Reviewerly/exHarmony huggingface dataset:
A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation

We propose a simple, yet powerful approach for unsupervised object segmentation in videos. We introduce an objective function whose minimum represents the mask of the main salient object over the input sequence. It only relies on independent image features and optical flows, which can be obtained using off-the-shelf self-supervised methods. It scales with the length of the sequence with no need for superpixels or sparsification, and it generalizes to different datasets without any specific training. This objective function can actually be derived from a form of spectral clustering applied to the entire video. Our method achieves on-par performance with the state of the art on standard benchmarks (DAVIS2016, SegTrack-v2, FBMS59), while being conceptually and practically much simpler. Code is available at https://ponimatkin.github.io/ssl-vos.
Mapping Climate Change Research via Open Repositories &amp; AI: advantages and limitations for an evidence-based R&amp;D policy-making

In the last few years, several initiatives have been starting to offer access to research outputs data and metadata in an open fashion. The platforms developed by those initiatives are opening up scientific production to the wider public and they can be an invaluable asset for evidence-based policy-making in Science, Technology and Innovation (STI). These resources can indeed facilitate knowledge discovery and help identify available R&D assets and relevant actors within specific research niches of interest. Ideally, to gain a comprehensive view of entire STI ecosystems, the information provided by each of these resources should be combined and analysed accordingly. To ensure so, at least a certain degree of interoperability should be guaranteed across data sources, so that data could be better aggregated and complemented and that evidence provided towards policy-making is more complete and reliable. Here, we study whether this is the case for the case of mapping Climate Action research in the whole Denmark STI ecosystem, by using 4 popular open access STI data sources, namely OpenAire, Open Alex, CORDIS and Kohesio.
Planarity in Generalized Scattering Amplitudes: PK Polytope, Generalized Root Systems and Worldsheet Associahedra

In this paper we study the role of planarity in generalized scattering amplitudes, through several closely interacting structures in combinatorics, algebraic and tropical geometry. The generalized biadjoint scalar amplitude, introduced recently by Cachazo-Early-Guevara-Mizera (CEGM), is a rational function of homogeneous degree $-(k-1)(n-k-1)$ in $\binom{n}{k}-n$ independent variables; its poles can be constructed directly from the rays of the positive tropical Grassmannian. We introduce for each pair of integers $(k,n)$ with $2\le k\le n-2$ a system of generalized positive roots which arises as a specialization of the planar basis of kinematic invariants. We prove that the higher root polytope $\mathcal{R}^{(k)}_{n-k}$ has volume the k-dimensional Catalan number $C^{(k)}_{n-k}$, via a flag unimodular triangulation into simplices, in bijection with noncrossing collections of $k$-element subsets. We also give a bijection between certain positroidal subdivisions, called tripods, of the hypersimplex $\Delta_{3,n}$ and noncrossing pairs of 3-element subsets that are not weakly separated. We show that the facets of the Planar Kinematics (PK) polytope, introduced recently by Cachazo and the author, are exactly the $\binom{n}{k}-n$ generalized positive roots. We show that the PK specialization of the generalized biadjoint amplitude evaluates to $C^{(k)}_{n-k}$. Looking forward, we give defining equations and conjecture explicit solutions using $(\mathbb{CP}^{n-k-1})^{\times (k-1)}$ via a notion of compatibility degree for noncrossing collections, for a two parameter family of generalized worldsheet associahedra $\mathcal{W}^+_{k,n}$. These specialize when $k=2$ to a certain dihedrally invariant partial compactification of the configuration space $M_{0,n}$ of $n$ distinct points in $\mathbb{CP}^{1}$. Many detailed examples are given throughout to motivate future work.
An Automatic Speech Recognition System for Bengali Language based on Wav2Vec2 and Transfer Learning

An independent, automated method of decoding and transcribing oral speech is known as automatic speech recognition (ASR). A typical ASR system extracts feature from audio recordings or streams and run one or more algorithms to map the features to corresponding texts. Numerous of research has been done in the field of speech signal processing in recent years. When given adequate resources, both conventional ASR and emerging end-to-end (E2E) speech recognition have produced promising results. However, for low-resource languages like Bengali, the current state of ASR lags behind, although the low resource state does not reflect upon the fact that this language is spoken by over 500 million people all over the world. Despite its popularity, there aren't many diverse open-source datasets available, which makes it difficult to conduct research on Bengali speech recognition systems. This paper is a part of the competition named `BUET CSE Fest DL Sprint'. The purpose of this paper is to improve the speech recognition performance of the Bengali language by adopting speech recognition technology on the E2E structure based on the transfer learning framework. The proposed method effectively models the Bengali language and achieves 3.819 score in `Levenshtein Mean Distance' on the test dataset of 7747 samples, when only 1000 samples of train dataset were used to train.
HyEnA: A Hybrid Method for Extracting Arguments from Opinions

The key arguments underlying a large and noisy set of opinions help understand the opinions quickly and accurately. Fully automated methods can extract arguments but (1) require large labeled datasets and (2) work well for known viewpoints, but not for novel points of view. We propose HyEnA, a hybrid (human + AI) method for extracting arguments from opinionated texts, combining the speed of automated processing with the understanding and reasoning capabilities of humans. We evaluate HyEnA on three feedback corpora. We find that, on the one hand, HyEnA achieves higher coverage and precision than a state-of-the-art automated method, when compared on a common set of diverse opinions, justifying the need for human insight. On the other hand, HyEnA requires less human effort and does not compromise quality compared to (fully manual) expert analysis, demonstrating the benefit of combining human and machine intelligence.
Karen, the Interrupting Customer

Karen is a conversational agent taking the role of an angry customer in a retail context. While the user (retail employee) tries to convince Karen to follow the rules, the agent interrupts the user, and verbally and nonverbally reacts to the user’s sentiments.
SENTIMENT ANALYSIS of CUSTOMER REVIEW in ONLINE FOOD DELIVERY INDUSTRY

During the COVID-19 crisis, the fact that customers prefer to have foods delivery to their door instead of going in a restaurant has fueled the growing of Online Food Delivery (OFD). Nearly all restaurants like UberEats and DoorDash coming online and bringing OFD on board, online platform user reviews of a company's performance have grown in importance as a source of data. OFD organizations give great importance on collecting complaints from customer feedback and using data effectively to identify fields of development to increase customer satisfaction. Online reviews remain important during the COVID-19 pandemic as they help customers make safe food decisions. It is one of the basic needs of company managers to get customer opinions about the products and services provided by companies and to develop products and services. This work uses a Natural Language Processing (NLP) based approach. Sentiment Analysis is an area of study that uses user-shared emotions on websites and social networking sites to discover meaningful information. It is helpful to categorize emotions as positive, negative, or neutral using this type of analysis. We have performed experimentations using three modes i.e. Unigram, Bigram, and Trigram. The findings indicate that the main issues with the OFD company are primarily related to food delivery issues, and both organizations generally experience the same issues. The proposed method can be used as a guide for catering companies to evaluate customer satisfaction and complaints and develop marketing strategies to o acquire new customers and increase their market share.
Reinforced securing of data leakage in Mobile Ad hoc Network (MANET) by hybrid mechanism of identity based encryption (IBE)

In Mobile Ad hoc Network (MANET), private data leakage that involves user’s private keys may become a threat to the computing systems security. It is essential that usual requirement of traditional cryptographic patterns or mechanisms have been performed for enduring several leakage attacks. However, the attacker can track and crack the cryptographic security due to the performance of continuous leakage attacks, which assist in introducing leakage resilience cryptography. But, still there are certain issues such as data loss, data failure system, data affecting and system break that are not yet resolved. As a result, various bounded-leakage models have been created for Identity Based Encryption (IBE) constructions that fail to fulfill their stated security under continuous-leakage assaults. This paper has introduced a hybrid mechanism of security to resolve the above listed issues by focusing in avoiding continuous leakage resilient through modified IBE schemes using Adaptive Chosen Cipher-text Attacks (ACCA) with Elliptical Curve Digital Signature Algorithm (ECDSA).
Hybrid Devices

Abstract Many devices that surround us are hybrid, combining digital functionality and physical form. However, more than thirty years of user interface design sought to abstract physical devices into hardware-neutral events. This chapter shows how we must rethink design taking into account the details of physicality, whilst also learning the lessons that enabled software to flourish despite a wide and changing array of computers: from desktop PCs to mobile phones. Two long-standing design concerns are mapping, the way physical location and form of controls relates to their digital function; and feedback, which enables us to assess the correctness of our understanding of devices as well the impact of actions. One way to unpack the crucial properties of hybrid devices, is, paradoxically, to study them ‘unplugged’: first see how they afford physical manipulation and then understand the way this should connect to internal digital behaviour. Incidentally, this chapter shows how driving backwards is both hard and easy.
The development of inclusive competence in the system of postgraduate education: theoretical, methodological and practical aspects

The relevance of the study is determined by the need to develop teachers’ inclusive competence in the system of postgraduate education. The article analyzes inclusive competence as a component of the professionalism of a modern teacher. The main attention is focused on the analysis of the peculiarities of the organization of the educational process at the Postgraduate Education Centre in the scope of formation of teachers’ inclusive competence. The content of educational programs has been revealed. The effectiveness of the implementation of the acmeological approach in the formation of inclusive competence of students of advanced training courses has been proved. In the practical section of the research it has been established that the developed educational programs not only contribute to the formation of inclusive competence, but also stimulate self-educational activities.
Kajian Desain Media Promosi Sebagai Strategi Pemasaran Di Smk Setia Negara Depok

The purpose of the study was to find out how the meaning of promotional media design as a marketing strategy at Setia Negara Vocational School was in order to meet the target in the acceptance of new students.The research method used in this research is to see the visual meaning of a promotional video that shows all activities and facilities and infrastructure at SMK Setia Negara, using a qualitative method with a Roland Barthes semiotic study approach.The results achieved in this study are by making scientific articles with the title Study of Promotional Media Design as a Marketing Strategy at SMK Setia Negara which will be published in 2022.The results of this research are expected to be useful both in concept and practice as well as one of the requirements to achieve a bachelor's degree and can also be useful for all parties involved in this study, especially at SMK Setia Negara.It is hoped that this research will also become a study of learning about how the content of the promotional video shown in the promotional video of Setia Negara Vocational School is related to social reality.
Content of the Educational Process in the Pedagogical Class As a Factor in the Graduates’ Choice of a Professional Trajectory

The article reflects the results of a study of factors influencing the professional trajectory of graduates of pedagogical classes. One of the external factors is the content of education, since it has serious motivating capabilities, stimulating interest in the teaching profession. The article defines the structure of the expert list for assessing the completeness of the content of work programs (in terms of orientation to the teaching profession) and the range of pedagogical technologies used in the educational process. The problematic points of the analyzed work programs of pedagogical classes from 10 regions of the Russian Federation are indicated. Based on a number of conceptual ideas and normative documents, a draft work program is presented, the content of which stimulates the choice of a professional path by graduates of pedagogical classes associated with pedagogical activity. The results of the study make it possible to identify the vectors for improving the quality of the content of education as a factor in choosing a professional trajectory for students in the teaching class.
A Sequence-Based Dialog Management Framework for Co-Regulated Dialog

We propose SUPPLE (Sequence-Update Pattern-Based Processing with Logical Expansions), a new dialog management framework that takes the core concept of a dialog sequence as its main starting point. SUPPLE naturally enables the integration of the flexible and re-usable conversation patterns from the Natural Conversation Framework (NCF). Whereas NCF primarily provides a design framework, we developed a dialog engine and authoring framework that builds on the notion of a pattern for specifying sequence structure. In our approach we combine patterns with the key concepts of update strategies and agenda adopted from the Information State Update (ISU) approach. The main contributions of our work are the introduction of concepts and mechanisms for automatically managing dialog sequences. The framework is implemented as a cognitive agent, and we show through a cooking assistant case study how the agent keeps track of a recipe instruction agenda while allowing for user- as well as agent-initiated sequence expansions. conversational agents to co-regulate the conversation and thus allows for more flexibility.
Björn: An Interrogation Simulator

This work presents a conversational agent (CA) that functions as a prototype for simulating interrogations. The solution implements a cognitive model that focuses on the interpersonal relationships between the CA and the user. This model can adjust the interpersonal stance of the CA based on the sentiment and phrasing of the user’s utterances. As a result, the CA updates the friendliness and truthfulness of its responses accordingly.
pEncode: A Tool for Visualizing Pen Signal Encodings in Real-Time

Many features have been proposed for encoding the input signal from digital pens and touch-based interaction. They are widely used for analyzing and classifying handwritten texts, sketches, or gestures. Although they are well defined mathematically, many features are non-trivial and therefore difficult to understand for a human. In this paper, we present an application that visualizes a subset from 114 digital pen features in real-time while drawing. It provides an easy-to-use interface that allows application developers and machine learning practitioners to learn how digital pen features encode their inputs, helps in the feature selection process, and enables rapid prototyping of sketch and gesture classifiers.
Comparative Analysis of Machine Learning-Based Approaches for Astrological Prediction of Profession

Astrology is an ancient concept. Each person’s astrological chart is unique and independent, which can be influenced by different factors. In the current world, there are no standard rules or guidelines for astrological prediction. Many applications can be used to predict and analyze data, thanks to advances in artificial intelligence. These applications make use of computers to analyze unknown, large, noisy, and complex data sets and to predict and classify them. This paper aims to establish universal rules and validate astrology by using various scientific methods. This research uses the positions of stars and planets at birth to determine the profession of a person. Logistic Regression, Naive Bayes Algorithm, and Catboost Algorithm use this information to predict a person’s profession. The learning classification dataset consisted of 6248 records covering 14 different professions.
Effective Leadership for Knowledge Sharing by Enterprise Social Networking (ESN) during Crisis

This chapter explores effective human resource development strategies during and post-crisis. Particularly, during the COVID-19 pandemic crisis, the remote and hybrid measures taken by multinational corporations (MNCs) play a significant role in the development of people. According to previous literature, the internal enterprise social network (ESN) of MNCs, localization of human resources in overseas subsidiaries, and flexibility and resilience of human resource systems have a positive impact on people’s development during this crisis. Leadership has a moderating effect on using the internal ESN for remote learning, people development performance, and organizational resilience building via crises. Based on a literature review and theoretical analysis, this chapter puts forward several propositions to clarify the kind of leadership and leadership competencies that positively influence knowledge sharing through ESNs and organizational resilience innovation. Suggestions for leadership development. This chapter serves as a good reference for both business professionals and academic researchers. It provides original suggestions for both MNCs and small and medium-sized enterprises.
AN ASSESSMENT OF THE BIOLOGICAL DAMAGE IN ITALIAN LEGAL FRAMEWORK

Non-pecuniary damage in itself is related with the human being, in his inner moral, biological and existential aspect.Compensation for non-pecuniary damage and its study are of great importance.Logically, it may be raised the question: What about the individual as a human being, can he/she be valued?According to Aristotle, justice is an instrument of adapting the law to the concrete case.Whereas, referring to Scholasticism, justice is the rule of the concrete case.Through the above statements, we understand that for Aristotle justice stood in the "beyond" view of the concrete case, defining the applicable norms from the right "law" ideal, whereas for the jurists of Scholasticism, justice stood in the "inside" view of the concrete case, emphasizing all those features that justified an adaptation with the pre-existing norms, but never the creation of new norms.Therefore, it seemed that the individual was valuable for what he/she was, not for what he/she produced.In this situation, it was deemed necessary to be found a standard economic value and the compensation of the damage occurred to be personalized based on defined criteria.In the present research paper, through a legal assessment, special attention has been paid to the biological damage in the Italian legislation, aiming to analyze the legal provisions and he judicial practice of the Italian courts in this regard.
EFN-SMOTE — An improved unbalanced data set oversampling based on fuzzy C-means for Credit Cards Fraud detection

Abstract Recent developments in e-payment systems have led to increased financial fraud, such as credit card fraud. It is therefore essential to implement detection mechanisms for credit card fraud. However, due to the unbalanced class distribution in the credit card dataset, the machine learning techniques are used to train the model based on the majority class, resulting in inaccurate fraud predictions. Therefore, this paper mainly focuses on processing unbalanced data using the oversampling technique called Elbow Fuzzy Noice filtering SMOTE (EFN-SMOTE). This method divides the dataset into multiple clusters. The number of clusters is determined by an algorithm known as the Elbow method, after which noise filtering is applied to each cluster, after that, we use the SMOTE in each cluster to synthesize a new minority instance based on the nearest majority instance of each minority instance to effectively perceive the decision boundary which leads to a balanced database by oversam-pling technique. On the other hand, the result show that EFN-SMOTE achieved better classification performance using Artificial Neural Network (ANN) with four hidden layers with 0.999 accuracy, 0.998 precision, 0.999 sensitivity, 0.998 specificity, 0.999 F-measure, and 0.999 G-Mean.
The Impact of Using Model and Augmented Reality Technology on Students’ Science Achievement, Motivation, and Interest Levels

In this study, it was aimed to investigate the effects of modelling and the material designed with augmented reality technology on the academic success of students, their motivation, and interest levels towards the science course. The study was conducted in 6th grade science units of "Support and Movement System" and "Systems in Our Body". In the study, quasi-experimental design with pre-test post-test control group was applied. The study was carried out with three groups: the control group, the augmented reality experimental group and the modeling experimental group. The research sample consists of a total of 66 students studying at a public middle school in the Küçükçekmece district of Istanbul province, during the 2020-2021 academic year. To collect data, a 30-item Science Achievement Test, a 23-item Science Motivation Scale, and a 27-item Science Interest Scale were applied. Kruskal Wallis test was applied for multiple comparisons, and Mann Whitney U tests were carried out for post-hoc comparasions. Wilcoxon Signed Ranks tests were applied for intragroup comparisons. According to the results of the study, it was concluded that the use of augmented reality and modeling technique in the science course had a statistically significant effect on the academic success, motivation, and interest of the students towards the science course.