Reviewerly/exHarmony huggingface dataset:
Optimal path planning for two-wheeled self-balancing vehicle pendulum robot based on quantum-behaved particle swarm optimization algorithm

Based on the self-balance of free-floating two-wheel self-balancing pendulum robot system, an optimal path planning of two-wheel self-balancing pendulum robot is proposed. Firstly, the corner trajectory of the two-wheel self-balancing pendulum robot is parameterized by quantum particle swarm optimization (QPSO), and the objective function is designed according to the base attitude and the control accuracy of the robot’s terminal position and attitude. The optimal path planning problem of the two-wheel self-balancing pendulum robot system is transformed into the optimization problem of the non-linear system. Quantum particle swarm optimization (QPSO) algorithm is used to solve the non-linear optimization problem, and the goal of optimal path planning is achieved. The experimental results show that the proposed method can converge to the global optimal value with fast convergence speed and less adjustment parameters. The planned joint path satisfies the range of corner, angular velocity, and angular acceleration. The joint path is smooth and suitable for the control of two-wheel self-balancing pendulum robot.
Ensemble Sentiment Analysis Method based on R-CNN and C-RNN with Fusion Gate

Text sentiment analysis is one of the most important tasks in the field of public opinion monitoring, service evaluation and satisfaction analysis in the current network environment. At present, the sentiment analysis algorithms with good effects are all based on statistical learning methods. The performance of this method depends on the quality of feature extraction, while good feature engineering requires a high degree of expertise and is also time-consuming, laborious, and affords poor opportunities for mobility. Neural networks can reduce dependence on feature engineering. Recurrent neural networks can obtain context information but the order of words will lead to bias; the text analysis method based on convolutional neural network can obtain important features of text through pooling but it is difficult to obtain contextual information. Aiming at the above problems, this paper proposes a sentiment analysis method based on the combination of R-CNN and C-RNN based on a fusion gate. Firstly, RNN and CNN are combined in different ways to alleviate the shortcomings of the two, and the sub-analysis network R-CNN and C-RNN finally combine the two networks through the gating unit to form the final analysis model. We performed experiments on different data sets to verify the effectiveness of the method.
THE CONNECTION BETWEEN DIFFERENT NEUROTRANSMITTERS INVOLVED IN COGNITIVE PROCESSES

Memory is one of the most complex cognitive functions.Conventionally, one the most important neurotransmitter system implicated in its regulation is the cholinergic system.However, literature certifies that other substances can be associated with certain memory processes as well, for example, the dopaminergic system, the NMDA receptors and the serotoninergic receptors, either direct, or by forming receptor complexes.Aside from these, the adrenergic system has been proven to work synergistically with the cholinergic system.Proving the interaction between nicotine and other neurotransmitters, as well as their involvement in cognition, opened a number of therapeutic possibilities for degenerative diseases such as Alzheimer's disease, Parkinson's disease and schizophrenia.That is to say, future research needs to focus on the potential therapies using these interactions. Rezumat
Robust Image Watermarking Based on Two-Layer Visual Saliency-Induced JND Profile

Visual saliency (VS) is an important mechanism for defining which areas of an image will attract more attention of the HVS.Thus, VS can be employed to weight the just noticeable difference (JND) with different attention levels.Some VS-based JND profiles have been proposed in the DCT domain, which used the bottom-up features, such as luminance and texture only.Recently, the research about saliency detection has shown that a better saliency model considering both bottom-up features and top-down features will lead to a significant improvement for the overall saliency detection performance.In this paper, we propose a novel two-layer VS-induced JND profile, which is composed of the bottom-up features and the top-down feature extracted from DCT blocks in the transformed domain.In this model, the luminance and texture features are adapted to calculate the bottom-up features maps, while the top-down feature of focus is used to guide the generation of final salient regions since the camerapersons are used to facilitate the attention regions in focus.The proposed two-layer saliency-induced JND model is further applied to modulate the quantization step in the watermarking framework, which can make full use of its individual merits to achieve a better tradeoff between fidelity and robustness.The experimental results show that the proposed scheme has superior performance than the previous watermarking schemes.
IVUS images segmentation using spatial fuzzy clustering and hierarchical level set evolution

The detection of the lumen and media-adventitia (MA) borders in intravascular ultrasound (IVUS) images is crucial for quantifying plaque burdens. The challenge of the segmentation work mainly roots in various artifacts in the image. Most of the published methods involve the establishment of complex models but do not behave well on images with artifacts. In this study, aiming at automatically delineating borders in IVUS frames acquired by 20 MHz ultrasound probes, we present a fuzzy clustering-initialized hierarchical level set evolution (FC-HLSE) method. A cluster selection strategy based on the spatial fuzzy c-means (FCM) is proposed to generate the initial value and regularization term of the level set evolution (LSE). The contour convergence splits into two LSE steps between which an ingenious contour extraction (consisting of the morphological processing, the seek and linear interpolation, the gradient-based and circular fitting-based refinement) is carried out. We evaluate the proposed methodology on the publicly available 435 images by comparing auto-segmented results with the ground truth. The performance of the method is quantified using the Jaccard measure (JM), the Hausdorff distance (HD), the percentage of area difference (PAD), the linear regression and Bland-Altman analysis. Results reveal that our method can handle images with or without artifacts. The algorithm is able to extract the lumen/MA border with the JM of 0.90/0.89, the HD of 0.31/0.40 mm, the PAD of 0.07/0.08 in average, which is better in some cases compared with several state-of-the-art methods.
Improved intuitionistic fuzzy composite relation and its application to medical diagnostic process

In this paper, we study the De et al.'s approach for the application of intuitionistic fuzzy relation (i.e., max-min-max composite relation), and improve the approach for better output.The validity of the improved intuitionistic fuzzy composite relation is carried out in comparison to De et al.'s approach using numerical experiments.It is shown that the improved intuitionistic fuzzy composite relation yields a better output.Finally, an application of the improved approach to medical diagnostic process is carried out using a hypothetical medical database.This improved intuitionistic fuzzy composite relation could be used as a sustainable approach in applying intuitionistic fuzzy sets to other real-life decision-making problems.
Developing Secure Services for IoT with OP-TEE: A First Look at Performance and Usability

The implementation, deployment and testing of secure services for Internet of Things devices is nowadays still at an early stage. Several frameworks have recently emerged to help developers realize such services, abstracting the complexity of the many types of underlying hardware platforms and software libraries. Assessing the performance and usability of a given framework remains challenging, as they are largely influenced by the application and workload considered, as well as the target hardware. Since 15 years, Arm processors are providing support for TrustZone, a set of security instructions that realize a trusted execution environment inside the processor. Op-Tee is a free-software framework to implement trusted applications and services for TrustZone. In this short paper we show how one can leverage Op-Tee for implementing a secure service (i.e., a key-value store). We deploy and evaluate the performance of this trusted service on common Raspberry Pi hardware platforms. We report our experimental results with the data store and also compare it against Op-Tee’s built-in secure storage.
The development of digital literacy of schoolchildren in conditions of creating a digital educational environment

Т. А. Бороненко, А. В. Кайсина, В
Challenges in NoSQL-Based Distributed Data Storage: A Systematic Literature Review

Key-Value stores (KVSs) are the most flexible and simplest model of NoSQL databases, which have become highly popular over the last few years due to their salient features such as availability, portability, reliability, and low operational cost. From the perspective of software engineering, the chief obstacle for KVSs is to achieve software quality attributes (consistency, throughput, latency, security, performance, load balancing, and query processing) to ensure quality. The presented research is a Systematic Literature Review (SLR) to find the state-of-the-art research in the KVS domain, and through doing so determine the major challenges and solutions. This work reviews the 45 papers between 2010–2018 that were found to be closely relevant to our study area. The results show that performance is addressed in 31% of the studies, consistency is addressed in 20% of the studies, latency and throughput are addressed in 16% of the studies, query processing is addressed in 13% of studies, security is addressed in 11% of the studies, and load balancing is addressed in 9% of the studies. Different models are used for execution. The indexing technique was used in 20% of the studies, the hashing technique was used in 13% of the studies, the caching and security techniques were used together in 9% of the studies, the batching technique was used in 5% of the studies, the encoding techniques and Paxos technique were used together in 4% of the studies, and 36% of the studies used other techniques. This systematic review will enable researchers to design key-value stores as efficient storage. Regarding future collaborations, trust and privacy are the quality attributes that can be addressed; KVS is an emerging facet due to its widespread popularity, opening the way to deploy it with proper protection.
Design and Analysis of a True Random Number Generator Based on GSR Signals for Body Sensor Networks

Today, medical equipment or general-purpose devices such as smart-watches or smart-textiles can acquire a person's vital signs. Regardless of the type of device and its purpose, they are all equipped with one or more sensors and often have wireless connectivity. Due to the transmission of sensitive data through the insecure radio channel and the need to ensure exclusive access to authorised entities, security mechanisms and cryptographic primitives must be incorporated onboard these devices. Random number generators are one such necessary cryptographic primitive. Motivated by this, we propose a True Random Number Generator (TRNG) that makes use of the GSR signal measured by a sensor on the body. After an exhaustive analysis of both the entropy source and the randomness of the output, we can conclude that the output generated by the proposed TRNG behaves as that produced by a random variable. Besides, and in comparison with the previous proposals, the performance offered is much higher than that of the earlier works.
Deriving Cyber Security Risks from Human and Organizational Factors – A Socio-technical Approach

Cyber security risks are socio-technical in nature. They result not just from technical vulnerabilities but also, more fundamentally, from the degradation of working practices over time – which move an organization across the boundary of secure practice to a place where attacks will not only succeed, but also have a significantly greater impact on the organization. Yet current risk analysis and management methodologies are not designed to detect these kinds of systemic risks. We present an approach, devised in the field, to deriving these risks – using a qualitative research methodology, akin to grounded theory, but based on preset coding descriptors. This allows organizational and individual behavior identified during interviews, observations or document research to be thematically analyzed, collated and mapped to potential risks, linked to poor working practices. The resulting risk factors can be linked together forming “risk narratives”, showing how the degradation of working practices in one part of the organization can contribute to undermining its ability to respond to cyber security threats in another part of the organization.
Imperceptible and secure image watermarking using DCT and random spread technique

Watermarking is a copyright protection technique, while cryptography is a message encoding technique. Imperceptibility, robustness, and safety are aspects that are often investigated in watermarking. Cryptography can be implemented to increase watermark security. Beaufort cipher is the algorithm proposed in this research to encrypt watermark. The new idea proposed in this research is the utilization of Beaufort key for watermark encryption process as well as for spread watermark when inserted as PN Sequence substitute with the aim to improve imperceptibility and security aspects. Where PN Sequence is widely used in spread spectrum watermarking technique. Based on the experimental results and testing of the proposed method proved that imperceptibility and watermark security are increased. Improved imperceptibility measured by PSNR rose by about 5dB and so did the MSE score better. Robustness aspect is also maintained which has been proven by the excellent value of NCC.
An Empirical Study for Detecting Fake Facebook Profiles Using Supervised Mining Techniques

Our social life and the way of people communicate are greatly affected by the social media technologies. The variety of stand-alone and built-in social media services such as Facebook, Twitter, LinkedIn, and alike facilitate users to create highly interactive platforms. However, these overwhelming technologies made us sank in an enormous amount of information. Recently, Facebook exposed data on 50 million Facebook unaware users for analytical purposes. Fake profiles are also used by Scammers to infiltrate networks of friends to wreak all sorts of havoc as stealing valuable information, financial fraud, or entering other user's social graph. In this paper, we turn our focus to Facebook fake profiles, and proposed a smart system (FBChecker) that enables users to check if any Facebook profile is fake. To achieve that, FBChecker utilizes the data mining approach to analyze and classify a set of behavioral and informational attributes provided in the personal profiles. Specifically, we empirically examine these attributes using four supervised data mining algorithms (e.g., k-NN, decision tree, SVM, and naïve Bayes) to determine how successfully we can recognize the fake profiles. To demonstrate the validity of our conceptual work, the selected classifiers have been implemented using RapidMiner data science platform with a dataset of 200 profiles collected from the authors’ profile and a honeypot page. Two experiments are developed; in the first one, the k-NN schema is applied as an estimator model for imputation the missing data with substituted values, whereas in the second experiment a filtering operator is applied to exclude the profiles with missing values. Results showed high accuracy rate with the all classifiers, however, the SVM outperforms other classifiers with an accuracy rate of 98.0% followed by Naïve Bayes.
Research on New English Mobile Teaching Mode under the Impact of Mobile Internet Age

The rapid development of mobile technology has provided a new opportunity for the reform of English listening and speaking teaching mode.Mobile technology enables learners to use mobile devices to learn anywhere, anytime.Mobile technology assists language learning with unparalleled advantages in extending learning time and space, enriching learning interactions, and improving learning efficiency.This paper introduces the concept and characteristics of mobile learning and smartphones, the theoretical basis of mobile learning and related technical support, through the feasibility analysis of mobile phone-based mobile learning in supporting English teaching.At the same time, a new English teaching mode under the background of mobile internet is summarized and designed.It provides a realistic basis for the design of mobile learning teaching mode with smartphone as the terminal, which adds practical application cases for mobile learning theory, solves the problem of lack of contextual interaction in traditional English teaching, and limitations of learning time and place.
Intelligent trajectory planning model for electric vehicle in unknown environment

Specific to the trajectory planning of intelligent electric vehicles with multi-constraints in some unknown environment, using rolling optimization principle of predictive model control for reference, an online trajectory planning algorithm based on a rolling window is proposed. Making full use of the local environmental information measured in real-time by intelligent electric vehicle, the path of continuous curvature is generated in the planning window by the Quintic Bézier curve and is optimized by the sequential quadratic planning algorithm. Furthermore, the optimal speed sequence for a given path is planed using the S-shaped curve velocity algorithm. Combining with the actual application scenario of an intelligent electric vehicle, the algorithm is simulated, and the trajectory planning experiment is carried out based on the actual environmental data. The results show that a safe trajectory with continuous curvature and optimal speed can be produced by using this algorithm in some unknown environment scenarios.
Skill Needs for Early Career Researchers—A Text Mining Approach

Research and development activities are one of the main drivers for progress, economic growth and wellbeing in many societies. This article proposes a text mining approach applied to a large amount of data extracted from job vacancies advertisements, aiming to shed light on the main skills and demands that characterize first stage research positions in Europe. Results show that data handling and processing skills are essential for early career researchers, irrespective of their research field. Also, as many analyzed first stage research positions are connected to universities, they include teaching activities to a great extent. Management of time, risks, projects, and resources plays an important part in the job requirements included in the analyzed advertisements. Such information is relevant not only for early career researchers who perform job selection taking into account the match of possessed skills with the required ones, but also for educational institutions that are responsible for skills development of the future R&amp;D professionals.
Prototype Reminding for Continual Learning.

Continual learning refers to the ability to acquire and transfer knowledge without catastrophically forgetting what was previously learned. In this work, we consider \emph{few-shot} continual learning in classification tasks, and we propose a novel method, Variational Prototype Replays, that efficiently consolidates and recalls previous knowledge to avoid catastrophic forgetting. In each classification task, our method learns a set of variational prototypes with their means and variances, where embedding of the samples from the same class can be represented in a prototypical distribution and class-representative prototypes are separated apart. To alleviate catastrophic forgetting, our method replays one sample per class from previous tasks, and correspondingly matches newly predicted embeddings to their nearest class-representative prototypes stored from previous tasks. Compared with recent continual learning approaches, our method can readily adapt to new tasks with more classes without requiring the addition of new units. Furthermore, our method is more memory efficient since only class-representative prototypes with their means and variances, as well as only one sample per class from previous tasks need to be stored. Without tampering with the performance on initial tasks, our method learns novel concepts given a few training examples of each class in new tasks.
Systolic Cube

3D convolutional neural networks (CNN) are gaining popularity in action/activity analysis. Compared to 2D convolutions that share the filters in 2D spatial domain, 3D convolutions further reuse filters in the temporal dimension to capture time-domain features. Prior works on specialized 3D-CNN accelerators employ additional on-chip memories and multi-cluster architecture to reuse data among the process element (PE) arrays, which is too expensive for low-power chips. Instead of harvesting in-memory locality, we propose a 3D systolic cube architecture to exploit the spatial-and-temporal localities of 3D CNNs, which moves the reusable data in-between PEs connected via a 3D-cube Network-on-Chip. Evaluation shows that systolic-cube contributes to considerable energy-efficiency boost for activity-recognition benchmarks.
A Dynamic Speaker Model for Conversational Interactions

Hao Cheng, Hao Fang, Mari Ostendorf. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Layer-Level Knowledge Distillation for Deep Neural Network Learning

Motivated by the recently developed distillation approaches that aim to obtain small and fast-to-execute models, in this paper a novel Layer Selectivity Learning (LSL) framework is proposed for learning deep models. We firstly use an asymmetric dual-model learning framework, called Auxiliary Structure Learning (ASL), to train a small model with the help of a larger and well-trained model. Then, the intermediate layer selection scheme, called the Layer Selectivity Procedure (LSP), is exploited to determine the corresponding intermediate layers of source and target models. The LSP is achieved by two novel matrices, the layered inter-class Gram matrix and the inter-layered Gram matrix, to evaluate the diversity and discrimination of feature maps. The experimental results, demonstrated using three publicly available datasets, present the superior performance of model training using the LSL deep model learning framework.
Asymmetric Cryptosystem Using Improved Equal Modulus Decomposition in Cylindrical Diffraction Domain

We present a simple and efficient asymmetric cryptosystem using an improved equal modulus decomposition (EMD) in the cylindrical diffraction domain. Being different from a cryptosystem based on the conventional EMD, whose outputs are complex-valued, in the planer diffraction domain, our proposed cryptosystem uses the improved EMD and the cylindrical diffraction. Therefore, the proposed system achieves high robustness against the special attack based on iterative retrieval algorithms. Moreover, it has a merit of real-valued outputs due to the improved EMD. In addition, our proposed asymmetric cryptosystem achieves comparative advantages on encryption time, optical decryption complexity, and the data amount of storage or transmission when it is compared with the conventional EMD-based cryptosystems. The simulation results verify the validity of the proposed system.