Reviewerly/exHarmony huggingface dataset:
Cognitive Capacity, Representation, and Instruction

The central argument of the present article is that Cognitive Psychology’s problems in dealing with the concept of “cognitive capacity” is intimately linked with Cognitive Psychology’s long-lasting failure of coming to terms with the concept of “representation” in general, and “task representation” in particular. From this perspective, the role of instructions in psychological experiments is emphasised. It is argued that both a careful conceptual analysis of instruction-induced task representations as well as an experimental variation of instructions promises to broaden our understanding of the role of task representations as a determinant of limited cognitive capacity.
Blind Side-Channel SIFA

Statistical Ineffective Fault Attacks (SIFA) have been recently proposed as very powerful key-recovery strategies on symmetric cryptographic primitives' implementations. Specifically, they have been shown to bypass many common countermeasures against faults such as redundancy or infection, and to remain applicable even when side-channel countermeasures are deployed. In this work, we investigate combined side-channel and fault attacks and show that a profiled, SIFA-like attack can be applied despite not having any direct ciphertext knowledge. The proposed attack exploits the ciphertext's side-channel and fault characteristics to mount successful key recoveries, even in the presence of masking and duplication countermeasures. We analyze the attack using simulations, discuss its requirements, strengths and limitations, and compare different approaches to distinguish the correct key. Finally, we demonstrate its applicability on an ARM Cortex-M4 device, utilizing a combination of laser-based fault injection and microprobe-based EM side-channel analysis.
A Correlation Context-Driven Method for Sea Fog Detection in Meteorological Satellite Imagery

Sea fog detection is a challenging and essential issue in satellite remote sensing. Although conventional threshold methods and deep learning methods can achieve pixel-level classification, it is difficult to distinguish ambiguous boundaries and thin structures from the background. Considering the correlations between neighbor pixels and the affinities between superpixels, a correlation context-driven method for sea fog detection is proposed in this letter, which mainly consists of a two-stage superpixel-based fully convolutional network (SFCNet), named SFCNet. A fully connected Conditional Random Field (CRF) is utilized to model the dependencies between pixels. To alleviate the problem of high cloud occlusion, an attentive Generative Adversarial Network (GAN) is implemented for image enhancement by exploiting contextual information. Experimental results demonstrate that our proposed method achieves 91.65&#x0025; mIoU and obtains more refined segmentation results, performing well in detecting fogs in small, broken bits and weak contrast thin structures, as well as detects more obscured parts.
A Resource Allocation Model Based on Trust Evaluation in Multi-Cloud Environments

Allocating and managing resources while considering the quality of service is considered a fundamental and complex research problem in a cloud environment. An optimal resource allocation optimizes several parameters such as optimizing cost and resource utilization or maximizing any quality parameters. However, to ensure better customer service, Cloud Service Providers (CSPs) should consider most of the quality attributes while allocating resources to the cloud infrastructures. Existing research does not evaluate trust as a quantitative attribute, thus a trade-off between trust and performance in resource allocation is also absent in the research area. We propose a model to consider both trust and delay in this paper. The trust of a CSP is quantitatively estimated through some attributes and metrics. Availability, reliability, data integrity, and efficiency are considered to estimate the trust. The objective is to maximize the trust of the allocation while minimizing the communication delay. The proposed joint optimization model combines the previous credentials of the CSPs and the present resource constraints. To solve the problem heuristically, a genetic algorithm is applied. The model uses a number of parameters that provide the flexibility to adapt several service requirements. The effectiveness and applicability of the proposed approach are demonstrated through experiments. The results ensure that the effectiveness in the estimation of trust evaluation for different CSPs with the proposed attributes. Moreover, integrating trust in the resource allocation model allocates appropriate resources while enhancing the trust and reducing the communication delay in the overall allocation.
Designing a Somatosensory Interactive Game of Lower Extremity Muscle Rehabilitation for the Elderly

Somatosensory game-based rehabilitation has recently experienced rapid increases in demand and scale in the health promotion market. However, the existing research on this topic mostly focuses on treatment efficacy rather than game usability, experience feedback, and the needs of the elderly when experiencing the games. To fill this knowledge gap, we designed a somatosensory interactive game focusing on lower extremity rehabilitation, explored the intention and motivation of elderly patients, and documented the difficulties and needs of these patients in using the game. We enrolled 15 elderly subjects with lower extremity degeneration and collected data using a one-group pre-test and post-test experiment. For the pre-test, the subjects were treated with a traditional lower extremity rehabilitation program for 1 min, followed by a 5-min questionnaire survey. Next, the subjects were asked to play the somatosensory interactive game for 1 min, followed by 15 min of post-test questionnaire survey and interview. The results showed a significant difference between the two types of rehabilitation in the us-ability constructs of "fun", "liking", "self-expression", "self-understanding", "intention", and "fatigue" in elderly subjects with no prior rehabilitation experience, indicating that, compared to the traditional ones, the rehabilitation game performed better in introducing rehabilitation to the elders, enhancing their motivation, intention, and increasing their self-confidence. The results also showed that the status of lower extremity disease in elderly users did not affect gameplay. Lastly, we provide suggestions for further improvements in the design of the operational interface, software, and hardware of lower extremity rehabilitation-dedicated somatosensory interactive games.
Semantic Meaning Based Bengali Web Text Categorization Using Deep Convolutional and Recurrent Neural Networks (DCRNNs)

Web text categorization is a procedure of deliberately assigning a web text document into one of the pre-defined classes or categories. It is a very challenging task to manipulate, organize, and categorize an enormous amount of web text data in manually. This paper proposes an automatic text categorization framework to classify Bengali web text data using deep learning. The proposed framework comprises of three key constituents: text to feature extraction, training, and testing. The categorization framework is trained, validated, and tested at 120K, 12K, and 36K datasets, respectively. The proposed system achieved \(99.00\%\) accuracy in the training phase, \(96.00\%\) in the validation phase, and \(95.83\%\) in the testing phase, respectively.
Controlling the uncontrollable: Quantum control of open-system dynamics

Control of open quantum systems is essential for the realization of contemporary quantum science and technology. We demonstrate such control using a thermodynamically consistent framework, taking into account the fact that the drive can modify the system's interaction with the environment. Such an effect is incorporated within the dynamical equation, leading to control-dependent dissipation. This relation serves as the key element for open-system control. The control paradigm is displayed by analyzing entropy-changing state-to-state transformations, such as heating and cooling. The difficult task of controlling quantum gates is achieved for nonunitary reset maps with complete memory loss. In addition, we identify a mechanism for controlling unitary gates by actively removing entropy from the system to the environment. We demonstrate a universal set of single- and double-qubit unitary gates under dissipation.
Predicting Literacy Skills via Stealth Assessment in a Simple Vocabulary Game

iSTART is a game-based intelligent tutoring system (ITS) designed to improve students' reading skills by providing training on reading comprehension strategies. Game-based practice in iSTART follows two main approaches: generative practice and identification practice. Generative practice games ask students to author self-explanations using one or more of the instructed strategies. Identification practice games require students to recognize or select appropriate strategies based on their analysis of example texts. This study explored the feasibility of implementing stealth assessments in iSTART using only an identification game. Specifically, this study examined the extent to which participants' performance and attitudes related to a simple vocabulary game could predict the outcomes of standardized reading assessments. MTurk participants (N = 211) played identification games in iSTART and then rated their subjective gameplay experience. Participants also completed measures of their vocabulary and reading comprehension skills. Results indicated that participants' performance in a vocabulary practice game was predictive of literacy skills. In addition, the possibility that students' attitude towards the game moderated the relation between game performance and literacy skills was ruled out. These findings argue for the feasibility of implementing stealth assessment in simple games to facilitate the adaptivity of ITSs.
Towards Dynamic Reconfiguration of a Composite Web Service: An Approach Based on QoS Prediction

Service-oriented architecture provides the ability to combine several web services in order to fulfil a user-specific requirement. In dynamic environments, the appearance of several unforeseen events can destabilize the composite web service (CWS) and affect its quality. To deal with these issues, the composite web service must be dynamically reconfigured. Dynamic reconfiguration may be enhanced by avoiding the invocation of degraded web services by predicting QoS for the candidate web service. In this paper, we propose a dynamic reconfiguration method based on HMM (Hidden Markov Model) states to predict the imminent degradation in QoS and prevent the invocation of partner web services with degraded QoS values. PSO (Particle Swarm Optimization) and SFLA (Shuffled Frog Leaping Algorithm) are used to improve the prediction efficiency of HMM. Through extensive experiments on a real-world dataset, WS-Dream, the results demonstrate that the proposed approach can achieve better prediction accuracy. Moreover, we carried out a case study where we revealed that the proposed approach outperforms several state-of-the-art methods in terms of execution time.
Leveraging Multi scale Backbone with Multilevel supervision for Thermal Image Super Resolution

This paper proposes an attention-based multi-level model with a multi-scale backbone for thermal image super-resolution. The model leverages the multi-scale backbone as well. The thermal image dataset is provided by PBVS 2020 in their thermal image super-resolution challenge. This dataset contains the images with three different resolution scales(low, medium, high) [1]. However, only the medium and high-resolution images are used to train the proposed architecture to generate the super-resolution images in x2, x4 scales. The proposed architecture is based on the Res2net blocks as the backbone of the network. Along with this, the coordinate convolution layer and dual attention are also used in the architecture. Further, multi-level supervision is implemented to supervise the output image resolution similarity with the real image at each block during training. To test the robustness of the proposed model, we evaluated our model on the Thermal-6 dataset [20]. The results show that our model is efficient to achieve state-of-the-art results on the PBVS dataset. Further the results on the Thermal-6 dataset show that the model has a decent generalization capacity.
Teaching Vehicles to Steer Themselves with Deep Learning

Traditional approaches for steering a vehicle using machine vision require large amounts of robust hand-crafted software which is both time consuming and expensive. The presented method uses a deep neural network to teach cars to steer themselves without any additional software. We created a labeled dataset for the ACTor (Autonomous Campus TranspORt) electric vehicle by pairing real world images taken during a drive with the associated steering wheel angle. We trained a model end to end using modern deep learning techniques including convolutional neural networks and transfer learning to automatically detect relevant features in the input and provide a predicted output. This means that no traditional hand engineered algorithm features were required for this implementation. We currently use an pretrained inception network on the ImageNet dataset to leverage the high level features learned from ImageNet to the steering problem through transfer learning. We removed the top portion of the network and replaced it with a linear regression node to provide the output. The model is trained end to end using backpropagation. The trained model is integrated with vehicle software on ROS (Robot Operating System) to read image data and provide a corresponding steering angle in real time. The current model achieves 15.2 degree error on average. As development continues the model may replace the current lane centering software and will be used for IGVC Self-Drive competition and campus transportation.
Differentially Private Attributed Network Releasing Based on Early Fusion

Vertex attributes exert huge impacts on the analysis of social networks. Since the attributes are often sensitive, it is necessary to seek effective ways to protect the privacy of graphs with correlated attributes. Prior work has focused mainly on the graph topological structure and the attributes, respectively, and combining them together by defining the relevancy between them. However, these methods need to add noise to them, respectively, and they produce a large number of required noise and reduce the data utility. In this paper, we introduce an approach to release graphs with correlated attributes under differential privacy based on early fusion. We combine the graph topological structure and the attributes together with a private probability model and generate a synthetic network satisfying differential privacy. We conduct extensive experiments to demonstrate that our approach could meet the request of attributed networks and achieve high data utility.
How does topology influence gradient propagation and model performance of deep networks with DenseNet-type skip connections?

DenseNets introduce concatenation-type skip connections that achieve state-of-the-art accuracy in several computer vision tasks. In this paper, we reveal that the topology of the concatenation-type skip connections is closely related to the gradient propagation which, in turn, enables a predictable behavior of DNNs’ test performance. To this end, we introduce a new metric called NN-Mass to quantify how effectively information flows through DNNs. Moreover, we empirically show that NN-Mass also works for other types of skip connections, e.g., for ResNets, Wide-ResNets (WRNs), and MobileNets, which contain addition-type skip connections (i.e., residuals or inverted residuals). As such, for both DenseNet-like CNNs and ResNets/WRNs/MobileNets, our theoretically grounded NN-Mass can identify models with similar accuracy, despite having significantly different size/compute requirements. Detailed experiments on both synthetic and real datasets (e.g., MNIST, CIFAR-10, CIFAR-100, ImageNet) provide extensive evidence for our insights. Finally, the closed-form equation of our NN-Mass enables us to design significantly compressed DenseNets (for CIFAR-10) and MobileNets (for ImageNet) directly at initialization without time-consuming training and/or searching. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Information and Entropy Measures of Rendered Literal Translation

The definition and effect of translation literality (word-for-word translation, as opposed to sense-for-sense translation) have been a topic in translation studies for very many years. Schaeffer and Carl (2014) introduced measures to quantify the literality of observed translations based on their distance to a hypothetical absolutely literal translation assuming monotonicity (identical word order), compositionality (one-to-one translation), and entrenchment (lack of translation variation). This chapter introduces measures to assess the rendered translation literality, which reformulates the three literality criteria in an information-theoretic framework. We introduce rendered literality measures on the word and segment levels as the joint self-information and the joint entropy of the three literality criteria. These literality measures respect the additional requirement for translations to be grammatical according to target language (TL) constraints (i.e., rendered) and rank sets of alternative translations according to their rendered literality. We evaluate the predictive potential of rendered translation literality on translation duration within the multiLing dataset for different languages and different translation production modes. Our data show that the most probable translations respecting TL constraints are also most monotone, compositional, and entrenched translations. We conclude that the information-theoretic literality measures are powerful new predictors for behavioral measures, such as word and segment translation duration.
Classification of Insincere Questions Using Deep Learning: Quora Dataset Case Study

In the recent few year internet has gained the attention of researchers from all over the world. Internet has become crucial part of people’s lives rapidly. The major cause of this rapid fame has been highlighted as its ability to make the work simpler and easier. Question answering websites have gained popularity due the human need of getting answers of queries. Such forums may be intruded by adversaries or hackers to ruin the forum’s reputation. The detection of questions from such rivalry is still a key challenge because of two major reasons: firstly, the rate of users may be affected by such kind of questions, and secondly such insincere questions may also be asked to harm some particular user. Due to these reasons this study has been done to purpose deep learning-based solution for insincere question classification. Deep learning has been used due to its marvelous results in text classification related to various fields. Dataset has been collected of Quora website from publicly available source Kaggle. The machine learning methods SVM (Support vecttor Machine), Logistic regression has been compared with deep learning approach LSTM neural network (Long short-term memory). The dataset has been preprocessed and then used to train the models. Models have been trained on datasets by using MATLAB tool. This study has been also done to consider the impact of using various feature engineering techniques with models. Extensive experiments have been performed and parameters of the model have been optimized. F1 score has been used to compare the model performance because the dataset was highly imbalanced. Our existential results have highlighted that LSTM outperforms in terms of F1 score. The findings of this research would be beneficial for such question asking forums.
On Self-equivalence Encodings in White-Box Implementations

All academic methods to secure software implementations of block ciphers against adversaries with full control of the device have been broken. Despite the huge progress in the cryptanalysis of these white-box implementations, no recent progress has been made on the design side. Most of the white-box designs follow the CEJO framework, where each round is encoded by composing it with small random permutations. While several generic attacks have been proposed on the CEJO framework, no generic analysis has been performed on self-equivalence encodings, a different design where only the affine layer of each round is encoded with random self-equivalences of the S-box layer, that is, affine permutations commuting with the non-linear layer. In this work, we analyse the security of white-box implementations based on self-equivalence encodings for a broad class of SPN ciphers. First, we characterize the self-equivalence groups of S-box layers, and we prove that all the self-equivalences of a cryptographically strong S-box layer have a diagonal shape. Then, we propose the first generic attack on self-equivalence encodings. Our attack, based on affine equivalence problems, identifies the connection between the security of self-equivalence encodings and the self-equivalence structure of the cipher components. While we show that traditional SPN ciphers with cryptographically strong S-box layers cannot be secured with self-equivalence encodings, our analysis shows that self-equivalence encodings resist the generic attack if the cipher components satisfy several conditions, revealing the potential of self-equivalence encodings to secure other types of ciphers.
Design and optimization of a TensorFlow Lite deep learning neural network for human activity recognition on a smartphone

Human Activity Recognition (HAR), using machine learning to identify times spent (for example) walking, sitting, and standing, is widely used in health and wellness wearable devices, in ambient assistant living devices, and in rehabilitation. In this paper, a stacked Long Short-Term Memory (LSTM) structure is designed for HAR to be implemented on a smartphone. The use of an edge device for the processing means that the raw collected data does not need to be passed to the cloud for processing, mitigating potential bandwidth, power consumption, and privacy concerns. Our offline prototype model achieves 92.8% classification accuracy when classifying 6 activities using a public dataset. Quantization techniques are shown to reduce the model's weight representations to achieve a >30x model size reduction for improved use on a smartphone. The end result is an on-phone HAR model with accuracy of 92.7% and a memory footprint of 27 KB.