Reviewerly/exHarmony huggingface dataset:
Embedding information in physically generated random bit sequences while maintaining certified randomness

Ultrafast physical random bit generation at hundreds of Gb/s rates, with verified randomness, is a crucial ingredient in secure communication and have recently emerged using optics based physical systems. Here we examine the inverse problem and measure the ratio of information bits that can be systematically embedded in a random bit sequence without degrading its certified randomness. These ratios exceed 0.01 in experimentally obtained long random bit sequences. Based on these findings we propose a high-capacity private-key cryptosystem with a finite key length, where the existence as well as the content of the communication is concealed in the random sequence. Our results call for a rethinking of the current quantitative definition of practical classical randomness as well as the measure of randomness generated by quantum methods, which have to include bounds using the proposed inverse information embedding method.
A calculation cost reduction method for a log-likelihood maximization in word2vec

Word2vec models learn text data and provide distributed representations to words. The distributed representations use vectors which show the meaning of the words. Thus the word2vec models are useful for Natural Language Processing (NLP). However, it is difficult to update the models for new data addition because it takes a long time to generate the word2vec model. This calculation time has become an impediment to analize text data which contains a lot of unknown words. This is caused by computational time in the calculation of the likelihood function. The purpose of this study was to speed up the training of Continuous Bag-of-Word Model(CBOW), which is one of the word2vec models, by reducing the calculation cost of the likelihood function. The likelihood function in CBOW has been expressed by the use of a softmax function and has a huge amount of computational time. In this paper, a sigmoid function replaces the softmax function as the approximated likelihood function, because the sigmoid function can reproduce the charactaristic change of the likelihood function in CBOW.
Automatic Detection of Incorrect Location Images Uploaded by Users

This paper proposes a system to automatically perform classification and data cleaning on location images in a database contributed by arbitrary users. Since human inspection is not feasible for large-scale databases, the ability to detect incorrect scenes uploaded by users is very important to maintain the correctness of the database. In this work, we compare different feature extractors using deep convolutional networks trained by massive datasets. Also, a detector is designed to identify incorrect scenes that can overcome the challenges of large intra-cluster distances. The experiments have validated the effectiveness of the proposed approach on a very challenging dataset.
Implementing Attacks on the Approximate Greatest Common Divisor Problem

The security of many fully homomorphic encryption (FHE) schemes is guaranteed by the difficulty of the approximate greatest common divisor (AGCD) problem. Therefore, the study of AGCD problem is of great significance to the security of the fully homomorphic encryption. This paper surveys three kinds of attacks on the AGCD problem, i.e. exhaustive search attack, simultaneous Diophantine approximation (SDA) attack and the orthogonal lattice (OL) attack. We utilize the Number Theory Library (NTL) to implement the SDA attack and the optimized OL attack on the AGCD problem. Comparisons are performed based on the experimental results to illustrate that the exhaustive search attack can be easily defended just by increasing the size of ρ. And increasing the length of the public key is the most effective way to defend SDA attack and OL attack. Meanwhile, we concluded that the success rate of SDA attack and OL attack can be improved by increasing the dimension of lattice at the expense of a certain time efficiency. In addition, the analysis and experiments show that the fully homomorphic computing efficiency of FHE scheme can’t be improved by simply increasing the private key without appropriately increasing the size of public key. Otherwise, the FHE scheme is vulnerable to OL and SDA attack. Besides, experimental results show that optimized OL attack performs better than both classical OL attack and SDA attack in terms of attack success rate and the time efficiency.
An Improved K-SVD Algorithm and Its Application for Image Denoising

The KSVD algorithm has been widely used in image processing fields due to its high efficiency for image sparse representation. The traditional KSVD alternately updates the dictionary atoms and sparse coefficients through iterative operations. The algorithm is with noise suppression ability, and the reconstructed image can maintain the internal structure information of the processed image. Thinking about the intrinsic structure information can be acquired by the dictionary update procedure, in this paper, the principal component analysis (PCA) process is adopted to replaces the singular value decomposition (SVD) performing on the error term, and the first principal component is extracted as update item for dictionary atom. Furthermore, the improved KSVD is applied to image denoising. The simulation results show that the improved algorithm can better reconstruct the essential features such as edge and texture of the denoising image, and obtain higher peak signal-to-noise ratio while significantly reducing the time consuming of the algorithm.
European Project Space Papers for the PROFES 2019 - Summary

The European Project Space at PROFES 2019 provides an opportunity for researchers involved in ongoing and recently completed research projects (national, European, and international) related to the topics of the conference to present their projects and disseminate the objectives, deliverables, or outcome.
Characteristics of IX1 Digital Acquisition System

Abstract Oil companies has spent decades in pursuit of obtaining high quality seismic data at lower cost. Until recent years, with the development of the geophysical theories, oil companies have shifted focus to low frequency seismic data while keeping the advantages of large number channel operations. The conventional geophones, due to the restriction of working principles, don't have good low frequency response. And, it usually costs more for geophysical companies to purchase large number instruments. The iX1 digital acquisition system is designated to offer fundamental improvement for seismic prospecting, acquiring high-resolution seismic data at economic prices. This article describes the characteristics of iX1 digital system, ground equipment and digital sensor AccuSeis SL11.
Reimagining the Role of the Expert: From Interface Design to Interface Curation

User Interface (UI) design has been a core topic of HCI research for several decades. Equipped with design skills and knowledge, the expert interface designer meticulously analyses a design brief, conceptualises design ideas, and constructs viable solutions. The intended outcome of this tedious process is a usable and aesthetically-pleasing UI. Classical approaches in HCI have relied upon providing designers with guidelines, heuristics, and best practices for realising good designs. In recent years, computational approaches have turned towards formalising and automating parts of the design process. In this provocation, I claim that the future expert will hand over the task of creating design solutions entirely to the machine, and instead take on the role of an interface curator who inspects a set of feasible designs and picks out the best possible solutions for a given problem. I discuss the current state of computational interface design, and suggest a path forward towards realising this vision.
The Algorithm of Direct Current Smooth Filtering Based on the Minimum Resource Consumption in FPGA

The algorithm of direct current smooth filtering was proposed in this paper to satisfy the requirement of minimum resource consumption in FPGA and to meet the goal of calculation period in minimum length. In this work, the detail circuit was designed by FPGA and the key circuit was simulated by the modalism. The result of board-level test is shown that the dither amplitude of original direct current was decreased significantly at 90%. The amplitude was in the stable state to reach expected smoothing effects.
On Representing the Set of All Parse Trees with a Decision Diagram

In this paper, we construct decision diagrams (DDs) representing the set of all parse trees of a context-free grammar (CFG) from a sequence data and analyze DD size. CFG is widely used in the field of natural language processing and bioinformatics to estimate the hidden structures of sequence data. A decision diagram is a data structure that represents a Boolean function in a concise form. By using DDs to represent the set of all parse trees, we can efficiently perform many useful operations over the parse trees, such as finding parse trees that satisfy additional constraints and finding the most probable parse tree. Since the time complexity of these operations strongly depends on DD size, selecting an appropriate DD variant is important. Experiments on the parse trees of a simple CFG show that the Zero-suppressed Sentential Decision Diagram (ZSDD) is better than other DDs; we also give theoretical upper bounds on ZSDD size of a simple CFG. Moreover, we propose an efficient method based on CYK (Cocke-Younger-Kasami) algorithm to construct ZSDDs that can represent the set of all parse trees. Experiments show that the method can construct ZSDDs much faster than the naive method based on compiling a Boolean function.
Database as a Service for Cloud Based Video Surveillance System

In recent times, cloud computing has become the widely accepted technology for moving the database over the cloud, which has brought revolution in the IT industry, this term is recently coined as Database as a Service (DBaaS). Mostly cloud databases are used for data intensive applications such as data warehousing, data mining and monitoring purpose. On the advent of smart cities concept, massively scalable video surveillance has become the necessity in public area. In this paper, we proposed a database as a service to support video surveillance system on the cloud environment with real time video analytics. In this regard, we have explored Trove (Open stack cloud DBaaS component) to facilitate scalability and availability for the video surveillance system furthermore we have also proposed data processing architecture based on open CV, Apache Kafka and Apache spark.
Implementation of security system on humanitarian organization: case study of dompet dhuafa foundation

Abstract System security management can increase public confidence in the information produced and processed by a company and increase the quality assurance of information. To reduce the threat to the company’s business process information system, there must be an assessment of information system security management needed to support the company’s business processes, especially in the humanitarian foundation, in this case, is “Dompet Dhuafa Foundation.” In the use of information systems, security risks will arise, which will hurt the institution. In order to reduce the negative impact, it is necessary to handle the security risks of the system. ISO / IEC 27001 offers a set of specifications, a code of ethics, and best practice guidelines to ensure the management of IT (Information Technology) services. ISO 27001 is a standard that is often used to determine the need to implement information system security. With the application of ISO / IEC 27001, It can protect aspects of information security, namely confidentiality, integrity, and availability.
Self-concept and self-care: implications for adolescents’ achievement

Abstract This study aims to shed light on: (1) self-concept, (2) self-care, (3) achievement, (4) the relationship between self-concept and self-care, (5) the relationship between self-care and achievement, (6) the relationship between self-concept and student achievement, and (7) the effect of self-concept and self-care on achievement. This study employed a quantitative approach. A sample of 95 students was selected as the research subjects using the probability random sampling technique. The instruments of this study were questionnaires and students’ report cards. The data were analyzed using the descriptive analysis technique and correlation. The results show that: (1) the students’ self-concept is in a high category (mean=137); (2) the students’ self-care is in a high category (mean=111); (3) the students’ achievement is good (mean=80); (4) there is a positive correlation between self-concept and self-care with a correlation coefficient of 0.578; (5) there is a positive correlation between self-care and student achievement with a correlation coefficient of 0.822; (6) there is a positive correlation between self-concept and students’ achievement with a correlation coefficient of 0.536; and (7) there is a significant effect of self-concept and self-care on students’ achievement.
Calligraphic Stylization Learning with Active Generation Networks

In this paper, we present a calligraphic stylization learning framework which achieve reasonable results. For this purpose, we propose a novel active generation networks inspired by active shape modeling. Combined with embedding layer, we exploit information across multiple fonts and rich models of calligraphic stylization can be built once in a time. More usefully, we perform a simulation of famous calligraphist’s cursive handwriting that shows our approach outperforms state-of-the-art calligraphic stylization learning.
Scene Text Aware Image Retargeting

Extensive use of text labels and symbols available in the digital media for interpretation and communication of information has gained a lot of attention in the era of digital media. Access of the images with scene text in it through different display devices tend to deform the scene text region while resizing for better viewing experience. We propose an image retargeting operator, which is aware of the scene text present in the image. We perform the normal seam carving depending on the content of the image for the non-text region. We find the target size of each scene text region during the seam carving process. Having the location and the size of the scene text region in the retargeted image, we perform content-aware warping for every scene text region in the image. We evaluate the performance of the proposed scene text aware image retargeting operator using image retargeting quality assessment metric for visual retargeting quality and text recognition efficiency for text readability. We show the quality of the proposed approach through results and discussion.
Worldview-3 and Sentinel-2 Imagery for Mapping Naturally Occurring Asbestos (NOA) in Serpentinites Rocks in Southern Italy

The paper compares the potential of WorldView-3 (WV-3) and Sentinel-2 (S-2) satellite data for mapping naturally occurring asbestos (NOA) outcrops to be used by geologists in the planning phase of environmental monitoring. The wide distribution as well as the variety and extent of asbestos-bearing rocks make the selected area a significant case study for the evaluation of the feasibility of multispectral VNIR-SWIR (0.425-2.330 μm) remote sensing observations for NOA outcrops mapping, in those areas where the density of vegetation allows their spectral identification. Different classification procedures were used to produce NOA outcrops maps for the study area. In our study, we found in general a good agreement (k > 0.8) between the produced NOA outcrops maps and the extensive available in situ data for the accessible locations.
Implementation of Innovative Educational Project of Additional Professional Education

This article considers the issue of improving the system of improvement of professional skill and retraining of pedagogical guidance and education through the implementation of the automated information system planning “Organization and management of additional professional education”, which is designed to improve the management of the additional professional education and continuous development of teaching staff in the region.
Multi-wavelength large optics wavefront error measurement in the SWIR range (Conference Presentation)

We present an optical metrology instrument for measuring both transmitted and reflected wavefront error (TWE and RWE) of coated or uncoated optics over a diameter of 4 inches in the SWIR range. Depending on the coating transmittance and reflectance, the measurements were done at different wavelengths from 1100 nm to 1650 nm. Fizeau interferometry is a standard technique to measure the TWE and RWE of uncoated optics. But in the case of coated optics (bandpass filters for example) measurement of TWE is not possible because the optics may not transmit the interferometer laser light. Moreover, for measurements at different wavelengths, a dedicated interferometer has to be built for each different wavelength. The chosen solution is based on a quadriwave lateral shearing interferometer (QWLSI) wavefront sensor. QWLSI is an achromatic technique, meaning that it measures optical path differences at any wavelength without any need for recalibration at specific wavelengths. Consequently, various sources at different wavelengths can be used with the same instrument and metrology bench. In addition, QWLSI measures the derivative of phase contrary to interferometer that measures phase. Therefore, QWLSI has by design a better WFE dynamic range for TWE and RWE measurement. Moreover, an achieved accuracy below 30 nm RMS is perfectly adapted to optical metrology measurement. The optical solution is a standard double pass configuration composed of a collimator and a beam expander to adapt the size of the beam to the aperture of the SWIR wavefront sensor. Different sources between 1100 nm and 1650 nm were integrated.
Placing Users and Nonusers at the Heart of Technology

Technologies need users. The machines built from waste materials, salvaged metal, and discarded clothing by the Swiss artist Jean Tinguely may be the only machines intended explicitly not to be used or produce anything. Inspired by Dadaism and the anticapitalist movement of the early 1960s, Tinguely aimed to “free machines” from their “slavery” in the mass production of consumer goods (Menil & Alexandre, 1980). These machines were made to disrupt the static presentation of art in conventional museums at that time and to destruct rather than produce anything (Stedelijk Museum, 2017).1 However, most machines are intended to produce consumer goods, and both depend on users. It is thus no surprise that users are an important theme in science and technology studies (STS). Criticizing a determinist linear view of technology that portrayed users as passive consumers, STS scholars have convincingly shown that users play an active role in all phases of technological development, from design to implementation and use.
Semantic Framework for Spatial Query Reformulation for Disaster Monitoring Applications

In disasters, since time is of the essence, quick decision making based on actionable insights is desired. In our earlier work, we have demonstrated that the spatial relationships-based queries can play a vital role in the disaster response phase. However, we found that the utilization of spatial relationships rules (i.e. encoded spatial knowledge) via rule reasoning process do not scale well with the increased number of image regions. Most of the available Resource Description Framework (RDF) triplestores do not support rule reasoning due to the computational complexity and undecidable nature of the rule reasoning process. In this paper, we propose an alternative approach for utilizing spatial knowledge encoded in the form of spatial relationship rules. The proposed approach reformulates the spatial query by expanding it with the configuration encoded in the corresponding spatial relationship rule. The preliminary results are promising and show the applicability of the proposed approach during the time critical events such as flood disaster.
Research on Extraction of Address from Criminal Case Records via Lattice LSTM

Abstract As an important carrier for recording the details of case in the field of criminal investigation, case records contain address of the crime scene which is crucial to the case analysis. This paper uses Lattice LSTM to extract address from case records which combines the advantages of the character-based and word-based models. It can exploit word and word sequence information without performing Chinese word segmentation. For the purpose of optimizing the model, this paper proposes a word vector template which is generated by address repository of Shanghai to pretrain more accurate and more controllable word embeddings. The experiment shows that the model achieves the performance with the precision rate of 96.49%, recall rate of 94.83% and F1 score of 95.65%.
Diversity Preservation in Genetic Algorithm by Lifespan Control

Genetic algorithms (GA) application to a real world problem which possesses various complicated constraints often requires to design each problem dependent genetic operations to keep the feasibility of the individual, which could decrease the population diversity and cause the early convergence. We propose Life Control Genetic Algorithm (LCGA) to maintain the diversity under such biased operations, by setting the lifespan of each individual depending on the relative fitness. LCGA is first applied to a typical functional optimization with largely biased crossover, and compared with several types of GA including uniform lifespan. Next, it is applied to a building design optimization as an example of a real world combinatorial optimization with complicated constraints, and the effectiveness is studied by numerical experiments.
Body Detection in Spectator Crowd Images Using Partial Heads

In spectator crowd images, the high number of people, small size and occlusion of body parts, make the body detection task challenging. Due to the similarity in facial features of different people, the variance in head features is less compared to the variation in the body features. Similarly, the visibility of the head in a crowd is more, compared to the visibility of the body. Therefore, the detection of only the head is more successful than the detection of the full body. We show that there exists a relation between head size and location, and the body size and location in the image. Therefore, head size and location can be leveraged to detect full bodies. This paper suggests that due to lack of visibility, more variance in body features, and lack of available training data of occluded bodies, full bodies should not be detected directly in occluded scenes. The proposed strategy is to detect full bodies using information extracted from head detection. Additionally, body detection technique should not be affected by the level of occlusion. Therefore, we propose to use only color matching for body detection. It does not require any explicit training data like CNN based body detection. To evaluate the effectiveness of this strategy, experiments are performed using the S-HOCK spectator crowd dataset. Using partial ground truth head information as the input, full bodies in a dense crowd is detected. Experimental results show that our technique using only head detection and color matching can detect occluded full bodies in a spectator crowd successfully.