Reviewerly/exHarmony huggingface dataset:
Non-Pauli topological stabilizer codes from twisted quantum doubles

It has long been known that long-ranged entangled topological phases can be exploited to protect quantum information against unwanted local errors. Indeed, conditions for intrinsic topological order are reminiscent of criteria for faithful quantum error correction. At the same time, the promise of using general topological orders for practical error correction remains largely unfulfilled to date. In this work, we significantly contribute to establishing such a connection by showing that Abelian twisted quantum double models can be used for quantum error correction. By exploiting the group cohomological data sitting at the heart of these lattice models, we transmute the terms of these Hamiltonians into full-rank, pairwise commuting operators, defining commuting stabilizers. The resulting codes are defined by non-Pauli commuting stabilizers, with local systems that can either be qubits or higher dimensional quantum systems. Thus, this work establishes a new connection between condensed matter physics and quantum information theory, and constructs tools to systematically devise new topological quantum error correcting codes beyond toric or surface code models.
Facial Expression Recognition Integrating Multiple CNN Models

In order to avoid the complex manual feature extraction process in traditional facial expression recognition and to extract more different facial expression features, a facial expression recognition method is proposed that integrates multiple Convolution Neural Network models and uses three different CNN subnetwork models for integrated prediction. Experiments show that the recognition accuracies of the method used on data sets Fer 2013 and CK+ are 70.1% and 94.9% respectively, which are better than those of the subnetwork models. It is proved that the integrated model has good performance and robustness.
Food Traceability and Prevention of Location Fraud using Blockchain

Food traceability has gained widespread prominence in recent years due to the widely reported food fraud and safety issues and there is a need for a simple low-cost solution. Adopting the design science research methodology, we proposed a design-based set of practical criteria for food tracking system that includes the prevention of location data input fraud. The design was successfully implemented on a blockchain as food tracking system. Based on our evaluation, the design and prototype demonstrate a viable approach to food tracking and limiting food fraud.
Tangible Body Maps of Felt-Sensing Experience

There is a growing interest amongst the HCI community to access and articulate the core of experiences for design use. The premise is that, by accessing detailed accounts of everyday experiences, we can obtain refined material for the design of interactive systems more connected with our bodies and emotions. This TEI studio aims to introduce participants to the basis of phenomenologically grounded techniques in combination with the use of tangible materials as a way to articulate experience from the inner self, applied to the evaluation of existing technologies. This studio offers an alternative to assessment tools that rely on a predefined repertoire of feelings, to instead focus on emergent, complex and unclear aspects of our emotions. These strange collections of emotions -or felt senses- will be further explored through self-reporting tools and group exercises.
Aggregating multi-scale contextual features from multiple stages for semantic image segmentation

Semantic segmentation plays a vital role in image understanding. Recent studies have attempted to achieve precise pixel-level classification by using deep networks that provide hierarchical features. These methods are trying to effectively utilise multi-level features that are extracted from the data and precisely reconstruct some characteristics of objects that are lost in producing high-level features. In this paper, we propose a multi-scale context U-net (MSCU-net) for semantic image segmentation. This network uses a multi-scale context block (MSCB) to aggregate multi-level features and employs the CRF layer to explicitly model the dependencies among pixels. This network significantly outperforms other state-of-the-art methods on both the PASCAL VOC 2012 and Cityscapes datasets.
Anticipatory Human-Robot Collaboration via Multi-Objective Trajectory Optimization

We address the problem of adapting robot trajectories to improve safety, comfort, and efficiency in humanrobot collaborative tasks. To this end, we propose CoMOTO, a trajectory optimization framework that utilizes stochastic motion prediction to anticipate the human's motion and adapt the robot's joint trajectory accordingly. We design a multiobjective cost function that simultaneously optimizes for i) separation distance, ii) visibility of the end-effector, iii) legibility, iv) efficiency, and v) smoothness. We evaluate CoMOTO against three existing methods for robot trajectory generation when in close proximity to humans. Our experimental results indicate that our approach consistently outperforms existing methods over a combined set of safety, comfort, and efficiency metrics.
Impact of technological pedagogical content knowledge on teachers’ digital proficiency at classroom in higher education institution of Pakistan

Purpose This paper aims to address the relationship of teachers’ technological pedagogical content knowledge (TPACK) and their technology proficiency according to the International Society for Technology in Education Standards for Teachers (ISTE-ST) in public and private universities of Karachi, Pakistan. Design/methodology/approach Quantitative approach with survey research design was used. In total, 520 teachers of public and private universities were selected on the basis of simple random sampling technique. Data was collected through questionnaire distribution. Findings Strong significant correlation was found between teacher’ TPACK and teachers’ technology proficiency, teachers’ ability to engage in professional development and teachers’ ability to promote digital citizenship and responsibilities in students. Originality/value TPACK and ISTE-ST are used for effective teaching. TPACK is used to measure teachers’ ability to integrate technology in their teaching, while ISTE-ST is used to measure the proficiency technology integration. This study is unique as it discusses the relationship of TPACK and ISTE-ST framework in term of teachers’ ability to promote digital citizenship and responsibilities in students and teachers’ ability to engage themselves in professional development program.
Data Generation Process Modeling for Activity Recognition

The dynamics of body movements are often driven by large and intricate low-level interactions involving various body parts. These dynamics are part of an underlying data generation process. Incorporating the data generation process into data-driven activity recognition systems has the potential to enhance their robustness and data-efficiency. In this paper, we propose to model the underlying data generation process and use it to constrain training of simpler learning models via sample selection. As deriving such models using human expertise is hard, we propose to frame this task as a large-scale exploration of architectures in charge of relating sensory information coming from the data sources. We report on experiments conducted on the Sussex-Huawei locomotion dataset featuring a sensor-rich environment in real-life settings. The derived model is found to be consistent with existing domain knowledge. Compared to the basic setting, our approach achieves up to 17.84% improvement, by simultaneously reducing the number of required data sources by one-half. Promising results open perspectives for deploying more robust and data-efficient learning models.
Differential Collisions in SHA-1

In paper highlights of hash functions differential cryptanalysis on an example of algorithm SHA-1 are considered. The technique of carrying out of the differential analysis of SHA-1 hash function and other hash functions having a similar structure is offered.
Motion Planning for Collision-resilient Mobile Robots in Obstacle-cluttered Unknown Environments with Risk Reward Trade-offs

Collision avoidance in unknown obstacle-cluttered environments may not always be feasible. This paper focuses on an emerging paradigm shift in which potential collisions with the environment can be harnessed instead of being avoided altogether. To this end, we introduce a new sampling-based online planning algorithm that can explicitly handle the risk of colliding with the environment and can switch between collision avoidance and collision exploitation. Central to the planner's capabilities is a novel joint optimization function that evaluates the effect of possible collisions using a reflection model. This way, the planner can make deliberate decisions to collide with the environment if such collision is expected to help the robot make progress toward its goal. To make the algorithm online, we present a state expansion pruning technique that significantly reduces the search space while ensuring completeness. The proposed algorithm is evaluated experimentally with a built-in-house holonomic wheeled robot that can withstand collisions. We perform an extensive parametric study to investigate trade-offs between (user-tuned) levels of risk, deliberate collision decision making, and trajectory statistics such as time to reach the goal and path length.
Research on cyberspace multi-objective security algorithm and decision mechanism of Energy Internet

The existing energy network security defense system uses firewall, intrusion detection, host monitoring, identity authentication, anti-virus software and vulnerability repair to build a fortress type rigid defense system to block or isolate external invasion. This static layered deep defense system is based on prior knowledge and has the advantages of rapid response and effective protection in the face of constant attacks When confronting the unknown attacking opponent, he is not able to do his best, and he is in danger of being attacked easily. In this context, multi-objective decision has more than two decision-making objectives and needs to use multiple criteria to evaluate and optimize the decision-making of alternatives. Due to the objectives of economic benefit, safety in production and environmental protection, it is necessary to use a variety of criteria to evaluate and optimize schemes. In this paper, we propose RBF neural network and weight-based algorithm to achieve multi-objective decision. We leverage RBF neural network to construct objective weight assignment model. The goal of our weight-based algorithm is that the multi-objective optimization problem is formulated as a single-objective optimization problem by assigning certain weights to each objective, and then the non-inferior solution of the multi-objective optimization problem is generated by changing the weights of each objective extensive.
COVID-19 Vulnerability Map Construction via Location Privacy Preserving Mobile Crowdsourcing

The pandemic of the coronavirus (COVID-19) has caused an unprecedented global public health crisis, and most countries in the world are running out of the healthcare resources.A fine-grained COVID-19 vulnerability map will be essential to track the number of people with covid-like symptoms, so that the the potential outbreak communities can be identified and the valuable healthcare resources can proactively and dynamically be allocated.Mobile crowdsourcing based symptom reporting is a promising and convenient option to construct such a map, while it may compromise the location privacy of crowdsourcing participants.In this work, we propose a novel approach to establish the COVID-19 vulnerability map based on the crowdsourced reporting without disclosing the participants' location privacy to a semi-honest crowdsourcing aggregator.Briefly, based on the differentially private geo-indistinguishability, the mobile participants are able to locally perturb their geographic data.With the masked geographic information, we employ the best linear unbiased prediction estimator with spatial smoothing to obtain the reliable vulnerability estimates in the areas of interest and construct the map.Given the fast spreading nature of coronavirus, we integrate the vulnerability estimates with a susceptible-exposed-infected-removed (SEIR) model to build up a future trend map.Extensive simulations based on real-world data verify the effectiveness of the proposed method.
Fast Model Predictive Image-Based Visual Servoing for Quadrotors

This paper studies the problem of Image-Based Visual Servo Control (IBVS) for quadrotors. Although the control of quadrotors has been extensively studied in the last decades, combining the IBVS module with the quadrotor's dynamics is still hard, mainly due to the under-actuation issues related to the quadrotor control as opposed to the 6 DoF control outputs generated by the IBVS modules. We propose an alternative formulation to solve this problem, by particularly using linear Model Predictive Control (MPC), that allows us to relax the UAVs under-actuation issues. Stability guarantees of the proposed scheme are presented. The proposed model is validated with synthetic data and tested in a real UAV's setup.
The dilemma of the direct answer

No Web technology has undergone such an impressive evolution as Web search engines did and still do. Starting with the promise of "Bringing order to the Web" 1 by compiling information sources matching a query, retrieval technology has been evolving to a kind of "oracle machinery", being able to recommend a single source, and even to provide direct answers extracted from that source. Notwithstanding the remarkable progress made and the apparent user preferences for direct answers, this paradigm shift comes at a price which is higher than one might expect at first sight, affecting both users and search engine developers in their own way. We call this tradeoff "the dilemma of the direct answer"; it deserves an analysis which has to go beyond system-oriented aspects but scrutinize the way our society deals with both their information needs and means to information access. The paper in hand contributes to this analysis by putting the evolution of retrieval technology and the expectations at it in the context of information retrieval history. Moreover, we discuss the trade offs in information behavior and information system design that users and developers may face in the future.
An inquiry on the potential of computational literary techniques towards successful destination branding and literary tourism

Sentiments and emotions are created for cities in literary texts; certain assets of cities are emphasized consistently as well. That's why, literary texts have the potential to create a desire to visit the city for the readers. Computational literary techniques offer opportunities for diagnosing sentimental, emotional and topic-based content in literary texts which can be used to enhance insight development for successful destination marketing. 'A Memento for Istanbul' was chosen as the case of this study as this novel has rich spatial and temporal content about the city. Computational sentiment analysis, emotional analysis and topic modelling were utilized to analyse this novel from a destination marketing perspective. Sentiment and emotional analyses demonstrated that information about present-day Istanbul, as well as both the distant and modern history of the city, was delivered with different positivity levels and with different emotional patterns, which creates insights for message strategies for destination branding. The results of the topic modelling revealed that computational literacy techniques can be used to reveal specific assets of the city such as iconic buildings or specific experiences that one should have in specific parts of the city. This information can shed light on strategies with high potential for successful branding.
Neural network surgery: Combining training with topology optimization

With ever increasing computational capacities, neural networks become more and more proficient at solving complex tasks. However, picking a sufficiently good network topology usually relies on expert human knowledge. Neural architecture search aims to reduce the extent of expertise that is needed. Modern architecture search techniques often rely on immense computational power, or apply trained meta-controllers for decision making. We develop a framework for a genetic algorithm that is both computationally cheap and makes decisions based on mathematical criteria rather than trained parameters. It is a hybrid approach that fuses training and topology optimization together into one process. Structural modifications that are performed include adding or removing layers of neurons, with some re-training applied to make up for any incurred change in input-output behaviour. Our ansatz is tested on several benchmark datasets with limited computational overhead compared to training only the baseline. This algorithm can achieve a significant increase in accuracy (as compared to a fully trained baseline), rescue insufficient topologies that in their current state are only able to learn to a limited extent, and dynamically reduce network size without loss in achieved accuracy. On standard ML datasets, accuracy improvements compared to baseline performance can range from 20% for well performing starting topologies to more than 40% in case of insufficient baselines, or reduce network size by almost 15%.
A Privacy-Preserving Data Inference Framework for Internet of Health Things Networks

Privacy protection in electronic healthcare applications is an important consideration due to the sensitive nature of personal health data. Internet of Health Things (IoHT) networks have privacy requirements within a healthcare setting. However, these networks have unique challenges and security requirements (integrity, authentication, privacy and availability) must also be balanced with the need to maintain efficiency in order to conserve battery power, which can be a significant limitation in IoHT devices and networks. Data are usually transferred without undergoing filtering or optimization, and this traffic can overload sensors and cause rapid battery consumption when interacting with IoHT networks. This consequently poses restrictions on the practical implementation of these devices. As a solution to address the issues, this paper proposes a privacy-preserving two-tier data inference framework - this can conserve battery consumption by reducing the data size required to transmit through inferring the sensed data and can also protect the sensitive data from leakage to adversaries. Results from experimental evaluations on privacy show the validity of the proposed scheme as well as significant data savings without compromising the accuracy of the data transmission, which contributes to energy efficiency of IoHT sensor devices.
Entropy Optimized Deep Feature Compression

This letter focuses on the compression of deep features. With the rapid expansion of deep feature data in various CNN-based analysis and processing tasks, the demand for efficient compression continues to increase. Product quantization (PQ) is widely used in the compact expression of features. In the quantization process, feature vectors are mapped into fixed-length codes based on a pre-trained codebook. However, PQ is not specifically designed for data compression, and the fixed-length codes are not suitable for further compression such as entropy coding. In this letter, we propose an entropy-optimized compression scheme for deep features. By introducing entropy into the loss function in the training process of quantization, the quantization and entropy coding modules are jointly optimized to minimize the total coding cost. We evaluate the proposed methods in retrieval tasks. Compared with fixed-length coding, the proposed scheme can be generally combined with PQ and its extended method and can achieve a better compression performance consistently.
An Exploratory Study on Readiness Framework in IoT Forensics

Forensic readiness is important to ensure that the organization is fully prepared and well-equipped to be forensically ready to conduct the digital forensic investigation. Moreover, forensic readiness in IoT forensic investigation is different from the usual computer forensic readiness. This research discovered the importance of having the forensic readiness in place for the organization before conducting the IoT forensic investigation. Therefore, a readiness framework was proposed as a groundwork before further research is carried out. Literature on related this issues was collected, examined and criticized in order to scrutinize the impact factors in IoT forensics investigations. Finally, the proposed framework was validated by thirty experts from digital forensics in Malaysia using triangulation methods. From the results, this framework will be used in developing an instruments to measure readiness factors among digital forensics stakeholders.
Face recognition in the scene of wearing a mask

Under the epidemic, face recognition technology in the scene of wearing a mask has brought convenience to people and reduced the risk of virus infection caused by mask removal. Face recognition technology is based on the extraction of a large number of feature points of human faces to make judgments. At present, the main realization method of face recognition technology with masks is to strengthen the recognition of key areas such as eyes and eyebrows through model training. LBP (Local Binary Pattern) feature is a feature that effectively depicts the texture of face images, and can further enhance the robustness of face recognition methods. Based on the above analysis, the article uses improved MTCNN (Multi-task Cascaded Convolutional Neural Networks) to detect the occlusion area caused by the mask, then finds the LBP feature of the non-occlusion area, and finally inputs the feature into the support vector machine for face recognition.
A Tibetan Thangka data set and relative tasks

Data set of high quality is the cornerstone of the current data-driven machine learning models, and plays an important role in promoting the development of various application areas. At present, image analysis and processing techniques have intensively involved into the tasks of inheriting and protecting culture resources. However, currently there are few effective image data sets about the traditional Chinese Tibetan culture. In this work, we provided a small data set referred as CYTKv1(Chomo Yarlung Tibet version 1) which includes 1700 + Thangka images (an important and representative carrier of Chinese Tibetan culture), and the main objects in each image are manually labeled and bounding-boxed with semantic words. In addition, we shared a list of tasks of processing and analyzing the Thangkas to enlighten researchers about the challenges and potential applications on this data set. At last, we tested several famous deep learning models for the purpose of validating the annotation task on the new data set and presented the results of them, and finally selected the best one as the baseline for the annotation task.
Ship Detection in SAR Images Using Convolutional Variational Autoencoders

We propose an unsupervised framework for ship detection in SAR image data, based on anomaly detection. We first learn representations of the SAR images with a convolutional Variational Autoencoder. Aftwerwards, we perform anomaly detection based on those representations, with a clustering algorithm. Experimental results with real SAR data are provided to illustrate the performance of the proposed algorithm.