Reviewerly/exHarmony huggingface dataset:
In-memory for the masses

With unrivaled performance, modern in-memory data management platforms such as SAP HANA [5] enable the creation of novel types of business applications. By keeping all data in memory, applications may combine both demanding transactional as well as complex analytical workloads in the context of a single system. While this excellent performance, data freshness, and flexibility gain is highly desirable in a vast range of modern business applications [6], the corresponding large appetite for main memory has significant implications on server sizing. Particularly, hardware costs on premise as well as in the cloud are at risk to increase significantly, driven by the high amount of DRAM that needs to be provisioned potentially. In this talk, we discuss a variety of challenges and opportunities that arise when running business applications in a cost-efficient manner on in-memory database systems.
Photon interference of second- and third-order correlation generated by two fluorescence sources

Abstract We demonstrate the second- and third-order temporal correlation of two independent pseudo-thermal fluorescence (FL) sources. The two distinguishable sources become indistinguishable following the delay in Feynman’s path. The second- and third-order correlation demonstrates the strong bunching amplitude surrounded by variable oscillations and interfering side bands (beats). Specifically, three-photon bunching is overlapped by two-photon bunching, and further decomposed into multiple lower order correlation functions. Here, the strong interference arises from source and path indistinguishable terms (varying time offset, laser frequency and the bandwidth of FL sources); The support for this idea comes from Feynman’s path integral theory and sub wavelength interference for such kind of sources with appropriate detection schemes. Such phenomena may serve as modulation and carrier for quantum communication channels.
Graph-Based Detection of Seams In 360-Degree Images

In this paper, we propose an algorithm to detect a specific kind of distortions, referred to as seams, which commonly occur when a 360-degree image is represented in planar domain by projecting the sphere to a polyhedron, e.g, via the Cube Map (CM) projection, and undergoes lossy compression. The proposed algorithm exploits a graph-based representation to account for the actual sampling density of the 360-degree signal in the native spherical domain. The CM image is considered as a signal lying on a graph defined on the spherical surface. The spectra of the processed and the original signals, computed by applying the Graph Fourier Transform, are compared to detect the seams. To test our method a dataset of compressed CM 360-degree images, annotated by experts, has been created. The performance of the proposed algorithm is compared to those achieved by baseline metrics, as well as to the same approach based on spectral comparison but ignoring the spherical nature of the signal. The experimental results show that the proposed method has the best performance and can successfully detect up to approximately 90% of visible seams on our dataset.
Perceptions of Risk, Benefits and Likelihood of Undertaking Password Management Behaviours: Four Components

Passwords remain the most common form of authentication in the digital world. People have increasing numbers of passwords, and research suggests that many people undertake risky password management behaviours such as re-using passwords, writing them down and sharing them with friends and colleagues. It is not clear whether people persist in these behaviours because they do not understand the risks involved or the benefits of the behavior outweigh the risk. An online survey was undertaken with 120 MTurk workers in which they rated the risks, benefits and likelihood of undertaking 15 password management behaviours. They also completed the Marlow-Crowne Social Desirability Scale to investigate whether their responses, particularly of the likelihood ratings, were affected by social desirability. An interesting pattern of responses was found with some groups of behaviours more affected by perceptions of the benefits and others equally affected by the perceptions of the risks and the benefits. These results have implications for how information about risky password behaviours in presented to users and general education about password security.
Learning in the Digital Era: Science Education Students’ Perception on the SNSs Use in the Context of English for Specific Course

Social Networking Services (SNSs) have influenced many aspects of life, including education and English as Foreign Language (EFL) teaching. Many studies have shown various kinds of SNSs applied in foreign language pedagogy. However, the research on the integration of SNSs in English for Specific Purposes (ESP) is limited. We collaborated with three lecturers of ESP who used Facebook, WhatsApp, and Youtube when they taught 8 classes of a science education program in one higher education institution in Indonesia. This mix-method study, survey and interview, explored the use of SNSs for both educational purposes, especially English for specific purposes. There were 311 respondents for the survey and 8 participants for the interview. The findings of this research showed that most participants were familiar with various kinds. This study also informed the high Mean (M) of positive statements regarding the use of SNSs in ESP course for science education students. There were more benefits than problems informed in the interview sessions reflecting that SNSs have potential impacts to improve higher education especially in foreign language classes. Some implications for instructors, policy makers, and researchers are recommended.
Penggunaan E-Learning sebagai Media Penunjang Mutu Pembelajaran di Prodi Pendidikan Ekonomi STKIP PGRI Lumajang

Digitalization is developing rapidly in the community, but not all levels of society can use technology properly including students. So that it needs an active role of universities in directing students to be more effective in using technology as a learning support facility. The purpose of the study was to determine the extent to which the use of e-learning in the Economic Education Study Program of STKIP PGRI Lumajang can spur student academic activities on an ongoing basis so as to improve the quality of learning. This type of research uses a qualitative phenomenological approach. The research method uses in-depth interviews, observations and questionnaires. Found the results of the first study, most of the students of Economic Education Study Program were able to use e-learning well. Second, the intensity of e-learning media usage is not routinely fully used by students because not all subjects are given through e-learning media. Finally, the use of e-learning media provides an attraction to encourage students to be more active, more effective in understanding the material, more efficient in terms of time and cost. The main problem that arises is the unstable internet network connectivity because most student residences are in locations that are difficult to reach with a strong internet network so they have to struggle more to be able to access e-learning.
Semantic segmentation of breast lesion using deep learning

Objectives Although breast ultrasound imaging is powerful and effective tool to detect breast lesions and have been widely performed worldwide, it is an operator-dependent test, hence the accuracy for detection and diagnosis of breast lesions depend on the operator. We develop a computer-aided segmentation system for breast lesions in ultrasound images using deep learning. Methods A data set containing 215 ultrasound images were collected from our institute. Ultrasound images were cropped at 512 × 512 pixel images automatically. The breast tissue area within each image was extracted and cropped to make a masking image. The U-Net algorithm was programmed to train the images with deep learning. The original ultrasound images were input as the input images, and the corresponding masking images were input as the output images to extract breast tissue area. DICE coefficient was used to evaluate its accuracy of extraction. Also, the breast lesion was cropped to make masking image and these images were also trained by U-Net to extract the area of the lesion. Results DICE coefficient achieved over 90% of accuracy to extract the area of breast tissue. The breast lesion was also successfully extracted from the images. Conclusions We achieved accurate segmentation of the area of breast tissue and breast lesions. Deep learning could potentially help detecting and diagnosing the breast cancer, improving accuracy and productivity of detecting and diagnosing breast cancer.
PROOF MINING IN <i>L</i><sup><i>p</i></sup> SPACES

Abstract We obtain an equivalent implicit characterization of L p Banach spaces that is amenable to a logical treatment. Using that, we obtain an axiomatization for such spaces into a higher order logical system, the kind of which is used in proof mining, a research program that aims to obtain the hidden computational content of mathematical proofs using tools from mathematical logic. As an aside, we obtain a concrete way of formalizing L p spaces in positive-bounded logic. The axiomatization is followed by a corresponding metatheorem in the style of proof mining. We illustrate its use with the derivation for this class of spaces of the standard modulus of uniform convexity.
Communication Management of Digital Information Data in Human Resources as a Policy Making Strategic Program for University

Modern universities aim at carrying capacity human resources component in its tri dharma (Three Pillars of Higher Education).UPI with sufficient conditions in the management of several resources has undoubtedly tried to establish itself as a modern university.The superior human resource support in every competition and the university's tri dharma is the primary resource of UPI.The results of all HR roles have become inputs for the management of digital data and information.Therefore, communication management containing the university's strategic policies can be effectively and promptly translated as well as implemented by human resources.To conduct dissemination and sustainability both nationally and internationally, this research formulated three main components in managing policy communication on human resources in a modern university, which include: (a) architecture vision, (b) value change; (c) flow diagrams, all of which refer to the achievement of vision and mission.
DISCRIMINATION OF CULTURAL RELICS SIMILARITY BASED ON PHASH ALGORITHM AND SIFT OPERATOR

Abstract. Among the immovable cultural relics such as grottoes, stone carvings of the same period usually show similar artistic styles and repetitive statues. Through the study of the similarity between cultural relics in the same period, we can have reference value for the historical significance, artistic characteristics and cultural relics restoration of cultural relics. The traditional discrimination of cultural relics similarity often depends on the subjective experience judgment of experts, which is time-consuming and inefficient. Often, different experts may issue different conclusions, the lack of convincing objective evaluation basis, can not provide effective scientific support for the protection and restoration of cultural relics. In order to provide objective criteria for evaluating the similarity of cultural relics and avoid the interference of human factors, this paper proposes the concept of similarity index of cultural relics based on perceptual Hash (pHsah) algorithm. For orthophoto images of cultural relics with similarity index within the threshold, the SIFT operator is further used to find the corresponding feature points. This paper takes the Buddha statues in Yungang Grottoes as the research object, objectively calculates the similarity index of cultural relics, combines the pHash algorithm and Sift operator, realizes the objective quantitative discrimination of cultural relics similarity, effectively avoids the subjective influence of experts on the judgment of cultural relics similarity.
InformatiCup Competition 2019: Fooling Traffic Sign Recognition

Neural networks are used more and more in critical areas such as autonomous driving. In such cases, their limitations might cause dangerous situations. Researchers were able to show that such limitations enable attacks on systems containing neural networks, which are even possible in real world scenarios. For example, a state-of-the-art network might misclassify modified traffic signs. Other researchers have shown that modern car assistants can easily be fooled to drive the car into the wrong lane on a street. The InformatiCup is a collegiate computer science competition in Germany, Switzerland and Austria for all students, with tasks based on real world problems. This year’s task is based on the above mentioned problem. To demonstrate this problem and to motivate students for experimenting with neural networks, participants were asked to generate fooling images for a traffic sign classifying neural network without having direct access to the network. The images should not be recognisable by humans as traffic signs, but be classified as such with a high confidence by the neural network.
FE analyses and structural optimization on refitted floating container

Abstract On the basis of the requirements of river-crossing emergency traffic guarantee, a structural modification was exerted on 40-foot Dry Cargo Container. A structure of refitted floating container was designed, which could be used in the assembly of emergency floating bridge. A finite element method (FEM) theoretical analysis was made on the static strength and stiffness of refitted floating container. Two kinds of load levels were brought in as the typical working conditions. Modelling in the ANSYS circumstance to simulation analysis and accomplishing optimization design based on the results. The results show that the floating bridge system assembled by refitted floating containers can’t support the load under the level of wheel type-20 and crawler type-500 for a stress concentration appears on the contact sites between floating container and main girder. The maximum stress goes beyond the limit of yield strength. An structural optimization was made to eliminate the influence of stress concentration and floating bridge system assembled by refitted floating containers can meet the requirements of load level of wheel type-20 and crawler type-500 after optimization. The refitted floating container can play a role in transporting troops and engineering emergency task crossing the river in a short period of time.
Dynamic Gesture Capture, Location and Tracking Based on MeanShift Algorithm

In this paper, dynamic gesture capture and tracking based on MeanShift algorithm for human-computer interaction system is proposed. In this method, the rectangular box representing the hand position is used as the control signal input and the initial detection target tracking. Thus the dynamic gesture video is effectively processed. In this paper, Python3 OpenCV library is adopted as the main processing library for experimental analysis. The initial capture and positioning of hand position and subsequent tracking experiments are carried out on a video of hand movement. Through the comparison and analysis with Camshift algorithm, the experimental results show that MeanShift algorithm achieves a good dynamic tracking effect of gesture position.
Graph-search Based UNet-d For The Analysis Of Endoscopic Images

While object recognition in deep neural networks (DNN)has shown remarkable success in natural images, endoscopicimages still cannot be fully analysed using DNNs, sinceanalysing endoscopic images must account for occlusion,light reflection and image blur. UNet based deep convolutionalneural networks (DNNs) offer great potential to extracthigh-level spatial features, thanks to its hierarchical naturewith multiple levels of abstraction, which is especially usefulfor working with multimodal endoscopic images with whitelight and fluoroscopy in the diagnosis of esophageal disease.However, the currently reported inference time for DNNs isabove 200ms, which is unsuitable to integrate into roboticcontrol loops. This work addresses real-time object detectionand semantic segmentation in endoscopic devices. Weshow that endoscopic assistive diagnosis can approach satisfydetection rates with a fast inference time.
A Virtual Image Accelerator for Graph Cuts Inference on FPGA

Graph Cuts is a popular technique for Maximum A Posteriori inference in computer vision. It transforms a Markov Random Field problem into a network flow problem, solved via the Push-Relabel algorithm. While attractively simple, the large size of a typical image and the large number of necessary pixel-level iterations render the technique computationally expensive. Prior accelerator attempts have been reported with GPUs and FPGAs. In 2017, we demonstrated the first pixel-parallel architecture on FPGA, but limited to only 256-pixel images. This paper extends this pixel-parallel concept and proposed a Virtual-Image architecture which solves the size limitation. We demonstrate the first working virtual-image Graph Cuts accelerator, implemented on a state of the art FPGA, applied to standard benchmark images for a background segmentation task. The design is 11-13× faster than other FPGA designs, and slightly faster than a modern GPU benchmark by about 30%.
Virtual Seismological Network of Sofia University - a research database

Summary Virtual Seismological Network of Sofia University (VSNSU) is operating since 2015 and it is supported by the Scientific Research Fund of Sofia University. The network consists of 16 seismic stations from 6 different countries in the Balkan Peninsula region. The seismological database consists of structured files containing: the characteristics of each analyzed earthquake (location, origin time, depth, etc.); different magnitudes; focal mechanisms; and seismic phases from the station records. The database structured a different measurements made since the operation of the VSNSU and included also the available information from different international datacenters. Database includes also the specific manuals for the used software explained by an individual example. The purpose of the established database is to simplify the education and research of the students as well as to contribute to the inter-institutional international cooperation.
Information seeking trends among the Law course entrants in two Sri Lankan universities: a comparative study

This comparative study is an attempt to understand the pre-university library experiences of two groups of Law course entrants in two universities. The study concentrated on the use of the school library before entering the university, some social media and the communication preferences, and established that both groups have effectively used the school libraries. Yet, there are variations between the groups in using the other information sources such as websites, smart phones, select social media, etc. The study recommends offering a well-planned support programme covering the available resources and services, paying special attention to the use of e-resources, online catalogue, e-mail and social media for their academic purposes
Controlling Web Traffic and Preventing DoS/DDoS Attacks in Networks with the Proxy Gateway Security Solution Built on Open Hardware

The emergence of the Internet of Things (IoT) and the advantages of computer network have attracted the attention of technological experts. However, network security issues remain a challenge. Controlling Web traffic and preventing attack to Web server, especially DoS/DDoS, are tremendous in the current computing ecosystem. In this paper, we propose a solution for controlling connections of the inside as well as the outside of network systems by using an integrated hardware, which would be deployed at gateway of the networks. It is not only used to implement rules but also monitor the network traffic, especially for web traffic which is considered as the common type in the Internet. In our solution, we prefer to use the IP SLA to control the web traffic, rather proxy to set up rules as well as a data stream algorithm for fast detecting Hot-IPs to prevent attacks from outside networks to the inside servers. The proposed solution is trouble-free to use and enhance security in small & medium networks as well.
Identifying High-Impact Opioid Products and Key Sellers in Dark Net Marketplaces: An Interpretable Text Analytics Approach

As the Internet based applications become more and more ubiquitous, drug retailing on Dark Net Marketplaces (DNMs) has raised public health and law enforcement concerns due to its highly accessible and anonymous nature. To combat illegal drug transaction among DNMs, authorities often require agents to impersonate DNM customers in order to identify key actors within the community. This process can be costly in time and resource. Research in DNMs have been conducted to provide better understanding of DNM characteristics and drug sellers’ behavior. Built upon the existing work, researchers can further leverage predictive analytics techniques to take proactive measures and reduce the associated costs. To this end, we propose a systematic analytical approach to identify key opioid sellers in DNMs. Utilizing machine learning and text analysis, this research provides prediction of high-impact opioid products in two major DNMs. Through linking the high-impact products and their sellers, we then identify the key opioid sellers among the communities. This work intends to help law enforcement authorities to formulate strategies by providing specific targets within the DNMs and reduce the time and resources required for prosecuting and eliminating the criminals from the market.
One-Shot Chinese Character Recognition Based on Deep Siamese Networks

This paper applies deep siamese network to one-shot Chinese handwritten character recognition. Different from common image classification tasks, the CASIA HWDB1.1 dataset used here contains more than 3000 categories, with only few samples in each one. We propose a basic deep siamese model as well as an improved model with multi-layer features mechanism and batch normalization for extracting the similarity of the input pairs, and implement one-shot recognition by categorizing the test example to the class where the support sample is the most similar. Experiments prove that our model is able to recognize Chinese characters of unseen classes in training with only one support example efficiently.
Convolution Neural Networks for Blind Image Steganalysis: A Comprehensive Study

Recently, Convolution Neural Network is widely applied in Image Classification, Object Detection, Scene labeling, Speech, Natural Language Processing and other fields. In this comprehensive study a variety of scenarios and efforts are surveyed since 2014 at yet, in order to provide a guide to further improve future researchers what CNN-based blind image steganalysis are presented its architecture, performance and limitations. Long-standing and important problem in image steganalysis difficulties mainly lie in how to give high accuracy and low payload in stego or cover images for improving performance of the network.&#x0D;