Reviewerly/exHarmony huggingface dataset:
Negative Lexical Constraints in Neural Machine Translation

This paper explores negative lexical constraining in English to Czech neural machine translation. Negative lexical constraining is used to prohibit certain words or expressions in the translation produced by the neural translation model. We compared various methods based on modifying either the decoding process or the training data. The comparison was performed on two tasks: paraphrasing and feedback-based translation refinement. We also studied to which extent these methods "evade" the constraints presented to the model (usually in the dictionary form) by generating a different surface form of a given constraint.We propose a way to mitigate the issue through training with stemmed negative constraints to counter the model's ability to induce a variety of the surface forms of a word that can result in bypassing the constraint. We demonstrate that our method improves the constraining, although the problem still persists in many cases.
KITLM: Domain-Specific Knowledge InTegration into Language Models for Question Answering

Large language models (LLMs) have demonstrated remarkable performance in a wide range of natural language tasks. However, as these models continue to grow in size, they face significant challenges in terms of computational costs. Additionally, LLMs often lack efficient domain-specific understanding, which is particularly crucial in specialized fields such as aviation and healthcare. To boost the domain-specific understanding, we propose, KITLM, a novel knowledge base integration approach into language model through relevant information infusion. By integrating pertinent knowledge, not only the performance of the language model is greatly enhanced, but the model size requirement is also significantly reduced while achieving comparable performance. Our proposed knowledge-infused model surpasses the performance of both GPT-3.5-turbo and the state-of-the-art knowledge infusion method, SKILL, achieving over 1.5 times improvement in exact match scores on the MetaQA. KITLM showed a similar performance boost in the aviation domain with AeroQA. The drastic performance improvement of KITLM over the existing methods can be attributed to the infusion of relevant knowledge while mitigating noise. In addition, we release two curated datasets to accelerate knowledge infusion research in specialized fields: a) AeroQA, a new benchmark dataset designed for multi-hop question-answering within the aviation domain, and b) Aviation Corpus, a dataset constructed from unstructured text extracted from the National Transportation Safety Board reports. Our research contributes to advancing the field of domain-specific language understanding and showcases the potential of knowledge infusion techniques in improving the performance of language models on question-answering.
Continuous Hamiltonian dynamics on digital quantum computers without discretization error

We introduce an algorithm to compute Hamiltonian dynamics on digital quantum computers that requires only a finite circuit depth to reach an arbitrary precision, i.e. achieves zero discretization error with finite depth. This finite number of gates comes at the cost of an attenuation of the measured expectation value by a known amplitude, requiring more shots per circuit. The gate count for simulation up to time $t$ is $O(t^2\mu^2)$ with $\mu$ the $1$-norm of the Hamiltonian, without dependence on the precision desired on the result, providing a significant improvement over previous algorithms. The only dependence in the norm makes it particularly adapted to non-sparse Hamiltonians. The algorithm generalizes to time-dependent Hamiltonians, appearing for example in adiabatic state preparation. These properties make it particularly suitable for present-day relatively noisy hardware that supports only circuits with moderate depth.
Bridging Trustworthiness and Open-World Learning: An Exploratory Neural Approach for Enhancing Interpretability, Generalization, and Robustness

As researchers strive to narrow the gap between machine intelligence and human through the development of artificial intelligence technologies, it is imperative that we recognize the critical importance of trustworthiness in open-world, which has become ubiquitous in all aspects of daily life for everyone. However, several challenges may create a crisis of trust in current artificial intelligence systems that need to be bridged: 1) Insufficient explanation of predictive results; 2) Inadequate generalization for learning models; 3) Poor adaptability to uncertain environments. Consequently, we explore a neural program to bridge trustworthiness and open-world learning, extending from single-modal to multi-modal scenarios for readers. 1) To enhance design-level interpretability, we first customize trustworthy networks with specific physical meanings; 2) We then design environmental well-being task-interfaces via flexible learning regularizers for improving the generalization of trustworthy learning; 3) We propose to increase the robustness of trustworthy learning by integrating open-world recognition losses with agent mechanisms. Eventually, we enhance various trustworthy properties through the establishment of design-level explainability, environmental well-being task-interfaces and open-world recognition programs. These designed open-world protocols are applicable across a wide range of surroundings, under open-world multimedia recognition scenarios with significant performance improvements observed.
Labeling without Seeing? Blind Annotation for Privacy-Preserving Entity Resolution

The entity resolution problem requires finding pairs across datasets that belong to different owners but refer to the same entity in the real world. To train and evaluate solutions (either rule-based or machine-learning-based) to the entity resolution problem, generating a ground truth dataset with entity pairs or clusters is needed. However, such a data annotation process involves humans as domain oracles to review the plaintext data for all candidate record pairs from different parties, which inevitably infringes the privacy of data owners, especially in privacy-sensitive cases like medical records. To the best of our knowledge, there is no prior work on privacy-preserving ground truth dataset generation, especially in the domain of entity resolution. We propose a novel blind annotation protocol based on homomorphic encryption that allows domain oracles to collaboratively label ground truths without sharing data in plaintext with other parties. In addition, we design a domain-specific easy-to-use language that hides the sophisticated underlying homomorphic encryption layer. Rigorous proof of the privacy guarantee is provided and our empirical experiments via an annotation simulator indicate the feasibility of our privacy-preserving protocol (f-measure on average achieves more than 90\% compared with the real ground truths).
FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels

LiDAR-based fully sparse architecture has garnered increasing attention. FSDv1 stands out as a representative work, achieving impressive efficacy and efficiency, albeit with intricate structures and handcrafted designs. In this paper, we present FSDv2, an evolution that aims to simplify the previous FSDv1 while eliminating the inductive bias introduced by its handcrafted instance-level representation, thus promoting better general applicability. To this end, we introduce the concept of \textbf{virtual voxels}, which takes over the clustering-based instance segmentation in FSDv1. Virtual voxels not only address the notorious issue of the Center Feature Missing problem in fully sparse detectors but also endow the framework with a more elegant and streamlined approach. Consequently, we develop a suite of components to complement the virtual voxel concept, including a virtual voxel encoder, a virtual voxel mixer, and a virtual voxel assignment strategy. Through empirical validation, we demonstrate that the virtual voxel mechanism is functionally similar to the handcrafted clustering in FSDv1 while being more general. We conduct experiments on three large-scale datasets: Waymo Open Dataset, Argoverse 2 dataset, and nuScenes dataset. Our results showcase state-of-the-art performance on all three datasets, highlighting the superiority of FSDv2 in long-range scenarios and its general applicability to achieve competitive performance across diverse scenarios. Moreover, we provide comprehensive experimental analysis to elucidate the workings of FSDv2. To foster reproducibility and further research, we have open-sourced FSDv2 at https://github.com/tusen-ai/SST.
From Prompt Injections to SQL Injection Attacks: How Protected is Your LLM-Integrated Web Application?

Large Language Models (LLMs) have found widespread applications in various domains, including web applications, where they facilitate human interaction via chatbots with natural language interfaces. Internally, aided by an LLM-integration middleware such as Langchain, user prompts are translated into SQL queries used by the LLM to provide meaningful responses to users. However, unsanitized user prompts can lead to SQL injection attacks, potentially compromising the security of the database. Despite the growing interest in prompt injection vulnerabilities targeting LLMs, the specific risks of generating SQL injection attacks through prompt injections have not been extensively studied. In this paper, we present a comprehensive examination of prompt-to-SQL (P$_2$SQL) injections targeting web applications based on the Langchain framework. Using Langchain as our case study, we characterize P$_2$SQL injections, exploring their variants and impact on application security through multiple concrete examples. Furthermore, we evaluate 7 state-of-the-art LLMs, demonstrating the pervasiveness of P$_2$SQL attacks across language models. Our findings indicate that LLM-integrated applications based on Langchain are highly susceptible to P$_2$SQL injection attacks, warranting the adoption of robust defenses. To counter these attacks, we propose four effective defense techniques that can be integrated as extensions to the Langchain framework. We validate the defenses through an experimental evaluation with a real-world use case application.
INCREASING LEARNING RESULTS THROUGH MODIFICATION OF LEARNING MEDIA IN CLASS 2 STUDENTS OF MADRASA ALIYAH NEGERI AMBON

This research is a classroom action research (CAR) which is carried out in two cycles and each cycle consists of 2 meetings with the research subjects of grade 2 students with a total of 20 students. The data sources come from students, teachers, and researchers. The data collection technique uses tests and observations. Data validation uses data triangulation techniques. Data analysis used qualitative descriptive analysis techniques using percentage techniques. Need back style as follows . in pre-cycle I, the percentage of completeness was 73.2%. 10 students had not completed while in the second cycle, 20 students had completed 79%. Based on the above analysis it can be concluded; that the use of learning modification tools can improve back-to-back shooting learning outcomes in grade 2 Man Ambon students in the 2021/2022 academic year
Development of Entrepreneurship Education Program Using Metaverse: Targeted at specialized high school students

The purpose of this study is to develop a program suitable for specialized high school students by operating an entrepreneurship education program to enhance the creativity of specialized high school students using the metaverse. In this study, research data were collected through the participation observation, interviews with students, FGI, and questionnaires. The learners who participated in the Entrepreneurship Education Program responded that they did not know why they had to study Entrepreneurship. However, the learners acquired new concepts and information each time the session progressed, and each team was able to come up with an idea to solve the problem in a creative way and implement a solution on the metaverse. In the process of implementing solutions after discovering problems, the method of using the metaverse provided an opportunity to understand the entrepreneurship program. Learners responded that it was difficult for the class to understand the concept of Entrepreneurship, but they recognized the whole program positively and said that participating in this class would help them in their social life. However, in the course of this research, it was judged that there is a need to develop and supplement teaching and learning methods that allow students of specialized high schools to understand entrepreneurship more easily. To do so, it is necessary to seek teaching methods based on an in-depth understanding of the school culture of specialized high schools and methods of rapport formation.
Qualitative Reasoning about 2D Cardinal Directions using Answer Set Programming

We introduce a formal framework (called NCDC-ASP) for representing and reasoning about cardinal directions between extended spatial objects on a plane, using Answer Set Programming (ASP). NCDC-ASP preserves the meaning of cardinal directional relations as in Cardinal Directional Calculus (CDC), and provides solutions to all consistency checking problems in CDC under various conditions (i.e., for a complete/incomplete set of basic/disjunctive CDC constraints over connected/disconnected spatial objects). In particular, NCDC-ASP models a discretized version of the consistency checking problem in ASP, over a finite grid (rather than a plane), where we provide new lower bounds on the grid size to guarantee that it correctly characterizes solutions for the consistency checking in CDC. In addition, NCDC-ASP has the following two novelties important for applications. NCDC-ASP introduces default CDC constraints to represent and reason about background or commonsense knowledge that involves default qualitative directional relations (e.g., "the ice cream truck is by default to the north of the playground" or "the keyboard is normally placed in front of the monitor"). NCDC-ASP introduces inferred CDC constraints to allow inference of missing CDC relations and to provide them as explanations. We illustrate the uses and usefulness of NCDC-ASP with interesting scenarios from the real-world. We design and develop a variety of benchmark instances, and comprehensively evaluate NCDC-ASP from the perspectives of computational efficiency.
Implementasi Media Big Book Dalam Mengembalikan Semangat Belajar Siswa Pasca Pandemi Pada Mata Pelajaran Akidah Akhlak Kelas VII DI MTs Tarbiyatul Islam Soko

It is important to restore learning during the Covid-19 pandemic to reduce learning losses. To restore students' enthusiasm for learning after the pandemic, teachers must choose the right media to make students excited and also increase the focus of learning. One of the methods used to generate enthusiasm and increase student focus is to use new learning media, namely big book media.The researcher used descriptive qualitative method. Data were obtained through unstructured and in-depth interviews, observation, and documentation. This study uses data analysis techniques in the form of data reduction, data presentation, drawing conclusions and checking the validity of the data.The results showed that students' enthusiasm for learning after the pandemic decreased. This is caused by several things, namely online learning is too long, not interested in learning methods and not using any media when learning. Meanwhile, the teacher's efforts to restore students' enthusiasm for learning after the pandemic is to combine the lecture method with big book media. Finally, learning becomes more effective, this can be seen from the increase in student literacy and the increase in students' creativity in imagining.&#x0D; Keywords : Implementasi, big book media, ethusiasm for learning, post-pandemic
Explainable AI and Interpretable Model for Insurance Premium Prediction

Traditional machine learning metrics (TMLMs) are quite useful for the current research work precision, recall, accuracy, MSE and RMSE.Not enough for a practitioner to be confident about the performance and dependability of innovative interpretable model 85%-92%.We included in the prediction process, machine learning models (MLMs) with greater than 99% accuracy with a sensitivity of 95%-98% and specifically in the database.We need to explain the model to domain specialists through the MLMs.Human-understandable explanations in addition to ML professionals must establish trust in the prediction of our model.This is achieved by creating a model-independent, locally accurate explanation set that makes it better than the primary model.As we know that human interaction with machine learning systems on this model's interpretability is more crucial.For supporting set validations in model selection insurance premium prediction.In this study, we proposed the use of the (LIME and SHAP) approach to understand research properly and explain a model developed using random forest regression to predict insurance premiums.The SHAP algorithm's drawback, as seen in our experiments, is its lengthy computing timeto produce the findings, it must compute every possible combination.In addition, the experiments conducted were intended to focus on the model's interpretability and explain its ability using LIME and SHAP, not the insurance premium charge prediction.Three experiments were conducted through experiment, one was to interpret the random forest regression model using LIME techniques.In experiment 2, we used the SHAP technique to interpret the model insurance premium prediction (IPP).
The Implementation of KWL Chart Strategy to Improve Worldwide EFL Learners’ Reading Comprehension Skills

There is a dire need for worldwide second language educators to sustainably invoke more interactive, favorable, and meaningful reading learning enterprises in which EFL learners are strongly willing to channel more effortful actions to hone their reading comprehension skills to the utmost development. KWL chart strategy can be one of the appropriate solutions to fulfill this reading learning demand by which EFL learners can activate their prior knowledge in harmony with the reading texts they are going to read, monitor their reading comprehension towards particular reading topics intensively, and profoundly evaluate their newly-acquired knowledge obtained through various passages. This present small-scale library study aimed to investigate the extent the KWL chart strategy can improve globalized EFL learners’ reading comprehension skills. 20 prior findings obtained from the KWL chart strategy in reading studies were thematically analyzed to yield more comprehensible and credible research results for the betterment of EFL reading learning enterprises nationwide. After analyzing those previous findings, two main research themes were generated namely: (1) KWL chart strategy transforms EFL learners into more spirited readers and (2) KWL chart strategy exceptionally escalates EFL learners’ reading proficiency level. In the end, it can be fairly concluded that the effective deployment of the KWL strategy not merely resulted in the significant enhancement of EFL learners’ reading comprehension skills but also their reading endeavor and knowledge. Keyword: KWL chart strategy, reading comprehension skills, EFL learners, library study, thematic analysis
A Bibliometric Analysis on Inventory Management: on a Time Horizon 2000-2022

Inventory management is an important process in which companies plan, organize and control the goods produced, based on techniques and tools such as demand analysis and production planning and control.This article presents a bibliometric analysis based on inventory management.The proposed methodology is based on the information obtained from Scopus databases, raises and provides answers to six (6) questions, through a descriptive, thematic, collaborative and interrelated analysis, using computer supports such as VosViewers and Bibliometrix -Biblioshiny.The findings identified a positive trend in the volume of annual publications, highlighting the interest of the scientific and academic community in this area of knowledge.The results also allowed the identification of relevant topics grouped by cluster and the future research perspective, oriented to the solution of problems framed in modeling, stochastic process analysis, application of metaheristical algorithms and product design.
Design and Application of a Moral Education Evaluation System Based on Big Data

Moral education in universities is an important component of higher education, which is related to the cultivation of higher talents. Traditional moral education evaluation mainly uses manual scoring, which is inefficient and subjective. Therefore, this article proposes a student moral education evaluation system based on big data. This system draws on other educational evaluation systems and takes into account the characteristics of moral education evaluation. This article provides a detailed explanation of the design scheme of the system and tests its functions. Result finding that when the number of users of the system increases, the system can still run stably and respond accordingly. During this process, the time consumption did not increase significantly, and the performance continued to remain stable. Moreover, the response time of this system was compared with other moral evaluation systems and the results show that it is significantly higher than other evaluation systems. The quality evaluation system for college students’ moral education can improve the efficiency of moral education evaluation, reduce office costs, and further improve the informationization level of moral education work.
Students' Perceptions of Information Security in Online Platforms

In academic institutions, e-learning platforms are widely used to manage learning contents, assessments, and student communication and act as a primary system for all learning needs.During Covid-19, online learning was the only medium of instruction used to teach students due to public health concerns.The world noticed the increased activity of identity theft, individual and institutional content theft, and ransom attacks in many educational institutions since most students were using digital technologies to learn, and information, including personal and intellectual content, was being shared online.E-learning platform providers often prioritize speedy delivery and deployment of learning solutions, which may result in inadequate attention to the security and privacy of personal information.The study aims to identify college students' perceptions of information security regarding online learning platforms.The global COVID-19 pandemic accelerated online learning methods, making it more critical for institutions to pay more attention to cybersecurity and ransom attacks.The results from this study will facilitate an understanding of students' confidence in the learning platforms and their level of knowledge related to information security while using the internet.The result of this study can help e-learning platform providers with insight into perceptions of their solutions, which ultimately can help them to create safer and more secure solutions that protect the integrity of information shared.