Reviewerly/exHarmony huggingface dataset:
Supporting secure dynamic alert zones using searchable encryption and graph embedding

Abstract Location-based alerts have gained increasing popularity in recent years, whether in the context of healthcare (e.g., COVID-19 contact tracing), marketing (e.g., location-based advertising), or public safety. However, serious privacy concerns arise when location data are used in clear in the process. Several solutions employ searchable encryption (SE) to achieve secure alerts directly on encrypted locations. While doing so preserves privacy, the performance overhead incurred is high. We focus on a prominent SE technique in the public-key setting–hidden vector encryption, and propose a graph embedding technique to encode location data in a way that significantly boosts the performance of processing on ciphertexts. We show that the optimal encoding is NP-hard, and we provide three heuristics that obtain significant performance gains: gray optimizer, multi-seed gray optimizer and scaled gray optimizer. Furthermore, we investigate the more challenging case of dynamic alert zones, where the area of interest changes over time. Our extensive experimental evaluation shows that our solutions can significantly improve computational overhead compared to existing baselines.
Trust, Sustainability and Learning@Scale

It is not overstating matters to say that humanity finds itself at an inflection point. The interlocking crises can feel overwhelming (ecological; political; financial; medical; technological; educational...), with recent leaps in AI closing the gap between human and machine cognition, raising many issues, including of course, educational questions. If a plausible diagnosis for our predicament as a species is "failure to learn", praxis questions assail us. What qualities should we cultivate most urgently, in which contexts? What literacies equip teachers and learners to engage critically with AI? How do we track progress meaningfully, at scale? And very pragmatically, when and why do people deem our tools trustworthy enough to trial, and if robust, embed sustainably into their teaching and learning practices?
Pseudolinear Kalman filter algorithms for single array bearings-only target motion analysis

In the research of target motion analysis, the target motion is usually approximated as a uniform linear target in most cases. However, in the actual environment, the target motion is usually a non-uniform linear motion and the target motion trajectory is mostly a curve, so it is particularly important to estimate the target state under this motion situation. In this paper, the motion analysis of low-speed and slow-turning targets is studied. Interested in low-speed and slow-maneuvering moving targets less than 6 knots, the deviation compensation algorithm of pseudo-linear Kalman filter in two-dimensional space and three-dimensional space is derived. Finally, the performance of the algorithm is verified by simulation, and the influence of measurement error and initial distance on the performance of the algorithm is studied.
Wh-AI-les: Exploring harmonized vision models robustness against distribution shift

The remarkable and increasing efficiency of learning-based vision strategies has induced strong paradigm shift in favor of neural architectures that are consequently finding their way into real-world applications with significant impact. Nevertheless, neural networks display a particular brittleness that can significantly hurt performance when deployed outside lab conditions, which is symptomatic of the independent and identically distributed (i.i.d.) hypothesis violation. This lack of consistency across domains is hurtful since it lessens reliability and trustworthiness and could have severe consequences in sensitive environments such as automated driving. Building on recent progresses in vision strategy harmonization and, through a flexible data generation process, this work explores the impact of alignment over robustness for a wide set of models as well as an encouraging theoretical configuration that uses aligned saliency features through a brute-force approach.
API 510 Section 5: <i>Inspection Practices</i>

Section 5 of API 510 is actually called Inspection, Examination and Pressure Testing Practices and contains many of the principles on which the BoK (and exam) are based. It’s 17 pages long but still relies on additional information included in Sections 6 and 7 to give the full picture of what API consider is involved in the inspection of pressure vessels. Look back at Figure 21.3. Taken together, these three sections make up 30 pages – more than 70% of the entire document and its source of exam questions.
PENGARUH GERAKAN LITERASI SEKOLAH TERHADAP PENINGKATAN MINAT BACA SISWA KELAS IV SD NEGERI 2 LANDUNGSARI

The aim of this research was to find the influence of Gerakan Literasi Sekolah (GLS) towards students’ reading interest in fourth grade of SDN 2 Landungsari. By applying 15 minutes reading before learning, this research was expected to increase students’ reading interest which was still categorized as low. The research method used was descriptive quantitative with associative approach. Fourth grader students of SDN 2 Landungsari was the population of this research consist of 17 students. This research was using population study which the subject was the whole population of fourth grader students of SDN 2 Landungsari. The data collected by distributing multiple choice and essai Pre-test and Post test questions. The data was analyzed by using ANOVA simple linear regression analysis in SPSS 25 for Windows. The result shows that there was a significant effect from Gerakan Literasi Sekolah (GLS) on increasing students’ of fourth grade reading interest at SDN 2 Landungsari, Dau, school year 2022/2023.
Toward frequency multiplexing for time-bin states

Time-bin entangled states are a promising paradigm for quantum communication between nodes of a quantum network. In addition, high-dimensional time-bin states are easy to generate and could offer significantly improved transmission fidelity compared to standard qubits. However, the overall rate of these transmissions is necessarily diminished because successive higher-dimensional time-bin states must be delayed such that they do not overlap in time. We propose to alleviate this concern by introducing an optical frequency shift on each time bin, taking advantage of quantum wavelength division multiplexing to greatly increase the rate of communication possible within a quantum channel. Here we report frequency shifts over a range of ∼ 2 nm (∼ 240 GHz) of telecom pulses in two time-bins separated by ∼ 250 ps, consistent with the requirements for multiplexing.
A Study on the Relationship between MSC and ADHD Adolescents in Improving Self-esteem

본 논문은 ADHD(Attention Deficit Hyperactivity Disorder) 청소년의 자존감 향상에 MSC(Mindful Self-Compassion)가 도움을 준다는 것을 밝힌 연구이다. ADHD는 주의력결핍 과잉행동장애를 의미하고 있으며, MSC는 자기 자신에 대한 사랑을 알 수 있는 자기공감명상 프로그램이다. ADHD는 과잉행동, 충동성, 주의력 결핍(부주의) 등의 핵심 증상이 있다. ADHD 청소년의 그 원인으로 ADHD는 전두엽의 실행기능에 관여하는 신경전달물진인 도파민(dopamine)의 불균형으로 비전형적인 도파민의 작용이다. 반면, MSC 프로그램은 하버드대학교 의과대학 임상심리전문가 크리스토퍼 거머 박사(Christopher K. Germer Ph.D)와 텍사스 대학교 심리학과 크리스틴 네프(Kristin Neff)교수가 자기 공감과 알아차림 명상을 결합한 8회기 명상치유프로그램이다. 이는 일상의 고통과 괴로움을 다루는 연습을 통하여 마음의 평온과 정서적 안정을 줄 수 있도록 돕는다. 본론에서는 우선 자존감 향상과 MSC의 이론적 배경을 알아보고, MSC 프로그램에 관하여 고찰하였다. 그리고 ADHD 청소년의 자아존중감 향상에 가능성이 있는지 논하였다. 이러한 연구를 통하여, ADHD 청소년은 주의력 결핍 과잉행동 및 충동성의 특징으로 인한 심리적 정서적 불안정감이 자아존중감(Self-esteem) 저하시킨다는 것을 확인하였다. 반면, MSC 명상은 코르티솔(Cortisol)의 수지가 낮아지고 심장 박동 수는 높아지면서 정서적 평정심으로 대처능력이 뛰어남으로 대인관계능력을 향상시키는 효과가 있다는 것을 입증하였다. MSC 프로그램을 통하여, ADHD청소년의 자아 존중감을 향상시키고 자기효능감을 키워 내적 성장 효과를 기대할 수 있게 되었다. This paper is a study that revealed that Mindful Self-Compassion (MSC) helps improve self-esteem of ADHD (Attention Deficit Hyperactivity Disorder) adolescents. ADHD refers to attention deficit hyperactivity disorder, and MSC is a self-sympathetic meditation program where you can learn about your love for yourself. ADHD has key symptoms such as hyperactivity, impulsivity, and attention deficit (attention). As a cause of ADHD adolescents, ADHD is an atypical dopamine action due to the imbalance of dopamine, a neurotransmitter involved in the executive function of the frontal lobe. On the other hand, the MSC program is an eighth-term meditation healing program that combines self-sympathy and awareness meditation by Dr. Christopher K. Germer, a clinical psychologist at Harvard Medical School, and Christine Neff, a professor of psychology at the University of Texas. This helps to provide peace of mind and emotional stability through practice dealing with daily pain and suffering. In this paper, first of all, the improvement of self-esteem and the theoretical background of MSC were investigated, and the MSC program was considered. In addition, it was discussed whether there is a possibility of improving self-esteem of ADHD adolescents. Through this study, it was confirmed that psychological and emotional instability due to attention deficit hyperactivity and impulsivity characteristics of ADHD adolescents decreased self-esteem. On the other hand, MSC meditation proved that it has the effect of improving interpersonal skills with excellent coping ability with emotional equilibrium as the balance of cortisol decreases and the heart rate increases. Through the MSC program, it is possible to expect internal growth effects by improving the self-esteem of ADHD adolescents and developing self-efficacy.
An Augmented Reality Book for Schematic English Reading Comprehension Development Skills in Grade 6 Students

Significant advancements are still required for augmented reality (AR). Augmented reality (AR) is a partly immersive experience in which users interact directly with a 3-D overlay onto the external reality in real time. This research aimed to examine the results of using an augmented reality (AR) book in improving students' schematic English reading comprehension skills, students' learning achievement, and students' knowledge retention, and to identify students' level of learning satisfaction. A total of 29 grade 6 students participated in the quasi-experimental study. The results revealed that students have significantly increased their English reading scores after exposing to the AR book at 0.01 significance level. There was non-significant difference between the first and second posttest scores as promoting knowledge retention. Students were satisfied in using the AR book at the highest level.
Data mining with Behaviour and Context-Based Optimal Navigation (BeaCON) for Optimal Navigation

We demonstrate the data mining process for the creation of optimal guidance information using BeaCON (Behaviour and Context-Based Optimal Navigation), a driving simulator and analytics framework. BeaCON implements a novel framework for optimal navigation by integrating user-behavioural and user-cognitive navigation processes. Integration of cognitive models along with route information and behavioural models enables BeaCON not only to identify the high cognitive load scenarios while driving but also the root cause of it. Analysis of high cognitive load scenarios and the root cause can be used to create the principles for the generation of optimal guidance information.
3D Prototype of an Interactive Adding Machine to Improve Basic Numerical Skills

3D printing in industries like medicine and education has led to major improvements in designing, developing, and printing useful models, including body prostheses and educational toys. The 3D printer's commercialization has risen thanks to more manufacturers, models, and reasonably priced equipment. As a result, prototypes and gadget creation have merged cutting-edge design techniques like gamification and electronic components, allowing accessibility and interactive and enjoyable devices. This paper describes the design, production, implementation, operation, codification, and printing of a 3D prototype of an interactive adding machine to improve basic numerical skills and help children and others with cognitive disabilities learn easier or lessen distractions. The prototype and all the necessary documentation are available for anyone to replicate. The paper also discusses difficulties encountered during the design process and proposes future improvements for the next version.
Training for Digital Forensics and Incident Response

The work of an digital forensics expert is far more extensive and varied today than it was just a few years ago. Especially after hacking attacks on organizations, experts in DFIR (Digital Forensics and Incident Response) come into play. In this paper, we present a learning platform that enables people to learn DFIR from scratch. To achieve this goal, the content of the learning platform was defined, evaluated and prepared with the help of experts from industry and government. For this purpose, expert interviews were conducted, which were subsequently evaluated. The results of these interviews were incorporated into initial scenarios that were implemented in individual modules on the learning platform Ilias, with a distinction being made between the basics and the main DFIR part. In the basic part, an introduction to IT forensics is offered, which is supplemented by further technical modules. This includes training in the use of the Linux operating system, which is frequently used in digital forensics, as well as the acquisition and analysis of RAM iand hard disk images. In the main part, the focus is to apply the learnings from the basic sections and to enhance them with incident related knowledge for DFIR projects, in which digital forensics experts gather and analyse evidence on various systems of the attacked organizations by searching and gathering so-called IoCs (Indicators of Compromise) from log files and other sources. Once the analysis part is complete, and all evidence has been collected, cleanup, recovery and restart of systems may take place, which is handled in the last section of the main training module.
CLIP-Based Search Engine for Retrieval of Label-Free Images Using a Text Query

In January 2021, OpenAI released the Contrastive Language-Image Pre-Training (CLIP) model, able to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the Internet. This model enables researchers to use natural language to reference learned visual concepts (or describe new ones), enabling the zero-shot transfer of the model to downstream tasks. One of the possible applications of CLIP is to look up images using natural language queries. This application is especially important in the context of the constantly growing amount of visual information created by people. This paper explores the application of the CLIP model to the image search problem. It proposes a practical and scalable implementation of the image search featuring the cache layer powered by SQLite 3 relational database management system (RDBMS) to enable performant repetitive image searches. The method allows efficient image retrieval using a text query when searching large image datasets. The method achieves 32.27% top-1 accuracy on the ImageNet-1k 1.28 million images train set and 55.15% top-1 accuracy on the CIFAR-100 10 thousand images test set. When applying the method, the image indexing time scales linearly with the number of images, and the image search time increases minorly. Indexing 50,000 images on Apple M1 Max CPU takes 19 minutes and 24 seconds while indexing 1,281,167 images on the same CPU takes 8 hours, 31 minutes, and 26 seconds. The query through 50,000 images on Apple M1 Max CPU executes in 4 seconds, while the same query through 1,281,167 images on the same CPU executes in 11 seconds.
Validation of virtual reality tools for unique aircraft interiors

A Virtual Reality (VR) application has been developed which supports engineers during product creation processes of unique aerospace interior. Ten trained engineers participated in a user study, which explored the differences in efficiency and user experience of two use cases within VR and conventional working methods. The study represented real day-to-day challenges and included two tasks: detecting the visibility of components for design reviews and cable routing for assembly processes. Five experts per task had to complete their assignment in both VR and their conventional working tools. In the visibility task participants had to decide whether the component of interest is visible or not in three different scenarios. For the cable task the experts were asked to route a specific cable for components in order to plan the amount of material that was needed in the final assembly. They had to estimate the cable length for four different cable routings and the outcome was compared to the optimal cable length for each given task. In both tasks the time until a decision has been measured. The results show that engineering processes can be supported by VR applications, which can help saving time in visibility testing and cable routing, as well as potentially saving resources by improving the accuracy of calculation for an ideal cable length. There are further potential benefits for users, as VR strains the workload less than conventional working methods. Furthermore, the study has shown that participants who were less experienced with VR technologies did significantly better with the VR application compared to conventional working methods during the visibility check.
Efficient Remote Entanglement Distribution in Quantum Networks: A Segment-based Method

Entanglement distribution between distant quantum nodes plays an essential role in realizing quantum networks’ capabilities. In addition to path selection, remote entanglement distribution involves two pivotal quantum operations, i.e., entanglement generation and entanglement swapping. The existing studies mainly adopt two methods, i.e., Tell-and-Generation (TAG) and Tell-and-Swapping (TAS), to manage these two quantum operations on a selected path. However, both methods fatally introduce redundant stop-and-wait processes, which are detrimental to the performance of remote entanglement distribution in terms of latency and fidelity. To achieve low-latency and high-fidelity entanglement distribution between far-off quantum nodes, we propose a segment-based method consisting of an entanglement generation algorithm and a segment design to diminish the unnecessary stop-and-wait processes. The entanglement generation algorithm adopts a concurrent design to establish entanglement links using the one-demand generation model, thus effectively reducing waiting time compared to hop-by-hop and parallel designs. The segment design is proposed to split a long-distance path into multiple short-haul segments with the similar ability to swap entanglement, and these segments build multi-hop entanglement connections in parallel. Extensive simulations show that the segment-based method significantly outperforms the existing methods, including TAG and TAS, in entanglement distribution latency and effectively mitigates fidelity attenuation.
Mobile money innovations and health performance in sub-Saharan Africa

This study assesses nexuses between mobile money innovations and health performance in terms of total life expectancy in 43 countries in Sub-Saharan Africa employing data for the period 2004-2018. Four mobile money innovation dynamics are proxied with registered mobile money agents and active mobile money agents. The empirical evidence is based on quantile regressions. The findings overwhelmingly show that mobile money innovations are relevant in improving health performance or total life expectancy exclusively in bottom quantiles of the conditional distribution of total life expectancy. In other words, countries with below-median levels of total life expectancy are more susceptible to benefit from mobile money innovations compared to countries with above-median levels of total life expectancy. It follows that common or general policy measures on the linkage between mobile money innovations and health performance are unlikely to succeed unless attendant policies are contingent on initial levels of health performance and hence, tailored differently across countries with various initial levels of health performance. More policy implications are discussed.
Online Query Expansion Hashing for Efficient Image Retrieval

Unsupervised hashing has the desirable advantages of label independence, high storage, and retrieval efficiency, which is suitable for scalable image retrieval. Most existing methods focus on enhancing the image hashing model training process at the offline stage. However, little attention has been paid to the query content analysis by them at the online retrieval stage. They still suffer from important query semantic shortages, and thus limit the online retrieval performance, which is the ultimate objective of the image retrieval system. In this paper, we propose an Online Query Expansion Hashing (OQEH) for efficient image retrieval, by adaptively enhancing the discriminative capability of query hash codes in an expansion manner at the online retrieval stage. Specifically, we first design a self-expansion network to learn semantically invariant feature representations from images and their visual augmentations. Then, we conduct neighborhood-expansion to search similar samples for each image from a query expansion set with the semantically invariant features and design a Transformer architecture to adaptively transfer the semantics of neighbor samples to their corresponding images. With the support of semantically invariant features, query expansion set, and adaptive semantic transfer, the representation capability of query hash codes can be enhanced at the online retrieval stage. Experimental results demonstrate that the proposed OQEH method achieves superior retrieval accuracy and comparable retrieval efficiency compared with the state-of-the-art methods. Particularly, on MS COCO dataset, OQEH can obtain about 6% performance improvement compared with the state-of-the-art results. The source codes of our method are available at: https://github.com/christinecui/OQEH.
Pseudo-Invertible Neural Networks

This work introduces novel architecture components and training procedures to create augmented neural networks with the ability to process data bidirectionally via an end-to-end approximate inverse. We develop Pseudo-Invertible Neural Network (PsI-NN) layers which function as drop-in replacements for corresponding convolutional and fully connected layers; by using these, existing architectures gain a pseudoinverse function which, with training, approximately reverses the forward function. For cases where learning both a task and its inverse are necessary or desirable, we show that PsI-NN enabled models match or exceed the quality of results generated by systems that use two separate models while drastically reducing system parameter count. We demonstrate this on two tasks: unpaired image translation and semi-supervised classification. In both, PsI-NN greatly reduces parameter count without any loss of output quality; in semi supervised image classification, PsI-NN improves classification performance beyond the baseline autoencoder method. Our approach creates pseudo-invertible versions of existing architectures, circumventing the stringent constraints required to create a true inverse while allowing a single neural network to learn a task in two directions simultaneously.