicolas de Hidalgo. The article briefly outlines the most relevant approaches on which this project is based and argues that its potential is constructed on the fusion of digital technologies and emerging pedagogies suitable for achieving an accessible curriculum for all students.
From Conventional Methods to Contemporary Neural Network Approaches: Financial Fraud Detection

This chapter provides insights on the underlying reasons to replace the conventional methods with contemporary approaches—the neural network-based machine learning methods—in financial fraud detection. To do this, we perform a systematic literature review on the evolution of financial fraud detection literature over the years from traditional techniques toward more advanced approaches such as modern machine learning methods like artificial neural networks. Additionally, this chapter provides concise chronological progress of the fraud literature and country-specific fraud-related regulations to draw a better framework and give the idea behind the corpus. Using the metadata in the existing literature, we show both benefits and costs of using machine learning-based methods in financial fraud detection. An accurate prediction using contemporary approaches is essential to minimize the potential costs of fraudulent financial activities for stakeholders, reduce the adverse effects of fraudsters’ and companies’ fraudulent activities, and increase trust in capital markets via continuous fraud risk assessment of companies.
SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution

In this paper, we tackle a fully unsupervised super-resolution problem, i.e., neither paired images nor ground truth HR images. We assume that low resolution (LR) images are relatively easy to collect compared to high resolution (HR) images. By allowing multiple LR images, we build a set of pseudo pairs by denoising and downsampling LR images and cast the original unsupervised problem into a supervised learning problem but in one level lower. Though this line of study is easy to think of and thus should have been investigated prior to any complicated unsupervised methods, surprisingly, there are currently none. Even more, we show that this simple method outperforms the state-of-the-art unsupervised method with a dramatically shorter latency at runtime, and significantly reduces the gap to the HR supervised models. We submitted our method in NTIRE 2020 super-resolution challenge and won 1st in PSNR, 2nd in SSIM, and 13th in LPIPS. This simple method should be used as the baseline to beat in the future, especially when multiple LR images are allowed during the training phase. However, even in the zero-shot condition, we argue that this method can serve as a useful baseline to see the gap between supervised and unsupervised frameworks.
Nigerian University Libraries’ Preparedness in the Provision of Electronic Books to Users:

This study examined the readiness and use of electronic books for information service provision in university libraries in Nigeria. The survey research design was adopted while a multi-stage sampling technique was used to select two universities from each of the six geopolitical zones based on electronic books' availability in the university libraries. A questionnaire was used as an instrument for data collection, while data collected were analysed with the use of descriptive statistics. Findings revealed that the level of institutional readiness is below 50%, while the majority of the respondents agreed that electronic books are useful for information service provision. The study recommended that universities should increase financial resources available to the university libraries in the area of ICT facilities acquisition among others.
MAN 1 Rokan Hilir, Data Akademik Analisis Strategi Untuk Meningkatkan Kualitas MAN 1 Rokan Hilir Dengan Business Intelligence

MAN 1 Rokan Hilir is an institution engaged in education located in Rokan Hilir Regency. Based on the results of questions and answers academic data in this school is still stored properly, academic data is still stored using the recording format in accordance with the type of data. However, the school experienced difficulties in knowing the academic development of students, so the school could not ascertain whether the operational funds incurred, available teaching staff and teaching and learning facilities available were able to provide a solution that was quite fitting to improve student achievement. Therefore a system was built that could assist the school in analyzing the progress of student achievement based on the allocation of funds, teaching staff and facilities provided by the school. This system was built by implementing business intelligence into the system with visualization in the form of column-charts, charts. designed using PHP and MySQL programming languages as its DBMS. This system is tested by using tenting black box testing, user acceptance test (UAT) and efficinecy so that it can find improvements desired by the user. Based on the user acceptance test (UAT) test that has been done shows that 88% of the system that has been built is acceptable to the user and can help the school in making decisions.
Block-Wise Image Compression Using Major and Minor Diagonal Pixels

The important role played by the data compression is in reducing the storage space required, and ensuring bandwidth utilization in efficient manner and low transmission delays. Image compression is also a part of data compression. Images contain important data that is to be transmitted or stored. The compression must not degrade the quality of the image, and should be acceptable. This paper presents simple, low complexity image compression algorithm. Algorithm scans the image in a raster san manner in terms of 4X4 blocks. Compression is achieved by the diagonally situated pixels and indices their surrounding pixels of the block. The mages produced by proposed algorithm are high fidelity images at low PSNR values. The results are comparable with standard algorithm results. The reconstructed image quality is better than JPEG images at same PSNR.
MODEL KOOPERATIF WHO AM I MENGGUNAKAN ZOOM MENDORONG LITERASI ANAK DALAM PEMBELAJARAN DARING

Abstract: Technological advances can help learning during the Covid-19 pandemic. The development of internet-based technology provides zoom video conference applications. The use of zoom helps teachers and students in virtual face-to-face interactions separated by space and distance. The Who Am I cooperative model uses zoom meetings to stimulate student literacy in online learning so as to increase literacy during the Covid 19 pandemic. Literacy stimulation is an inner drive (without coercion) to carry out reading and writing activities. Who Am I model that uses game tournaments as a learning-while-playing strategy. Learning while playing is able to provide a good stimulus for literacy activities. The characteristics of the Who Am I learning model provide a stimulus to literacy. These characteristics are: 1) learning in groups, 2) learning while playing, 3) fun learning, 4) learning vocabulary mastery.&#x0D;
Deep Network Approximation With Accuracy Independent of Number of Neurons

This paper develops simple feed-forward neural networks that achieve theuniversal approximation property for all continuous functions with a fixedfinite number of neurons. These neural networks are simple because they aredesigned with a simple and computable continuous activation function $\sigma$leveraging a triangular-wave function and a softsign function. We prove that$\sigma$-activated networks with width $36d(2d+1)$ and depth $11$ canapproximate any continuous function on a $d$-dimensioanl hypercube within anarbitrarily small error. Hence, for supervised learning and its relatedregression problems, the hypothesis space generated by these networks with asize not smaller than $36d(2d+1)\times 11$ is dense in the space of continuousfunctions. Furthermore, classification functions arising from image and signalclassification are in the hypothesis space generated by $\sigma$-activatednetworks with width $36d(2d+1)$ and depth $12$, when there exist pairwisedisjoint closed bounded subsets of $\mathbb{R}^d$ such that the samples of thesame class are located in the same subset.
Breaking a Masked AES Implementation Using a Deep Learning-based Attack

The block cipher AES (Advanced Encryption Standard) is a cryptographic algorithm used to guarantee the confidentiality of a message. A masked implementation of AES is often used to increase resistance against SCA (Side Channel Attacks). This paper presents some deep learning-based attacks for extracting AES secret keys embedded in cryptographic devices. The proposed attack methods represent new approaches to computing the secret key by applying the mask profiling techniques. The MLP (Multi-Layer Perceptron) and CNN (Convolutional Neural Network) deep learning models are developed to break the masked AES implementation. Our experimental results show the overwhelming advantages of the novel attack methods when targeting both unmasked and masked implementation of AES.
Quantization of Weights of Neural Networks with Negligible Decreasing of Prediction Accuracy

Quantization and compression of neural network parameters using the uniform scalar quantization is carried out in this paper. The attractiveness of the uniform scalar quantizer is reflected in a low complexity and relatively good performance, making it the most popular quantization model. We present a design approach for the memoryless Laplacian source with zero-mean and unit variance, which is based on iterative rule and uses the minimal mean-squared error distortion as a performance criterion. In addition, we derive closed-form expressions for SQNR (Signal to Quantization Noise Ratio) in a wide dynamic range of variance of input data. To show effectiveness on real data, the proposed quantizer is used to compress the weights of neural networks using bit rates from 9 to 16 bps (bits/sample) instead of standardly used 32 bps full precision bit rate. The impact of weights compression on the NN (neural network) performance is analyzed, indicating good matching with the theoretical results and showing negligible decreasing of the prediction accuracy of the NN even in the case of high variance-mismatch between the variance of NN weights and the variance used for the design of quantizer, if the value of the bit-rate is properly chosen according to the rule proposed in the paper.
Pengaruh Kecerdasan Linguistik dan Minat Belajar Terhadap Keterampilan Menyimak Berita

The purpose of this study was to determine 1) the effect of linguistic intelligence on news listening skills; 2) the effect of interest in learning on news listening skills; 3) the influence of linguistic intelligence and interest in learning on news listening skills. A research method is the expostfacto correlation method. The research population was all SMP IT Al-Anis Sukoharjo academic year 2020/2021. The research sample is some students of SMP IT Al-Anis Sukoharjo taken by random sampling. Techniques Data collection using documentation and questionnaires. The results are 1) the influence of linguistic intelligence on news listening skills is positive and significant. It means that if students have a high level of linguistic intelligence, they can improve their listening skills well; 2) the effect of interest in learning on news listening skills is positive and significant. It means that if students have a high interest in learning, the students' news listening skills are also good; 3) there is an influence of linguistic intelligence and students' interest in learning on news listening skills, which means that there has been a correlation between linguistic, intelligence, and interest in learning (independent variable) with news listening skills (dependent variable).
Perception of University Students toward the Effectiveness of E- Learning during Covid-19 Pandemic

The COVID-19 pandemic has transformed education systems throughout the planet from traditionalcourses to online learning. Aim: this study aimed to explore the perception of university studentstoward the effectiveness of E- Learning during Covid-19 pandemic. Design: An exploratorydescriptive research design was utilized to conduct this study. Subjects: by using stratified randomsampling technique eight hundred and seventy-five (857) fourth year students were selected. Tools.Data was collected using an electronic questionnaire that consists of four parts: part I: Sociodemographic characteristics of the students, Part II: perception of students towards E-learning viaMicrosoft Teams application, Part III: challenges facing the students and their suggestions toovercome them. Results: The rate of the student's agreement on the perceived usefulness,behavioral intention and perceived ease of use was (72.6%,64.7%,63.7%). The highest challengereported by the students was cost of the internet (66.5%) followed by unavailability of the internet athome (56.8%), absence of collaboration between the students in the web-based class (44.2%) andlimited technology skills of the students (40.8%).Conclusion. This study sheds the light on thechallenges and factors affecting the perception of the college students towards E-learning as a newlyadopted teaching strategy. So, it will assist to create an effective plan for E-learning successfulimplementation and view innovative technology as a good step to progress and evolution.Recommendations: use of offline downloadable learning materials would overcome the challengesrelated to the variable quality of internet access in the country.
Sentiment Analysis in Twitter for Macedonian

We present work on sentiment analysis in Twitter for Macedonian. As this is pioneering work for this combination of language and genre, we created suitable resources for training and evaluating a system for sentiment analysis of Macedonian tweets. In particular, we developed a corpus of tweets annotated with tweet-level sentiment polarity (positive, negative, and neutral), as well as with phrase-level sentiment, which we made freely available for research purposes. We further bootstrapped several large-scale sentiment lexicons for Macedonian, motivated by previous work for English. The impact of several different pre-processing steps as well as of various features is shown in experiments that represent the first attempt to build a system for sentiment analysis in Twitter for the morphologically rich Macedonian language. Overall, our experimental results show an F1-score of 92.16, which is very strong and is on par with the best results for English, which were achieved in recent SemEval competitions.
Breast Cancer Detection via Global and Local Features using Digital Histology Images

Globally, breast cancer (BC) is the prevailing cause of unusual deaths in women. Breast tumor (BT) is a primary symptom and may lead to BC. Digital histology (DH) image modality is a gold standard medical test for a definite diagnosis of BC. Traditionally, DH images are visually examined by two or more pathologists to come up with a consensus for authentic BC detection which may cause a high error rate. Therefore, researchers had developed automated BC detection models using a machine learning (ML) based approach. Thus, this study aims to develop a BC detection model through ten feature extraction methods which extract both local and global type features from publicly available breast histology dataset. The extracted features are sorted by their weights, which are computed by the neighborhood component analysis method. A feature selection algorithm is developed to find the minimum number of discriminating features, evaluated through seven heterogeneous traditional ML classifiers. The proposed ML-based BC detection model acquired 90% accuracy for the initial testing set using 51 Harris features. Whereas, for the extended testing set, only three Harris features is shown 93% accuracy. The proposed BC detection model can assist the doctor in giving a second opinion.
Bounds on stabilizer measurement circuits and obstructions to local implementations of quantum LDPC codes

In this work we establish lower bounds on the size of Clifford circuits that measure a family of commuting Pauli operators. Our bounds depend on the interplay between a pair of graphs: the Tanner graph of the set of measured Pauli operators, and the connectivity graph which represents the qubit connections required to implement the circuit. For local-expander quantum codes, which are promising for low-overhead quantum error correction, we prove that any syndrome extraction circuit implemented with local Clifford gates in a 2D square patch of $N$ qubits has depth at least $Ω(n/\sqrt{N})$ where $n$ is the code length. Then, we propose two families of quantum circuits saturating this bound. First, we construct 2D local syndrome extraction circuits for quantum LDPC codes with bounded depth using only $O(n^2)$ ancilla qubits. Second, we design a family of 2D local syndrome extraction circuits for hypergraph product codes using $O(n)$ ancilla qubits with depth $O(\sqrt{n})$. Finally, we use circuit noise simulations to compare the performance of a family of hypergraph product codes using this last family of 2D syndrome extraction circuits with a syndrome extraction circuit implemented with fully connected qubits. While there is a threshold of about $10^{-3}$ for a fully connected implementation, we observe no threshold for the 2D local implementation despite simulating error rates of as low as $10^{-6}$. This suggests that quantum LDPC codes are impractical with 2D local quantum hardware. We believe that our proof technique is of independent interest and could find other applications. Our bounds on circuit sizes are derived from a lower bound on the amount of correlations between two subsets of qubits of the circuit and an upper bound on the amount of correlations introduced by each circuit gate, which together provide a lower bound on the circuit size.