Reviewerly/exHarmony huggingface dataset:
Application-Agnostic Chatbot Deployment Considerations: A Case Study

Advances in machine learning are making possible the interaction between humans and machines, coming closer to passing the Turing test. Chatbots, specifically, are a technology that uses the latest advances in natural language processing and machine learning to understand text and produce text in response to input. While this is an important achievement today, we must consider specific challenges that chatbot deployments might pose. This paper looks back to a historical event that took place in 2016 with the purpose of extracting important, memorable, lessons. The study suggests that certain assumptions with respect to societal values are of paramount importance and need to be considered carefully along with a proper platform selection.
Direct Tracking of Co-Channel Moving Sources With Different Cyclic Frequencies

Mobile source tracking is an important topic in the field of radio monitoring. A direct position determination method based on particle filtering is proposed in this letter for tracking multiple co-channel mobile transmitters. The co-channel signals, which have different symbol rates, overlap in both the time and frequency domains but can be distinguished in the cyclic frequency domain. We determine the positions of mobile targets directly from different cyclic cross-spectrum slices without time difference of arrival (TDOA) measurements. The particle filtering technique is employed to improve the accuracy in estimating motion states. Simulation results are given to demonstrate that the proposed algorithm outperforms other mobile tracking algorithms in the presence of co-channel interference.
Improving Design Quality of Software Using Machine Learning Techniques

Design patterns commonly pre-owned in Object-Software oriented Development. It provides Information helping to overcome recurrent design challenges (problems). Recognition of patterns can be described as identification and representation of information based on knowledge obtained from statistical data derived from patterns. Detection of design patterns provides the advantages of an extensive investigation of object-oriented software applications. When using various pattern detection techniques depending on static research issues to identify patterns it detects similar pattern structures. For this, we seek to identify patterns in design of Software by using classification-based methods and information metrics. The JHotDraw, QuickUML, and Junit of object-oriented software are used to detect software problems in the patterns of designing a Software. The datasets are manual creations which are designed utilizing software criteria for classifying learners. It is carried out in two steps, namely the development of a statistics-oriented dataset and the identification of trends in software trends in software design. Tests are carried out by Usage of 3 open source computer programs to test the process, then the tests are examined.
Biological Recursion and Digital Systems: Conceptual Tools for Analysing Man-Machine Interaction

The theory of numbers, the theory of computation and well-known biological and neurological studies on cognition and consciousness all indicate the concept of recursion as their common denominator. Mathematical recursion owes its meaning and properties to a dual relationship between its results, which always constitute a sequence, and the operator that generated them, which is instead invariant. This article proposes that this duality in recursion originates from the duality between the biological homeostatic equilibrium in living systems and the adaptive physico-chemical changes required to sustain such equilibria. Such duality gives order and meaning to the experiences of a living system. One of the many implications of this innovative perspective is that this duality can decouple computational results from our intuitive order relations, and that this can cause a rarefaction of the capacity of digital systems to convey communication and favour adaptation to the environment.
Adaptive 3D Model-Based Facial Expression Synthesis and Pose Frontalization

Facial expressions are one of the important non-verbal ways used to understand human emotions during communication. Thus, acquiring and reproducing facial expressions is helpful in analyzing human emotional states. However, owing to complex and subtle facial muscle movements, facial expression modeling from images with face poses is difficult to achieve. To handle this issue, we present a method for acquiring facial expressions from a non-frontal single photograph using a 3D-aided approach. In addition, we propose a contour-fitting method that improves the modeling accuracy by automatically rearranging 3D contour landmarks corresponding to fixed 2D image landmarks. The acquired facial expression input can be parametrically manipulated to create various facial expressions through a blendshape or expression transfer based on the FACS (Facial Action Coding System). To achieve a realistic facial expression synthesis, we propose an exemplar-texture wrinkle synthesis method that extracts and synthesizes appropriate expression wrinkles according to the target expression. To do so, we constructed a wrinkle table of various facial expressions from 400 people. As one of the applications, we proved that the expression-pose synthesis method is suitable for expression-invariant face recognition through a quantitative evaluation, and showed the effectiveness based on a qualitative evaluation. We expect our system to be a benefit to various fields such as face recognition, HCI, and data augmentation for deep learning.
A lossless blind image data hiding scheme for semi-fragile image watermark

In this work, we propose a digital watermark approach that is invariable to rotation and guarantees the integrity of the inserted mark. The size of the resulting image is identical to the original image and the process guarantees acceptable transparency. For this, we have experimented eight substitution processes to conclude that the substitution of four bits gives a great capacity of insertion while guaranteeing an acceptable transparency for the insertion of text. However for the insertion of an image, it is recommended to substitute only one bit. This reduces the capacity but guarantees transparency of the watermarking process.
Scalable quantum computation with fast gates in two-dimensional microtrap arrays of trapped ions

We theoretically investigate the use of fast pulsed two-qubit gates for trapped ion quantum computing in a two-dimensional microtrap architecture. In one dimension, such fast gates are optimal when employed between nearest neighbors, and we examine the generalization to a two-dimensional geometry. We demonstrate that fast pulsed gates are capable of implementing high-fidelity entangling operations between ions in neighboring traps faster than the trapping period, with experimentally demonstrated laser repetition rates. Notably, we find that without increasing the gate duration high-fidelity gates are achievable even in large arrays with hundreds of ions. To demonstrate the usefulness of this proposal, we investigate the application of these gates to the digital simulation of a 40-mode Fermi-Hubbard model. This also demonstrates why shorter chains of gates required to connect arbitrary pairs of ions make this geometry well suited for large-scale computation.
A Focused Study to Compare Arabic Pre-training Models on Newswire IE Tasks

The Arabic language is a morphological rich language, posing many challenges for information extraction (IE) tasks, including Named Entity Recognition (NER), Part-of-Speech tagging (POS), Argument Role Labeling (ARL) and Relation Extraction (RE). A few multilingual pre-trained models have been proposed and show good performance for Arabic, however, most experiment results are reported on language understanding tasks, such as natural language inference, question answering and sentiment analysis. Their performance on the IE tasks is less known, in particular, the cross-lingual transfer capability from English to Arabic. In this work, we pre-train a Gigaword-based bilingual language model (GigaBERT) to study these two distant languages as well as zero-short transfer learning on the information extraction tasks. Our GigaBERT model can outperform mBERT and XLM-R-base on NER, POS and ARL tasks, with regarding to the per-language and/or zero-transfer performance. We make our pre-trained models publicly available at this https URL to facilitate the research of this field.
Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction

As an effective learning paradigm against insufficient training samples, multi-task learning (MTL) encourages knowledge sharing across multiple related tasks so as to improve the overall performance. In MTL, a major challenge springs from the phenomenon that sharing the knowledge with dissimilar and hard tasks, known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">negative transfer</i> , often results in a worsened performance. Though a substantial amount of studies have been carried out against the negative transfer, most of the existing methods only model the transfer relationship as task correlations, with the transfer across features and tasks left unconsidered. Different from the existing methods, our goal is to alleviate negative transfer collaboratively across features and tasks. To this end, we propose a novel multi-task learning method called task-feature collaborative learning (TFCL). Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks and suppressing inter-group knowledge sharing. We then propose an optimization method for the model. Extensive theoretical analysis shows that our proposed method has the following benefits: (a) it enjoys the global convergence property and (b) it provides a block-diagonal structure recovery guarantee. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks. We further apply it to the personalized attribute prediction problem with fine-grained modeling of user behaviors. Finally, experimental results on both simulated dataset and real-world datasets demonstrate the effectiveness of our proposed method.
Discrete-Event Simulation of Quantum Walks

We use discrete-event simulation on a digital computer to study two different models of experimentally realizable quantum walks. The simulation models comply with Einstein locality, are as "realistic" as the one of the simple random walk in that the particles follow well-defined trajectories, are void of concepts such as particle-wave duality and wave-function collapse, and reproduce the quantum-theoretical results by means of a cause-and-effect, event-by-event process. Our simulation model for the quantum walk experiment presented in [C. Robens et al., Phys. Rev. X 5, 011003 (2015)] reproduces the result of that experiment. Therefore, the claim that the result of the experiment "rigorously excludes (i.e., falsifies) any explanation of quantum transport based on classical, well-defined trajectories" needs to be revised.
Electronic Module (E-Module) Berbasis Component Display Theory (CDT) Untuk Matakuliah Pembelajaran Terpadu

Abstrak: Modul elektronik merupakan bahan ajar mandiri yang disusun secara sistematis ke dalam pembelajaran untuk mencapai tujuan pembelajaran tertentu yang disajikan dalam bentuk elektronik. Penelitian ini bertujuan untuk mengembangkan modul elektronik berbasis component d isplay t heory melalui model R&amp;D yang telah diadaptasi sesuai kebutuhan pada penelitian ini. Data hasil penelitian dikumpulkan dengan teknik kuisioner dan catatan lapangan untuk validasi ahli dan uji pengguna yang dianalisis dengan teknik deskriptif kuantitatif dan deskriptif kualitatif. Hasil pengembangan modul elektronik menunjukkan bahwa modul telah tervalidasi layak dari ahli teknologi dan materi untuk mata kuliah pembelajaran terpadu mahasiswa S1 PGMI. Hasil uji pengguna menunjukkan bahwa modul dapat diterapkan dalam pembelajaran sebagai bahan ajar pendamping perkuliahan. Kelebihan elektronik modul berbasis component d isplay t heory ini secara tampilan memiliki visual design yang menarik dan fleksibel dalam penggunaannya, sehingga mahasiswa dapat mengakses serta belajar kapanpun dan dimanapun mereka berada. Abstract: Electronic m odule are independent teaching materials that are arranged systematically into learning to achieve certain learning goals that are presented in electronic form. This study aims to develop CDT-based electronic modules through the R&amp;D model which have been adapted according to the needs of researchers. Research data were collected using questionnaire techniques and field notes for expert validation and user tests which were analyzed by quantitative descriptive and qualitative descriptive techniques. The results of the development of the electronic module show that the module has been validated properly from technology experts and materials for integrated learning courses of PGMI students. User test results also indicate that the module can be applied in learning as a teaching companion. The advantages of electronic module based on component display theory show that the display has an attractive visual design and the flexible in its use, so students can access and learn whenever and wherever they are.
Solid-state quantum memory for hybrid entanglement involving three degrees of freedom

A quantum memory for photonic entanglement is the basic component in quantum networks. Photons entangled in multiple degrees of freedom enable the mutual transformation of Hilbert spaces for the same particle and different particles, thereby connecting heterogeneous nodes in the quantum network. So far, the quantum storage for photonic entanglement is limited to two degrees of freedom. Here, we demonstrate the heralded storage of single-photon hybrid entanglement involving three degrees of freedom (polarization, orbital angular momentum, and path) using a solid-state medium. The fidelity of the output state with the ideal maximum entangled state is $94.0\ifmmode\pm\else\textpm\fi{}0.8%$. The quantum property of the retrieved state is verified by testing the hypothesis of noncontextual hidden-variable theories. These results demonstrate the entanglement interface between light and matter involving multiple degrees of freedom, paving the way for the construction of hybrid quantum networks.
Contact tracing during the COVID-19 pandemic: Protection of personal information in South Africa

Containing the COVID-19 pandemic necessitates the use of personal information without the consent of the person. The protection of personal information is fundamental to the rights that ensure an open and democratic society. When regulations that limit the right to privacy are issued outside of the democratic process, every effort must be made to protect personal information and privacy. The limitation of human rights must be treated as an exception to the norm, and any regulations should be drafted to ensure minimum limitation of rights, rather than to the minimum acceptable standard. The contact tracing regulations included in the COVID-19 disaster regulations include some basic principles to ensure privacy; however, other important principles are not addressed. These include principles of transparency and data security. The envisaged future use of human data for research purposes, albeit de-identified, needs to be addressed by the COVID-19 designated judge appointed under the regulations.
GPU Accelerated Polar Harmonic Transforms for Feature Extraction in ITS Applications

Polar Harmonic Transform (PHT) is termed to represent a set of transforms those kernels are basic waves and harmonic in nature, which can improve the effect in Intelligent Transportation System (ITS) applications. PHTs consist of Polar Complex Exponential Transform (PCET), Polar Cosine Transform (PCT) and Polar Sine Transform (PST). PHTs can extract orthogonal and rotation invariant features and demonstrated superior performance in various image processing and computer vision applications. For real time systems and large multimedia databases, execution efficiency is always a significant challenge. With widespread use of Graphics Processing Unit (GPU), this study presents GPU based PHTs. Proposed methods are based on mathematical properties of PHTs and optimization techniques of GPU. Optimal parameter selections for GPU execution are also discussed. In our experiments, proposed methods are over 1800 times faster.
Gentle Versus Strong Touch Classification: Preliminary Results, Challenges, and Potentials

Touch plays a crucial role in humans’ nonverbal social and affective communication. It then comes as no surprise to observe a considerable effort that has been placed on devising methodologies for automated touch classification. For instance, such an ability allows for the use of smart touch sensors in such real-life application domains as socially-assistive robots and embodied telecommunication. In fact, touch classification literature represents an undeniably progressive result. However, these results are limited in two important ways. First, they are mostly based on overall (i.e., average) accuracy of different classifiers. As a result, they fall short in providing an insight on performance of these approaches as per different types of touch. Second, they do not consider the same type of touch with different level of strength (e.g., gentle versus strong touch). This is certainly an important factor that deserves investigating since the intensity of a touch can utterly transform its meaning (e.g., from an affectionate gesture to a sign of punishment). The current study provides a preliminary investigation of these shortcomings by considering the accuracy of a number of classifiers for both, within- (i.e., same type of touch with differing strengths) and between-touch (i.e., different types of touch) classifications. Our results help verify the strength and shortcoming of different machine learning algorithms for touch classification. They also highlight some of the challenges whose solution concepts can pave the path for integration of touch sensors in such application domains as human–robot interaction (HRI).
Vertical lossless genomic data compression tools for assembled genomes: A systematic literature review

The recent decrease in cost and time to sequence and assemble of complete genomes created an increased demand for data storage. As a consequence, several strategies for assembled biological data compression were created. Vertical compression tools implement strategies that take advantage of the high level of similarity between multiple assembled genomic sequences for better compression results. However, current reviews on vertical compression do not compare the execution flow of each tool, which is constituted by phases of preprocessing, transformation, and data encoding. We performed a systematic literature review to identify and compare existing tools for vertical compression of assembled genomic sequences. The review was centered on PubMed and Scopus, in which 45726 distinct papers were considered. Next, 32 papers were selected according to the following criteria: to present a lossless vertical compression tool; to use the information contained in other sequences for the compression; to be able to manipulate genomic sequences in FASTA format; and no need prior knowledge. Although we extracted performance compression results, they were not compared as the tools did not use a standardized evaluation protocol. Thus, we conclude that there’s a lack of definition of an evaluation protocol that must be applied by each tool.
Generative Design in Minecraft: Chronicle Challenge

We introduce the Chronicle Challenge as an optional addition to the Settlement Generation Challenge in Minecraft. One of the foci of the overall competition is adaptive procedural content generation (PCG), an arguably under-explored problem in computational creativity. In the base challenge, participants must generate new settlements that respond to and ideally interact with existing content in the world, such as the landscape or climate. The goal is to understand the underlying creative process, and to design better PCG systems. The Chronicle Challenge in particular focuses on the generation of a narrative based on the history of a generated settlement, expressed in natural language. We discuss the unique features of the Chronicle Challenge in comparison to other competitions, clarify the characteristics of a chronicle eligible for submission and describe the evaluation criteria. We furthermore draw on simulation-based approaches in computational storytelling as examples to how this challenge could be approached.
A Framework for Emulating Database Operations in Cloud Data Warehouses

In recent years, increased interest in cloud-based data warehousing technologies has emerged with many enterprises moving away from on-premise data warehousing solutions. The incentives for adopting cloud data warehousing technologies are many: cost-cutting, on-demand pricing, offloading data centers, unlimited hardware resources, built-in disaster recovery, to name a few. There is inherent difference in the language surface and feature sets of on-premise and cloud data warehousing solutions. This could range from subtle syntactic and semantic differences, with potentially big impact on result correctness, to complete features that exist in one system but are missing in other systems. While there have been some efforts to help automate the migration of on-premise applications to new cloud environments, a major challenge that slows down the migration pace is the handling of features not yet supported, or partially supported, by the cloud technologies. In this paper we build on our earlier work in adaptive data virtualization and present novel techniques that allow running applications utilizing sophisticated database features within foreign query engines lacking the native support of such features. In particular, we introduce a framework to manage discrepancy of metadata across heterogeneous query engines, and various mechanisms to emulate database applications code in cloud environments without any need to rewrite or change the application code.