Reviewerly/exHarmony huggingface dataset:
Formal and Semantic Hierarchy of Turkish Transitive Verbs

In Turkish, transitivity is defined according to both syntactic and semantic criteria. However, the types of transitional verbs and sentences, determined by the semantic and syntactic criteria, often contradict each other. Sometimes, a verb or sentence which meets the semantic condition may not satisfy the syntactic or vice versa. There are two reasons cause this contradiction. The first is due to the reason for adherence of transitivity to strict syntactic qualification and not taking the specific encoding situation of prototype transitivity [NOM-ACC] into account or ignoring the frames out of valency-related verb-meaning codification. The second is the restraint of transitivity in prototype transitivity as a rigid semantic concept. However, transitivity is a gradual and multifactorial concept. Therefore, it is not possible to talk about a single transitivity, hence a single verb type and a single case frame in which transitivity is encoded. There are different degrees of transitivity, and they can be labeled in the range of high degree to low degree transitivity. The degrees of transitivity in Turkish are formally coded up to a point with different case frames. It has been investigated which phases of transitivity in Turkish and how far different degrees of transitivity can be coded with variable case frames in this work. Turkish transitive verb hierarchy and related classes are proposed based on semantic and syntactic parameters. The transitivity scale of Turkish and the semantic map of verb classes will be formed as the final stage of this study.
Usability Evaluation of Augmented Reality: A Neuro-Information-Systems Study

This study introduces an experimental paradigm for a usability test of emerging technologies in a management information system (MIS). The usability test included both subjective and objective evaluations. For the subjective evaluation, a usability questionnaire and a NASA-TLX scale were adopted. For the objective evaluation, methods of Neuro-Information-Systems (NeuroIS) were used. From a NeuroIS perspective, this study used mobile fNIRS and eye tracking glasses for multimodal measurements, which solved the problem of ecological validity of cognitive neuroscience tools used in real-world behavior experiments. This study used Augmented Reality (AR) integrated into the Internet of Things (IoT) as an experimental object. Comparing the differences in the neuroimaging data, the physiological data, the usability questionnaire, and the NASA-TLX scale data between the two information-search modes (AR versus a website), information search with AR had a higher efficiency and a lower cognitive load compared with information search with a website during the process of consumption decision-making. The usability experiment results demonstrate that AR, as an emerging technology in retail, can effectively enhance consumer experiences and increase their purchase intention. The experimental paradigm, combining both subjective and objective evaluations in this study, could be applied to a usability test for emerging technologies, such as augmented reality, virtual reality, artificial intelligence, wearable technology, robotics, and big data. It provides a practical experimental solution for the user experience in human-computer-interactions with the adoption of emerging technologies.
A SYSTEMATIC REVIEW ON FLEXIBILITY ENABLERS IN LOGISTICS SURVIVABILITY

The demand uncertainty particularly during the outbreak of COVID-19 and the increasing competition of market have imposed pressures on the logistics industry to embrace the technology innovation. The pandemic caused a huge impact on world economies and communities and many companies were struggling to survive in this changing environment, thus leaving a question to the resilience of industry. In this situation, flexibility plays the key role for the logistics industry to recover from the supply chain crisis and get prepared for future uncertainties. This paper aims to provide a comprehensive insight on the technology innovations which enable the function of flexibility in logistics through a systematic literature analysis of papers from 2017 to 2022. Bibliometric analysis and content analysis are the methods used to analyse the multiple sources from different databases. The main research gaps in this paper is the flexibility enablers available in the logistics field, their implications on the logistics operations in terms of survivability performance are analysed and the challenges for the adoption of these flexibility enablers to the logistics service providers (LSPs) are identified. The identified flexibility enablers are blockchain, automated guided vehicle (AGV), drones, big data, radio frequency identification (RFID), cyber-physical system (CPS), sensors, cloud computing, augmented reality (AR), unmanned aerial vehicle (UAV), internet of things (IoT), digital twin, artificial Intelligence (AI) and information and communications technology (ICT). An implication model is set up to show the relationship of these flexibility with the logistics. The barriers to the LSPs to adopt the flexibility enablers are categorised into economic barriers, managerial barriers and operational barriers. Overall, these could provide some suggestions to the LSPs when they are considering the technological transformation and assist technology developers in identifying commercial opportunities.
Application Performance Monitoring System Design Using Opentelemetry and Grafana Stack

The increasingly massive use of digital technology requires that the application architecture be designed to have high availability and reliability. This is because when an application cannot be accessed, it will cause no small loss to the organization. Therefore, the development and operation teams must be able to detect when their system is not working well. For that, we need a system that can monitor application performance. In this research, a system is developed to collect telemetry data, namely metrics and traces from an online donation backend application based on the REST API. OpenTelemetry produces telemetry as an open-source telemetry instrumentation tool. Then the telemetry data is collected by the OpenTelemetry Collector which is then stored on the backend of each telemetry. Metrics are sent to Prometheus and traces are sent to Jaeger. The data metrics collected are throughput, request latency, and error rate which are visualized using the Grafana dashboard. The test results show that the monitoring system can collect real-time metrics data with an average delay of 13,8 seconds. The system can also detect when an anomaly occurs in the app and sends notifications via Slack. In addition, the trace data collected can be used to simplify the debugging process when an error occurs in the application. However, the implementation of OpenTelemetry in a REST API-based backend application to monitor metrics and traces has a negative impact on the performance of the application itself, which can reduce the number of request throughput with an average decrease of 23.32% and increase request latency with an average increase of 22.80%.
Question Answering over Knowledge Graphs for Thai Retail Banking Products

Question Answering over Knowledge Graphs (KGQA) extracts the answer entity directly from the graph, given a natural language question, offering scalability to applications that need to readily provide information to the end users, such as chatbots. Nevertheless, KGQA specifically designed for Knowledge Graphs in Thai has not yet been well investigated. In this paper, we adapt multi-hop KGQA using Graph Embedding to handle Thai dataset while being able to extract answer entities that do not have explicit relation to the head node. We also construct a Thai Knowledge Graph with the ontology based on retail banking products. The model achieves a HITS @ 1 score of 80.8 on our annotated dataset. The results confirm that, aside from reaching multi-hop answers, using Graph Embedding in KGQA helps improve the overall score, especially in sparse Knowledge Graphs. Moreover, augmenting the training questions to include more entities in the graph can further help increase the performance.
TEACHERS’ READINESS TOWARD THE NEW PARADIGM OF ENGLISH LANGUAGE TEACHING: A NARRATIVE INQUIRY

The development of English Language Teaching grows rapidly. The pandemic of Covid-19 became the turning point for teachers' awareness of the need for changes in language teaching practices. The advance in technology also takes an important role in the current trends of Language teaching. A narrative inquiry research method was applied to investigate the teachers' perception of their readiness toward the paradigm shift of English Language Teaching. Eight English teachers from many areas in Indonesia such as Semarang, Lampung, Jombang, Surakarta, Yogyakarta, and Purwokerto participated in this research through in-depth interviews. Then, the data collected were classified into several points regarding the new paradigms of education. The teachers' views on the paradigm shift in Education and the current practice of English Language Teaching were presented in this paper. Finally, teachers as the spearhead of education are required to be lifelong learners who are creative, innovative, and adaptive to the rapid change in world trends.
Higher-Order DCA Attacks on White-Box Implementations with Masking and Shuffling Countermeasures

On white-box implementations, it has been proven that differential computation analysis (DCA) can recover secret keys without time-costly reverse engineering. At CHES 2021, Seker et al. combined linear and non-linear masking protections (SEL masking) to prevent sensitive variables from being predicted by DCA. At Eurocrypt 2021, Biryukov and Udovenko introduced a public dummy shuffling construction (BU shuffling) to protect sensitive functions. In this paper, we extend higher-order DCA (HO-DCA) to higher-degree context for exploiting the vulnerabilities against the state-of-the-art countermeasures. The data-dependency HO-DCA (DDHO-DCA), which is proposed at CHES 2020, is improved to successfully recover the correct key of SEL masking. In specific, our improved DDHO-DCA can also enhance the attack result of #100 which is the third winning challenge in WhibOx 2019. Since the XOR phase plays the same role as linear masking, we prove that a specific BU shuffling is vulnerable to HO-DCA attacks. Furthermore, we demonstrate that the combination of SEL masking and the specific BU shuffling still cannot defeat our higher-degree HO-DCA and improved DDHO-DCA attacks.
Convolutional Neural Networks Trained to Identify Words Provide a Good Account of Visual Form Priming Effects

Abstract A wide variety of orthographic coding schemes and models of visual word identification have been developed to account for masked priming data that provide a measure of orthographic similarity between letter strings. These models tend to include hand-coded orthographic representations with single unit coding for specific forms of knowledge (e.g., units coding for a letter in a given position or a letter sequence). Here we assess how well a range of these coding schemes and models account for the pattern of form priming effects taken from the Form Priming Project and compare these findings to results observed in with 11 standard deep neural network models (DNNs) developed in computer science. We find that deep convolutional networks perform as well or better than the coding schemes and word recognition models, whereas transformer networks did less well. The success of convolutional networks is remarkable as their architectures were not developed to support word recognition (they were designed to perform well on object recognition) and they classify pixel images of words (rather artificial encodings of letter strings). The findings add to the recent work of (Hannagan et al., 2021) suggesting that convolutional networks may capture key aspects of visual word identification.
Suppose You Had Blocks within a Notebook

Computational notebooks have been gaining prominence as a development environment suitable for non-experienced developers. However, it requires proficiency in writing syntactically and semantically correct code. In this article, we propose integrating a block-based approach into computational notebooks to prevent syntactical errors and ease the non-expert developers' adoption. Furthermore, we rely on two tools previously implemented (Bacatá and Kogi) to (i) create a computational notebook for Domain-Specific Languages and (ii) generate a block-based representation upon the language definition. Consequently, our approach does not exclusively focus on integrating a block-based environment into computational notebooks but on enabling the creation and integration of domain-specific block-based environments into notebooks. Future work concerns the evaluation of our proposal through a user study.
Recognizing elderly peoples by analyzing their walking pattern using body posture skeleton

The increasing age of the population has become a significant concern internationally. During the COVID-19 pandemic situation, it has been seen that the most sensitive and affected class of the population is the class of Elder’s. It is therefore necessary to track the movement and behavior of the old persons. This kind of monitoring could help them in providing assistance in their needy time. Our objective is to develop an approach to classify elderly people using skeleton data for their assistance. OpenPose algorithm is used here to detect human skeletons (joint positions) from the video sequences. OpenPose algorithm with a sliding window of size ‘N’ is used to achieve a real-time posture recognition framework. Posture features from each extracted skeleton are then used to build a classifier for recognizing elderly people. We also introduce here a new dataset that includes old person walk and young person walk video’s. The experimental outcomes reveal that the proposed method has achieved up to 98.45% training accuracy and 96.16% testing accuracy for deep feed-forward neural network (FFNN) classifier. This asserts the effectiveness of the approach.
Partial Parsing for Structured Editors

Creating structured editors, which maintain a valid syntax tree at all times rather than allowing to edit program text, is typically a time consuming task. Recent work has investigated the use of existing general-purpose language grammars as a basis for automatically generating structured editors, thus considerably reducing the effort required. However, in these generated editors, input occurs through menu and mouse-based interaction, rather than via keyboard entry that is familiar to most users.
Qubit seriation: Improving data-model alignment using spectral ordering

With the advent of quantum and quantum-inspired machine learning, adapting the structure of learning models to match the structure of target datasets has been shown to be crucial for obtaining high performance. Probabilistic models based on tensor networks (TNs) are prime candidates to benefit from data-dependent design considerations, owing to their bias towards correlations which are local with respect to the topology of the model. In this work, we use methods from spectral graph theory to search for optimal permutations of model sites which are adapted to the structure of an input dataset. Our method uses pairwise mutual information estimates from the target dataset to ensure that strongly correlated bits are placed closer to each other relative to the model's topology. We demonstrate the effectiveness of such preprocessing for probabilistic modeling tasks, finding substantial improvements in the performance of generative models based on matrix product states (MPS) across a variety of datasets. We also show how spectral embedding, a dimensionality reduction technique from spectral graph theory, can be used to gain further insights into the structure of datasets of interest.
MIGRATION MANAGEMENT IN EDUCATION: TRAINING OF THE HEADS OF EDUCATIONAL INSTITUTIONS AT A BORDER UNIVERSITY

Migration has actualised the problems in the education of migrants and refugees’ children. Therefore, training of the heads of educational institutions at the border territories is required for the migration management in educational institutions. The study presents a model of management training in a cross-border Federal university. The proposed model is based on the competence and personality-oriented approaches in education in Russia and abroad. The data for the study was collected through observation and semi-structured interviews with open-ended questions. The development of the model was supported by a set of developed educational programs for master's degree and additional professional courses. The effectiveness of scientific and methodological educational software has been experimentally proven during the in-depth interviews. The results of the study contribute to the development of heads and teachers training and curricula. These results are used in the continuing professional educational programs, development of managerial and teaching staff in the field of education, in pre-professional education, and various special courses and training programs for the students of pedagogical specialties. In conclusion, the study develops a vision for the further development of the system for training managerial and pedagogical personnel in education for migration management in education.
Mesin Absensi Face Recognition Berbasis Raspberry Pi

Mesin absensi sidik jari gagal merekap absensi dengan kondisi jari yang terluka, tergores, kulit jari terkelupas dan lainnya sehingga kehadiran tidak tercatat. Penelitian ini bertujuan membuat mesin absensi pengenalan wajah untuk meminimalisir kegagalan seperti pada mesin sidik jari. Penelitian ini merupakan pengembangan penelitian sebelumnya dengan sistem absensi Radio Frequency Identification (RFID) karena sistem absensi dengan RFID dapat dicurangi. Penggabungan sistem rekognisi wajah dan RFID pada sebuah mesin absensi dengan raspberry pi diharapkan dapat menimalisir kegagalan pada saat melakukan absensi. Seandainya terjadi kegagalan rekognisi wajah, pengguna dapat melakukan absensi dengan RFID. Absensi dengan RFID pada sistem ini hanya dapat dilakukan ketika terjadi kegagalan deteksi wajah. Untuk mengetahui persentase keberhasilan dan keakurasian mesin, setiap pengguna melakukan beberapa kali percobaan. Selanjutnya dicatat jumlah keberhasilan rekognisi wajah dan dihitung nilai akurasinya dengan metode perhitungan akurasi. Hasil percobaan identifikasi wajah, diperoleh keakurasian pengguna pertama sebesar 53 %, pengguna kedua 48 %, pengguna ketiga 45 % dan pengguna keempat 52 %. Mesin mampu memprediksi 4 gambar pengguna dengan 4 posisi wajah yang berbeda dengan waktu rata-rata proses identifikasi selama 7 detik. Kata kunci : Face recognition, mesin absensi, raspberry pi . The fingerprint attendance machine failed to record attendance with injured, scratched, peeled finger skin and others so that attendance was not recorded. This research is a development of previous research with Radio Frequency Identification (RFID) attendance system because attendance system with RFID can be rigged. The incorporation of a facial recognition system and RFID on an attendance machine with a raspberry pi is expected to minimize failures during attendance. If there is a facial recognition failure, the user can make attendance with RFID. Attendance with RFID in this system can only be done when there is a face detection failure. To find out the percentage of success and accuracy of the machine, each user performs several trials. Furthermore, the number of facial recognition successes was recorded and the accuracy value was calculated using the accuracy calculation method. The results of the face identification experiment showed that the accuracy of the first user was 53%, the second user was 48%, the third user was 45% and the fourth user was 52%. The machine is able to predict 4 user images with 4 different face positions with an average identification process time of 7 seconds. Keywords: Face recognition, attendance machine, raspberry pi.
Enabling cross-border trade in the face of regulatory barriers to data flow – the case of the blockchain-based service network

The adoption of digital technologies has transformed the way of international trade, and cross-border data flow has become an important enabler for trade growth across countries. Meanwhile, regulations that govern cross-border data flows for the sake of protecting producers, consumers, and the data sovereignty of nation states are increasing at both national and trans-national levels, which could put a brake on data flows across national borders. Insofar as data regulations are concerned in an increasingly regulatory landscape in the trans-national trade context, this chapter examines how regulatory and technical responses – blockchain-related technologies – can ensure compliance with cross-border rules and regulations for data flows. One of the most recent and profound initiatives – the blockchain-based service network (BSN) – that are seeking to address challenges given rise to by new regulatory systems of governance and establish a jurisdictionally compliant digital environment for streamlined cross-border trade was investigated. Built on the reflection on the limitations of the BSN approach associated with its architectural design for cross-border trade in accordance with national/trans-national regulatory requirements, this chapter proposes a BSN-based ‘hub-to-hub’ cross-border trade framework that incorporates Zero Knowledge Proofs into multi-party governance mechanisms to enable international trade in the face of increasingly regulatory barriers to cross-border data flow.
CE-BART: Cause-and-Effect BART for Visual Commonsense Generation

"A Picture is worth a thousand words". Given an image, humans are able to deduce various cause-and-effect captions of past, current, and future events beyond the image. The task of visual commonsense generation has the aim of generating three cause-and-effect captions for a given image: (1) what needed to happen before, (2) what is the current intent, and (3) what will happen after. However, this task is challenging for machines, owing to two limitations: existing approaches (1) directly utilize conventional vision-language transformers to learn relationships between input modalities and (2) ignore relations among target cause-and-effect captions, but consider each caption independently. Herein, we propose Cause-and-Effect BART (CE-BART), which is based on (1) a structured graph reasoner that captures intra- and inter-modality relationships among visual and textual representations and (2) a cause-and-effect generator that generates cause-and-effect captions by considering the causal relations among inferences. We demonstrate the validity of CE-BART on the VisualCOMET and AVSD benchmarks. CE-BART achieved SOTA performance on both benchmarks, while an extensive ablation study and qualitative analysis demonstrated the performance gain and improved interpretability.