Reviewerly/exHarmony huggingface dataset:
Analysis of the Artificial Neural Network Approach in the Extreme Learning Machine Method for Mining Sales Forecasting Development

Forecasting is an accurate indicator to support management decisions.This study aimed to mining sales forecasting on Indonesia's consumer goods companies with business warehouses engaged in the dynamic movement of large data using the Artificial Neural Network method.The sales forecasting used traditional method by inputting data and improvising simple patterns by collecting historical sales and remaining stock.Furthermore, several data variables in business warehouses were employed for sales forecasting.The study also used qualitative method to investigate the quality of data that cannot be measured quantitatively.The results showed with Mean Square Error score of 0.02716 in forecasting sales.The average accuracy generated by the Extreme Learning Machine after nine data tests is 111%.The result shows an opportunity for the company to further analyze the sales profit growth potential.The predicted value generated by Extreme Learning Machine for the last three months reaches 132%.The company's improved decision-making enlarge potential production line demonstrates the usefulness of this study.
EXPERIENCE USING VOICE ASSISTANTS

Natural user interfaces are becoming popular. One of the most common today is interfaces with activated voice, including smart personal assistants such as Google Assistant, Alexa, Cortana, Siri, Alice, Bixby, Mycroft. This article presents the results of their evaluation in three dimensions: capabilities, language support, and how natural responses users experience. Evaluations were performed by analyzing existing reviews. The results show that Alexa and Google Assistant are much better than Siri and Cortana. However, there is no statistically significant difference between Alexa and Google Assistant, and neither of them integrates into modern messengers with a note-taking function, which is a significant disadvantage of such devices.
Developing a Computer Simulation to Study the Behavior of Factors Affecting the Flooding of the Gash River

In recent years, the city of Kassala has suffered from frequent flooding disasters in the Gash River, which is the city's lifeblood.But the problem of frequent flooding of the river has made it a life-threatening nightmare.The importance of research lies in the fact that it is one of the few attempts to discuss and study the causes and effects of the Gash River floods.It aims to identify the factors affecting river floods.It proposes an algorithm to simulate flooding by randomly generating different factors that effectively affect river flooding.The descriptive analytical approach, the analytical, inductive approach, and the analytical deductive approach to desk research were used, taking advantage of the primary statistical method in its observation and evaluation, which relies on primary and secondary information to help make scientific, practical, and objective.The research came out with significant results related to the problems that threaten the town of Kassala from the frequent floods of the Gash River.The study's results proved that there is a deviation and discrepancy between the floods rate during the year, which gives a negative indication, and that deposited quantities vary in different proportions from one period to another, which causes a significant threat in the future.The research suggests other solutions that help reduce the problems and their effects.In addition to the above, the study proposes various recommendations that will be the basis for future studies to reach the required solutions and goals.
An Integrated Framework on Human-in-the-Loop Risk Analytics

Risk analytics is an integral component in the overall assessment of the risk profile for potential and existing obligors. For example, credit worthiness is often assessed via the use of scorecards, which are regulatory credit risk models developed based on historical data and domain expertise in banks and financial institutions. A pure statistical model, however, often fails to entertain regulatory requirements on both predictiveness and interpretability at the same time. Instead, practical risk models are developed by incorporating expert opinions within the development process, such as forcing the direction of travel for certain financial factors. In this article, the author proposes a unified framework, termed constrained and partially regularized logistic regression (CPR-LR) model, on how human inputs could be embedded in the statistical estimation procedure when developing credit risk models. By expressing such inputs as model constraints at different levels, the proposed approach serves as an effective solution to developing intuitive, easy-to-interpret, and statistically robust credit risk models, as demonstrated in the author’s experiments. This work also contributes to the growing field of human-in-the-loop model development, in which the author shows that domain expertise can be formulated as model constraints, thus biasing the resulting statistical model to be more interpretable and regulation compliant.
Pengolahan arsip dinamis inaktif sebagai upaya efektivitas dan efisiensi temu kembali arsip di Badan Perencanaan Pembangunan Daerah (BAPPEDA) Kabupaten Bantul

The Regional Development Planning Agency (BAPPEDA) of Bantul Regency is an agency engaged in research and regional development planning. This agency produces a large number of archives. Archive have a very important role in many aspects in life. Archive as an information source and an authentic evidence than can be accounted for righteousness. The archive over time will decrease the level of use and become an inactive dynamic archive. Inactive archives that are managed properly will produce archives that can be maintain information and physical archives and make it easier the process of archive retrieval. The purpose of this research was to study how to manage inactive dynamic archives and process of retrieval in BAPPEDA, Bantul Regency. This research used descriptive qualitative research method. Data collection techniques were carried out by interview, observation, and documentation with three informants with the positions of implementing archivist, PHL archive manager, and staffing administration. The data analysis was done through data reduction, data presentation, and drawing conclusions. The results showed that the processing of inactive dynamic archives in BAPPEDA Bantul office is starting from transferring, identifying archives, describing, maneuvering files, giving definitive numbers, wrapping, arranging archives in boxes, labeling, and assigning boxes on the shelf. Archive retrieval process quite well and have been using archives retrieval tools, it is Archive Listings (DPA) and laptop. There are three supporting factors for the effectiveness and efficiency of archive retrieval, that is the archive storage/processing system, human resources , and infrastructure. In conclusion, the inactive dynamic archive processing carried out at the BAPPEDA office is quite good and the retrieval time process is relatively fast.
Evolution of Random Forest from Decision Tree and Bagging: A Bias-Variance Perspective

The ensemble methods are one of the most heavily used techniques in machine learning. The random forest arguably spearheads this army of learners. Being sprung from the decision tree in the late 90s, the benefits of a random forest have rightfully attracted practitioners to widely and successfully apply this powerful yet simple-to-understand technique to numerous applications. In this study we explain the evolution of a random forest from a decision tree in the context of bias and variance of learning theory. While doing so, we focus on the interplay between the correlation and generalization error of the random forest. This analysis is expected to enrich the literature of random forests by providing further insight into its working mechanism. These insights will assist the practitioners of the random forest implement this algorithm more wisely and in an informed way. DUJASE Vol. 7(1) 67-71, 2022 (January)
Discourse-Oriented Speech Act Theory of Quotation Quotation, Indirect Quotation and Japanese Quasi-Quotation

Issues of quotation have attracted philosophers’ and linguists’ interest from the mid-20th century. Philosophers took notice of uniqueness of pure quotation, and the use-mention distinction was introduced to account for it. Theories such as (Proper) Name Theory, Demonstrative Theory and Identity Theory were advocated to describe pure quotation, while other types of quotation, namely, mixed quotation, scare/emphatic quotes and open quotation pose challenges. To adequately analyze these types of quotation, the present paper introduces a discourse-oriented speech act theory of quotation: (i) a phone, a pheme and a rheme of the locutionary act are enclosed within quotation marks, and (ii) how each contributes to the meaning of the utterance depends on the type of the illocutionary act the whole utterance performs (i.e., illocutionary-act-type sensitive). The theory is extended to explain differences between direct/mixed/open quotation (enclosed within quotation marks) and indirect quotation (without quotation marks): a direct/mixed/open quotation is used when (i) the rhetic act was significant for performing the illocutionary act in the original discourse, and (ii) indicating the rhetic act of the original discourse is important for producing illocutionary and perlocutionary act in the current discourse. Furthermore, the paper describes a type of quasi-quotation in Japanese.
Investigating the Input Validation Vulnerabilities in C Programs

Input validation is a fairly universal programming practice that helps reduce the chances of producing protectionrelated vulnerabilities in software.In this paper, an experiment is conducted to specifically determine the input validation issues found in programs and the problematic functions that lead to such issues.The experiment evaluated 12 arbitrarily selected open source C projects written by different programmers.The top two most common input validation problems are buffer overflow/XSS and potential memory mismanagement.In addition, the functions that caused the first problem are: (a) strings/text functions (e.g., strcpy and strcmp), and (b) functions that read from standard input, STDIN (e.g., scanf and gets).In contrast, the functions that caused the second problem are (a) memory allocation/deallocation functions (e.g., memmove and malloc), and (b) file manipulation functions (e.g., fopen and fseek).Furthermore, the goto construct-to a small extent-plays a role.The recommendations are that (a) developers are encouraged to use memory-safe programming languages, otherwise, they should perform different types of checks for the validity of inputs as soon as they are entered, and (b) they should have the required knowledge of secure source code and use tools/suites to manage malformed strings.
SECOND LAW OF DIALECTICS OR CONSOLIDATION OF DIGITAL RESOURCES OF ODABA LIBRARY UNDER CONDITIONS OF QUARANTINE RESTRICTIONS

Summary In connection with the pandemic, which caused the transition of educational activities to distance learning in front of the library, there was a need to review the requirements for information (resource) support for readers. The gradual transition from the usual information and library services to the process management of information and communication resources increases the efficiency of library management as a unit of the academy, focusing on consumers and their satisfaction. For its implementation in the own activity the library offers a list of modern management tools based on the quality management system: Developed model, policy, strategic goal and mission of the library, strategic development plan, drawn up a scheme of the main stages of achieving compliance with ISO 9001, made PDCA 3 cycle of continuous improvement of the quality of information and bibliographic services (https://odaba.edu.ua/library/quality-management-system). Analyzed strengths, weaknesses, opportunities and potential obstacles in the quality provision of electronic services On the basis of the Gantt chart the step-by-step use of the object-oriented approach to remote service of readers is designed and put into operation. According to the protocols of quality of results of service by library, the system of advanced training of employees is adjusted. Thus, we can conclude that the constant development, updating of existing skills and abilities of library staff in combination with the right project-oriented approach to work is becoming a traditional the practice of improving the quality of information and digital service to readers.
Implementation of the Association Method in the Analysis of Sales Data from Manufacturing Companies

The company produces sales data every day. Over time, the data increases, and the amount becomes very large. The data is only stored without understanding the benefits that exist from these data due to limitations in proper knowledge in analyzing the data, especially transaction data. Sale. To overcome these problems, a study focused on reprocessing sales transaction data in 2018 with a data mining technique approach using the Knowledge Discovery in Database (KDD) concept using the association method and apriori algorithm and a supporting application, namely RapidMiner. This study aims to help companies find customer buying habits or patterns based on 2018 sales transaction data. The results of this study produce 316 association rules where the best rules are generated on record 309 with PRO 889 &amp; PRO 868 PRO 869 rules.
Dynamic Storyboard Generation in an Engine-based Virtual Environment for Video Production

Amateurs working on mini-films and short-form videos usually spend lots of time and effort on the multi-round complicated process of setting and adjusting scenes, plots, and cameras to deliver satisfying video shots. We present Virtual Dynamic Storyboard (VDS) to allow users storyboarding shots in virtual environments, where the filming staff can easily test the settings of shots before the actual filming. VDS runs on a "propose-simulate-discriminate" mode: Given a formatted story script and a camera script as input, it generates several character animation and camera movement proposals following predefined story and cinematic rules to allow an off-the-shelf simulation engine to render videos. To pick up the top-quality dynamic storyboard from the candidates, we equip it with a shot ranking discriminator based on shot quality criteria learned from professional manual-created data. VDS is comprehensively validated via extensive experiments and user studies, demonstrating its efficiency, effectiveness, and great potential in assisting amateur video production.
Don't Explain Noise: Robust Counterfactuals for Randomized Ensembles

Counterfactual explanations describe how to modify a feature vector in order to flip the outcome of a trained classifier. Obtaining robust counterfactual explanations is essential to provide valid algorithmic recourse and meaningful explanations. We study the robustness of explanations of randomized ensembles, which are always subject to algorithmic uncertainty even when the training data is fixed. We formalize the generation of robust counterfactual explanations as a probabilistic problem and show the link between the robustness of ensemble models and the robustness of base learners. We develop a practical method with good empirical performance and support it with theoretical guarantees for ensembles of convex base learners. Our results show that existing methods give surprisingly low robustness: the validity of naive counterfactuals is below $50\%$ on most data sets and can fall to $20\%$ on problems with many features. In contrast, our method achieves high robustness with only a small increase in the distance from counterfactual explanations to their initial observations.
SoftTreeMax: Exponential Variance Reduction in Policy Gradient via Tree Search

Despite the popularity of policy gradient methods, they are known to suffer from large variance and high sample complexity. To mitigate this, we introduce SoftTreeMax -- a generalization of softmax that takes planning into account. In SoftTreeMax, we extend the traditional logits with the multi-step discounted cumulative reward, topped with the logits of future states. We consider two variants of SoftTreeMax, one for cumulative reward and one for exponentiated reward. For both, we analyze the gradient variance and reveal for the first time the role of a tree expansion policy in mitigating this variance. We prove that the resulting variance decays exponentially with the planning horizon as a function of the expansion policy. Specifically, we show that the closer the resulting state transitions are to uniform, the faster the decay. In a practical implementation, we utilize a parallelized GPU-based simulator for fast and efficient tree search. Our differentiable tree-based policy leverages all gradients at the tree leaves in each environment step instead of the traditional single-sample-based gradient. We then show in simulation how the variance of the gradient is reduced by three orders of magnitude, leading to better sample complexity compared to the standard policy gradient. On Atari, SoftTreeMax demonstrates up to 5x better performance in a faster run time compared to distributed PPO. Lastly, we demonstrate that high reward correlates with lower variance.
Mono-STAR: Mono-camera Scene-level Tracking and Reconstruction

We present Mono-STAR, the first real-time 3D reconstruction system that simultaneously supports semantic fusion, fast motion tracking, non-rigid object deformation, and topological change under a unified framework. The proposed system solves a new optimization problem incorporating optical-flow-based 2D constraints to deal with fast motion and a novel semantic-aware deformation graph (SAD-graph) for handling topology change. We test the proposed system under various challenging scenes and demonstrate that it significantly outperforms existing state-of-the-art methods.
Deep Monocular Hazard Detection for Safe Small Body Landing

Hazard detection and avoidance is a key technology for future robotic small body sample return and lander missions. Current state-of-the-practice methods rely on high-fidelity, a priori terrain maps, which require extensive human-in-the-loop verification and expensive reconnaissance campaigns to resolve mapping uncertainties. We propose a novel safety mapping paradigm that leverages deep semantic segmentation techniques to predict landing safety directly from a single monocular image, thus reducing reliance on high-fidelity, a priori data products. We demonstrate precise and accurate safety mapping performance on real in-situ imagery of prospective sample sites from the OSIRIS-REx mission.
Contextual Dynamic Prompting for Response Generation in Task-oriented Dialog Systems

Response generation is one of the critical components in task-oriented dialog systems. Existing studies have shown that large pre-trained language models can be adapted to this task. The typical paradigm of adapting such extremely large language models would be by fine-tuning on the downstream tasks which is not only time-consuming but also involves significant resources and access to fine-tuning data. Prompting (Schick and Sch\"utze, 2020) has been an alternative to fine-tuning in many NLP tasks. In our work, we explore the idea of using prompting for response generation in task-oriented dialog systems. Specifically, we propose an approach that performs contextual dynamic prompting where the prompts are learnt from dialog contexts. We aim to distill useful prompting signals from the dialog context. On experiments with MultiWOZ 2.2 dataset (Zang et al., 2020), we show that contextual dynamic prompts improve response generation in terms of combined score (Mehri et al., 2019) by 3 absolute points, and a massive 20 points when dialog states are incorporated. Furthermore, human annotation on these conversations found that agents which incorporate context were preferred over agents with vanilla prefix-tuning.
Emergence of Maps in the Memories of Blind Navigation Agents

Animal navigation research posits that organisms build and maintain internal spatial representations, or maps, of their environment. We ask if machines -- specifically, artificial intelligence (AI) navigation agents -- also build implicit (or 'mental') maps. A positive answer to this question would (a) explain the surprising phenomenon in recent literature of ostensibly map-free neural-networks achieving strong performance, and (b) strengthen the evidence of mapping as a fundamental mechanism for navigation by intelligent embodied agents, whether they be biological or artificial. Unlike animal navigation, we can judiciously design the agent's perceptual system and control the learning paradigm to nullify alternative navigation mechanisms. Specifically, we train 'blind' agents -- with sensing limited to only egomotion and no other sensing of any kind -- to perform PointGoal navigation ('go to $\Delta$ x, $\Delta$ y') via reinforcement learning. Our agents are composed of navigation-agnostic components (fully-connected and recurrent neural networks), and our experimental setup provides no inductive bias towards mapping. Despite these harsh conditions, we find that blind agents are (1) surprisingly effective navigators in new environments (~95% success); (2) they utilize memory over long horizons (remembering ~1,000 steps of past experience in an episode); (3) this memory enables them to exhibit intelligent behavior (following walls, detecting collisions, taking shortcuts); (4) there is emergence of maps and collision detection neurons in the representations of the environment built by a blind agent as it navigates; and (5) the emergent maps are selective and task dependent (e.g. the agent 'forgets' exploratory detours). Overall, this paper presents no new techniques for the AI audience, but a surprising finding, an insight, and an explanation.
Disentangling Model Multiplicity in Deep Learning

Model multiplicity is a well-known but poorly understood phenomenon that undermines the generalisation guarantees of machine learning models. It appears when two models with similar training-time performance differ in their predictions and real-world performance characteristics. This observed 'predictive' multiplicity (PM) also implies elusive differences in the internals of the models, their 'representational' multiplicity (RM). We introduce a conceptual and experimental setup for analysing RM by measuring activation similarity via singular vector canonical correlation analysis (SVCCA). We show that certain differences in training methods systematically result in larger RM than others and evaluate RM and PM over a finite sample as predictors for generalizability. We further correlate RM with PM measured by the variance in i.i.d. and out-of-distribution test predictions in four standard image data sets. Finally, instead of attempting to eliminate RM, we call for its systematic measurement and maximal exposure.