Reviewerly/exHarmony huggingface dataset:
Operator and Graph Theoretic Techniques for Distinguishing Quantum States via One-Way LOCC

We bring together in one place some of the main results and applications from our recent work on quantum information theory, in which we have brought techniques from operator theory, operator algebras, and graph theory for the first time to investigate the topic of distinguishability of sets of quantum states in quantum communication, with particular reference to the framework of one-way local quantum operations and classical communication (LOCC). We also derive a new graph-theoretic description of distinguishability in the case of a single-qubit sender.
Efektivitas Model Problem Based Learning Untuk Meningkatkan Hasil Belajar Matematika Siswa Kelas 2 SD

This study aims to determine the effectiveness of the Problem Based Learning model to improve mathematics learning outcomes for second grade students at SDN 31 VII Koto Sungai Sarik. This research is Classroom Action Research (CAR). The learning outcomes of cognitive aspects in the first cycle have increased from the initial ability test with an average value of 60.23% to 86.74%. The affective aspect of learning outcomes obtained an average score of 21.07% in the sufficient category and the psychomotor aspect learning outcomes obtained an average score of 24.57% in the sufficient category. So there is an increase in learning outcomes from the initial ability test to the first cycle, which is 26.51%. In cycle II, learning outcomes have increased again, namely in the cognitive aspect with an average value of 86.74%; affective aspect with an average score of 28.43% in good category; and psychomotor aspect learning outcomes with an average score of 32.62% in the good category. These results indicate that the Problem Based Learning model can improve student learning outcomes in Mathematics class 2 SDN 31 VII Koto Sungai Sarik.
CoCo-BERT: Improving Video-Language Pre-training with Contrastive Cross-modal Matching and Denoising

BERT-type structure has led to the revolution of vision-language pre-training and the achievement of state-of-the-art results on numerous vision-language downstream tasks. Existing solutions dominantly capitalize on the multi-modal inputs with mask tokens to trigger mask-based proxy pre-training tasks (e.g., masked language modeling and masked object/frame prediction). In this work, we argue that such masked inputs would inevitably introduce noise for cross-modal matching proxy task, and thus leave the inherent vision-language association under-explored. As an alternative, we derive a particular form of cross-modal proxy objective for video-language pre-training, i.e., Contrastive Cross-modal matching and denoising (CoCo). By viewing the masked frame/word sequences as the noisy augmentation of primary unmasked ones, CoCo strengthens video-language association by simultaneously pursuing inter-modal matching and intra-modal denoising between masked and unmasked inputs in a contrastive manner. Our CoCo proxy objective can be further integrated into any BERT-type encoder-decoder structure for video-language pre-training, named as Contrastive Cross-modal BERT (CoCo-BERT). We pre-train CoCo-BERT on TV dataset and a newly collected large-scale GIF video dataset (ACTION). Through extensive experiments over a wide range of downstream tasks (e.g., cross-modal retrieval, video question answering, and video captioning), we demonstrate the superiority of CoCo-BERT as a pre-trained structure.
Differentiable Network Pruning for Microcontrollers.

Embedded and personal IoT devices are powered by microcontroller units (MCUs), whose extreme resource scarcity is a major obstacle for applications relying on on-device deep learning inference. Orders of magnitude less storage, memory and computational capacity, compared to what is typically required to execute neural networks, impose strict structural constraints on the network architecture and call for specialist model compression methodology. In this work, we present a differentiable structured network pruning method for convolutional neural networks, which integrates a model's MCU-specific resource usage and parameter importance feedback to obtain highly compressed yet accurate classification models. Our methodology (a) improves key resource usage of models up to 80x; (b) prunes iteratively while a model is trained, resulting in little to no overhead or even improved training time; (c) produces compressed models with matching or improved resource usage up to 1.7x in less time compared to prior MCU-specific methods. Compressed models are available for download.
Recognition and Analysis of Emotional Words in Ancient Chinese Poetry under Different Themes

Abstract The emotional connotation in ancient poetry is a valuable human spiritual culture, adopting the key semantic technique to parse the emotional word in poetry under different themes is beneficial to discover the relationship between poetry theme and emotion. In a “cold environment” (without learning corpus) of Tang poetry, this work presents, for the first time, the automatic recognition and analysis of large‐scale humanistic emotional words within ancient Chinese poetry from different themes. A “cold start” automatic citation method for character sequences is proposed to obtain the learning corpus. The best F1 and F1_distinct of trained BERT‐BiLSTM‐CRFs model respectively reach 96.27% and 86.04%. Deep learning expanded imagery words that convey emotion to realize knowledge discovery. The relationships between theme and emotion word show that Chinese poetry is good at using natural objects to express various sentiments to people, with each theme of poetry owns distinguished emotion feature.
Bag-of-Vectors Autoencoders for Unsupervised Conditional Text Generation

Text autoencoders are often used for unsupervised conditional text generation by applying mappings in the latent space to change attributes to the desired values. Recently, Mai et al. (2020) proposed Emb2Emb, a method to learn these mappings in the embedding space of an autoencoder. However, their method is restricted to autoencoders with a single-vector embedding, which limits how much information can be retained. We address this issue by extending their method to Bag-of-Vectors Autoencoders (BoV-AEs), which encode the text into a variable-size bag of vectors that grows with the size of the text, as in attention-based models. This allows to encode and reconstruct much longer texts than standard autoencoders. Analogous to conventional autoencoders, we propose regularization techniques that facilitate learning meaningful operations in the latent space. Finally, we adapt Emb2Emb for a training scheme that learns to map an input bag to an output bag, including a novel loss function and neural architecture. Our empirical evaluations on unsupervised sentiment transfer show that our method performs substantially better than a standard autoencoder.
O’SPEAK FOR ENGLISH PHONETIC AND PHONOLOGY CLASS: WHAT’S ON STUDENTS’ MIND?

In the era of Education 5.0, mobile applications for the teaching-learning process are proliferating. The mobile application also exists in English Phonetics and Phonology Classes. Its name is O’Speak. The use of O’Speak in English Phonetic and Phonology Class derives from the idea of technological development in this digital era. Virtues and hurdles of using O’Speak come out in students’ viewpoints. Therefore, this study is conducted to know the students’ perceptions toward the use of O’Speak in English Phonetic and Phonology Classes. The research method used in this study is a case study that focuses on the phenomenon of O’Speak. English Phonetics and Phonology students are the participants in this study. The finding shows that the students have several overviews toward O’Speak, such as students' concept of O’Speak, O’Speak designs, and O’Speak comments.
SMART MIRROR 2.0:A touch-free reflective interaction that unveil user customizable intelligence

In today’s environment, increment is addressable to us rather well through with our phones, our portable computer, our screen, and much. As machinery cultivates, knowledge would breed and further not here from the outmoded flair of collaboration with campaigns. In the bygone, evidence was dispatched through rag, then through CPUs, and in today’s daylight and stage of development, through our phones and manifold other means. Expertise should industrialize more cohesive into our breathes - more unbroken and more imperceptible. We anticipate to shove the casing further, into the forthcoming. We recommend an innovative modest approach of fixing with your daybreak rag. We extant our impression, the Smart Mirror, facts at a glimpse.
An Interoperable Open Data Portal for Climate Analysis

This work proposes an open interoperable data portal that offers access to a Web-wide climate domain knowledge graph created for Ireland and England's NOAA climate daily data. There are three main components contributing to this data portal: the first is the upper layer schema of the knowledge graph -- the climate analysis (CA) ontology -- the second is an ad hoc SPARQL server by which to store the graph data and provide public Web access, the last is a dereferencing engine deployed to resolve URIs for entity information. Our knowledge graph form of NOAA climate data facilitates the supply of semantic climate information to researchers and offers a variety of semantic applications that can be built on top of it.
Multimodality analysis of Jokowi's Social Exchange Theory and Political Marketing to Ma'ruf Amin's infidelity at the 2019 Presidential Election Contestation

In the constellation of the 2019 presidential election, there is something very interesting. Prabowo's camp quickly announced his vice-presidential candidate but Jokowi did not immediately announce his chosen vice-presidential candidate. Even until the last moment, Jokowi let the issue of the vice-presidential candidate rolling in the community. Unexpectedly, Jokowi chose a man who was never included in consideration of public. Jokowi's choice also made several names with the initials M as stated himself that his chosen vice-presidential candidate was M. Surprisingly, he announced that his chosen vice-presidential candidate was Ma'ruf Amin, a senior cleric and Chairman of the MUI (Majelis Ulama Indonesia). The issue of Ma'ruf Amin's selection in this study focuses on social exchange theory from Richard Emerson and the concept of political marketing from Jennifer Lees, especially the MOP (Market Oriented Product) model with a qualitative multimodality approach. Data were analysed using a visual text analysis to dig deeper into Jokowi's non-verbal meaning by deciding to choose Ma'ruf Amin as a vice-presidential candidate. The data were photos of the nomination of the presidential and vice-presidential. The findings of this study are that the election of Ma'ruf Amin is the most appropriate and accurate choice to defeat Prabowo. By choosing Ma'ruf Amin, Jokowi won before competing against Prabowo. Ma'ruf is a symbol, means, and source of Jokowi's victory
Contextual Sentence Classification: Detecting Sustainability Initiatives in Company Reports

We introduce the novel task of detecting sustainability initiatives in company reports. Given a full report, the aim is to automatically identify mentions of practical activities that a company has performed in order to tackle specific societal issues. New methods for identifying continuous sentence spans need to be developed for capturing the multi-sentence structure of individual sustainability initiatives. We release a new dataset of company reports in which the text has been manually annotated with sustainability initiatives. We also evaluate different models for initiative detection, introducing a novel aggregation and evaluation methodology. Our proposed architecture uses sequences of consecutive sentences to account for contextual information when making classification decisions at the individual sentence level.
Oblivious Data for Fairness with Kernels

We investigate the problem of algorithmic fairness in the case where sensitive and non-sensitive features are available and one aims to generate new, `oblivious', features that closely approximate the non-sensitive features, and are only minimally dependent on the sensitive ones. We study this question in the context of kernel methods. We analyze a relaxed version of the Maximum Mean Discrepancy criterion which does not guarantee full independence but makes the optimization problem tractable. We derive a closed-form solution for this relaxed optimization problem and complement the result with a study of the dependencies between the newly generated features and the sensitive ones. Our key ingredient for generating such oblivious features is a Hilbert-space-valued conditional expectation, which needs to be estimated from data. We propose a plug-in approach and demonstrate how the estimation errors can be controlled. Our theoretical results are accompanied by experimental evaluations.
The role of AI assisted socio-cultural frameworks in academic change and higher education growth

The 'growth' of academics in higher education is focused on quality improvements in teaching. However, the analysis has shown that the sense of academics significantly affects their practices that ...
ModeRNN: Harnessing Spatiotemporal Mode Collapse in Unsupervised Predictive Learning

Learning predictive models for unlabeled spatiotemporal data is challenging in part because visual dynamics can be highly entangled in real scenes, making existing approaches prone to overfit partial modes of physical processes while neglecting to reason about others. We name this phenomenon spatiotemporal mode collapse and explore it for the first time in predictive learning. The key is to provide the model with a strong inductive bias to discover the compositional structures of latent modes. To this end, we propose ModeRNN, which introduces a novel method to learn structured hidden representations between recurrent states. The core idea of this framework is to first extract various components of visual dynamics using a set of spatiotemporal slots with independent parameters. Considering that multiple space-time patterns may co-exist in a sequence, we leverage learnable importance weights to adaptively aggregate slot features into a unified hidden representation, which is then used to update the recurrent states. Across the entire dataset, different modes result in different responses on the mixtures of slots, which enhances the ability of ModeRNN to build structured representations and thus prevents the so-called mode collapse. Unlike existing models, ModeRNN is shown to prevent spatiotemporal mode collapse and further benefit from learning mixed visual dynamics.
Socio-Cognitive Concept of Digital Socialization: А New Ecosystem and Social Evolution of the Mind

Within the framework of a transdisciplinary approach and based on years of empirical research, a socio-cognitive concept of digital socialization has been developed, reflecting the process of adaptation of a changing person to the opportunities and risks of a dynamic socio-technological environment. The key element of the concept of digital socialization is a hyperconnected, technologically upgraded digital personality as a part of a real personality; the concept reflects the current stage of the social and cognitive evolution of the human mind, when the developing consciousness actually merges with external tools (gadgets) and semiotic reality (the Internet). Digital technologies are being integrated into our cognitive and social system, determining the digital expansion (upgrade) of a person. Key dimensions of digital socialization are Internet hyperconnectivity, mixed reality, augmented personality, and digital sociality. Being hyperconnected to the Internet in mixed reality means not only the basic and defining characteristics of digital socialization for both adults and children, but also the main basis for the changes that are taking place with a person today. The extended self - a constantly developing cultural tool - is formed in its socio-technological embodiment, and it modifies the human ecosystem. The emerging and transforming personality expands and absorbs a technosystem as a significant element that promotes the implementation of psychological mechanisms of externalization and includes digital devices and computer programs along with methods of their application. A new anthropological type of digital man - Man Connected and Upgraded - lives in multiple realities and already interacts with inanimate systems (chat bots, household robots, etc.), so a paradigm change, an update of theoretical and methodological models is required. All this determines the cumulative nature of the theoretical and methodological approach, based mainly on the Lev Vygotsky’s (and his followers’) cultural psychology, which has absorbed classical and modern humanitarian and natural-scientific concepts. This approach is a promising basis for studying the specifics of digital reality, capable of withstanding the challenges of the “new normality”.
Space recognition with path optimization using the “turtlebot3 burger” robot

This research is a contribution to mobile robotics for the planning of trajectories of an autonomous robot. For the case study, the TURTLEBOT3 BURGER robot will be used. With thisdevice performance tests were carried out inside a hexagonal track. Each test has an increasing level of complexity, which allowed determining an algorithmic execution with results that lead to incremental development in robot programming. The results obtained are conditioned by the hardware and software connection and programming modes, through the use of the Robot Operating System (ROS), and the execution of a series of tests that focus on applying Dijkstra and A Star algorithms that allow optimizing routes with a robot and simulate them in Rviz as a 3D visualization tool for ROS applications. Finally, the theoretical part related to the work of algorithms and their programming was verified using this robotic platform, which manages to demonstrate that the initially proposed, defined objectives are met, such as the recognition and planning of a trajectory on flat terrain with obstacles , from a starting point to an end point as a target or arrival point.
Research of Stability and Convergence of Decentralized Coordination of Local Control of Distributed Cyber-physical Systems

The use of decentralized coordination of state control of distributed cyber-physical systems with continuous objects, in which, in addition to the physical interaction of the elements of a continuous object, there is information interaction of local coordinators as part of agents of a multi-agent system, leads to the formation of multi-loop control systems. The features of decentralized coordination control of such systems (nonlinearity of coordinators, mutual physical impact of the elements of the object, the presence of production costs of the accumulated resource, which determines the state of the elements, etc.) necessitate additional studies of the stability of the system and the convergence of the coordination process. The aim of the work is to study the conditions of stability and convergence of decentralized coordination of a distributed cyber-physical control system with a wave coordination method. A condition for the stability of such systems is obtained. The model of a system of two connected controllable elements and local control systems has been developed. The transfer function of the system is obtained using the method of equivalent transformations. It is shown that in the absence of coordination, such system is stable under the condition of attenuation under the propagation of influences on the elements of a continuous object. The stability and convergence of decentralized coordination of local control systems with the wave coordination method based on a simulation model is investigated using the example of three-element systems. The simulation model was created in the Scilab/Xcos system. As a result of the studies, it was shown that although the system is stable, the state of the object's elements coincides with the specified one, however, the duration of the coordination process significantly exceeds the duration of the transient processes of individual elements. Further research is supposed to be directed at the proof the hipotesis that the stability of the system independent of the number of controlled elements of a continuous object and studying the conditions for the stability of active systems (with an increase in the influence in the process of propagation).
SISTEM INFORMASI KEANGGOTAAN PERGERAKAN MAHASISWA ISLAM INDONESIA (PMII) KABUPATEN BANJAR

The Indonesian Islamic Student Movement (PMII) Banjar Regency is a forum for association of student organizations within the Banjar Regency. The registration process for organizational members is carried out conventionally, which must come to the PMII Kabupaten Banjar then filled out all available forms and collected them back to the administration department. Then a written test is held which will be held on the specified day, after the participant is declared to have passed, then the participant will be invited to carry out an interview test. Documentation of activities on books and paper causes data recording to take a long time due to different handwriting and difficulty in reading them, books and paper are also prone to damage and loss. It is very difficult to find data from previous registered member because there is no good documentation. There is also no standard committee election fee, which creates difficulties in choosing a new committee. With the Information System, the registration service process, written tests until interviews can be well documented and stored, data retrieval becomes faster also data management is more structured. The management system for each member also facilitates the management of existing members in the PMII Kabupaten Banjar and can be used as a reference when forming a committee. Testing User Acceptance on the application of the system gets Cronbach Alpha value of 0.654, which means that the reliability is included.
A note on fully homomorphic encryption of real-life data

The purpose of this short paper is to explain the difference between encrypting real-life data and encrypting elements with a ring structure in the context of fully homomorphic encryption (FHE). Specifically, our encryption of real-life data is in two stages. First, we use a private-key embedding of real-life data in a ring; this embedding does not have to be fully homomorphic. This private embedding, speaking informally, takes most of the security burden off the second part of our encryption procedure, namely FHE between rings. The whole encryption function is then not fully homomorphic, but we show that it still provides for the most popular functionalities one expects from FHE, including private search on encrypted data.
How to conduct semantic network analysis

The aim of this chapter is to offer basic guidelines and tools to conduct semantic network analysis and to outline the possible uses of such an analysis in the social sciences. As many readers may not have previous knowledge and experience in computation analysis, this chapter provides step-by-step instructions for how to choose and prepare the corpus for analysis, select desired words, construct simple and readable networks, and even perform some basic analyses such as cluster analysis to reveal the structure, the topics, and ultimately the meanings of those networks. Bearing in mind that social scientists often do not get proper training in programming, this chapter also presents some of the publicly available tools, such as word frequency calculators and text-to-network convertors, to enable researchers to easily engage in text analysis related research.