Reviewerly/exHarmony huggingface dataset:
Learning Multi-Step Manipulation Tasks from A Single Human Demonstration

Learning from human demonstrations has exhibited remarkable achievements in robot manipulation. However, the challenge remains to develop a robot system that matches human capabilities and data efficiency in learning and generalizability, particularly in complex, unstructured real-world scenarios. We propose a system that processes RGBD videos to translate human actions to robot primitives and identifies task-relevant key poses of objects using Grounded Segment Anything. We then address challenges for robots in replicating human actions, considering the human-robot differences in kinematics and collision geometry. To test the effectiveness of our system, we conducted experiments focusing on manual dishwashing. With a single human demonstration recorded in a mockup kitchen, the system achieved 50-100% success for each step and up to a 40% success rate for the whole task with different objects in a home kitchen. Videos are available at https://robot-dishwashing.github.io
Why Not Mitigate Vulnerabilities in Helm Charts?

[Context]: Containerization ensures the resilience of distributed applications by Kubernetes. Helm is a package manager for Kubernetes applications. A Helm package, namely "Chart'', is a set of pre-configured resources that one could quickly deploy a complex application. However, Helm broadens the attack surface of the distributed applications. [Objective]: This study aims to investigate the prevalence of fixable vulnerabilities, the factors related to the vulnerabilities, and current mitigation strategies in Helm Charts. [Method]: We conduct a mixed-methods study on 11,035 Helm Charts affected by 10,982 fixable vulnerabilities. We analyze the complexity of Charts and compare the distribution of vulnerabilities between official and unofficial Charts. Subsequently, we investigate vulnerability mitigation strategies from the Chart-associated repositories by a grounded theory. [Results]: Our findings highlight that the complexity of a Chart correlates with the number of vulnerabilities, and the official Charts do not contain fewer vulnerabilities compared to unofficial Charts. The 10,982 fixable vulnerabilities are at a median of high severity and can be easily exploited. In addition, we identify 11 vulnerability mitigation strategies in three categories. Due to the complexity of Charts, maintainers are required to investigate where a vulnerability impacts and how to mitigate it. The use of automated strategies is low as automation has limited capability(e.g., a higher number of false positives) in such complex Charts. [Conclusion]: There exists need for automation tools that assist maintainers in mitigating vulnerabilities to reduce manual effort. In addition, Chart maintainers lack incentives to mitigate vulnerabilities, given a lack of guidelines for mitigation responsibilities. Adopting a shared responsibility model in the Helm ecosystem would increase its security.
Harnessing Pre-trained Generalist Agents for Software Engineering Tasks

Nowadays, we are witnessing an increasing adoption of Artificial Intelligence (AI) to develop techniques aimed at improving the reliability, effectiveness, and overall quality of software systems. Deep reinforcement learning (DRL) has recently been successfully used for automation in complex tasks such as game testing and solving the job-shop scheduling problem. However, these specialized DRL agents, trained from scratch on specific tasks, suffer from a lack of generalizability to other tasks and they need substantial time to be developed and re-trained effectively. Recently, DRL researchers have begun to develop generalist agents, able to learn a policy from various environments and capable of achieving performances similar to or better than specialist agents in new tasks. In the Natural Language Processing or Computer Vision domain, these generalist agents are showing promising adaptation capabilities to never-before-seen tasks after a light fine-tuning phase and achieving high performance. This paper investigates the potential of generalist agents for solving SE tasks. Specifically, we conduct an empirical study aimed at assessing the performance of two generalist agents on two important SE tasks: the detection of bugs in games (for two games) and the minimization of makespan in a scheduling task, to solve the job-shop scheduling problem (for two instances). Our results show that the generalist agents outperform the specialist agents with very little effort for fine-tuning, achieving a 20% reduction of the makespan over specialized agent performance on task-based scheduling. In the context of game testing, some generalist agent configurations detect 85% more bugs than the specialist agents. Building on our analysis, we provide recommendations for researchers and practitioners looking to select generalist agents for SE tasks, to ensure that they perform effectively.
BEV-CV: Birds-Eye-View Transform for Cross-View Geo-Localisation

Cross-view image matching for geo-localisation is a challenging problem due to the significant visual difference between aerial and ground-level viewpoints. The method provides localisation capabilities from geo-referenced images, eliminating the need for external devices or costly equipment. This enhances the capacity of agents to autonomously determine their position, navigate, and operate effectively in environments where GPS signals are unavailable. Current research employs a variety of techniques to reduce the domain gap such as applying polar transforms to aerial images or synthesising between perspectives. However, these approaches generally rely on having a 360{\deg} field of view, limiting real-world feasibility. We propose BEV-CV, an approach which introduces two key novelties. Firstly we bring ground-level images into a semantic Birds-Eye-View before matching embeddings, allowing for direct comparison with aerial segmentation representations. Secondly, we introduce the use of a Normalised Temperature-scaled Cross Entropy Loss to the sub-field, achieving faster convergence than with the standard triplet loss. BEV-CV achieves state-of-the-art recall accuracies, improving feature extraction Top-1 rates by more than 300%, and Top-1% rates by approximately 150% for 70{\deg} crops, and for orientation-aware application we achieve a 35% Top-1 accuracy increase with 70{\deg} crops.
Towards Learning Geometric Eigen-Lengths Crucial for Fitting Tasks

Some extremely low-dimensional yet crucial geometric eigen-lengths often determine the success of some geometric tasks. For example, the height of an object is important to measure to check if it can fit between the shelves of a cabinet, while the width of a couch is crucial when trying to move it through a doorway. Humans have materialized such crucial geometric eigen-lengths in common sense since they are very useful in serving as succinct yet effective, highly interpretable, and universal object representations. However, it remains obscure and underexplored if learning systems can be equipped with similar capabilities of automatically discovering such key geometric quantities from doing tasks. In this work, we therefore for the first time formulate and propose a novel learning problem on this question and set up a benchmark suite including tasks, data, and evaluation metrics for studying the problem. We focus on a family of common fitting tasks as the testbed for the proposed learning problem. We explore potential solutions and demonstrate the feasibility of learning eigen-lengths from simply observing successful and failed fitting trials. We also attempt geometric grounding for more accurate eigen-length measurement and study the reusability of the learned eigen-lengths across multiple tasks. Our work marks the first exploratory step toward learning crucial geometric eigen-lengths and we hope it can inspire future research in tackling this important yet underexplored problem.
Preconditioning for a Variational Quantum Linear Solver

We apply preconditioning, widely used in classical solvers for linear systems $A\textbf{x}=\textbf{b}$, to the Variational Quantum Linear Solver. By utilizing Incomplete LU factorization as a preconditioner for linear equations formed by $128\times128$ random sparse matrices, we numerically demonstrate a notable reduction in the required ansatz depth, thereby clarifying that preconditioning is useful for quantum algorithms as well. This reduction in circuit depth is key to improving the efficiency and accuracy of Noisy Intermediate-Scale Quantum (NISQ) algorithms. Our findings suggest that combining classical computing techniques like preconditioning with quantum algorithms can significantly enhance their performance in NISQ algorithms.
Punctuation Matters! Stealthy Backdoor Attack for Language Models

Recent studies have pointed out that natural language processing (NLP) models are vulnerable to backdoor attacks. A backdoored model produces normal outputs on the clean samples while performing improperly on the texts with triggers that the adversary injects. However, previous studies on textual backdoor attack pay little attention to stealthiness. Moreover, some attack methods even cause grammatical issues or change the semantic meaning of the original texts. Therefore, they can easily be detected by humans or defense systems. In this paper, we propose a novel stealthy backdoor attack method against textual models, which is called \textbf{PuncAttack}. It leverages combinations of punctuation marks as the trigger and chooses proper locations strategically to replace them. Through extensive experiments, we demonstrate that the proposed method can effectively compromise multiple models in various tasks. Meanwhile, we conduct automatic evaluation and human inspection, which indicate the proposed method possesses good performance of stealthiness without bringing grammatical issues and altering the meaning of sentences.
Task-Disruptive Background Suppression for Few-Shot Segmentation

Few-shot segmentation aims to accurately segment novel target objects within query images using only a limited number of annotated support images. The recent works exploit support background as well as its foreground to precisely compute the dense correlations between query and support. However, they overlook the characteristics of the background that generally contains various types of objects. In this paper, we highlight this characteristic of background which can bring problematic cases as follows: (1) when the query and support backgrounds are dissimilar and (2) when objects in the support background are similar to the target object in the query. Without any consideration of the above cases, adopting the entire support background leads to a misprediction of the query foreground as background. To address this issue, we propose Task-disruptive Background Suppression (TBS), a module to suppress those disruptive support background features based on two spatial-wise scores: query-relevant and target-relevant scores. The former aims to mitigate the impact of unshared features solely existing in the support background, while the latter aims to reduce the influence of target-similar support background features. Based on these two scores, we define a query background relevant score that captures the similarity between the backgrounds of the query and the support, and utilize it to scale support background features to adaptively restrict the impact of disruptive support backgrounds. Our proposed method achieves state-of-the-art performance on PASCAL-5 and COCO-20 datasets on 1-shot segmentation. Our official code is available at github.com/SuhoPark0706/TBSNet.
Stronger resilience to disorder in 2D quantum walks than in 1D

We study the response of spreading behavior, of two-dimensional discrete-time quantum walks, to glassy disorder in the jump length. We consider different discrete probability distributions to mimic the disorder, and three types of coin operators, viz., Grover, Fourier, and Hadamard, to analyze the scale exponent of the disorder-averaged spreading. We find that the ballistic spreading of the clean walk is inhibited in presence of disorder, and the walk becomes sub-ballistic but remains super-diffusive. The resilience to disorder-induced inhibition is stronger in two-dimensional walks, for all the considered coin operations, in comparison to the same in one dimension. The quantum advantage of quantum walks is therefore more secure in two dimensions than in one.
EFFECTS OF LANGUAGE USE THROUGH AN INTEGRATED SKILLS INTERVENTION ON ENGLISH LANGUAGE LEARNERS' SPEAKING SKILLS: A FOCUS ON VOCABULARY AND GRAMMAR

This study examines the effects of language use through a thorough skills intervention on students' general level of speaking skills, focusing on vocabulary and grammar. A quasi-experimental investigation was conducted to fulfill the research objective. The total number of participants was selected by systematic random sampling and divided into control and experimental groups. During the 2022 academic year, 92 Shambu High School of 10th grade students participated in the study. Tests with oral questions were given before and after the intervention and the results were evaluated using SPSS. Independent samples t-test results showed that there was no significant difference between the pretest scores of the two groups on the oral presentation samples, either in general speaking ability or in the use of grammar and vocabulary. However, the general spoken language abilities of the two groups and their consumption of vocabulary and grammatical elements differed significantly in the post-test. According to the paired sample t-test, the experimental group improved their basic speaking skills more than the control group. Therefore, it can be argued that teaching English through an integrated skills intervention had a better impact on students' speaking abilities than a more conventional method of teaching speaking skills. The study found that integrated skill strategies should be used in EFL lecture sessions to help students develop their interpersonal skills
EXAMINING VARIABILITY: A GENRE ANALYSIS OF ENGLISH LANGUAGE EDUCATION STUDENTS' THESIS ABSTRACTS

The abstract of the thesis is the initial segment that researchers scrutinize to ascertain its pertinence to their interests. Researchers must possess an inherent comprehension of the section's rhetorical progression, structure, and arrangement. This study investigates the genre of thesis abstract sections produced by undergraduate students from Aceh who are majoring in English Education. The writers are interested in examining the writing style of Acehnese undergraduate students when composing their thesis abstracts. The dataset utilized in this study comprises 50 specifically chosen thesis abstracts from 5 universities in Aceh. The data analysis utilizes Hyland's five-move structural model of the abstract section, as well as the status of the motions as described by Huttner. The findings indicate that Hyland's five-move structure model is a useful tool for examining the move structure of the abstract section. Furthermore, there is no notable variation in the move structures across all datasets. The moves that occur most frequently are move 2, move 3, and move 4, making them the mandatory moves. The results could assist English as a Foreign Language (EFL) instructors in instructing and preparing their students to write the abstract section. Additionally, it could enhance the effectiveness of EFL students in composing well-organized abstract sections and improve researchers' understanding and sensitivity while crafting their thesis abstracts
PHRASAL VERBS IN RICK RIORDAN’S NOVEL THE HAMMER OF THOR: A SYNTACTIC ANALYSIS

The study about syntax is quite significant because some people may write sentences yet they have no idea the proper structure of grammar. In this study, the writer focuses on syntax which has something to do with phrasal verbs discussed in tree diagram. This study uses The Hammer of Thor novel by Rick Riordan as the data source. The objective of the study is to find out kinds of phrasal verbs in The Hammer of Thor novel and the meaning of those phrasal verbs. This study used qualitative method. There are 33 phrasal verbs found in the novel as the source of data. Those particles which form the phrasal verbs are out, up, for, down, about, back, on, off, into, away, through, from, and over. Based on the data found, the phrasal verbs are different from verb phrases in written form. Furthermore, the tree diagram gives more understanding how phrasal verbs are formed and how to use them syntactically.
Probabilistic Approach to Gramian Inversion

The meaning of Gramian and its role in the joint inversion are explained using a probabilistic approach to inverse problem solution. We introduce a Hilbert space of random variables with the metric defined by the covariance matrix between random variables, representing different model parameters. Using this Hilbert space, the probabilistic Gramian is represented as the determinant of the covariance matrix between the different model parameters and their attributes. By minimizing the probabilistic Gramian, we enforce the linear correlation between various inverse models produced by joint inversion. Several gradient-type techniques are considered for solving this minimization problem.
Sistem Informasi Anggaran Pendapatan Dan Belanja Sekolah Pada Smk N 1 Kendal Dengan Metode Bottom-Up Berbasis Web

Currently SMK N 1 Kendal is still recording school income and expenditure budgets manually, but the more stringent accountability reporting on the use of the budget, it is necessary to have an information technology system that is efficient, useful and makes it easier for the treasurer to process and report the revenue and budget budgets. school shopping.&#x0D; This study aims to implement a school income and expenditure budget information system application with a web-based bottom-up method so that the process of recording school income and expenditure data is more effective. The application that will be used is developed using Macromedia Dreamweaver 8 as a coding tool and MySQL as the database.&#x0D; With the implementation of this school income and expenditure budget information system, errors and loss of income and expenditure data will be minimized at SMK N 1 Kendal, besides the time required to obtain the required data will be much faster and more accurate. The process of reporting receipts and expenses automatically aims to make it easier for users. In addition, the time required to complete the school income and expenditure budget report will be much shorter.&#x0D;
Novel data encryption scheme and its application to secure multi-party communication

Recent advances in cellular mobile telecommunication field have dramatically facilitated the multi-party collaborative work in social networks. However, the privacy issues exposed by insecure network channels and semi-trusted service providers, such as underlying data analysis and mining, have also gradually aroused public concerns. In this context, a novel Multi-Party Privacy Data Encryption (MP-PDE) scheme built upon the deep learning framework is proposed. In this scheme, a four-dimensional autonomous chaotic system is initially leveraged to configurate the key-controlled cipher streams. Under the guidance of a multi-objective optimization function, the proposed encryption network manipulates the multi party private data into a cipher image with the statistical pseudo-randomness. At the recipient side, distinct participants can decrypt the matching data availing their own licensing key from the identical cipher image. Furthermore, the encryption and decryption networks are equivalent except for their network parameters. Finally, numerous experiments are conducted to verify the effectiveness and security of the proposed scheme.&#xD
ARTIFICIAL INTELLIGENCE AND NATIONAL SECURITY: PERSPECTIVE OF THE GLOBAL SOUTH

More than six decades since its inception, Artificial Intelligence (AI) stands at the cusp of a transformative shift. The global perspective on AI has evolved optimistically, as it increasingly permeates every facet of human life. AI is revolutionizing national security strategies and capabilities worldwide, but its impact on the Global South remains a topic of growing significance and concern. Every nation actively seeks to bolster internal security through AI-driven initiatives, including surveillance, cyber security, and autonomous technologies. This review paper delves into AI's role in analyzing vast datasets, uncovering patterns, and identifying security threats and challenges focusing specifically on the Global South. It considers the potential advantages AI offers in enhancing national security capabilities while addressing concerns surrounding its integration. Drawing from existing literature, it presents a comprehensive analysis of AI's prospective future in the cyber and national security domains within these nations. Ultimately, this paper aims to answer whether AI serves as a facilitator in strengthening internal security or poses unforeseen challenges and raises the importance of capacity-building, technology transfer, and international cooperation. It provides valuable insights into the evolving landscape of AI in the context of national security in the Global South.
ARTIFICIAL INTELLIGENCE AND ITS ROLE IN THE DEVELOPMENT OF THE FUTURE OF ARBITRATION

AI has proven to be a powerful tool in various fields, and its integration into arbitration is highly anticipated due to its potential to improve efficiency, accuracy, and objectivity. This paper aimed to analyze how AI can help streamline arbitration, reduce costs, ensure faster dispute resolution, and improve accessibility. By using machine learning algorithms and natural language processing techniques, AI systems can analyze large volumes of legal text, extract relevant information, recognize patterns, and predict case outcomes. In addition, AI-driven chatbots could provide users with instant support and assistance in navigating the complex arbitration process. However, ethical considerations such as privacy and bias must be taken into account to ensure that AI does not compromise fairness or jeopardize confidentiality in arbitration proceedings. The article concludes with an examination of the transformative impact that artificial intelligence will have on the future of arbitration and emphasizes the needfor continued research and collaboration to realize its full potential while preserving the integrity of arbitration practice.
Securing academic certificate verification with blockchain-based algorithmic rules

In this current digital world, academic certificates are digitalized by universities and distributed to students. It is difficult to maintain these certificates and verification of these certificates is difficult and time-consuming. The proposed system will solve this problem of storage of certificates and verification/validation of academic certificates. The certificates are first converted to digital certificates and algorithmic rules are designed to convert certificates to hash. This hash is in blockchain storage. Then verification process can be done by comparing the hashes. Using blockchain technology we propose a scalable and secure model for verifying academic certificates. It has been found that 95% of utilization of CPU and response time in average is 30%. The analysis of the vulnerability of this blockchain decentralized application (Dapp) to security threats and its possible countermeasures is identified.