Reviewerly/exHarmony huggingface dataset:
A RESEARCH NOTE ON TEXTUAL DISCLOSURES ABOUT BLOCKCHAIN TECHNOLOGY IN 10-KS

Fiscal year-end 2018 and 2019 10-Ks filed with the SEC provide evidence of increasing use of blockchain technology by companies, but do not indicate an immediate disruptive revolution. In recent years, several large companies that were identified as using blockchain technology in business publications and press releases did not mention their efforts in their 10-Ks. This omission may reveal that information about leading-edge technological applications is not being properly discussed and disclosed in 10-K filings.
PENGEMBANGAN MEDIA VIDEO MOTION GRAPHIC DALAM PEMBELAJARAN MENULIS PANTUN PADA MASA PANDEMI COVID 19

The main objective of the research is to design and develop motion graphic video media in learning to write rhymes. The research method used is design and development (D&amp;D) and the ADDIE development model. This study concludes that based on the assessment of media experts, the developed media shows a percentage of 88% with a very decent category. The assessment of the material expert shows a percentage of 71.2% with a decent category. Based on user ratings, namely teachers and students, the media developed was categorized as very feasible based on the teacher's view and received an assessment in the very appropriate category based on the responses of students. This shows that motion graphic video media can be used as a medium for learning to write poem.
BANK CONFERENCE CALLS: A TEXTUAL ANALYSIS APPROACH

While firms are increasingly providing conference calls to help investors better understand and contextualize financial information, banks’ public disclosures are more opaque than that of non-banks. This study examines the likelihood and information content of bank conference calls as well as related market outcomes. The empirical results document that a higher level of bank systemic risk is associated with a lower likelihood of the bank providing earnings conference calls. However, banks with a higher level of disclosure transparency are more likely to provide conference calls to investors. Furthermore, the empirical evidence suggests that managers demonstrate a consistent behavior in terms of disclosure contents during conference calls. Higher obfuscation is associated with higher bank systemic risk and lower disclosure transparency. When banks have a higher level of disclosure transparency, information components in conference calls are higher, suggesting that managers disclose more information during conference calls. Finally, consistent with theoretical predictions, obfuscation (information) components in bank conference call disclosures are associated with higher (lower) information asymmetry (measured by market illiquidity and analyst forecast errors). Banks are black boxes. Money goes in, and money goes out, but the risks taken in the process of intermediation are hard to observe from outside the bank. —Donald P. Morgan (2002)
Literature Review : Kelengkapan Kode Topography dan Morphology pada Kasus Neoplasma

In the Medical Record for determining the cancer diagnosis code (Neoplasm), there are 2 codes, namely the topographic code and the morphology code. These two codes are very important because the topographic code is a code that shows the location of the tumor, while the morphology code is a code that shows the nature of the tumor. If the two codes are not included, it will not determine the level of malignancy of the tumor. The aim of the study was to identify the completeness of the topographic and morphological codes in neoplasm cases. Literature study conducted on 7 journals uploaded online in the span of 2011-2021. Search journals in this study using the keywords "completeness", "Topography and Morphology code" obtained through Google Scholar. The results of the literature review show that the completeness of topographic and morphological codes in neoplasm cases has not yet reached 100%. The highest completeness of topographic codes is 98% at Aisyiyah Hospital Malang in 2018. While the lowest percentage is 0% at MRCCC Siloam Semanggi Hospital in 2020 and Karanganyar District Hospital in 2011. The highest completeness of morphology codes is 82.4% at Santa Elisabeth Hospital Medan while The lowest percentage was 0% at MRCCC Siloam Semanggi Hospital in 2020, Bhayangkara Hospital, Aisyiyah Hospital, Dr Moewardi Hospital, and Karanganyar District Hospital. The incompleteness is due to 2 factors Man: Coder inaccuracy in coding, officers have not implemented coding procedures in neoplasm cases. Method: there is no SOP for coding neoplasms, there is no PA result sheet, and the doctor's writing is not clear and complete. In assigning codes to neoplasm cases, officers should code according to the SOP, so that the resulting code is complete and accurate.
Silicon photonic delay-based reservoir computing incorporated with a multimode interference structure

We propose and experimentally demonstrate delayed-feedback type reservoir computing with a multimode interference structure in a silicon photonics. By combining both temporal and spatial nodes, the total number of nodes is increased to 1100, and the accuracy for the time series prediction is greatly improved.
Estimating the Presence of Blood with Reference to Anti-Forensic-A Comparative Study

Number of presumptive tests is there for detection of blood in forensic science. But problems arise in choosing the presumptive test that gives the least false positive result. Difficulties arise due to the small quantity of blood found, so to prevent damage of the sample appropriate reagent is required for the detection of blood. Blood, artificial blood, fake blood, and some false positive-giving substances (including commercial products such as Bleach, Enamel paint, Sauce, and Betadine, chemicals such as copper sulphate, ferrous oxide, and potassium permanganate, vegetables/cereals/fruits such as cauliflower, strawberries, red kidney beans, and rust) were used in these studies. Presumptive tests like Kastle-Meyer, Tetra methyl benzidine, leucomalachite green, BLUESTAR® FORENSIC and Fluorescein test were used. To overcome this problems, comparison of these test reagents are done. In comparison to all other test reagents, Lecomalachite green has the highest accuracy, followed by BLUESTAR® FORENSIC, Fluorescein, Kastle-Meyer, and Tetramethylbenzidine test, which has the lowest accuracy.
Using DRG to analyze hospital production: a re-classification model based on a linear tree-network topology

&#x0D; Background: Hospital discharge records are widely classified through the Diagnosis Related Group (DRG) system; the version currently used in Italy counts 538 different codes, including thousands of diagnosis and procedures. These numbers reflect the considerable effort of simplification, yet the current classification system is of little use to evaluate hospital production and performance.&#x0D; Methods: As the case-mix of a given Hospital Unit (HU) is driven by its physicians’ specializations, a grouping of DRGs into a specialization-driven classification system has been conceived through the analysis of HUs discharging and the ICD-9-CM codes. We propose a three-folded classification, based on the analysis of 1,670,755 Hospital Discharge Cards (HDCs) produced by Lombardy Hospitals in 2010; it consists of 32 specializations (e.g. Neurosurgery), 124 sub-specialization (e.g. skull surgery) and 337 sub-sub-specialization (e.g. craniotomy).&#x0D; Results: We give a practical application of the three-layered approach, based on the production of a Neurosurgical HU; we observe synthetically the profile of production (1,305 hospital discharges for 79 different DRG codes of 16 different MDC are grouped in few groups of homogeneous DRG codes), a more informative production comparison (through process-specific comparisons, rather than crude or case-mix standardized comparisons) and a potentially more adequate production planning (considering the Neurosurgical HUs of the same city, those produce a limited quote of the whole neurosurgical production, because the same activity can be realized by non-Neurosugical HUs).&#x0D; Conclusion: Our work may help to evaluate the hospital production for a rational planning of available resources, blunting information asymmetries between physicians and managers. &#x0D;
A Knowledge Revision Learner Using Artificially Generated Examples

Attribute-type-learners which express examples as attribute vectors cover many application fields. When it is difficult to collect a sufficient number of examples, from which to acquire knowledge, the most appropriate learners are revision learners. Such learners can acquire knowledge from a small number of examples because they also employ knowledge provided by human experts. There are, however, only a few revision learners of the attribute-type. Thus, a framework is required that would allow non-revision learners, which cannot learn from both given knowledge and examples, to be used as revision-learners. We propose a revision learner that generates artificial examples from knowledge given by humans, inputs these artificial examples together with collected examples, and acquires knowledge by using non-revision-learners. Evaluating the proposed revision learner, which uses the IrvineDatabase and the ID3 learner, reveals that knowledge acquisition can be performed more accurately than that based on either given knowledge or collected examples alone. Thus its effectiveness as a knowledge acquisition tool is demonstrated.
Design and Implementation of Multiple-Context Truth Maintenance System with Binary Decision Diagram

Implicit enumeration of prime implicates in Truth Maintenance System (TMS) is investigated. CMS (Clause Management System), an extension of Assumption-based TMS (ATMS), that accepts any type of justi cation has a burden to compute all prime implicates, since its complexity is NP-complete. To improve the performance of multiple-context TMS such compact representation of boolean functions. In this paper, we propose a BDD-based Multiple-context TMS (BMTMS) and present the design and implementation of interface between TMS and BDD. The interface provides high level speci cations of logical formulas, and has mechanisms to schedule BDD commands to avoid combinatorial explosions in constructing BDDs. In BMTMS, most TMS operations are carried out without enumerating all prime implicates.
Confidence Backup Updates for Aggregating MDP State Values in Monte-Carlo Tree Search

Monte-Carlo Tree Search (MCTS) algorithms estimate the value of MDP states based on rewards received by performing multiple random simulations. MCTS algorithms can use different strategies to aggregate these rewards and provide an estimation for the states’ values. The most common aggregation method is to store the mean reward of all simulations. Another common approach stores the best observed reward from each state. Both of these methods have complementary benefits and drawbacks. In this paper, we show that both of these methods are biased estimators for the real expected value of MDP states. We propose an hybrid approach that uses the best reward for states with low noise, and otherwise uses the mean. Experimental results on the Sailing MDP domain show that our method has a considerable advantage when the rewards are drawn from a noisy distribution.
Dynamic Causal Calculus

We introduce dynamic causal calculus, a nonmonotonic formalism that can be viewed as a direct logical counterpart of the action description language C+ from (Giunchiglia et al. 2004). We formulate a nonmonotonic semantics of the associated causal language, and compare this semantics with the indirect, two-stage semantics for C+, given in (Giunchiglia et al. 2004). It will be shown, in particular, that the suggested semantics allows us to alleviate syntactic distinctions between propositional atoms, maintained by C+, as well as type restrictions imposed on its causal laws. We will describe also a logical formalism of dynamic causal inference that constitutes a complete description of the logic that is adequate for this dynamic calculus.
Rowson's new crime novel released as ebook and published in USA

Silent Running the explosive new marine crime novel by Pauline Rowson is published by Severn House in ebook and in hardcover in USA and UK
Intelligent Fuzzy Controllers Laboratory

The Intelligent Fuzzy Controllers Laboratory has been developed in the Department of Electrical and Computer Engineering of Western Michigan University with the help of a DURIP grant by the Department of Defense [6] and generous donations by ABB Automation Technology Products.This new lab is to support research, the development of advanced courses, and graduate projects in the area of intelligent controls for large, complex hybrid systems.One of the targeted applications is the supervisory level of flatness control for cold rolling mills.Contemporary industrial control systems are designed using Object Oriented methods and software agents.It i s required that the system should reach its goals even when unexpected events occur in an uncertain environment.The current research focus is on fault detection and automated recovery from faults.A fuzzy automaton has been proposed to model the system at critical junctions of the control algorithm.A research project is currently underway to develop a Generic Encapsulated Fuzzy Automaton Software Agent for Object Oriented Control Systems.The aim of the project is twofold: 1) to develop and evaluate the performance of new methods and architectures for this area of research, and 2) to create a laboratory environment in which masters and doctoral students can work on independent projects as well as course assignments.In the first part of the paper, we will summarize the key research ideas behind the lab.The second part of the paper will focus on creating a software environment for conducting research and supporting graduate courses in the intelligent supervisory controls area.Students will be developing solutions for plant control problems using skeleton codes provided and will be verifying the performance of their solutions with the help of the Fuzzy Automaton Software Agent and a plant simulator program.Both the Fuzzy Automaton Software Age nt and the plant simulator are written in Java.These two programs are added to ABB's industrial software control system.The overall system will Page 9.787.
Reviews Of Curriculum Guides For Professional Software Engineers

Software Engineering (SE) is currently recognized as a stand-alone field of study within the computing disciplines enabling academia's emergence of Bachelor degrees in Software Engineering.This paper examines the recent successful accreditation in the United States of BS in Software Engineering programs by ABET to answer the question of what to teach future professional software engineers.The six accredited program's curriculum is reviewed for similarities and differences.The different licensing views for Software Engineers is presented for insight to what traditional engineering fundamentals should be part of the SE curriculum.The paper begins with the historical evolution of software engineering over the decades.The cornerstones that created the foundations of what we as educators viewed as relevant and current software engineering over the years are explored as a continuum of curriculum progress extending over three decades.The impact and involvement of the SWECC and the SWEBOK project on what we teach in software engineering curriculum are also discussed.
HATE SPEECH ON SOCIAL NETWORK SITES: PERPETRATOR AND SERVICE PROVIDERS’ LIABILITY

The article investigates the phenomenon of hate speech on social network sites and gives an overview of the national and international legal instruments which are available to combat hate speech. After an overview of the nature of hate speech andthe early international attempts to curb it, hate speech in South Africa is investigated. The question is posed whether statements of hatred made on the Internet, especially if published from sites such as Facebook which is external to South Africa, can leadto liability for perpetrators in South Africa. International responses to hate speech in cyberspace are then investigated with specific reference to the possible liability of Internet service providers for hate speech posted by third parties on their websites. Itis shown that, although service providers in the United States enjoy more protection than those in European Union, Canada and South Africa, hate speech on social network sites can be legally curbed. It is concluded that the myth that the Internet as a godless, lawless zone can and must be dismissed.
Graph Attention Tracking

Siamese network based trackers formulate the visual tracking task as a similarity matching problem. Almost all popular Siamese trackers realize the similarity learning via convolutional feature cross-correlation between a target branch and a search branch. However, since the size of target feature region needs to be pre-fixed, these cross-correlation base methods suffer from either reserving much adverse background information or missing a great deal of foreground information. Moreover, the global matching be-tween the target and search region also largely neglects the target structure and part-level information.In this paper, to solve the above issues, we propose a simple target-aware Siamese graph attention network for general object tracking. We propose to establish part-to-part correspondence between the target and the search region with a complete bipartite graph, and apply the graph attention mechanism to propagate target information from the template feature to the search feature. Further, instead of using the pre-fixed region cropping for template-feature-area selection, we investigate a target-aware area selection mechanism to fit the size and aspect ratio variations of different objects. Experiments on challenging benchmarks including GOT-10k, UAV123, OTB-100 and LaSOT demonstrate that the proposed SiamGAT outperforms many state-of-the-art trackers and achieves leading performance. Code is available at: https://git.io/SiamGAT
Incremental Learning Through Deep Adaptation

Given an existing trained neural network, it is often desirable to learn new capabilities without hindering performance of those already learned. Existing approaches either learn sub-optimal solutions, require joint training, or incur a substantial increment in the number of parameters for each added domain, typically as many as the original network. We propose a method called Deep Adaptation Modules (DAM) that constrains newly learned filters to be linear combinations of existing ones. DAMs precisely preserve performance on the original domain, require a fraction (typically 13 percent, dependent on network architecture) of the number of parameters compared to standard fine-tuning procedures and converge in less cycles of training to a comparable or better level of performance. When coupled with standard network quantization techniques, we further reduce the parameter cost to around 3 percent of the original with negligible or no loss in accuracy. The learned architecture can be controlled to switch between various learned representations, enabling a single network to solve a task from multiple different domains. We conduct extensive experiments showing the effectiveness of our method on a range of image classification tasks and explore different aspects of its behavior.
Psychometric evaluation of Simulator Sickness Questionnaire and its variants as a measure of cybersickness in consumer virtual environments

Cybersickness, i.e. visually induced motion sickness, remains as a negative effect that is detrimental to the user experience of VEs (virtual environments) developed for VR (virtual reality) consumers. As the VR technology evolves, it is rather triggered by application aspects rather than hardware limitations. For this reason, there is still a need for a measurement method to assess and compare VEs for cybersickness effects. SSQ (Simulation Sickness Questionnaire) is used for measuring users' level of sickness symptoms and is highly appreciated in VR research. However, it is criticized for its psychometric qualities and applicability in VR, as a measure of cybersickness. Recently, two variants of SSQ were offered for measuring cybersickness, CSQ (Cybersickness Questionnaire) and VRSQ (Virtual Reality Sickness Questionnaire). There is also another variant with a different factor structure, which we call FSSQ, that is based on French translation of SSQ. Our study compares SSQ and these variants for their psychometric qualities; construct validity, discriminant validity, internal reliability, test-retest reliability and sensitivity to distinguish application aspects of VEs that are related to cybersickness. Using a within-subjects experiment design, we evaluated 7 different VEs with 32 participants through 9 sessions, resulting with 288 responses to the 16-item SSQ. Results suggested that both VRSQ and CSQ were valid and reliable measures of cybersickness, as well as being sensitive to application aspects such as translational and rotational movements required by users for navigation in VEs. Compared to SSQ and FSSQ; the cybersickness questionnaires, CSQ and VRSQ, revealed better indicators of validity. On the other hand, we assume that the development of the two cybersickness scales had limitations in sample size to represent VR consumers and limitations in stimuli to represent the applications aspects of consumer VEs. We suggest further evaluation of cybersickness symptoms with larger samples and broader range of applications to identify the symptoms and the construct of a subjective measurement tool.
PREDICT &amp; CLUSTER: Unsupervised Skeleton Based Action Recognition

We propose a novel system for unsupervised skeleton-based action recognition. Given inputs of body-keypoints sequences obtained during various movements, our system associates the sequences with actions. Our system is based on an encoder-decoder recurrent neural network, where the encoder learns a separable feature representation within its hidden states formed by training the model to perform the prediction task. We show that according to such unsupervised training, the decoder and the encoder self-organize their hidden states into a feature space which clusters similar movements into the same cluster and distinct movements into distant clusters. Current state-of-the-art methods for action recognition are strongly supervised, i.e., rely on providing labels for training. Unsupervised methods have been proposed, however, they require camera and depth inputs (RGB+D) at each time step. In contrast, our system is fully unsupervised, does not require action labels at any stage and can operate with body-keypoints input only. Furthermore, the method can perform on various dimensions of body-keypoints (2D or 3D) and can include additional cues describing movements. We evaluate our system on three action recognition benchmarks with different numbers of actions and examples. Our results outperform prior unsupervised skeleton-based methods, unsupervised RGB+D based methods on cross-view tests and while being unsupervised have similar performance to supervised skeleton-based action recognition.
Revisiting Post-quantum Fiat-Shamir

The Fiat-Shamir transformation is a useful approach to building non-interactive arguments (of knowledge) in the random oracle model. Unfortunately, existing proof techniques are incapable of proving the security of Fiat-Shamir in the quantum setting. The problem stems from (1) the difficulty of quantum rewinding, and (2) the inability of current techniques to adaptively program random oracles in the quantum setting. In this work, we show how to overcome the limitations above in many settings. In particular, we give mild conditions under which Fiat-Shamir is secure in the quantum setting. As an application, we show that existing lattice signatures based on Fiat-Shamir are secure without any modifications.