Reviewerly/exHarmony huggingface dataset:
Entanglement of nitrogen-vacancy-center ensembles with initial squeezing

In this paper, we investigate entanglement of an experimental system of two nitrogen-vacancy-center ensembles which are initially squeezed under the one-axis twisting Hamiltonian. We take into account three scenarios in which initial squeezing and entanglement are mediated by phonons or photons: (a) the phonon-squeezed photon-entangled scenario, (b) the phonon-squeezed phonon-entangled scenario, and (c) the photon-squeezed photon-entangled scenario. For our investigation, we employ the Tavis-Cummings model, which includes dissipative decoherence of the collective spin ensemble, and analyze the system both for a relatively small number of spins and in the limit of a large number of spins using the approach of a quantum master equation. Although evidence in the literature on idealized coupled oscillator systems and coupled quantum kicked tops suggests that initial squeezing can enhance entanglement, we find that, in the realistic system studied in this paper, initial squeezing can improve entanglement overall when the field mode interacts in a particular manner with the two spin ensembles. Our analysis using the Holstein-Primakoff transformation and Wigner characteristic function in the rotating frame of reference shows that the entanglement enhancement is a subtle consequence of the way in which the dissipative decoherence rotates the state of the collective spin ensemble such that enhancement depends on the time-evolved rotated states between the presence and absence of initial squeezing.
HCt-SNE: Hierarchical Constraints with t-SNE

Dimensionality reduction (DR) methods are useful when analyzing high dimensional data, in particular, if one wants to visualize them. t-distributed stochastic neighbor embedding (t-SNE), one of the most widely used DR methods, can preserve neighborhood information and reveal groups in embeddings. However, it may not preserve the global structure and fail to reveal the semantic information in the visualization. From a user point-of-view, a DR visualization is useful if it not only reveals hidden structures in the data but also corresponds to the user knowledge. This paper addresses these problems by proposing Hierarchical Constraint t-SNE (HC <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> -SNE), a method that allows users to integrate hierarchical constraints directly into <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> -SNE embeddings. The user constraints are encoded in an explicit tree. We transform the hierarchical information in this tree into a novel regularization term based on triplet constraints among the nodes at different levels in the tree. Our method takes advantage of semantic information provided in class labels and outperforms the original <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> -SNE and two other supervised DR methods in terms of both visual assessment and quality metrics on three classic image datasets: MNIST, Fashion-MNIST and CIFAR10.
Unsupervised Subspace Learning With Flexible Neighboring

Graph-based subspace learning has been widely used in various applications as the rapid growth of data dimension, while the graph is constructed by affinity matrix of input data. However, it is difficult for these subspace learning methods to preserve the intrinsic local structure of data with the high-dimensional noise. To address this problem, we proposed a novel unsupervised dimensionality reduction approach named unsupervised subspace learning with flexible neighboring (USFN). We learn a similarity graph by adaptive probabilistic neighborhood learning process to preserve the manifold structure of high-dimensional data. In addition, we utilize the flexible neighboring to learn projection and latent representation of manifold structure of high-dimensional data to remove the impact of noise. The adaptive similarity graph and latent representation are jointly learned by integrating adaptive probabilistic neighborhood learning and manifold residue term into a unified objection function. The experimental results on synthetic and real-world datasets demonstrate the performance of the proposed unsupervised subspace learning USFN method.
A Path Planning Algorithm based on Artificial Potential Field Method and Ant Colony Algorithm

In the UUV searching task, a key step is the path planning process, in the known search environment to plan a satisfactory path from the beginning to the end, directly determines the final success of the search task. There are many path planning algorithms and environment modeling methods for the purpose of searching a target. Artificial potential field method has the advantages of small computation, strong real-time performance and easy implementation, but it also has the problem of local minimum point, so that UUV can't move forward in the vicinity of the target point. Aiming at the above problems, in this paper, a new method based on artificial potential field method and ant colony algorithm is proposed. The repulsive force field in the artificial potential field method is combined with the pheromone in the ant colony algorithm. Firstly, the initial target path point set is assigned to the UUV, and then the target path point of the next iteration is selected from the path point set, and the trajectory points of the next iteration are selected according to the path points. During the iteration, the UUV gets closer and closer to the target, and the path point set is updated. The results show that the improved algorithm solves the path planning problem of UUV search in complex environment, has certain adaptability to different search environment, and is more in line with the actual application environment, and has better search performance.
On the Optimality of Binary AIFV Codes with Two Code Trees

Huffman code is the optimal code in the class of uniquely decodable codes in the sense of the average length of codeword when a single code tree can represent the code. This paper defines hierarchical subclasses of noiseless source codes by allowing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -bit decoding delay for positive integer <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> and clarifies a necessary and sufficient condition for the uniquely decodable codes with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -bit decoding delay. Furthermore, we show that AIFV code is the optimal code in the class of uniquely decodable codes with 2-bit decoding delay when two code trees represent the noiseless source code.
Towards Context-Awareness for Enhanced Safety of Autonomous Vehicles

Autonomous vehicles operate in dynamic environments continuously encountering safety-critical scenarios. This necessitates employing methodologies that can handle these scenarios and ensure safety of the vehicle as well as other traffic participants. Besides, random failures or malfunctions in its components might result in hazardous situation(s), further raising concerns regarding safety. The intensity of these hazards caused by the malfunctions depends upon the current state of the operational context in which they occur. Thus to guarantee safe behavior of the vehicle, one must be aware of its operational context in the first place. To this end, we propose to systematically model the operational context of an autonomous vehicle apropos its safety-relevant aspects. This paper puts forth our initial work for context-awareness aided safety, including our perspective towards context and its modeling, and its categorization based on relevance and goal. We also propose a context meta-model and its fundamental elements crucial for developing a safety-relevant context model.
Completeness of string analysis for dynamic languages

In Abstract Interpretation, completeness ensures that the analysis does not lose information with respect to the property of interest. In particular, for dynamic languages like JavaScript, completeness of string analysis is a key security issue, as poorly managed string manipulation code may easily lead to significant security flaws. In this paper, we provide a systematic and constructive approach for generating the completion of string domains for dynamic languages, and we apply it to the refinement of existing string abstractions. We also provide an effective procedure to measure the precision improvement obtained when lifting the analysis to complete domains.
Maintaining Student Engagement: The Digital Shift during the Coronavirus Pandemic a Case of the Library at the University of Pretoria

The South African Higher Education Institution (HEI) sector had to move to 'emergency' online learning during the covid-19 pandemic, and they embraced a humanistic approach underpinned by the need to complete the academic year in 2020 and support all students. Because this emergency online learning accelerated the digital shift, evidence of pre-existing disparities and inequalities have been magnified, such as the digital divide, infrastructure, connectivity, access to data, digital literacies, vastly different learning environments, software and hardware tools, and others. The Department of Library Services (DLS) is aligned to the University of Pretoria's shift to online learning and to reimaging its service offerings and innovation readiness. It deliberately adopted a responsive engagement strategy for students, staff, senior management and service providers. Therefore, the DLS now offers a mix of services and programmes, with more reliance on virtual services to support and sustain the academic agenda. This paper provides real-life case study experiences of student engagement during the covid-19 pandemic, with reference to the relevant student engagement with literature. It also reflects on how the DLS previously applied attributes of service innovation, such as 'employing' a client-service robot at an HEI library in Africa, this being in keeping with the UP's innovation culture.
Lineament mapping for a part of the Central Sulaiman Fold–Thrust Belt (SFTB), Pakistan

Abstract This study aims to analyze the lineaments using the field data and a remote sensing approach, to describe their relationship with the folds, faults, and regional tectonic stress of the central Sulaiman Fold-Thrust Belt. Joint data, from nine anticlines, has been collected using the scanline method and classified into three sets (JS1, JS2, and JS3) based on their geometrical relationships. Lineaments extracted from the 30 m digital elevation model have been classified subsequently into three lineament sets (LS1, LS2, and LS3) based on the azimuths from the corresponding joint sets. A very high correlation coefficient (rs = 0.97), between the azimuths of the field joints and the remotely sensed lineaments, has been observed which validates that the lineaments are the regional representation of the local field joints. The geometrical relationship of the lineament sets with the fold hinges indicates that the older LS1 and LS2 are strongly related to the regional folding episode, while the younger LS3 is a result of local shears. The chronological interpretation of the deformational events responsible for the lineament sets is constrained by the presence of the Kingri Fault, which induces a strike-slip component within the study area. Furthermore, the controls on the joint and lineament sets, established using multivariate statistics to decipher the effects of lithological and structural contrasts on the lineament density, reveal that an increase in the lineament density can be attributed to the competence and thickness of the rock units as well as the variable local stresses across the different folds. Based on the orientations of these lineament sets, the cumulative direction of the compressive event in the NW-SE direction (310–320) coincides with the regional stress direction of the SFTB.
CyberPolice: Classification of Cyber Sexual Harassment

Online sexual harassment is defined as unwanted sexual conduct on any digital platform and it is recognised as a form of sexual violence which can make a person feel threatened, exploited, coerced, humiliated, upset, sexualised or discriminated against. With our work we contend that such content can be leveraged to create models that could automatically detect such malicious online behaviour and thereby, ban such users from posting such content in the future, without having to wait for the other users to report them and thereby, create a safe space on social media. A major attribute of our proposed model is that it focuses on how sexual harassment can be hard to classify on social media. These spaces, unlike a formal environment, have no rigid set of rules or code of conduct to adhere to and therefore it can be very difficult to draw the line between a joke and a more malicious comment. To be able to discern the differences between such analogous statements we must have a model which can read and understand the context clues to better classify. In our paper we have worked with state-of-the-art Machine Learning and Deep Learning models and conducted extensive comparison to find the most effective model to better realise this vision of fair space by achieving the most accurate predictions.
Citizen e-governance using blockchain

As recently as 2016, there was barely any literature available which could connect the breakthrough technology of blockchain to any potential use case in the public sector. As of 2020, numerous use cases have not just been proposed but have also been implemented by governments all over the world. However, in a large number of government offices, documents are still printed, signed, and then uploaded for public access. This method, though quite successful in terms of making information accessible, partially defeats the aim of digitalization. This chapter discusses various use cases which blockchain finds in e-governance. The benefits it offers over the current stack of technology being used and the challenges posed in the switchover are also mentioned. Various case studies of governments which have successfully implemented blockchain-based solutions are also discussed in brief.
Scaling Digital Design Literacy in K-9 Education Through the Fellowship Program

We report from a research-based professional development program - the fellowship program - aimed at scaling digital design literacy in Danish K-9 education. The fellowship program was designed to engage with experienced K-9 teachers to provide them with the knowledge, experience and resources needed to further develop digital design literacy in their local school environment and to create networks for continuous exchange between researchers, educators of teachers and in-service teachers. In this paper we present the motivation, strategy, partnerships and execution of the fellowship program and report from survey results assessing participants gains and learning outcomes. We position the fellowship program within the broader discourse of leveraging teacher training in computing education and digital design literacy. We suggest that the fellowship format has a number of qualities that make it a potentially valuable addition to established teacher training initiatives. This includes the establishment of feedback loops and networks between researchers and practitioners, scaling through local peer-to-peer training, a modular and agile setup and the collaboration between educators of teachers and in-service teachers.
Multiscale fractal dimension applied to tactical analysis in football: A novel approach to evaluate the shapes of team organization on the pitch

The aim of this study was to evaluate different shape descriptors applied to images of polygons that represent the organization of football teams on the pitch. The effectiveness of different shape descriptors (area/perimeter, fractal area, circularity, maximum fractal, rectangularity, multiscale fractal curve—MFC), and the concatenation of all shape descriptors (except MFC), denominated Alldescriptors (AllD)) was evaluated and applied to polygons corresponding to the shapes represented by the convex hull obtained from players’ 2D coordinates. A content-based image retrieval system (CBIR) was applied for 25 users (mean age of 31.9 ± 8.4 years) to evaluate the relevant images. Measures of effectiveness were used to evaluate the shape descriptors (P@n and R@n). The MFD (P@5, 0.46±0.37 and P@10, 0.40±0.31, p &lt; 0.001; R@5, 0.14±0.13 and R@10, 0.24±0.19, p &lt; 0.001) and AllD (P@5 = 0.43±0.36 and P@10 = 0.39±0.32, p &lt; 0.001; R@5 = 0.13±0.11 and R@10 = 0.24±0.20, p &lt; 0.001) descriptors presented higher values of effectiveness. As a practical demonstration, the best evaluated shape descriptor (MFC) was applied for tactical analysis of an official match. K-means clustering technique was applied, and different shapes of organization could be identified throughout the match. The MFC was the most effective shape descriptor in relation to all others, making it possible to apply this descriptor in the analysis of professional football matches.
LGTM: A Fast and Accurate kNN Search Algorithm in High-Dimensional Spaces

Approximate k nearest neighbor (AkNN) search is a primitive operator for many applications, such as computer vision and machine learning. As these applications deal with a large set of high-dimensional points, a fast and accurate solution is required. It is known that graph-based AkNN search algorithms are faster and more accurate than other approaches, including hash- and quantization-based ones. However, existing graph-based AkNN search algorithms rely purely on heuristics, i.e., their performances are not theoretically supported. This paper proposes LGTM, a new algorithm for AkNN search, that exploits both locality-sensitive hashing and a proximity graph. The performance of LGTM is theoretically supported. Our experiments on real datasets show that LGTM outperforms state-of-the-art AkNN search algorithms.
Importance of Data Re-Sampling and Dimensionality Reduction in Predicting Students' Success

In this paper, we present the importance of data pre-processing in predicting students' success. We implemented Principal Component Analysis for dimensionality reduction to achieve better model performance. Data re-sampling technique was also utilized to handle the imbalanced class problem that is one of the significant issues in effective classification in Educational Data Mining due to the nature of the data from educational settings. We also performed a comparative analysis on the impacts of Random Under-Sampling (RUS), Random Over-Sampling (ROS), and Synthetic Minority Over-Sampling Technique (SMOTE) to an imbalanced dataset used in this study. SMOTE and PCA techniques application offer better performance compared to RUS and ROS with PCA. Support Vector Machine had the best accuracy value of 0.94 after the application of SMOTE and PCA. The application of PCA on the imbalanced data also positively affected the accuracy of the models used in this study. We used other performance metrics to evaluate our models: Kappa, Area Under Curve, and Precision-Recall curve. Our finding shows that the predictive models can predict student success with the proper application of PCA and data re-samnling techniques.
Parallel Swarm Intelligent Motion Planning with Energy-Balanced for Multirobot in Obstacle Environment

Multirobot motion planning is always one of the critical techniques in edge intelligent systems, which involve a variety of algorithms, such as map modeling, path search, and trajectory optimization and smoothing. To overcome the slow running speed and imbalance of energy consumption, a swarm intelligence solution based on parallel computing is proposed to plan motion paths for multirobot with many task nodes in a complex scene that have multiple irregularly-shaped obstacles, which objective is to find a smooth trajectory under the constraints of the shortest total distance and the energy-balanced consumption for all robots to travel between nodes. In a practical scenario, the imbalance of task allocation will inevitably lead to some robots stopping on the way. Thus, we firstly model a gridded scene as a weighted MTSP (multitraveling salesman problem) in which the weights are the energies of obstacle constraints and path length. Then, a hybridization of particle swarm and ant colony optimization (GPSO-AC) based on a platform of Compute Unified Device Architecture (CUDA) is presented to find the optimal path for the weighted MTSPs. Next, we improve the A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mo>∗</mo> </math> algorithm to generate a weighted obstacle avoidance path on the gridded map, but there are still many sharp turns on it. Therefore, an improved smooth grid path algorithm is proposed by integrating the dynamic constraints in this paper to optimize the trajectory smoothly, to be more in line with the law of robot motion, which can more realistically simulate the multirobot in a real scene. Finally, experimental comparisons with other methods on the designed platform of GPUs demonstrate the applicability of the proposed algorithm in different scenarios, and our method strikes a good balance between energy consumption and optimality, with significantly faster and better performance than other considered approaches, and the effects of the adjustment coefficient <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>q</mi> </math> on the performance of the algorithm are also discussed in the experiments.
Odyssey: Creation, Analysis and Detection of Trojan Models

Along with the success of deep neural network (DNN) models, rise the threats to the integrity of these models. A recent threat is the Trojan attack where an attacker interferes with the training pipeline by inserting triggers into some of the training samples and trains the model to act maliciously only for samples that contain the trigger. Since the knowledge of triggers is privy to the attacker, detection of Trojan networks is challenging. Existing Trojan detectors make strong assumptions about the types of triggers and attacks. We propose a detector that is based on the analysis of the intrinsic DNN properties; that are affected due to the Trojan insertion process. For a comprehensive analysis, we develop Odyssey, the most diverse dataset to date with over 3,000 clean and Trojan models. Odyssey covers a large spectrum of attacks; generated by leveraging the versatility in trigger designs and source to target class mappings. Our analysis results show that Trojan attacks affect the classifier margin and shape of decision boundary around the manifold of clean data. Exploiting these two factors, we propose an efficient Trojan detector that operates without any knowledge of the attack and significantly outperforms existing methods. Through a comprehensive set of experiments we demonstrate the efficacy of the detector on cross model architectures, unseen Triggers and regularized models.