Reviewerly/exHarmony huggingface dataset:
CADGE: Context-Aware Dialogue Generation Enhanced with Graph-Structured Knowledge Aggregation

Commonsense knowledge is crucial to many natural language processing tasks. Existing works usually incorporate graph knowledge with conventional graph neural networks (GNNs), leading to the text and graph knowledge encoding processes being separated in a serial pipeline. We argue that these separate representation learning stages may be suboptimal for neural networks to learn the overall context contained in both types of input knowledge. In this paper, we propose a novel context-aware graph-attention model (Context-aware GAT), which can effectively incorporate global features of relevant knowledge graphs based on a context-enhanced knowledge aggregation process. Specifically, our framework leverages a novel representation learning approach to process heterogeneous features - combining flattened graph knowledge with text. To the best of our knowledge, this is the first attempt at hierarchically applying graph knowledge aggregation on a connected subgraph in addition to contextual information to support commonsense dialogue generation. This framework shows superior performance compared to conventional GNN-based language frameworks. Both automatic and human evaluation demonstrates that our proposed model has significant performance uplifts over state-of-the-art baselines.
SepMark: Deep Separable Watermarking for Unified Source Tracing and Deepfake Detection

Malicious Deepfakes have led to a sharp conflict over distinguishing between genuine and forged faces. Although many countermeasures have been developed to detect Deepfakes ex-post, undoubtedly, passive forensics has not considered any preventive measures for the pristine face before foreseeable manipulations. To complete this forensics ecosystem, we thus put forward the proactive solution dubbed SepMark, which provides a unified framework for source tracing and Deepfake detection. SepMark originates from encoder-decoder-based deep watermarking but with two separable decoders. For the first time the deep separable watermarking, SepMark brings a new paradigm to the established study of deep watermarking, where a single encoder embeds one watermark elegantly, while two decoders can extract the watermark separately at different levels of robustness. The robust decoder termed Tracer that resists various distortions may have an overly high level of robustness, allowing the watermark to survive both before and after Deepfake. The semi-robust one termed Detector is selectively sensitive to malicious distortions, making the watermark disappear after Deepfake. Only SepMark comprising of Tracer and Detector can reliably trace the trusted source of the marked face and detect whether it has been altered since being marked; neither of the two alone can achieve this. Extensive experiments demonstrate the effectiveness of the proposed SepMark on typical Deepfakes, including face swapping, expression reenactment, and attribute editing.
On the number of inequivalent monotone Boolean functions of 9 variables

We provide the first-ever calculation of the number of inequivalent monotone Boolean functions of 9 variables, which is equal to 789,204,635,842,035,040,527,740,846,300,252,680.
RECKONING: Reasoning through Dynamic Knowledge Encoding

Recent studies on transformer-based language models show that they can answer questions by reasoning over knowledge provided as part of the context (i.e., in-context reasoning). However, since the available knowledge is often not filtered for a particular question, in-context reasoning can be sensitive to distractor facts, additional content that is irrelevant to a question but that may be relevant for a different question (i.e., not necessarily random noise). In these situations, the model fails to distinguish the knowledge that is necessary to answer the question, leading to spurious reasoning and degraded performance. This reasoning failure contrasts with the model's apparent ability to distinguish its contextual knowledge from all the knowledge it has memorized during pre-training. Following this observation, we propose teaching the model to reason more robustly by folding the provided contextual knowledge into the model's parameters before presenting it with a question. Our method, RECKONING, is a bi-level learning algorithm that teaches language models to reason by updating their parametric knowledge through back-propagation, allowing them to then answer questions using the updated parameters. During training, the inner loop rapidly adapts a copy of the model weights to encode contextual knowledge into its parameters. In the outer loop, the model learns to use the updated weights to reproduce and answer reasoning questions about the memorized knowledge. Our experiments on two multi-hop reasoning datasets show that RECKONING's performance improves over the in-context reasoning baseline (by up to 4.5%). We also find that compared to in-context reasoning, RECKONING generalizes better to longer reasoning chains unseen during training, is more robust to distractors in the context, and is more computationally efficient when multiple questions are asked about the same knowledge.
Triple Rounds to Protect Text Files

Text file may be confidential and it can contain secret information that required protection from being hacked. In this research paper a simple method of text files cryptography will be introduced, this method will suit any text file with any size; the sequence of required operations will remain the same without any change even if we change the text file and the private key. The introduced method of data cryptography will provide a high level of security; the private key will provide a huge key space making the hacking process impossible. The private key will have a complicated structure with multiple components, these components are to be used to generate three keys needed to apply three rounds of encryption-decryption, the keys will be very sensitive to any minor changes, any changes in the private key components in the decryption phase will be considered as a hacking attempt. The private key will base on chaotic logistic map model to generate 2D chaotic keys. The proposed method will be implemented and the obtained results will be analyzed using various types of analysis to prove the efficiency enhancement provide by the proposed method.
A Study of Interactional Metadiscourse and Its Persuasive Function in Advertising Discourse

Advertising plays an indispensable role in spreading information and it highly influences and persuades consumers to act in a variety of ways. This paper investigates the distribution and use of interactional metadiscourse in Cosmopolitan in 2022, as well as analyzes the role they play in the construction of persuasion. A total of 50 advertisements were included, and both qualitative and quantitive methods were used in the analysis. Based on the classification model of interactional metadiscourse proposed by Hyland (2005), this study calculated the frequency of interactional metadiscourse in ads and explained how the author used these metadiscourse markers to interact with consumers. Results revealed that attitude markers and engagement markers appeared most frequently. It means advertisers are primarily focused on consumers, as they are effective means of persuasion. The study concluded that interactional metadiscourse successfully fulfills its persuasive function and contributes effectively to the success of ads.
Exploiting Action Impact Regularity and Exogenous State Variables for Offline Reinforcement Learning

Offline reinforcement learning—learning a policy from a batch of data—is known to be hard for general MDPs. These results motivate the need to look at specific classes of MDPs where offline reinforcement learning might be feasible. In this work, we explore a restricted class of MDPs to obtain guarantees for offline reinforcement learning. The key property, which we call Action Impact Regularity (AIR), is that actions primarily impact a part of the state (an endogenous component) and have limited impact on the remaining part of the state (an exogenous component). AIR is a strong assumption, but it nonetheless holds in a number of real-world domains including financial markets. We discuss algorithms that exploit the AIR property, and provide a theoretical analysis for an algorithm based on Fitted-Q Iteration. Finally, we demonstrate that the algorithm outperforms existing offline reinforcement learning algorithms across different data collection policies in simulated and real world environments where the regularity holds.
What controls b-value variations: insights from a physics based numerical model

&amp;lt;p&amp;gt;The magnitudes of earthquakes are known to follow a power-law distribution, where the frequency of earthquake occurrence decreases with the magnitude. This decay is usually characterized by the power exponent, the so-called b-value. Typical observations report b-values in the range 0.5-2. The origin of b-value variations is however still debated. Seismological observations of natural seismicity indicate a dependence of the b-value with depth, and with faulting style, which could be interpreted as a signature of a stress dependence. Within creeping regions of major tectonic faults, the b-value of microseismicity increases with creep rate. Stress dependent b-value of acoustic emissions is also commonly reported during rock failure experiments in the laboratory. Natural and laboratory observations all support a decrease of b-value with increasing differential stress. I report here on the origin of b-value variations obtained in a fault model consisting in a planar 2D rate-and-state frictional fault embedded between 3D elastic slabs. This model assumes heterogeneous frictional properties in the form of overlapping asperities with size-dependent critical slip distance distributed on a creeping segment. This allows to get complex sequences of earthquakes characterized by realistic b-values. The role of frictional heterogeneity, normal stress, shear stress, and creep rate on the b-value variations is systematically explored. It is shown that the size distribution of asperities is not the only feature controlling the b-value, which indicates an important contribution from partial ruptures, and cascading events. In this model cascades of events (and thus b-value) is strongly influenced by frictional heterogeneity and normal stress through fracture energy distribution. If the decrease of b-value with differential stress is reproduced in these simulations, it is also shown that part of the b-value fluctuations could be attributed to changes of nucleation length and stress drop with normal stress. A slight increase of b-value with slip rate exists but remains an order of magnitude smaller than the observations.&amp;lt;/p&amp;gt;
The Dominance of Subconscious Thinking in Shireihou's Novel Obsessive Loves

Women are the most potential victims of sexual harassment. What is often overlooked in the issue of sexual harassment is the psychological condition of women. Women victims of sexual harassment should get special attention from the surrounding environment because victims will experience trauma. One of the traumas that can be experienced by female victims of sexual abuse is traumatic pistanthrophobia. Pistanthrophobia is an excessive fear of trusting others. This study aims to determine the personality characteristics of female characters who experience traumatic pistanthrophobia psychoanalysis by Sigmund Freud in the novel Obsessive Loves by Shireishou. The research method used is descriptive qualitative. The data collection techniques used are tapping techniques and recording techniques. Researchers will tap into the meaning of Shireishou's novel Obsessive Loves by reading it carefully and repeatedly. The results of this study show that the personality characteristics of Syaira characters are seen in two aspects, namely, the dominance of subconscious thinking and the meaning of dreams. The dominance of subconscious thinking makes Syaira often hallucinate, daydream excessively, and ignore others. The sexual incident committed by his uncle to Syaira had a big impact on Syaira'smindset.
Analisa Sistem Administrasi Pergudangan Pada Departemen Warehouse PT. Transkon Jaya Tbk Kota Balikpapan

In companies, office administration systems have an important role, especially warehousing administration in the warehouse section that handles inventory. Warehousing administration in the warehouse department is the spearhead of all records of incoming and outgoing goods. PT. Transkon Jaya has a warehouse section that handles spare parts inventory for vehicles or equipment that is rented, the inaccuracy of data in recording administration at the warehouse will have an impact on the company's operations. Based on the description above, the authors conducted a study entitled Analysis of Warehouse Administration Systems at the Warehouse Department at PT Transkon Jaya Tbk, Balikpapan City. The research method used in this research is Descriptive Qualitative Research which uses comparative analysis techniques through literature study, observation, and interviews. From the results of research and analysis, it can be concluded that the warehousing administration system in the Warehouse Department at PT. Transkon Jaya Tbk Balikpapan uses 2 (two) mechanisms, namely a computerized mechanism that uses the MYOB application and a manual mechanism. However, the application used is an accounting computer application, not an application specifically for inventory administration, which can provide information regarding the location of goods, the number of goods, the date of goods coming in, the date of goods left, and the amount of stock in the warehouse in real-time.
PENERAPAN POLA ARSITEKTUR MVVM PADA PERANCANGAN APLIKASI PENGADUAN MASYARAKAT BERBASIS ANDROID

The Model-View-ViewModel (MVVM) architectural pattern can be applied in designing a mobile complaint application. MVVM separates the graphical interface and business processes, making it easier to develop and maintain applications. In an MVVM implementation, event and observer data are separated, observing calls from event listeners are avoided, and state is used for data. The MVVM architectural pattern can also reduce dependencies so that there is no close relationship between the view and the viewmodel. In addition, MVVM can also be used to manage the concentration of Android application development that functions as a public complaint. In this community complaint application it is intended that the public can notify their complaints without having to come to the location of the complaint. In addition, it can make it easier for the authorities to follow up on these complaints, so that the public government can provide better service and be responsive to complaints from the public.
Multicriteria approach applied to knowledge management: A review

Purpose: This paper aims to propose a multicriteria knowledge management instrument, built from a comprehensive systematic review of the literature, to support decision-making. Design/methodology/approach: The study consists of a systematic review of 20 articles, resulting from the InOrdinatio Index Method or Methodi Ordinatio (Pagani et al., 2017). Findings: The use of the multicriteria approach as a tool in the evaluation or selection of methods or instruments for knowledge management resulted in 74 criteria, relevant to the definition of a knowledge management system considering the different scenarios. The findings demonstrate the complexity of a problem given the variety of criteria that can be used to assess or select different knowledge management systems. Additionally, it is possible to observe that there is no prioritization of one criterion when compared to another. Research limitations/implications: The criteria found with the application of the InOrdinatio Index Method may not cover all the literature regarding the research topic. Yet, it is believed that the findings provide a valuable understanding of the current situation in this research field. The study proposes several future research directions, on how to verify the adherence of the criteria found in decision making in knowledge management systems. Originality: To the best of the authors’ knowledge, no systematic literature review about Knowledge Management with the application InOrdinatio Index method has previously been published in academic journals.
Pengelolaan Penataan Arsip Bagian SDM dan Umum PT. PELINDO IV (PERSERO) Cabang Balikpapan

Archiving is a process or activity that starts from creation, receipt, collection, regulation, control, maintenance, care, and storage as well as evaluation according to a certain predetermined system. As time goes by and also the many activities that have been carried out at PT Pelindo IV (Persero) Balikpapan Branch, more and more archives have been created. The purpose of this study was to find out the causes of archive management and to find out the efforts made in overcoming obstacles to archive management in the HR and general division at PT Pelindo IV (Persero) Balikpapan Branch. The type of research used in this research is descriptive research with a qualitative approach in which data is collected through observations, interviews, and documentation. Based on the research results, several things can be improved for more efficiency and effectiveness in archiving 1) What can be improved is the system currently used. Pelindo IV (Persero) Balikpapan Branch, namely the system regarding this is quite good, it's just that in recovering it is still difficult to find because letters are in other classification sections such as payment archives in the information system classification. 2) Tools and equipment used in managing archives are wooden and glass cabinets. The arrangement is still disorganized because the order folders are just piled up. The archive storage space is too small so many archives accumulate and cannot be put in the cupboard. 3) There are still many archives whose duration of archive destruction is too long, considering the number of incoming documents or document production is quite high and quite a lot because archives that are old and no longer needed will be destroyed within a yearly period.
Analisis Sistem Informasi Akuntansi Penggajian Karyawan Pada PT. Star Rubber Kecamatan Jujuhan Kabupaten Bungo Provinsi Jambi

Analysis of employee payroll accounting information systems should be carried out effectively and required relevant information and a system, namely an accounting information system as a means of communication which can later be used for the needs of company management. Likewise things at PT. Star Rubber Jujuhan Bungo Jambi Regency where employee payroll accounting is very important. The purpose of this study was to determine the payroll accounting information system for employees at PT. Star Rubber, obstacles in employee payroll accounting information systems and efforts to overcome obstacles in employee payroll accounting information systems at PT. Star Rubber. This study used qualitative research methods. Data were obtained from subjects in the study including the Director, Expenditure Treasurer and employees regarding the payroll system of PT. Star Rubber Jujuhan and Employees of PT. Star Rubber. The results showed that the Employee Payroll Accounting Information System at PT. Star Rubber has been designed to support the achievement of company goals and the creation of good internal control. In addition, the payroll and remuneration system is also designed to ensure that employees' salaries and wages are paid. While the related functions at PT. Star Rubber is a staffing function that is held by the personnel department, the function of recording employee attendance times is held by the timekeeper, the function of making payroll and wages is handled by the personnel section, the cashier function, and the function of paying salaries and wages is authorized by the personnel and cashier section.
Fall Risk Assessment and Ranking using Survival Analysis

Abstract Falling is the second leading cause of unintentional injury-related deaths, but fall injuries in older adults are often underreported, reported too late or reported with a lack of detail due to the stigma that surrounds falling. This makes it hard to locate and target the right citizens with fall prevention programs. In this paper, we propose a novel approach to assess fall risk over time and thus identify who would benefit from such a program. Using survival analysis, we train machine learning models to rank citizens by their predicted risk of getting a personal alarm within six months based on home care observations. Data were gathered from July 2021 to December 2021 for 57 types of home care from a large municipality in Denmark. After preprocessing, a total of 5713 observations for 346 citizens with mean age 81 (std. 5 years) are available for model training. Results show that a plain Cox proportional hazards model obtain a mean concordance index of 0.76 (95\% CI: 0.7-0.82), Random Survival Forest obtain 0.84 (95\% CI: 0.76-0.92) and XGBoost obtain 0.94 (95\% CI: 0.88-1) over 25 randomized folds using cross-validation. Censoring rate is 94.5\% on average. Most predictive features are help with personal hygiene, administration of medicine, excretions, cleaning and dispensing of medicine according to a SHAP summary plot. We also identify correlations between care usage in minutes per week and risk using SHAP dependency plots, but the correlation is not always positive. With our method, professionals at municipalities in Denmark or elsewhere, where home care services are recorded, will have a noninvasive decision-support tool that can predict individual fall risk over time.
Trajectory tracking control method of UAV formation based on fuzzy control

This paper presents a method of UAV formation trajectory tracking control based on fuzzy control. Aiming at the trajectory tracking problem of UAV formation, starting from the UAV formation control, the fuzzy control method is adopted to obtain the revised PD parameters through fuzzification, determination of membership, fuzzy rule reasoning, and clarification, so as to realize the real-time adjustment of UAV formation control parameters, and finally make the UAV formation reach the docking position quickly and stably. Through simulation, the effectiveness of the fuzzy algorithm is verified, the control logic of UAV formation is simplified, and the flight stability of UAV formation is improved.
Analisis Sentimen Pengguna Twitter Terhadap Kinerja Walikota Medan Menggunakan Metode Naive Bayes Classifier

Technological developments today make it easy for people to use social media as a means of expressing opinions, including Twitter. The case study taken by the researcher is the sentiment towards the performance of the mayor of Medan. The case was taken because it was widely discussed by Indonesian people, especially the city of Medan on Twitter social media. One of the uses of this research is to find out the trend of Twitter user comments on the performance of the mayor of Medan by conducting a sentiment analysis. Sentiment will be classified as positive, negative and neutral. The algorithm used in sentiment analysis is Naïve Bayes. The stages in conducting sentiment analysis in this study are data preprocessing, data processing, classification, and evaluation. The results of this study are using the SMOTE method, the training and testing ratio is 80:20 because it has the highest accuracy, which is 78% compared to other ratios. The prediction results resulting from the classification turned out to be more dominant towards neutral labels. In addition to classifying for sentiment analysis, this study also measures the performance of the model created. The results showed that the Naïve Bayes algorithm has a precision value of 78%, a recall of 78%, and an f1-score of 77%.