Reviewerly/exHarmony huggingface dataset:
Automating Public Announcement Logic with Relativized Common Knowledge as a Fragment of HOL in LogiKEy

A shallow semantical embedding for public announcement logic with relativized common knowledge is presented. This embedding enables the first-time automation of this logic with off-the-shelf theorem provers for classical higher-order logic. It is demonstrated (i) how meta-theoretical studies can be automated this way, and (ii) how non-trivial reasoning in the target logic (public announcement logic), required e.g. to obtain a convincing encoding and automation of the wise men puzzle, can be realized. Key to the presented semantical embedding is that evaluation domains are modeled explicitly and treated as an additional parameter in the encodings of the constituents of the embedded target logic; in previous related works, e.g. on the embedding of normal modal logics, evaluation domains were implicitly shared between meta-logic and target logic. The work presented in this article constitutes an important addition to the pluralist LogiKEy knowledge engineering methodology, which enables experimentation with logics and their combinations, with general and domain knowledge, and with concrete use cases -- all at the same time.
Information-Theoretic Characterization of the Generalization Error for Iterative Semi-Supervised Learning

Using information-theoretic principles, we consider the generalization error (gen-error) of iterative semi-supervised learning (SSL) algorithms that iteratively generate pseudo-labels for a large amount of unlabelled data to progressively refine the model parameters. In contrast to most previous works that {\em bound} the gen-error, we provide an {\em exact} expression for the gen-error and particularize it to the binary Gaussian mixture model. Our theoretical results suggest that when the class conditional variances are not too large, the gen-error decreases with the number of iterations, but quickly saturates. On the flip side, if the class conditional variances (and so amount of overlap between the classes) are large, the gen-error increases with the number of iterations. To mitigate this undesirable effect, we show that regularization can reduce the gen-error. The theoretical results are corroborated by extensive experiments on the MNIST and CIFAR datasets in which we notice that for easy-to-distinguish classes, the gen-error improves after several pseudo-labelling iterations, but saturates afterwards, and for more difficult-to-distinguish classes, regularization improves the generalization performance.
Self-Supervised Learning from Semantically Imprecise Data

Learning from imprecise labels such as "animal" or "bird", but making precise predictions like "snow bunting" at inference time is an important capability for any classifier when expertly labeled training data is scarce. Contributions by volunteers or results of web crawling lack precision in this manner, but are still valuable. And crucially, these weakly labeled examples are available in larger quantities for lower cost than high-quality bespoke training data. CHILLAX, a recently proposed method to tackle this task, leverages a hierarchical classifier to learn from imprecise labels. However, it has two major limitations. First, it does not learn from examples labeled as the root of the hierarchy, e.g., "object". Second, an extrapolation of annotations to precise labels is only performed at test time, where confident extrapolations could be already used as training data. In this work, we extend CHILLAX with a self-supervised scheme using constrained semantic extrapolation to generate pseudo-labels. This addresses the second concern, which in turn solves the first problem, enabling an even weaker supervision requirement than CHILLAX. We evaluate our approach empirically, showing that our method allows for a consistent accuracy improvement of 0.84 to 1.19 percent points over CHILLAX and is suitable as a drop-in replacement without any negative consequences such as longer training times.
The Effect of Buginese Language Transfer on Studentsâ€™ English Pronunciation: A Case Study at SMAN 4 Barru

Indonesia is a country consists of various cultures and possesses hundreds of native language. Therefore, in the process of l2 acquisition, the impact of L1 on English articulation certainly is seen as a tough obstacle for the Indonesian EFL learners. In SLA, it is known as language transfer. Buginese language as one of the native language existed in South Sulawesi also gave positive and negative transfer towards English pronunciation. It was proven through a qualitative case study employed towards 20 students from XI IPA 2 at SMAN 4 Barru. To obtain the data, several methods were undergone such as questionnaires, studentsâ€™ recording, interview and observation. The results of the study showed that Buginese language gave major negative transfer towards vowels /É™/ and /Ã¦/, diphthongs /ÉªÉ™/, /eÉ™/, /ÊŠÉ™/ and /É™ÊŠ/, consonants /p/, /f/, /Å‹/ and /n/, and also clusters skr/, /spl/ (initial), /sk/, and /bl/. Moreover, this language gave minor negative transfer towards long vowels /i:/, /É‘:/, /É”:/, and /u:/ and vowels /É’/, also consonants /Ê¤/, /Ê’/, /z/, /v/, /Ã°/, /Î¸/, /Ê§/, and /Êƒ/. It did not give any transfer towards diphthongs /eÉª/ (initial), /aÉª/ (initial and final) and /aÊŠ/. Besides that, Buginese language also gave minor positive transfer towards; vowels /ÊŒ/, /Éª/, /e/, /ÊŠ/ and /É”/, diphthongs /É”Éª/, /eÉª/ (middle and final), and /aÉª/ (middle), and also consonants /b/, /d/, /g/, /h/, /k/, /l/, /m/, /r/, /s/, /t/, /w/ and /y/. Last, the role of the teacher in improving studentsâ€™ pronunciation was considered lack and need to be improved.
Attention model for articulatory features detection

Articulatory distinctive features, as well as phonetic transcription, play important role in speech-related tasks: computer-assisted pronunciation training, text-to-speech conversion (TTS), studying speech production mechanisms, speech recognition for low-resourced languages. End-to-end approaches to speech-related tasks got a lot of traction in recent years. We apply Listen, Attend and Spell~(LAS)~\cite{Chan-LAS2016} architecture to phones recognition on a small small training set, like TIMIT~\cite{TIMIT-1992}. Also, we introduce a novel decoding technique that allows to train manners and places of articulation detectors end-to-end using attention models. We also explore joint phones recognition and articulatory features detection in multitask learning setting.
Unsupervised Anomalous Trajectory Detection for Crowded Scenes

We present an improved clustering based, unsupervised anomalous trajectory detection algorithm for crowded scenes. The proposed work is based on four major steps, namely, extraction of trajectories from crowded scene video, extraction of several features from these trajectories, independent mean-shift clustering and anomaly detection. First, the trajectories of all moving objects in a crowd are extracted using a multi feature video object tracker. These trajectories are then transformed into a set of feature spaces. Mean shift clustering is applied on these feature matrices to obtain distinct clusters, while a Shannon Entropy based anomaly detector identifies corresponding anomalies. In the final step, a voting mechanism identifies the trajectories that exhibit anomalous characteristics. The algorithm is tested on crowd scene videos from datasets. The videos represent various possible crowd scenes with different motion patterns and the method performs well to detect the expected anomalous trajectories from the scene.
Ensemble of Similar Differential Evolution Algorithms With Independent Option on Operators for Individuals

&lt;p&gt;A differential evolution algorithm&lt;/p&gt;
Code-switching in EFL classrooms: factors influencing teachers to use code-switching and its types used in the classrooms

Among myriad prior studies on code-switching, little has been done on the factors influencing teachers to use code-switching and their classroom practices. The current research was aimed at investigating the motives of EFL teachers applying code-switching and the sorts of code-switching used by the EFL teachers in the classroom. This study used a qualitative case study as a research design by conducting in-depth interviews and observations as a data collection process. The obtained data were analyzed through coding, i.e., finding the similarities of the data and theming. The research participants were two EFL teachers at a private university in Yogyakarta Indonesia who have code-switching experience when teaching. The research found five reasons teachers used code-switching in EFL teaching and learning: discussing specific topics, making teaching and learning more practical, managing the classroom, building social relationship, and encouraging students' active participation. In addition, the observation found three code-switching types used by EFL teachers in their classrooms. The study implies that while code-switching offers some benefits, teachers should use code-switching at a minimum rate to keep students with maximum exposure to the English language.
Vulnerability Scanning

&lt;p&gt;The commercial value of web applications has significantly increased in recent years. They progressed from simple information-sharing platforms to more sophisticated business applications. Web-based apps, unlike most other technologies, are always accessible from anywhere in the world. This makes them ideal targets for malevolent cyberattacks. Scanners can identify and mitigate an organization's vulnerabilities. However, without a thorough awareness of a system's weaknesses, it would be difficult to undertake effective network defense in order to keep intruders out in the real world. As a result, vulnerability scanning is an important part of a cybersecurity curriculum's success. In this paper, we'll look at the present state of open-source vulnerability scanning technologies. A literature review of vulnerability assessment and reporting, vulnerability scanning, vulnerability scanning technologies, security vulnerabilities, system and application security, and malicious cyber-attacks reveals that a lot of work is being done in this area. This research provides an in-depth examination of vulnerability scanning technologies. In this paper, we covered two important topics: vulnerability scanning and reporting. Then, after identifying gaps in relevant practices and presenting chosen findings, we emphasize future directions and bring this study to a conclusion. The top open-source network vulnerability scanning tools are described in detail.&lt;/p&gt;
Secure quantum key distribution system by applying decoy states protocol

Secure quantum key distribution (QKD) promises a revolutionizing in optical applications such as encryption, and imaging. However, their implementation in real-world scenarios continues to be challenged. The goal of this work is to verify the presence of photon number splitting (PNS) attack in quantum cryptography system based on BB84 protocol and to obtain a maximum secure key length as possible. This was realized through randomly interleaving decoy states with mean photon numbers of 5.38, 1.588, and 0.48 between the signal states with mean photon numbers of 2.69, 0.794, and 0.24. Experiment results show that a maximum secure key length obtained from our system, which ignores eavesdropping cases, is 125 with 20% decoy states and 82 with 50% decoy states for mean photon number of 0.794 for signal states and 1.588 for decoy states.
Similarity measurement on digital mammogram classification

Breast cancer is one of the dominant causes of death in the world. Mammography is the standard for early detection of breast cancer. In examining mammograms, the overall parenchyma pattern of the left and right breast was placed side by side for symmetry assessed of left and right breast tissue by radiologist. Thus, in building computer-aided diagnosis (CAD) system for screening mammography, it is necessary to adapt the working procedure of the radiologist. In this study, 30 training images and 30 testing images from Kotabaru Oncology Clinic in Yogyakarta were used. The first step was to enhance the image quality with median filter and contrast limited adaptive histogram equalization (CLAHE). Then, feature extraction was processed by histogram-based and by gray level co-occurrence matrix (GLCM) based. Furthermore, the similarity measurement process was used to measure the difference value between selected features, i.e. angular second moment (ASM), inverse difference moment (IDM), contrast, entropy based GLCM, and energy, on the left and right mammograms. This process was intended to assess the symmetry of left and right mammograms as radiologists do in mammography screening. The obtained results of the classification between normal and abnormal images with backpropagation algorithm were accuracy of 0.933, sensitivity of 0.833, and specificity of 1.000.
Learning Strategies of ELT Students for IELTS Test Preparation to Meet English Learning Outcomes

The present research aimed to examine the learning strategies for the IELTS test preparation to meet the English learning outcomes (ELOs) used by students majoring in English Language Teaching (ELT) in Vietnamese universities, especially at the University of Foreign Language Studies – the University of Da Nang (UFLS-UD) and provided solutions to improve their IELTS test preparation efficiency. Data were collected from the questionnaires completed by 120 English language teaching (ELT) students, the in-depth interviews with 10 student informants, 5 experts in English teaching, and 10 students who have taken the IETLS test to meet the ELOs. The findings showed that most student informants chose the IELTS test to meet the ELOs requirements and did some preparation for the test. Regarding the learning strategies, practicing English pronunciation and communication skills were used for speaking, listening to the audio, watching English videos for listening, reading the IELTS writing samples, and learning grammar for writing. To overcome problems faced by informants, some suggested solutions were proposed, such as employing self-study strategies, practicing the IELTS sample test, attending IELTS courses, sitting for the IELTS mocks, and finding support from lecturers.
Müşteri Deneyimini İyileştirmede Yapay Zeka Uygulamaları: Çevrimiçi Perakende Firması Örneği

Artificial Intelligence, which can be basicly defined as systems or machines that can perform behaviors of human and human intelligence, is one of the concepts that have been flourishing and maintaining the popularity since its first appereance. Simultaneously with the development of this concept day by day, its execution areas have expanded and become significant part of life. There is no doubt, marketing is one of the areas where it expands and provides maximum benefit. With the use of Artificial Intelligence based applications in marketing, the customer experience is have been tried to be improved gradually. The aim of this study is to understand the point of view of companies, operating the online retail market, towards technology, to observe how they benefit from artificial intelligence-based applications, and to reveal how they try to enrich the customer experience with artificial intelligence-based applications. In this context, qualitative research methods and the personal interview technique were used in accordance with the main purpose of the study. It is aimed to explore detail about how is companies’ customer journey map for the customer while enriching the customer experience, and how they benefit from artificial intelligence, with the information provided by the experts who work in company while conducting a personal interview. For this reason, interviews were conducted with the regional sales manager and sales specialists of the online retail company.The limitations of the study are that the study only provides an information from the company, does not include whole sample in the market, and the methods applied by the company are open to change due to the dynamic structure of the market and technology. Results revealed that, suggesting some special opportunities to customer, taking decisions to accelerate the delivery of the order, using virtual assistant in customer communication and providing dashboard to see the order status are some of the areas where improvement has been made thanks to Artificial Intelligence. Thus, while the problems stem from human nature are minimized, customer experience is tried to be improvement. Keywords: Artificial Intelligence, Customer Experience, Customer Journey Map, Brand Experience, Differentiation
An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms

Abstract Machine learning strategy has changed the face of automated models by integrating themselves into many application domains. The spectrum of applications ranges over various domains, from atmospheric analysis to medical diagnosis. All these applications are design-sensitive, implying that the model's performance depends highly on the selected machine learning algorithm, training procedures, regularization methods, and most importantly, how the hyperparameters are tuned. With the advent of AutoML systems, all these processes including hyperparameter optimization can be automated, producing better performance and faster results. The ideal hyperparameter setting for machine learning models has a direct and significant impact on the model's performance. This paper studies different AutoML models and the hyperparameter techniques used by them for image classification problems. We also discuss some of the libraries for hyperparameter optimization and analyse how they work for different image classification problems. Moreover, the paper proposes a framework for hyperparameter optimization that combines Bayesian optimization and evolutionary algorithms. Experiments are carried out on image classification benchmark datasets to assess the performance of different optimization approaches including the proposed model.
Nonclassical correlations in subsystems of globally entangled quantum states

The relation between genuine multipartite entanglement in the pure state of a collection of N qubits and the nonclassical correlations in its two-qubit subsystems is studied. Quantum discord is used as the quantifier of nonclassical correlations in the subsystem while the generalised geometric measure (GGM) [Phys. Rev. A. 81, 012308 (2010)] is used to quantify global entanglement in the N-qubit state. While no definite discernible dependence between the two can be found for randomly generated global states, for those with additional structure like weighted graph states we find that local discord is indicative of global multipartite entanglement. Global states that admit efficient classical descriptions like stabilizer states furnish an exception in which despite multipartite entanglement, nonclassical correlation is absent in two qubit subsystems. We discuss these results in the context of mixed state quantum computation where nonclassical correlation is considered a candidate resource that enables exponential speedup over classical computers.
Quadtree Segmentation Network for Obstacle Avoidance in Monocular Navigation

Monocular depth map prediction has become in recent years a major research topic in computer vision. Especially with the emergence of self-supervised methods that have demonstrated that based on geometric properties, it is possible to obtain good results without human generated labels. But these methods are, for the moment, mainly oriented towards high resolution dense reconstruction and therefore neglect applications for robotic navigation. In this paper, we propose addressing this problem by developing a solution for navigation and obstacle avoidance. The method takes advantage of dense depth prediction to segment the view into a limited number of classes of interest for navigation. Four classes have been thus retained: one to segment the road and three to cluster obstacles by their distance (close, middle and far). As a result, the system is able to directly identify threats and areas where it can navigate. Furthermore, efficient information compression can be considered using a quadtree data structure derived from Quadtree Generating Network. Experiments conducted on the Kitti dataset have shown our proposed method can efficiently predict a quadtree segmentation directly from monocular input images. In addition, the approach tends to significantly reduce the amount of information to be predicted without any loss of accuracy.
Peran Supporting Agency Komite Sekolah dalam Pembiayaan Pendidikan

Weak allocation and procurement of funds in educational institutions, affects the educational process. The reason is that the budget is the main means of supporting and carrying out activities in education. The school committee is an organizational body that is held to support solutions, provide considerations, and complete the results of coordination through its role in communicating the institution by providing a positive image to the community and partners. One of the committee's roles as a supporting agency is to support procurement activities and the allocation of school budgets. The research approach used is descriptive qualitative, which then uses interview and observation data analysis techniques. The purpose of the study focused on social conditions in the role of the school committee's supporting agency in financing education. Research that has been carried out at Madrasah Ibtidaiyah Swasta Al-Ittihadiyah Al-Islamiyah (MIS AIAI) identified the following results: 1) the role of mobilizing the education financing budget with good management functions, 2) the committee carries out monitoring and evaluation to determine the level of effectiveness of the implementation of activities carried out, 3) Monitoring and evaluation is carried out by the committee (MIS AIAI) by holding committee meetings between the community and the school, and 4) The role in organizing the education budget is also pursued through visits to people who are in the capable category to support the education process.
Development of Science Digital Scrapbook as Authentic Assessment to Measure Learning Outcome of Junior High School Students

Learning during the COVID-19 pandemic has an impact on students’ learning outcomes. Observation results in SMP 2 Dawe Kudus showed that students’ learning outcomes in science subjects are still low. This school was chosen because of the location of the school in remote areas which resulted in not optimal use of digital devices for assessment in learning. The purpose of this study is to analyze the characteristics, feasibility, and profile of learning outcomes using digital science scrapbooks as authentic assessment to measure the learning outcomes of junior high school students on the theme of Additives and Addictive Substances. This study used a Research and Development (RD) design with ADDIE model. The science digital scrapbook as an authentic assessment developed in this study was declared feasible to use based on the assessment of material experts and evaluations. The reliability coefficient values obtained from small-scale tests for essay questions, attitude assessment instruments, and skills assessment instruments were obtained 0.62; 0.84; and 0.38. Students’ learning outcomes measured using a digital science scrapbook as an authentic assessment showed 83.3% of students were complete in the realm of knowledge, 100% completed in the realm of attitudes, and 100% completed in the realm of skills. The conclusion of this study is science digital scrapbook as an authentic assessment developed in this study was feasible to be used to measure the learning outcomes of junior high school students on additives and addictive substances concept