Reviewerly/exHarmony huggingface dataset:
Fast Blind Image Super Resolution Using Matrix-Variable Optimization

Super resolution image reconstruction under unknown Gaussian blur has been a challenging topic. Advanced optimization-based works for blind image super-resolution (SR) were reported to be effective, but there exist both large data space storage and time consuming due to vector-variable optimization. This paper proposes a matrix-variable optimization method for fast blind image SR. We first present an accurate blur kernel estimation-based matrix decomposition method. Then we propose minimizing a matrix-variable optimization problem with sparse representation and TV regularization terms. The proposed method can exactly estimate the unknown blur kernel and blur matrix. Compared with vector-variable optimization based methods for blind image SR, the proposed method can greatly reduce their data space storage and computation time. Compared with deep learning methods, the proposed method can directly deal with multiframe SR problem without training and learning task. Experimental results show that the proposed algorithm is superior to conventional optimization-based method in terms of solution quality and computation time. Moreover, the proposed method can obtain higher reconstruction quality than the deep learning methods, specially in the case of large blur kernels.
Improved IMM Algorithm based on RNNs

Abstract The Interactive Multi-Model (IMM) algorithm uses multiple motion models to simultaneously track the target, which effectively solves the problem of model mismatch when a single model tracks the maneuvering target, and is widely used in maneuvering target tracking tasks. However, the Interactive Multi-Model recognition motion model is not accurate enough, and there is a certain delay in the maneuver recognition of the target, which leads to a reduction in tracking accuracy. To solve this problem, considering that deep neural networks are very good at processing classification tasks, we introduce it into target tracking tasks, combining the respective of deep neural networks and traditional tracking filtering methods for maneuvering target tracking. we use the Recurrent Neural Networks to identify the motion model of the target and propose an improved LSTM-IMM model algorithm based on the interactive multi-model algorithm. Finally, we compare the traditional interactive multi-model algorithm and verify the algorithm using Monte Carlo simulation. The results show that the proposed algorithm has improved the recognition accuracy and recognition speed of the model, and the tracking accuracy has been improved.
Sustainable peacebuilding and development in Nigeria's <scp>post‐amnesty</scp> programme: The role of corporate social responsibility in oil host communities

The activities and violence of militants in the Niger Delta which saw the capacity for production of oil in Nigeria fall to an all‐time low resulted in federal government of Nigeria announcing the Presidential Amnesty Programme in return for peace in the region. We examine how the multinational oil companies corporate social responsibility impact on entrepreneurship development and job creations to absorb the youths. A total of 1,200 youths were sampled across the nine states of Niger Delta. Results from the use of estimated logit model reveal that general memorandum of understanding interventions are prevalent with communities with greater ownership creating room for better projects, sustainability and improved trust; yet the interventions failed to make significant impact on entrepreneurship development and job creations. Clearly, facilitating how youths get involved in skill acquisition and empowerment programmes would help them become entrepreneurs, improving their self‐assurance that they can prosper outside militancy activities and violence.
Move Your Body: Engaging Museum Visitors with Human-Data Interaction

Museums have embraced embodied interaction: its novelty generates buzz and excitement among their patrons, and it has enormous educational potential. Human-Data Interaction (HDI) is a class of embodied interactions that enables people to explore large sets of data using interactive visualizations that users control with gestures and body movements. In museums, however, HDI installations have no utility if visitors do not engage with them. In this paper, we present a quasi-experimental study that investigates how different ways of representing the user ("mode type") next-to a data visualization alters the way in which people engage with a HDI system. We consider four mode types: avatar, skeleton, camera overlay, and control. Our findings indicate that the mode type impacts the number of visitors that interact with the installation, the gestures that people do, and the amount of time that visitors spend observing the data on display and interacting with the system.
Vehicle collision avoidance motion planning strategy using artificial potential field with adaptive multi‐speed scheduler

This study presents an adaptive motion planning strategy for automated vehicle collision avoidance systems to be associated with the variation of collision speed region based on the position of the obstacle. This is done by designing the motion planner using an artificial potential field (APF) with the incorporation of an adaptive multi-speed scheduler using fuzzy system in the motion planning structure. The knowledge database information is developed based on the risk perception of the driver that consists of APF parameters and was optimised by using particle swarm optimisation algorithm. This study contributes to the improvement of a feasible reference motion generated by the motion planner that can be converted into desired control signals. The reference motion resulted to provide the control command that managed to avoid collision successfully by evasive manoeuvre without lane departure when adapting to variation in the vehicle speeds with different obstacle positions. The results indicated the reduction of the lateral error with respect to the reference avoidance trajectory data of up to 87% compared to base-type APF with maximum reference lateral motion is reduced of up to 26%. Then, a hardware-in-loop test is conducted to verify the proposed strategy using a steering wheel system.
Speeding Up GED Verification for Graph Similarity Search

Graph similarity search retrieves from a database all graphs whose edit distance (GED) to a query graph is within a threshold. As GED computation is NP-hard, the existing works adopt the filtering-and-verification paradigm to reduce the number of GED verifications, and they mainly focus on designing filtering techniques while using the now out-dated algorithm A*GED for verification. In this paper, we aim to speed up GED verification, which is orthogonal to the index structures used in the filtering phase. We propose a best-first search algorithm AStar <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> -LSa which improves A*GED by (1) reducing memory consumption, (2) tightening lower bound estimation, and (3) improving the time complexity for lower bound computation. We formally show that AStar <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> -LSa has a lower space and time complexity than A*GED. We further modify AStar <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> -LSa into a depth-first search algorithm to contrast these two search paradigms, and we extend our algorithms for exact GED computation. We conduct extensive empirical studies on real graph datasets, and show that our algorithm AStar <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> -LSa outperforms the state-of-the-art algorithms by several orders of magnitude for both GED verification and GED computation.
Synthetic Sensor Data for Human Activity Recognition

Human activity recognition (HAR) based on wearable sensors has emerged as an active topic of research in machine learning and human behavior analysis because of its applications in several fields, including health, security and surveillance, and remote monitoring. Machine learning algorithms are frequently applied in HAR systems to learn from labeled sensor data. The effectiveness of these algorithms generally relies on having access to lots of accurately labeled training data. But labeled data for HAR is hard to come by and is often heavily imbalanced in favor of one or other dominant classes, which in turn leads to poor recognition performance. In this study we introduce a generative adversarial network (GAN)-based approach for HAR that we use to automatically synthesize balanced and realistic sensor data. GANs are robust generative networks, typically used to create synthetic images that cannot be distinguished from real images. Here we explore and construct a model for generating several types of human activity sensor data using a Wasserstein GAN (WGAN). We assess the synthetic data using two commonly-used classifier models, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). We evaluate the quality and diversity of the synthetic data by training on synthetic data and testing on real sensor data, and vice versa. We then use synthetic sensor data to oversample the imbalanced training set. We demonstrate the efficacy of the proposed method on two publicly available human activity datasets, the Sussex-Huawei Locomotion (SHL) and Smoking Activity Dataset (SAD). We achieve improvements of using WGAN augmented training data over the imbalanced case, for both SHL (0.85 to 0.95 F1-score), and for SAD (0.70 to 0.77 F1-score) when using a CNN activity classifier.
"All in the Same Boat": Tradeoffs of Voice Assistant Ownership for Mixed-Visual-Ability Families

A growing body of evidence suggests Voice Assistants (VAs) are highly valued by people with vision impairments (PWVI) and much less so by sighted users. Yet, many are deployed in homes where both PWVI and sighted family members reside. Researchers have yet to study whether VA use and perceived benefits are affected in settings where one person has a visual impairment and others do not. We conducted six in-depth interviews with partners to understand patterns of domestic VA use in mixed-visual-ability families. Although PWVI were more motivated to acquire VAs, used them more frequently, and learned more proactively about their features, partners with vision identified similar benefits and disadvantages of having VAs in their home. We found that the universal usability of VAs both equalizes experience across abilities and presents complex tradeoffs for families-regarding interpersonal relationships, domestic labor, and physical safety-which are weighed against accessibility benefits for PWVI and complicate the decision to fully integrate VAs in the home.
Aggression and Misogyny Detection using BERT: A Multi-Task Approach

In recent times, the focus of the NLP community has increased towards offensive language, aggression, and hate-speech detection.This paper presents our system for TRAC-2 shared task on “Aggression Identification” (sub-task A) and “Misogynistic Aggression Identification” (sub-task B). The data for this shared task is provided in three different languages - English, Hindi, and Bengali. Each data instance is annotated into one of the three aggression classes - Not Aggressive, Covertly Aggressive, Overtly Aggressive, as well as one of the two misogyny classes - Gendered and Non-Gendered. We propose an end-to-end neural model using attention on top of BERT that incorporates a multi-task learning paradigm to address both the sub-tasks simultaneously. Our team, “na14”, scored 0.8579 weighted F1-measure on the English sub-task B and secured 3rd rank out of 15 teams for the task. The code and the model weights are publicly available at https://github.com/NiloofarSafi/TRAC-2. Keywords: Aggression, Misogyny, Abusive Language, Hate-Speech Detection, BERT, NLP, Neural Networks, Social Media
Privacy-preserving Real-time Anomaly Detection Using Edge Computing

Anomaly detection on data collected by devices, such as sensors and IoT objects, is inevitable for many critical systems, e.g., an anomaly in the data of a patient's health monitoring device may indicate a medical emergency situation. Because of the resource-constrained nature of these devices, data collected by such devices are usually off-loaded to the cloud/edge for storage and/or further analysis. However, to ensure data privacy it is critical that the data be transferred to and managed by the cloud/edge in an encrypted form which necessitates efficient processing of such encrypted data for real-time anomaly detection. Motivated by the simultaneous demands for data privacy and real-time data processing, in this paper, we investigate the problem of a privacy-preserving real-time anomaly detection service on sensitive, time series, streaming data. We propose a privacy-preserving framework that enables efficient anomaly detection on encrypted data by leveraging a lightweight and aggregation optimized encryption scheme to encrypt the data before off-loading the data to the edge. We demonstrate our solution for a widely used anomaly detection algorithm, windowed Gaussian anomaly detector and evaluate the performance of the solution in terms of the obtained model privacy, accuracy, latency, and communication cost.
Comparison of chemical and physical activation processes at obtaining adsorbents from Moroccan oil shale

Within the Moroccan natural resources valorisation scheme, new adsorbents have been prepared from oil shale by chemical and physical activation processes.The activation process the authors have developed in this study give effective adsorbent materials.In view of the physico-chemical properties of these materials and application to the treatment of water loaded with a metal (Cr 6+ ion) or organic (methylene blue (MB)) pollutant, it is concluded that the chemical activation process of oil shale at low temperature (250 °C) affords the best material.The material's yield is good in comparison with the physical activation at the same temperature and the process is energy saving differently from that at 450 °C.Moreover, the chemical activation of oil shale with phosphoric acid at 250 °C produces a material with a good yield (about 70%), a high specific surface area (approximately 600 m 2 /g) and a highly porous structure, which gives it a high retention of methylene blue and the Cr 6+ ion.
BZIP: A compact data memory system for UTXO-based blockchains

Unspent Transaction Output (UTXO) set is the foundational model used in many blockchain systems to represent assets. The benefits of UTXO representation include parallel processing, privacy, etc. However, the increasing size of UTXO set is degrading the access performance and severely brings down the validation speed of blockchain further, especially in resource-constrained scenerios, such as IoT. In this paper, we present a memory-economical storage system for UTXO-based blockchain. Based on the inherent properties of UTXO set, we propose two lossless compression techniques to reduce the memory space occupied by UTXO set. Besides, the database related operations are adapted to make the proposed mechanism easily applied in current blockchain system. Taking Bitcoin as the object of study, our mechanism can deliver 2.9-4.5x memory reduction and orders of magnitude validation speed improvement in resource-constrained situations. This compact system will improve validation performance and extend applied scope of blockchains.
The Role of Barite in the Post-Mining Stabilization of Radium-226: A Modeling Contribution for Sequential Extractions

Barite is ubiquitous and known to incorporate 226Ra through the formation of a solid-solution. In U mining mill tailings, barite is one of the dominant sulfate-binding minerals. In such environments, sequential extractions are generally used to identify the U- and 226Ra-binding phases and their associated reactivity. To better decipher the main processes governing the behavior of 226Ra during such sequential extractions, a geochemical model was developed with PHREEQC mimicking the sequential extraction of U and 226Ra from Bois-Noirs Limouzat U mine tailings, France. The model results were compared with a dataset produced by an experimental sequential extraction from the same mine tailings and including data on the solids and selective extraction results with the major elements, U and 226Ra. The simulations reproduced the results of the experimental chemical extractions accurately, with iron oxyhydroxides being the major U binding phase. However, the modeling indicated rather that barite would be the main 226Ra binding phase, instead of the iron oxyhydroxides identified by the experimental extractions. This is consistent with the 226Ra concentration measured in pore water, but in disagreement with the direct interpretation of the sequential extractions. The direct interpretation disregarded the role of barite in the geochemical behavior of 226Ra because barite was not specifically targeted by any of the extraction steps. However, the modeling showed that the dissolution of 226Ra-binding barite by reactants would lead to a 226Ra redistribution among the clay minerals, resulting in a skew in the experimental results. Similar results were achieved by referring simply to the bulk mineralogy of the tailings. This study highlights the importance of considering the mineralogy, mineral reactivity and retention capacity for more realistic interpretation of sequential extractions. Moreover, this paper provides new perspectives on the long-term consequences of these mill tailings in which barite controls the geochemical behavior of the 226Ra.
Reviewing inference performance of state-of-the-art deep learning frameworks

Deep learning models have replaced conventional methods for machine learning tasks. Efficient inference on edge devices with limited resources is key for broader deployment. In this work, we focus on the tool selection challenge for inference deployment. We present an extensive evaluation of the inference performance of deep learning software tools using state-of-the-art CNN architectures for multiple hardware platforms. We benchmark these hardware-software pairs for a broad range of network architectures, inference batch sizes, and floating-point precision, focusing on latency and throughput. Our results reveal interesting combinations for optimal tool selection, resulting in different optima when considering minimum latency and maximum throughput.
Memory-Aware Framework for Efficient Second-Order Random Walk on Large Graphs

Second-order random walk is an important technique for graph analysis. Many applications use it to capture higher-order patterns in the graph, thus improving the model accuracy. However, the memory explosion problem of this technique hinders it from analyzing large graphs. When processing a billion-edge graph like Twitter, existing solutions (e.g., alias method) of the second-order random walk may take up 1796TB memory. Such high memory overhead comes from the memory-unaware strategies for node sampling across the graph. In this paper, to clearly study the efficiency of various node sampling methods in the context of second-order random walk, we design a cost model, and then propose a new node sampling method following the acceptance-rejection paradigm to achieve a better balance between memory and time cost. Further, to guarantee the efficiency of the second-order random walk within arbitrary memory budgets, we propose a memory-aware framework on the basis of the cost model. The framework applies a cost-based optimizer to assign desirable node sampling method for each node in the graph within a memory budget while minimizing the time cost. Finally, we provide general programming interfaces for users to benefit from the memory-aware framework easily. The empirical studies demonstrate that our memory-aware framework is robust with respect to memory and is able to achieve considerable efficiency by reducing 90% of the memory cost.
Philosophy of Education in a New Key: East Asia

Ruyu HungNational Chiayi University, TaiwanThis is a collective writing experiment of PESA members, orchestrating the Philosophy of Education in a New Key regarding East Asia. In 2016 the pioneerin...
Social media reduce users' moral sensitivity: Online shaming as a possible consequence

Abstract In this study, we propose that social media reduce users' moral sensitivity through the mediation of the perceived moral intensity of hostile comments, which leads to behavioral consequences for online shaming. Three separate studies were conducted to explore this statement. Study 1 ( N = 160) compared moral sensitivity between participants in simulated social media situations and a control group. Study 2 ( N = 412) tested the mediating role of perceived moral intensity through self‐rated questionnaires. Study 3 ( N = 295) examined the behavioral consequences of reduced moral sensitivity on online shaming by manipulating social media and perceived moral intensity. Across these three studies with their different methodologies, we found consistent support for our prediction that social media reduce users' moral sensitivity. Also, our findings shed light on perceived moral intensity as a mediator. As expected, less perceived moral intensity and less moral sensitivity (as serial mediators) induced by social media led to a higher tendency to participate in online shaming. In addition, our research suggests that the harmful effects of social media could be restricted by improving users' perceived moral intensity in the form of reminders. These findings provide novel insights into the underlying mechanism of cyberviolence on social media and also contribute to the literature on the antecedents and consequences of moral sensitivity.
Soft sensor design using transductive moving window learner

Changes in characteristics of the industrial processes require implementation of adaptive mechanisms in soft sensors. In the current study, we propose combining two common adaptation methods, moving window (MW) and Just-In-Time-Learning (JITL), using transductive inference (MWtr). Transductive learning exploits the knowledge from the feature values of the query points in determining predictions of the response variable at these points. Here, we use JITL predictions for obtaining predictions for the query points, which are used in training the MW learner in a weighted Lasso regression setting. Tests of the proposed method on three publicly available industrial real datasets show that prediction accuracy of MWtr is higher than both MW, and JITL models, and MWtr is able to combat against various types of concept drifts via integrating the capabilities of MW and JITL methods. Ease of implementation, robustness and stability render MWtr a convenient adaptive method in industrial soft sensor design.