Reviewerly/exHarmony huggingface dataset:
Addressing NIME's Prevailing Sociotechnical, Political, and Epistemological Exigencies

Abstract Nearly two decades after its inception as a workshop at the Association for Computing Machinery Conference on Human Factors in Computing Systems, New Interfaces for Musical Expression (NIME) exists as an established international conference significantly distinct from its precursor. Although this origin story is often noted, the implications of NIME's history as emerging from a field predominantly dealing with human–computer interaction (HCI) have rarely been discussed. In this paper we highlight many of the recent—and some not so recent—challenges that have been brought upon the NIME community as it attempts to maintain and expand its identity as a platform for multidisciplinary research into HCI, interface design, and electronic and computer music. We discuss the relationship between the market demands of the neoliberal university—which have underpinned academia's drive for innovation—and the quantification and economization of research performance that have facilitated certain disciplinary and social frictions to emerge within NIME-related research and practice. Drawing on work that engages with feminist theory and cultural studies, we suggest that critical reflection and, moreover, mediation is necessary to address burgeoning concerns that have been raised within the NIME discourse in relation to methodological approaches, “equity, diversity, and inclusion,” “accessibility,” and the fostering of “rigorous” interdisciplinary research.
Efficient Java implementation of image cloning method based on gradient processing

In the processing of photographic images, it is common to manipulate them to include objects that are not present in the original image, to make them more appealing or to change the environment. Therefore, they are manually edited and the object to be included is cut out of a source image, to join it to the destination and then, through the use of filters, the contour is softened to make the union between both images less noticeable. Perez et al. introduced a technique, called perfect cloning, based on processing the image gradient to integrate them, using sparse matrices, allowing to automate this process. However, its implementation in languages such as Java is complex due to memory limitations for large matrices. Hence, this paper introduces a method of implementing sparse arrays that allow their use in Java. It has been implemented as a plugin for the free software ImageJ. In addition, the technique is compared with the Multiresolution method, developed by Burt and Adelson, which is considered a reference in the field.
Detecting Deep Neural Network Defects with Data Flow Analysis

Deep neural networks (DNNs) are shown to be promising solutions in many challenging artificial intelligence tasks, including object recognition, natural language processing, and even unmanned driving. A DNN model, generally based on statistical summarization of in-house training data, aims to predict correct output given an input encountered in the wild. In general, 100% precision is therefore impossible due to its probabilistic nature. For DNN practitioners, it is very hard, if not impossible, to figure out whether the low precision of a DNN model is an inevitable result, or caused by defects such as bad network design or improper training process. This paper aims at addressing this challenging problem. We approach with a careful categorization of the root causes of low precision. We find that the internal data flow footprints of a DNN model can provide insights to locate the root cause effectively. We then develop a tool, namely, DeepMorph (DNN Tomography) to analyze the root cause, which can instantly guide a DNN developer to improve the model. Case studies on four popular datasets show the effectiveness of DeepMorph.
Design of a GPS Enabled Maximum Power Point Solar Tracker for Mobile Platform

Due to the large power potential of solar energy and the high cost of power generation by conventional sources, people are now shifting towards solar energy. Electricity generation from solar power is not a new concept, but earlier due to the lack of proper technology the capital cost involved in electricity generation was very high. But now, the advancement in technology and the use of new techniques have reduced the generation cost. This paper discusses about obe such technique called maximum power point tracking (MPPT). It was a revolutionary concept that helped in reducing one of the limitations of solar power, i.e. irregularities in power output. Here, this research work discusses about the available different types of MPPT methods, their advantages, and limitations and then a new design of solar tracker will be proposed to generate more power on a moving platform. Also, the proposed research work will discuss its challenges and advantages in detail and also about its different components. A detailed design of the proposed system has also been provided.
MicroSTARLING: A Millimeter Scale Self-Organizing Optical Communication Processor for Embedded Multihop Sensor and Computational Networks

In the development of small scale, networked nodes, design inspiration can be found in the decentralized and simplistic communication used by starlings in flight. Here, we present a novel node processor based on simple nearest neighbor connections to yield a self-organized linear optical sensor network. We present the processor state machine design and evaluation of the Verilog code. Each node, termed a MicroSTARLING, contains a processor fabricated using 180nm CMOS technology. The chips are 1.2mm ×1.2mm and the digital processor occupies 0.063 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of the chip. We assemble three test bench nodes to evaluate the processor function in wired, wireless communication and fully wireless circuits. When nodes are directly wired, the MicroSTARLING processor functions with a 25MHz input clock enabling a linear multihop network readout of 8 devices in 1.8ms. With a 20 μW, 524KHz input clock, the processor requires 12 μW at 1.2V. We evaluate the processor function under optical communication obtaining 6-hops between 7 nodes with little noticeable reduction in communication quality. Our fully wireless node required 3mW of power harvested via a 940nm light emitting diode (LED) and communicated via separate LEDs. To our knowledge this is the first demonstration of more than 3 hops of linear wireless communication with a mm scale self-organizing node processor. The processor size and power make possible the concept of smart dust networks with multihop communication between nodes for an embedded wireless sensor network in biological tissue structures or as the foundation of an optical neural network for edge computing.
Generation of 1 GB full entropy random numbers with the enhanced-NRBG method

Abstract Random numbers have significant applications in fundamental science, high-level scientific research, cryptography and several other areas where there is a pressing need for high-quality random numbers. We present an experimental demonstration of a non-deterministic random bit generator from a quantum entropy source and a deterministic random bit generator mechanism to provide high quality random numbers of 1 GB data size. Quantum entropy is realized by a series of quantum chips based on radioactive isotope Americium-241. The extracted raw random numbers are further post-processed to generate a high-entropy seed for the hash based deterministic random bit generator. We discuss the implementation of randomness extraction algorithm and Hash-DRBG algorithm in detail. The random numbers pass all the randomness measures provided in standard statistical test suites.
PROTOTYPING IN SOCIAL VR: ANTICIPATE THE UNANTICIPATED OUTCOMES OF INTERACTIONS BETWEEN AI-POWERED SOLUTIONS AND USERS

Abstract Interactions of users with AI powered solutions (AIPS) have the potential to affect collective behaviours and amplify unanticipated outcomes. Product developers, organisations, and companies are increasingly being expected to take responsibility for the unanticipated outcomes of their products. In this paper we explore a proactive approach to prototyping AIPS-user interactions using Social Virtual Reality (SVR) environments that allows for the anticipation of potential outcomes. We contend that doing so would limit the detrimental effect outcomes could have on product developers' resources and reputation
Chinese Proprietary Domain Text Named Entity Recognition

Chinese named entity recognition(CNER) is a basic task of natural language processing, the purpose of which is to identify the boundary and type of entity existing in the text. In this paper, we first deeply analyze the classical Chinese named entity recognition algorithm from two perspectives. On the basis of statistical knowledge of the conditional random field(CRF) and bidirectional long short term memory(Bi-Lstm) recurrent neural network for Chinese named entity recognition task. In combination with the advantages of Bi-Lstm deep learning entity features, to make up for the shortcoming that Bi-Lstm cannot dynamically output the results at the current moment by using the last output results. We introduce the CRF algorithm after the BI-Lstm algorithm, which makes the model can obtain the context semantic relations and ultimately improve the effectiveness of the experiment.
Research on the Requirement Analysis Method of Emergency Communication Task List

Task list can effectively solve the differences in concept understanding and action cognition of emergency communication joint command and control organization and management. Full and correct requirement analysis is the basis and premise of developing emergency communication task list (ECTL). Based on DoDAF version 2.0, this paper proposes a multi viewpoint requirement analysis method, and establishes an ECTL requirement analysis model from the viewpoint of full, operational, capability and system viewpoints, which provides a basis for the next task list requirement analysis.
Application of Gaze-Based Adaptive Notifications to Support Image Analysts

An application that emulates the working environment of an image analyst has been developed. An eye-tracker is used to observe the viewing behavior and based on this viewing behavior and image analyst's computer keyboard interaction, the application tries to optimize the presentation of notifications (namely, adaptive notifications). Within the study, three notification modes were explored: adaptive notifications, non-adaptive notifications and no notifications. The goal of the study was to explore whether adaptive notifications result in higher task performance, situational awareness, and sense of trust, and a lower sense of irritation. A total of fifteen participants participated. They observed two simultaneous video streams and reported mission-relevant information. Each participant performed three scenarios with a different notification mode in each scenario. Task performance was logged in the background by the application. Results indicate that task performance was higher when using the adaptive notification mode compared to the other modes. More specifically, the mode with adapted notifications resulted in fewer false alarms and more correct rejections compared to other modes. There were no significant differences in situational awareness, trust and irritation between the different notification modes. Adaptive notifications based on gaze input can increase the image analyst's task performance.
Homomorphic Filtering Approach for Eliminating Multiplicative Noise in a Monochromatic Based Images

Digital image processing has led to a more significant contributions in the meadow of computing science ranging from image enhancement, restoration, detection down to image analysis used in both scientific and medical field. Digital image processing perhaps being used to enhance images with a multiplicative noise or an unwanted signals during capture or transmission via mobile device or camera. Filtering, specifically Homomorphic Filtering is one of the digital image processing technique for processing of both convolved and nonlinearly related signals from an image. With a use of homomorphic filtering through illumination-reflectance model, an image appearance can be improved usually by range compression and contrast enhancement in a frequency domain. However, the main approach of this study is to separate illumination from reflectance component by considering logarithm and applying fourier transform and inverse exponential operations yielding to a better yet enhanced image. Homomorphic filter considered as a generalized technique for image enhancement has proven to be most effective filtering technique in the vast field of digital processing.
Results of the NATO project: "analysis, design, and implementation of an end-to-end QKD link"

This manuscript discusses the most relevant aspects of the practical implementation of a long-range Quantum Key Distribution (QKD) link with trusted nodes, achieving the highest possible secret key rate generation within the security and system-level constraints. To this purpose, it describes the implementation of an end-to-end QKD system, including implementation aspects from the physical transmission of photon states through a standard telecommunications grade optical fiber, to consideration of quantum metrology and information reconciliation protocols based on forward error correction codes. In addition, since there are circumstances when a fiber optical link may not be available, it examines the problems involved with the implementation of a Free Space Optics (FSO) QKD link. The manuscript also discusses the problem of information reconciliation in Continuous Variable (CV) QKD scenarios on FSO links, showing that in long distance links, since the sign of the received Gaussian samples contains the largest fraction of information, Unequal Error Protection (UEP) reverse reconciliation schemes can be designed. The presented results have been achieved within the NATO SPS project “Analysis, design and implementation of an end-to-end 400 km QKD link”.
Security for User Profile Matching in Social Networks

We consider a situation where a client tends to have a client profile database, kept up quite a while ago can handle by corresponding association supplier. In order to perceive clients’ organized profile Data it should undergo for Customer investigating. A common example of this functionality is on the social media site. Also starting late, a cloud-based dating site, Internet dating, was compromised, which reveals a lot of online customer accounts to goliaths. This information break has mentioned that bosses investigate viable security insurance for client profiles in a social affiliation. We propose an affirmation protecting response for profile sorting out in easygoing systems by utilizing server. Our solution relies upon homomorphic encryption and enables a client to discover sorting out clients with the help of servers. This is done without uncovering to anybody the request and the tended to client profiles in clear.
Spirituality: The pillar of true education

There has been a constant discussion on the role of Spirituality in education. Everyone knows that the goal of education is not to create a generation only capable of understanding the languages of materials and machines. Education should help us to attain SAMSKARA- spiritual cultural values. If culture merely denotes a refined sense of judgment regarding worldly things, we don’t need education to cultivate it. The question is why do we see spirituality as something separate from life? This attitude has already created a big problem by branding spirituality as not part of our day to day life. Due to this thinking, we have disconnected the external from the internal. It is important to create a balance between the external and the internal, the body and the soul. The present paper focuses on how spirituality connects human values to human conscience.
A Covid-19 Lockdown Indoor Electric Arm Training Machine’ Design and Mechanism

Along with the spread of Covid-19, many countries are locked down, and people cannot go out for exercises as they were before. Additionally, along with the spread of streaming media, people are spending more and more time on TVs, and Internet Media, leaving less time for exercise, which is an essential factor in promoting healthy life. Current indoor exercising devices are either not fully integrated with the household environment or cannot let people exercise while watching TV. Thus, this paper introduces an electric arm training apparatus, including a housing, an arms training drive structure, an assembly matching structure and a microprocessor, said housing having an arms training drive structure mounted inside, and an assembly matching structure fixed to a lower end face of the housing, said housing having an operating structure at one end, and a mobile phone paired code at one side of the surface of the operating structure, and a voice broadcast being set above the mobile phone paired code. The device is equipped with a training drive structure that allows the user to rewind the connecting rope and handle. The training drive structure allows the user to reel in the stretched cord and handle, thus ensuring that the trainer’s arm is contracted and stretched in a cyclic manner, and the assembly and matching structure allows the device to be easily interfaced with the customizable external mounting bracket—the ability to increase the diversity of device assemblies.
The pages in the history of Culture Faculty of Dagestan State University. On the occasion of the 80-th anniversary of Kazim O. Omarov (14.09.1941–6.05.2008)

The article is written on the 80-th anniversary of Kazim Omarovich Omarov, Candidaate of Sciences in Pedagogy, Professor, and President of Dagestan Association of Librarians. He made a great contribution to library professional education and training librarians in the Republic of Dagestan and Northern Caucasus on the whole. Omarov’s biography is reviewed — his pedagogical, academic, research and public activities. His contribution as the head of library studies and bibliography (1983—2008) is emphasized.
FEBR: Expert-Based Recommendation Framework for Beneficial and Personalized Content

So far, most research on recommender systems focused on maintaining long-term user engagement and satisfaction, by promoting relevant and personalized content. However, it is still very challenging to evaluate the quality and the reliability of this content. In this paper, we propose FEBR (Expert-Based Recommendation Framework), a collaborative recommendation framework based on apprenticeship learning to assess the quality of the recommended content on online platforms. FEBR exploits the demonstrated trajectories of a set of trusted users (chosen to be experts with reliable behavior) in a recommendation evaluation environment, to recover an unknown utility function. This function is used to learn an optimal policy describing the experts’ behavior, which is then used in the framework to optimize a user-expert-based recommendation policy with an adapted Q-learning algorithm, providing high-quality and personalized recommendations. We evaluate the performance of our solution through a user interest simulation environment (using RecSim), and compare its efficiency with standard recommendation methods. The results show that our approach provides a significant gain in terms of content quality (evaluated by experts and watched by users) while maintaining an important engagement rate.
Fuzzy Rule Interpolation with a Transformed Rule Base

Traditional fuzzy rule interpolation (FRI) methods typically utilise Euclidean distances between an observation and the rules in a given sparse rule base to select a set of rules closest to the observation to perform interpolation. However, simply applying the Euclidean distance metric may frequently lead to inferior results, because it cannot take into consideration the relevance degree of different features. To address this important issue, this paper presents an initial framework for a novel FRI approach which works based on exploiting a transformed rule base. Mahalanobis matrix learned by metric learning methods is used herein to transform the given sparse rule base to a new coordinates system where the distance between instances of the same category is closer, and instances of different categories is distant from each other. When a new observation is present which matches no rules, the selection of the nearest rules to implement the required interpolation is carried out in the transformed coordinates system. Then, the scale and move factors within the classical transformation-based FRI procedure are modified by Choquet integral. Experimental results obtained by employing different metric learning methods and Choquet integral over seven classification problems demonstrate that the proposed approach remarkably outperforms existing FRI methods.
Two-Stage Voice Application Recommender System for Unhandled Utterances in Intelligent Personal Assistant

Intelligent personal assistants (IPA) enable voice applications that facilitate people's daily tasks. However, due to the complexity and ambiguity of voice requests, some requests may not be handled properly by the standard natural language understanding (NLU) component. In such cases, a simple reply like "Sorry, I don't know" hurts the user's experience and limits the functionality of IPA. In this paper, we propose a two-stage shortlister-reranker recommender system to match third-party voice applications (skills) to unhandled utterances. In this approach, a skill shortlister is proposed to retrieve candidate skills from the skill catalog by calculating both lexical and semantic similarity between skills and user requests. We also illustrate how to build a new system by using observed data collected from a baseline rule-based system, and how the exposure biases can generate discrepancy between offline and human metrics. Lastly, we present two relabeling methods that can handle the incomplete ground truth, and mitigate exposure bias. We demonstrate the effectiveness of our proposed system through extensive offline experiments. Furthermore, we present online A/B testing results that show a significant boost on user experience satisfaction.
Maneuver Control and Avoidance System For Tracked Vehicle Robot

Over the past decade, nuclear and chemical substances have been part of the world's technological advancement. However, these technologies also have a negative impact towards the environment once disasters happen. In the case of contaminations, most operation will deploy human as the first responder. Alternatively, a mobile operator robot or semi-autonomous vehicle robot can be deploy instead to this unknown territory. To achieve this, the tracked vehicle must be able to be operated blindly and assist the operators during reconnaissance and assessment. This research focused on the maneuvering capability and the avoidance system of the tracked vehicle robot during operation. The maneuver control system based on a motion tracking device that used inertial measurement unit (IMU) that combines with a three-axis gyroscope and accelerometer to ensure stability. On the other hand, collision avoidance primarily used infrared (IR) sensors to ensure the safety due to its low dynamic applications. The proposed system shows good respond with the unknown and uneven terrain and able to halt movement accordingly. The next phase will explore multiple sensors in order to enhance its overall capability regarding the efficiently of its maneuvering capabilities with great responses.