Reviewerly/exHarmony huggingface dataset:
Using Dilated Residual Network to Model Distantly Supervised Relation Extraction

Distantly supervised relation extraction has been widely used to find relational facts in the text. However, distant supervision inevitably brings in noise that can lead to a bad relation contextual representation. In this paper, we propose a deep dilated residual network (DRN) model to address the noise of in distantly supervised relation extraction. Specifically, we design a module which employs dilated convolution in cascade to capture multi-scale context features by adopting multiple dilation rates. By combining them with residual learning, the model is more powerful than traditional CNN model. Our model significantly improves the performance for distantly supervised relation extraction on the large NYT-Freebase dataset compared to various baselines.
What Does My Shirt Tell Me? The Integrating of Electronics in Clothing

The era of smart textiles has begun. Sensors become smaller, more reliable and require less power and the market of garments for health monitoring increases exponentially. The challenges still are in the seamless integration, robustness and ease of use. In this paper the developments and trends are discussed.
PSMSP: A Parallelized Sampling-Based Approach for Mining Top-k Sequential Patterns in Database Graphs

We study to improve the efficiency of finding top-k sequential patterns in database graphs, where each edge (or vertex) is associated with multiple transactions and a transaction consists of a set of items. This task is to discover the subsequences of transaction sequences that frequently appear in many paths. We propose PSMSP, a Parallelized Sampling-based Approach For Mining Top-k Sequential Patterns, which involves: (a) a parallelized unbiased sequence sampling approach, and (b) a novel PSP-Tree structure to efficiently mine the patterns based on the anti-monotonicity properties. We validate our approach via extensive experiments with real-world datasets.
Network Improved by Auxiliary Part Features for Person Re-identification

Person re-identification (ReID) is an important issue in computer vision area. It focuses on identifying people under different scenarios. In this paper, we test the contributions of local part features in ReID system. With the auxiliary of local part features, our model achieves significantly improvements, which achieves rank-1 accuracy of 91.7% on market1501 dataset and 82.6% on MARS dataset. We also test the feasibility of using densenet as backbone model in ReID system. With densenet as our backbone model, our method achieves state-of-art performance and simultaneously reduces the model size enormously.
Data-Intensive Computing Acceleration with Python in Xilinx FPGA

Data-intensive workloads drive the development of hardware design. Such data intensive services are driven the raising trend of novel machine learning techniques, such as CNN/RNN, over massive chunks of data objects. These services require novel devices with configurable high throughput in I/O (i.e., data-based model training), and uniquely large computation capability (i.e., large number of convolutional operations). In this paper, we present our early work on realizing a python-based Field-Programmable Gate Array (FPGA) system to support such data-intensive services. In our current system, we deploy a light layer of CNN optimization and a mixed hardware setup, including multiple FPGA/GPU nodes, to provide performance acceleration on the run. Our prototype can support popular machine learning platform, such as Caffe, etc. Our initial empirical results show that our system can perfect handling all data-intensive learning services.
GPU-Accelerated Dynamic Graph Coloring

The graph coloring is a classic problem in the graph theory, which can be leveraged to mark two objects with a certain relationship with different colors. Existing graph coloring solutions mainly focus on efficiently calculating high-quality coloring of static graphs. However, many graphs in the real world are highly dynamic and the coloring result changes when the graph is updated. Repeated adoption of static graph coloring schemes will incur prohibitive costs. Although some CPU-based incremental graph coloring methods have been proposed recently, they become inefficient when facing dense graphs and large batch updates. In this paper, we explore the dynamic graph coloring solution by utilizing the powerful parallel processing capabilities of GPU and propose a CPU-GPU heterogeneous method. We conduct extensive experiments comparing our algorithm with the existing methods. The results confirm that our algorithm is superior to others in many aspects such as coloring efficiency.
Comparative study of temporal phase shifting algorithms and their effect on surface profiles

Temporal phase shifting algorithms are classified according to number of phase shifted images required for determination of phase map. This research article reports the comparative study of different temporal phase shifting algorithms and their effect on calculation of surface profiles. Performance of three, four and five step phase shifting algorithms is comparatively reported. It was found that use of five step phase shifting technique yields more realistic surface profile values than compared to three and four step phase shifting algorithms.
Diana and Juno Destroyed: Statues and Pagan Deities in two <i>Meisterlieder</i> by Hans Sachs

This essay analyzes two of Hans Sachs's Meisterlieder which treat ancient tales of a statue and its destruction. Both unpublished, these songs have not received scholarly attention, and yet their subject matter gains a particular resonance against the background of the early modern debate in German regions on images and their proper function. Drawing on a distinction made in ancient Greek between an eikōn and an eidōlon , the essay explores how the songs depict different attitudes towards statuary: what a statue is, what it claims to represent, and how its representational strategies can fail or be foiled. Through comparison with Sachs's purported source, the German mythographer Johannes Herold's work, this essay posits that both songs suggest that iconoclasm can be an appropriate response to statues.
Security elements used in color three-dimensional security holograms, methods for their production and control

The article describes the features of the security elements used in color three-dimensional security holograms. Unlike classical security holograms - "rainbow" relief - phase holograms, color three-dimensional security holograms have spectral and angular selectivity. This feature allows them to create new security elements or use the properties of already known elements as a new. The article describes the schemes and devices for creating such security elements, as well as devices for their visualization and control. Examples of typical security elements used in color three-dimensional security holograms are presented. This work was supported by the Russian Science Foundation (Project No. 18-79-00304).
Deep Cross-Modal Age Estimation

Automatic age and gender classification systems can play a vital role in a number of applications including a variety of recommendation systems, face recognition across age progression, and security applications. Current age and gender classifiers, are lacking crucial accuracy and reliability in order to be used in real world applications since most real-time systems have zero fault tolerant. This paper develops an end-to-end, deep architecture aiming to improve the accuracy and reliability of the age estimation task. We design a deep convolutional neural network (CNN) architecture for age estimation that builds upon a gender classification model. The system leverages a gender classifier to improve the accuracy of the age estimator. We investigate several architectures and techniques for the age estimator model with cross-modal learning, including an end-to-end model, using gender embedding of the input image, which leads to an increased accuracy. We evaluated our system on the Adience benchmark, which consists of real-world in-the-wild pictures of faces. We have shown that our system outperforms state-of-the-art age classifiers, such as [1] by $$9\%$$ , by training a cross-modal age classifier.
A Style Image Confrontation Generation Network Based on Markov Random Field

A style-transfer generates against network based on Markov random field is proposed in this paper. Based on the original image, a new image is generated by generate network, and then the error between the original image and the style image is calculated using the discriminant network and backward propagation to the generate network, high-quality style transfer images are generated through the continuous confrontation of the two networks. In the quantification of style loss and content loss, we have introduced Markov random field, which uses its limitation on the spatial layout to reduce the distorted distortion of the generated image and improve the quality of the generated image. Experiments show that the network can quickly generate high-quality style transition images in a short time.
Analysis and Application of Normalization Methods with Supervised Feature Weighting to Improve K-means Accuracy

Normalization methods are widely employed for transforming the variables or features of a given dataset. In this paper three classical feature normalization methods, Standardization (St), Min-Max (MM) and Median Absolute Deviation (MAD), are studied in different synthetic datasets from UCI repository. An exhaustive analysis of the transformed features’ ranges and their influence on the Euclidean distance is performed, concluding that knowledge about the group structure gathered by each feature is needed to select the best normalization method for a given dataset. In order to effectively collect the features’ importance and adjust their contribution, this paper proposes a two-stage methodology for normalization and supervised feature weighting based on a Pearson correlation coefficient and on a Random Forest Feature Importance estimation method. Simulations on five different datasets reveal that our two-stage proposed methodology, in terms of accuracy, outperforms or at least maintains the K-means performance obtained if only normalization is applied.
Management Leadership and Its Development

Global development of digital economy in the twenty-first century leads to changes in understanding leadership phenomenon as a socio-psychological factor that can unite groups of people for reaching certain objectives and fulfilling tasks. Leadership phenomenon as a management technique is a research topic of current interest. In conditions of digital economy, an ability to create the systems of leadership internals in management contributes to growth in the frame of effective running of a socioeconomic system on the whole. In our opinion, the most importance should be attributed to the issues of uniting a group of people for achieving the objectives in times of uncertainty that supposes a current and unpredictable change of external environment. Our paper studies individual and psychological characteristics of the leadership development, but also tackles the group context of different approaches, such as socio-psychological, philosophic, and behavioral, situational. Our interest and focus in these issues are logical as far as management of the leadership has always been valued, especially nowadays when the demand among organizations is focused on the issues related to transferring leadership practices into company’s brand. Thence, our paper is devoted to processes of developing different types of leaders: a manager, a leader, or a managing leader.
Implementation of ECC on FPGA for WSN- A Survey

The use of WSN is wide spread in various fields and areas. There are many possibilities that the WSN nodes are in open to all possible attacks and they are easily available. There are few challenges in terms of the nodes speed and the security. There are many possible solutions, but we intend to use one simple and effective solution, that is FPGA. FPGA re programmable and can be reconfigurable to any type of situation as per the requirement, making very suitable for WSN's. The other issue is the security factor. Implementing any type of cryptography is not suitable for WSN as well as FPGA, though there are various cryptographic algorithm implemented on FPGA. As per our survey on of the possible and suitable cryptographic algorithm is Elliptic Curve Cryptography (ECC). The WSN's have constraints like limited area, power and memory which restrict all the categories of cryptographic algorithm. Performance of ECC on FPGA may be similar to other algorithms but we need to find out the best possible algorithm with respect to WSN. In this paper we comparison of FPGA with ASIC and microcontroller, further how ECC can be implemented on FPGA for WSN.
Conceptual approach to building information security systems for telecommunication systems using artificial immune systems

In article, it is shown that distributed nature the infotelekommunikation of systems and networks, a large number of users in them leads to considerable increase of "bottlenecks" and because of vulnerabilities of these structures. It steadily lowers the level of information security, increases risks of damage or destruction of data, and negatively affects confidentiality of information. Further, the concept of application at the heart of mechanisms of information security of the distributed system of the principles of functioning of immune system of the person as effective remedy of counteraction to different types of threats is considered. This publication is one more step in the direction of discussion of a possibility of creation of the program technical means imitating functioning of biological systems.
Reproducing tonguing strategies in single-reed woodwinds using an artificial blowing machine

Articulation on woodwind instruments is achieved inside the player’s mouth, where the tongue interacts with the vibrating reed, while the player adjusts the blowing pressure, the lip force, and the vocal tract configuration. The performed articulation technique defines the characteristics of the attack and release transients and thus the transitions between tones. In this study, an artificial blowing machine with a built-in tonguing system is used to analyze different tonguing strategies in single-reed woodwinds. The tonguing system is controlled via an electronically monitored shaker, offering the possibility to reproduce tongue articulation, while assuring repeatability. To reproduce different playing techniques, parameters obtained from measurements with players are used to set up the pressure in the artificial mouth and the behaviour of the tonguing system. During the experiment, the artificial-mouth pressure, the mouthpiece pressure, the reed displacement, and the shaker acceleration are recorded. The recorded signals are then compared to real-playing clarinet and saxophone measurements. Different trajectories for the artificial tongue are tested as well as different tongue-reed-contact durations. The artificial blowing and tonguing set-up, along with the images obtained with a high-speed camera, provide an in-depth understanding of the processes taking place inside the player's mouth.
Fault Analysis of the New Ukrainian Hash Function Standard: Kupyna

Kupyna has been selected by the Ukrainian government as the new national hash function standard in 2015. In this paper, we apply two fault attacks on Kupyna. In the first attack, we assume that the attacker knows all the hash parameters and aims to recover the input to the hash function. We experiment using three different fault models which are random byte fault model, known byte unique fault model and known byte random fault model. In the second fault attack, we assume that the attacker does not know the entries of the SBoxes used in Kupyna and aims to recover the SBox entries. Our experimental results in both attacks illustrate the importance of protecting implementations of Kupyna against fault analysis attacks.
The endpoint epidemic of IoT – just a bad dream?

Over the past decade we've heard a lot of concern from security vendors about the rise of the Internet of Things (IoT) and the growth of smart devices. On a daily basis, technology professionals are struggling to keep their organisations secure, even though the endpoint epidemic that has kept experts up at night hasn't fully manifested itself in the way we feared. The growth of the Internet of Things (IoT) has been a key theme of the past few years but the endpoint epidemic that has kept experts up at night hasn't fully manifested itself in the way we feared. Yet despite its slow burn, IoT should remain a top priority for today's cyber security professionals. A self-healing, automated network can detect threats, keep data from being compromised and reduce downtime. Infrastructure teams within today's digital businesses need to operate at a speed that leaves little room for manual updates and almost certainly requires automation, suggests Sascha Giese at SolarWinds.
Crushed it!

We introduceCrushed It!, an interactive game on a sensor floor. This floor is combined with a multiple projector system to reduce occlusions from players' interactions with the floor. Individual displays, an HTC Vive to track player position, and smart watches were added to provide an extra layer of interactivity. We created this interactive experience to explore collaboration between people when interacting with large displays. We contribute a novel combination of different technologies for this game system and our studies showed this game is both entertaining and provides players with motivation to stay physically active. We believe presenting at interactivity would be a benefit to both our research and to the attendees of CHI 2019.
Visualization and Cognitive Graphics in Medical Scientific Research

The aim of the research is to apply methods of structural analysis of multidimensional data using different approaches to visualizing the results of experimental studies. To solve applied problems, the authors used NovoSparkVisualizer (demo) system, as well as the R scripting language. The leading approach to the study of this problem is the mapping of multidimensional experimental data in the form of generalized graphic images on the basis of original methods and approaches developed by the authors. The paper presents the results of solving two applied problems illustrating the effectiveness of the method for visualization of multidimensional experimental data: In the first case, the use of cognitive graphics tools made it possible to propose an effective way of displaying the dynamics of the state of the bio-object (for example, comparing the patient's condition before and after treatment). In the second case, it helped to reveal some previously unknown patterns of physiological response of the bronchial-pulmonary system to psycho-physiological exposure. The results of the research allow the authors to state that the methods and approaches presented in the paper can be regarded as promising directions in the analysis and presentation of multidimensional experimental data.
Low Cost Hand Gesture Recognition System Design and Implementation

The design and development of a low cost hand gesture recognition computer interface, using a standard laptop webcam is presented. The purpose of the system is to recognise both static and dynamic hand gestures from a user in real-time and perform basic macro instructions on the Windows operating system. The calibration and gesture recognition processes are discussed. The system is able to correctly classify 19 static hand gestures and recognise 6 dynamic hand gestures with greater than 95% accuracy. However, the system has a varying latency of between 50-500 milliseconds due to inefficient interfacing with the operating system. It was concluded that an optimised operating system interface would improve system performance and user experience dramatically.
PENGARUH METODE PEMBELAJARAN TGT TERHADAP KEMAMPUAN BERBAHASA MANDARIN SISWA SMP METHODIST CHARLES WESLEY MEDAN

Learning Method is a way that is done by a teacher so that learning processes occur in students to achieve learning goals. This study aims to know significantly the influence of the Team Games Tournament learning method on the ability to recognize vocabulary about nouns students of Medan Charles Wesley Methodist Middle School. The learning material taught to students of Medan's Charles Wesley Methodist Middle School is a book from Taiwan, namely Hello, 华语! which is a Chinese language textbook at the Medan Charles Wesley Methodist Middle School. This research was conducted in the school of Charles Wesley Methodist Middle School in Medan, specifically 7th grade students of junior high school, namely classes 7A and 7B. The research design used was a quantitative research design. The analysis of the results of the research used is the analysis of the t-test. The results of this study indicate that the Team Games Tournament learning method significantly influences the mandarin language skills of Medan Charles Wesley Methodist Middle School students
Co ed

The current Indian education system promotes competition among students, emphasizing highly on ranks and marks scored. As a consequence, students tend to drift away from a collaborative mindset to a competitive one. The learning gap is even larger in schools catering to lower income groups due to the absence of digital infrastructure and digital knowledge. As a result, a large section of Indian students is cut off from a major source of knowledge, the internet, causing the gap to increase between students in the society. With co ed, we attempt to bridge these gaps by providing a platform to foster mutual learning as well as combining digital mediums and the long-established pen and paper in a seamless manner.
The Four I-s of E-skills

Nationwide E-inclusion is yet to be realised in many countries, which could explain the E-skills diversities that exist in the workplace and amongst university students. Literature confirms the existence of E-skills diversity however fails to provide a comprehensive solution. Rapid E-learning adoption in South African universities make additional demands on first year students to use technology for learning, hence clear direction on how to address E-skill diversity is critical for university students' success. A longitudinal case study of sixteen undergraduate first year students with diverse E-skill levels was conducted to gather qualitative data needed to understand how using E-learning could contribute towards improving students' E-skills capabilities. Focus group interviews and course assessments were used to gather data from participants and Straussian-grounded-theory methods were employed to ensure rigorous, structured analysis of student experiences with technology and related E-skill development. Emerging categories show that diversity may be addressed by introducing carefully designed incrementally complex E-learning tasks, complemented by instructor interventions, beginning with instruction, then involvement, thereafter facilitating interaction and finally encouraging independence to stimulate E-skill development from fundamental to strategic levels.
An efficient deep learning platform for detecting objects

Real-time object detection models based on deep learning are being studied. However, when using deep learning, the user must directly select one of the various object detection models, and the result of object detection may vary depending on the selected object detection model. Therefore, in this paper, we propose an efficient deep learning platform for object detection technology. The proposed platform estimates learning results based on benchmark results and recommends proper object detection model based on deep learning to minimizes user intervention.