Reviewerly/exHarmony huggingface dataset:
A Face Recognition Technique by Representation Learning with Quadruplets

Face recognition is a key task of computer vision research that has been employed for various purposes. There are many studies in the literature that attempts for solving this problem. Especially in recent years, deep learning methods have been utilized. With these methods, algorithms can reach higher accuracy levels. In addition to deep learning, the use of representative learning is used to aggregate face images in multidimensional metric spaces. While clustering, the similarities in the faces are generally utilized with the help of Siamese or triplet cost functions. In the studies following this approach, the recognition of the faces is done only by using identity labels. However, in this paper, we propose the method which is utilizing both the identity labels and the attribute labels such as the light intensity of the images and the direction of the face. Therefore, two labels are available for each image. In this study, the images in the Yale-B face dataset are used for training a state-of-the-art architecture, ResNet with the labels which are re-organized by us. In order to benefit from two labels during the training, we employ quadruplet selection methods and a quadratic cost function. The contribution of secondary labels on recognition accuracy was measured in experiments. In addition, the methods for selecting quadruplets are compared.
Comparative study of methods to obtain the number of hidden neurons of an auto-encoder in a high-dimensionality context

Fourteen formulas from the state-of-art were used in this paper to find the optimal number of neurons in the hidden layer of an autoencoder neural network. The latter is employed to reduce the dataset dimension on high-dimensionality scenarios with not significant reduction in classification accuracy in comparison to the use of the whole dataset. A Deep Learning neural network was employed to analyze the effectiveness of the studied formulas in classification terms (accuracy). Eight high-dimensional datasets were processed in an experimental set in order to assess this proposal. Results presented in this work show that formula 13 (used to find the number of hidden neurons of the auto-encoder) is effective to reduce the data dimensionality without a statistically significant reduction of the classification performance, as it was confirmed by the Freidman test and the Holm's post-hoc test.
HVLSeg: An Ensemble Model for Instance Segmentation on Satellite Images

Automatic object detection and segmentation algorithms are essential for automatic analysis of high resolution remote sensing images. In order to make automatic analysis more healthy in remote sensing images, the sensitive boundaries of the objects detected in the images should also be segmented. In this study, an approach for automatic detection of aircrafts, ships, buildings/hangars, POL and heliports in electro-optic satellite images is proposed. The developed method is a hybrid approach in which multiple models are used together depending on the target type . The proposed approach won the General Classification first phlce in the automatic target detection competition (Innovative Software Competition) in satellite images organized by SSB and METU OGAM in 2019.
Decentralized machine learning using compressed push-pull averaging

For decentralized learning algorithms communication efficiency is a central issue. On the one hand, good machine learning models require more and more parameters. On the other hand, there is a relatively large cost for transferring data via P2P channels due to bandwidth and unreliability issues. Here, we propose a novel compression mechanism for P2P machine learning that is based on the application of stateful codecs over P2P links. In addition, we also rely on transfer learning for extra compression. This means that we train a relatively small model on top of a high quality pre-trained feature set that is fixed. We demonstrate these contributions through an experimental analysis over a real smartphone trace.
ANALISIS RESISTANCE FACTOR DALAM PENERAPAN PEMBELAJARAN BERBASIS ELEKTRONIK MENGGUNAKAN PENDEKATAN HUMAN-CENTERED-APPROACH

E-learning becomes an alternative way to transfer information effectively and efficiently in adopting information technology as a means of teaching and learning. Information technology applied must be Acceptable, if it’s not, it will display the behavior of resistance to change. This study uses the Human-Centered Approach-related resistance to change in preparation of the questionnaire instrument and using factor analysis in data processing and analysis. The process of taking samples using simple random sampling technique in which the taking of samples of the population that was randomly without regard to strata that exist in the population. Factor analysis model used is Principal Component Analysis (PCA). Phases of analysis of factors such as the correlation matrix, factor extraction, factor rotation, and interpretation of factors. Results from this research can be concluded that these factors play a role in the emergence of resistance to change in sequence from the most dominant factor Competence Factor, Motivation and Planning Implementation Factors, Communications in Leadership Factors, Culture Factor, Situations and Conditions Factors, Personality Factors, and Security Factor. It would be better if the sampling is not only for teachers but also involves students and mapped based on certain criteria which are equipped with the characteristics of each sample.
Validation of Corrupted Medical Image Labelling using Deep Neural Network

Introduction: Deep Neural Networks (DNN) has been successfully applied in a variety of medical image analysis. However, in building a good DNN model, it requires validated label from medical experts. The task of getting labels validated by the medical expert is quite a challenge due to the complexity of the image as well as large variability between the experts. Thus, this research investigates the effect of corrupted medical label on deep neural network for analysing diseases. Materials and method: In investigating this issue, a chest X-ray dataset comprising of 2560 images with 14 multilabel diseases from the National Institutes of Health (NIH) are examined. The DNN models that are explored are MobileNet, which is a base network that uses a depth-wise separable convolutional in building lightweight DNN and Xception, which is also utilised depth-wise separable convolutional with inception modules as an intermediate step in between regular convolutional and the depthwise convolutional. Different training and testing sizes are presented to the models with different ranges of corrupted labels from 10% to 50%. The training and validation accuracy is compared for each of the test cases together with other statistical analysis. Results: Based on the conducted experiments, it can be highlighted that both models can classify the diseases with the accuracy of more than 80% if the images have been previously trained by the model for different sizes of the corrupted labels. For the unseen or previously untrained images, the models can reach the accuracy of 70% and as the sizes of corrupted label increases, the accuracy will decreases as low as 50%. Conclusion: To conclude, corrupted labels have a critical effect on the DNN models. Thus, it is very essential to study the effect of the corrupted medical images label to ensure the reliability of the DNN models for medical imaging.
New Challenges for the Right to Privacy During the COVID-19 Pandemic - The Outline of the Problem

At the beginning 2020, the vast majority of countries worldwide were forced to confront and face the SARS-CoV-2 pandemic.The rapid spread of this new disease resulted in radical changes in the basic principles of the functioning and organization of states and entire societies.The novel circumstances which both nation states and the international community are facing induce reflection on the need of redefining the right to privacy.This paper aims to answer, whether the current situation will lead to lasting changes in the way privacy is now perceived in Europe and what threats may be associated with the possible changes.
A Genetic Algorithm Approach for Inter and Intra Homogeneous Grouping Considering Multi-student Characteristics

This paper addresses the problem of group formation in collaborative learning by considering the students' characteristics.The proposed solution is based on a Genetic Algorithm (GA), which minimizes an objective function that has two main aims.Indeed, the proposed GA's fitness function helps to achieve two objectives: Fairness in the formation of different groups, resulting in intergroup homogeneity, and a low gap in the levels of students within a group, which corresponds to intragroup homogeneity.Exhaustive experiments were conducted using three different sizes of randomly generated data sets and several crossover operators.Indeed, the order crossover and the crossovers based on random keys representation are experimented.The reported results show that the proposed approach guarantees the efficient grouping of students.In addition, comparisons with existing approaches based on GA confirm the ability of the proposed approach to provide greater intergroup and intragroup homogeneity.In addition, the uniform crossover based on random keys representation ensures better grouping quality than do the other experimented crossover operators.
Joint Distance and Representation Learning for Sign Language Videos

Scientific progress in sign language applications has a crucial importance, since it can increase the quality of the lives of deaf and mute people. In this study, frame level distance metric and representation learning problems are studied to represent and classify sign language video frame sequences in an efficient way. For the joint representation and distance metric learning, a Siamese deep neural network is employed. This network is trained with the examples extracted from the automatically acquired transcription of the speech signal found in the sign language videos. The performance of the proposed system is measured by utilizing the same-different sign-discrimination task. Our method considerably outperforms the considered baseline systems that are based on the dynamic time warping (DTW) algorithm used with common distance metrics. This success can be considered as a first step towards an efficient query-by-sign search system.
Multi-model inference on the edge: Scheduling for multi-model execution on resource constrained devices

Deep neural networks (DNNs) are becoming the core components of many applications running on edge devices,especially for image-based analysis, e.g., identifying objects, faces, and genders. While very successful in resource rich environments like the cloud of powerful computers, utilizing Deep Learning on edge devices for inference is not used to great extend. The resource constraints of edge devices present bottlenecks that prevent all but small networks from on-device inference. Existing solutions like compression or off-loading to the cloud sacrifice either model accuracy or inference latency. To counter the aforementioned shortcomings, Masa and EdgeCaffe are proposed as a solution. The process of DNN inference by existing frameworks is to loaded and execute a model in its entirety. As this ignores the resource limitations that are inherent to edge devices, a new framework EdgeCaffe is proposed. EdgeCaffe is able to execute models partially and thereby reducing the memory footprint of the model. Additional, EdgeCaffe allows for coordination between models when a multi-model DNN inference is executed. Masa , a memory-aware multi-DNN scheduling algorithm, featuring on modeling inter-and intra-network dependency and leveraging complimentary memory usage of each layer. Masa can consistently ensure the average response time when deterministically and stochastically executing multiple DNN-based image analysis. With extensive evaluations, Masa can consistently ensure the average response time when deterministically and stochastically executing multiple DNN-based image analyses. We extensively evaluate Masa on three configurations of Raspberry Pi and a large set of popular DNN models triggered by different arrival patterns of images. Our evaluation results show that Masa can achieve lower average response times by up to 8× and 16× for deterministic and stochastic arrivals respectively on devices with small memory, i.e., 512 MB to 1 GB, compared to the state of the art multi-DNN scheduling solutions.
Adapting to Reward Progressivity via Spectral Reinforcement Learning

In this paper we consider reinforcement learning tasks with progressive rewards; that is, tasks where the rewards tend to increase in magnitude over time. We hypothesise that this property may be problematic for value-based deep reinforcement learning agents, particularly if the agent must first succeed in relatively unrewarding regions of the task in order to reach more rewarding regions. To address this issue, we propose Spectral DQN, which decomposes the reward into frequencies such that the high frequencies only activate when large rewards are found. This allows the training loss to be balanced so that it gives more even weighting across small and large reward regions. In two domains with extreme reward progressivity, where standard value-based methods struggle significantly, Spectral DQN is able to make much farther progress. Moreover, when evaluated on a set of six standard Atari games that do not overtly favour the approach, Spectral DQN remains more than competitive: While it underperforms one of the benchmarks in a single game, it comfortably surpasses the benchmarks in three games. These results demonstrate that the approach is not overfit to its target problem, and suggest that Spectral DQN may have advantages beyond addressing reward progressivity.
Differentially Private Parameter Estimation: Optimal Noise Insertion and Data Owner Selection

In this paper, we study differentially private parameter estimation in which a data acquisitor (DA) accesses data (or signals) from multiple privacy-aware data owners (DOs) to estimate some random parameters. To ensure differential privacy, the DOs add Laplace noises to their private signals and only reveal the noisy signals to the DA. Our goal is to add optimal amount of noises (measured by their variances) so that the mean squared error (MSE) of the DA's estimate is minimized while differential privacy is satisfied. In the general case, the optimal private estimation can be formulated as a semidefinite program (SDP), which can be readily solved by off-the-shelf optimization methods. In the special case where different DOs have uncorrelated signals, the optimization problem is decomposed across DOs and can be solved very efficiently in almost closed-form. We observe that, in the optimal solution, the DOs should add more noises to the signal entries that are less useful for estimation. Further, when the DA has DO selection constraint (e.g., due to limited budget), a relaxed SDP is put forth to compute a suboptimal solution. Finally, several numerical examples are presented.
Increasing instructors’ teaching innovation: a significant role of leaders in higher education institutions

In the society of high-speed operation and extreme uncertainty nowadays, innovation becomes a pivotal contributor to enable us to keep up with the advancement of the changing world. In China’s universities, how to cultivate innovative talents has become a focus of education. Since the university is the place where instructors may be the most direct influence factor in the process of student training, their abilities in teaching innovation, which is affected by various factors including leaders who are the key decision-maker, are worthy of attention. Nevertheless, leaders, in reality, tend to place themselves in the secondary position of teaching innovation and neglect to provide necessary resources to support the learning. This paper infers the crucial role of university leaders in improving teaching innovation by analyzing the connotation, the importance, the influencing factors of teaching innovation, and recommendations. The recommendations provided for the university leaders to support instructors in consolidating the importance and procedure of teaching innovation through “GISS”, a guide of action that includes goal-setting, implementation, supporting, and sharing.
Continual learning in recurrent neural networks

While a diverse collection of continual learning (CL) methods has been proposed to prevent catastrophic forgetting, a thorough investigation of their effectiveness for processing sequential data with recurrent neural networks (RNNs) is lacking. Here, we provide the first comprehensive evaluation of established CL methods on a variety of sequential data benchmarks. Specifically, we shed light on the particularities that arise when applying weight-importance methods, such as elastic weight consolidation, to RNNs. In contrast to feedforward networks, RNNs iteratively reuse a shared set of weights and require working memory to process input samples. We show that the performance of weight-importance methods is not directly affected by the length of the processed sequences, but rather by high working memory requirements, which lead to an increased need for stability at the cost of decreased plasticity for learning subsequent tasks. We additionally provide theoretical arguments supporting this interpretation by studying linear RNNs. Our study shows that established CL methods can be successfully ported to the recurrent case, and that a recent regularization approach based on hypernetworks outperforms weight-importance methods, thus emerging as a promising candidate for CL in RNNs. Overall, we provide insights on the differences between CL in feedforward networks and RNNs, while guiding towards effective solutions to tackle CL on sequential data.
Design and evaluation of virtual reality model based on eye movement analysis in street-crossing training of autistics

In order to satisfy the demand of autistic patients to integrate into social life, a street-crossing training model based on the virtual reality technology is built in this paper. Concerning about the reality of insufficient language expression ability of autistic patients, this paper proposes the application of eye tracking technology to realize the digitization of the training process. Therefore, the establishing of training database makes it possible to systematically design a rehabilitation training plan for each autistic patient. At the same time, this paper innovatively uses the radar chart to evaluate the street-crossing ability of autistic patients from six dimensions: Traffic light observation, Start in time, Sidewalk observation, Pedestrian observation and avoidance, Green light time planning and Vehicles observation.
Load Balancing in VM in Cloud Computing Using CloudSim

Cloud computing infrastructure is a combination of software and hardware resources for performing the efficient computing. In this context a number of contributions are established for optimizing their performance more. The resource scheduling and the workload balancing is the similar directional effort for optimizing the computational performance of the cloud infrastructures. The proposed work is intended to measure the performance of cloud scheduling approaches that are claimed to optimize the performance of the cloud computing. Therefore two popular and frequently used scheduling approaches i.e. round robin and first come first serve techniques are implemented with the help of cloudSim simulator. The Round Robin Technique allocates a fixed amount of time for all the resources to a given job. Therefore, this concept works as the time shared manner. Similarly the FCFS technique allocates jobs according to their appearance or sequence therefore that technique is functions according to the space shared manner. Additionally the performance of both the approaches are measured and compared. In order to compare the performance of both the techniques the average processing time, average processing cost, average waiting time and the CPU utilization is computed using the simulation trace. According to the obtained performance The Round Robin Technique found much efficient in all the parameters. Therefore, it is acceptable for future extension of the proposed work.
Review of Tools for Analyzing Security Vulnerabilities in Ethereum based Smart Contracts

One of the most promising features of blockchain technology are software programs; smart contracts. A smart contract is a digital agreement that allows a significant amount of cryptocurrency transactions and processes without the need of third parties. They are the fundamental component for any decentralized application and are irreplaceable. They are legally self-enforced lines of a computer program. This paper is motivated by financial losses that occurred due to known attacks such as DAO attack ($150M) , Parity Multi-Sig Wallet attack (30M USD) and integer underflow/overflow attack during 2016-2018. This paper provides a detailed study of the known security vulnerabilities and security analysis tools that are used to detect them. This review explores possible attack techniques along with presenting a systematic study of the tools such as Oyente, Smart Check, Securify, GASPER, ZEUS, MAIAN and F* Framework used to detect these vulnerabilities.
The influence and countermeasures of the application of blockchain on the investment efficiency of cultural industry ——- DEA Analysis of listed companies based on blockchain concept

Block chain technology has changed the traditional digital economy, promoted the formation of a new value and trust system, and at the same time triggered the transformation of cultural industry, providing a means to improve investment efficiency. This paper takes the listed companies of cultural industry in the concept of block chain as the research sample, and uses DEA efficiency evaluation method to empirically analyze the change of cultural industry investment efficiency. The results show that: block chain technology has an impact on pure technical efficiency and also promotes profit growth. However, the development and application of block chain technology need to be more integrated with cultural industry investment to open up a new development path for the traditional information society.
Integral Thermo-Anemometers for Average Temperature and Airflow Measurement in Ducts, at Anemostat Outlets and in Ventilation Grilles

When creating ventilation systems, it is important to correctly calculate the volumes of air inflow and outflow. If an error is made in the calculation or a redistribution of air flows is required, measurements are indispensable. The existing methods for determining the air flow rate by using point measurements in the cross-section are laborious and time-consuming, and taking readings at different time points introduces a significant error into the result. A. M. Pidhornyi Institute of Mechanical Engineering Problems of the National Academy of Sciences of Ukraine has developed a new hot-wire anemometer whose use greatly simplifies the measuring process. This device allows one to measure the average values of temperature and air velocity (flow rate) in the cross-section of air ducts or at the inlets and outlets of grilles and anemostats, and can be used in real time to monitor and control air flow rate and temperature in ventilation systems. The probe of the hot-wire anemometer is a metal shell with guides on which a sensitive element is laid. Its principle of operation is to change the heat transfer coefficient at different air leakage velocities. The anemometer is preliminarily calibrated in laboratory conditions at various velocities. There has been obtained a calibration dependence that can be used to measure the air flow rate at the inlets and outlets of air distribution devices and directly in the air ducts. To improve the measurement accuracy, it is necessary to provide the 90° angle of airflow leakage on the hot-wire anemometer probe. For this, special air collectors and air flow rectifiers are used.