Reviewerly/exHarmony huggingface dataset:
Development of a personality recognition system by voice

Abstract— the paper discusses the development of a voice recognition system based on cepstral coefficients on the chalk scale (Mel-frequency Cepstral Coefficients, MFCC) and mixed Gaussian models (Gaussian Mixture Models, GMM). The aim of the research is to create a simple and convenient automatic speaker recognition system. The description of the created speech database for the Kazakh language is given. Experiments have shown quite good results of the system FAR 8.05%.
Estudio y análisis del aprendizaje y la enseñanza en la educación superior

This article shows the result of the research carried out during the 2020/21 academic year, in the subject of Information Security, taught in Higher Education, in the fourth year of the Degree in Telematics Engineering in Telecommunications and also in the Degree in Computer Engineering in Information Technology, at the University Center of Mérida of the University of Extremadura.&nbsp; The motivation of the research was to monitor the teaching, in a non-invasive way that helps in the teaching / learning process, while the students do not feel that they are being evaluated, minimizing the stress produced by the exams, and trying to automate the learning assessment process so that the teacher has a quick and easy-to-use tool that provides information on the evolution of the students' mental structure, online.&nbsp; The bases of the study that we present are different investigations on pathfinder associative networks and nuclear concepts. The research process developed in several stages. The first step consisted in obtaining the prior knowledge of the students with the help of software created for this purpose.&nbsp; With the data obtained, the associative pathfinder networks of each student were created to know their mental structure.&nbsp; Data were collected at various moments of the teaching course, to verify the evolution of the teaching-learning process and through associative pathfinder networks, the mental structures of the students were graphically represented, being able to observe the evolution of learning during the teaching period.&nbsp
Pronominal variation and layers in grammaticalization: the enclitic forms -ello and -lo in the Italo-Romance dialect of Lizzano in Belvedere

This study investigates the rare phenomenon of the co-occurrence (in Lizzanese, an Italo-Romance dialect spoken in the Tuscan-Emilian Apennines) of two types of enclitic pronoun in interrogative contexts: the unstressed mono-syllabic forms -lo (3sg.m)/-la (3sg.f) and the stressed disyllabic forms -ello(3sg.m)/-ella (3sg.f). The coexistence of -lo and -ello is analysed as an instance of ‘layering’ (Hopper 1991) and it is claimed that a peculiar characteristic of Lizzanese historical phonology, the preservation of geminate consonants in post-tonic contexts, played a crucial role in the emergence of layering. Moreover, fol-lowing Loporcaro 2012, this study claims that -ello should be analysed as a syn-tactic clitic and -lo as a syntactic-and-phonological clitic and that these two clitic categories should be assigned distinct positions in a more refined and enriched cline of grammaticality
Legal and ethical issues of COVID-19 Digital Contact Tracing" measures in Taiwan"

Objectives: COVID-19 has posed unprecedented challenges to governments and civil society worldwide. Before effective treatments and vaccines become available, nonpharmaceutical interventions (NPIs), including digital contact tracing technologies, are critical for delaying or controlling the spread of COVID-19. This paper investigates the practices, discussions, and legal and ethical issues of the contact tracing measures imposed in Taiwan during the COVID-19 pandemic. Methods: By analyzing official documents from the Ministry of Health and Welfare website, media reports, legal regulations, international organizations' ethical guidelines, and research on contact tracing measures, this paper categorically presents the applications, effectiveness, and legal and ethical concerns of these measures in Taiwan. Results: Although the rates of confirmed cases and deaths from COVID-19 are low in Taiwan, the Electronic Fence of Surveillance, a digital contact tracing measure implemented by the National Health Command Center, was found to lack legal certainty. Some proactive deployments by public and private organizations that have used location-based services, the National Health Insurance system, and the center's data on of the digital footprints of confirmed cases do not meet ethical and scientific validity standards. Oversight mechanisms have not been established, and public reflection has not been addressed. Conclusions: This paper argues that the choice of upholding individual privacy rights versus performing digital contact tracing is not binary. To make digital technologies part of measures against COVID-19, and future public health crises, it is crucial to include diverse experts not only of information technology and biomedicine, but also of epidemiology, public health, law, ethics, and social sciences, as well as civil organizations in the design, application, oversight, and evaluation of new public health technologies.
PERANCANGAN SISTEM DOKUMENTASIELEKTRONIK SISTEM PENJAMINAN MUTUINTERNAL MENGGUNAKAN METODE RAPIDAPPLICATION DEVELOPMENT

The purpose of the Higher Education Quality Assurance System (SPMI) is to ensurethe fulfillment of Higher Education Standards in a systematic and sustainable manner, so thatthe quality culture will continue to grow and develop. To support higher education qualityassurance activities quickly and precisely, it must have an electronic documentation system.Electronic Document Management System (EDMS) is a document management systemframework that is more specific for building and storing computerized documents or reports.This electronic documentation system is designed using the Rapid Application Development(RAD) software development method. The result is an EDMS model for SPMI, a usermanagement system and a system design in the form of features that will be applied to theSPMI electronic documentation system.
Examining the uncanny valley effect in virtual character design for digital games

Today, games, where visuality is at the forefront, have similar features with a cinema project in terms of both the content and the technologies used. When the realistic appearance of robots and digital designs that do not fully resemble humans exceeds a point, the images created leave their place to negative emotions such as disgust, fear, and hate. The feeling of disgust emerges when the level of affinity reaches the highest level. The negative mood status which took its place in the literature as an uncanny valley is an important reference for game designers and researchers. Masahiro Mori’s uncanny valley theory about humanoid robots is used by animators and video game designers in the content creation process in video games, every day. In this study, the design evolution of Lara Croft, who has found an important place in the video game world, has been evaluated in terms of exposure to the uncanny valley. This study has used images and videos representing Lara Croft, the strong and smart female icon of the Tomb Raider games. A total of 67 undergraduate students (32 for experiments and 35 as a control group), 34 male (50.7 %), and 33 female (49.3 %), voluntarily participated in this study. In this study, the quasi-experimental design was used with experimental and control groups, since the data collection tool used to compare the experimental and control groups did not allow them to reflect the student’s past game experiences. The findings of this study suggest the following design principles: to use a high polygon count to design attractive, ideal faces that are not spooky.
Guides: Adding MUSC Libraries' Resources to Brightspace: Questions?

This guide is for MUSC instructional designers, course developers, faculty, and staff that want to follow best practices to include accessible links to library resources in Brightspace.
A Hybrid Machine Learning Approach for Breast Cancer Detection

Now a day, cancer is one of most common and internecine disease among all disease present in the world. A cancer disease is classified into different types based on the body location like breast cancer, kidney cancer, liver cancer etc. but breast cancer is one of the most common cancer in woman and 8% of woman were diagnosed breast cancer in 2016. It is found that if the cancer is diagnosed in the early stage than the probability of the survival is higher. Now a day, Machine learning play a vital role in order to detect cancer in the early stage. Lots of work has been done previously which uses machine learning approach like support vector machine, Naive Bayes, logistic regression etc. In this paper w proposed hybrid approach for detecting cancer in the early stage. This hybrid approach is the combination of Support vector machine and Naive Bayes approach. In order to evaluate the performance of the proposed approach uses Wisconsin Breast Cancer (WBC) which is downloaded from the UCI machine learning repository. Performance of the proposed approach is measured in term of accuracy, F- value. Experiment results shows that proposed approach gives better result as compare to the competitive approach.
NeLF: Neural Light-transport Field for Portrait View Synthesis and Relighting

Human portraits exhibit various appearances when observed from different views under different lighting conditions. We can easily imagine how the face will look like in another setup, but computer algorithms still fail on this problem given limited observations. To this end, we present a system for portrait view synthesis and relighting: given multiple portraits, we use a neural network to predict the light-transport field in 3D space, and from the predicted Neural Light-transport Field (NeLF) produce a portrait from a new camera view under a new environmental lighting. Our system is trained on a large number of synthetic models, and can generalize to different synthetic and real portraits under various lighting conditions. Our method achieves simultaneous view synthesis and relighting given multi-view portraits as the input, and achieves state-of-the-art results.
The Promises and Pitfalls of Deep Kernel Learning

Deep kernel learning and related techniques promise to combine the representational power of neural networks with the reliable uncertainty estimates of Gaussian processes. One crucial aspect of these models is an expectation that, because they are treated as Gaussian process models optimized using the marginal likelihood, they are protected from overfitting. However, we identify pathological behavior, including overfitting, on a simple toy example. We explore this pathology, explaining its origins and considering how it applies to real datasets. Through careful experimentation on UCI datasets, CIFAR-10, and the UTKFace dataset, we find that the overfitting from overparameterized deep kernel learning, in which the model is ``somewhat Bayesian'', can in certain scenarios be worse than that from not being Bayesian at all. However, we find that a fully Bayesian treatment of deep kernel learning can rectify this overfitting and obtain the desired performance improvements over standard neural networks and Gaussian processes.
Соционические малые группы. К истории открытия, обоснования и информационного наполнения

In this article, we tried to fully describe the history of the development of this direction of socionics, considered different approaches to dividing socion into 4 small groups, noted the connection of such an approach with the developments in MBTI and other well-known classifiers of the functioning of the human psyche.
THE INFLUENCE OF USING CLOZE PROCEDURE TECHNIQUE TOWARDS STUDENTS’ READING ABILITY AT THE TENTH CLASS OF SMK PRAJA UTAMA LAMPUNG TIMUR IN 2019/2020

Reading is essentsial skill and so much part of daily life for those of us who live in the literate communicative that much of the time we hardly consider other the purpose or process involved. It means that reading is a part of our daily activities when we live in the country where people think that education is very important.The objective of this research was to know and describe the influence of Cloze procedure technique towards students’ reading ability. In this research, the writer used experimental method. The population of this research was the students at the tenth class of  SMK PRAJA UTAMA Lampung Timur in academic year 2019/2020 consisted of 242 students in 8 classes. The sample of this research was X Administrasi Perkantoran 1 as the experimental class and X Pemasaran 1 was the control class. In taking the sample, the writer used cluster random sampling technique. In collecting the data, the writer used multiple choice test consisting of 40 item test options ( a,b,c,d and e ). In analyzing the data, the writer used t-testformula. The result of this research showed that the average score in experimental class was 77.65 and average score of control class 49.75. it also supported by t-testwas higher than t-table with significant level 5% and 1% (3.56 > 1.67 < 2.39 ). It means that there is positive and significant influence of Cloze Procedure Technique towards students’ reading ability.
Detection of Double Compression in MPEG-4 Videos Using Refined Features-based CNN.

Double compression is accompanied by various types of video manipulation and its traces can be exploited to determine whether a video is a forgery. This Letter presents a convolutional neural network for detecting double compression in MPEG-4 videos. Through analysis of the intra-coding process, we utilize two refined features for capturing the subtle artifacts caused by double compression. The discrete cosine transform (DCT) histogram feature effectively detects the change of statistical characteristics in DCT coefficients and the parameter-based feature is utilized as auxiliary information to help the network learn double compression artifacts. When compared with state-of-the-art networks and forensic method, the results show that the proposed approach achieves a higher performance.
LARC: Language annotated Abstraction and Reasoning Corpus

Author(s): Acquaviva, Sam; Pu, Yewen; Nye, Maxwell; Wong, Catherine; Tessler, Michael Henry; Tenenbaum, Josh | Abstract: The Abstraction and Reasoning Challenge (ARC) is a set of tasks where one must induce a program from a few given input-output examples, and apply it to a new input. Although humans can easily solve most tasks, ARC is challenging for state-of-the-art algorithms. We hypothesize that humans use intuitive program induction, and interpret ARC tasks by constructing programs'' in language. We experimentally study programs'' by formulating a two-player game: A participant solves and then communicates the program to another participant using natural language; the second participant must solve the task using the description alone. We find at least 361 out of 400 tasks can be solved from a natural language description, demonstrating that natural language is sufficient in transmitting these natural programs. We compared the natural language programs to computer programs constructed using two separate state-of-the-art program synthesis approaches, and conducted a study on leveraging natural language annotations to improve performance of program-synthesis tools.
The Development of Seabin Boat As a Waste Collector for Marine Pollution

70 percent of the surface of the earth is covered by the ocean and it is our responsibility to save it from pollution. Over the years, marine pollution has become a serious issue especially due to the mismanagement of plastic waste. In this project, a prototype of a seabin boat that could collect trash on the water surface was developed. The working performance of the boat in terms of buoyancy, stability, the speed of the boat, and energy supply were also tested. This project was started with the design of the prototype. Then the body of the prototype was set up by using styrofoam which is the main material for this project. Besides that, a table mat was used to act as a conveyer with the power of 9 V batteries and 6 V motors. The working performance of the boat in terms of buoyancy, stability, speed of the boat, and energy supply was tested in a pool in UTHM Pagoh. Archimede’s principle was also applied to do the testing. Based on the result obtained, the boat could perform well in terms of buoyancy, stability, speed, and energy supply. The boat could collect a 240 g load of weight with maximum speed, 0.043m/s, and energy capacity equals 500 mAh. Further research needs to be conducted to improve the performance of the prototype.
An Adaptive Approach to implement an Object Storage for Private Cloud

Data is everywhere in both the structured and unstructured form and growing continuously over the years. With this much of huge data, cloud storage is required where data can be stored in a more efficient and structured way from where the data can be retrieved easily. Object based storage is one way of storing data in metadata and more structured way.  Cloud storage services are used everywhere in enterprises, by researchers, educational institutes and many more to store data in various formats and sizes. Huge storage of data requires a more efficient way of storing the data. This paper will cover the basic of object storage, the methodology of creating the Object storage and implementation of Object storage in Linux based system by creating the private cloud.
The identity of the partner matters even when naming everyday objects

Author(s): Yoon, Si On; Pratley, Breanna Brigitte, Pratley; Heller, Daphna | Abstract: Social factors, such as partner familiarity (e.g., talking to a friend vs. stranger) may affect some conversations but not others. While researchers do not always control the partner identity when conducting interactive studies, the current empirical report of a language production experiment conducted via Zoom presents effects of partner familiarity (friends vs. strangers) on the form and content of referring expressions in the mundane task of describing everyday objects. First, speakers interacting with a friend were less disfluent than speakers interacting with a stranger, showing that more effort is invested in interactions with strangers. Second, speakers interacting with a friend showed more sensitivity to prior context. Surprisingly, these effects reveal that speakers are sensitive to the partner identity even when describing everyday objects whose labels are shared across all language users. The current findings suggest that researchers should consider social factors as part of the experimental design of interactive tasks.
Imbalanced Big Data Oversampling: Taxonomy, Algorithms, Software, Guidelines and Future Directions.

Learning from imbalanced data is among the most challenging areas in contemporary machine learning. This becomes even more difficult when considered the context of big data that calls for dedicated architectures capable of high-performance processing. Apache Spark is a highly efficient and popular architecture, but it poses specific challenges for algorithms to be implemented for it. While oversampling algorithms are an effective way for handling class imbalance, they have not been designed for distributed environments. In this paper, we propose a holistic look on oversampling algorithms for imbalanced big data. We discuss the taxonomy of oversampling algorithms and their mechanisms used to handle skewed class distributions. We introduce a Spark library with 14 state-of-the-art oversampling algorithms implemented and evaluate their efficacy via extensive experimental study. Using binary and multi-class massive data sets, we analyze the effectiveness of oversampling algorithms and their relationships with different types of classifiers. We evaluate the trade-off between accuracy and time complexity of oversampling algorithms, as well as their scalability when increasing the size of data. This allows us to gain insight into the usefulness of specific components of oversampling algorithms for big data, as well as formulate guidelines and recommendations for designing future resampling approaches for massive imbalanced data. Our library can be downloaded from this https URL.
Contextual Policy Transfer in Reinforcement Learning Domains via Deep Mixtures-of-Experts

In reinforcement learning, agents that consider the context, or current state, when selecting source policies for transfer have been shown to outperform context-free approaches. However, existing approaches suffer from limitations, including sensitivity to sparse or delayed rewards and estimation errors in value functions. One important insight is that explicit learned models of the source dynamics, when available, could benefit contextual transfer in such settings. In this paper, we assume a family of tasks with shared sub-goals but different dynamics, and availability of estimated dynamics and policies for source tasks. To deal with possible estimation errors in dynamics, we introduce a novel Bayesian mixture-of-experts for learning state-dependent beliefs over source task dynamics that match the target dynamics using state transitions collected from the target task. The mixture is easy to interpret, demonstrates robustness to estimation errors in dynamics, and is compatible with most learning algorithms. We incorporate it into standard policy reuse frameworks and demonstrate its effectiveness on benchmarks from OpenAI gym.
Modelling document-query interaction in a hierarchical neural model for IR

Recent deep approaches to information retrieval are either representation-oriented or interaction-oriented, depending on how they view the modelling of document and query representations and their interactions. We explore a hierarchical approach to document encoding that enables modelling the query-document interaction at different levels of granularity. The proposed model splits the input documents into blocks that are individually matched to a given query through a series of self-attention modules, along with pooling and projection layers. We test our method on the LETOR 4.0 MQ2007 standard IR collection. The approach shows promising preliminary results, albeit a more in-depth exploration of the modelling choices could provide further gains.
Evaluating user experience of different angle VR images

This paper describes a comparison of user experience of virtual reality (VR) image angles. 7 angles conditions are prepared and evaluated the user experience during viewing VR images with a headset by measuring subjective and objective indexes. Angle conditions were every 30 degrees from 180 to 360 degrees. From the results of the subjective indexes (reality, presence, and depth sensation), a 360-degree image was evaluated highest, and different evaluations were made between 240 and 270 degrees.In addition, from the results of the objective indexes (eye and head tracking), a tendency to spread the eye and head movement was found as the image angle increases.