Miracle-dz/UniFAR-FTUs huggingface dataset:
Current developments of robotic hip exoskeleton toward sensing, decision, and actuation: A review

Abstract The aging population is now a global challenge, and impaired walking ability is a common feature in the elderly. In addition, some occupations such as military and relief workers require extra physical help to perform tasks efficiently. Robotic hip exoskeletons can support ambulatory functions in the elderly and augment human performance in healthy people during normal walking and loaded walking by providing assistive torque. In this review, the current development of robotic hip exoskeletons is presented. In addition, the framework of actuation joints and the high-level control strategy (including the sensors and data collection, the way to recognize gait phase, the algorithms to generate the assist torque) are described. The exoskeleton prototypes proposed by researchers in recent years are organized to benefit the related fields realizing the limitations of the available robotic hip exoskeletons, therefore, this work tends to be an influential factor with a better understanding of the development and state-of-the-art technology.
Comparing energy levels in brain regions of interest in ADHD subjects

Energy Landscape is a method to analyze fMRI resting state data to compare activity in regions of interest (ROIs) of the brain. The fMRI date from two groups, one with Attention Deficit Hyperactivity Disorder (ADHD) symptoms and another Control group, were examined using energy landscape analyzed. Preliminary results indicate potential biomarkers for ADHD in ROI network states that involve areas of the brain that control language, self-awareness, spatial attention, and visual processing.
The Intention of Using Web Captioner as Verbal Language Translator to Improve Academic Self-Efficacy for The Deaf

This study aims to determine the relationship between the intention to use Web Captioner as a verbal language translator with the academic self-efficacy of deaf individuals. This research is motivated by the problems faced by teachers and deaf individuals in learning, especially in terms of communication so that misconceptions occur between teachers and deaf students. With the assistive technology in the form of a Web Captioner that functions as a verbal language translator, it is easier for the deaf to understand what the teacher is saying in learning. The question is whether the higher the intention to use the Web Captioner can increase the academic self-efficacy of deaf individuals. This research is a correlational study using two variables, namely the intention to use the Web Captioner as the independent variable and academic self-efficacy as the dependent variable. The subjects for this study amounted to more than 30 deaf children. The data collection method used the intention scale of using Web Captioner and the academic self-efficacy scale compiled by the researcher. Data were analyzed using correlational statistical analysis. Testing using the Pearson Product Moment correlation test then obtained that rcount of 0.334 which is greater than the value of rtable of 0.279. The conclusion obtained from the research results is that there is a significant relationship between the intense use of Web Captioners and the academic self-efficacy of deaf individuals, then the Web Captioner is proven to increase the academic self-efficacy of deaf students.
SpecChecker-ISA: a data sharing analyzer for interrupt-driven embedded software

Concurrency bugs are common in interrupt-driven programs, which are widely used in safety-critical areas. These bugs are often caused by incorrect data sharing among tasks and interrupts. Therefore, data sharing analysis is crucial to reason about the concurrency behaviours of interrupt-driven programs. Due to the variety of data access forms, existing tools suffer from both extensive false positives and false negatives while applying to interrupt-driven programs. This paper presents SpecChecker-ISA, a tool that provides sound and precise data sharing analysis for interrupt-driven embedded software. The tool uses a memory access model parameterized by numerical invariants, which are computed by abstract interpretation based value analysis, to describe data accesses of various kinds, and then uses numerical meet operations to obtain the final result of data sharing. Our experiments on 4 real-world aerospace embedded software show that SpecChecker-ISA can find all shared data accesses with few false positives, significantly outperforming other existing tools. The demo can be accessed at https://github.com/wangilson/specchecker-isa.
Finding bugs in Gremlin-based graph database systems via Randomized differential testing

Graph database systems (GDBs) allow efficiently storing and retrieving graph data, and have become the critical component in many applications, e.g., knowledge graphs, social networks, and fraud detection. It is important to ensure that GDBs operate correctly. Logic bugs can occur and make GDBs return an incorrect result for a given query. These bugs are critical and can easily go unnoticed by developers when the graph and queries become complicated. Despite the importance of GDBs, logic bugs in GDBs have received less attention than those in relational database systems. In this paper, we present Grand, an approach for automatically finding logic bugs in GDBs that adopt Gremlin as their query language. The core idea of Grand is to construct semantically equivalent databases for multiple GDBs, and then compare the results of a Gremlin query on these databases. If the return results of a query on multiple GDBs are different, the likely cause is a logic bug in these GDBs. To effectively test GDBs, we propose a model-based query generation approach to generate valid Gremlin queries that can potentially return non-empty results, and a data mapping approach to unify the format of query results for different GDBs. We evaluate Grand on six widely-used GDBs, e.g., Neo4j and HugeGraph. In total, we have found 21 previously-unknown logic bugs in these GDBs. Among them, developers have confirmed 18 bugs, and fixed 7 bugs.
Deadlock prediction via generalized dependency

Deadlocks are notorious bugs in multithreaded programs, causing serious reliability issues. However, they are difficult to be fully expunged before deployment, as their appearances typically depend on specific inputs and thread schedules, which require the assistance of dynamic tools. However, existing deadlock detection tools mainly focus on locks, but cannot detect deadlocks related to condition variables. This paper presents a novel approach to fill this gap. It extends the classic lock dependency to generalized dependency by abstracting the signal for the condition variable as a special resource so that communication deadlocks can be modeled as hold-and-wait cycles as well. It further designs multiple practical mechanisms to record and analyze generalized dependencies. In the end, this paper presents the implementation of the tool, called UnHang. Experimental results on real applications show that UnHang is able to find all known deadlocks and uncover two new deadlocks. Overall, UnHang only imposes around 3% performance overhead and 8% memory overhead, making it a practical tool for the deployment environment.
Teacher's Perception towards Assessing of Higher Order Thinking Skills (HOTS) in Elementary Schools

The higher order thinking test was developed as an instrument to measure students' abilities based on the Cognitive level of Bloom's Taxonomy, which consists of analysing, evaluating, and creating levels. Higher Order Thinking Skills (HOTS) includes critical, logical, reflective, metacognitive, and creative thinking. HOTS must be balanced and in line between learning activities and assessments. The purpose of this study was to describe teachers' perceptions of the assessment of Higher Order Thinking Skills (HOTS) in the curriculum in elementary schools. This research used qualitative research methods. The data collection method used a questionnaire. The subject of this research is the fifth-grade teacher as many as 27 people in Tulung sub-district, Klaten district. The data were analysed using descriptive and content analysis. Teachers understand the importance of the HOTS assessment in elementary schools and have tried using the HOTS assessment. But they found a difficulties to make sure that the instrument was objective and valid is suitable with the achievement indicators. Accordingly, as a teacher it is necessary to make and develop a HOTS assessment instrument in the form of a test.
A unifying model for distributed data-intensive systems

Modern applications handle increasingly larger volumes of data, generated at an unprecedented and constantly growing rate. They introduce challenges that are radically transforming the research fields that gravitate around data management and processing, resulting in a blooming of distributed data-intensive systems. Each such system comes with its specific assumptions, data and processing model, design choices, implementation strategies, and guarantees. Yet, the problems data-intensive systems face and the solutions they propose are frequently overlapping. This tutorial presents a unifying model for data-intensive systems that dissects them into core building blocks, enabling a precise and unambiguous description and a detailed comparison. From the model, we derive a list of classification criteria and we use them to build a taxonomy of state-of-the-art systems. The tutorial offers a global view of the vast research field of data-intensive systems, highlighting interesting observations on the current state of things, and suggesting promising research directions.
Maestro: a platform for benchmarking automatic program repair tools on software vulnerabilities

Automating the repair of vulnerabilities is emerging in the field of software security. Previous efforts have leveraged Automated Program Repair (APR) for the task. Reproducible pipelines of repair tools on vulnerability benchmarks can promote advances in the field, such as new repair techniques. We propose Maestro, a decentralized platform with RESTful APIs for performing automated software vulnerability repair. Our platform connects benchmarks of vulnerabilities with APR tools for performing controlled experiments. It also promotes fair comparisons among different APR tools. We compare the performance of Maestro with previous studies on four APR tools in finding repairs for ten projects. Our execution time results indicate an overhead of 23 seconds for projects in C and a reduction of 14 seconds for Java projects. We introduce an agnostic platform for vulnerability repair with preliminary tools/datasets for both C and Java. Maestro is modular and can accommodate tools, benchmarks, and repair workflows with dedicated plugins.
A large-scale empirical analysis of the vulnerabilities introduced by third-party components in IoT firmware

As the core of IoT devices, firmware is undoubtedly vital. Currently, the development of IoT firmware heavily depends on third-party components (TPCs), which significantly improves the development efficiency and reduces the cost. Nevertheless, TPCs are not secure, and the vulnerabilities in TPCs will turn back influence the security of IoT firmware. Currently, existing works pay less attention to the vulnerabilities caused by TPCs, and we still lack a comprehensive understanding of the security impact of TPC vulnerability against firmware. To fill in the knowledge gap, we design and implement FirmSec, which leverages syntactical features and control-flow graph features to detect the TPCs at version-level in firmware, and then recognizes the corresponding vulnerabilities. Based on FirmSec, we present the first large-scale analysis of the usage of TPCs and the corresponding vulnerabilities in firmware. More specifically, we perform an analysis on 34,136 firmware images, including 11,086 publicly accessible firmware images, and 23,050 private firmware images from TSmart. We successfully detect 584 TPCs and identify 128,757 vulnerabilities caused by 429 CVEs. Our in-depth analysis reveals the diversity of security issues for different kinds of firmware from various vendors, and discovers some well-known vulnerabilities are still deeply rooted in many firmware images. We also find that the TPCs used in firmware have fallen behind by five years on average. Besides, we explore the geographical distribution of vulnerable devices, and confirm the security situation of devices in several regions, e.g., South Korea and China, is more severe than in other regions. Further analysis shows 2,478 commercial firmware images have potentially violated GPL/AGPL licensing terms.
Capturing Conversational Interaction for Question Answering via Global History Reasoning

Conversational Question Answering (ConvQA) is required to answer the current question, con-ditioned on the observable paragraph-level context and conversation history. Previous works have intensively studied history-dependent reasoning. They perceive and absorb topic-related information of prior utterances in the interac-tive encoding stage. It yielded significant improvement compared to history-independent reasoning. This paper further strengthens the ConvQA encoder by establishing long-distance dependency among global utterances in multi-turn conversation. We use multi-layer transformers to resolve long-distance relationships, which potentially contribute to the reweighting of attentive information in historical utterances. Experiments on QuAC show that our method obtains a substantial improvement (1%), yield-ing the F1 score of 73.7%. All source codes are available at https://github.com/ jaytsien/GHR .
Talaromyces marneffei suppresses human macrophages inflammatory by producing the truncated protein NCOR2-013 via TUT1-regulated alternative splicing

Talaromyces marneffei (T. marneffei) immune-escaping is an important factor for high mortality of talaromycosis. It is currently known that T. marneffei performs these functions through a variety of strategies, however, the role of alternative splicing (AS) in this process is poorly understood. Here we depicted the AS landscape in the macrophage upon T. marneffei infection via high-throughput RNA sequencing. Moreover, we identified a truncated protein of NCOR2/SMRT, namedly NCOR2-013, was significantly upregulated upon T. marneffei infection. Mechanistic analysis indicates that NCOR2-013 forms a co-repression complex with TBL1XR1/TBLR1 and HDAC3, thereby inhibiting JunB-mediated transcriptional activation of pro-inflammatory cytokines via the inhibition of histone acetylation. Also, we identified TUT1 as the AS regulator that involved in facilitating T. marneffei immune evasion via regulation of NCOR2-013 production. Collectively, the findings indicate that T. marneffei escapes macrophages killing through the TUT1-mediated the alternative splicing of NCOR2-013, which providing a new insight into the molecular mechanisms of T. marneffei immune evasion, and a potential targets for talaromycosis therapy.
Paternal transmission of behavioural and metabolic traits induced by postnatal stress to the 5th generation in mice

Life experiences and environmental conditions in childhood can change the physiology and behaviour of exposed individuals and in some cases, of their offspring. In rodent models, stress/trauma, poor diet and endocrine disruptors in a parent have been shown to cause phenotypes in the direct progeny, suggesting intergenerational inheritance. A few models also examined transmission to further offspring and suggested transgenerational inheritance, but such multi-generations inheritance is not well characterized. Our previous work in a mouse model of early postnatal stress showed that behaviour and metabolism are altered in the offspring of exposed males up to the 4th generation in the patriline and up to the 2nd generation in the matriline. The present study examined if in the patriline, symptoms can be transmitted beyond the 4th generation. Analyses of the 5th and 6th generation of mice revealed that altered risk-taking and glucose regulation caused by postnatal stress are still manifested in the 5th generation but are attenuated in the 6th generation. Some of the symptoms are expressed in both males and females, but some are sex-dependent and sometimes opposite. These results indicate that postnatal trauma can affect behaviour and metabolism over many generations, suggesting epigenetic mechanisms of transmission.
Conditional forest models built using metagenomic data could accurately predict Salmonella contamination in Northeastern streams

The use of water contaminated with Salmonella for produce production contributes to foodborne disease burden. To reduce human health risks, there is a need for novel, targeted approaches for assessing the pathogen status of agricultural water. We investigated the utility of water microbiome data for predicting Salmonella contamination of streams used to source water for produce production. Grab samples were collected from 60 New York streams in 2018 and tested for Salmonella. Separately, DNA was extracted from the samples and used for Illumina shotgun metagenomic sequencing. Reads were trimmed and used to assign taxonomy with Kraken2. Conditional forest (CF), regularized random forest (RRF), and support vector machine (SVM) models were implemented to predict Salmonella contamination. Model performance was determined using 10-fold cross-validation repeated 10 times to quantify area under the curve (AUC) and Kappa score. Taxa identified as the most informative for accurately predicting Salmonella contamination based on conditional variable importance were compared to taxa identified by ALDEx2 as being differentially abundant between Salmonella-positive and - negative samples. CF models outperformed the other two algorithms based on AUC (0.82 - CF, 0.76 - RRF, 0.67 - SVM) and Kappa score (0.41- CF, 0.38 - RRF, 0.19 - SVM). CF and differential abundance tests both identified Aeromonas (VI = 0.32) and Tabrizicola (VI = 0.12) as the two most informative taxa for predicting Salmonella contamination. The taxa identified in this study warrant further investigation as indicators of Salmonella contamination in Northeastern freshwater streams. IMPORTANCE Understanding the associations between surface water microbiome composition and the presence of foodborne pathogens, such as Salmonella, can facilitate the identification of novel indicators of Salmonella contamination. This study assessed the utility of microbiome data and three machine learning algorithms for predicting Salmonella contamination of Northeastern streams. The research reported here both expanded the knowledge on the microbiome composition of surface waters and identified putative novel indicators (i.e., Aeromonas and Tabrizicola) for Salmonella in Northeastern streams. These putative indicators warrant further research to assess whether they are consistent indicators of Salmonella for regions, waterways, and years not represented in the dataset used in this study.
MultiNERD: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)

Named Entity Recognition (NER) is the task of identifying named entities in texts and classify-ing them through specific semantic categories, a process which is crucial for a wide range of NLP applications. Current datasets for NER focus mainly on coarse-grained entity types, tend to consider a single textual genre and to cover a narrow set of languages, thus limit-ing the general applicability of NER systems. In this work, we design a new methodology for automatically producing NER annotations, and address the aforementioned limitations by introducing a novel dataset that covers 10 languages, 15 NER categories and 2 textual genres. We also introduce a manually-annotated test set, and extensively evaluate the quality of our novel dataset on both this new test set and standard benchmarks for NER. In addition, in our dataset, we include: i) disambiguation information to enable the development of multilingual entity linking systems, and ii) image URLs to encourage the creation of multimodal systems. We release our dataset at https://github. com/Babelscape/multinerd .
Pruning Adatperfusion with Lottery Ticket Hypothesis

Pre-trained language models have shown great 001 success in multiple downstream tasks. How-002 ever, they are computationally expensive to 003 fine-tune. Thus, transfer learning with adapter 004 modules has been introduced to alleviate this 005 problem, helping to extract knowledge of the 006 downstream tasks. And the latest Adapterfu-007 sion model can further merge multiple adapters 008 to incorporate knowledge from different tasks. 009 However, merging multiple adapters will in-010 evitably cause redundancies, increasing the 011 training and inference time massively. There-012 fore, in this paper, we propose an approach 013 to identify the influence of each adapter mod-014 ule and a novel way to prune adapters based 015 on the prestigious Lottery Ticket Hypothesis. 016 Experiments on GLUE datasets show that the 017 pruned Adapterfusion model with our scheme 018 can achieve state-of-the-art results, reducing 019 sizes significantly while keeping performance 020 intact. 021
All Information is Valuable: Question Matching over Full Information Transmission Network

Question matching is the task of identifying whether two questions have the same intent. For better reasoning the relationship between questions, existing studies adopt multiple interaction modules and perform multi-round reasoning via deep neural networks. In this process, there are two kinds of critical information that are commonly employed: the representation information of original questions and the interactive information between pairs of questions. However, previous studies tend to transmit only one kind of information, while failing to utilize both kinds of information simultane-ously. To address this problem, in this paper, we propose a Full Information Transmission Network (FITN) that can transmit both representation and interactive information together in a simultaneous fashion. More specifically, we employ a novel memory-based attention for keeping and transmitting the interactive information through a global interaction matrix. Be-sides, we apply an original-average mixed connection method to effectively transmit the representation information between different reasoning rounds, which helps to preserve the original representation features of questions along with the historical hidden features. Experiments on two standard benchmarks demonstrate that our approach outperforms strong baseline models.
A Dog Is Passing Over The Jet? A Text-Generation Dataset for Korean Commonsense Reasoning and Evaluation

Recent natural language understanding (NLU) research on the Korean language has been vigorously maturing with the advancements of pretrained language models and datasets. However, Korean pretrained language models still struggle to generate a short sentence with a given condition based on compositionality and commonsense reasoning (i.e., generative commonsense reasoning). The two ma-jor challenges are inadequate data resources to develop generative commonsense reasoning regarding Korean linguistic features and to evaluate language models which are necessary for natural language generation (NLG). To solve these problems, we propose a text-generation dataset for Korean generative commonsense reasoning and language model evaluation. In this work, a semi-automatic dataset construction approach filters out contents in-explicable to commonsense, ascertains quality, and reduces the cost of building the dataset. We also present an in-depth analysis of the generation results of language models with various evaluation metrics along with human-annotated scores. The whole dataset is publicly available at ( https://aihub.or. kr/opendata/korea-university ).