Reviewerly/exHarmony huggingface dataset:
Online Discussion Support System with Facilitation Function

In this paper, we present a discussion board system (DBS) with a facilitator function that we developed for the purpose of facilitating discussions that require decision-making that incorporates diverse values and opinions.Its function is to constantly extract nouns from the utterances of discussion participants and display them on the DBS on each participant's personal computer.Items to be decided in the discussion are displayed together with a frame (called a "box"), and each participant puts the displayed keywords in the box according to their own opinions and intentions.No other participant can see what the individual is doing.The color of any keyword that all participants put in the same box changes to green.Furthermore, comments are automatically presented based on the time when each participant last spoke and the time when the keyword was moved.This is intended to encourage participants who appear to be less involved to join the discussion.Experiments with the DBS suggested that it might be possible to capture the will of participants who disagree but do not speak.However, it was also deemed necessary to post comments that encourage participants to express their intentions independently, and to have a mechanism that can link the motivated manifestation of intentions to appropriate actions.
Ground-Level Pressing Issues at the Intersection of AI and IP

Humankind has always sought to solve problems. This impetus has transformed hunters and gatherers into a society beginning to enjoy the fruits of the fourth industrial revolution. As part of the fourth industrial revolution, and the increased computing power accompanying it, the long-theorized concept of artificial intelligence (“AI”) is finally becoming a reality. This raises new issues in myriad fields—from the moral and ethical implications of replacing human activity with machines to who will own inventions created by AI. While these questions are worth exploring, they have already received a fair amount of coverage in popular and theoretical writing. This paper will take a different direction, focusing on the current and near-future issues arising on the ground at the intersection of AI and intellectual property (“IP”). After providing a brief overview of AI, we will analyze legal issues unique to AI, including access to data, patent requirements, open source licenses and trade secrecy. We will then suggest best practices for obtaining and preserving IP protection for AI-related innovations through the United States and European Union IP systems. By addressing these issues, the intellectual property system will be better positioned to do its part in unlocking AI’s immense potential.
SISTEM MANAJEMEN SANGGAR SENI SARAI SARUMPUN DI KOTA PADANG

Abstract This article aims to describe and explain the management system of the art workshop Sarai Sarumpun in order to manage the bun well to be able to set the schedule to appear so much in one day. This type of research is qualitative research with a descriptive method. The main instrument in this research is its own researchers and assisted with supporting instruments such as notebooks, note taking tools, video cameras, photo cameras, and tape recorders. The data types in this study are primary data and secondary data. Data collection techniques are conducted by way of library studies, observation, interviews, documentation. The steps for analyzing data are data reduction, data presentation and withdrawal of conclusions. The results showed that the management system run by the Art Gallery of Sarai Sarumpun can run well. The management system carried out by the art Snggar Sarai Sarumpun is included in the organization of the line (a form of organization where direct authority is in a vertical and fully from leadership to its subordinates). The advantage of the line organization, all decisions can be resolved directly because in a particular situation the leader becomes one of the main targets in problem solving. Disadvantages of the line organization, if there is an issue or problem can not be resolved quickly because the leader is not in place. Thus the management of the line Oganisasi to solve the problem or problems in the workshop, the head of the workshop became the first person to complete and resolve the issue. Keywords: management system, art of gallery Sarai Sarumpun
Ranking Association Rules from Data Mining for Health Outcomes: A Case Study of Effect of Industrial Airborne Pollutant Mixtures on Birth Outcomes

Association rule mining can be a powerful computational tool for exploring complex interactions between high-dimensional exposures and health outcomes. Given the high-dimensional nature of the data, many complex association rules may be identified. To narrow down on the most important rules for hypothesis-generating and future investigation in the context of health research, we need an objective approach to reduce the ruleset. The ranking is often based on the lift, a widely used measure of association strength in data mining. In this paper, we show why the lift-based ranking is undesirable from a population health perspective. We propose a new approach to select rules obtained from association rule mining. This new approach considers both association strength measured by relative risk and the excessive health burden in the target population. We use a case study of rules mined from industrial airborne pollutant mixtures and birth outcomes, comparing rules selected using our proposed approach to those selected using lift.
On Conducting Tests in Software Engineering Courses during the COVID-19 Pandemic (S)

The circumstances presented by the COVID-19 pandemic have had a severely debilitating impact on education, in general, and software engineering education, in particular.This paper describes the author's experience over three semesters in conducting oral and written tests in six software engineering-related courses with around 500 students overall.The technical as well as non-technical challenges encountered are discussed, and educational lessons based on the reactions and responses of students are given.The proposed concepts, decisions, and processes herein are effectively generalizable and potentially applicable to other types of courses and to (not necessarily pandemic-related) online teaching-related situations in general.
Unveiling MIMETIC: Interpreting Deep Learning Traffic Classifiers via XAI Techniques

The widespread use of powerful mobile devices has deeply affected the mix of traffic traversing both the Internet and enterprise networks (with bring-your-own-device policies). Traffic encryption has become extremely common, and the quick proliferation of mobile apps and their simple distribution and update have created a specifically challenging scenario for traffic classification and its uses, especially network-security related ones. The recent rise of Deep Learning (DL) has responded to this challenge, by providing a solution to the time-consuming and human-limited handcrafted feature design, and better clas-sification performance. The counterpart of the advantages is the lack of interpretability of these black-box approaches, limiting or preventing their adoption in contexts where the reliability of results, or interpretability of polices is necessary. To cope with these limitations, eXplainable Artificial Intelligence (XAI) techniques have seen recent intensive research. Along these lines, our work applies XAI-based techniques (namely, Deep SHAP) to interpret the behavior of a state-of-the-art multimodal DL traffic classifier. As opposed to common results seen in XAI, we aim at a global interpretation, rather than sample-based ones. The results quantify the importance of each modality (payload- or header-based), and of specific subsets of inputs (e.g., TLS SNI and TCP Window Size) in determining the classification outcome, down to per-class (viz. application) level. The analysis is based on a publicly-released recent dataset focused on mobile app traffic.
Enhancing the Software Clone Detection in BigCloneBench

In the software system, the code snippets that are copied and pasted in the same software or another software result in cloning. The basic cause of cloning is either a programmer‘s constraint or language constraints. An increase in the maintenance cost of software is the major drawback of code clones. So, clone detection techniques are required to remove or refactor the code clone. Recent studies exhibit the abstract syntax tree (AST) captures the structural information of source code appropriately. Many researchers used tree-based convolution for identifying the clone, but this technique has certain drawbacks. Therefore, in this paper, the authors propose an approach that finds the semantic clone through square-based convolution by taking abstract syntax representation of source code. Experimental results show the effectiveness of the approach to the popular BigCloneBench benchmark.
Smoothing Dialogue States for Open Conversational Machine Reading

Conversational machine reading (CMR) requires machines to communicate with humans through multi-turn interactions between two salient dialogue states of decision making and question generation processes.In open CMR settings, as the more realistic scenario, the retrieved background knowledge would be noisy, which results in severe challenges in the information transmission.Existing studies commonly train independent or pipeline systems for the two subtasks.However, those methods are trivial by using hard-label decisions to activate question generation, which eventually hinders the model performance.In this work, we propose an effective gating strategy by smoothing the two dialogue states in only one decoder and bridge decision making and question generation to provide a richer dialogue state reference.Experiments on the OR-ShARC dataset show the effectiveness of our method, which achieves new state-of-the-art results.
DIGITAL MUSICAL INSTRUMENTS, MAKING AND LEARNING

This paper offers an overview of the activities of the interdisciplinary project Sparkling Instruments. This STEAM project used playful design and the technical development of digital musical instruments so as to raise interest in school subjects such as computer science, music and physics. With musicians, game designers and music technology experts, we presented a series of workshops that showed how such a project can be implemented in schools. We then presented our findings from the perspectives of students, teachers, the school and the project.
Metareasoning for Interleaved Planning and Execution

Agents that plan and act in the real world must deal with the fact that time passes as they are planning. In the presence of tight deadlines, there may be insufficient time to complete the search for a plan before it is time to act. One can gain additional time to search by starting to act before a complete plan is found, incurring the risk of making incorrect action choices. This tradeoff between opportunity and risk, inherent in interleaving planning and execution, is a non-trivial metareasoning problem addressed in this paper.
Mental Health Education for Students from Junior Colleges Moving into Undergraduate Level in Private Colleges from the Perspective of Three-Dimensional Education

Good psychological quality is the basic requirement for college students in the new era. Students from junior colleges moving into undergraduate level in private colleges are a special group as they are already an important part of students in private colleges. Based on the mental health status of these students, private colleges have analyzed the issues of mental health education which include weak teaching staffs, lack attention, and traditional education methods. From the perspective of three-dimensional education, the psychological education work model in private colleges is explored to build an education team to promote the participation of all employees in the work of psychological education, strengthen the attention of college students who are moving into the undergraduate level, and to pay full attention to these students. Private colleges should try to innovate education methods and improve psychological education in all aspects.
Toward an Incremental Classification Process of Document Stream Using a Cascade of Systems

In the context of imbalanced classification, deep neural networks suffer from the lack of samples provided by low represented classes. They can’t train enough their weights with a statistically reliable set. All solutions in the state of the art that could offer better performance for those classes, sacrifice in return a huge part of their precision on bigger classes. In this paper, we propose a solution to this problem by introducing a system cascade concept that could integrate deep neural network. This system is designed to keep as mush as possible the original network performance while it reinforces the classification of the minor classes by the addition of stages with more specialised systems. This cascade offers the possibility to integrate few-shot learning or incremental architecture following the deep neural network without major restrictions on system internal architecture. Our method keeps intact or slightly improves the performances of a deep neural network (used as first stage) in conventional cases and improves performances in strongly imbalanced cases by around +8% accuracy.
A corpus-based approach in archaeolinguistics

The article focuses on archaeolinguistics as a separate field of knowledge and outlines the features that distinguish it from other disciplines in comparative studies. It analyses the existing text collections and shows how they may find application in a corpus-based research in ancient languages. It also discusses approaches to creating new corpora of texts. The study focuses on Old Church Slavonic and Ancient Greek, in particular, it analyses the existing corpora in these languages, e. g., Corpus Cyrillo-Methodianum Helsingiense. Most of the corpora under study are not tagged. Some of them change the original writing system (from Glagolitic to Latin, using, for instance, ASCII), while the others have a restricted access. Some of the corpora are no longer available at all or available as part of local databases only. Thus, corpus-based resources in ancient languages in question are obviously insufficient. To facilitate more effective research, the easiest possible solution is to develop new corpora by using platforms specializing in linguistic analysis (e. g., CDLI or Lingvodoc) or systems that support DIY corpora. However, such platforms are often paywalled, may have limited functionality, or lack comprehensive user guides. With all the above in mind, there seems to be no ready solution for archaeolinguists who want to use a corpus-based approach in their study. They either have to make a considerable effort to modify an existing system for their purposes, or to build one of their own. In conclusion, the article proposes one of the possible ways to address these issues.
Clausal Arguments and Peripheries in ASD-STE100: the Parsing of Subordination in ARTEMIS

espanolUno de los principales objetivos del procesamiento del lenguaje natural es la simulacion de la comprension del mismo. Entre sus aplicaciones, ARTEMIS se basa en el modelo teorico de la GPR, cuyo algoritmo de enlace fundamenta su proceso de parseado. Componente esencial de ARTEMIS es el modulo EDG, en donde se almacenan las reglas de produccion y las MAVs, vitales para el proceso de generacion de expresiones del lenguaje natural. Ya se han descrito las reglas de produccion para el sintagma, la oracion simple y la subordinada adverbial, pero aun quedan por desarrollar otras estructuras complejas y contribuir al enriquecimiento de las reglas de parseado para la oracion subordinada en ARTEMIS. Teniendo en cuenta la validacion a la que estas reglas deberan someterse, este trabajo se centra en el diseno de reglas de produccion para las subordinadas detectadas en el lenguaje controlado ASD-STE100 EnglishOne of the main objectives of Natural Language Processing is the simulation of natural language understanding. Within its applications, ARTEMIS (Automatically Representing TExt Meaning via an Interlingua-Based System) is theoretically grounded in RRG, whose linking algorithm lies at the basis of our interlingua-based system. A fundamental component of ARTEMIS is the GDE module, where feature-based production rules and AVMs are stored to allow the generation of natural language expressions. Production rules for phrasal constituents, simple sentences and adverbial subordinates have already been described, but it is now turn to delve into other types of complex structures and generally contribute to the enhancement of parsing rules for clausal subordination in ARTEMIS. Bearing in mind the validation process these production rules should undergo, as well as the common problems that may arise in such parsing applications, our research will concentrate, precisely, on the analysis of these subordinate structures as found in a CNL like ASD-STE100
Seasonal variation of some atmospheric parameters in fresh water swamp and Sudan Savanna areas of Nigeria

&#x0D; &#x0D; &#x0D; The year in year out variation in atmospheric parameters, solar radiation, and meteorological variables such as ambient temperature, relative humidity RH, wind speed etc, are posies that can be and are used to describe the atmospheric conditions. Ten years of data obtained from the Nigerian Meteorological Agency (NiMet) was analysed. Results showed that solar radiation rises from January to get to its peak in April which is maintained till August before it begins to fall again with the Sudan savanna area (Maiduguri) having a value of (15.70 MJm-2month-1) and freshwater swamp area (Ikeja) having the value of (10.16 MJm-2month-1). The extraterrestrial radiations calculated for the two stations are 333.53 (MJm-2month-1) and 195.53 (MJm-2month-1) respectively. However, the relative humidity of Ikeja (84.54%) is higher as compared to that of Maiduguri (42.23%). The minimum temperature ranges observed for the two stations varies from (22 - 24)0C and (12 - 26)°C, while the maximum temperature was as high as 33°C and 40°C obtained in April for Ikeja and Maiduguri, respectively. Similarly, the average wind speed is higher for Ikeja (4.97m/s) than for Maiduguri (4.62m/s). The result of the statistical correlation reveals that, in Maiduguri, solar radiation was found to have a significant negative relationship with relative humidity (r = -.256, p&lt;0.01) and a significant positive relationship with minimum and maximum temperature (p&lt;0.05). This means that minimum and maximum temperatures increase as solar radiation increases (p&lt;0.05). Relative humidity decreases as solar radiation increases. In Ikeja, solar radiation was found to have a significant negative relationship with relative humidity (r =-.350, p&lt;0.01) and wind speed (r = -146, p&lt;0.05) and significant positive relationship with minimum temperature (r =.410, p&lt;0.05) and maximum temperature (r =.575, p&lt;0.01). In conclusion, the variables like relative humidity, minimum temperature and wind speed are higher in the freshwater swamp area of Nigeria as compared to the Sudan savanna area, while the solar radiation, extraterrestrial radiation and maximum temperature are generally higher in the Sudan savanna area of Nigeria.&#x0D; &#x0D; &#x0D;
A low-cost passenger safety protection system based on laser ranging

Abstract In 2021, the total number of motor vehicles in China has exceeded 370 million. In the process of getting off the car, avoiding accidents between passengers and non-motor vehicles is of great significance for reducing casualties and economic losses. Therefore, this paper designs a low-cost passenger safety protection system based on laser ranging. Following the principle of easy integration and miniaturization, the hardware design and software design of the system have been completed. The mathematical model of arrival time-distance of non-motor vehicle under various speeds is established. Laser-ranging data is used to build the map, and the map can be used for non-motor vehicle identification and speed estimation. The function and precision of the system are tested. The experimental results show that 1) the established model can automatically determine whether passengers can get off the car safely and close the door in dangerous situations; 2) the mapping method based on laser ranging can effectively distinguish between non-motorized vehicles and roadsides. The accuracy of non-motor vehicle speed measurement is about 90%. The designed passenger safety protection system has high practicability.
Research on Blockchain-Based Power Data Storage Scheme

Blockchain technology was invented for bitcoin. It serves as a new computing paradigm with a decentralized framework. For its characteristics such as decentralization, tamper-proofing, and traceability, this technology has been widely used in various industry sectors. Currently, the power industry, as China's basic energy industry, is closely linked to national economic development. In the power industry, power data storage and dispatching are of paramount importance. However, there are some security problems with traditional storage methods. Given this, this paper proposed a blockchain-based power data storage scheme, to enhance the security of power data storage.
Multiclass Language Identification Using CNN-Bigru-Attention Model on Spectrogram of Audio Signals

Aiming at the problems of low recognition rate and uneven distribution of language information in language identification tasks, a language recognition method based on the CNN-Bigru-Attention model is proposed. This method first extracts the spectrogram of audio signals and converts it into a gray-scale spectrogram as input, then uses CNN (convolutional neural network) to capture the local features, and extracts the temporal features through the Bigru (Bidirectional gated recurrent unit), and then local features and temporal features are passed to the attention mechanism layer to focus on the information related to the language features and suppress useless information. Finally the classes of language is output through the fully connected layer. Experiments on the Common voice dataset show that the method has achieved good results and improves the performance of language identification.
Beyond the Baseline: 3D Reconstruction of Tiny Objects With <u>Si</u>ngle Camera <u>Ste</u>reo <u>R</u>obot

Self-aware robots rely on depth sensing to interact with the surrounding environment, e.g. to pursue object grasping. Yet, dealing with tiny items, often occurring in industrial robotics scenarios, may represent a challenge due to lack of sensors yielding sufficiently accurate depth measurements. Existing active sensors fail at measuring details of small objects (<1cm) because of limitations in the working range, e.g. usually beyond 50 cm away, while off-the-shelf stereo cameras are not suited to close-range acquisitions due to the need for extremely short baselines. Therefore, we propose a framework designed for accurate depth sensing and particularly amenable to reconstruction of miniature objects. By leveraging on a single camera mounted in eye-on-hand configuration and the high repeatability of a robot, we acquire multiple images and process them through a stereo algorithm revised to fully exploit multiple vantage points. Using a novel dataset addressing performance evaluation in industrial applications, our Single camera Stereo Robot (SiSteR) delivers high accuracy even when dealing with miniature objects. We will provide a public dataset and an open-source implementation of our proposal to foster further development in this field.