Reviewerly/exHarmony huggingface dataset:
Bayesian-Optimized Impedance Control of an Aerial Robot for Safe Physical Interaction with the Environment

Impedance control is a widely used interaction-control technique for aerial and ground robots. To achieve consistent performance during impedance control tasks, an a-priori knowledge of the environment parameters is needed to adjust the controller's impedance parameters accordingly. Concentrating on tasks requiring constant impedance parameters throughout operation, a model-free learning framework is proposed to autonomously find the suitable parameters values. The framework relies on Bayesian optimization and episodic reward calculation requiring the drone to repeatedly perform a predetermined task in the environment actively searching in the impedance parameters space. The sample-efficiency and safety of learning were improved by adding two novel modifications to standard Bayesian optimization. The proposed technique was validated in a high fidelity simulation environment. The results show that the proposed framework is able to automatically find suitable impedance parameters values in different situations given the same initial knowledge and that the learned parameters values can be generalized to similar interaction tasks.
Segregated Ballots for Voters with Disabilities? An Analysis of Policies and Use of the ExpressVote Ballot Marking Device

When the Help America Vote Act (HAVA) was enacted in 2002, it meant that, for the first time, people with disabilities were given the right to vote privately and independently. Post-HAVA, most states switched to direct recording electronic (DRE) voting machines, which allowed for people with disabilities to use the same machines, with alternate input/output modalities (e.g., blind voters could use the audio option and a set of headphones to ensure privacy). However, in the light of potential hacking threats (or even just the fear of hacking threats), many states are now moving back to hand-marked paper ballots such as optical scan ballots. Voters with print-related disabilities, unable to use an optical scan ballot by hand, are now forced to use a separate system, called a ballot marking device (BMD), to mark up paper ballots. Some BMDs, such as the ExpressVote®, produce a ballot that is different in size and content from the hand-marked ballot used in the jurisdiction. If only a small number of people with disabilities are using this BMD in each polling place, this allows for the possibility of determining which votes were cast by people with disabilities, and if only one ballot was cast using the BMD in a precinct, it might eliminate the secrecy of the ballot for that voter. This article presents a case study of Maryland, describing how ballot secrecy may have been violated in the 2016 and 2018 elections. The article also presents empirical data from the 19 other states (and Washington DC) where the ExpressVote BMD is used, on their policies related to the use of the BMD.
Smart Contracts as Authorized Production Rules

Rainfall is a smart contract programming model that allows mutually distrusting parties to manage assets on a distributed ledger. The model consists of a tuple space of authorized facts, and a set of production rules. Rules match on authorized facts, gaining their authority, and produce new facts with a subset of the gained authority. Rainfall allows assets such as crypto currencies to be defined in user code, rather than being baked directly into the ledger framework. Our authorization model also provides a natural privacy model, where not all rules or facts need to be revealed to all parties.
A sinusoidal model to assess PV generation from daily irradiation data

The estimation of photovoltaic (PV) efficiency depends on the solar cell temperature, which varies with ambient temperature and solar irradiation. When only daily averages are available, for instance, when assessing solar potential in a future climate, the standard procedure leads to a non-negligible error in the estimation of PV generation, as it disregards the fact that changes in efficiency at low irradiance are less relevant than changes in efficiency at high irradiance. A correction factor based on a sinusoidal model for solar irradiation and temperature is proposed and tested for locations with diverse latitudes and climates. The results show that this approach features random and bias errors below 2%, at least three times smaller than the standard averaging method, thus validating its application for estimation of PV generation.
Discussion: Object-Oriented Data Analysis, Power Metrics, and Graph Laplacians

"Discussion: Object-Oriented Data Analysis, Power Metrics, and Graph Laplacians." Journal of the American Statistical Association, 114(527), pp. 1097–1098
Expanding the boundaries of community by making space for inclusive participation

This experience report describes how our team restructured a cultural heritage website as part of our overall goal to become a more welcoming, equitable space for diverse community members. We describe the traditional categories that stakeholders have used in the past, why those categories caused concern, and discuss how restructuring the site's information architecture helped us address some of these concerns and managed to create new concerns. We recommend ways of thoughtfully engaging with the community and making improvements to help the community move towards a more open and accepting future.
Hierarchical-Gate Multimodal Network for Human Communication Comprehension

Computational modeling of human multimodal language is an emerging research area in natural language processing spanning the language, visual and acoustic modalities. Comprehending multimodal language requires modeling not only the interactions within each modality (intra-modal interactions) but more importantly the interactions between modalities (cross-modal interactions). In this paper, we present a novel neural architecture for understanding human communication called the Hierarchical-gate Multimodal Network (HGMN). Specifically, each modality is first encoded by Bi-LSTM which aims to capture the intra-modal interactions within single modality. Subsequently, we merge the independent information of multi-modality using two gated layers. The first gate which is named as modality-gate will calculate the weight of each modality. And the other gate called temporal-gate will control each time-step contribution for final prediction. Finally, the max-pooling strategy is used to reduce the dimension of the multimodal representation, which will be fed to the prediction layer. We perform extensive comparisons on five publicly available datasets for multimodal sentiment analysis, emotion recognition and speaker trait recognition. HGMN shows state-of-the-art performance on all the datasets.
Design of filler and compactor for oyster mushroom growing medium (baglog)

Abstract Oyster mushroom cultivation requires good quality growing medium (baglog). In addition to good baglog material, a suitable density level is needed for mushroom growth. Filling and compaction of baglog is often found use manual methods so it takes longer time. The objective of this study is to design a filler and compactor mechanical device for mushroom growing medium. The design obtained dimensions of length 150 cm, width 90 cm and height 153.06 cm. The device is operated by an electric motor of 1 Hp with rotation of 1450 rpm. The device capacity is 180 baglog/hour. The design test showed that the material for the main frame component consist of angle bar with size of 40 mm x 40 mm and thickness of 4 mm, was able to load the device with minimum safety factor of 3.58.
Query Based Graph Data Reduction Algorithms and Application in Education

Graph is a commonly used data structure to store large relational data in today's education networks. With the growing demand for storing and processing large graph data, graph data compression is becoming more important. By reducing large graph data itself, we will also be able to reduce memory space, processing time and transmission cost. While most existing compression methods compress general graphs by generating an encoded representation, we propose query based graph reduction algorithms. Query based graph reduction algorithms, by taking advantage of structural properties of graph and edge weights, compute reduced graphs that preserves necessary information to answer specific queries through disposing irrelevant nodes and edges. We study graph reduction algorithms based on two typical queries: shortest path queries and minimum spanning tree queries. In this paper, we illustrate our algorithm in detail, provide proof for the correctness of the algorithms and show estimation of their reduction ratio on actual graphs.
GestTalk—Real-Time Gesture to Speech Conversion Glove

It is often observed that people who are speech or hearing impaired find it difficult to communicate with others. According to WHO [5], over 5% of the global population has disabling hearing loss, while many more are speech impaired. These people mainly rely on sign languages for their daily communication. Sign language is quite complicated for an average person to understand, which makes the world less accessible to a person who has acquired this disability. Hence, to solve this existing problem and make the world more accessible for such people, we propose GestTalk, a smart glove specifically designed to enable speech-impaired people to communicate with others by translating their performed gesture to speech with the help of machine learning and IoT. We are using hardware-based glove loaded with home-made flex sensor to capture data points and machine learning algorithm to map these gestures to speech dynamically in real time. Usage of home-made flex sensor, each costing under 10 INR, plays the vital role in capturing finger movement during the gesture, and certainly makes GestTalk a cost-effective gadget. We were able to achieve ~95% of prediction accuracy on the trained sample with our minimal prototype. We present this case study to show how GestTalk can aid the hearing/speech impaired by enabling them to communicate with the world via speech cheaply and economically.
Innovation in the Spirit of Design Thinking

Abstract We define an innovation process that was designed for the provision of analytic services and software products. Our iterative process, which is based on design thinking, captures the need to both create value and accelerate value creation. The four steps in our iterative process include illuminate, ideate, create, and evaluate. We present a case study for which we used this process in the successful creation of a conversational non‐player character within a virtual training environment for the Army.
Elliptic Feature Recognition and Positioning Method for Disc Parts

Machine vision technology is more and more widely used in industrial production. Parts' sorting is a very common application scenario in industrial production. Considering the particularity of circular Parts, a method of part identification and positioning based on ellipticity is proposed. The method firstly preprocesses the image, then performs threshold segmentation to extract the edge contour, and then further filters the extracted edge contour, and performs least squares circle fitting on the basis of the retained edge contour to solve the Parts' pose. Finally, similarity matching is performed to determine the type of Parts' information. The experimental results show that the proposed algorithm can effectively identify and locate the parts.
REPRESENTATION OF SOCIOLOGICAL DATA IN THE CONTEXT OF LEGAL PROTECTION OF COPYRIGHT PROPERTY IN THE DIGITAL ECONOMY

This article presents the modern aspects of the formation digital objects of copyright (DOC) in the digital economy, as well as their function in modern scientific communication. The peculiarities of copyright protection are analyzed within the framework of traditional protection institutions, as well as using digital technologies based on distributed registries. Using the example of an object of copyright “sociological data” presented in digital form, its structure and description are presented for placement on the IPUniversity platform created by leading Russian universities to test fixing models and sharing the results of intellectual activity.
Learning Dialogue Strategies for Interview Dialogue Systems that Can Engage in Small Talk

This paper proposesNakamura, Tomoaki a method with which an interview dialogue system canKobori, Takahiro learn user-friendly dialogue strategies. Conventional interview dialogueNakano, Mikio systems mainly focus on collecting the user’s information and simply repeat questions. We have previously proposed a method for improving user impressions by engaging in small talk during interviews that performs frame-based dialogue management and generates small-talk utterances after the user answers the system’s questions. However, the utterance selection strategy in the method was fixed, making it difficult to give users a good impression of the system. This paper proposes a method for learning strategies for selecting system utterances based on a corpus of dialogues between human users and a text-based interview dialogue system in which each system utterance was evaluated by human annotators. This paper also reports the results of a user study that compared the proposed method with fixed utterance selection strategies.
Direction-invariant binary pattern-encoded descriptor for texture classification and retrieval

Texture feature captures the structural arrangement details of pixels present in a given image. The local binary pattern (LBP) and its modified versions have been used to extract discriminant information about the structural arrangement. A unique textural representation obtained from LBP and its modified versions are affected by rotating the structural arrangement of the pixels available in a particular region. The weight of the binary values obtained from the LBP and its modified versions are constant. The structural arrangement of the local region is effectively determined from the direction-oriented edge information [i.e., local directional pattern (LDP)]. The LDP is also nonrobust toward the structural arrangement changes in the local region. We give a direction-invariant binary pattern to represent the texture details available in a particular location by encoding edge response along with the LBP of that region. The direction-invariant property is derived from the weight of the edge response available in a particular region. The edge response around each local region of the image is captured using the compass edge detector. It detects the edge details from eight directions. The process of encoding eight direction edge responses along with a binary pattern obtained from the center pixel difference gives a more robust texture descriptor than the other rotation-invariant descriptors used in texture feature extraction. The proposed descriptor is tested over five standard databases, namely Brodatz, color Brodatz, KTH-TIPS2b, FTVL, and free photo. The performance of the proposed rotation-invariant texture feature is more effective than the existing rotation-invariant local texture feature representations involved in the image classification and retrieval experiments.
Network Architecture Framework Applications with FOCUS-Compliant Ship Designs

Ongoing development of the US Navy's LEAPS data repository, research exploring the use of networks in ship system design by the Naval International Cooperative Opportunities in Science and Technology Program (NICOP), and tool development by the Electric Ship Research and Development Consortium (ESRDC) have provided an excellent opportunity to interface efforts to address preliminary distributed system design and analysis in early-stage ship design. This paper builds on the architecture framework and network-based methods by demonstrating new ways to process FOCUS-compliant data, develop network views, and analyze connectivity and flow of distributed ship systems. This framework decomposes system architecture into three primary views: physical, logical, and operational. Network-based tools targeted on these views and their intersections efficiently explore and analyze broad ranges in the ship system design space. These explorations generate knowledge, encourage innovation, and support the synthesis of affordable, effective ship designs. The authors present the findings of these efforts in consideration for future changes to the FOCUS Product Meta-Model to support integrated advanced network architecture analyses.
Applying Horner's Rule to Optimize Lightweight MDS Matrices

This article is concerned with the problem of constructing lightweight MDS matrices. The authors present a new construction of 4 × 4 MDS matrices over GL(F2, m) for any integer m. They give sufficient and necessary conditions to determine whether the construction is an MDS matrix. Further, for any even number m ≥ 4, they construct lightweight MDS matrices in this structure. Applying Horner's rule to implement MDS matrices, the authors constructions need only 8+4×3×m XOR operations.
An Empirical Study on Acquisition of Digital Evidences from Facebook Using Various Tools and Techniques

The activities on Social Networking Sites have grown exponentially over the years, with majority of the population shifting to the virtual paradigm. The huge amount of information shared by users on Social Media not only act as breeding ground for criminals but also as a storehouse of evidence for Digital Forensic Investigators. Social Media Forensics, which is a branch of Digital Forensics, is used for collecting and analyzing user information from Social networking sites. Apart from the information available on social media, the different memory locations such as RAM, browser cache, browsing history etc. also include various artifacts useful in investigation. The available proprietary Social Media Forensic tools are expensive and might not be able to gather all evidences at a given time. Hence, the need of the hour is to use a combination of different tools and techniques to achieve best results. In this paper, we have conducted an empirical study to demonstrate different open-source tools and in-built native techniques for acquisition of user information and other artifacts from social media and the hard disk of computer system, followed by analysis of the same for various types of evidences. Although the scope of this study is limited to Facebook, the findings would be useful for Digital Forensic Investigators and Law Enforcement Personnel to investigate social media crimes in an efficient manner.
On Generalized Measures of Entropy for Fuzzy Sets

A probability distribution may have uncertainty. Now the question arises how to measure the uncertainty that exists within the probability distribution. Shannon developed the basic idea related to entropy in the year 1948. Later on, this concept extended by Zadeh. According to Zadeh the concept to measure the uncertainty of an event of statement having specific doubtfulness or it may depends linguistic variables. There are various application used in everyday life related to entropy measure such as cloud computing application, pattern recognition, traffic signal processing, interpersonal communication etc. Keeping in view of the above new generalized measure of entropy developed for fuzzy sets with its proof of validity. During this development phase, few of the properties of the measure has also been discussed. Graphical representations are also used to verify the measure with different values of parameters.
The feasibility of electronic voting technologies in Africa: Selected case examples

The use of electronic voting technologies (EVTs) presents hurdles to election management bodies (EMBs) and other election stakeholders in Africa. The constitutionality and feasibility of such technologies provoke several questions that are tackled in this article. A key question is whether voting technologies such as electronic voting machines (EVMs) comply with national election legislation. The article’s methodology includes a review of the literature, official reports and policy documents, media and other articles, including public pronouncements on such technologies in Africa’s elections. These are supplemented by case examples of interviewees from selected African countries, including Namibia, Kenya, Uganda, Nigeria, South Africa and Botswana. Its main contention is that African countries should evaluate the constitutionality and legitimacy of EVTs before their use. Finally, it will conclude that any use of voting technologies that does not meet constitutional and legal obligations will render the outcome of elections thereof flawed or dogged by irregularities, with questionable legitimacy, as seen recently in a few African countries.
Exploring Variants of Fully Convolutional Networks with Local and Global Contexts in Semantic Segmentation Problem

Recently, the semantic inference from images is widely used for various applications, such as augmented reality, autonomous robots, and indoor navigation. As a pioneering work for semantic segmentation, the fully convolutional networks (FCN) was introduced and outperformed traditional methods. However, since FCN only takes account of the local contextual dependency, it does not reflect the global contextual dependency. In this paper, we explore variants of FCN with local and global contextual dependencies in the semantic segmentation problem. In addition, we tried to improve the performance of semantic segmentation with extra depth information from a commercial RGBD camera. Our experiment result indicates that exploiting the global contextual dependencies and the additional depth information improves the quality of semantic segmentation
The Application Of Critical Thinking In C Programming Language Course

C programming language is an important course for undergraduates. Algorithmic design thought is the essence of programming course. But it is very difficult for undergraduates to master it. Most students master grammer of C programming language only. About algorithm, They are still in the imitation stage. Application of Critical thinking on teaching could improve teaching effect greatly, which would promote students to form their own ideas for algorithm design. To extend this mode to more departments or more computer basic courses is our future work.
Developing Intellectual Competence of Future Biotechnologists in the Process of Learning a Foreign Language

The article discusses the methodology for the development of intellectual competence of biotechnology students in the process of learning a foreign language, and organizational and methodological learning environment that provide a comprehensive, holistic development of intellectual skills in the context of a competence-based approach to modern professional-oriented education of technical university students enrolled in the field of biotechnology. The article focuses on the analysis of the concept of intellectual competence, the components and levels of competence development, considered from linguistic and methodological points of view. As a result of the research, the role of development of cognitive processes (knowledge, understanding, application, analysis, evaluation, synthesis) in a hierarchical sequence from simple to complex as target components of educational activities in the development of the future biotechnologist’s intellectual competence has been revealed.