Reviewerly/exHarmony huggingface dataset:
Multi-Hop Fact Checking of Political Claims

Recent work has proposed multi-hop models and datasets for studying complex natural language reasoning. One notable task requiring multi-hop reasoning is fact checking, where a set of connected evidence pieces leads to the final verdict of a claim. However, existing datasets either do not provide annotations for gold evidence pages, or the only dataset which does (FEVER) mostly consists of claims which can be fact-checked with simple reasoning and is constructed artificially. Here, we study more complex claim verification of naturally occurring claims with multiple hops over interconnected evidence chunks. We: (1) construct a small annotated dataset, PolitiHop, of evidence sentences for claim verification; (2) compare it to existing multi-hop datasets; and (3) study how to transfer knowledge from more extensive in- and out-of-domain resources to PolitiHop. We find that the task is complex and achieve the best performance with an architecture that specifically models reasoning over evidence pieces in combination with in-domain transfer learning (The content in this chapter has been reprinted, with permission, from the original work in Ostrowski et al. (2021)).
Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing

Fact-checking systems have become important tools to verify fake and misguiding news. These systems become more trustworthy when human-readable explanations accompany the veracity labels. However, manual collection of these explanations is expensive and time-consuming. Recent work has used extractive summarization to select a sufficient subset of the most important facts from the ruling comments (RCs) of a professional journalist to obtain fact-checking explanations. However, these explanations lack fluency and sentence coherence. In this work, we present an iterative edit-based algorithm that uses only phrase-level edits to perform unsupervised post-editing of disconnected RCs. To regulate our editing algorithm, we use a scoring function with components including fluency and semantic preservation. In addition, we show the applicability of our approach in a completely unsupervised setting. We experiment with two benchmark datasets, namely LIAR-PLUS and PubHealth. We show that our model generates explanations that are fluent, readable, non-redundant, and cover important information for the fact check (The content in this chapter has been reprinted, with permission, from the original work in Jolly et al., Information 13(10), 2022).
PEMBELAJARAN E-LEARNING PADA MATA PELAJARAN PAI DI SD NEGERI 1 PANDEGLANG

Learning is a human need that must be met under any circumstances, including during the Covid-19 pandemic which required distance learning (PJJ). For this reason, the majority of schools organize online learning using e-learning, and SD Negeri 1 Muruy is no exception. e-learning which is electronic-based learning both online and offline has obstacles.This study aims to 1) Assess the design of e-learning learning plans in PAI Subjects at SD Negeri 1 Muruy Pandeglang Banten; 2) Assessing the implementation of e-learning learning in PAI subjects at SD Negeri 1 Muruy Pandeglang Banten Public Middle School; 3) Reviewing the implementation of e-learning learning evaluation in PAI subjects at SD Negeri 1 Muruy Pandeglang Banten Public Middle School; 4) Analyzing the results of the implementation of learning PAI subjects at SD Negeri 1 Muruy Pandeglang Banten. This study uses a qualitative method with a phenomenological approach. The results of the study show that the design of e-learning learning plans for PAI Subjects at SD Negeri 1 Muruy Pandeglang Banten is to prepare CBT/CBE applications, prepare student and teacher accounts to access CBT/CBE, arrange full online learning schedules or shift systems, compile RPP blended, holding training related to e-learning and installing CBT/CBE replacement applications. The implementation of e-learning learning in PAI subjects was mostly carried out online which initially used the CBT Al Fattah application, then changed to using WAG which included links to other applications, such as Zoom, Google Meet, Plif Book, Youtube, PPt and Podcast for audio. . Implementation of e-learning learning evaluations using the CBT Application or Google Form, E-mail, or Youtube, both Daily Assessments, Formative Tests and Summative Tests. Based on the documentation study of PTS report cards, it shows that the cognitive value of students in PAI Subjects increases when face-to-face learning, on the contrary when PJJ, there are more scores below the KKM. Therefore, it is necessary to improve the quality of e-learning learning in PAI Subjects
GUARANTEES FOR THE PROTECTION OF JOURNALISTIC SOURCES IN THE APPLICATION OF TEMPORARY ACCESS TO THINGS AND DOCUMENTS

The article is devoted to the study of guarantees of protection of journalistic sources when applying temporary access to things and documents in the criminal procedural law of Ukraine. Based on the analysis of the current criminal procedural legislation of Ukraine, the author defines the subject matter of journalistic secrecy, which is: 1) a source of information; 2) information which is held/received by a journalist or a media outlet with a view to its dissemination or use in their professional activities; 3) any other information (documents, records) which allow establishing a connection between a journalist and a source of information. The author establishes that the lack of proper judicial control over the legality of investigators’ claims, the formal approach to consideration of applications for temporary access to things and documents, and the impossibility of appealing against the relevant rulings on appeal create a risk of disclosure of information in the possession of a media outlet or a journalist and provided to them on condition that the authorship or source of information is not disclosed. It is proved that the use of temporary access to things and documents containing legally protected information, in particular, information in the possession of a media outlet or a journalist and provided to them on condition that the authorship or source of information is not disclosed, without a ruling of an investigating judge or a court is inadmissible. The European Court of Human Rights has determined in a number of decisions that disclosure of sources is acceptable only in exceptional cases when it comes to protecting vital interests. These include cases of sexual abuse of children, disclosure of particularly serious crimes, etc. However, the decision to disclose the source of information must be made by an independent and impartial judicial authority and must contain a detailed justification why the disclosure of journalistic «sources» of information outweighs the journalist’s right to keep them secret. It is important to emphasize the need to eliminate formalism in the consideration of motions for temporary access to things and documents, and especially in cases involving information containing legally protected secrets (in particular, the secrecy of journalistic sources). In such cases, the law requires that the investigating judge find a fair balance between the right to secure criminal proceedings for the purpose of to solve the crime and the right to keep secrets protected by law, in particular the secrecy of journalistic sources (see para. 1, part 1, Article 162, part 6, Article 163 of the CPC of Ukraine). In view of this, the practice of considering such motions within 5–5 minutes should be recognized as unacceptable. motions within 4–5 minutes, as this does not allow for a thorough familiarization with the case file, conduct an appropriate analysis, and make a reasoned decision.
An Overview of Cloud Computing Solutions in Library Services

This article intends to provide a comprehensive overview of the current applications of cloud computing services in libraries. This study investigates the ways in which cloud-based information systems and services can administer library records in a cloud computing environment and support the evolving needs of library patrons. The authors endeavor to rectify the identified deficiency, advantages, and prospects associated with the integration of cloud storage at the Infrastructure as a Service (IaaS) level with the remaining two tiers of cloud computing Platform as a Service (PaaS) and Software as a Service (SaaS).
Multi-path fusion network based global attention for brain tumor segmentation

In many clinical applications, medical picture segmentation is essential for the precise delineation of anatomical features and diseased areas. However, due to the complexity and variability of medical images, traditional segmentation methods often face challenges in achieving robust and accurate results. This paper proposes a novel method called Multi-Path Fusion Network (MPFN) for segmenting medical image to overcome these drawbacks. The MPFN leverages the power of deep learning and incorporates multiple pathways to gather contextual information on both local and global scale. The network architecture consists of parallel pathways. Each pathways is designed to extract various levels of spatial features from the input MRI image. These pathways are then fused using an attention mechanism, allowing the network to selectively emphasize relevant features while suppressing noise and irrelevant information. Furthermore, the MPFN introduces a multi-scale strategy to deal with variations in object sizes and shapes within medical images. By incorporating feature maps at different resolutions, the network can effectively capture both fine-grained details and high-level context, improving segmentation accuracy. The experiment show that the proposed method achieves competitive result in Brat 2015 segmentation tasks, outperforming existing traditional neural network.
RESEARCH ON QUESTION-ANSWERING SYSTEM OF CHILDREN'S DISEASE BASED ON ALBERT

Due to the immature development of children and their weak immune systems, children are more susceptible to illnesses, and it can be difficult for parents to find accurate answers online. This paper proposes a method for constructing an ALBERT-based question-answering system for pediatric diseases, using semi-structured medical-related websites and structured open-source medical knowledge graphs as data sources. The method uses the ALBERT pre-training model and Bidirectional Long Short-Term Memory (BiLSTM) to extract entities and relationships and construct a knowledge graph for pediatric diseases. The ALBERT-TextCNN model is used for question classification, and Cypher statements are used to query the Neo4j graph database, returning answers to the user. Experimental results show that the ALBERT-BiLSTM-CRF model and the ALBERT-BiLSTM-Self-Attention model have better entity and relationship extraction capabilities, with F₁ scores of 93.17 and 94.21, respectively. The ALBERT-based pediatric disease question-answering system effectively enables people to utilize vast amounts of specialized medical knowledge to solve problems and has significant practical value.
On One-Pixel Attacking Medical Images against Deep Learning Models

Deep neural network models has made impressive achievements in various domains, along with their non-negligible vulnerability to adversarial attacks. This intrinsic weakness of deep learning systems is crucially concerned with practical clinic deployment, in addition to explainability. This paper explores the vulnerability of well-trained deep learning models for medical images by one-pixel adversarial attack. Specifically, an enhanced success history adaptive differential evolution(DE) with greedy mutation strategy (EBL) is employed to search adversarial pixels of diabetic retinopathy, pneumothorax chest X-ray, melanoma and brain tumor images. The well-trained models for the first three classes of images are ResNet-50, while it is UTnet for the last. Experiments show that EBL approach speeds up about 30 times on the classical DE one, without loss of attacking success rate. The overall attacking success rate varies from 2.7% for negative chest X-ray images to 99.3% for positive diabetic retinopathy images. Repeating one-pixel attacks confirm the receptive fields of vulnerability for the first three classes of images, but not for the brain tumor ones. To successfully attack such medical images, it may need up to hundreds of attacking pixels. For the brain tumor dataset, the success rate is about 47% using 1024 attacking pixels.
Design of Intelligent Community Medical Station Software and Hardware System IntegrationDesign of Intelligent Community Medical StationStudy on Intelligent Medical Station Software and Hardware System Integration Design for the Community

The design concept of community intelligent medical station is proposed to solve the problem of difficult physical examination and medical consultation for residents in old communities. On the basis of hardware and software systems, an intelligent medical station that can be used for self-help medical checkups and self-help consultations for community residents is developed and designed. The hardware system of the medical station is a series of flexible medical and health testing equipment, and the software system of the medical station automatically accomplishes the functions of symptom analysis, disease prediction, diagnosis, and recommended medication. At the same time, the terminal of the medical station can also be connected with residents' cell phones and other devices which greatly benefits community residents.
An Entity Prediction Method for Chinese Medical Knowledge Graph via Bert Sentence Embedding and Classification

Automatic Knowledge Graph Completion is becoming the main research direction of Knowledge graph construction. Among, the entity prediction method can complete the complement of entities in RDF triples, and is widely used in the generation process of the Knowledge graph. In order to complete the Chinese medical Knowledge graph, this paper proposes an entity prediction method based on BRRT sentence embedding and classification. This method needs three steps, the first step is to introduce a large-scale medical corpus to fine tune the basic BERT model into a BERT Domain model. The second step is obtaining the sentence embedding through the model for candidate triples. The third step is to obtain the top N candidate entity lists according to the ranking of classifier probabilities of all candidate. In order to verify the effectiveness of this method, a series of experiments are conducted on the BIOS. The experimental results show that the optimal accuracy of the entity prediction method in this paper is 20.5%, which is 7.2% higher than that using Word embedding+distance.
Hybrid Attention-based Semantic Segmentation for Hand Acupoint Reflex Zones

The significance of acupoint reflex zones in Traditional Chinese Medicine is well acknowledged, aiding considerably in the comprehension and learning of the theory. This study addresses the segmentation challenges brought forth by skin color variations and hand sizes in the identification of acupoint reflex zones. An enhanced semantic segmentation methodology for hand acupoint reflex zones employing an augmented Deeplabv3+ network is proposed. Initially, a hybrid attention module, integrating the merits of spatial attention and channel attention mechanisms, is utilized to refine the Deeplabv3+ network, thereby enhancing its feature selection proficiency for acupoint reflex zones. To further augment the ASPP module's capability in low-resolution feature extraction, a dilated convolution with a dilation rate of 4 is incorporated into the initial setup. Subsequently, the focal loss function is adopted to heighten the segmentation precision of intricate samples. Through experimental evaluations, a notable enhancement in the segmentation clarity of diminutive reflex zones is demonstrated, with a mIoU improvement of 3.09% compared to the original DeeplabV3+ network.
Comparative analysis of machine learning techniques for credit card fraud detection: Dealing with imbalanced datasets

The main objective of this research is to evaluate the performance of machine learning algorithms in the field of credit card fraud detection and then compare them according to various performance metrics. Seven different supervised classification algorithms including Logistic Regression, Decision Trees, Random Forest, XGBoost, Naive Bayes, K-Nearest Neighbors and Support Vector Machine were used. The performance of these algorithms was measured through a comprehensive evaluation of metrics including Accuracy, Precision, Recall, F-Score, AUC and AUPRC values. Furthermore, ROC curves and confusion matrices were used to evaluate these algorithms. The data preparation phase is critical in this study. The data imbalance problem arises as an unequal distribution between fraudulent and non-fraudulent transactions. Addressing this imbalance is imperative for successful model training and subsequent reliable results. Various techniques, such as Scaling and Distribution, Random Under-Sampling, Dimensionality Reduction, and Clustering, are employed to ensure an accurate evaluation of model performance and its ability to generalize effectively. As a result, the "Random Forest" and "K-Nearest Neighbors" algorithms exhibit the highest performance levels in this research with 97% accuracy rates. This study contributes significantly to the ongoing fight against financial fraud and provides valuable guidance for future research efforts.
A Semantic Study of Intensifiers

This research investigates the new insights on intensification scale and intensifiers as target, and the sources of semantic change, the data collected from semantic references to build clear boundaries and to avoid vagueness between adverbs of degree and adverbs of intensity. Intensifier taxonomy is a cover term to refer to those adverbs that influence and thus strength or weaken the meaning of the adjective or adverb .Negativity will show new intensifiers with the cognitive psychological research. In gradability, intensifiers determine measurement and markedness , while binary features in intensification and comparison scales have grammaticalized adjectives and adverbs intensifiers. Finally completeness is an opposition model of markedness. Conclusion states that intensifiers are semantic prosody used to describe positive or negative meaning especially their connotation in semantic meaning or attitudinal meaning and by the semantics of pragmatic expression associated with particular collocation.
An Empirical Analysis on Memcached's Replacement Policies

The performance of large-scale web services heavily relies on the hit ratio of the key-value caches. One core component of a high-performance key-value cache is the replacement policy. A right replacement policy can help the caching system achieve a better hit ratio with no extra space cost, thereby improving the system's throughput and end-to-end latency. Memcached and Redis are two widely used in-memory key-value caching software in many production systems. Both Memcached and Redis are simple to use and capable of ensuring end-to-end latency requirements for latency critical services. Memcached and Redis use different policies for cache replacement. In contrast, Memcached uses LRU or a variant of segmented-LRU (SegLRU) for replacement, while Redis uses KLRU, a random sampling-based LRU policy which evicts the LRU object from K randomly selected samples. This naturally leads to the question: "how does one compare to the other in actual production usage?" To answer the question, we implement the KLRU policy on Memcached. We evaluate the effectiveness of these three policies using both synthetic and actual production workloads. Our empirical analysis shows that, both SegLRU and KLRU outperform LRU in scalability for write-intensive workloads. However, despite the fact that SegLRU and KLRU are considerably different in terms of their heuristic and implementation, they yield very similar cache hit ratios, throughput, and scalability, with the random sampling-based LRU slightly winning over write-heavy workloads. KLRU also shows advantages in its simplicity in data structures and flexibility in adjusting the sampling size K to adapt to different workloads.
Large-scale Graph Processing on Commodity Systems: Understanding and Mitigating the Impact of Swapping

Graph workloads are critical in many areas. Unfortunately, graph sizes have been increasing faster than DRAM capacity. As a result, large-scale graph processing necessarily falls back to virtual memory paging, resulting in tremendous performance losses.
RAMPART: RowHammer Mitigation and Repair for Server Memory Systems

RowHammer attacks are a growing security and reliability concern for DRAMs and computer systems as they can induce many bit errors that overwhelm error detection and correction capabilities. System-level solutions are needed as process technology and circuit improvements alone are unlikely to provide complete protection against RowHammer attacks in the future. This paper introduces RAMPART, a novel approach to mitigating RowHammer attacks and improving server memory system reliability by remapping addresses in each DRAM in a way that confines RowHammer bit flips to a single device for any victim row address. When RAMPART is paired with Single Device Data Correction (SDDC) and patrol scrub, error detection and correction methods in use today, the system can detect and correct bit flips from a successful attack, allowing the memory system to heal itself. RAMPART is compatible with DDR5's RowHammer mitigation features, as well as a wide variety of algorithmic and probabilistic tracking methods. We also introduce BRC-VL, a variation of DDR5's Bounded Refresh Configuration (BRC) that improves system performance by reducing mitigation overhead and show that it works well with probabilistic sampling methods to combat traditional and victim-focused mitigation attacks like Half-Double. The combination of RAMPART, SDDC, and scrubbing enables stronger RowHammer resistance by correcting bit flips from one successful attack. Uncorrectable errors are much less likely, requiring two successful attacks before the memory system is scrubbed.
Linear-Mark: Locality vs. Accuracy in Mark-Sweep Garbage Collection

Tracing garbage collectors are widely deployed in modern programming languages. But tracing an arbitrary heap shape incurs poor locality and may hinder scalability. In this paper, we explore an avenue for mitigating these inefficiencies at the expense of conservative, less accurate identification of live objects. We do this by proposing and studying an alternative to the Mark-Sweep tracing algorithm, called Linear-Mark. It turns out that although Linear-Mark improves locality and scalability, the accuracy of Mark-Sweep outweighs the achieved enhancements. We present the Linear-Mark garbage-collecting algorithm and provide an evaluation that highlights the trade-offs between the Linear-Mark and the Mark-Sweep approaches. Our hope is that this research will inspire further algorithmic improvements, ultimately leading to better garbage collection algorithms.