Reviewerly/exHarmony huggingface dataset:
A literature review on optimization techniques for adaptation planning in adaptive systems: State of the art and research directions

Recent developments in modern IT systems including internet of things, edge/fog computing, or cyber–physical systems support intelligent and seamless interaction between users and systems. This requires a reaction to changes in their environment or the system. Adaptive systems provide mechanisms for these reactions. To implement this functionality, several approaches for the planning of adaptations exist that rely on rules, utility functions, or advanced techniques, such as machine learning. As the adaptation space with possible options is often extensively huge, optimization techniques might support efficient determination of the adaptation space and identify the system's optimal configuration. With this paper, we provide a systematic review of adaptation planning as the optimization target. In this paper, we review which optimization techniques are applied for adaptation planning in adaptive systems using a systematic literature review approach. We reviewed 115 paper in detail out of an initial search set of 9,588 papers. Our analysis reveals that learning techniques and genetic algorithms are by far dominant; in total, heuristics (anytime learning) are more frequently applied as exact algorithms. We observed that around 57% of the approaches target multi-objectiveness and around 30% integrate distributed optimization. As last dimension, we focused on situation-awareness, which is only supported by two approaches. In this paper, we provide an overview of the current state of the art of approaches that rely on optimization techniques for planning adaptations in adaptive systems and further derive open research challenges, in particular regarding the integration of distributed optimization and situation-awareness.
Coursework, field experiences, and the technology beliefs and practices of preservice teachers

Studies of technology in teacher education programs have rarely investigated how both coursework and field experiences impacted the beliefs and technology integration behaviors of preservice teachers. To address this gap, this study utilized online surveys administered to student teachers across ten different institutions to determine how the prevalence of technological pedagogical content knowledge (TPACK) in their field observations and the teacher educator technology competencies (TETCs) in their university coursework predicted preservice teachers' beliefs and teaching behaviors with technology. Analyses with structural equation modeling indicated that preservice teachers’ interest in technology and beliefs in its utility predicted their TPACK application. Additionally, their self-efficacy to improve student learning with technology predicted their value beliefs and their TPACK application. While TETC prevalence in university coursework directly predicted their self-efficacy and teaching behaviors, the prevalence of TPACK in their field placements did not predict beliefs but were associated with TPACK application. • Intrinsic interest and utility value predicted TPACK application. • Self-efficacy to improve learning with technology predicted value beliefs and TPACK application. • Program characteristics predicted preservice teachers' self-efficacy and TPACK application. • TPACK-rich field observations predicted applied TPACK, but not value beliefs or self-efficacy.
FitAct: Error Resilient Deep Neural Networks via Fine-Grained Post-Trainable Activation Functions

Deep neural networks (DNNs) are increasingly being deployed in safety-critical systems such as personal healthcare devices and self-driving cars. In such DNN-based systems, error resilience is a top priority since faults in DNN inference could lead to mispredictions and safety hazards. For latency-critical DNN inference on resource-constrained edge devices, it is nontrivial to apply conventional redundancy-based fault tolerance techniques. In this paper, we propose FitAct, a low-cost approach to enhance the error resilience of DNNs by deploying fine-grained post-trainable activation functions. The main idea is to precisely bound the activation value of each individual neuron via neuron-wise bounded activation functions, so that it could prevent the fault propagation in the network. To avoid complex DNN model re-training, we propose to decouple the accuracy training and resilience training, and develop a lightweight post-training phase to learn these activation functions with precise bound values. Experimental results on widely used DNN models such as AlexNet, VGG16, and ResNet50 demonstrate that FitAct outperform state-of-the-art studies such as Clip-Act and Ranger in enhancing the DNN error resilience for a wide range of fault rates, while adding manageable runtime and memory space overheads.
Differentially private recommender system with variational autoencoders

To provide precise recommendations, traditional recommender systems (RS) collect personal data, user preference and feedback, which are sensitive to each user if such information is maliciously used for extra analysis. In recent years, differential privacy (DP) has been widely applied in RS to provide privacy protection for sensitive information. Prior studies explored the combination of DP and RS, while neglecting the disparate effect on model accuracy of imbalanced subgroups as large user groups control the trained model, and DP can worsen the disparate effect of degrading the performance of recommender systems significantly. Besides, the number of uploaded contributions can differ among users for training a recommender system, so it is necessary to set the user-level privacy guarantee. In this paper, we make four contributions. First, we propose an efficient way of constructing datasets for training a recommender system based on prior theories. Second, we compute the user-level priors based on user metadata to optimize the VAE model. Besides, we add noise into the calculation process to protect user metadata. Third, we analyze and propose a tighter theoretical bound on gradient updates for DP Stochastic Gradient Descent (DPSGD). Finally, we exploit these theoretical results and propose a novel DP-VAE based recommender system. Extensive experimental results on multiple datasets show that our system can achieve high recommendation precision while maintaining a reasonable privacy guarantee.
Not All SWAPs Have the Same Cost: A Case for Optimization-Aware Qubit Routing

Despite rapid advances in quantum computing technologies, the qubit connectivity limitation remains to be a critical challenge. Both near-term NISQ quantum computers and relatively long-term scalable quantum architectures do not offer full connectivity. As a result, quantum circuits may not be directly executed on quantum hardware, and a quantum compiler needs to perform qubit routing to make the circuit compatible with the device layout. During the qubit routing step, the compiler inserts SWAP gates and performs circuit transformations. Given the connectivity topology of the target hardware, there are typically multiple qubit routing candidates. The state-of-the-art compilers use a cost function to evaluate the number of SWAP gates for different routes and then select the one with the minimum number of SWAP gates. After qubit routing, the quantum compiler performs gate optimizations upon the circuit with the newly inserted SWAP gates.In this paper, we observe that the aforementioned qubit routing is not optimal, and qubit routing should not be independent on subsequent gate optimizations. We find that with the consideration of gate optimizations, not all of the SWAP gates have the same basis-gate cost. These insights lead to the development of our qubit routing algorithm, NASSC (Not All Swaps have the Same Cost). NASSC is the first algorithm that considers the subsequent optimizations during the routing step. Our optimization-aware qubit routing leads to better routing decisions and benefits subsequent optimizations. We also propose a new optimization-aware decomposition for the inserted SWAP gates. Our experiments show that the routing overhead compiled with our routing algorithm is reduced by up to 69.30% (21.30% on average) in the number of CNOT gates and up to 43.50% (7.61% on average) in the circuit depth compared with the state-of-the-art scheme, SABRE.
Authorization schemes for internet of things: requirements, weaknesses, future challenges and trends

Abstract Scalable and secure authorization of smart things is of the crucial essence for the successful deployment of the Internet of Things (IoT). Unauthorized access to smart things could exacerbate the security and privacy concern, which could, in turn, lead to the reduced adoption of the IoT, and ultimately to the emergence of severe threats. Even though there are a variety of IoT solutions for secure authorization, authorization schemes in highly dynamic distributed environments remain a daunting challenge. Access rights can dynamically change due to the heterogeneous nature of shared IoT devices and, thus, the identity and access control management are challenging. This survey provides a comprehensive comparative analysis of the current state-of-the-art IoT authorization schemes to highlight their strengths and weaknesses. Then, it defines the most important requirements and highlights the authorization threats and weaknesses impacting authorization in the IoT. Finally, the survey presents the ongoing open authorization challenges and provides recommendations for future research.
Leakage Prediction in Machine Learning Models When Using Data from Sports Wearable Sensors

One of the major problems in machine learning is data leakage, which can be directly related to adversarial type attacks, raising serious concerns about the validity and reliability of artificial intelligence. Data leakage occurs when the independent variables used to teach the machine learning algorithm include either the dependent variable itself or a variable that contains clear information that the model is trying to predict. This data leakage results in unreliable and poor predictive results after the development and use of the model. It prevents the model from generalizing, which is required in a machine learning problem and thus causes false assumptions about its performance. To have a solid and generalized forecasting model, which will be able to produce remarkable forecasting results, we must pay great attention to detecting and preventing data leakage. This study presents an innovative system of leakage prediction in machine learning models, which is based on Bayesian inference to produce a thorough approach to calculating the reverse probability of unseen variables in order to make statistical conclusions about the relevant correlated variables and to calculate accordingly a lower limit on the marginal likelihood of the observed variables being derived from some coupling method. The main notion is that a higher marginal probability for a set of variables suggests a better fit of the data and thus a greater likelihood of a data leak in the model. The methodology is evaluated in a specialized dataset derived from sports wearable sensors.
SyntEO: Synthetic dataset generation for earth observation and deep learning – Demonstrated for offshore wind farm detection

With the emergence of deep learning in the last years, new opportunities arose in Earth observation research. Nevertheless, they also brought with them new challenges. The data-hungry training processes of deep learning models demand large, resource expensive, annotated datasets and partly replaced knowledge-driven approaches so that model behaviour and the final prediction process became a black box. The proposed SyntEO approach enables Earth observation researchers to automatically generate large deep learning ready datasets by merging existing and procedural data. SyntEO does this by including expert knowledge in the data generation process in a highly structured manner to control the automatic image and label generation by employing an ontology. In this way, fully controllable experiment environments are set up, which support insights in the model training on the synthetic datasets. Thus, SyntEO makes the learning process approachable, which is an important cornerstone for explainable machine learning. We demonstrate the SyntEO approach by predicting offshore wind farms in Sentinel-1 images on two of the worlds largest offshore wind energy production sites. The largest generated dataset has 90,000 training examples. A basic convolutional neural network for object detection, that is only trained on this synthetic data, confidently detects offshore wind farms by minimising false detections in challenging environments. In addition, four sequential datasets are generated, demonstrating how the SyntEO approach can precisely define the dataset structure and influence the training process. SyntEO is thus a hybrid approach that creates an interface between expert knowledge and data-driven image analysis.
Fake Profile Identification in Social Network using Machine Learning and NLP

In present times, social media plays a key role in every individual life. Everyday majority of the people are spending their time on social media platforms. The number of accounts in these social networking sites has dramatically increasingday-by-day and many of the users are interacting with others irrespective of their timeand location. These social media sites have both pros and cons and providesecurity problems to us also for our information. To scrutinize, who are giving threats in thesenetworking sites we need to organize these social networking accounts into genuine accountsand fake accounts. Traditionally, we are having different classification methods to point outthe fake accounts on social media. But we must increase the accuracy rate in identifying fakeaccounts on these sites. In our paper we are going with Machine Learning technologies and Natural Language processing (NLP) to increase the accuracy rate of detecting the fake accounts. We opted for Random Forest tree classifier algorithm.
Natural scene text detection and recognition based on saturation-incorporated multi-channel MSER

The detection and recognition of natural scene text is a crucial computer task for the automatic identification and classification of industrial resources with words or labels, and it has gained much attention recently because of its potential to boost production efficiency. Maximally Stable Extremal Region (MSER) is an effective detection method for the natural scene text. However, MSER and its variants have not adequately considered the color information like Red, Green, and Blue (RGB). To address this problem, we present a combined detection method by Canny edge detector focusing on the edge and the improved multi-channel MSER concentrating on the area, where the channels incorporated into MSER consist of R, G, and B channels of the RGB color space and the S channel of the Hue, Saturation and Intensity (HSI) color space. Such a multi-channel MSER method takes full consideration of the color information into the text detection without causing a significant increase in the computational cost. In addition, a text recognition system is presented on the basis of the proposed text detection method. The proposed method for text detection and recognition is evaluated on three datasets including the ICDAR2013, the ICDAR2015, and the ICDAR2017-MLT. Experimental results show that our method outperforms the state-of-the-art methods with the highest Precision of 89.4%, Recall of 93.7%, F-score of 91.5%, and about the shortest runtime of 0.76s on an image of 512 × 512 pixels.
CS-MIA: Membership inference attack based on prediction confidence series in federated learning

Federated learning (FL) is vulnerable to membership inference attacks even it is designed to protect users' data during model training, as model parameters remember the information of training data. However, existing inference attacks against FL perform poorly in multi-participant scenarios. We propose CS-MIA, a novel membership inference based on prediction confidence series, posing a more critical privacy threat to FL. The inspirations of CS-MIA are the different prediction confidence of a model on training and testing data, and multiple versions of target models over rounds during FL. We use a neural network to learn individual features of confidence series on training and testing data for subsequent membership inference. We design inference algorithms for both local and global adversaries in FL. And we also design an active attack for global adversaries to extract more information. Our confidence-series-based membership inference outperforms most state-of-the-art attacks on various datasets in different scenarios, demonstrating the severe privacy leakage in FL.
Architectural design game: A serious game approach to promote teaching and learning using multimodal interfaces

The present article introduces and develops an educational tool as an interactive digital game for architectural design, allowing the architectural students to challenge their knowledge and experiences. The framework of this educational tool supports a serious open-ended game, which helps students get involved with the game through self-assessment and a multi-modal natural user interface, including gesture recognition and speech recognition in a familiar CAD environment without any right or wrong solutions. The students can immediately compare their game results with the architecture of iconic buildings and get familiar with the complexity of the design process through five different games in the initial version of this tool without the fear of being judged. According to the results of the questionnaire, this tool can simulate the design process, enhance its quality, and thus, assist the learners with developing their required skills with a wide variety of motivations and opportunities for engagement while helping them connect their experiences and activities to their learning and development in a meaningful way to fill the gap between their knowledge acquisition and knowledge application.
BARA: A blockchain-aided auction-based resource allocation in edge computing enabled industrial internet of things

The recent emergence of Industrial Internet of Things (IIoT), a novel subset of Internet of Things (IoT), integrates sensors and intelligent devices with industry applications to develop a self-organizing system for creating enhanced and adaptive industrial environments. IIoT devices usually have limited computational power, while IIoT applications are mostly mission-critical and/or safety-critical. Therefore, these applications need computing resources that are closer to the devices. This work proposes a decentralized auction-based resource allocation mechanism in edge computing enabled IIoT using consortium blockchain and smart contract that shelves the involvement of a trusted third party, i.e. auctioneer. Various quality parameters are considered during resource allocation to address the mobility of both IIoT devices and edge resources, heterogeneity of edge servers, false assurance of edge servers, reliability, delay in results, and responsiveness of edge servers. Utility functions for IIoT devices are designed to calculate their degree of satisfaction depending on various quality parameters. The proposed blockchain-aided auction based mechanism fulfills various auction-based resource allocation requirements such as seal bidding, no impersonation of the bidder, no modification in any bid or result of allocation by the auctioneer, and proof for winners. The proposed work encourages edge servers for truthful bidding and furnishes the allocation results in polynomial time. Performance evaluation of the proposed model exhibits encouraging results.
Machine Learning Algorithm for Distinguishing Ductal Carcinoma In Situ from Invasive Breast Cancer

Given that early identification of breast cancer type allows for less-invasive therapies, we aimed to develop a machine learning model to discriminate between ductal carcinoma in situ (DCIS) and minimally invasive breast cancer (MIBC).In this retrospective study, the health records of 420 women who underwent biopsies between 2010 and 2020 to confirm breast cancer were collected. A trained XGBoost algorithm was used to classify cancers as either DCIS or MIBC using clinical characteristics, mammographic findings, ultrasonographic findings, and histopathological features. Its performance was measured against other methods using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, precision, and F1 score.The model was trained using 357 women and tested using 63 women with an overall 420 patients (mean [standard deviation] age, 57.1 [12.0] years). The model performed well when feature importance was determined, reaching an accuracy of 0.84 (95% confidence interval [CI], 0.76-0.91), an AUC of 0.93 (95% CI, 0.87-0.95), a specificity of 0.75 (95% CI, 0.67-0.83), and a sensitivity of 0.91 (95% CI, 0.76-0.94).The XGBoost model, combining clinical, mammographic, ultrasonographic, and histopathologic findings, can be used to discriminate DCIS from MIBC with an accuracy equivalent to that of experienced radiologists, thereby giving patients the widest range of therapeutic options.
Cryogenic CMOS for Qubit Control and Readout

Quantum computers have been heralded as a novel paradigm for the solution of today's intractable problems, whereas the core principles of quantum computation are superposition, entanglement and interference, three fundamental properties of quantum mechanics [1]. A quantum computer generally comprises a quantum processor, made of an array of quantum bits or qubits, and a classical controller, which is used to control and read out the qubits. Quantum algorithms are generally mapped onto a circuit of quantum gates that operate on multiple qubits. Unlike conventional digital bits, qubits can take a coherent state ranging from |0〉 to |1〉 on a continuous sphere, known as the Bloch Sphere and they are implemented based on several mechanisms. While many solid-state implementations of qubits exist, an exhaustive description of available technologies is beyond the scope of this paper [2] [3].
On the Practical Use of Immersive Virtual Reality for Rehabilitation of Intimate Partner Violence Perpetrators in Prison

Virtual reality (VR) allows the user to be immersed in environments in which they can experience situations and social interactions from different perspectives by means of virtual embodiment. In the context of rehabilitation of violent behaviors, a participant could experience a virtual violent confrontation from different perspectives, including that of the victim and bystanders. This approach and other virtual scenes can be used as a useful tool for the rehabilitation of intimate partner violence (IPV) perpetrators, through improvement of their empathic skills or for training in non-violent responses. In this perspective, we revise and discuss the use of this tool in a prison environment for the rehabilitation of IPV perpetrators with a particular focus on practical aspects based on our experience.