Reviewerly/exHarmony huggingface dataset:
Maintenance assessment guidelines for autonomic system using ANP approach

Autonomic Computing based development is emerging in the IT sector which performs software management intelligently. As a result, the software development team are now using their efficiency more on developing high-performance optimal software rather than on maintenance task. This concept was introduced by IBM & now the development has reached a stage where autonomicity of particular autonomic application may also be determined. In this paper, System Autonomicity Level has been estimated as one of the objective using Analytical Network Process applied on our proposed Maintenance Model. Further, some guidelines for the maintenance of autonomic systems are also explored. Using these guidelines, the domain-specific development and maintenance process may be handled in such a way that it will save time and cost required for developing such systems. These guidelines also work as pre-post guidelines in Software Development Life Cycle. Testing phase may also become efficient using proposed domain specific maintenance guidelines.
Holes in the Outline

Scientific literature search engines typically index abstracts instead of the full-text of publications. The expectation is that the abstract provides a comprehensive summary of the article, enumerating key points for the reader to assess whether their information needs could be satisfied by reading the full-text. Furthermore, from a practical standpoint, obtaining the full-text is more complicated due to licensing issues, in the case of commercial publishers, and resource limitations of public repositories and pre-print servers.
Skip-Gram-KR: Korean Word Embedding for Semantic Clustering

Deep learning algorithms are used in various applications for pattern recognition, natural language processing, speech recognition, and so on. Recently, neural network-based natural language processing techniques use fixed length word embedding. Word embedding is a method of digitizing a word at a specific position into a low-dimensional dense vector with fixed length while preserving the similarity of the distribution of its surrounding words. Currently, the word embedding methods for foreign language are used for Korean words; however, existing word embedding methods are developed for English originally, so they do not reflect the order and structure of the Korean words. In this paper, we propose a word embedding method for Korean, which is called Skip-gram-KR, and a Korean affix tokenizer. Skip-gram-KR creates similar word training data through backward mapping and the two-word skipping method. The experiment results show the proposed method achieved the most accurate performance.
Building and Studying a Password Store that Perfectly Hides Passwords from Itself

We introduce a novel approach to password management, called SPHINX, which remains secure even when the password manager itself has been compromised. In SPHINX, the information stored on the device is theoretically independent of the user's master password. Moreover, an attacker with full control of the device, even at the time the user interacts with it, learns nothing about the master password - the password is not entered into the device in plaintext form or in any other way that may leak information on it. Unlike existing managers, SPHINX produces strictly high-entropy passwords and makes it compulsory for the users to register these passwords with the web services, which defeats online guessing attacks and offline dictionary attack upon service compromise. We present the design, implementation and performance evaluation of SPHINX, offering prototype browser plugins, smartphone apps and transparent device-client communication. We further provide a comparative analytical evaluation of SPHINX with other password managers based on a formal framework consisting of security, usability, and deployability metrics.
Cultural Embodiment in Virtual Reality Education and Training: A Reflection on Representation of Diversity

This reflection paper presents an argument with justification for including diverse representations of embodied self in the design of virtual training systems and smart learning environments. A great number of VR training programs exist and they are becoming more widespread with the advent on consumer VR. Evidence supports that embodiment of self through avatars impact engagement and transfer of behavior outside of virtual space. It is important that these factors are taken into consideration during the initial stages of development, as it is much easier to design a system with a diversity component and opportunities for customization than it is to redesign an existing system to represent diverse users. This discourse is informed by perspectives from literature on cognitive and behavioral effects of embodiment in virtual spaces. This is also informed by insights from the multi-billion dollar game industry that has struggled to include more diverse representations in recent years.
Filtered strong quantum correlation of resonance fluorescence from a two-atom radiating system with interatomic coherence

Frequency-resolved quantum correlation of resonance fluorescence is investigated in a two-atom radiating system. In this quantum radiating system, only one atom is driven by a laser field, and the spontaneous transition of the undriven atom resonates with one of the Rabi sidebands of the driven atom. A single-mode empty cavity is applied to serve as a Lorentzian filter to output the superbunched fluorescent photon pairs when its frequency is tuned to halfway between the central peak and one of the side peaks. In the case of large filter width, two-photon correlation signal and its physical correspondence can be bridged analytically in our approach. It reveals that this superbunching effect turns out to be the constructive quantum interference between a pair of coupled two-photon cascaded transitions. Ulteriorly, it is the consequence of the modulations of the unfiltered dressed-state transition amplitudes by the filter. Our analytical formalism also shows that, although the dipole-dipole interaction is usually weak, the interatomic coherence caused by this weak perturbation can also play a crucial role in breaking through the superbunching limit obtained from a single two-level atom in the same parameter regime. In addition to being a treasurable quantum pump to probe into the target quantum system, it is also found in our investigation that this superbunched fluorescence can serve as a promising quantum response in detecting this weak perturbation in the interior of the quantum source. A general case is also considered when the two-atom radiating system is monitored by two filter-detector monitoring systems. It is found that this filtered strong quantum correlation can be maintained even though the two photons are spatially separated.
Authentication Protocols Based on One-Time Passwords

Nowadays one-time passwords are used in a lot of areas of information technologies including e-commerce. A few vulnerabilities in authentication protocols based on one-time passwords are widely known. In current work, we analyze authentication protocols based on one-time passwords and their vulnerabilities. Both simple and complicated protocols which are implementing cryptographic algorithms are reviewed. For example, an analysis of relatively old Lamport's hash-chain protocol is provided. At the same time, we examine HOTP and TOTP protocols which are actively used nowadays. The main result of the work are conclusions about the security of reviewed protocols based on one-time passwords.
The autohomeomorphism group of connected homogeneous functionally Alexandroff spaces

If f:X→X is a map, then the family {A⊆X:f(A)⊆A} gives the closed sets of a topology P(f) on X called a functionally Alexandroff topology. If (X,P(f)) is a connected homogeneous functionally Alexandroff space, we give an algebraic characterization and some basic properties of its autohomeomorphism group H(X).
Reducing overfitting of a deep learning breast mass detection algorithm in mammography using synthetic images

We evaluated whether using synthetic mammograms for training data augmentation may reduce the effects of overfitting and increase the performance of a deep learning algorithm for breast mass detection. Synthetic mammograms were generated using a combination of an in-silico random breast generation algorithm and x-ray transport simulation. In-silico breast phantoms containing masses were modeled across the four BI-RADS breast density categories, and the masses were modeled with different sizes, shapes and margins. A Monte Carlo-based xray transport simulation code, MC-GPU, was used to project the 3D phantoms into realistic synthetic mammograms. A training data set of 2,000 mammograms with 2,522 masses were generated and used for augmenting a data set of real mammograms for training. The data set of real mammograms included all the masses in the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and consisted of 1,112 mammograms (1,198 masses) for training, 120 mammograms (120 masses) for validation, and 361 mammograms (378 masses) for testing. We used Faster R-CNN for our deep learning network with pre-training from ImageNet using Resnet-101 architecture. We compared the detection performance when the network was trained using only the CBIS-DDSM training images, and when subsets of the training set were augmented with 250, 500, 1,000 and 2,000 synthetic mammograms. FROC analysis was performed to compare performances with and without the synthetic mammograms. Our study showed that enlarging the training data with synthetic mammograms shows promise in reducing the overfitting, and that the inclusion of the synthetic images for training increased the performance of the deep learning algorithm for mass detection on mammograms.
Real-time opponent learning in automated negotiation using recursive Bayesian filtering

Automated negotiation is a toolset to model human interactions during a negotiation process with the aim of improving the efficiency and quality of decision-making using advanced information analytics. During the negotiation, the participants share their viewpoints and concerns about the negotiation issues. However, in reality, they usually do not reveal the details of their preferences to one another. Therefore, modeling and learning opponents’ behavior is a crucial component of automated negotiation. In this paper, we propose an estimation technique based on recursive Bayesian filtering to facilitate opponent-modeling and -learning in the context of multi-participant, multi-issue negotiations. In the proposed technique, opponents’ preference profiles are modeled using fuzzy functions, which are very close to the way humans evaluate alternatives. As the negotiation progresses, the agents can recursively learn the parameters of these models in real time. The only required information for this learning process includes the feedback and the arguments the participants may provide in support of their decisions. At each round, a probabilistic graphical model is also implemented that utilizes the learned preference limits of the participants to offer a new proposal with a high probability of satisfying the participants and reaching an agreement. The proposed methodology is examined in two different negotiation contexts: energy-system development and real estate service. The experiments show that the proposed opponent modeling/learning approach increases the efficiency of the negotiation up to 85% and facilitates reaching an agreement in fewer rounds of negotiation without requiring any prior understanding of the negotiation participants.
A Pattern-Based, Design Framework for Designing Collaborative Environments

Communication theory suggests that people tend to interact with interactive artifacts as if these were human. For decades, this understanding has been applied to designing singular, embedded artifacts at a small physical scale. In this paper, we extend the same theory and practice to the dimension of space-to designing interactive, physical environments and their components. A conceptual ground for this is found in a "pattern language" developed by Alexander et al. for designing static physical environments. Upon this ground, we construct a systematic framework for designing "collaborative environments" shaped, as well, by our own concepts, Direct Mapping, Conveyed Mapping, and Space Agency, to strive for more human-human-like interactions between human beings and their physical surroundings. Our lab-based study generates a hypothetical design as qualitative validation of the framework, which has significance for designing tangible, embedded, and embodied interaction as it extends, inevitably, to the dimension of space, entertaining, serving, and augmenting us.
Distance and word order between lexical heads and noun dependents in Chinese–English code-switching

This paper investigates the effects of code-switching on the dependency distance and the word order of the dependencies with lexical heads and noun dependents, using data from a Chinese–English code-mixed treebank. It is found that (1) except the object relation, mixed dependencies with English lexical heads and Chinese noun dependents present shorter dependency distances than monolingual ones; (2) mixed dependencies with Chinese lexical heads and English noun dependents present longer dependency distances than monolingual ones; (3) word order differences are mainly found in the adverbial dependencies with verb heads and noun dependents, or with English preposition heads and Chinese noun dependents. These findings suggest that: (1) the heads, either the first language or the second language, tend to have greater influence on the dependency distance of the mixed dependencies; (2) the word order of the mixed dependencies is greatly influenced by the first language and the dependents tend to play the greater role.
Joint dialog act segmentation and recognition in human conversations using attention to dialog context

A dialog act represents the communicative function of an utterance in a conversation, and thus provides informative cues for understanding, managing, and generating dialog. While most spoken dialog systems process user input and system output at the turn level, a single turn can consist of multiple dialog acts in human conversations. Therefore, segmenting turn-level tokens into a meaningful dialog act unit is just as important as recognizing the dialog act. Towards joint segmentation and recognition of dialog acts, we propose an encoder–decoder model featuring joint coding and incorporate contextual information by means of an attentional mechanism. The proposed encoder–decoder outperforms other models in segmentation, and the application of attentions significantly reduces recognition error rates. By combining the encoder–decoder model with contextual attention, we achieve state-of-the-art performance in the joint evaluation of dialog act segmentation and recognition.
Can ImageNet feature maps be applied to small histopathological datasets for the classification of breast cancer metastatic tissue in whole slide images?

The scarcity of large histopathological datasets can be problematic for Deep Learning in medical imaging and digital pathology. However, transfer Learning has been shown to be promising for the effective training of classifiers on smaller datasets. ImageNet is a popular dataset that is commonly used for transfer learning in various domains. The features extracted from the ImageNet dataset are generalizable and can be applied to alternative tasks and datasets. Deep Learning typically requires a vast amount of data for training, however, in our study we interrogated two datasets with patches extracted from only 30 whole slide images (WSIs) and 60 WSIs respectively. As a consequence, we decided to extract features and feed them into separate classifier models such as a fully connected softmax layer, Support Vector Machines (SVM) and Logistic Regression. This study demonstrated that for the small dataset, the best pretrained feature extractor was DenseNet201, whereas the best model for training was a fully connected softmax layer with a reported accuracy of 88.20% and an average f1-score of 0.881. For the larger dataset size, the best feature extractor was InceptionResNetV2 where the highest accuracy and f1-score of 90.60% and 0.908 was produced when classified using a fully connected softmax layer. All models, apart from ResNet50 demonstrated an improvement in performance when pretraining using ImageNet for bottleneck feature extraction.
An ElGamal-like Secure Channel Free Public Key Encryption with Keyword Search Scheme

The idea of public key encryption with keyword search (PEKS), proposed by Boneh et al., enables one to send a trapdoor containing a encrypted keyword to query data without revealing the keyword. In Boneh et al.’s design, the trapdoor has to be transferred through a secure channel, which is both costly and inefficient. Baek et al. then proposed an efficient secure channel free public key encryption scheme with keyword search (SCF-PEKS). After that, vast amounts of research have focused on the protection against the off-line keyword guessing attack (OKGA) by enhancing the model. However, most of the PEKS/SCF-PEKS schemes developed so far are constructed by applying bilinear pairing and are susceptible to off-line keyword guessing attacks. In this paper, we propose a new SCF-PEKS scheme based on the ElGamal cryptosystem. The proposed scheme is not only secure against off-line keyword guessing attacks but also improves the efficiency.
Facial expression recognition using feature level fusion

AbstractAbstractIn this paper, a model for facial expression recognition (FER) fusing local and global features is proposed. Local features are extracted by dividing the face into multiple regions and carefully selecting regions of interest which assists in reducing redundancy. Global features are extracted from the entire subject face and features of interest are extracted for expression recognition. The major drawback with previously available techniques in literature is that they do not take finer details into account along with global geometric features. In our work, using concatenation fusion, FER is performed in an effective way; one part of model recognizes facial expressions on a complete scale while the other performs the same task on a finer scale, thereby improving the accuracy. Accuracy with the proposed fused model is increased to 93.52% in comparison to the accuracy of 74.11% with local binary patterns and 90.64% with global features when used as standalone technique.Keywords: Facial expression recognitionfacial expressionsupport vector machineslocal featuresglobal featureslocal binary patternsregion-specific features
"What is Fair Shipping, Anyway?"

The HCI community cares for the human and social aspects of technologies. Ethical discussion on the social implications of new technologies often happen among researchers, but it is important to raise this discussion also in the industry that designs and implements new systems. In this paper, we introduce a case in which design fiction was used as an ethical discussion tool among company partners. We report the process of creating and prototyping a fictional world embedded with conflicting values that aimed to shift the focus from industrial merits towards societal values and raise discussion among participants. Moreover, we examine the challenges and propose suggestions in crafting critiques and friction to the industrial context. Our findings suggest why and how one should use design fiction as a means to raise ethical awareness in a technology- and profit-focused context, to support further activities on developing more humane technological futures.
AsiANet: Autoencoders in Autoencoder for Unsupervised Monocular Depth Estimation

Monocular depth estimation is extremely challenging because it is inherently an ambiguous and ill-posed problem. The unsupervised approach to monocular depth estimation using convolutional neural networks is gaining a lot of interest since learning from a set of rectified stereo image pairs without ground truth depths and predicting scene geometry from a single image have become feasible. The proposed approach requires training an encoder-decoder network architecture, referred to as autoencoders in autoencoder (AsiANet), in an unsupervised fashion to discover the implicit relationship between a single image and its corresponding depth map. AsiANet uses a unique Inception-like pooling module based on fractional max-pooling for dimensionality reduction. Experiments on the KITTI benchmark dataset show that the proposed architecture trained using the Charbonnier loss function achieved superior performance on depth map prediction compared to previous unsupervised monocular depth estimation methods.
Optically guided level set for underwater object segmentation

Combining underwater optical imaging principles and the level set, this paper proposes a novel type of level set method called optically guided level set. This novel method can transform optical challenges in underwater environments (such as the illumination bias and wavelength-selective absorption) into valuable guidance for underwater object segmentation. Using the underwater optical guidance, our novel method can generate accurate object segmentation results by suitable initialization and regular evolving of the level set. The optical guidance core lies in two observations pertaining to the underwater optical imaging process: (i) the overlap between the object region and optical collimation region and (ii) the correspondence between the object structure and irradiation distribution inside the optical collimation. The high accuracy of our proposed method is demonstrated via comparisons to the state-of-the-art level set and salient object detection methods for public underwater images collected in diverse environments. Moreover, by using the work presented in this paper, we plan to demonstrate optical principles' potential for improving computer vision research, which is a promising research topic with many practical applications.
How to Verify Conway’s Law for Open Source Projects

Conway's Law states that in successful software projects, the organizational structure of programmer teams corresponds to the architecture of the developed system. It means that, ideally, each developer team works on its software module, and only on that module. We propose an approach to assess the difference between code structure and organizational structure. It is based on agglomerative clustering of modules and developers, followed by the search for best possible mapping between the groups. We applied the approach to a number of popular open source projects. The results show that these projects hardly obey Conway's law, due to the scale-free nature of both types of deduced networks, i.e., of software modules and the developers.