Reviewerly/exHarmony huggingface dataset:
Combining Deep Neural Network with Traditional Classifier to Recognize Facial Expressions

Facial expressions are important in people's daily communications. Recognising facial expressions also has many important applications in the areas such as healthcare and e-learning. Existing facial expression recognition systems have problems such as background interference. Furthermore, systems using traditional approaches like SVM (Support Vector Machine) have weakness in dealing with unseen images. Systems using deep neural network have problems such as requirement for GPU, longer training time and requirement for large memory. To overcome the shortcomings of pure deep neural network and traditional facial recognition approaches, this paper presents a new facial expression recognition approach which has image pre-processing techniques to remove unnecessary background information and combines deep neural network ResNet50 and a traditional classifier-- the multiclass model for Support Vector Machine to recognise facial expressions. The proposed approach has better recognition accuracy than traditional approaches like Support Vector Machine and doesn't need GPU. We have compared 3 proposed frameworks with a traditional SVM approach against the Karolinska Directed Emotional Faces (KDEF) Database, the Japanese Female Facial Expression (JAFFE) Database and the extended Cohn-Kanade dataset (CK+), respectively. The experiment results show that the features extracted from the layer 49Relu have the best performance for these three datasets.
A Novel Residual Dense Pyramid Network for&#x0D; Image Dehazing

Recently, convolutional neural network (CNN) based on the encoder-decoder structurehave been successfully applied to image dehazing. However, these CNN based dehazing methodshave two limitations: First, these dehazing models are large in size with enormous parameters, whichnot only consumes much GPU memory, but also is hard to train from scratch. Second, these models,which ignore the structural information at different resolutions of intermediate layers, cannot captureinformative texture and edge information for dehazing by stacking more layers. In this paper, wepropose a light-weight end-to-end network named the residual dense pyramid network (RDPN)to address the above problems. To exploit the structural information at different resolutions ofintermediate layers fully, a new residual dense pyramid (RDP) is proposed as a building block.By introducing a dense information fusion layer and the residual learning module, the RDP canmaximize the information flow and extract local features. Furthermore, the RDP further learnsthe structural information from intermediate layers via a multiscale pyramid fusion mechanism.To reduce the number of network parameters and to ease the training process, we use one RDPin the encoder and two RDPs in the decoder, following a multilevel pyramid pooling layer forincorporating global context features before estimating the final result. The extensive experimentalresults on a synthetic dataset and real-world images demonstrate that the new RDPN achievesfavourable performance compared with some state-of-the-art methods, e.g., the recent denselyconnected pyramid dehazing network, the all-in-one dehazing network, the enhanced pix2pixdehazing network, pixel-based alpha blending, artificial multi-exposure image fusions and thegenetic programming estimator, in terms of accuracy, run time and number of parameters. To bespecific, RDPN outperforms all of the above methods in terms of PSNR by at least 4.25 dB. The runtime of the proposed method is 0.021 s, and the number of parameters is 1,534,799, only 6% of thatused by the densely connected pyramid dehazing network.
Comparison of three focus sensors for optical topography measurement of rough surfaces

The study compares three variants of focus sensors designed for the optical topography measurement of rough surface specimens with submicron accuracy. We present a theoretical analysis of the focus sensor principles and the experimental measurements with a single point laser probe. A low coherent illumination beam was provided by a monochromatic laser source and a rotating diffuser, which reduced the speckles generated by the rough surface. The reflected beam was modulated by three specific optical elements (axicon, double wedge prism, four spherical lenses) realized by a spatial light modulator. A digital camera detected the output intensity patterns that were evaluated by the intensity centroid method. The results showed a good coincidence of the surface profiles obtained by the three sensor variants with the root-mean-square deviations below one micron. We discuss the results obtained for several specimens with various surface roughness and compare the differences between the three focus sensor variants.
Makespan–Cost–Reliability-Optimized Workflow Scheduling Using Evolutionary Techniques in Clouds

Most popular scientific workflow systems can now support the deployment of tasks to the cloud. The execution of workflow on cloud has become a multi-objective scheduling in order to meet the needs of users in many aspects. Cost and makespan are considered to be the two most important objects. In addition to these, there are some other Quality-of-Service (QoS) parameters including system reliability, energy consumption and so on. Here, we focus on three objectives: cost, makespan and system reliability. In this paper, we propose a Multi-objective Evolutionary Algorithm on the Cloud (MEAC). In the algorithm, we design some novel schemes including problem-specific encoding and also evolutionary operations, such as crossover and mutation. Simulations on real-world and random workflows are conducted and the results show that MEAC can get on average about 5% higher hypervolume value than some other workflow scheduling algorithms.
Deploying Hash Tables on Die-Stacked High Bandwidth Memory

Die-stacked High Bandwidth Memory (HBM) is an emerging memory architecture that achieves much higher memory bandwidth with similar or lower memory access latency and smaller capacity, compared with main memories. Memory-intensive database algorithms may potentially benefit from these new features. Due to the small capacity of such die-stacked HBM, a hybrid memory architecture comprising both main memories and HBMs is promising for main-memory databases. As a starting point, we study a key data structure, hash tables, in such a hybrid memory architecture. In a large hash table distributed among multiple NUMA (non-uniform memory accesses) nodes and accessed by multiple CPU sockets, the data placement and memory access scheduling for workload balance are challenging due to the random memory accesses involved that are difficult to predict. In this work, we propose a deployment algorithm that first estimates the memory access cost and then places data in a way that exploits the hybrid memory architecture in a balanced manner. Evaluation results show that the proposed deployment is able to achieve up to three times performance improvement over the state-of-the-art NUMA-aware scheduling algorithms for hash joins in relational databases on present and simulated future hybrid memory architectures.
Testing Stylistic Interventions to Reduce Emotional Impact of Content Moderation Workers

With the rise in user generated content, there is a greater need for content reviews. While machines and technology play a critical role in content moderation, the need for manual reviews still remains. It is known that such manual reviews could be emotionally challenging. We test the effects of simple interventions like grayscaling and blurring to reduce the emotional impact of such reviews. We demonstrate this by bringing in interventions in a live content review setup thus allowing us to maximize external validity. We use a pre-test post-test experiment design and measure review quality, average handling time and emotional affect using the PANAS scale. We find that simple grayscale transformations can provide an easy to implement and use solution that can significantly change the emotional impact of content reviews. We observe, however, that a full blur intervention can be challenging to reviewers.
Study of Russian and the UK Legislations in Combating Digital Crimes

The purpose of this article is to formulate proposals to improve the criminal law of the Russian Federation on liability for digital crimes on the basis of a comparative legal study of the UK legislation in this area.The work provides a detailed comparative legal analysis of the UK legislation in the field of combating digital crimes.Based on the study, we proposed some mechanisms for ensuring the security of relations in the digital field.The cross-border nature of these attacks among the main tasks includes unification of legal norms governing the IT sphere, creation of a single mechanism to hold accountable for attacks in the IT sphere worldwide, regardless of geopolitical boundaries.A separate direction of the internal criminal policy of all countries shall be the creation of effective mechanisms for applying the provisions of legislation in the digital sphere; any legislation, even the most progressive, is useless and only declarative in nature without the necessary mechanism for its application.Evidence of the person's guilt is required in order to prosecute.The informational nature of infringements necessitates expanding the boundaries of the powers of law enforcement agencies, which inextricably leads to the problem of finding a balance between observing the freedoms of citizens in the information space and ensuring the universal information security.In the UK, as in all countries of the world, the answer to this question has not yet been found.
Global Autoregressive Models for Data-Efficient Sequence Learning

Standard autoregressive seq2seq models are easily trained by max-likelihood, but tend to show poor results under small-data conditions.We introduce a class of seq2seq models, GAMs (Global Autoregressive Models), which combine an autoregressive component with a log-linear component, allowing the use of global a priori features to compensate for lack of data.We train these models in two steps.In the first step, we obtain an unnormalized GAM that maximizes the likelihood of the data, but is improper for fast inference or evaluation.In the second step, we use this GAM to train (by distillation) a second autoregressive model that approximates the normalized distribution associated with the GAM, and can be used for fast inference and evaluation.Our experiments focus on language modelling under synthetic conditions and show a strong perplexity reduction of using the second autoregressive model over the standard one.1 To borrow terminology from Reinforcement Learning (RL) (Sutton and Barto, 2018), such NLP models work by "imitation learning", without any representation of "objectives" to be realized.While this defect can be mitigated in the presence of large training sets, it can become serious when this condition is not met.
An anti-noise ensemble algorithm for imbalance classification

Ensemble learning is an excellent method for imbalance classification. However, the existing ensemble methods often ignore noise in the dataset, which may reduce the accuracy of classifier. In this paper, we propose a density-based undersampling algorithm (DBU) and integrate it with AdaBoost (DBUBo ost) to improve the classification performance. The major contribution of this paper is the development of an undersampling strategy for dealing with both noise and class imbalance problem. We first divide the examples from each class into three categories: useful examples, noise and potentially useful examples. Then we introduce a similarity coefficient to distinguish the examples from each category. Through a selection mechanism based on similarity coefficients, we retain the useful examples and remove the noisy examples. To demonstrate the effectiveness, we compare our DBUBoost with four ensemble methods and three anti-noise methods. The experiments were conducted on 9 KEEL datasets and their noise-modified datasets. Experimental results have shown that our DBUBoost performs better than other state-of-the-art methods.
Internet of Medical Things: Services, Applications and Technologies

With constant evolution of technology, Internet of Things (IoT) is drawing much attention in recent years in various application domains from Wearables and Agriculture to Smart Cities, with Healthcare being one of the arenas. With Internet of Things in action, a new generation of healthcare is being introduced with assuring outlooks. This paper reviews the progress that has been made with the successful amalgamation of internet and healthcare department. Further this paper discusses various types of IoT based healthcare services and applications that have been introduced so far along with different prospects and scope of future research.
Bi-objective Motion Planning Approach for Safe Motions: Application to a Collaborative Robot

This paper presents a new bi-objective safety-oriented path planning strategy for robotic manipulators. Integrated into a sampling-based algorithm, our approach can successfully enhance the task safety by guiding the expansion of the path towards the safest configurations. Our safety notion consists of avoiding dangerous situations, e.g. being very close to the obstacles, human awareness, e.g. being as much as possible in the human vision field, as well as ensuring human safety by being as far as possible from human with hierarchical priority between human body parts. Experimental validations are conducted in simulation and on the real Baxter research robot. They revealed the efficiency of the proposed method, mainly in the case of a collaborative robot sharing the workspace with humans.
A Color-Image Encryption Scheme Using a 2D Chaotic System and DNA Coding

This paper proposes a method of encrypting images with password protection for secure sharing based on deoxyribonucleic acid (DNA) sequence operations and the tangent-delay ellipse reflecting the cavity-map system (TD-ERCS). The initial values of the TD-ERCS system are generated from a user’s password, and the TD-ERCS system is used to scramble the pixel locations of the R, G, and B matrices of the original image. Next, three DNA-sequence matrices are generated by encoding the permuted color image such that it can be transformed into three matrices. Then, the TD-ERCS system is employed to generate three chaotic sequences before encoding the DNA into the three matrices. Thereafter, a DNA exclusive OR (XOR) operation is executed between the DNA sequences of the permuted image and the DNA sequences generated by the TD-ERCS system to produce three encrypted scrambled matrices. Finally, the matrices of the DNA sequences are decoded, and the R, G, and B channels are recombined to form an encrypted color image. The results of simulation and security tests reveal that the proposed algorithm offers robust encryption and demonstrates the ability to resist exhaustive, statistical, and differential attacks.
Digital communication, social media, and Englishes

Abstract Scholarship in world Englishes has long been interested in the impact of digital media on the spread of English worldwide. This special issue on ‘World Englishes and digital media’ examines how English(es) are positioned and used in relation to other languages in digital communication, what pragmatic functions English and other languages serve, and how various linguistic choices affect identity work in the context of digital communication. It is hoped that this special issue will make an important contribution to research in this field.
Edge Gradient-Based Active Learning for Hyperspectral Image Classification

In active learning (AL)-based remote sensing (RS) image classification tasks, the acquisition of labeled data depends not only on the informativeness and representativeness measured in feature space but also on the spatial distributions and relations in an image plane. However, very few studies have investigated the advantages of integrating spatial constraints into the AL paradigm. Hence, under the basic assumption “instances that are difficult to classify are usually located around edges between different objects or land-cover types,” edge gradient information was integrated into the conventional AL paradigm using popular uncertainty and diversity measurements. The experimental results with two real hyperspectral images confirmed the advantages of the proposed edge gradient-based AL (EGAL) approach from the aspects of fast convergence and computationally efficient operation.
Spatial Distribution of Minor Elements in the Tazlău River Sediments: Source Identification and Evaluation of Ecological Risk

Minor elements received more attention in recent years due to their contamination susceptibility and environmental impact. Surface sediment samples were collected from 29 sites and total contents of eight minor elements (Cr, Co, Ni, Cu, Zn, As, Cd, and Pb) were investigated in order to determine the geostatistical distribution and to predict ecological implications. The relationship between metals and ecological implications was analyzed by using the geochemical normalization approach and ecological prediction indicators such as the enrichment factor (EF), the contamination degree (CD), the environmental toxicity quotient (ETQ), and the health risk assessment. Based on the studied toxic metals, it was observed that the most toxic element in Tazlău River sediments is Cr. The assessment results of carcinogenic and non-carcinogenic risks via dermal contact indicate that the study area shows no human health risk. The correlation matrix and principal component analysis (PCA) provide an overview of the major sources, anthropogenic versus geogenic, where Cr and Cd mainly originate from anthropogenic sources, while Pb is derived from a geogenic source. The approaches used in this study will provide a baseline regarding the accumulation of minor elements in the sediment and will be useful for other studies to easily identify the major contaminates and to estimate the health human risk.
Virtual and augmented reality enhanced by touch

Conventional technologies for virtual and augmented reality simulate interactive experiences through visual and auditory stimuli. A technology that adds sensations of touch could find uses in areas from gaming to prosthetic feedback. Skin-integrated wireless touch-based interfaces.
Determination of Student Opinions on Usage of Social Media and Mobile Tools in Student-Teacher, Student-Student Communication

Since Depending on technological advances, development of&#x0D; social media and mobile applications today reshape communication, education and teaching system. The extent to which social media and mobile applications which are especially used by new generation youth frequently will be beneficial in student-teacher and student-student communication is becoming more and more important. This research is based on the opinions of students on the usage of social media and mobile applications in education which take considerable time of teachers and students, social networks and mobile applications were used with the purpose of communication in science and communication technologies for 12 weeks. Thirty-five teacher candidates attended the study. At the end of the application interviews were held with 20 students and according&#x0D; to the findings obtained, an effort was paid to determine the impact of social media and mobile applications in student-teacher and student-student communication. Discussions and recommendations on obtained findings were presented.
Neural Task Scheduling with Reinforcement Learning for Fog Computing Systems

A key challenge in the design space of fog computing systems is online task scheduling, i.e., to allocate multiple types of resources to pending tasks that are constantly generated from end devices. It is challenging because of the online, intensive, and time-varying nature of task arrival, the varieties in the amounts and durations of task resource demands, as well as the unattainability of such priori information due to the online nature of task arrivals. To handle such uncertainties, an online task scheduler design with flexibility to process sequences of task arrivals with variable lengths is highly demanded. Existing works have adopted deep reinforcement learning (DRL) techniques to develop online task schedulers in a data-driven fashion by constructing them as neural networks and training using empirical data. However, hindered by the intrinsic restriction of the underlying neural network design, such schedulers often suffer from poor flexibility that may induce resource under- utilization, or overly fine-grained control that induces considerable overheads. In this paper, we address the above challenges by integrating pointer network architecture with the scheduler design, and proposing Neural Task Scheduling (NTS), an online flexible task scheduling scheme which effectively reduces average task slowdown to facilitate best quality-of-service. Simulation results show that NTS consistently outperforms state-of-the-art schemes under different settings.
VaryBlock: A Novel Approach for Object Detection in Remote Sensed Images

In recent years, the research on optical remote sensing images has received greater and greater attention. Object detection, as one of the most challenging tasks in the area of remote sensing, has been remarkably promoted by convolutional neural network (CNN)-based methods like You Only Look Once (YOLO) and Faster R-CNN. However, due to the complexity of backgrounds and the distinctive object distribution, directly applying these general object detection methods to the remote sensing object detection usually renders poor performance. To tackle this problem, a highly efficient and robust framework based on YOLO is proposed. We devise and integrate VaryBlock to the architecture which effectively offsets some of the information loss caused by downsampling. In addition, some techniques are utilized to facilitate the performance and to avoid overfitting. Experimental results show that our proposed method can enormously improve the mean average precision by a large margin on the NWPU VHR-10 dataset.