Reviewerly/exHarmony huggingface dataset:
Clustering Secondary Education and the Focus on Science: Impacts on Higher Education and the Job Market in Bangladesh

Many countries continue to follow a policy of clustering educational provision by disciplinary stream as developed during the British colonial era. Whether such an approach enables education to fulfill its developmental purpose is still open to debate. Using multiple qualitative tools, this study investigates the impact of clustering education in Bangladesh. The findings suggest that the clustering system favors science graduates, creating an atmosphere of educational disparity. This disparity affects the symbiotic and reciprocal relationship that exists between different levels (i.e., secondary, tertiary) of education. The clustering system further contributes to a larger "horizontal educational mismatch" in the job market. As a consequence, it is argued that, in Bangladesh, the education system fails adequately to support social and economic developments. This article suggests a revision of the prevailing policy to address the constraints.
Resource Allocation for Latency-Aware Federated Learning in Industrial Internet of Things

Federated learning (FL) has been employed for numerous privacy-sensitive applications, where distributed devices collaboratively train a global model. In industrial Internet of things (IIoT) systems, training latency is the key performance metric as the automated manufacture usually requires timely processing. The existing works increase the number of effective devices to accelerate the training. However, devices in IIoT systems are usually deployed densely; increasing the number of clients can potentially cause serious interference and prolonged training latency. In this article, we propose a resource allocation scheme for FL, namely RaFed. We formulate the problem of reducing training latency as an optimization problem, which is proved to be NP-hard. We propose a heuristic algorithm to select appropriate devices for achieving a good tradeoff between the interference and convergence time. We conduct experiments using an RGB-D dataset in an IIoT system. The results show that RaFed significantly reduces the latency by 29.9%, compared to the state-of-the-art works.
Distributed ledger technology as a catalyst for open innovation adoption among small and medium-sized enterprises

Open innovation and distributed ledger technology (DLT) are both based on the underlying principles of distribution and sharing. While open innovation is about sharing knowledge to improve innovation processes and performance, DLT is a distributed data ledger that is utilized to enhance efficiency, reduce costs, and ensure immutability, traceability, security, and transparency. In this paper, we investigate the barriers to open innovation currently faced by small and medium-sized companies (SMEs) that DLT can solve. To achieve this goal, we conducted semi-structured interviews with 11 experts in open innovation and DLTs from Spain, Germany, Australia, and India. The results of our exploratory study show that DLTs can help to solve several problems, including external barriers, such as problems with contracts, financing, lack of trust, raw materials, lack of information, domestic and international market limitations, IP rights, and governmental regulations as well as bureaucracy. Internal challenges include insufficient funding, organizational systems that are out of date, and lack of trust. When it comes to difficulties associated with the management of open innovation, external barriers are frequently caused by customers' demands, while internal barriers are frequently caused by organizational culture or human nature, which cannot be improved by DLTs. Finally, SMEs might face new obstacles when integrating DLTs, such as integration problems, complex transition phases, and high setup costs as well as problems with attracting and retaining qualified employees.
Effect of marker position and size on the registration accuracy of HoloLens in a non-clinical setting with implications for high-precision surgical tasks

Abstract Purpose Emerging holographic headsets can be used to register patient-specific virtual models obtained from medical scans with the patient’s body. Maximising accuracy of the virtual models’ inclination angle and position (ideally, ≤ 2° and ≤ 2 mm, respectively, as in currently approved navigation systems) is vital for this application to be useful. This study investigated the accuracy with which a holographic headset registers virtual models with real-world features based on the position and size of image markers. Methods HoloLens ® and the image-pattern-recognition tool Vuforia Engine™ were used to overlay a 5-cm-radius virtual hexagon on a monitor’s surface in a predefined position. The headset’s camera detection of an image marker (displayed on the monitor) triggered the rendering of the virtual hexagon on the headset’s lenses. 4 × 4, 8 × 8 and 12 × 12 cm image markers displayed at nine different positions were used. In total, the position and dimensions of 114 virtual hexagons were measured on photographs captured by the headset’s camera. Results Some image marker positions and the smallest image marker (4 × 4 cm) led to larger errors in the perceived dimensions of the virtual models than other image marker positions and larger markers (8 × 8 and 12 × 12 cm). ≤ 2° and ≤ 2 mm errors were found in 70.7% and 76% of cases, respectively. Conclusion Errors obtained in a non-negligible percentage of cases are not acceptable for certain surgical tasks (e.g. the identification of correct trajectories of surgical instruments). Achieving sufficient accuracy with image marker sizes that meet surgical needs and regardless of image marker position remains a challenge.
Mobile App Cross-Domain Recommendation with Multi-Graph Neural Network

With the rapid development of mobile app ecosystem, mobile apps have grown greatly popular. The explosive growth of apps makes it difficult for users to find apps that meet their interests. Therefore, it is necessary to recommend user with a personalized set of apps. However, one of the challenges is data sparsity, as users’ historical behavior data are usually insufficient. In fact, user’s behaviors from different domains in app store regarding the same apps are usually relevant. Therefore, we can alleviate the sparsity using complementary information from correlated domains. It is intuitive to model users’ behaviors using graph, and graph neural networks have shown the great power for representation learning. In this article, we propose a novel model, Deep Multi-Graph Embedding (DMGE), to learn cross-domain app embedding. Specifically, we first construct a multi-graph based on users’ behaviors from different domains, and then propose a multi-graph neural network to learn cross-domain app embedding. Particularly, we present an adaptive method to balance the weight of each domain and efficiently train the model. Finally, we achieve cross-domain app recommendation based on the learned app embedding. Extensive experiments on real-world datasets show that DMGE outperforms other state-of-art embedding methods.
Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach

We consider a many-to-one wireless architecture for federated learning at the network edge, where multiple edge devices collaboratively train a model using local data. The unreliable nature of wireless connectivity, together with constraints in computing resources at edge devices, dictates that the local updates at edge devices should be carefully crafted and compressed to match the wireless communication resources available and should work in concert with the receiver. Thus motivated, we propose SGD-based bandlimited coordinate descent algorithms for such settings. Specifically, for the wireless edge employing over-the-air computing, a common subset of k-coordinates of the gradient updates across edge devices are selected by the receiver in each iteration, and then transmitted simultaneously over k sub-carriers, each experiencing time-varying channel conditions. We characterize the impact of communication error and compression, in terms of the resulting gradient bias and mean squared error, on the convergence of the proposed algorithms. We then study learning-driven communication error minimization via joint optimization of power allocation and learning rates. Our findings reveal that optimal power allocation across different sub-carriers should take into account both the gradient values and channel conditions, thus generalizing the widely used water-filling policy. We also develop sub-optimal distributed solutions amenable to implementation.
Trav-SHACL: Efficiently Validating Networks of SHACL Constraints

Knowledge graphs have emerged as expressive data structures for Web data. Knowledge graph potential and the demand for ecosystems to facilitate their creation, curation, and understanding, is testified in diverse domains, e.g., biomedicine. The Shapes Constraint Language (SHACL) is the W3C recommendation language for integrity constraints over RDF knowledge graphs. Enabling quality assements of knowledge graphs, SHACL is rapidly gaining attention in real-world scenarios. SHACL models integrity constraints as a network of shapes, where a shape contains the constraints to be fullfiled by the same entities. The validation of a SHACL shape schema can face the issue of tractability during validation. To facilitate full adoption, efficient computational methods are required. We present Trav-SHACL, a SHACL engine capable of planning the traversal and execution of a shape schema in a way that invalid entities are detected early and needless validations are minimized. Trav-SHACL reorders the shapes in a shape schema for efficient validation and rewrites target and constraint queries for fast detection of invalid entities. Trav-SHACL is empirically evaluated on 27 testbeds executed against knowledge graphs of up to 34M triples. Our experimental results suggest that Trav-SHACL exhibits high performance gradually and reduces validation time by a factor of up to 28.93 compared to the state of the art.
Exposure to social suffering in virtual reality boosts compassion and facial synchrony

Virtual reality (VR) has recently been popularized as the “ultimate empathy machine,” and yet the science behind the effects of VR on empathy is scarce. In this media-comparison study, we assessed the impact of VR upon physiological and subjective indicators of empathy. Furthermore, we investigated the role of social presence, i.e. the experience of being there with real others, in modulating the empathic responses. We filmed a 360° 3D video of a person disclosing a painful autobiographical story from her past. This empathy-eliciting video was presented either in a VR head-mounted display or on a computer screen. Centrally, autonomic and facial responses were recorded both from the target in the video and from participants who viewed the video. We found that the VR condition enhanced viewers’ empathic care and facial synchrony with the target. Viewers in the VR condition also reported higher levels of social presence, which mediated the effects of VR on empathic care as well as on facial synchrony. The current study highlights the potential of VR to elicit social connectedness and a caring motivation to help. Our findings imply that VR has a unique potential to motivate empathy in situations where face-to-face encounters are not possible. • Exposure to social suffering in virtual reality boosts compassion and facial synchrony. • Participants in the 360° VR condition felt higher social connection and compassion. • They also exhibited enhanced facial synchrony with the suffering video character. • These effects were mediated by increased social presence in the 360° VR condition.
Organizing workers and machine learning tools for a less oppressive workplace

Machine learning tools are increasingly infiltrating everyday work life with implications for workers. By looking at machine learning tools as part of a sociotechnical system, we explore how machine learning tools enforce oppression of workers. We theorize, normatively, that with reorganizing processes in place, oppressive characteristics could be converted to emancipatory characteristics. Drawing on Paulo Freire’s critical theory of emancipatory pedagogy, we outline similarities between the characteristics Freire saw in oppressive societies and the characteristics of currently designed partnerships between humans and machine learning tools. Freire’s theory offers a way forward in reorganizing humans and machine learning tools in the workplace. Rather than advocating human control or the decoupling of workers and machines, we follow Freire’s theory in proposing four processes for emancipatory organizing of human and machine learning partnership: 1) awakening of a critical consciousness, 2) enabling role freedom, 3) instituting incentives and sanctions for accountability, and 4) identifying alternative emancipatory futures. Theoretical and practical implications of this emancipatory organizing theory are drawn.
LSTM-based Dead Reckoning Navigation for Autonomous Underwater Vehicles

Autonomous Underwater Vehicles (AUVs) do need to be equipped with highly-accurate and robust navigation systems in order to perform challenging operations and complex missions. Nevertheless, since the Global Positioning System (GPS) is not a feasible, functional solution in the underwater scenario, the localization task is fulfilled by employing filtering techniques - as Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) - or Dead Reckoning (DR) strategies. Even though the specific algorithm architecture may vary, such methodologies usually rely on the direct linear speed measurements provided by specialized and expensive sensors, such as the Doppler Velocity Log (DVL). Thus, DVL failures or fallacies alongside DVL-denied environments may arise as unexpected causes for a severe malfunction of the whole navigation system. Motivated by the aforementioned considerations and the outstanding performance of Long Short-Term Memory (LSTM) neural networks in time-series regression problems, an LSTM-based DR approach has been developed to estimate the surge and sway body-fixed frame velocities, without canonically employing the DVL sensor.
Natural Language Description of Videos for Smart Surveillance

After the September 11 attacks, security and surveillance measures have changed across the globe. Now, surveillance cameras are installed almost everywhere to monitor video footage. Though quite handy, these cameras produce videos in a massive size and volume. The major challenge faced by security agencies is the effort of analyzing the surveillance video data collected and generated daily. Problems related to these videos are twofold: (1) understanding the contents of video streams, and (2) conversion of the video contents to condensed formats, such as textual interpretations and summaries, to save storage space. In this paper, we have proposed a video description framework on a surveillance dataset. This framework is based on the multitask learning of high-level features (HLFs) using a convolutional neural network (CNN) and natural language generation (NLG) through bidirectional recurrent networks. For each specific task, a parallel pipeline is derived from the base visual geometry group (VGG)-16 model. Tasks include scene recognition, action recognition, object recognition and human face specific feature recognition. Experimental results on the TRECViD, UET Video Surveillance (UETVS) and AGRIINTRUSION datasets depict that the model outperforms state-of-the-art methods by a METEOR (Metric for Evaluation of Translation with Explicit ORdering) score of 33.9%, 34.3%, and 31.2%, respectively. Our results show that our framework has distinct advantages over traditional rule-based models for the recognition and generation of natural language descriptions.
Defensive Technology Use by Political Activists During the Sudanese Revolution

Political activism is a worldwide force in geopolitical change and has, historically, helped lead to greater justice, equality, and stopping human rights abuses. A modern revolution—an extreme form of political activism—pits activists, who rely on technology for critical operational tasks, against a resource-rich government that controls the very telecommunications network they must use to operationalize, putting the technology they use under extreme stress. Our work presents insights about activists’ technological defense strategies from interviews with 13 political activists who were active during the 2018-2019 Sudanese revolution. We find that politics and society are driving factors of security and privacy behavior and app adoption. Moreover, a social media blockade can trigger a series of anti-censorship approaches at scale, while a complete internet blackout can cripple activists’ use of technology. Even though the activists’ technological defenses against the threats of surveillance, arrest and physical device seizure were low tech, they were largely sufficient against their adversary. Through these results, we surface key design principles, but we observe that the generalization of design recommendations often runs into fundamental tensions between the security and usability needs of different user groups. Thus, we provide a set of structured questions in an attempt to turn these tensions into opportunities for technology designers and policy makers.
Convolutional Dynamic Alignment Networks for Interpretable Classifications

We introduce a new family of neural network models called Convolutional Dynamic Alignment Networks <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> (CoDA-Nets), which are performant classifiers with a high degree of inherent interpretability. Their core building blocks are Dynamic Alignment Units (DAUs), which linearly transform their input with weight vectors that dynamically align with task-relevant patterns. As a result, CoDA-Nets model the classification prediction through a series of input-dependent linear transformations, allowing for linear decomposition of the output into individual input contributions. Given the alignment of the DAUs, the resulting contribution maps align with discriminative input patterns. These model-inherent decompositions are of high visual quality and outperform existing attribution methods under quantitative metrics. Further, CoDA-Nets constitute performant classifiers, achieving on par results to ResNet and VGG models on e.g. CIFAR-10 and TinyImagenet.
Handwritten Digit Recognition of MNIST dataset using Deep Learning state-of-the-art Artificial Neural Network (ANN) and Convolutional Neural Network (CNN)

Handwritten digit recognition is an intricate assignment that is vital for developing applications, in computer vision digit recognition is one of the major applications. There has been a copious exploration done in the Handwritten Character Recognition utilizing different deep learning models. Deep learning is rapidly increasing in demand due to its resemblance to the human brain. The two major Deep learning algorithms Artificial Neural Network and Convolutional Neural Network which have been compared in this paper considering their feature extraction and classification stages of recognition. The models were trained using categorical cross-entropy loss and ADAM optimizer on the MNIST dataset. Backpropagation along with Gradient Descent is being used to train the networks along with reLU activations in the network which do automatic feature extraction. In neural networks, Convolution Neural Network (ConvNets or Convolutional neural networks) is one of the primary classifiers to do image recognition, image classification tasks in Computer Vision.
Saliency-Based Multilabel Linear Discriminant Analysis

Linear discriminant analysis (LDA) is a classical statistical machine-learning method, which aims to find a linear data transformation increasing class discrimination in an optimal discriminant subspace. Traditional LDA sets assumptions related to the Gaussian class distributions and single-label data annotations. In this article, we propose a new variant of LDA to be used in multilabel classification tasks for dimensionality reduction on original data to enhance the subsequent performance of any multilabel classifier. A probabilistic class saliency estimation approach is introduced for computing saliency-based weights for all instances. We use the weights to redefine the between-class and within-class scatter matrices needed for calculating the projection matrix. We formulate six different variants of the proposed saliency-based multilabel LDA (SMLDA) based on different prior information on the importance of each instance for their class(es) extracted from labels and features. Our experiments show that the proposed SMLDA leads to performance improvements in various multilabel classification problems compared to several competing dimensionality reduction methods.
Attack Surface Assessment for Cybersecurity Engineering in the Automotive Domain

Connected smart cars enable new attacks that may have serious consequences. Thus, the development of new cars must follow a cybersecurity engineering process as defined for example in ISO/SAE 21434. A central part of such a process is the threat and risk assessment including an attack feasibility rating. In this paper, we present an attack surface assessment with focus on the attack feasibility rating compliant to ISO/SAE 21434. We introduce a reference architecture with assets constituting the attack surface, the attack feasibility rating for these assets, and the application of this rating on typical use cases. The attack feasibility rating assigns attacks and assets to an evaluation of the attacker dimensions such as the required knowledge and the feasibility of attacks derived from it. Our application of sample use cases shows how this rating can be used to assess the feasibility of an entire attack path. The attack feasibility rating can be used as a building block in a threat and risk assessment according to ISO/SAE 21434.
Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder

In this paper, we propose a new method to perform data augmentation in a reliable way in the High Dimensional Low Sample Size (HDLSS) setting using a geometry-based variational autoencoder (VAE). Our approach combines the proposal of 1) a new VAE model, the latent space of which is modeled as a Riemannian manifold and which combines both Riemannian metric learning and normalizing flows and 2) a new generation scheme which produces more meaningful samples especially in the context of small data sets. The method is tested through a wide experimental study where its robustness to data sets, classifiers and training samples size is stressed. It is also validated on a medical imaging classification task on the challenging ADNI database where a small number of 3D brain magnetic resonance images (MRIs) are considered and augmented using the proposed VAE framework. In each case, the proposed method allows for a significant and reliable gain in the classification metrics. For instance, balanced accuracy jumps from 66.3% to 74.3% for a state-of-the-art convolutional neural network classifier trained with 50 MRIs of cognitively normal (CN) and 50 Alzheimer disease (AD) patients and from 77.7% to 86.3% when trained with 243 CN and 210 AD while improving greatly sensitivity and specificity metrics.