Reviewerly/exHarmony huggingface dataset:
Limits and Risks of Digital Transformation

Currently, the process of digital transformation is actively going on in the economy, science, education, and society as a whole. This process has a number of restrictions and risks we consider. The mathematical theory of complexity reveals a large class of the restrictions. The exact solution of a number of simple-looking problems with a small amount of input data requires resources many times greater than the capabilities of all available computers. On the "border" between natural and artificial intelligence lies the "cognitive barrier". This, as a rule, makes it impossible to use the results of a number of artificial intelligence systems to adjust our strategies. We and computers "think" differently. They have to be considered as "black boxes". It is very likely that the tester of artificial intelligence systems will become one of the mass professions in the close future. We give examples to show that the "translation" from "continuous" to "discrete" language can lead to qualitatively different behavior of mathematical models. In a number of problems associated with a computational experiment this can be quite significant. Great risks arise when passing to the "fast world", approaching the "Lem's barrier". It happens when artificial intelligence systems are assigned strategically important tasks that they must solve at a speed inaccessible to humans. The analysis shows that managing the risks of digital transformation and its limitations requires the attention of the scientific and expert community, as well as active participants in this process.
MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation

The recent breakthroughs of Neural Architecture Search (NAS) have motivated various applications in medical image segmentation. However, most existing work either simply rely on hyper-parameter tuning or stick to a fixed network backbone, thereby limiting the underlying search space to identify more efficient architecture. This paper presents a Multi-Scale NAS (MS-NAS) framework that is featured with multi-scale search space from network backbone to cell operation, and multi-scale fusion capability to fuse features with different sizes. To mitigate the computational overhead due to the larger search space, a partial channel connection scheme and a two-step decoding method are utilized to reduce computational overhead while maintaining optimization quality. Experimental results show that on various datasets for segmentation, MS-NAS outperforms the state-of-the-art methods and achieves 0.6-5.4% mIOU and 0.4-3.5% DSC improvements, while the computational resource consumption is reduced by 18.0-24.9%.
SeqHAND:RGB-Sequence-Based 3D Hand Pose and Shape Estimation

3D hand pose estimation based on RGB images has been studied for a long time. Most of the studies, however, have performed frame-by-frame estimation based on independent static images. In this paper, we attempt to not only consider the appearance of a hand but incorporate the temporal movement information of a hand in motion into the learning framework for better 3D hand pose estimation performance, which leads to the necessity of a large scale dataset with sequential RGB hand images. We propose a novel method that generates a synthetic dataset that mimics natural human hand movements by re-engineering annotations of an extant static hand pose dataset into pose-flows. With the generated dataset, we train a newly proposed recurrent framework, exploiting visuo-temporal features from sequential images of synthetic hands in motion and emphasizing temporal smoothness of estimations with a temporal consistency constraint. Our novel training strategy of detaching the recurrent layer of the framework during domain finetuning from synthetic to real allows preservation of the visuo-temporal features learned from sequential synthetic hand images. Hand poses that are sequentially estimated consequently produce natural and smooth hand movements which lead to more robust estimations. We show that utilizing temporal information for 3D hand pose estimation significantly enhances general pose estimations by outperforming state-of-the-art methods in experiments on hand pose estimation benchmarks.
Linux Security using Blockchain

Blockchain is a secure and distributed ledger structure and each block is a cryptographic hash of some other factors like a timestamp.The chain is formed by linking the blocks and distributed data is present on multiple computers.The blockchain made system are more fault-tolerant and has high availability even in times of database failures.Though Linux is considered a benchmark for security, it is susceptible to various attacks.With the use of blockchain, Linux logs can be made more transparent among multiple root users and secured by hosting the logs on a decentralized Ethereum blockchain which is customized as per the requirement.It has been observed that in a scenario with multiple root users working simultaneously on same system, transparency between multiple root users can be compromised if some appropriate changes are made in few lines containing the history, Hence, with the help of Ethereum blockchain and log monitoring using log monitors, it is much easier to track the intrusions to identify the source of unauthorized access to logs or changes in them.
Machine Translation from Japanese to Robot Language for Human-Friendly Communication

Humanoid robots should be capable of communicating naturally with humans to act as good partners to humans. However, there are several challenges associated with ensuring that humanoid robots are able to speak human languages. For example, to enable the humanoid robot NAO/Pepper of SoftBank Robotics Corp. to speak human-friendly Japanese, sentences in Japanese must first be translated into robot language. The target language is manually converted to the robot language to ensure that humanoid robots can communicate appropriately using human languages. Two facets ought to be incorporated into such translations: a dictionary for correct pronunciation must be created and another dictionary for expressing emotions must be created. This paper describes an approach that enables the humanoid robot NAO/Pepper to execute human-friendly communication. We manually prepared 32,293 pairs of sentences in Japanese and robot language as training data. Further, we created dictionaries to associate the correct pronunciations and emotional expressions with each pair of sentences using the morphemic analyzer MeCab. Finally, we developed a machine translation system by incorporating these dictionaries to translate Japanese to robot language. The performance of the machine translation system was verified using test sentences, and the results demonstrate the effectiveness of the proposed machine translation system, although some challenges still remain.
Research on the Influence of Entrepreneurs’ Creative Thinking and Optimism Degree on Innovation Opportunity Identification

Innovative entrepreneurial activities originate from the identification of entrepreneurs' opportunities for innovation.Using the questionnaire survey method, the hypothesis of complex relationship between entrepreneurial creative thinking, optimism degree and innovation opportunity identification was proposed and tested.The results show that there is a significant inverted U-shaped relationship between entrepreneurial creative thinking, optimism degree and innovation opportunity identification; Optimism degree positively regulates the promoting effect of creative thinking on innovation opportunity identification.
Detection and Prevention of Session Hijacking in Web Application Management

Web applications are programs that are available on the go.The increase in the number of customers accessing the web demands for technological complexity to manage the operation.The session established between the user and the server can be hijacked by an attacker by masquerading as an authorized user called Man-in-the-Middle (MITM).The target of the attacker is to have access to users' confidential records in the server for their own financial gain.It was predicted by Juniper research that by 2023 over 146 billion records will be tampered with and also electronic commerce will progressively increase by 66% in 2024 as the number on online transaction reaches $18.7 trillion.The security of Web applications have been a great concern to many online services.The paper, therefore developed a web application for e-Commerce for the detection and prevention of session hijacking in order to protect individual records from unauthorized user.
A Mutation Triggering Method for Genetic Algorithm to Solve Traveling Salesman Problem

This chapter reveals the effect of combining mutation operators in Genetic Algorithm. Though mutation is a very effective genetic operator to escape the local optima, it has an adverse effect on computation time. It is quite a challenge to say how much probability is appropriate for mutation. The major contribution of this chapter is to design a mutation triggering method by combining three mutation operators (Swap, Insertion and 2-Opt) and applying adaptive probability. To decide which mutation can be activated at a given generation, a decision-making method named Mutation Triggering Method is proposed. To measure the performance of the proposed method, the classical traveling salesman problem was considered. The experimental result shows that the proposed method was able to find a better solution (travel cost) than the other approaches. For computation time, the proposed strategy did as well as other mutation strategies. In conclusion, the combination of several mutation operators can ensure the benefits of diversity as well as the benefits of faster convergence.
Broadening Label-based Argumentation Semantics with May-Must Scales (May-Must Argumentation)

The semantics as to which set of arguments in a given argumentation graph may be acceptable (acceptability semantics) can be characterised in a few different ways. Among them, labelling-based approach allows for concise and flexible determination of acceptability statuses of arguments through assignment of a label indicating acceptance, rejection, or undecided to each argument. In this work, we contemplate a way of broadening it by accommodating may- and must- conditions for an argument to be accepted or rejected, as determined by the number(s) of rejected and accepted attacking arguments. We show that the broadened label-based semantics can be used to express more mild indeterminacy than inconsistency for acceptability judgement when, for example, it may be the case that an argument is accepted and when it may also be the case that it is rejected. We identify that finding which conditions a labelling satisfies for every argument can be an undecidable problem, which has an unfavourable implication to existence of a semantics. We propose to address this problem by enforcing a labelling to maximally respect the conditions, while keeping the rest that would necessarily cause non-termination labelled undecided. Several semantics will be presented and the relation among them will be noted. Towards the end, we will touch upon possible research directions that can be pursued further.
Towards a practical measure of interference for reinforcement learning

Catastrophic interference is common in many network-based learning systems, and many proposals exist for mitigating it. But, before we overcome interference we must understand it better. In this work, we provide a definition of interference for control in reinforcement learning. We systematically evaluate our new measures, by assessing correlation with several measures of learning performance, including stability, sample efficiency, and online and offline control performance across a variety of learning architectures. Our new interference measure allows us to ask novel scientific questions about commonly used deep learning architectures. In particular we show that target network frequency is a dominating factor for interference, and that updates on the last layer result in significantly higher interference than updates internal to the network. This new measure can be expensive to compute; we conclude with motivation for an efficient proxy measure and empirically demonstrate it is correlated with our definition of interference.
Finding distinguishers for pseudorandom number generators based on permutations

In this paper, we consider properties of a pseudorandom number generator (PRNG) based on two random permutations. We investigate the possibility to distinguish this PRNG from a true random number generator. A black-box setting is used when a generator is put in the box, the output sequences from that box are available to the adversary, and also she can restart the box as many times as needed not changing the generator in it. The adversary tries to find out which generator is in the box. We built two statistical distinguishers. One of them is based on the probabilities to observe some particular types of output sequences of finite lengths. The other one checks all output sequences of small fixed lengths. For both distinguishers exact probabilities to observe particular outputs were computed. It was found that both distinguishers require a very large amount of the PRNG output to analyze before they can tell the difference with high certainty. In particular the best distinguisher found requires no less than $$O\left( (2^n)^2\right) $$ restarts and altogether at least $$O\left( (2^n)^3\right) $$ symbols for analysis in case of n-bit permutations. This indicates that PRNGs on two permutations exhibit very good probabilistic properties close to those of a true RNG.
Research on the Application of Automatic Monitoring Software for Seismic Observation Instruments

Abstract Combining with the actual monitoring work of seismic geophysical observation stations, a seismic monitoring instrument automatic monitoring software was developed. The software is suitable for real-time or regular automatic network monitoring of IP instruments and equipment under different network environments. Automatic monitoring of the clock, status and observation data of geophysical observation instruments, which can automatically identify invalid data and abnormal amplitude changes in the observation data of the day, interference, earthquake and amplitude limitation, etc. Without other hardware and software, the alarm information can be sent to the duty personnel and management personnel by SMS, which can realize automatic backup and manual recovery of the original observation data of the instrument, and One-click restart and clock correction and other control operations, you can view the integrity and validity of the observed data curve and analysis data of the day without cross-platform operation, thereby achieving unified and efficient management of the instrument.
Perancangan Knowledge Management System pada Komite Teknis

Standar Nasional Indonesia (SNI) has important role, among others, for consumer protection.The formulation and review of SNI is carried out by Technical Committee involving expert, government, business actor, and consumer.Document storage is not integrated yet and the absences of good knowledge-management-system cause several processes in formulation and review to run less smoothless.
HyperGrid: Efficient Multi-Task Transformers with Grid-wise Decomposable Hyper Projections

Achieving state-of-the-art performance on natural language understanding tasks typically relies on fine-tuning a fresh model for every task. Consequently, this approach leads to a higher overall parameter cost, along with higher technical maintenance for serving multiple models. Learning a single multi-task model that is able to do well for all the tasks has been a challenging and yet attractive proposition. In this paper, we propose \textsc{HyperGrid}, a new approach for highly effective multi-task learning. The proposed approach is based on a decomposable hypernetwork that learns grid-wise projections that help to specialize regions in weight matrices for different tasks. In order to construct the proposed hypernetwork, our method learns the interactions and composition between a global (task-agnostic) state and a local task-specific state. We apply our proposed \textsc{HyperGrid} on the current state-of-the-art T5 model, demonstrating strong performance across the GLUE and SuperGLUE benchmarks when using only a single multi-task model. Our method helps bridge the gap between fine-tuning and multi-task learning approaches.
Preference Similarity Analysis of User Preference Rules Using a Character Coordination System

In this study, we analyzed user preference rules obtained from a character coordination system using the Kansei retrieval agent (KaRA) model with fuzzy inference for the acquisition of user rules related to user preferences. The preference rules of the character coordination system were expressed by if-then rules. Previous studies have demonstrated that the character coordination system was effective in terms of acquiring preference rules that were the evaluation criteria of users. In this study, we measured the similarity of preference rules by calculating the distance between preference rules. As a result, the proposed system generated fuzzy rules that matched with more than 60% of the users’ preferences. The similarity with antecedent labels (if part) of each was high when the consequent labels (then part) were the same.
A Logical Interpretation of Silence

In human communication process, we often face situations where decisions have to be made, regardless of silence of one of the interlocutors. That is, we have to decide from incomplete information, guessing the knowledge or intentions of the silent person. This behaviour has been studied by several disciplines but barely touched in logic or artificial intelligence. After reviewing some previous studies of silence and conversational implicature of Grice, we focus on apuzzle formerly expressed and solved in Answer Set Programming, to analyze the implications of two different interpretations of silence (Defensive and Acquiescent Silence), in terms of the Says () predicate. Several conclusions are derived from the different possibilities that opened for analysis. In addition, a general strategy for analysis of problems involving testimonies and silence is proposed.
A Memetic Approach for the Orienteering Problem

In this paper we present a new memetic approach to solve the orienteering problem. The key method of our proposal is the procedure ReduceExtend which, starting from a permutation of all the vertices in the orienteering problem, produces a feasible path with a locally optimal score. This procedure is coupled with an evolutionary algorithm which navigate the search space of permutations. In our experiments we have considered the following algorithms: the algebraic differential evolution algorithm, and the three continuous algorithms CMA-ES, DE and PSO equipped with the random key technique. The experimental results show that the proposed approach is competitive with the state of the art results of some selected benchmark instances .
Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion

Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. Although several efforts have been made for CRS, two major issues still remain to be solved. First, the conversation data itself lacks of sufficient contextual information for accurately understanding users' preference. Second, there is a semantic gap between natural language expression and item-level user preference. To address these issues, we incorporate both word-oriented and entity-oriented knowledge graphs (KG) to enhance the data representations in CRSs, and adopt Mutual Information Maximization to align the word-level and entity-level semantic spaces. Based on the aligned semantic representations, we further develop a KG-enhanced recommender component for making accurate recommendations, and a KG-enhanced dialog component that can generate informative keywords or entities in the response text. Extensive experiments have demonstrated the effectiveness of our approach in yielding better performance on both recommendation and conversation tasks.
EVALUATING THE FIVE KAIZEN SUCCESS MEASUREMENTS THROUGH EMPLOYEES WORK IMPROVEMENT AND ITS EFFECTS ON OVERALL WORK AND QUALITY OF SERVICES: EMPIRICAL STUDY OF INSURANCE COMPANIES IN JORDAN

The study aimed to understand the impact of the five Kaizen success measurements (Organizational Policy and Awareness, Education and training, Cultural factors, Internal processes assessment and recognition) on the overall quality of work and the quality through by Employees Work Improvement as mediated factor on Insurance Companies in Amman. The study used a questionnaire to gather data from distributed on the 300 employees used random sample was applied to the population of this study consisting of all executives, department managers and employees of the Insurance Companies in Amman, Jordan. The questionnaire consists of three sections. Least Square-Structure Equation Modeling (PLS-SEM) program was chosen and used in this study in an effort to examine and test the data associated with the hypotheses. The results show Kaizen methods positively effect on the employee work improvement of the companies of. The kaizen implement employee work improvement positively effect on overall work and quality of service, Kaizen implementation positively affects overall work and quality of service, Employee work improvement on overall work and quality of service is partially mediated between kaizen implementation and on overall work and quality of service of Insurance companies in Jordan. Additionally, small companies can use Kaizen to enhance the overall work with low cost compared to other methods of improvement, which can be of a high cost that small companies cannot afford. Keywords: Kaizen implementation, Education and training, Cultural factors, Employees work improvement, Overall work and quality of services, Insurance Companies JEL Classifications: G22, P36 DOI: https://doi.org/10.32479/irmm.9994
A Personal Health-Tracking System Focused on Social Communication for Motivation

Personal health-tracking technology which is to record and improve health is becoming popular. The personal health-tracking tools collect user's living data and analyze it, planning for user to change his unhealthy behavior. However, there is was a lack of motivation in the current personal health-tracking system because it mainly focused on individuals. The importance of others in health behaviors and in personal health-tracking was well-established but designer still focused on the individual. How to design a part which allows other users to take part in personal health-tracking technologies system is a new challenge. Our research aims to help people to reach the right audiences and guidance when they share health information to get the motivation to change their behavior to maintain health. We designed a system to help people to finish the process: record data, find friends and coach, change behavior, help others. We use Augmented Reality technology to make the communication and interaction smooth. Also, an Augmented Reality game part is designed in our system to offer people more motivation to change their behavior. Preliminary experiment was carried out to verify the system is effective in terms of providing people with motivation.