Reviewerly/exHarmony huggingface dataset:
Domain-Size Aware Markov Logic Networks

Several domains in AI need to represent the relational structure as well as model uncertainty. Markov Logic is a powerful formalism which achieves this by attaching weights to formulas in finite first-order logic. Though Markov Logic Networks (MLNs) have been used for a wide variety of applications, a significant challenge remainsthat weights do not generalize well when training domain sizes are different from those seen during testing. In particular, it has been observed that marginal probabilities tend to extremes in the limit of increasing domain sizes. As the first contribution of our work, we further characterize the distribution and show thatmarginal probabilities tend to a constant independent of weights and not always to extremes as was previously observed. As our second contribution, we present a principled solution to this problem by defining Domain-size Aware Markov Logic Networks (DA-MLNs) which can be seen as re-parameterizing the MLNs after taking domain size into consideration. For some simple but representative MLN formulas, we formally prove that probabilities defined by DA-MLNs are well behaved. On a practical side, DA-MLNs allow us to generalize the weights learned over small-sized training data to much larger domains. Experiments on three different benchmark MLNs show that our approach results in significant performance gains compared to existing methods.
A New Copyright- and Privacy-Protected Image Trading System Using a Novel SteganographyBased Visual Encryption Scheme

In copyright- and privacy-protected image trading systems, the image sent to the trusted third party (TTP) is visually encrypted, and the image is traditionally unrecognizable. The image, however, is suspicious and has possibility to be attacked. Image steganography then becomes more interesting than image encryption for this application, however, applying image steganography instead of image encryption degrades the fingerprint extraction performance. In addition, the CP is allowed to directly contact with the consumer in the conventional systems. Thus, the consumer’s privacy is not protected completely. This paper then proposes a new copyright- and privacy-protected image trading system with a novel steganography-based visual encryption scheme, where the scheme protects a commercial image much more securely by generating a recognizable image instead of a suspicious encrypted image. By replacing amplitude components of a dummy image by those of a commercial image, the output image looks like a degraded dummy image instead of the commercial image, while it contains some details of the commercial image, i.e., the amplitude components of the commercial image are hidden into a dummy cover image. A discrete cosine transform-based fingerprinting method, which is compatible with the proposed amplitude component replacing scheme, is also proposed in this paper to solve the problem of applying image steganography. As another contribution of this paper, the CP is not allowed to directly send the image reconstruction key to the consumer, for further consumer’s privacy protection. The second TTP is then introduced to the proposed system, and in addition, the image reconstruction key is encrypted by the CP before being sent to the consumer via the second TTP for more security. Experimental results show that the proposed scheme generates recognizable images and perfectly visually encrypts the commercial images. It also achieves much higher reconstructed image qualities than those of the conventional scheme, and the proposed system simultaneously enhances the fingerprinting performance using the proposed compatible fingerprinting method.
Using Smart Glasses for Facial Recognition

Facial recognition is one of the most promising applications of smart glasses and can help many organizations become more efficient. For example, police traditionally identify criminals by manually going through pictures in a database which makes face matching a slow process. However, with the combination of facial recognition software, smart glasses, and databases, the police can quickly scan through multiple databases of faces to find a match. The police would also be able to spot criminals in crowds, identify unknown victims at crime scenes, retrieve background information on individuals, and verify if someone is a missing person. The Transportation Security Administration (TSA) can also use this combination to identify potential terror suspects or verify the identity of travelers. Lastly, academia can benefit from these tools by being able to identify individuals at events (e.g. conferences) and display relevant information about them. The goal of this project is to write an Android program that takes a photo via Google Glass, compares it with a predefined sample database held within the smartphone, and outputs information based on its analysis. The results are displayed with an accuracy acceptance level to the user both on their Android smartphone and on their Google Glass.
HIGH SCHOOL STUDENTS’ READING HABIT AND PERCEPTION ON READING FOR PLEASURE

This paper aims to identify high school students’ reading habit and describe their perceptions on reading for pleasure. There were 41 high school students grade XI involved in this study. Data were collected from questionnaires, observation, and an interview. Results indicate that during holidays nearly 50% of the students read sometimes, 12% read once a week, 9% once in a month, and 29% never read although books are available. The students agree that reading for pleasure give many benefits, but the quantitative data indicate students’ low interest in reading. This may be due to the reading culture which has not been sufficiently developed since the early ages. In addition, the availability and accessibility of reading materials seems to play roles in determining the students’ reading interest. Reading materials should also vary in genres, in themes, and in forms of both printed and on-screen regarding students’ digital nativity. This study has, therefore, initiated the needs for libraries with good book collection, facilities, and interesting design to attract students. DOI : doi.org/10.24071/ijiet.2019.030108
Dynamic Configuration of CUDA Runtime Variables for CDP-Based Divide-and-Conquer Algorithms

CUDA Dynamic Parallelism (CDP) is an extension of the GPGPU programming model proposed to better address irregular applications and recursive patterns of computation. However, processing memory demanding problems by using CDP is not straightforward, because of its particular memory organization. This work presents an algorithm to deal with such an issue. It dynamically calculates and configures the CDP runtime variables and the GPU heap on the basis of an analysis of the partial backtracking tree. The proposed algorithm was implemented for solving permutation combinatorial problems and experimented on two test-cases: N-Queens and the Asymmetric Travelling Salesman Problem. The proposed algorithm allows different CDP-based backtracking from the literature to solve memory demanding problems, adaptively with respect to the number of recursive kernel generations and the presence of dynamic allocations on GPU.
Online Trolls, Journalism and the Freedom of Speech: Are the Bullies Taking Over?

This paper explores the legal and ethical challenges faced by journalists in retaining open channels of discourse online. Quantitative and qualitative research involving twenty experienced reporters, twenty new media industry recruits and forty journalism undergraduates assesses the extent to which internet trolls impacted on their work. It found that while the sample was required to have an online presence and to interact with their audiences through social media, they received little guidance in dealing with abuse and threats. A significant number, including the journalism students developing their online presence, felt that hostile experiences had affected their ability to express themselves freely. This has an impact on a journalist’s critical role as a guardian of free speech in a democratic society. Social media adds to the pressures on journalists and raises concerns over the effectiveness of existing complaint mechanisms. A case is made for more support from both educators and the media industry to ensure these freedoms are not eroded.
KEEFEKTIFAN MODEL PEMBELAJARAN INOVATIF DENGAN MENGGUNAKAN MIND MAPPING DALAM PEMBELAJARAN MENULIS NARASI SISWA SD

The purpose of this study was to determine the effectiveness of innovative learning models using mind mapping in learning to write narratives of second grade Elementary School students.The effectiveness of learning can be seen from the results of observations from students 'activities during learning activities, while the improvement of students' narrative essay writing skills can be seen from the results of tests on limited trials and extensive trials.The results of the research that have been conducted show that the effectiveness of innovative learning models using mind mapping in learning to write narratives of second grade Elementary School students is very good.This can be seen based on the recapitulation of observations of student activities in limited trials and extensive trials at presentations of 81% -100% which means very good.Furthermore, based on the results of the recapitulation, the improvement of narrative writing skills of the second grade students showed the results that students could improve the students' narrative writing skills.The results of the recapitulation of the improvement of the narrative writing skills of the second grade elementary school students in the limited trial were 21.97%, and the extensive trial was 19.68%.
Urban planning support based on the photovoltaic potential of buildings: a multi-scenario ranking system

Urban planning support based on the photovoltaic potential of buildings: a multi-scenario ranking system
Close Approximation of Kullback–Leibler Divergence for Sparse Source Retrieval

In this letter, we propose a fast and accurate approximation of the Kullback-Leibler divergence (KLD) between two Bernoulli-Generalized Gaussian (Ber-GG) distributions. Such a distribution has been found to be well suited for modeling sparse signals like wavelet-based representations. On the basis of high-bitrate approximations of the entropy of quantized Ber-GG sources, we provide a close approximation of the KLD without resorting to the conventional time-consuming Monte Carlo estimation approach. The developed approximation formula is then validated in the context of depth map and stereo image retrieval.
Reducing the Key Size of McEliece Cryptosystem from Automorphism-induced Goppa Codes via Permutations

In this paper, we propose a new general construction to reduce the public key size of McEliece cryptosystems constructed from automorphism-induced Goppa codes. In particular, we generalize the ideas of automorphism-induced Goppa codes by considering nontrivial subsets of automorphism groups to construct Goppa codes with a nice block structure. By considering additive and multiplicative automorphism subgroups, we provide explicit constructions to demonstrate our technique. We show that our technique can be applied to automorphism-induced Goppa codes based cryptosystems to further reduce their key sizes.
Cyberbullying in India’s Capital

Over the period of time, data charges in India are decreasing, infrastructure of data lines is increasing, due to good competition in the market availability of smart phones on cheap rates is also increasing. Other than this, users of social networking sites and mobile Apps are also increasing. Smart phones with attractive data plans are easily available these days to every economic group. Although the facility of getting connected with the world anytime through internet connected device has proved to be a boon for the society, yet it has helped introducing cybercrimes in the society. Cyberbullying is becoming a prominent cybercrime these days. Cyberbullying involves an aggressive behavior of an individual or a group of individuals whose intention is to insult others through social or electronic media. This paper informs the status of this crime in the capital of India, shows the awareness of cyber bullying among different age groups in Delhi and NCR and how much Delhi is getting affected out of it. The paper also gives few ideas to save people from being the victim of cyberbullying.
Multi-class Semi-supervised Logistic I-RELIEF Feature Selection Based on Nearest Neighbor

The multi-class semi-supervised logistic I-RELEIF (MSLIR) algorithm has been proposed and showed its feature selection ability using both labeled and unlabeled samples. Unfortunately, MSLIR is poor when predicting labels for unlabeled samples. To solve this issue, this paper presents a novel multi-class semi-supervised logistic I-RELEIF based on nearest neighbor (MSLIR-NN) for multi-class feature selection tasks. To generate better margin vectors for unlabeled samples, MSLIR-NN uses the nearest neighbor scheme to first predict the labels of unlabeled samples and then calculates their margin vectors according to these estimated labels. Experimental results demonstrate that MSLIR-NN can improve the prediction accuracy of unlabeled data.
PCANE: Preserving Context Attributes for Network Embedding

Through mapping network nodes into low-dimensional vectors, network embedding methods have shown promising results for many downstream tasks, such as link prediction and node classification. Recently, attributed network embedding obtained progress on the network associated with node attributes. However, it is insufficient to ignore the attributes of the context nodes, which are also helpful for node proximity. In this paper, we propose a new attributed network embedding method named PCANE (Preserving Context Attributes for Network Embedding). PCANE preserves both network structure and the context attributes by optimizing new object functions, and further produces more informative node representations. PCANE++ is also proposed to represent the isolated nodes, and is better to represent high degree nodes. Experiments on 3 real-world attributed networks show that our methods outperform the other network embedding methods on link prediction and node classification tasks.
Efficient preference clustering via random fourier features

Approximations based on random Fourier features have recently emerged as an efficient and elegant method for designing large-scale machine learning tasks.Unlike approaches using the Nystr öm method, which randomly samples the training examples, we make use of random Fourier features, whose basis functions (i.e., cosine and sine ) are sampled from a distribution independent from the training sample set, to cluster preference data which appears extensively in recommender systems.Firstly, we propose a two-stage preference clustering framework.In this framework, we make use of random Fourier features to map the preference matrix into the feature matrix, soon afterwards, utilize the traditional k-means approach to cluster preference data in the transformed feature space.Compared with traditional preference clustering, our method solves the problem of insufficient memory and greatly improves the efficiency of the operation.Experiments on movie data sets containing 100 000 ratings, show that the proposed method is more effective in clustering accuracy than the Nystr öm and k-means, while also achieving better performance than these clustering approaches.
Negative Consequences of Anthropomorphized Technology: A Bias-Threat-Illusion Model

Attributing human-like traits to information technology (IT) -leading to what is called anthropomorphized technology (AT)-is increasingly common by users of IT. Previous IS research has offered varying perspectives on AT, although it primarily focuses on the positive consequences. This paper aims to clarify the construct of AT and proposes a "bias-threat-illusion" model to classify the negative consequences of AT. Drawing on "threefactor theory of anthropomorphism" from social psychology and integrating self-regulation theory, we propose that failing to regulate the use of elicited agent knowledge and to control the intensified psychological needs (i.e., sociality and effectance) when interacting withAT leads to negative consequences: "transferring human bias," "inducing threat to human agency," and "creating illusionary relationship."Based on this bias-threat-illusion model, we propose theory-driven remedies to attenuate negative consequences.We conclude with implications for IS theories and practice.
Locally adaptive super-resolution through spatially variant interpolation

Systems that do not meet the requirements of the sampling theorem produce images corrupted by aliasing. Higher resolution images are attainable by unfolding aliased spatial frequencies. Multiple-image super-resolution has seen much attention in the literature though with no clear optimum algorithm for many real-world applications. We propose a method of multiframe super-resolution using a set of convolutional sinc kernels, tailored to the specific shifts between images, capable of resolving up to the diffraction limit. We demonstrate our method for the case of global shifts before we treat a pixel-level super-resolution.
SENTINEL-1A ANALYSIS FOR DAMAGE ASSESSMENT: A CASE STUDY OF KUMAMOTO EARTHQUAKE IN 2016

The powerful earthquakes occurred in Kumamoto at the highest magnitude 7.3 hitting this prefecture on April 16, 2016 at 01.25 A.M. local time. This study proposed a method to utilize Sentinel-1A images to detect earthquake damaged areas. There were two images being used. One was before-earthquake images (on March 3, 2016 and March, 27 2016) and another was an after-earthquake image (on April 20, 2016). The method operated on interferometric coherence of sentinel-1A image. In order to estimate the damaged areas, the coherence change between the before-earthquake and during-earthquake pairs were processed. The damage map was produced by concerning into different classes comprised of the urban damaged areas, none and less damaged of the urban areas, and non-urban areas. This approach was compared with the ground-truth data, which provides high overall accuracy at 88% (kappa coefficient is 0.82). Consequently, Sentinel-1A could contribute the insightful geospatial information of the earthquake situation and support the disaster management.
The Impact of Gender, Avatar and Height in Distance Perception in Virtual Environments

Virtual Reality is becoming more popular over the years because it allows the user to be the main actor in another environment and interact with it in real time. New interaction methods are being studied, like tangible interfaces, but there is little work done related to small distances when grabbing objects through a virtual environment. This study is important because, in our perspective, interaction in virtual reality will be at arms reach, meaning that the user will interact within very close distances (under 1 m). In this paper, the research team further evaluate distance perception using gender, the presence of avatar and height (fixed or personalised). The sample consisted of 64 participants (32 females and 32 males) evenly distributed between all four conditions (8 males and 8 females for each condition). Results revealed that gender does have an impact on small distance estimation; height does not have an impact on distance estimation; and avatar does make a difference when trying to grab a real object through the virtual environment.
What Do Cryptocurrencies Do?

Cryptocurrencies, like Bitcoin, are designed to facilitate payments even when there is a lack of trust. In this article, the authors discuss the features that are necessary for cryptocurrencies to play this role. They conclude that the specific features required to allow cryptocurrencies to facilitate payments when there is a lack of trust make these currencies less convenient as a payment mechanism than alternatives that rely on trust. The authors conclude that as long as sufficient trust exist in payment intermediaries, cryptocurrencies are likely too inconvenient to be widely adopted. <b>TOPICS:</b>Currency, portfolio construction, wealth management
Thickened 2D Networks for 3D Medical Image Segmentation.

There has been a debate in 3D medical image segmentation on whether to use 2D or 3D networks, where both pipelines have advantages and disadvantages. 2D methods enjoy a low inference time and greater transfer-ability while 3D methods are superior in performance for hard targets requiring contextual information. This paper investigates efficient 3D segmentation from another perspective, which uses 2D networks to mimic 3D segmentation. To compensate the lack of contextual information in 2D manner, we propose to thicken the 2D network inputs by feeding multiple slices as multiple channels into 2D networks and thus 3D contextual information is incorporated. We also put forward to use early-stage multiplexing and slice sensitive attention to solve the confusion problem of information loss which occurs when 2D networks face inputs. With this design, we achieve a higher performance while maintaining a lower inference latency on a few abdominal organs from CT scans, in particular when the organ has a peculiar 3D shape and thus strongly requires contextual information, demonstrating our method's effectiveness and ability in capturing 3D information. We also point out that thickened 2D inputs pave a new method of 3D segmentation, and look forward to more efforts in this direction. Experiments on segmenting a few abdominal targets in particular blood vessels which require strong 3D contexts demonstrate the advantages of our approach.
Software Adaptability Metrics Model Using Ordinary Logistic Regression

Adaptability is an important software quality characteristic and a major non-functional requirement in software and should therefore be given adequate attention during software quality measurement and predictions especially now that the environment in which software products operate becomes highly unpredictable due to rapid changes in hardware platform as well as changes in the operating system requirements.In this work the results from the analysis of object-oriented software source code using our previously developed software analyser to measure the values of some internal properties using object-oriented software metrics based on formulation of decision rules in conjunction with binary logic combination of the possible internal software properties which aided in the prediction of adaptability level of a given software, is used as the dataset for ordinal logistic regression analysis.This analysis was used to formulate the adaptability model based on proportional odds assumption.The results showed that software with low coupling, inheritance and complexity were more likely to be adaptable than those with high values.Conversely, software with low cohesion were less likely to be adaptable than those with high cohesion.High cohesion was associated with adaptability since its odds ratio (low/high) was 7.97 (>1) while low coupling, inheritance and complexity were associated with adaptability since their odds ratios (low/high) were 0.15, 0.22 and 0.05 (<1) respectively.The resulted model fitted the data well and the estimated cumulative odds were the same across all the ordinal categories, thus the proportional odds assumption held.