Reviewerly/exHarmony huggingface dataset:
Analisis Pengaruh Angkatan Kerja, Angka Melek Huruf, Angka Putus Sekolah Dalam Disparitas Anggaran Pendidikan

The study aims to determine the effect of the labor force on the education budget, between the literacy rate on the education budget, and between the dropouts rate on the education budget. This research is a quantitative study with multiple linear regression analysis using the Eviews 11 application. The secondary data used is sourced from the Central Statistics Agency, the Ministry of Education and Culture Data Statistics, and the 2015-2019 Regional Education Balance. Partially, the results state that the labor force has a positive and significant effect on the education budget, the literacy rate has a positive and significant effect on the education budget, and the dropouts rate has a negative but not significant effect on the education budget. Furthermore, there is a significant simultaneous effect between the labor force, literacy rate, dropouts rate on education budget disparities.
Unmanned Aerial Vehicles Traffic Management Solution Using Crowd-sensing and Blockchain

Unmanned aerial vehicles (UAVs) are gaining immense attention due to their potential to revolutionize various businesses and industries. However, the adoption of UAV-assisted applications will strongly rely on the provision of reliable systems that allow managing UAV operations at high levels of safety and security. Recently, the concept of UAV traffic management (UTM) has been introduced to support safe, efficient, and fair access to low-altitude airspace for commercial UAVs. A UTM system identifies multiple cooperating parties with different roles and levels of authority to provide real-time services to airspace users. However, current UTM systems are centralized and lack a clear definition of protocols that govern a secure interaction between authorities, service providers, and end-users. The lack of such protocols renders the UTM system unscalable and prone to various cyber attacks. Another limitation of the currently proposed UTM architecture is the absence of an efficient mechanism to enforce airspace rules and regulations. To address this issue, we propose a decentralized UTM protocol that controls access to airspace while ensuring high levels of integrity, availability, and confidentiality of airspace operations. To achieve this, we exploit key features of the blockchain and smart contract technologies. In addition, we employ a mobile crowdsensing (MCS) mechanism to seamlessly enforce airspace rules and regulations that govern the UAV operations. The solution is implemented on top of the Etheruem platform and verified using four different smart contract verification tools. We also provided a security and cost analysis of our solution. For reproducibility, we made our implementation publicly available on Github
UNISON: Unpaired Cross-lingual Image Captioning

Image captioning has emerged as an interesting research field in recent years due to its broad application scenarios. The traditional paradigm of image captioning relies on paired image-caption datasets to train the model in a supervised manner. However, creating such paired datasets for every target language is prohibitively expensive, which hinders the extensibility of captioning technology and deprives a large part of the world population of its benefit. In this work, we present a novel unpaired cross-lingual method to generate image captions without relying on any caption corpus in the source or the target language. Specifically, our method consists of two phases: (i) a cross-lingual auto-encoding process, which utilizing a sentence parallel (bitext) corpus to learn the mapping from the source to the target language in the scene graph encoding space and decode sentences in the target language, and (ii) a cross-modal unsupervised feature mapping, which seeks to map the encoded scene graph features from image modality to language modality. We verify the effectiveness of our proposed method on the Chinese image caption generation task. The comparisons against several existing methods demonstrate the effectiveness of our approach.
Off-line English character recognition system

Purpose of the study: This paper aims to recognition of handwritten English characters in offline mode. It develops an efficient character recognition model avoiding large variations in handwriting by using better feature extraction techniques. &#x0D; Methodology: The samples of characters are preprocessed by applying a sequence of operations in succession like Thickening, Thresholding, Filtering, and Thinning. Efficient features like Gradient features and Zonal features have been extracted. Gradient features are helpful to find out stroke information in the character whereas Zonal features detail out local information in a more précised way. Hidden Markov Model is the classifier. &#x0D; Main Findings: Classification has been started with only a 5-state HMM model but it is observed that as the number of states of HMM model is increased, the corresponding recognition rate is also improved. Finally, with the 36 states HMM model we have got the expected result. This produces an overall average recognition rate of 92.6%. For the letters ‘A’ and ‘W’, the recognition rate is found to be very low, because of a lot of variations in writing style of these letters.&#x0D; Applications of this study: HMM is a flexible tool which is capable of absorbing variations in character images. The future works will be concentrated on improvement of recognition rate of such letters by finding some demarcating features and post processing. The proposed method can be well used in Natural Language Processing, Signature verification, Face recognition like other Pattern Recognition applications. &#x0D; Novelty/Originality of this study: Preprocessing uses Median filter which removes all stray marks in samples and hence avoids any possibility of false pixels. The combination of Gradient features and Zonal features leads to a recognition accuracy of 92.6% which may be used by researchers in any other domains for the purpose of classification. The application of HMM will motivate the readers to use it for better results of classification.
ANALYSIS OF THE OCCURRENCE OF EVACUATION SUPPORT FOR WHEELCHAIR USERS BY TSUNAMI EVACUATION DRILL OBSERVATIONS

The purpose of this study is to analyze of the occurrence of evacuation support for wheelchair users in order to enable more efficient wheelchair evacuation. A tsunami evacuation drill has been conducted for residents in Nakatosa Town, Kochi Prefecture, where tsunamis are expected due to the Nankai earthquake. In the evacuation drill, we measured the speed of evacuation of vulnerable people and observed behavior of the residents who supported their evacuation. As a result, factors to be reflected in evacuation drill and support behavior types for wheelchair evacuation were derived.
El MOOC, un entorno virtual para la resolución de problemas matemáticos

This study aims to analyze the way participants of a Massive Open Online Curse (MOOC) accomplish the processes of problem-solving when they use GeoGebra and interacts in forum discussions.The processes were analyzed according to: the construction of dynamic representations of the problems; the exploration of different properties and relationships between its elements; and, the use of strategies such as dragging objects, quantification of its attributes and locus identification.Virtual ethnography was the used method to analyze the asynchronous communication between participants in the forums.The problem-solving process started a conversation, that is, a network of interactions that led to the use of new solution strategies and the construction of new knowledge, where the participants were able to advance at their own pace and to break with geographical and temporary limitations.The interactions in the forums took place between people with different professional activities or academic degrees.
An Exploration Method of Useful Open Data for Application Ideation based on Data Structure Change after Combination of the Data

Open data is a data that is provided by public sector or social infrastructure companies like transportation service providers, and recently tried to be utilized in various applications. When plural open data are successfully used in combination for an application, its usefulness would certainly exist. However, it is a little difficult to straightly retrieve such combinable open data, and the difficulty brings a lot of trial process or go and back process in ideation and development of applications that use open data. In this paper, we propose an exploration method of useful open data for combinational use. In the method, an evaluation approach of the data for application ideation in advance to start application ideation is embedded. The method evaluates the combinability and the usefulness based on the structure change of consolidated data, especially in the amount change on its volume and attributes, also uniqueness of its cell values. The result of experiments applying the method to some actual municipal open data shows sufficient capability to distinguish the combinability and the usefulness of plural open data for application ideation.
PHENOMENON OF MAX FACTOR: MAKEUP FATHER AND HOLLYWOOD WIZARD

Today, the art of make-up occupies an important place in the structure of the fashion image: thematic, catwalk, festive, stage, entertainment. The meaning of the term makeup is well-known for everybody nowadays. It is a subset of cosmetics, which refers primarily to coloured products intended to alter the user’s appearance [1]. Beauty products business is huge, this is a whole empire with its own rules. This part of life is extremely important for every person, therefor there is a bunch of new beauty products and their advertising that we face every day.
VERBAL, NON-VERBAL, AND PARAVERBAL COMMUNICATION MEANS INTERACTION IN INSTAGRAM REELS

Nowadays, with the rapid development of technologies, information delivery in the form of ordinary linear text is not relevant. Therefore, experts from various fields present themselves and their activities with the help of multimodal messages that are posted on social networks. A multimodal message is a combination of verbal and non-verbal means, and according to Gunther Kress these means, or modes overlap and interact in order to create a complex meaning [2].
Real-Time Application for Detection of Raised Hands and Personal Identification by Deep Learning Algorithms for Camera Images

Students often raise their hands in school classes, and the teacher calls on a student among those who raise their hands. However, when many people raise their hands, counting the number of raised hands takes time. Moreover, there is also the problem of missing some people and being confused about whom to call on. This paper uses YOLO to detect people who raise their hands and identify individuals using a face recognition system. Here we use the zoom function of the PTZ camera to perform face recognition in a wide-area. In addition, we construct a prototype system to automatically calls on a person among those who raised their hands on Jetson AGX Xavier.
Sentence-Level Sentiment Analysis of Financial News Using Distributed Text Representations and Multi-Instance Learning

Researchers and financial professionals require robust computerized tools that allow users to rapidly operationalize and assess the semantic textual content in financial news. However, existing methods commonly work at the document-level while deeper insights into the actual structure and the sentiment of individual sentences remain blurred. As a result, investors are required to apply the utmost attention and detailed, domain-specific knowledge in order to assess the information on a fine-grained basis. To facilitate this manual process, this paper proposes the use of distributed text representations and multi-instance learning to transfer information from the document-level to the sentence-level. Compared to alternative approaches, this method features superior predictive performance while preserving context and interpretability. Our analysis of a manually-labeled dataset yields a predictive accuracy of up to 69.90%, exceeding the performance of alternative approaches by at least 3.80 percentage points. Accordingly, this study not only benefits investors with regard to their financial decision-making, but also helps companies to communicate their messages as intended.
Convex Relaxations of Convolutional Neural Nets

We propose convex relaxations for convolutional neural nets with one hidden layer where the output weights are fixed. For convex activation functions such as rectified linear units, the relaxations are convex second order cone programs which can be solved very efficiently. We prove that the relaxation recovers the global minimum under a planted model assumption, given sufficiently many training samples from a Gaussian distribution. We also identify a phase transition phenomenon in recovering the global minimum for the relaxation.
Impossibility and Uncertainty Theorems in AI Value Alignment (or why your AGI should not have a utility function)

Utility functions or their equivalents (value functions, objective functions, loss functions, reward functions, preference orderings) are a central tool in most current machine learning systems. These mechanisms for defining goals and guiding optimization run into practical and conceptual difficulty when there are independent, multi-dimensional objectives that need to be pursued simultaneously and cannot be reduced to each other. Ethicists have proved several impossibility theorems that stem from this origin; those results appear to show that there is no way of formally specifying what it means for an outcome to be good for a population without violating strong human ethical intuitions (in such cases, the objective function is a social welfare function). We argue that this is a practical problem for any machine learning system (such as medical decision support systems or autonomous weapons) or rigidly rule-based bureaucracy that will make high stakes decisions about human lives: such systems should not use objective functions in the strict mathematical sense. We explore the alternative of using uncertain objectives, represented for instance as partially ordered preferences, or as probability distributions over total orders. We show that previously known impossibility theorems can be transformed into uncertainty theorems in both of those settings, and prove lower bounds on how much uncertainty is implied by the impossibility results. We close by proposing two conjectures about the relationship between uncertainty in objectives and severe unintended consequences from AI systems.
Recognition of Aircraft Activities at Airports on Video Micro-Satellites: Methodology and Experimental Validation

The remote sensing satellite constellation based on micro-satellites is an important means to construct a global and all-sky earth observation system in the future. Therefore, realizing the recognition of aircraft activities on video micro-satellites is a key technology that needs to be solved urgently. In this paper, an efficient algorithm for aircraft activity recognition that can be deployed on video micro-satellites was proposed. First, aircraft detection was performed on the first incoming remote sensing image using a robust DCNN-based object detection model. Then, a multi-target tracking model incorporating geospatial information was built for aircraft tracking and activity recognition. The algorithm was deployed on an embedded AI computer which was a COTS component. The algorithm was verified using remote sensing videos from commercial micro-satellites. Experimental results show that the algorithm can process aircraft targets of different sizes, and is equally effective even with complex environmental backgrounds, lighting conditions, and various movements of the aircraft, such as turning, entering, and exiting. Based on aircraft tracking results and geospatial information, the motion speed of each aircraft can be obtained, and its activity can be divided into parking, taxiing, or flying. The scheme proposed in this paper has good application prospects in the realization of on-orbit event recognition in micro-satellites with limited computing and memory resources.
Spoiled for choice? Uncertainty facing options in translation

Uncertainty is inherent to translating. Texts tend to have many valid renditions, and the outcome of a translation can never be fully anticipated. This study investigates the relationship between uncertainty in translation and the range of options available to translation students. To do so, we applied Muñoz &amp; Cardona’s (2019) approach to task segmentation and pause analysis to study translation processes in 19 translation trainees working on the same source text with a key-logger. We identified 10 potential hesitation indicator types in the key-logged processes. Target texts were then scrutinized using choice network analysis (Campbell, 2000) in order to identify and quantify the choices made by the participants. The relationship between the quantified hesitation indicators and the number of options for each segment was explored, and no correlation was found between them. Participants did not always hesitate between all available options, their decisions did not always determine further ones in a linear way, and indecision sometimes remained after the end of the process. The results suggest that decision making processes in translation cannot be fruitfully approached as games with complete information, but they could be explained from a broader perspective that accounts for risk taking and incomplete information.
The Use of Mobile Assisted Language Learning (MALL) In English Teaching for Hospitality Vocational Students

&#x0D; &#x0D; &#x0D; &#x0D; The purpose of this study is to analyze the perception of vocational students about the perception of use (PU) of Mobile Assisted Language Learning (MALL), the second goal is to analyze the perception of ease of use (PEoU) regarding Mobile Assisted Language Learning (MALL). This study uses a quantitative approach. The purpose of this study was to determine the perception of the use of Mobile Assisted Language Learning (MALL) in learning English for vocational students at the Mataram Tourism College. 100 respondents were involved in this study. Respondents answered statements about perceived usefulness and ease of use of MALL. Responses to the questionnaire were measured on a 5-point Likert scale, from 5 (“strongly agree”) to 1 (“strongly disagree”). The results of this study indicate that respondents as learners of English as a second language show a positive attitude in terms of usability and ease of use in MALL. In addition, the role of MALL is believed to be able to improve the results and teaching and learning process of foreign language learning.&#x0D; &#x0D; &#x0D; &#x0D;
SAT-Based Proof Search in Intermediate Propositional Logics

Abstract We present a decision procedure for intermediate logics relying on a modular extension of the SAT-based prover $$\texttt {intuitR} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>intuitR</mml:mi></mml:math> for $$\mathrm {IPL} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>IPL</mml:mi></mml:math> (Intuitionistic Propositional Logic). Given an intermediate logic L and a formula $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>α</mml:mi></mml:math> , the procedure outputs either a Kripke countermodel for $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>α</mml:mi></mml:math> or the instances of the characteristic axioms of L that must be added to $$\mathrm {IPL} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>IPL</mml:mi></mml:math> in order to prove $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>α</mml:mi></mml:math> . The procedure exploits an incremental SAT-solver; during the computation, new clauses are learned and added to the solver.
Using Automated Reasoning Techniques for Enhancing the Efficiency and Security of (Ethereum) Smart Contracts

Abstract The use of the Ethereum blockchain platform [17] has experienced an enormous growth since its very first transaction back in 2015 and, along with it, the verification and optimization of the programs executed in the blockchain (known as Ethereum smart contracts ) have raised considerable interest within the research community.
Neural Graph Similarity Computation with Contrastive Learning

Computing the similarity between graphs is a longstanding and challenging problem with many real-world applications. Recent years have witnessed a rapid increase in neural-network-based methods, which project graphs into embedding space and devise end-to-end frameworks to learn to estimate graph similarity. Nevertheless, these solutions usually design complicated networks to capture the fine-grained interactions between graphs, and hence have low efficiency. Additionally, they rely on labeled data for training the neural networks and overlook the useful information hidden in the graphs themselves. To address the aforementioned issues, in this work, we put forward a contrastive neural graph similarity learning framework, Conga. Specifically, we utilize vanilla graph convolutional networks to generate the graph representations and capture the cross-graph interactions via a simple multilayer perceptron. We further devise an unsupervised contrastive loss to discriminate the graph embeddings and guide the training process by learning more expressive entity representations. Extensive experiment results on public datasets validate that our proposal has more robust performance and higher efficiency compared with state-of-the-art methods.
Analisis Penghargaan Finansial, Pertimbangan Pasar Kerja Dan Persepsi Standar Audit Terhadap Minat Mahasiswa Akuntansi Dalam Pemilihan Karir Menjadi Auditor

A career as an auditor is considered to have good prospects. However, the fact is, the number of accounting graduates each year is not proportional to the growth of auditors in Indonesia. This phenomenon can be a big enough opportunity for graduate accounting students to have a career in the field of auditors. This study aims to determine the effect of financial rewards, labor market considerations, work environment and perceptions of auditing standards on the interest of accounting students in choosing a career as an auditor. This type of research includes quantitative research using primary data obtained from distributing questionnaires. The population in this study were Accounting students of ITB AAS Indonesia with a sample of 86 students determined by purposive sampling technique. The analytical method in this study uses multiple regression analysis statistics. The results of this study are the financial rewards variable and the perception of audit standards have a significant effect on the interest of accounting students in choosing a career to become auditors, while the labor market considerations variable have no significant effect on the interest of accounting students in choosing a career as an auditor.
Penguatan Keterampilan Guru dalam Pemanfaatan GeoGebra sebagai Media Pembelajaran Program Linear

To support students facing rapid technological development, teachers must provide real experience in the learning process. Linear programming materials that require visualization in their understanding can be taught by integrating GeoGebra software. This community service aims to strengthen teacher skills in using GeoGebra for linear programming learning media. Community service is carried out through training and mentoring with an active learning participant approach. The activities are carried out through three core stages: teacher training in mastering the GeoGebra interface, assistance in visualizing linear programming problems, and aid in solving linear programs. Although most of the participants stated that GeoGebra could help learn linear programming in the classroom, its implementation needs to consider various aspects, such as the preparation of learning tools and equipment, learning time, and student readiness.