Reviewerly/exHarmony huggingface dataset:
Designing experiences for IoT products: A case study testing existing UX frameworks

IoT products are believed to be a new type of things that combine both material and immaterial resources.Their unique attributes can result in user experiences strongly differing from traditional products, imposing new design challenges.This study aims to 1) test whether existing UX frameworks are applicable to IoT products in design practices, 2) identify essential elements in experience design for IoT products, and 3) explore new associated experience design opportunities.We conducted a workshop including 25 design-engineering students testing two UX frameworks.The participants designed new experiences for a representative IoT product (i.e., smartwatch) in the workshop and presented the experience scenarios by roleplaying, revealing existing frameworks' insufficiencies and highlighting how interactions in an IoT network influenced experiences.The study critically discusses whether pleasurable elements should be prioritised in IoT products' experience design and how the agency of IoT products can be a tool for designers to shape experiences.
Learning Towards Synchronous Network Memorizability and Generalizability for Continual Segmentation Across Multiple Sites

In clinical practice, a segmentation network is often required to continually learn on a sequential data stream from multiple sites rather than a consolidated set, due to the storage cost and privacy restriction. However, during the continual learning process, existing methods are usually restricted in either network memorizability on previous sites or generalizability on unseen sites. This paper aims to tackle the challenging problem of Synchronous Memorizability and Generalizability (SMG) and to simultaneously improve performance on both previous and unseen sites, with a novel proposed SMG-learning framework. First, we propose a Synchronous Gradient Alignment (SGA) objective, which not only promotes the network memorizability by enforcing coordinated optimization for a small exemplar set from previous sites (called replay buffer), but also enhances the generalizability by facilitating site-invariance under simulated domain shift. Second, to simplify the optimization of SGA objective, we design a Dual-Meta algorithm that approximates the SGA objective as dual meta-objectives for optimization without expensive computation overhead. Third, for efficient rehearsal, we configure the replay buffer comprehensively considering additional inter-site diversity to reduce redundancy. Experiments on prostate MRI data sequentially acquired from six institutes demonstrate that our method can simultaneously achieve higher memorizability and generalizability over state-of-the-art methods. Code is available at https://github.com/jingyzhang/SMG-Learning .
A better entity detection of question for knowledge graph question answering through extracting position-based patterns

Abstract Entity detection task on knowledge graph question answering systems has been studied well on simple questions. However, the task is still challenging on complex questions. It is due to a complex question is composed of more than one fact or triple. This paper proposes a method to detect entities and their position on triples mentioned in a question. Unlike existing approaches that only focus on detecting the entity name, our method can determine in which triple an entity is located. Furthermore, our approach can also define if an entity is a head or a tail of a triple mentioned in a question. We tested our approach to SimpleQuestions, LC-QuAD 2.0, and QALD series benchmarks. The experiment result demonstrates that our model outperforms the previous works on SimpleQuestions and QALD series datasets. 99.15% accuracy and 96.15% accuracy on average, respectively. Our model can also improve entity detection performance on LC-QuAD 2.0 with a merged dataset, namely, 97.4% accuracy. This paper also presents Wikidata QALD series version that is helpful for researchers to assess the knowledge graph question answering system they develop.
Digital Camouflage Pattern Design Based on the Biased Random Walk

Digital camouflage is a common countermeasure against military reconnaissance. In the face of high-tech imaging reconnaissance, battlefield detection means tend to be automated and refined. In order to adapt to the concealment requirements under various environmental backgrounds, combined with the camouflage performance of digital camouflage and its feedback mechanism in camouflage pattern design, this paper proposed a digital camouflage pattern design method based on biased random walk. Firstly, the original background is preprocessed, and the background texture’s direction, corner, step length, and pixel intensity difference are statistically analyzed, and the boundary probability between pixel nodes is estimated. Then, a biased random walk is used to outline the camouflage patches. The edge scatter is enriched according to the density of the patches, and the camouflage patches are filled according to the proportion of the main color of the background. Finally, a digital camouflage pattern is obtained. The quantitative analysis results show that the mean heart rate of the digital camouflage pattern based on multiscene design is at least 31.0% higher than that of the original background segmentation texture, and the standard deviation index of equivalent diameter is increased by 14.9% on average. In addition, the results of simulation camouflage image detection in multiple scenes show that the proposed method can effectively deal with camouflage target detection on the basis of fully retaining the original background texture information and has strong camouflage concealment effect in the scene.
Comprehensive Preparation for the English Language Olympiad as Part of Improving the Quality of Language Education in Russia

Modern foreign language education in Russia is riddled with various challenges, ranging from the forced introduction of interactive technologies to complications imposed by the political agenda. The article analyses Olympiad tasks and their peculiarities at different Olympiad stages, considering various competitive projects, both university Olympiad projects and the All-Russian Olympiad of schoolchildren. The author offers some recommendations on better preparation for the regional and final rounds of the All-Russian School Olympiad, which the author trialled while working with groups and individual students from Moscow and which have proven to be successful. The article specifies manuals which can be used for this purpose as well. Empirical material for the study comprises a survey, aimed at students who attended the courses to prepare for the Olympiad to determine their attitudes toward online courses. The suggested system can function in any region of Russia, which will help the development of young talent throughout the country.
DPSSD: Dual-Path Single-Shot Detector

Object detection is one of the most important and challenging branches of computer vision. It has been widely used in people's lives, such as for surveillance security and autonomous driving. We propose a novel dual-path multi-scale object detection paradigm in order to extract more abundant feature information for the object detection task and optimize the multi-scale object detection problem, and based on this, we design a single-stage general object detection algorithm called Dual-Path Single-Shot Detector (DPSSD). The dual path ensures that shallow features, i.e., residual path and concatenation path, can be more easily utilized to improve detection accuracy. Our improved dual-path network is more adaptable to multi-scale object detection tasks, and we combine it with the feature fusion module to generate a multi-scale feature learning paradigm called the "Dual-Path Feature Pyramid". We trained the models on PASCAL VOC datasets and COCO datasets with 320 pixels and 512 pixels input, respectively, and performed inference experiments to validate the structures in the neural network. The experimental results show that our algorithm has an advantage over anchor-based single-stage object detection algorithms and achieves an advanced level in average accuracy. Researchers can replicate the reported results of this paper.
Natural Language Processing of Electronic Patient Records to Predict Psychiatric Inpatients at Risk of Early Readmission to Hospital Using Predictive Models Derived Through Machine Learning

Aims Psychiatric readmissions cause a burden on the healthcare system, incur a monetary cost and cause additional distress to acutely unwell patients. This project explores the use of the free-text of electronic patient records to predict inpatients in psychiatric hospitals at risk of readmission using predictive models generated by machine learning. Methods Free-text was extracted from the electronic patient records of patients admitted to hospitals in Birmingham and Solihull Mental Health Foundation Trust (BSMHFT) during the five years 2015–2019 inclusive. The anonymised records were obtained via the CRIS (Clinical Record Interactive Search) database. A total of 17208 records were extracted. The free-text entered by clinicians during an admission was extracted and processed using techniques of natural language processing to generate input vectors suitable to be used with machine learning algorithms. tf-idf (term frequency-inverse document frequency) vectors were used. A selection of algorithms were used to train predictive models. Two-thirds of the records were used as training data with the remainder as test data. Baseline model performance was assessed and then best-performing candidates underwent hyperparameter optimisation using five-fold cross-validation to improve performance. Bayesian optimisation was used to automate hyperparameter tuning during cross-validation. Hyperparameters were optimised on the log loss function. As the dataset was imbalanced with negative instances outnumbering positive instances to a significant degree, various techniques such as random undersampling of negative instances in the training data were used to deal with class imbalance throughout this process. Following cross-validation, the best-performing models underwent performance analysis. Models were used to make predictions on the test data. Performance was assessed using F1-measures, precision-recall curves and the average precision metric (equivalent to area under the precision-recall curve). These metrics were chosen due to their suitability in assessing models trained on imbalanced datasets. Results The best F1 score obtained was 0.233 using a Random Forest model trained using unigram tf-idf vectors of 500 token dimension. The best average precision obtained was 0.157 using a Support Vector Machine trained using unigram tf-idf vectors of 2000 token dimension. Both the above results required the use of random oversampling of positive instances to improve performance on the imbalanced dataset. Conclusion The performance indicates that the models generated are unlikely to have significant practical utility. Nevertheless, this exploratory project has produced a processed dataset with knowledge about its characteristics. This could be used for the further development of models using more complex techniques such as language modelling using neural networks.
Virtual and in-person co-design workshops: from alternative to complementary approaches.

Co-design methods and toolkits are commonly used to involve people from diverse backgrounds and disciplines in design processes, promoting collaboration, design thinking, shared decision making, and creativity. These methods and toolkits are generally tailored to in-person workshops supported by different physical artifacts (e.g. card-sets) in a shared physical location. Physical co-location and artifacts allow participants to interact in seamless ways, relying on everyday modalities of interaction. The CoViD-19 pandemic has forced many of such workshops online. This required transforming location, methods, toolkits and to rethink interaction among participants. With this workshop we aim to look back at these experiences of transformation and to reflect on the affordances of the physical and the virtual in co-design workshops. What are the challenges of transforming location, methods, and toolkits that are designed for in-person workshops into the digital? In which ways can in-person and virtual workshops co-exist and complement each other? We invite participants to share their experiences and reflect on how to bring together virtual and in-person co-design workshops.
Pedagogical Model of Forming Cross-Cutting Digital Competences in Students of Supplementary Higher Education

The Workforce for Digital Economy Federal Project is aimed at developing professional competencies in the field of cross-cutting digital technologies. Digital economy competences depend on the requirements of potential employers, as well as on those of society, state, and labor market. The article introduces the definition of cross-cutting digital competences and describes their basic, personal, and professional components. The author constructed a pedagogical model of developing cross-cutting digital competences in students of supplementary higher education. The model includes goals, objectives, pedagogical principles, internal and external factors, content components, organizational foundations, and pedagogical conditions.
Target detection based on a new triple activation function

As one of the important parts of Neural Network, activation function plays a very important role in model training in Neural Network. In this paper, the status quo, advantages and disadvantages of the existing common activation functions are analysed, and a new activation function is proposed and applied to target detection. To test the performance of the new activation function, this paper compares it with the ReLU activation functions on a variety of Neural Networks and data sets, and not only analyses the performance of the activation function itself but also verifies the effectiveness of the activation function in target detection.
A Deep Neural Network Technique for Detecting Real-Time Drifted Twitter Spam

The social network is considered a part of most user’s lives as it contains more than a billion users, which makes it a source for spammers to spread their harmful activities. Most of the recent research focuses on detecting spammers using statistical features. However, such statistical features are changed over time, and spammers can defeat all detection systems by changing their behavior and using text paraphrasing. Therefore, we propose a novel technique for spam detection using deep neural network. We combine the tweet level detection with statistical feature detection and group their results over meta-classifier to build a robust technique. Moreover, we embed our technique with initial text paraphrasing for each detected tweet spam. We train our model using different datasets: random, continuous, balanced, and imbalanced. The obtained experimental results showed that our model has promising results in terms of accuracy, precision, and time, which make it applicable to be used in social networks.
End-to-end capacities of imperfect-repeater quantum networks

The optimal performance of a communication network is limited not only by the quality of point-to-point channels, but by the efficacy of its constituent technologies. Understanding the limits of quantum networks requires an understanding of both the ultimate capacities of quantum channels and the efficiency of imperfect quantum repeaters. In this work, using a recently developed node-splitting technique which introduces internal losses and noise into repeater devices, we present achievable end-to-end rates for noisy-repeater quantum networks. These are obtained by extending the coherent and reverse coherent information (single channel capacity lower bounds) into end-to-end capacity lower bounds, both in the context of single-path and multi-path routing. These achievable rates are completely general, and apply to networks composed of arbitrary channels arranged in general topologies. Through this general formalism, we show how tight upper-bounds can also be derived by supplementing appropriate single-edge capacity bounds. As a result, we develop tools which provide tight performance bounds for quantum networks constituent of channels whose capacities are not exactly known, and reveal critical network properties which are necessary for high-rate quantum communications. This permits the investigation of pertinent classes of quantum networks with realistic technologies; qubit amplitude damping networks and bosonic thermal-loss networks.
Phrase-level Prediction for Video Temporal Localization

Video temporal localization aims to locate a period that semantically matches a natural language query in a given untrimmed video. We empirically observe that although existing approaches gain steady progress on sentence localization, the performance of phrase localization is far from satisfactory. In principle, the phrase should be easier to localize as fewer combinations of visual concepts need to be considered; such incapability indicates that the existing models only capture the sentence annotation bias in the benchmark but lack sufficient understanding of the intrinsic relationship between simple visual and language concepts, thus the model generalization and interpretability is questioned. This paper proposes a unified framework that can deal with both sentence and phrase-level localization, namely Phrase Level Prediction Net (PLPNet). Specifically, based on the hypothesis that similar phrases tend to focus on similar video cues, while dissimilar ones should not, we build a contrastive mechanism to restrain phrase-level localization without fine-grained phrase boundary annotation required in training. Moreover, considering the sentence's flexibility and wide discrepancy among phrases, we propose a clustering-based batch sampler to ensure that contrastive learning can be conducted efficiently. Extensive experiments demonstrate that our method surpasses state-of-the-art methods of phrase-level temporal localization while maintaining high performance in sentence localization and boosting the model's interpretability and generalization capability. Our code is available at https://github.com/sizhelee/PLPNet.
40 Years of Designing Code Comprehension Experiments: A Systematic Mapping Study

The relevance of code comprehension in a developer's daily work was recognized more than 40 years ago. Consequently, many experiments were conducted to find out how developers could be supported during code comprehension and which code characteristics contribute to better comprehension. Today, such studies are more common than ever. While this is great for advancing the field, the number of publications makes it difficult to keep an overview. Additionally, designing rigorous code comprehension experiments with human participants is a challenging task, and the multitude of design options can make it difficult for researchers, especially newcomers to the field, to select a suitable design. We therefore conducted a systematic mapping study of 95 source code comprehension experiments published between 1979 and 2019. By structuring the design characteristics of code comprehension studies, we provide a basis for subsequent discussion of the huge diversity of design options in the face of a lack of basic research on their consequences and comparability. We describe what topics have been studied, as well as how these studies have been designed, conducted, and reported. Frequently chosen design options and deficiencies are pointed out to support researchers of all levels of domain expertise in designing their own studies.
Neural Moving Horizon Estimation for Robust Flight Control

Estimating and reacting to disturbances is crucial for robust flight control of quadrotors. Existing estimators typically require significant tuning for a specific flight scenario or training with extensive ground-truth disturbance data to achieve satisfactory performance. In this paper, we propose a neural moving horizon estimator (NeuroMHE) that can automatically tune its key parameters modeled by a neural network and adapt to different flight scenarios. We achieve this by deriving the analytical gradients of the MHE estimates with respect to the MHE weighting matrices, which enables a seamless embedding of the MHE as a learnable layer into the neural network for highly effective learning. Interestingly, we show that the gradients can be computed efficiently using a Kalman filter in a recursive form. Moreover, we develop a model-based policy gradient algorithm to train NeuroMHE directly from the quadrotor trajectory tracking error without needing the ground-truth disturbance data. The effectiveness of NeuroMHE is verified extensively via both simulations and physical experiments on quadrotors in various challenging flights. Notably, NeuroMHE outperforms a state-of-the-art neural network-based estimator, reducing force estimation errors by up to 76.7%, while using a portable neural network that has only 7.7% of the learnable parameters of the latter. The proposed method is general and can be applied to robust adaptive control of other robotic systems.
A Cost-Optimized Data Parallel Task Scheduling with Deadline Constraints in Cloud

Large-scale distributed systems have the advantages of high processing speeds and large communication bandwidths over the network. The processing of huge real-world data within a time constraint becomes tricky, due to the complexity of data parallel task scheduling in a time constrained environment. This paper proposes data parallel task scheduling in cloud to address the minimization of cost and time constraints. By running concurrent executions of tasks on multi-core cloud resources, the number of parallel executions could be increased correspondingly, thereby, finishing the task within the deadline is possible. A mathematical model is developed here to minimize the operational cost of data parallel tasks by feasibly assigning a load to each virtual machine in the cloud data center. This work experiments with a machine learning model that is replicated on the multi-core cloud heterogeneous resources to execute different input data concurrently to accomplish distributive learning. The outcome of concurrent execution of data-intensive tasks on different parts of the input dataset gives better solutions in terms of processing the task by the deadline at optimized cost.
Differential privacy preserving dynamic data release scheme based on Jensen-Shannon divergence

Health monitoring data or the data about infectious diseases such as COVID-19 may need to be constantly updated and dynamically released, but they may contain user's sensitive information. Thus, how to preserve the user's privacy before their release is critically important yet challenging. Differential Privacy (DP) is well-known to provide effective privacy protection, and thus the dynamic DP preserving data release was designed to publish a histogram to meet DP guarantee. Unfortunately, this scheme may result in high cumulative errors and lower the data availability. To address this problem, in this paper, we apply Jensen-Shannon (JS) divergence to design the OPTICS (Ordering Points To Identify The Clustering Structure) scheme. It uses JS divergence to measure the difference between the updated data set at the current release time and private data set at the previous release time. By comparing the difference with a threshold, only when the difference is greater than the threshold, can we apply OPTICS to publish DP protected data sets. Our experimental results show that the absolute errors and average relative errors are significantly lower than those existing works.