Reviewerly/exHarmony huggingface dataset:
Fuzzy AHP based identification model for efficient application development

Irrespective of the nature of the software, the choice of a software development process plays a crucial role. A good choice may help expand the business to heights while a bad choice may wreak havoc for the stakeholders. Therefore, selecting the right software development process is an indispensab le element during project planning. Undoubtedly, web-based applications differ from conventional software and are becoming so popular in society due to their distinguishing features and widespread coverage. Web application software is generally found fit candidates to be developed according to agile software processes. This article attempts to provide a model for advising an agile software process during the planning phase of a web application development project. The proposed model takes into account the underlying web project characteristics and uses the Fuzzy Analytic Hierarchy Process (FAHP) along with TOPSIS techniques for suitable agile candidate selection. The model is also validated on a primary dataset obtained by developing twenty web application projects through four student teams. The results show that the proposed model is successful in suggesting a correct agile process with a probability of the order of 0.8.
Public-Key Function-Private Hidden Vector Encryption (and More)

We construct public-key function-private predicate encryption for the "small superset functionality," recently introduced by Beullens and Wee (PKC 2019). This functionality captures several important classes of predicates: Our construction extends the group-based obfuscation schemes of Bishop et al. (CRYPTO 2018), Beullens and Wee (PKC 2019), and Bartusek et al. (EUROCRYPT 2019) to the setting of public-key function-private predicate encryption. We achieve an average-case notion of function privacy, which guarantees that a decryption key $$\mathsf {sk} _f$$ reveals nothing about f as long as f is drawn from a distribution with sufficient entropy. We formalize this security notion as a generalization of the (enhanced) real-or-random function privacy definition of Boneh, Raghunathan, and Segev (CRYPTO 2013). Our construction relies on bilinear groups, and we prove security in the generic bilinear group model.
Locally and multiply distorted image quality assessment via multi-stage CNNs

The majority of existing objective Image Quality Assessment (IQA) methods are designed specifically for singly and globally distorted images, which are incapable of dealing with locally and multiply distorted images effectively. On the one hand, artificially extracted features in traditional IQA methods are insufficient to represent quality variations in locally and multiply distorted images. On the other hand, the IQA methods suitable for both locally and multiply distorted images are scarce. In view of this, an IQA method based on multi-stage deep Convolutional Neural Networks (CNNs) is proposed for locally and multiply distorted images in this paper. The method adopts a three-stage strategy, which are distortion classification, quality prediction of single distortion and comprehensive assessment, respectively. Firstly, three datasets of locally, multiply and singly distorted images are designed and established. Secondly, a local and multiple distortion classifier, a distortion type classifier and prediction models of single distortions are obtained based on CNN models in their corresponding stages. Thirdly, the predicted results of single distortions are weighted by the output confidence probability of the classifiers, thus obtaining the final comprehensive quality. Experimental results verified the advantages of the proposed method in measuring the quality of locally and multiply distorted images.
Inequalities for the Schmidt number of bipartite states

In this short note we show two completely opposite methods of constructing entangled states. Given a bipartite state $\gamma\in M_k\otimes M_k$, define $\gamma_S=(Id+F)\gamma (Id+F)$, $\gamma_A=(Id-F)\gamma(Id-F)$, where $F\in M_k\otimes M_k$ is the flip operator. In the first method, entanglement is a consequence of the inequality $\text{rank}(\gamma_S)<\sqrt{\text{rank}(\gamma_A)}$. In the second method, there is no correlation between $\gamma_S$ and $\gamma_A$. These two methods show how diverse is quantum entanglement. We prove that any bipartite state $\gamma\in M_k\otimes M_k$ satisfies $\displaystyle SN(\gamma)\geq\max \left\{ \frac{\text{rank}(\gamma_L)}{\text{rank}(\gamma)}, \frac{\text{rank}(\gamma_R)}{\text{rank}(\gamma)}, \frac{SN(\gamma_S)}{2}, \frac{SN(\gamma_A)}{2} \right\},$ where $SN(\gamma)$ stands for the Schmidt number of $\gamma$ and $\gamma_L,\gamma_R$ are the marginal states of $\gamma$. We also present a family of PPT states in $M_k\otimes M_k$, whose members have Schmidt number equal to $n$, for any given $1\leq n\leq \left\lceil\frac{k-1}{2}\right\rceil$. This is a new contribution to the open problem of finding the best possible Schmidt number for PPT states.
Cognitive Robotics: Making Robots Sense, Understand, and Interact

Robots have spread from manufacturing floors to spaces occupied by humans.Although robots in these settings may improve the way humans work, the programming by hand of collaborative robots in such environments is increasingly difficult.We predict that recent breakthroughs in large-scale simulations, deep reinforcement learning, and computer vision collectively bring forth a basic level of cognitive abilities to robots that will lead to significant improvements of robotic applications over the next few years.
Implementing Blockchain to Enhance Usability of Patient-Generated Data

The adoption of blockchain shows a variety of benefits owing to an incorruptible digital ledger and a decentralized database. This has eliminated the need for a gatekeeper to oversee all associated transactions. Blockchain, the underlying technology behind Bitcoin and other crypto-currencies, has found use in many industries besides finance, such as healthcare, where it is used for verifying medical licensing and credentialing, for tracking medical equipment (or consumables) from production to usage, and in cases associated with high levels of privacy and security. Patient data is collected using a plethora of patient-generated data devices, such as Internet of Things (IoT)-enabled wearables, health trackers, and home use medical devices. As a result, the data is siloed amongst several applications and/or vendors’ proprietary solutions. Of all this data, only some of it is transmitted to Electronic Medical Records. This produces the risk that not all data collected will be reviewed at the point of care due to the abundance of data collected. This article explains the areas within healthcare where blockchain could address data usability challenges and analyzes new ways in which patient health data can be collected to address the increasing number of challenges associated with the amount of data being generated over time. It also describes the drivers behind this data collection trend, the associated challenges and the subsequent ramifications. It concludes with a review of previous studies on data usability challenges and the means by which blockchain can be used to overcome these challenges.
Automatically Optimized Gradient Boosting Trees for Classifying Large Volume High Cardinality Data Streams Under Concept Drift

Data abundance along with scarcity of machine learning experts and domain specialists necessitates progressive automation of end-to-end machine learning workflows. To this end, Automated Machine Learning (AutoML) has emerged as a prominent research area. Real world data often arrives as streams or batches, and data distribution evolves over time causing concept drift. Models need to handle data that is not independent and identically distributed (iid), and transfer knowledge across time through continuous self-evaluation and adaptation adhering to resource constraints. Creating autonomous self-maintaining models which not only discover an optimal pipeline, but also automatically adapt to concept drift to operate in a lifelong learning setting was the crux of NeurIPS 2018 AutoML challenge. We describe our winning solution to the challenge, entitled AutoGBT, which combines an adaptive self-optimized end-to-end machine learning pipeline based on gradient boosting trees with automatic hyper-parameter tuning using Sequential Model-Based Optimization (SMBO). We report experimental results on the challenge datasets as well as several benchmark datasets affected by concept drift and compare it with the baseline model for the challenge and Auto-sklearn. Results indicate the effectiveness of the proposed methodology in this context.
Comparison of monocular depth estimation methods using geometrically relevant metrics on the IBims-1 dataset

The task of predicting a dense depth map from a monocular RGB image, commonly known as single-image depth estimation (SIDE) or monocular depth estimation (MDE), is an active research topic in computer vision for decades. With the significant progress of deep models in recent years, new standards were set yielding remarkable results in capturing the 3D structure from a single image. However, established evaluation schemes of predicted depth maps are still limited, as they only consider global statistics of the depth residuals. In order to allow for a geometry-aware analysis, we propose a set of novel quality criteria addressing the preservation of depth discontinuities and planar regions, the depth consistency across the image, and a distance-related assessment. As current datasets do not fulfill the requirements of all proposed error metrics, we provide a new high-quality indoor RGB-D test dataset, acquired by a digital single-lens reflex (DSLR) camera together with a laser scanner. New insights into the performance of current state-of-the-art SIDE approaches, as well as subtle differences among them, could be unveiled by employing the proposed error metrics on our reference dataset. Additionally, investigations on the real-world applicability of SIDE methods by a series of experiments regarding different image augmentations, illumination changes and textured planar regions have shown current limitations in this research field.
A Study on Architectural Smells Prediction

Architectural smells can be detrimental to the system maintainability, evolvability and represent a source of architectural debt. Thus, it is very important to be able to understand how they evolved in the past and to predict their future evolution. In this paper, we evaluate if the existence of architectural smells in the past versions of a project can be used to predict their presence in the future. We analyzed four Java projects in 295 Github releases and we applied for the prediction four different supervised learning models in a repeated cross-validation setting. We found that historical architectural smell information can be used to predict the presence of architectural smells in the future. Hence, practitioners should carefully monitor the evolution of architectural smells and take preventative actions to avoid introducing them and stave off their progressive growth.
A Multi-cascaded Deep Model for Bilingual SMS Classification

Most studies on text classification are focused on the English language. However, short texts such as SMS are influenced by regional languages. This makes the automatic text classification task challenging due to the multilingual, informal, and noisy nature of language in the text. In this work, we propose a novel multi-cascaded deep learning model called McM for bilingual SMS classification. McM exploits $n$-gram level information as well as long-term dependencies of text for learning. Our approach aims to learn a model without any code-switching indication, lexical normalization, language translation, or language transliteration. The model relies entirely upon the text as no external knowledge base is utilized for learning. For this purpose, a 12 class bilingual text dataset is developed from SMS feedbacks of citizens on public services containing mixed Roman Urdu and English languages. Our model achieves high accuracy for classification on this dataset and outperforms the previous model for multilingual text classification, highlighting language independence of McM.
Interaction Guidelines for Personal Voice Assistants in Smart Homes

The use of Personal Voice Assistants (PVAs) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> such as Alexa and the Google Assistant is rising steadily, but there is a lack of research investigating common issues and requests of PVAs in the context of smart home control. We address this research question with an online survey (n = 65), using a qualitative evaluation of users’ problems and improvement requests. Our analysis leads to a partly hierarchic clustering of issues & recommendations for interaction capabilities of PVAs into seven basic categories, allowing us in turn to derive implications and to condense them into design guidelines for future Human-Agent Interaction (HAI) with PVAs. Specifically, we formulate and elaborate the concepts Authentication & Authorization, Activity-Based Interaction, Situated Dialogue, and Explainability & Transparency as key topics for making progress towards smooth interaction with smart homes.
Smart Ontology-Based Event Identification

Identifying an event and all its attributes help in timely response to emergencies or business decisions. Although accurate event identification has been studied in the last decade, fewer thoughts have been put into determining actions with context-dependent effects. This paper is motivated by the desire to develop a synergy between the different answers on the same query posed by users of differing priority. The proposed approach exploits semantic technologies to model the personalized behavior. We provide a control protocol that recognizes the pattern in the flow of precision as the priority of user changes. The control protocol has been utilized to define the priority of the user and is exploited in an efficient algorithm to yield good tradeoffs between various attributes of the decision. Both bottom-up and top-down parsing of the ontological knowledge base is depicted depending on whether the event object is available in the knowledge base or not. The algorithm is then tested on the real-world use case of events of terrorist attacks. The algorithm renders varying answer with varying precision based on a balance between the available resources, the required certainty, the required specificity level, and the acceptable threshold value. The proposed control protocol and the algorithm proved to be logically sound and seem to be a direct consequence of representing knowledge in a manner that is complete.
A Schema-First Formalism for Labeled Property Graph Databases

Graph databases provide better support for highly interconnected datasets than relational databases. However, labeled property graph databases, which have become increasingly popular, are schema-optional, making them prone to data corruption, especially when new users switch from relational databases to graph databases. In this work, we provide a schema-driven formalism for graph databases. This formalism enables schema-driven loading of graph databases from other sources, such as relational databases. Also, this formalism enables schema-driven data analytics that allows for a more structured analysis of data stored in graph databases. Such analytics are based on a boilerplate approach allowing users who are not experts in the use of graph database query languages to carry out analytics efficiently. We showcase the utility of the proposed formalism by considering a case study from Airbnb for illustrating schema-based loading procedures. The proposed schema-driven analytics process is illustrated using another case study from an industrial cyber-physical systems standard. Overall, the schema-driven formalism provides several useful features, such as preventing both data corruption and long-term degradation of graph database structures.
Stress-Plus-X (SPX) Graph Layout

Stress, edge crossings, and crossing angles play an important role in the quality and readability of graph drawings. Most standard graph drawing algorithms optimize one of these criteria which may lead to layouts that are deficient in other criteria. We introduce an optimization framework, Stress-Plus-X (SPX), that simultaneously optimizes stress together with several other criteria: edge crossings, minimum crossing angle, and upwardness (for directed acyclic graphs). SPX achieves results that are close to the state-of-the-art algorithms that optimize these metrics individually. SPX is flexible and extensible and can optimize a subset or all of these criteria simultaneously. Our experimental analysis shows that our joint optimization approach is successful in drawing graphs with good performance across readability criteria.
Authorable augmented reality instructions for assistance and training in work environments

Augmented Reality (AR) is a promising technology for assistance and training in work environments, as it can provide instructions and feedback contextualised. Not only, but especially impaired workers can benefit from this technology. While previous work mostly focused on using AR to assist or train specific predefined tasks, "general purpose" AR applications, that can be used to intuitively author new tasks at run-time, are widely missing.
IoT Devices Recognition through Object Detection and Classification Techniques

Internet of Things (IoT) has dramatically penetrated into today's daily routine, with the number of new devices being growing exponentially, anticipating by year 2035 over one trillion of IoT devices to be connected around the world. However, IoT is still in its infant stage, regarding mainly the heterogeneity of the connected devices. In order to contribute to this gap, this paper proposes a mechanism for recognizing heterogeneous IoT devices of unknown device type, by applying specific computer vision techniques. This mechanism implements four (4) stages to successfully recognize a device of unknown nature. Initially the discovery and the connection of both known and unknown devices occur, followed by the online search of their images, so as to categorize and map the unknown devices' images with the images of the known devices. As a result, since the device type of the known devices is a priory known, and each unknown device is mapped with one or more images of these known devices, the device type of each unknown device becomes known, as well. The prototype associated with this paper provides an example of this mechanism, demonstrating in detail each discrete stage.
A combinative knowledge-driven integration method for integrating geophysical layers with geological and geochemical datasets

In this research, we establish the ability of some famous Multi Criteria Decision Making (MCDM) techniques which are called "The technique for order preference by similarity to ideal solution" (TOPSIS) and evidential belief functions (EBFs) in deposit scale studies. Zafarqand copper‑molybdenum deposit (located in centeral region of the Urumieh-Dokhtar Volcanic Arc (UDVA) in Iran) was chosen as case study for this research. Various evidential layers (geophysical, geological and geochemical) were usually raster-based maps which were integrated to attain mineral prospectivity mapping (MPM) by variants of TOPSIS. In this research, TOPSIS variants were completer each other in locations of mineralization zones applying EBFs method with Dempster-Shafer's rules. EBFs method can avoid of losing high value pixels in final produced MPM and clear uncertainty degree. Field checking and microscopic studies of borehole's samples verified high confidence level of final MPM (up to 93%) and percent of missed high values pixels of TOPSIS variants (up to 40%). Accordingly, it is better that knowledge-driven integration methods not be separately executed in deposit scale studies.
Towards a Control Strategy Based on Type-2 Fuzzy Logic for an Autonomous Mobile Robot

The main purpose considered in this paper is to maintain a specific location and behavior for a robot that uses type-2 fuzzy logic for controlling its behavior. In this work, we propose a combination of behaviors by following a trajectory without leaving or losing it and avoiding obstacles in an omnidirectional mobile platform. The results of the simulation show the advantages of the proposed approach. We describe the previous knowledge about type-2 fuzzy logic, the virtualization of the mobile robot and its modeling according to real situations. The proposed control system is developed in Matlab-Simulink, the system can model and guide a mobile robot, successfully in simulated and real environments.
Primitive Shape Fitting in Point Clouds Using the Bees Algorithm

In this study the problem of fitting shape primitives to point cloud scenes was tackled as a parameter optimisation procedure, and solved using the popular bees algorithm. Tested on three sets of clean and differently blurred point cloud models, the bees algorithm obtained performances comparable to those obtained using the state-of-the-art random sample consensus (RANSAC) method, and superior to those obtained by an evolutionary algorithm. Shape fitting times were compatible with real-time application. The main advantage of the bees algorithm over standard methods is that it doesn’t rely on ad hoc assumptions about the nature of the point cloud model like RANSAC approximation tolerance.
An Analysis Method of Crowd Abnormal Behavior for Video Service Robot

Abnormal behavior analysis in crowded scenes is a challenging task in the computer vision field. Determining how to quickly and accurately locate abnormal behavior in a crowd, especially in airports, shopping malls, stations, and other practical application scenarios, has become the key to the timely handling of mass incidents. The authors have developed an embedded video analysis robot capable of local processing for crowd anomaly detection in application scenarios. In recent years, many methods of crowd anomaly detection have been proposed by scholars, but the performance of these methods when applied to video service robots is unsatisfactory. In this paper, an anomaly analysis method based on improved k-means is proposed. This method combines mean shift and the k-means classification method to achieve rapid and accurate crowd anomaly detection. A quantitative experimental evaluation was carried out on a video service robot, and the results demonstrate that the method is effective for the detection of abnormal behavior on multiple and publicly available video sequences.
A Novel Approach to Data Extraction on Hyperlinked Webpages

The World Wide Web has an enormous amount of useful data presented as HTML tables. These tables are often linked to other web pages, providing further detailed information to certain attribute values. Extracting schema of such relational tables is a challenge due to the non-existence of a standard format and a lack of published algorithms. We downloaded 15,000 web pages using our in-house developed web-crawler, from various web sites. Tables from the HTML code were extracted and table rows were labeled with appropriate class labels. Conditional random fields (CRF) were used for the classification of table rows, and a nondeterministic finite automaton (NFA) algorithm was designed to identify simple, complex, hyperlinked, or non-linked tables. A simple schema for non-linked tables was extracted and for the linked-tables, relational schema in the form of primary and foreign-keys (PK and FK) were developed. Child tables were concatenated with the parent table’s attribute value (PK), serving as foreign keys (FKs). Resultantly, these tables could assist with performing better and stronger queries using the join operation. A manual checking of the linked web table results revealed a 99% precision and 68% recall values. Our 15,000-strong downloadable corpus and a novel algorithm will provide the basis for further research in this field.