Reviewerly/exHarmony huggingface dataset:
Design and Implementation of a Mobile-Based Personal Digital Assistant (MPDA)

In this work we present a mobile-based personal digital assistance for students of higher institutions of learning. These days, the need for a To-Do list or a daily plan cannot be over-emphasized. This is because so many things compete for our valuable. It appears that the 24 h of a day are no longer sufficient for our daily activities. Although this appears true, however, prioritizing our activities generally may go a long way in helping to manage our time. Several platforms and media that existed some years ago were based on the use of pen and paper to organize activities for the day. These can no longer match up recent advances in technology and information flow which are at a very great speed. This work leverages on the current proliferation of mobile devices where each student now has a mobile device for personal use. In this work a mobile-based personal digital assistant is developed specifically on the Android platform. The system adopts Google’s material guidelines for the design of its user interface. Android Java was used for the implementation of the core aspects of the system while SQLite was used for the database. The application will help students in prioritizing and organizing their daily activities thereby making them more responsible.
Architecture Search for Image Inpainting

Neural Architecture Search (NAS) shows the ability to automate the architecture engineering for specific tasks recently which is extremely promising. Many published works apply reinforcement learning or evolutionary algorithm to design the neural architecture for image classification and achieve state-of-the-art performance. However, using NAS to perform other challenging tasks, such as inpainting irregular regions in an image, has not been explored yet. The target of image inpainting is to generate plausible image regions to fill the missing regions in the original image. It has been widely used in many applications. In this paper, we are interested in applying neural architecture search methods to image inpainting tasks. We propose to use reinforcement learning to automatically design the network architecture. Our method can efficiently explore new network structure based on existing architecture. The experiment result demonstrates that the proposed method can design an efficient and high-quality architecture for image inpainting.
A theory of change for prioritised resilient and evolvable software systems

The process of completing, correcting and prioritising specifications is an essential but very complex task for the maintenance and improvement of software systems. The preservation of functionalities and the ability to accommodate changes are main objectives of the software development cycle to guarantee system reliability. Logical theories able to fully model such processes are still insufficient. In this paper we propose a full formalisation of such operations on software systems inspired by the Alchourrón–Gärdenfors–Makinson (AGM) paradigm for belief revision of human epistemic states. We represent specifications as finite sets of formulas equipped with a priority relation that models functional entrenchment of properties. We propose to handle specification incompleteness through ordered expansion, inconsistency through ordered safe contraction and prioritisation through revision with reordering, and model all three in an algorithmic fashion. We show how the system satisfies basic properties of the AGM paradigm, including Levi's and Harper's identities. We offer a concrete example and complexity results for the inference and model checking problems on revision. We conclude by describing resilience and evolvability of software systems based on such revision operators.
Clustering tuberculosis in children using K-Means based on geographic information system

The prevalence of tuberculosis (TBC) in children tends to increase and become a serious public health problem. The higher case of TBC in children requires comprehensive surveillance. Surveillance can be carried out by clustering the cases of TBC in children based on region. One of the clustering methods that can be used is the K-Means method. Furthermore, Geographic Information Systems (GIS) has been successfully used in health fields and have the advantages in the processing and presentation of spatial and non-spatial information. The present research aims to classify the cases of TBC in children using K-Means clustering and identify the distribution patterns using GIS. Based on the analysis, the spatial pattern of the distribution of TBC in children can be categorized into: areas with high prevalence, areas with moderate prevalence, and areas with low prevalence. The result can be used to help decision making in controlling the cases of TBC in children.
Contextualized Translations of Phrasal Verbs with Distributional Compositional Semantics and Monolingual Corpora

This article describes a compositional distributional method to generate contextualized senses of words and identify their appropriate translations in the target language using monolingual corpora. Word translation is modeled in the same way as contextualization of word meaning, but in a bilingual vector space. The contextualization of meaning is carried out by means of distributional composition within a structured vector space with syntactic dependencies, and the bilingual space is created by means of transfer rules and a bilingual dictionary. A phrase in the source language, consisting of a head and a dependent, is translated into the target language by selecting both the nearest neighbor of the head given the dependent, and the nearest neighbor of the dependent given the head. This process is expanded to larger phrases by means of incremental composition. Experiments were performed on English and Spanish monolingual corpora in order to translate phrasal verbs in context. A new bilingual data set to evaluate strategies aimed at translating phrasal verbs in restricted syntactic domains has been created and released.
Application of Lean-Agile Approach for Medical Wearable Device Development

Currently the healthcare industry undergoes disruptive changes so that the design of medical products requires much better speed, flexibility and inter-connectivity. Research and development of mobile health products require an intensive collaboration from cross-disciplinary teams. In order to succeed, healthcare product providers should adapt their engineering processes to the fast improving environment. This paper introduces a hybrid Lean-Agile approach for the medical wearable device development process with respect to the learning outcomes and quality management system.
Predicting Online User Purchase Behavior Based on Browsing History

Recently, people tend to purchase through websites. This change allows e-commerce sites to collect user behavior data from web logs. E-commerce marketing forces usually make use of such data to come up with subsequent promotional campaign to drive more traffic, and converting into paying customers. In this paper we consider a special kind of e-commerce companies which sell products with similar property and usually at a high price. Therefore, the recommendation becomes less important than prediction of items(if any) bought. We want to discover potential buyers and deliver ads or even coupons to them, expecting them to be real buyers. In this paper, we model the buying behaviors from clicking records with patterns extracted using feature engineering approach. Our solution was to model two kinds of browsing behaviors, namely hesitant and impulsive respectively. In the model, we define some interaction features from click-streams which uncover users' purchase intention with the product pages, how long the user stays on that page, and then build a model which can predict users' preference. Experimental results on a real dataset from an e-commerce company demonstrate the effectiveness of the proposed method. The approaches in our work can be used to model user purchasing intent and applied to e-commerce sites which sell high-end products.
Evaluation of Avatar and Voice Transform in Programming E-Learning Lectures

This article reports the effectiveness of high frame rate facial animated avatar and voice transformer in eLearning. Three avatars: (Real male professor, Male avatar, Female avatar) were combined with male professor's voice or VT-4 vocoder transformed voice to create 6 distinguished videos which were then viewed university freshmen students. A total of 186 students divided into 15 groups participated in this experiment. Female avatar was the most appealing avatar visually, but its combination with voice transform severely hinders its overall score. This research can be extended to real time live with with preferences of students and draw more connections between student perception of avatar and actual lecturers.
Fast ship contour extraction in SAR images

Fast ship contour extraction in synthetic aperture radar (SAR) images plays an important role in sea surface surveillance. Ship contours and sizes and shapes extracted from them are salient features in ship classification and recognition. This study presents a two-step method to fast extract ship contours. At the first step, an existing object detection method is used to extract bounding boxes of ships. Next, the morphological operators are applied to each box to yield a compact external contour. At the second step, using the compact external contour as the initial contour, the active contour algorithm is used to iteratively generate ship contours. Owing to compact initial external contour, the active contour algorithm avoids the topological merging and splitting, two time-consuming operations. In this way, ship contours can be fast extracted.
Learning to Reason with Relational Video Representation for Question Answering.

How does machine learn to reason about the content of a video in answering a question? A Video QA system must simultaneously understand language, represent visual content over space-time, and iteratively transform these representations in response to lingual content in the query, and finally arriving at a sensible answer. While recent advances in textual and visual question answering have come up with sophisticated visual representation and neural reasoning mechanisms, major challenges in Video QA remain on dynamic grounding of concepts, relations and actions to support the reasoning process. We present a new end-to-end layered architecture for Video QA, which is composed of a question-guided video representation layer and a generic reasoning layer to produce answer. The video is represented using a hierarchical model that encodes visual information about objects, actions and relations in space-time given the textual cues from the question. The encoded representation is then passed to a reasoning module, which in this paper, is implemented as a MAC net. The system is evaluated on the SVQA (synthetic) and TGIF-QA datasets (real), demonstrating state-of-the-art results, with a large margin in the case of multi-step reasoning.
Adaptive VP8 Steganography Based on Deblocking Filtering

In this paper, a novel deblocking filtering-based VP8 steganographic scheme is proposed. The unique aspect of this work and one that distinguishes it from the prior art is that we effectively exploit the characteristics of deblocking filtering. We propose to embed the secret messages by comparing the quantized discrete cosine transform coefficients before and after the in-loop filtering. In the process of encoding, given one frame, first, we encode it to obtain the quantized discrete cosine transform coefficients. Second, a new set of coefficients is obtained by re-encoding the filtered frame. Third, the distortion function is defined by comparing the difference between the two sets of coefficients. Finally, adaptive embedding is realized by using the syndrome-trellis codes. Experimental results show that satisfactory levels of visual quality and steganographic security could be achieved with adequate payloads.
Robust Video Face Recognition From a Single Still Using a Synthetic Plus Variational Model

Sparse representation-based classification (SRC) techniques have been shown to achieve a high level of performance in video-based face recognition (FR). However, matching faces captured in uncontrolled video conditions against a gallery with a single reference facial still per individual typically yields low accuracy. To improve robustness to intra-class variations, SRC techniques for FR have recently been extended to incorporate variational information from an external generic set into an auxiliary variational dictionary. Despite their success in handling linear variations, probe facial images with non-linear variations due to e.g., changes in pose and expressions, cannot be accurately reconstructed with a linear combination of images from gallery and auxiliary dictionaries because they do not share the same type of variations. In this paper, a new synthetic plus variational model is proposed to account for the non-linearities, particularly with pose variations. It reconstructs a probe image using (1) an auxiliary variational dictionary and (2) an augmented gallery dictionary enriched with a set of synthetic images generated from the reference faces with a wide diversity of pose angles. By solving a newly formulated simultaneous sparsity-based optimization problem, the augmented gallery dictionary is encouraged to share the same sparsity pattern with the variational dictionary for the same pose angles. In this way, each synthetic face in the augmented dictionary is combined with similar facial viewpoint in the variational dictionary. Experimental results obtained on Chokepoint and COX-S2V datasets, using different face representations, indicate that the proposed approach can outperform state-of-the-art SRC-based methods for still-to-video FR with a SSPP.
Multiple security anti-counterfeit applications to QR code payment based on visual secret sharing and QR code

In this paper, we propose a novel mechanism for QR code security anti-counterfeit based on the fusion of visual secret sharing (VSS) and QR code (called VSSQR scheme), which can greatly improve the security of QR code payment. Due to different application scenarios, the background security anti-counterfeit application and the prospects security anti-counterfeit application are shown for QR code payment authentication. The basic idea of the two applications can be characterized as follows. First, two QR code shares that contain the information of the merchant can be generated based on VSSQR scheme with an original secret image. Second, the secret image can be revealed by stacking two QR code shares to obtain the original information. Finally, whether the stacking result is the same as the original secret image or not can determine the authenticity of QR code share used for payment. The analyses show the security of our method. The applications are conducted to show the effectiveness and practicability.
Risk Assessment Methods for Cloud Computing Platforms

Risk assessment (RA) use cases for cloud computing platforms are presented in the context of an ISO 27001 Information Security Management System (ISMS) developed for Alcohol Monitoring Systems (AMS) across a portfolio of products and services.
CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model

Continuous Bag of Words (CBOW) is a powerful text embedding method. Due to its strong capabilities to encode word content, CBOW embeddings perform well on a wide range of downstream tasks while being efficient to compute. However, CBOW is not capable of capturing the word order. The reason is that the computation of CBOW's word embeddings is commutative, i.e., embeddings of XYZ and ZYX are the same. In order to address this shortcoming, we propose a learning algorithm for the Continuous Matrix Space Model, which we call Continual Multiplication of Words (CMOW). Our algorithm is an adaptation of word2vec, so that it can be trained on large quantities of unlabeled text. We empirically show that CMOW better captures linguistic properties, but it is inferior to CBOW in memorizing word content. Motivated by these findings, we propose a hybrid model that combines the strengths of CBOW and CMOW. Our results show that the hybrid CBOW-CMOW-model retains CBOW's strong ability to memorize word content while at the same time substantially improving its ability to encode other linguistic information by 8%. As a result, the hybrid also performs better on 8 out of 11 supervised downstream tasks with an average improvement of 1.2%
Accelerated Work Stealing

Realizing scalable performance with irregular parallel applications is challenging on large-scale distributed memory clusters. These applications typically require continuous, dynamic load balancing to maintain efficiency. Work stealing is a common approach to dynamic distributed load balancing. However its use in conjunction with advanced network offload capabilities is not well understood. We present a distributed work-stealing system that is amenable to acceleration using the Portals 4 network programming interface. Our work shows that the structures provided by Portals to handle two-sided communication are general-purpose and can accelerate work stealing. We demonstrate the effectiveness of this approach using known benchmarks from computational chemistry and for performing unbalanced tree searches. Results show that Portals accelerated work-stealing can greatly reduce communication overhead, task acquisition time, and termination detection.
TermEnsembler

Abstract This paper describes TermEnsembler, a bilingual term extraction and alignment system utilizing a novel ensemble learning approach to bilingual term alignment. In the proposed system, the processing starts with monolingual term extraction from a language industry standard file type containing aligned English and Slovenian texts. The two separate term lists are then automatically aligned using an ensemble of seven bilingual alignment methods, which are first executed separately and then merged using the weights learned with an evolutionary algorithm. In the experiments, the weights were learned on one domain and tested on two other domains. When evaluated on the top 400 aligned term pairs, the precision of term alignment is over 96%, while the number of correctly aligned multi-word unit terms exceeds 30% when evaluated on the top 400 term pairs.
A Novel Rate-Control for Predictive Image Coding With Constant Quality

Predictive image coding systems yield a high-compression performance at low computational complexity, and are therefore popular in standards and prominent coding techniques for both lossless and near-lossless compression. However, few prediction-based coding techniques include rate-control approaches because of the difficulty in properly combining the prediction feedback loop and the quantization. None of the rate-control approaches for predictive image coding are focused on delivering homogeneous quality along the whole image. The main objective of this paper is to define a novel rate-control approach based on prediction that homogenises the quality over the entire image. The extensive experimental results, on medical, remote sensing, and natural images, suggest that our proposal attains more regular image quality and lower peak absolute error than the state-of-the-art approaches based on prediction and also transform-based techniques such as JPEG2000 and CCSDS-122.
Analysis of lymphatic metastasis and progression patterns for clinical target volume (CTV) definition in head and neck squamous cell carcinoma (HNSCC)

Treatment planning in radiotherapy distinguishes three target volume concepts: the gross tumor volume (GTV), the clinical target volume (CTV) and the planning target volume (PTV). In contrast to GT...
Generalized Potential Heuristics for Classical Planning

Generalized planning aims at computing solutions that work for all instances of the same domain. In this paper, we show that several interesting planning domains possess compact generalized heuristics that can guide a greedy search in guaranteed polynomial time to the goal, and which work for any instance of the domain. These heuristics are weighted sums of state features that capture the number of objects satisfying a certain first-order logic property in any given state. These features have a meaningful interpretation and generalize naturally to the whole domain. Additionally, we present an approach based on mixed integer linear programming to compute such heuristics automatically from the observation of small training instances. We develop two variations of the approach that progressively refine the heuristic as new states are encountered. We illustrate the approach empirically on a number of standard domains, where we show that the generated heuristics will correctly generalize to all possible instances.
Social Justice, Recognition Theory and the First Amendment: A New Approach to Hate Speech Restriction

There is no way to overstate the severity of the problems related to social inequality today, and treatment of hate speech in the United States is problematic in light of these escalating tensions. Long-standing arguments that free speech serves as a societal pressure valve and that open speech leads to truth hold little sway when, 200 years later, hatred against groups based on their identities is still rampant and insidious. The concept of hate speech and the subsequent calls for possible restriction raise complicated issues. This article proposes a shift in emphasis from the negative protection of individual rights instead toward a more positive support of social equality. Utilizing Axel Honneth’s theory of recognition, this article develops a two-tiered framework for free speech analysis that will promote a strategy for combating hate speech in the global twenty-first century.
Detection and Analysis of Tor Onion Services

Tor onion services can be accessed and hosted anonymously on the Tor network. We analyze the protocols, software types, popularity and uptime of these services by collecting a large amount of .onion addresses. Websites are crawled and clustered based on their respective language. In order to also determine the amount of unique websites a de-duplication approach is implemented. To achieve this, we introduce a modular system for the real-time detection and analysis of onion services. Address resolution of onion services is realized via descriptors that are published to and requested from servers on the Tor network that volunteer for this task. We place a set of 20 volunteer servers on the Tor network in order to collect .onion addresses. The analysis of the collected data and its comparison to previous research provides new insights into the current state of Tor onion services and their development. The service scans show a vast variety of protocols with a significant increase in the popularity of anonymous mail servers and Bitcoin clients since 2013. The popularity analysis shows that the majority of Tor client requests is performed only for a small subset of addresses. The overall data reveals further that a large amount of permanent services provide no actual content for Tor users. A significant part consists instead of bots, services offered via multiple domains, or duplicated websites for phishing attacks. The total amount of onion services is thus significantly smaller than current statistics suggest.
Dynamic Multimodal Optimization: A Preliminary Study

The benchmark problems have played a fundamental role in verifying the algorithm's search ability. A dynamic multimodal optimization (DMO) problem is defined as an optimization problem with multiple global optima and characteristics of global optima which are changed during the search process. Two cases are used to illustrate the application scenario of DMO. A set of benchmark functions on DMO, which contains eight problems, are proposed to show the difficulty of DMO. The properties of the proposed benchmark problems, such as the distribution of solutions, the scalability, the number of global/local optima, are discussed.