Reviewerly/exHarmony huggingface dataset:
A Model for Encoding Multiple Logical Qubit States into the Energy Eigenstates of a Transmon System

One of the main challenges in implementing a Quantum Computer is the realization of two-qubit gates. In contrast to single qubit gates, for which advanced quantum controls are available for a variety of physical systems, the coupling required for two-qubit systems typically introduces more noise and poses an obstacle for arbitrary scaling of the underlying architecture. In this paper, we investigate a new model that partially overcomes the coupling problem. The model is based on encoding multiple logical states into the energy eigenstates of a single physical system, thereby allowing two qubit gates to be realized using the logical states that are spanned by the energy spectrum of the system. The work presented here expands upon previous work by the authors that was implemented and validated using tarnsmon technology in IBM's quantum experience for the two-qubit register case; here the model is expanded to demonstrate how it can encode quantum registers of arbitrary size.
Relevance-Sensitive Belief Revision in the Realm of Partial Preorders

The AGM paradigm, proposed by Alchourrón, Gärdenfors and Makinson, constitutes a prominent formal framework that models rational belief revision. Parikh formulated an additional constraint, encoded in the so-called axiom (P), which strengthens the AGM paradigm so that it adequately encodes the notion of relevance. In a subsequent work by Peppas, Williams, Chopra and Foo, axiom (P) was semantically characterized in terms of total preorders over possible worlds. In this article, we point out that this characterization is applicable in the realm of partial (not necessarily total) preorders as well. Furthermore, we show that Satoh’s popular revision operator —which is defined through non-total preorders over possible worlds— respects Parikh’s notion of relevance.
A high performance architecture for object detection in drones

Nowadays the use of drones in daily life is becoming more and more frequent. One important service of drones is object detection which is used in surveillance and search-and-rescue missions. High performance is a critical requirement for object detection, since drones travel at high flying speeds. Current systems mainly use software platforms which have limited performance. In this paper a hardware architecture is presented for object detection in drones. The ability of the proposed architecture to exploit parallelization in object detection tasks more efficiently than in software platforms, offers lower execution time and more accurate and faster results. The proposed architecture is implemented using an FPGA platform. Experimental results show the benefits of this architecture compared to a software platform in terms of performance and detection-precision.
International Patent Ethical Issues: Inventorship, AI, Ownership, and Compensation

Patent requirements may vary from country to country or region with respect to issues including, but not limited to, who (or what) qualifies as an inventor as well as the benefits afforded to inventors, whether through patent ownership or remuneration for invention, both, and neither. This Chapter provides an overview of the law related to inventorship in the United States compared to other countries/regions having robust patent systems. Comparisons also will be made as to who or what qualifies as an inventor and the benefits afforded to inventors in different countries/regions. Ethical issues related to patents in the context of inventorship also will be discussed, particularly as it relates to issues that may arise when an employer may have more than one place of business where invention occurs and/or the invention is to be protected in more than one country/region. While issues of invention ownership or employee-inventor compensation may not be the determining factor as to where an employer may base its business, there are ethical issues that may arise as laws may act as a deterrent in innovative countries or may lead to relocation of research and development (R&D) facilities to countries where ownership defaults to the employer or compensation to employees for inventions may be less frequently granted.
Ethics of Data Sharing

Cultural influences impact data collection, analysis, reporting and sharing of published work, code, and datasets. Sharing data with collaborators but also other scientists in peer-reviewed publications makes science accessible and enables follow-up research that promotes the advancement of science. Nonetheless, some scientists resist sharing data for many reasons—even if they agree with sharing in principle. To encourage and promote data sharing, many journal publishers and grant agencies have instituted practices that require data to be deposited into public repositories as a condition of publication or funding. In some cases, researchers can develop servers and webpages funded in part by the grant award to organize and make the data accessible. In this chapter, I review approaches used by scientists to share data, data sharing best practices, barriers to data sharing, systems that encourage data sharing, and recommendations to increase data sharing.
Strong homotopy induced by adjacency structure

This paper generalizes the concept of SA-homotopy in finite topological adjacency category, which is introduced in our previous work, to graph category and discusses its properties. We prove that every SA-strong deformation retract of a simple graph G could be obtained by removing trivial vertices one by one, which makes it possible to allow an iterative algorithm of finding all SA-strong deformation retracts of G. We also obtain that two simple graphs are SA-homotopy equivalent if and only if they have graph isomorphic cores. Compared with the graph homotopy transformation defined by S.T. Yau et al. and the s-homotopy transformation defined by R. Boulet et al., the main advantage of SA-homotopy transformation is that it could reflect correspondences between vertices, and hence it more accurately describe the transformation process than the graph homotopy transformation and s-homotopy transformation. As an application of SA-homotopy on graphs, we introduce the mapping class group of a graph, which also shows its advantage over the graph homotopy transformation and the s-homotopy transformation.
Bayesian network parameter learning algorithm based on improved QMAP

Small data sets make the statistical information in Bayesian network parameter learning inaccurate, which makes it difficult to get accurate Bayesian network parameters based on data. Qualitative maximum a posteriori estimation (QMAP) is the most accurate algorithm for Bayesian network parameter learning under the condition of small data sets. However, when the number of parameter constraints is large or the parameter feasible region is small, the rejection-acceptance sampling process in QMAP algorithm will become extremely time-consuming. In order to improve the learning efficiency of QMAP algorithm and not affect its learning accuracy as much as possible, a new analytical calculation method of the center point of constrained region is designed to replace the original rejection-acceptance sampling calculation method. Firstly, a new objective function is designed, and a constrained objective optimization problem for solving the boundary points of the constrained region is constructed. Secondly, a new optimization engine is used to solve the objective optimization problem, and the boundary points and center points of the constrained region are obtained. Finally, the existing QMAP algorithm is improved by the obtained center points. The simulation results show that the CMAP algorithm proposed in this paper has a slightly worse parameter learning accuracy than the QMAP algorithm, but its computational efficiency is 2-5 times higher than that of the QMAP algorithm.
A relation detection method based on multi semantic similarity

Relation detection is a critical step of knowledge base question answering, which directly affects the quality of question answering. Among the existing methods, the encoding-comparison method extracts text global semantic information for matching, which often ignores the local semantic feature of text sequence. The interaction approach performs the comparison on low-level representations based on the sequence local information, which fails to consider the global semantic information of the input sequences. To solve the issues, this paper proposes a relation detection model based on Bert model and multi-semantic similarity considering global and local semantic information. First, our model introduces Bert as a text encoding layer to represent questions and relations as sequences of vectors. And then, a bi-directional long short-term memory (Bi-LSTM) layer with the attention mechanism is used to analyze the local semantic relevance and calculate the local similarity. Finally, our model uses a distance calculation formula to measure the global semantic relevance between questions and relations. The experimental results on two benchmark datasets, SimpleQuestions and WebQSP, show that the proposed model achieves the accuracy of 93.92% and 87.81% respectively, performs better than state-of-the-art approaches.
Optic Flow-based Vision System for Autonomous and Collision-free Navigation of Micro Aerial Vehicles

In this work, a monocular camera-based obstacle avoidance system was designed to improve autonomous operation of quadcopters, already hindered by sensors&#x0027; heavyweight and processing and energy requirements. The work underwent two stages. First, the system dynamics were modeled, linearized, and controlled using proportional and derivative (PD) controllers. Then, an optic flow-based hybrid obstacle avoidance algorithm was developed. The algorithm consisted of three approaches that account for avoiding frontal and peripheral objects, while aiming to the final position. The developed system was tested in a challenging scenario that mimicked a real forest using Webots simulator. Results demonstrated an 85&#x0025; success rate of avoiding obstacles. Cases of system failure resulted due to the linearization constraint which blocked ad hoc aggressive behaviour.
Global Periodic Monopoles and Filtered Difference Modules

We shall prove the main theorem (Theorem 9.1.2) of this monograph. In Sect. 9.3, we shall prove that for a given periodic monopole of GCK-type, the associated good filtered bundle is polystable of degree 0. In Sect. 9.4, we shall prove the existence of a monopole of GCK type which induces a given polystable good filtered bundle with degree 0. Then, Theorem 9.1.2 follows. As one of the consequences of Theorem 9.1.2, the classification of singular monopoles of GCK-type is reduced to the classification of polystable parabolic difference modules of degree 0. In Sect. 9.5, we explain smooth parabolic 0-difference modules of rank 2 are equivalent to filtered torsion-free sheaves of rank one on spectral curves, which is a variant of equivalences between Higgs bundles and sheaves on the spectral curves due to Hitchin and Beauville-Narasimhan-Ramanan, and we revisit the classification of SU(2)-monopoles of GCK-type without Dirac type singularity studied by Harland.
Spontaneous magnetization and electroweak phase transition in the scalar sector of 2HDM

We discuss theoretical and phenomenological aspects of the two­-Higgs­-doublet extensions of the Standard model. In modern notation the h2HDM is the model with mh = 125.26 GeV, where the lightest neutral scalar particle in 2HDM is the usual Higgs boson in Standard Model.&#x0D; The parametric space of this model is very rich and admits many scenarios of New Physics’ phenomenol­ogy.The many restrictions and conditions should be satisfied. One of them is the Sakharov’s conditions for the formation of the baryon asymmetry of the Universe in models with extra scalar fields. We try to show that we can satisfy them in the h2HDM and find the region of parametric space where the electroweak phase transition is first order. The Sakharov’s conditions are not satisfied in Standard Model but it is fundamental problem of modeling the Universe, so the searching of New physics this way is relevant.
Designing and Developing A Smart Application for Improving the Communication Between the Deaf, Dumb People and Society

Technology and Science have made Human life addictive to luxury but still there exists poor people who are trying to find innovative ways which make the communication process easier. Regarding to the World Health Organization, about 1 million people are dumb and 300 million are deaf in the world. Whereas; the communication is one of the most important issues for deaf and dumb people. This work aims to propose a smart application named "Jusoor" to improve the communication process and bridge the gap between the deaf, dumb People, and society. The proposed application provides the user with a dictionary that contains many vocabularies with videos explaining the way that the word is indicated, the most important sentences used in public places (markets, parks, restaurants, etc.), Emergency calls that enable recognizing the type assistance needed with the feature of locating the person which makes it easier to reach and provide the assistance, and service of communication with sign language experts. The proposed work was analyzed using the Unified Modeling Language (UML), Firebase, XML and Java programming languages.
Large Vocabulary Continuous Speech Recognition for Nepali Language

Speech Recognition is a widely studied topic for high-resource languages like English and Mandarin. A plethora of publications exist that study the performance of several recognition methods for these languages. However differences in phonetics, accent, language model, etc between any two different languages demand for a study of speech recognition methodologies and components separately for each language. In this paper, we present a comparative study of popular speech recognition methods for Nepali, a low-resource Indo-Aryan language. We describe our approach to building the phonetic dictionary and present our findings for DNN and GMM based techniques with speaker adaptation on 50K vocabulary speech recognition task.
On the least amount of training data for a machine learning model

Whether the exact amount of training data is enough for a specific task is an important question in machine learning, since it is always very expensive to label many data while insufficient data lead to underfitting. In this paper, the topic that what is the least amount of training data for a model is discussed from the perspective of sampling theorem. If the target function of supervised learning is taken as a multi-dimensional signal and the labeled data as samples, the training process can be regarded as the process of signal recovery. The main result is that the least amount of training data for a bandlimited task signal corresponds to a sampling rate which is larger than the Nyquist rate. Some numerical experiments are carried out to show the comparison between the learning process and the signal recovery, which demonstrates our result. Based on the equivalence between supervised learning and signal recovery, some spectral methods can be used to reveal underlying mechanisms of various supervised learning models, especially those “black-box” neural networks.
Learning neighborhood-based embedding sequence for link prediction in temporal knowledge graphs

Many real-world knowledge graphs are complex and keep evolving over time. Inferring missing facts in temporal knowledge graphs is a fundamental and challenging task. Previous studies focus on link prediction in static knowledge graphs which hardly extracts the temporal features effectively. In this paper, we propose a novel deep learning model, namely KBGAT-BiLSTM, which is capable of solving long-term predict problems and is suitable for temporal knowledge graph with complex structures. First, we adapt the Graph Attention Network (GAT) to learn the structural features of knowledge graph. Then we utilize the Bidirectional Long Short-Term Memory Networks (BiLSTM) to learn the temporal features and obtain the low-dimensional embeddings of entities and relations. Finally, we employ a scoring function for link prediction in temporal knowledge graphs. Through extensive experiments on YAGO, WIKI, and ICEWS18 datasets, we demonstrate the effectiveness of our model, compare the performance of our model with several different state-of-the-art methods and further analyze the properties of the proposed method.
Exploring digital transformation of teacher education in the Western Balkans: Case of Kosovo

BACKGROUND: The whole educational system from pre-university to university level has been challenged with the immediate pressure to transform during the lockdown period of the novel coronavirus disease 2019 (COVID-19). The question of how to ensure digital transformation in universities become topical at policy, institutional, and classroom practice dimensions. OBJECTIVE: The main objective of this study is to examine and explore how digital transformation in higher education in Kosovo is conceptualised in a changing context. The study focuses on teacher education as a pathway that links pre-university education with higher education system to ensure future teacher preparation. METHODS: The study used a qualitative research design. The data were collected through interviews with management staff (n = 3) and teacher educators (n = 7), and group interviews with student teachers (n = 15), at the University in Kosovo and teacher education institution. Following the principles of the hermeneutical circle, conceptualisations of digital transformation in teacher education were clustered into the following dimensions: (i) policy, (ii) organisation culture and management, and (iii) teaching and learning. RESULTS: The paper analyses the Western Balkans, specifically Kosovo, as the country faced the transition challenges including institutional building, limited administrative capabilities, and other challenges regarding educational process and digitalization. The paper contributes to the discussions with the emerging conceptualisations of digital transformation in teacher education by drawing from thematic areas concerning policy response mechanisms, teacher education policy as isolated from higher education framework, change management process, missing organisational culture, rigidity and resistance in teaching. CONCLUSIONS: The paper concludes that teacher education context should be reconceptualised within the higher education framework to better manage a transition and change settings for a shared conceptualisation and sustainable digital transformation.
Efficient feature based video retrieval and indexing using pattern change with invariance algorithm

The amount of audio visual content kept in networked repositories has increased dramatically in recent years. Many video hosting websites exist, such as YouTube, Metacafe, and Google Video. Currently, indexing and categorising these videos is a time-consuming task. The system either asks the user to provide tags for the videos they submit, or manual labelling is used. The aim of this research is to develop a classifier that can accurately identify videos. Every video has content that is either visual, audio, or text. Researchers categorised the videos based on any of these three variables. With the Pattern Change with Size Invariance (PCSI) algorithm, this study provides a hybrid model that takes into account all three components of the video: audio, visual, and textual content. This study tries to classify videos into broad categories such as education, sports, movies, and amateur videos. Key feature extraction and pattern matching would be used to accomplish this. A fuzzy logic and ranking system would be used to assign the tag to the video. The proposed system is tested only on a virtual device in addition a legitimate distributed cluster for the aim of reviewing real-time performance, especially once the amount and duration of films are considerable. The efficiency of video retrieval is measured with metrics like accuracy, precision, and recall is over 99% success.
Participation Rate of Fifth Graders During COVID-19 Emergency: Synchronous Versus Asynchronous Learning Activities

The COVID-19 pandemic has influenced teaching and learning in dramatic ways. Educators have made significant efforts to facilitate learning in dynamically changing formats, and students have experienced a very difficult time to learn in non-traditional environments and settings. This study focuses on participation rates of 5th grade students on a variety of learning activities or assignments in Language Arts and Social Studies that occurred over a three-month period in which the school pivoted from full remote learning to a hybrid model. Results from a non-parametric analysis on 42 learning activities indicate that students’ participation rates on synchronous assignments are higher than that on asynchronous assignments. Implications for increased attention to instructional design as they relate to technology integration and the unique circumstances of remote learning are discussed.
Value economy: A meta-analysis of the challenges and opportunities of blockchain technology for businesses

Significant business growth is the result of innovations in their process and product, which is made possible by the use of high technologies such as blockchain. Technology has a huge impact on the world, not just through social media, big data, robotics and artificial intelligence, but these big changes can be achieved by the blockchain integration potential. By studying the fundamental concepts of the peer-to-peer blockchain networks, we find that this technology can record any tangible and intangible valuable assets. Its immutability, distributability and encryption capability revolutionizes the business centralism. One of the achievements of this technology is a fundamental revolution in the definition of trust that eliminates market frictions. In this study, we aim to have a meta-analysis on the role of blockchain technologies in businesses to express the opportunities and challenges of its implications for businesses. Also, we explain the facilitating role of blockchain in different industries by stating how to take advantage of these opportunities. Despite the comprehensive acceptance issue of this technology, new players have been made and that’s why blockchain tools can be the foundation of an intelligent economy through the transformation of businesses by changing systems. This article describes the fundamental features of blockchain technologies and the business opportunities that results from it and takes a futuristic insight for the evolution or development procedures of businesses.
Using Eye-Tracking Data and Mouse Cursor Location To Examine Visual Alerting in a Multi-Display Environment

A study was conducted to assess the effectiveness of visual alerting during a task that required full attention and that used multiple displays. Alert detection time was collected, and eye-tracking data was recorded to determine where participants were looking, particularly when an alert appeared. Results showed that a full border around the display was detected faster than a short bar at the top of the display. This finding is in contrast to previous work in our lab, where the bar alert has always been superior for detection. Previous findings had inferred that the bar alert can be included in a spotlight of attention created during the task. The new findings suggest that the spotlight was expanded in the current experiment as a consequence of limitations in head movement imposed by wearing the eye-tracking equipment. As a result the bar was not captured and detection time was slower. The eye-tracking data was also used to validate mouse cursor location as a reasonable indication of where eyes are looking. The data showed a relatively strong correlation between eye and cursor and indicated that for the task used the cursor is a suitable tool for collecting data on where an individual is looking.