Reviewerly/exHarmony huggingface dataset:
Level set framework with transcendental constraint for robust and fast image segmentation

Though image segmentation models are plentiful and have many applications nowadays, it can be difficult to segment images with complex boundaries and serious intensity inhomogeneity. To some extent, the region-scalable fitting energy model can segment images suffering from intensity inhomogeneity since it considers image intensity as a function, but it relies on initial conditions dramatically. Nowadays, prior knowledge has been widely applied in image segmentation models, which can integrate automatic method and experts experience in one robust and fast segmentation model. In this paper we present a new model that can segment various images accurately by taking the advantages of the region-scalable fitting energy model and the advanced transcendental constraint from artificial experience. The proposed energy functional consists of a smooth length term, a target image data term and a transcendental constraint term. The transcendental constraint term plays a key role in the proposed model, which not only gives the accurate segmentation results but also provides us the chance to carry out the parallel computation. In the proposed-parallel model, the efficiency is improved a lot and the results become more precise compared with other methods. The split Bregman method is applied to minimize the energy functional. Furthermore, we present the convergence analysis and the time complexity analysis of our algorithm. Multiple experimental results and comparisons including parameters sensitivity discussion are shown to demonstrate the superiority of the proposed model such as high accuracy, robustness and efficiency.
History of Gamification and Its Role in the Educational Process

This article tells about the history of gamification of education, its current state and the practice of introducing game elements into education in different countries of the world. The experience of scientists from the USA, Sweden, Russia and Canada on the use of gamification in the lesson is analyzed. It also discusses the issues of gamification of education, the results of this process and its use as an educational innovation, describes the versatile potential of gamification, as well as programs and platforms that use partially or fully the elements of gamification in the process of creating educational resources.
FIVER – Robust Verification of Countermeasures against Fault Injections

Fault Injection Analysis is seen as a powerful attack against implementations of cryptographic algorithms. Over the last two decades, researchers proposed a plethora of countermeasures to secure such implementations. However, the design process and implementation are still error-prone, complex, and manual tasks which require long-standing experience in hardware design and physical security. Moreover, the validation of the claimed security is often only done by empirical testing in a very late stage of the design process. To prevent such empirical testing strategies, approaches based on formal verification are applied instead providing the designer early feedback.In this work, we present a fault verification framework to validate the security of countermeasures against fault-injection attacks designed for ICs. The verification framework works on netlist-level, parses the given digital circuit into a model based on Binary Decision Diagrams, and performs symbolic fault injections. This verification approach constitutes a novel strategy to evaluate protected hardware designs against fault injections offering new opportunities as performing full analyses under a given fault models.Eventually, we apply the proposed verification framework to real-world implementations of well-established countermeasures against fault-injection attacks. Here, we consider protected designs of the lightweight ciphers CRAFT and LED-64 as well as AES. Due to several optimization strategies, our tool is able to perform more than 90 million fault injections in a single-round CRAFT design and evaluate the security in under 50 min while the symbolic simulation approach considers all 2128 primary inputs.
Learning in Feedforward Neural Networks Accelerated by Transfer Entropy

Current neural networks architectures are many times harder to train because of the increasing size and complexity of the used datasets. Our objective is to design more efficient training algorithms utilizing causal relationships inferred from neural networks. The transfer entropy (TE) was initially introduced as an information transfer measure used to quantify the statistical coherence between events (time series). Later, it was related to causality, even if they are not the same. There are only few papers reporting applications of causality or TE in neural networks. Our contribution is an information-theoretical method for analyzing information transfer between the nodes of feedforward neural networks. The information transfer is measured by the TE of feedback neural connections. Intuitively, TE measures the relevance of a connection in the network and the feedback amplifies this connection. We introduce a backpropagation type training algorithm that uses TE feedback connections to improve its performance.
A study of knowledge management alignment with production management: A study of carpet manufacture in Kurdistan region of Iraq

The manufacturing sector has been profoundly impacted by advances in project management knowledge. As a result, it is critical to optimize service development processes in every project. The aim of this research was to increase cement manufacturing production by applying knowledge management concepts and Knowledge management as a mediator. The study was conducted at various cement manufacturing facilities in Iraq's Kurdistan province. To quantify production improvement in cement companies, the researchers examined four facets of knowledge management: Knowledge creation, Knowledge storage management, knowledge sharing, and Knowledge application, with Knowledge management serving as a mediator. To conduct the current analysis, the researchers used a quantitative research approach in the form of a survey. The questionnaire was randomly distributed to 140 administrative employees of various cement manufacturing companies in the Kurdistan region. The researchers did, however, collect 128completed questionnaires. To assess developed research hypotheses, the study used hierarchical multiple regression analysis and the Sobel test. The findings indicated that all knowledge management elements (Knowledge creation, Knowledge storage development, knowledge sharing, and Knowledge application) had a clear and important positive relationship with increased production at carpet manufacturers in Iraq's Kurdistan region. Additionally, the findings indicated that all knowledge management concepts with a mediator (project management development program) had an indirect positive and important relationship with increased production at cement manufacturing facilities in Iraq's Kurdistan region.
Playing Lottery Tickets with Vision and Language

Large-scale pre-training has recently revolutionized vision-and-language (VL) research. Models such as LXMERT and UNITER have significantly lifted the state of the art over a wide range of VL tasks. However, the large number of parameters in such models hinders their application in practice. In parallel, work on the lottery ticket hypothesis (LTH) has shown that deep neural networks contain small matching subnetworks that can achieve on par or even better performance than the dense networks when trained in isolation. In this work, we perform the first empirical study to assess whether such trainable subnetworks also exist in pre-trained VL models. We use UNITER as the main testbed (also test on LXMERT and ViLT), and consolidate 7 representative VL tasks for experiments, including visual question answering, visual commonsense reasoning, visual entailment, referring expression comprehension, image-text retrieval, GQA, and NLVR2. Through comprehensive analysis, we summarize our main findings as follows. (i) It is difficult to find subnetworks that strictly match the performance of the full model. However, we can find relaxed winning tickets at 50%-70% sparsity that maintain 99% of the full accuracy. (ii) Subnetworks found by task-specific pruning transfer reasonably well to the other tasks, while those found on the pre-training tasks at 60%/70% sparsity transfer universally, matching 98%/96% of the full accuracy on average over all the tasks. (iii) Besides UNITER, other models such as LXMERT and ViLT can also play lottery tickets. However, the highest sparsity we can achieve for ViLT is far lower than LXMERT and UNITER (30% vs. 70%). (iv) LTH also remains relevant when using other training methods (e.g., adversarial training).
Superpolynomial quantum enhancement in polaritonic neuromorphic computing

Recent proof-of-principle experiments have demonstrated the implementation of neuromorphic computing using exciton-polaritons, making use of coherent classical states [D. Ballarini et al., Nano Lett. 20, 3506 (2020)]. At the same time, it is expected that nonlinear exciton-polaritons can reach a quantum regime forming non-classical states. Here we consider theoretically the quantum nature of exciton polaritons and predict a superpolynomial quantum enhancement in image recognition tasks. This is achieved within experimentally accessible parameters.
Hazard Perception, Presence, and Simulation Sickness—A Comparison of Desktop and Head-Mounted Display for Driving Simulation

Driving simulators are becoming increasingly common in driver training and assessment. Since virtual reality is generally regarded as an appropriate environment for measuring risk behavior, simulators are also used to assess hazard perception, which is considered to be one of the most important skills for safe driving. Simulators, which offer challenges that are indeed comparable to driving in real traffic, but at a very low risk of physical injury, have the potential to complement theoretical and practical driver trainings and tests. Although configurations and fidelity differ considerably between driving simulators, studies comparing the impact of their distinct features on driving performance and test validity remain rare. In this context, prior research demonstrated that a wider field of view (three monitors compared to a single monitor) led to earlier speed adjustments in response to potential hazards — especially for experienced drivers. The wider field of view was assumed to cause the drivers to be more present in the virtual world, which in turn provoked more natural scanning of the road and therefore, earlier hazard detection in experienced drivers. Research on spatial presence in other contexts support this assumption. The present experiment investigated whether this effect could be enhanced by an even more immersive presentation technique for driving simulation: a head-mounted display (HMD). Moreover, we studied the interplay between display mode, sense of presence and simulation sickness. Eighty experienced and less experienced drivers completed six simulation-based hazard perception scenarios, which were displayed either via a triple-monitor set-up or an HMD. Results indicate that the experienced drivers showed very similar driving and risk behavior as the inexperienced drivers in both experimental conditions. However, there were significant differences between the two display conditions. The use of an HMD resulted in a clearer and more abrupt speed reduction, more virtual presence, and a higher degree of simulation sickness. However, the interrelation between these three variables could not be conclusively clarified in the present study and thus represents a research aim that could be addressed in future studies.
Blockchain Technology for a Safe and Transparent Covid-19 Vaccination

In late 2019, we witnessed the apparition of the covid-19 virus. The virus appeared first in Wuhan, and due to people travel was spread worldwide. Exponential spread as well as high mortality rates, the two characteristics of the SARS-CoV-2 virus that pushed the entire world into a global lock-down. Health and economic crisis, along with social distancing have put the globe in a highly challenging situation. Unprecedented pressure on the health care system exposed many loopholes not only in this industry but many other sectors, which resulted in a set of new challenges that researchers and scientists among others must face. In all these circumstances, we could attend, in a surprisingly short amount of time, the creation of multiple vaccine candidates. The vaccines were clinically tested and approved, which brought us to the phase of vaccination. Safety, security, transparency, and traceability are highly required in this context. As a contribution to assure an efficient vaccination campaign, in this paper we suggest a Blockchain-based system to manage the registration, storage, and distribution of the vaccines. This manuscript has been presented as preprint in Blockchain technology for a Safe and Transparent Covid-19 Vaccination: https://arxiv.org/abs/2104.05428
Image resizing by reconstruction from deep features

Abstract Traditional image resizing methods usually work in pixel space and use various saliency measures. The challenge is to adjust the image shape while trying to preserve important content. In this paper we perform image resizing in feature space using the deep layers of a neural network containing rich important semantic information. We directly adjust the image feature maps, extracted from a pre-trained classification network, and reconstruct the resized image using neural-network based optimization. This novel approach leverages the hierarchical encoding of the network, and in particular, the high-level discriminative power of its deeper layers, that can recognize semantic regions and objects, thereby allowing maintenance of their aspect ratios. Our use of reconstruction from deep features results in less noticeable artifacts than use of imagespace resizing operators. We evaluate our method on benchmarks, compare it to alternative approaches, and demonstrate its strengths on challenging images.
Use of Mobile Phones in Teaching English in Bangladesh: A Systematic Review (2010–2020)

The use of mobile devices for English language teaching (ELT) is increasing rapidly all over the world. This review study surveys the empirical research on using mobile phones in ELT published in Scopus and Web of Science indexing journals from 2010 to 2020 in Bangladesh. Out of 103 studies, 11 studies met the criteria of this study to analyze the effects of mobile phones on ELT. The findings show that the major research trends of these studies aim at teachers’ professional development using mobile phones for teaching language. The findings also reveal that the use of mobile phones is effective in ELT through facilitating feasible, ubiquitous, and effective learning environments with some limitations, i.e., an issue with charging, a small screen, affection, and a lack of teacher confidence. Of the studies conducted, 83% have employed a qualitative research design for investigating learners’ readiness and concepts on the use of this device. In addition, there is a lack of empirical studies with the intention to observe and justify the effect of mobile phones on developing learners’ language skills. There is also a lack of evidence describing which mobile applications are effective for developing relevant language skills. Overall, the results of this systematic review might be applicable in the context of similarly developing countries, as well as triggering empirical research in the field of technology-enhanced ELT in these countries.
How important are faces for person re-identification?

This paper investigates the dependence of existing state-of-the-art person re-identification models on the presence and visibility of human faces. We apply a face detection and blurring algorithm to create anonymized versions of several popular person re-identification datasets including Market1501, DukeMTMC-reID, CUHK03, Viper, and Airport. Using a cross-section of existing state-of-the-art models that range in accuracy and computational efficiency, we evaluate the effect of this anonymization on re-identification performance using standard metrics. Perhaps surprisingly, the effect on mAP is very small, and accuracy is recovered by simply training on the anonymized versions of the data rather than the original data. These findings are consistent across multiple models and datasets. These results indicate that datasets can be safely anonymized by blurring faces without significantly impacting the performance of person reidentification systems, and may allow for the release of new richer re-identification datasets where previously there were privacy or data protection concerns.
Scalability of All-Optical Neural Networks Based on Spatial Light Modulators

Optical implementation of artificial neural networks has been attracting great attention due to its potential in parallel computation at speed of light. Although all-optical deep neural networks (AODNNs) with a few neurons have been experimentally demonstrated with acceptable errors recently, the feasibility of large-scale AODNNs remains unknown because error might accumulate inevitably with increasing number of neurons and connections. Here, we demonstrate a scalable AODNN with programmable linear operations and tunable nonlinear activation functions. We verify its scalability by measuring and analyzing errors propagating from a single neuron to the entire network. The feasibility of AODNNs is further confirmed by recognizing handwritten digits and fashions, respectively.
A hybrid recommender system for recommending smartphones to prospective customers

Recommender Systems are a subclass of machine learning systems that employ sophisticated information filtering strategies to reduce the search time and suggest the most relevant items to any particular user. Hybrid recommender systems combine multiple recommendation strategies in different ways to benefit from their complementary advantages. Some hybrid recommender systems have combined collaborative filtering and content-based approaches to build systems that are more robust. In this paper, we propose a hybrid recommender system, which combines Alternating Least Squares (ALS) based collaborative filtering with deep learning to enhance recommendation performance as well as overcome the limitations associated with the collaborative filtering approach, especially concerning its cold start problem. In essence, we use the outputs from ALS (collaborative filtering) to influence the recommendations from a Deep Neural Network (DNN), which combines characteristic, contextual, structural and sequential information, in a big data processing framework. We have conducted several experiments in testing the efficacy of the proposed hybrid architecture in recommending smartphones to prospective customers and compared its performance with other open-source recommenders. The results have shown that the proposed system has outperformed several existing hybrid recommender systems.
Adversarial attacks and mitigation for anomaly detectors of cyber-physical systems

The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated research into a multitude of attack detection mechanisms, including anomaly detectors based on neural network models. The effectiveness of anomaly detectors can be assessed by subjecting them to test suites of attacks, but less consideration has been given to adversarial attackers that craft noise specifically designed to deceive them. While successfully applied in domains such as images and audio, adversarial attacks are much harder to implement in CPSs due to the presence of other built-in defence mechanisms such as rule checkers (or invariant checkers). In this work, we present an adversarial attack that simultaneously evades the anomaly detectors and rule checkers of a CPS. Inspired by existing gradient-based approaches, our adversarial attack crafts noise over the sensor and actuator values, then uses a genetic algorithm to optimise the latter, ensuring that the neural network and the rule checking system are both deceived. We implemented our approach for two real-world critical infrastructure testbeds, successfully reducing the classification accuracy of their detectors by over 50% on average, while simultaneously avoiding detection by rule checkers. Finally, we explore whether these attacks can be mitigated by training the detectors on adversarial samples.
Memory for a virtual reality experience in children and adults according to image quality, emotion, and sense of presence

Numerous studies have explored the effects of virtual reality (VR) on adults' cognition. Little is known, however, of these effects in children. The aim of this study was to explore, in both children and adults, the respective roles of the specific factors of VR, such as immersion, sense of presence and emotion, on memory performance. To do so, we used a head-mounted display to present a VR experience in which we manipulated immersion by varying 3D asset quality (High and Low) and emotion by presenting negative, neutral and positive stimuli. 48 adults (Mage = 20.65) and 40 children (Mage = 11.63) were both divided into two experimental groups (High vs. Low 3D model quality). Valence, arousal, and sense of presence were self-assessed by means of questionnaires, while memory of the presented stimuli was assessed using a free recall task. We also performed physiological measurements to provide objective support for our data. Results showed that memory performance was better for emotional than for neutral stimuli regardless of age group, even though children seemed to avoid looking at negative stimuli compared to neutral ones. Memory was predicted by arousal and presence in adults and only by arousal in children. Memory was not impaired by using poor image quality when highly arousing content was displayed. This study revealed that, contrary to adults, the use of poor image quality did not protect children from strong emotional experiences in VR. The roles of familiarity and arousal are discussed to help explain these results.The online version contains supplementary material available at 10.1007/s10055-021-00537-y.
On Attention Redundancy: A Comprehensive Study

Multi-layer multi-head self-attention mechanism is widely applied in modern neural language models.Attention redundancy has been observed among attention heads but has not been deeply studied in the literature.Using BERT-base model as an example, this paper provides a comprehensive study on attention redundancy which is helpful for model interpretation and model compression.We analyze the attention redundancy with Five-Ws and How.(What) We define and focus the study on redundancy matrices generated from pre-trained and fine-tuned BERT-base model for GLUE datasets.(How) We use both token-based and sentence-based distance functions to measure the redundancy.(Where) Clear and similar redundancy patterns (cluster structure) are observed among attention heads.(When) Redundancy patterns are similar in both pre-training and fine-tuning phases.(Who) We discover that redundancy patterns are task-agnostic.Similar redundancy patterns even exist for randomly generated token sequences.("Why") We also evaluate influences of the pre-training dropout ratios on attention redundancy.Based on the phaseindependent and task-agnostic attention redundancy patterns, we propose a simple zero-shot pruning method as a case study.Experiments on fine-tuning GLUE tasks verify its effectiveness.The comprehensive analyses on attention redundancy make model understanding and zero-shot model pruning promising.