Reviewerly/exHarmony huggingface dataset:
Fractional Backpropagation Algorithm – Convergence for the Fluent Shapes of the Neuron Transfer Function

The classic algorithm of backpropagation of errors requires that the neural transfer function is differentiable and usually the algebraic form of this derivative determines the implementation of the algorithm minimizing the SSE error function. The paper extends the idea of homogeneous ANNs of the feed-forward type, which can be designed with the use of calculus of finite differences. We present a novel model of a neural network which uses a fractional order derivative mechanism. It has been shown that by using numerical approximation of a fractional order derivative, it is possible to smoothly model the dynamics of the transfer function of a single neuron without the need to modify the algebraic form of its base functions like sigmoid. This approach universalizes the neural network model, and enhance the area of possible applications.
Recursividad educativa en tiempos de COVID-19 en Venezuela

In response to a call by the Angolan Scientific Magazine to disseminate papers addressing the current covid-19 pandemic and its development throughout the world, this hermeneutical research is presented interpreting reliable sources that tackle this world crisis focusing on Venezuela. The author indulges in a subjective interpretation for he also encounters the problem, thus he is a victim of the process. Moreover, he encounters social quarantine, and his family and school experiences as well as his inter-relation in general with people in the society, all intervene on his dialectical interpretation of the sources. To emerge as an agent of change to the problem, this study intends to analyze the relation -family-school-family in times of pandemic in Venezuela. The necessary measures have emerged in this cycle whereby the child who left his family where he received a process of primary socialization, goes to school to continue his education process, but must now return to the family to continue his education in the midst of the urgency caused by the crisis and then return to school in order to complete it. The back and forth cycle bring to the forefront a series of problems to the process of primary socialization and schooling of the child which the family and the school must address through an open and straightforward dialogue. It is an opportunity for both actors to unite, considering the educational demands, to face our coexistence and survival as a species, our interaction with the environment and our goals to reach the heights.
A formative study on the use of augmented reality in technical drawing courses

Augmented reality (AR), a mixture of virtual reality and reality in a computerized virtual object mounted on real environments, has been used extensively in educational studies in recent years. This study analyzed the use of a mobile AR application (AR tool) in a technical drawing course formatively to evaluate its usefulness for 40 engineering students that failed the technical drawing course. The students who took the course a second time in a summer semester used the AR application. The AR tool brought the three-dimensional models prepared in computer-aided design. The markers or trackers where the model can be handled freely from its page were brought to the students to examine the model. The students were able to move and rotate the objects freely, and they were able to look at the objects from many angles. The results indicated that the AR tool used in the course is useful in teaching the subjects of the course. As the use of AR increased, the benefits to the students were quite positive in terms of teaching and learning. This study has shown that AR contributes greatly to the easier comprehension of technical drawings of parts that are quite complex to understand.
‘Bring Your Own Device’ in VR: Intuitive Second-Screen Experiences in VR Isolation

Intuitive second screen experiences involving smartphone or tablet use are currently not possible with existing VR systems. Whenever users are immersed in a virtual reality world, interactive features and content are usually presented as pop-up windows and floating-text notifications, immediately breaking user immersion, serving as a reminder they are navigating a computer-generated environment. In this paper we discuss how to introduce second screen experiences in VR by orchestrating interplay between using handheld devices while using a VR headset. This entails allowing the users to interface with the touchscreen display despite the opaque, view-obstructing visor. We propose achieving this through a “mobile second-screen interreality system” (MSIS), referring a virtual 3D smart device representation inside the VR world, which is tightly coupled with its real-world counterpart, allowing users to manipulate, interface with, and look at the actual device screen, as if the head-mounted display were not present. This can be achieved via advanced screen-sharing technology, and pixel-perfect mapping between the real device and its virtual “double”, ensuring basic functions (such as typing on an on-screen keyboard and/or tapping on application icons) can be carried out effortlessly. Advanced hand and finger-tracking can be used to represent the user’s virtual hands’ and fingers’ movements on the screen, further enhancing virtual presence, and cementing the links established between the human brain and virtual reality space.
Eliminating the Effect of Reflectance Properties on Reconstruction in Stripe Structured Light System

The acquisition of the geometry of general scenes is related to the interplay of surface geometry, material properties and illumination characteristics. Surface texture and non-Lambertian reflectance properties degrade the reconstruction results by structured light technique. Existing structured light techniques focus on different coding strategy and light sources to improve reconstruction accuracy. The hybrid system consisting of a structured light technique and photometric stereo combines the depth value with normal information to refine the reconstruction results. In this paper, we propose a novel hybrid system consisting of stripe-based structured light and photometric stereo. The effect of surface texture and non-Lambertian reflection on stripe detection is first concluded. Contrary to existing fusion strategy, we propose an improved method for stripe detection to reduce the above factor's effects on accuracy. The reconstruction problem for general scene comes down to using reflectance properties to improve the accuracy of stripe detection. Several objects, including checkerboard, metal-flat plane and free-form objects with complex reflectance properties, were reconstructed to validate our proposed method, which illustrates the effectiveness on improving the reconstruction accuracy of complex objects. The three-step phase-shifting algorithm was implemented and the reconstruction results were given and also compared with ours. In addition, our proposed framework provides a new feasible scheme for solving the ongoing problem of the reconstruction of complex objects with variant reflectance. The problem can be solved by subtracting the non-Lambertian components from the original grey values of stripe to improve the accuracy of stripe detection. In the future, based on stripe structured light technique, more general reflection models can be used to model different types of reflection properties of complex objects.
Language and Intercultural Information Ethics Concepts: A Preliminary Discussion of Privacy

This paper introduces the perspective to understand privacy via language as an intercultural information ethics (IIE) concept. This research perspective carries two goals: to understand privacy as an IIE concept and to do so via natural language. The paper suggests that studying privacy through language answers the challenge faced by IIE work; in addition, studying privacy as an information ethics concept through language seems most appropriate considering that language both embodies and shapes meaning. Specifically, this paper briefly discusses privacy and some of its language expressions in the Chinese and English languages, through which it hopes to reveal the richness and possibilities of using natural language as a research instrument to understand privacy in intercultural settings, which is an area of researching privacy that has attracted little discussion so far.
Electronic Equipment Loads Telemetry in Ferry Ship Passenger Room Based on Long Range Wireless Communication (LoRa)

The development of information technology both software and hardware support development in various fields, especially in the field of electronics. This progress can be used in making automation systems and controlling electronic and electrical components. With the increasing need for system monitoring in real-time, a model that is more than just portable storage can be developed, using telemetry (remote measurement)systems. This research makes a prototype that is used for remote monitoring of electronic equipment telemetry in the passenger space of ferries using wireless communications. Radiofrequency is used for electronic equipment located in the passenger space. The system is made using ACS712 current sensor and ZMPT101B voltage sensor to measure current and voltage monitored by long-range wireless communications (LoRa) to maintain the strength of the amplifier signal and the distance that the LoRa can reach. By utilizing the frequency of the LoRa, it is expected to monitor the load current and voltage generated by electronic equipment in the ferry passenger's passenger space. From the monitoring tool obtained the test results of testing Television with power 27 W and Voltage 19 Volt obtained the percentage of voltage error is 1.09 % and the percentage of current error is 1.15 %.3. Based on the results of the test data, it can be concluded that the current, voltage in the passenger room of the JokoTole KMP ship is fluctuating. This needs to be done monitoring the current load on the ship, especially in the safety alarm system, electronic devices, pumps for the safety of shipping, which at KMP Joko Tole still uses conventional technology and the absence of adequate electrical system data support.
Prototype Design of Mobile Application ‘Hydrolite’ for Hydroponics Marketplace

Hydroponics is one of the effective farming methods to apply in big cities because it does not require extensive agricultural land. In addition, hydroponic products are cleaner, higher quality and free from pesticides. However, the development of hydroponic products in Indonesia is relatively slow. One of the factors causing the slow development of hydroponic agribusiness is that online sales media for hydroponic products are still limited, especially android-based e-marketplace application. Hydrolite is present as an e-marketplace that specifically sells vegetables grown using the hydroponic method, and sells all the equipment needed to farm hydroponically. Hydrolite is a prototype e-marketplace application designed using the Marvelapp platform. Further, Marvelapp is one of the best prototyping tools to support application development on mobile devices.
An overview of the 2020 NISO plus inaugural annual conference: A grand experiment

This paper offers an overview of the highlights of the 2020 NISO Plus inaugural conference that was held in Baltimore, MD from February 23–February 25, 2020. This conference replaced what would have been the 62nd Annual NFAIS conference. However, NISO and NFAIS merged in June 2019, resulting in the conference being renamed NISO Plus and taking on a new format. The goal was to continue some of the best traditions of past NFAIS conferences while building in time for discussions. With two and a half days of networking and education on tap, attendees had the opportunity to learn about emerging and exciting areas of change and development such as Artificial Intelligence and Machine Learning and new content types such as Augmented Reality/Virtual Reality. There were very practical sessions that focused on the status of standards, current issues and problems, with the goal of working towards innovative solutions and developing plans for moving forward.
Synthesizing Parallel Data of User-Generated Texts with Zero-Shot Neural Machine Translation

Neural machine translation (NMT) systems are usually trained on clean parallel data. They can perform very well for translating clean in-domain texts. However, as demonstrated by previous work, the translation quality significantly worsens when translating noisy texts, such as user-generated texts (UGT) from online social media. Given the lack of parallel data of UGT that can be used to train or adapt NMT systems, we synthesize parallel data of UGT, exploiting monolingual data of UGT through crosslingual language model pre-training and zero-shot NMT systems. This paper presents two different but complementary approaches: One alters given clean parallel data into UGT-like parallel data whereas the other generates translations from monolingual data of UGT. On the MTNT translation tasks, we show that our synthesized parallel data can lead to better NMT systems for UGT while making them more robust in translating texts from various domains and styles.
Internet-Mediated Genre Studies: An Integrative Literature Review (2005–2019)

Background: As all sorts of communications have substantially moved to the internet, volumes of literature on internet-mediated communication have emerged in professional and technical communication in different research paradigms, including studies on internet-mediated genres, which often have generic features beyond traditional conception and thus require updated understanding. This study systematically explores the emerging body of internet-mediated genre studies and identifies the specific genres that researchers have studied, the analytical components and research methods used, and conclusions reached to characterize the current state of the research. Research questions: 1. Which internet-mediated genres have been studied in existing literature (2005-2019)? 2. What affordances have been considered in existing studies on internet-mediated genres? 3. Which research methods have been used to study internet-mediated genres? Literature review: Major issues affecting prior studies of internet-mediated genres include inconsistent terminology used by researchers, the ways that affordances are considered, and the inadequacies of current genre analysis methods to explain features arising from those affordances. Methodology: Employing an integrative literature review, we conducted a systematic search resulting in 35 qualified studies published in journals indexed in the Social Sciences Citation Index between 2005 and 2019. Each was systematically analyzed to identify the genre addressed, communicative goal, medium, affordances addressed, and research methods used. Results/discussion: Three main types of internet-mediated genres-including email, website, and social media, and several subtypes-were identified, each distinguished by their medium and communicative goal. The affordances were either treated monomodally, mentioned as contextual information, or integrated into the analytical framework. Researchers relied on a variety of methods to study internet-mediated genres, with mixed methods most commonly used. Conclusions and further research: The data show that both the genres of interest and methods used to study them vary with time, suggesting that this area of research continues to evolve. Future studies could probe into a larger variety of internet-mediated genres with more diverse analytical components and methods.
Factoid Question Answering over Bangla Comprehension

Question Answering (QA) framework is a data recovery framework in which an explicit reply is required because of a submitted query, as opposed to a lot of references that may contain the appropriate responses. It is a man-machine specialized gadget. The essential thought of QA frameworks in Natural Language Processing (NLP) is to give the right responses to the queries. This paper shows a step towards building a question answering framework in Bangla. A significant initial phase in building up a QA framework is the dataset. As the Bangla QA framework is at the beginning phase of advancement, we have prepared our own Bangla question answering dataset. The proposed model for Bangla-QA contains careful stacking of several deep learning-based sub-models to grasp the context from a comprehension and generate appropriate answers for diverse question types. We have accomplished up to 92.16% F1 score considering partial match and 76.8% exact match on test dataset utilizing LSTM.
Optimal quantum transfer from input flying qubit to lossy quantum memory

Abstract In a quantum network, a key challenge is to minimize the direct reflection of flying qubits as they couple to stationary, resonator-based memory qubits, as the reflected amplitude represents state transfer infidelity that cannot be directly recovered. Optimizing the transfer fidelity can be accomplished by dynamically varying the resonator’s coupling rate to the flying qubit field. Here, we analytically derive the optimal coupling rate profile in the presence of intrinsic loss of the quantum memory using an open quantum systems method that can account for intrinsic resonator losses. We show that, since the resonator field must be initially empty, an initial amplitude in the resonator must be generated in order to cancel reflections via destructive interference; moreover, we show that this initial amplitude can be made sufficiently small as to allow the net infidelity throughout the complete transfer process to be close to unity. We then derive the time-varying resonator coupling that maximizes the state transfer fidelity as a function of the initial population and intrinsic loss rate, providing a complete protocol for optimal quantum state transfer between the flying qubit and resonator qubit. We present analytical expressions and numerical examples of the fidelities for the complete protocol using exponential and Gaussian profiles. We show that a state transfer fidelity of around 99.9% can be reached momentarily before the quantum information is lost due to the intrinsic loss in practical resonators used as quantum memories.
Intra-domain Knowledge Generalization in Cross-Domain Lifelong Reinforcement Learning

Lifelong reinforcement learning (LRL) is an important approach to achieve continual lifelong learning of multiple reinforcement learning tasks. The two major methods used in LRL are task decomposition and policy knowledge extraction. Policy knowledge extraction method in LRL can share knowledge for tasks in different task domains and for tasks in the same task domain with different system environmental coefficients. However, the generalization ability of policy knowledge extraction method is limited on learned tasks rather than learned task domains. In this paper, we propose a cross-domain lifelong reinforcement learning algorithm with intra-domain knowledge generalization ability (CDLRL-DKG) to improve generalization ability of policy knowledge extraction method from learned tasks to learned task domains. In experiments, we evaluated CDLRL-DKG performance on three task domains. And our results show that the proposed algorithm can directly get approximate optimal policy from given dynamical system environmental coefficients in task domain without added cost of computing time and storage space.
Constrained Center Loss for Image Classification

In feature representation learning, robust features are expected to have intra-class compactness and inter-class separability. The traditional softmax loss concept ignores the intra-class compactness. Hence the discriminative power of deep features is weakened. This paper proposes a constrained center loss (CCL) to enable CNNs to extract robust features. Unlike the general center loss (CL) concept, class centers are analytically updated from the deep features in our formulation. In addition, we propose to use the entire training set to approximate class centers. By doing so, class centers can better capture the global information of feature space. To improve training efficiency, an alternative algorithm is proposed to optimize the joint supervision of softmax loss and CCL. Experiments are performed on four benchmark datasets. The results demonstrate that the proposed scheme outperforms several existing architectures.
Transfer Dataset in Image Segmentation Use Case

The most labour-intensive stage of machine learning (ML) modelling is the appropriate preparation of correct dataset. This paper aims to show transfer dataset approach in image segmentation use case to lower labour intensity. Moreover, we test the effectiveness of this approach by training deep learning models on our prepared dataset. The models achieved high-performance metrics, even on very hard test data.
Game-theoretic Model of Agents’ Motion over a Terrain with Obstacles

In the paper, we studied the continuous optimal control problem for multiagent pathfinding which is suitable for modelling various agents such as UAVs, swarms etc. Here, multiple agents try to find the best routes avoiding the obstacles and collisions. At first, we define path-traversing time cost functional. We show continuity of this functional and existence of the optimal path for a single agent. Then, we describe the multiagent pathfinding problem. Also, we consider game theory interpretation for the multiagent pathfinding with continuous routes and social behaviour. Various examples of optimal routes and agent behaviour strategies are provided.
ARFIMA Model for Short Term Forecasting of New Death Cases COVID-19

COVID-19 is an infectious disease that can spread from one person to another and has a high potential for death. The infection of COVID-19 is spreading massive and fast that causes the extreme fluctuating data spread and long memory effects. One of the ways in which the death of COVID-19 can be reduce is to produce a prediction model that could be used as a reference in taking countermeasures. There are various prediction models, from regression to Autoregressive Fractional Integrated Moving Average (ARIMA), but it still shows shortcomings when disturbances occur from extreme fluctuations and the existence of long memory effects in the form of analysis of a series of data becomes biased, and the power of statistical tests generated for identification become weak. Therefore, the prediction model with the Autoregressive Fractional Integrated Moving Average (ARFIMA) approach was used in this study to accommodate these weaknesses because of their flexible nature and high accuracy. The results of this study prove that ARFIMA (1,0,431.0) with an RMSE of 2,853 is the best model to predict data on the addition of new cases of patients dying from COVID-19.