Reviewerly/exHarmony huggingface dataset:
Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation

With the wide use of deep neural networks (DNN), model interpretability has become a critical concern, since explainable decisions are preferred in high-stake scenarios. Current interpretation techniques mainly focus on the feature attribution perspective, which are limited in indicating why and how particular explanations are related to the prediction. To this end, an intriguing class of explanations, named counterfactuals, has been developed to further explore the "what-if" circumstances for interpretation, and enables the reasoning capability on black-box models. However, generating counterfactuals for raw data instances (i.e., text and image) is still in the early stage due to its challenges on high data dimensionality and unsemantic raw features. In this paper, we design a framework to generate counterfactuals specifically for raw data instances with the proposed Attribute-Informed Perturbation (AIP). By utilizing generative models conditioned with different attributes, counterfactuals with desired labels can be obtained effectively and efficiently. Instead of directly modifying instances in the data space, we iteratively optimize the constructed attributeinformed latent space, where features are more robust and semantic. Experimental results on real-world texts and images demonstrate the effectiveness, sample quality as well as efficiency of our designed framework, and show the superiority over other alternatives. Besides, we also introduce some practical applications based on our framework, indicating its potential beyond the model interpretability aspect.
Frequency Principle in Broad Learning System

Deep neural networks have achieved breakthrough improvement in various application fields. Nevertheless, they usually suffer from a time-consuming training process because of the complicated structures of neural networks with a huge number of parameters. As an alternative, a fast and efficient discriminative broad learning system (BLS) is proposed, which takes the advantages of flat structure and incremental learning. The BLS has achieved outstanding performance in classification and regression problems. However, the previous studies ignored the reason why the BLS can generalize well. In this article, we focus on the interpretation from the viewpoint of the frequency domain. We discover the existence of the frequency principle in BLS, i.e., the BLS preferentially captures low-frequency components quickly and then fits the high frequencies during the incremental process of adding feature nodes and enhancement nodes. The frequency principle may be of great inspiration for expanding the application of BLS.
FPGA Implementation of high speed-low energy RNS based Reconfigurable-FIR Filter for Cognitive Radio Applications

The Finite impulse response (FIR) filter is prominently employed in many digital signal processing (DSP) systems for various applications. In this paper, we present a high-performance RNS based FIR filter design for filtration in SDR applications. In general, the residue number system (RNS) gives significant metrics over FIR implementation with its inherent parallelism and data partitioning mechanism. But with increased bit width cause considerable performance trade-off due to both residue computation and reverse conversion. In this paper optimized Residue Number System (RNS) arithmetic is proposed which includes distributed arithmetic based residue computation during RNS multiplication followed by speculative delay optimized reverse computation to mitigate the FIR filter trade-off characteristics with filter length. The proposed RNS design utilizes built-in RAMs block present in the devices of FPGA to accomplish the process of reverse conversion and to store pre-computational values. A distinctive feature of the proposed FIR filter implementation with core optimized RNS is to minimize hardware complexity overhead with the improved operating speed. Initially, fetal audio signal detection is carried out to validate the functionality of FIR filter core and FPGA hardware synthesis is carried out for various input word size and FIR length. From the experimental, it is proved that the trade-off exists in conventional RNS FIR over filter length is narrow down along with considerable complexity reduction with our proposed optimized RNS system.
RETRA: Recurrent Transformers for Learning Temporally Contextualized Knowledge Graph Embeddings

Knowledge graph embeddings (KGE) are vector representations that capture the global distributional semantics of each entity instance and relation type in a static Knowledge Graph (KG). While KGEs have the capability to embed information related to an entity into a single representation, they are not customizable to a specific context.This is fundamentally limiting for many applications, since the latent state of an entity can change depending on the current situation and the entity’s history of related observations. Such context-specific roles an entity might play cannot be captured in global KGEs, since it requires to generate an embedding unique for each situation.This paper proposes a KG modeling template for temporally contextualized observations and introduces the Recurrent Transformer (RETRA), a neural encoder stack with a feedback loop and constrained multi-headed self-attention layers. RETRA enables to transform global KGEs into custom embeddings, given the situation-specific factors of the relation and the subjective history of the entity.This way, entity embeddings for down-stream Knowledge Graph Tasks (KGT) can be contextualized, like link prediction for location recommendation, event prediction, or driving-scene classification. Our experimental results demonstrate the performance gains standard KGEs can obtain, if they are customized according to the situational context.
Automatic construction of RDF with web tables

Being a metadata model, the Resource Description Framework (RDF) has been widely used in the context of semantic extraction, unified organization and intelligent processing of a large number of data because RDF's machine understandability. Knowledge graph based on RDF model, for example, is predominantly generated from massive data with various models and formats. While many efforts have been carried out in converting databases and XML into RDF, the generation of RDF from Web tables currently receives less attention due to their flexible and complex structure as well as difficult understanding. Given that billions of Web tables are widely used in real-world applications, in this paper, we devote to the automatic construction of RDF with diverse Web tables. We recognize total six representative types of Web tables that have been extensively applied in Web-based applications, including Web tables with simple structure and complex structure. For each type of Web tables, we propose our approach for converting it into RDF. Our approaches can well identify the semantics of Web tables with complex structure and then generate the corresponding RDF. We implement an RDF generation system and experimental results show the feasibility of our proposed approaches.
Colon10k: A Benchmark For Place Recognition In Colonoscopy

Place recognition in colonoscopy is needed for various reasons. 1) If a certain region needs to be rechecked during an endoscopy, the endoscopist needs to re-localize the camera accurately to the region of interest. 2) Place recognition is needed for same-patient follow-up colonoscopy to localize the region where a polyp was cut off. 3) Recent development in colonoscopic 3D reconstruction needs place recognition to establish long-range correspondence, e.g., for loop closure. However, traditional image retrieval techniques do not generalize well in colonic images. Moreover, although place recognition or instance-level image retrieval is a widely researched topic in computer vision and several benchmarks have been published for it, there has been no specific research or benchmarks in endoscopic images, which are significantly different from common images used in traditional computer vision tasks. In this paper we present a testing dataset with manually labeled groundtruth which comprises 10126 images from 20 colonoscopic subsequences. We perform an extensive evaluation on different existing place recognition techniques using different metrics.
Discovering Research Hypotheses in Social Science Using Knowledge Graph Embeddings

In an era of ever-increasing scientific publications available, scientists struggle to keep pace with the literature, interpret research results and identify new research hypotheses to falsify. This is particularly in fields such as the social sciences, where automated support for scientific discovery is still widely unavailable and unimplemented. In this work, we introduce an automated system that supports social scientists in identifying new research hypotheses. With the idea that knowledge graphs help modeling domain-specific information, and that machine learning can be used to identify the most relevant facts therein, we frame the problem of hypothesis discovery as a link prediction task, where the ComplEx model is used to predict new relationships between entities of a knowledge graph representing scientific papers and their experimental details. The final output consists in fully formulated hypotheses including the newly discovered triples (hypothesis statement), along with supporting statements from the knowledge graph (hypothesis evidence and hypothesis history). A quantitative and qualitative evaluation is carried using experts in the field. Encouraging results show that a simple combination of machine learning and knowledge graph methods can serve as a basis for automated scientific discovery.
Aspect-level sentiment capsule network for micro-video click-through rate prediction

Micro-videos, a new form of videos that are constrained in duration, gain significant popularity in recent years. The volume and rate of online micro-videos urgently calls for effective recommendation algorithms to help users find their interested ones. Although some previous works have investigated how to model users' historical behaviors to predict the click-through rate of micro-videos, they are generally based on positive feedback only but overlook the negative which can help understand user preference at a finer granularity. The positive and negative feedback jointly imply the user's different sentiments on different aspects, where each aspect is one component of a micro-video such as video_scene and video_subject. To this end, we propose an a spect-level s entiment cap sule network(ASCap) for micro-video click-through rate prediction by aggregating both positive and negative feedback, with an attempt to make the prediction more explainable. More specifically, an aspect-specific gating mechanism is firstly utilized to extract the aspect-level features from the target micro-video and the user's positive and negative feedback. Then, in the following sentiment capsule network, the aspect-level features of the target micro-video are paired with those of positive and negative feedback respectively to identify their sentiments and form the sentiment capsules. Finally, the prediction layer is employed to calculate the overall click probability based on the sentiment capsules. Experimental results on two real-world micro-video datasets demonstrate that the proposed method significantly outperforms the state-of-the-art methods.
Automatic Comic Generation with Stylistic Multi-page Layouts and Emotion-driven Text Balloon Generation

In this article, we propose a fully automatic system for generating comic books from videos without any human intervention. Given an input video along with its subtitles, our approach first extracts informative keyframes by analyzing the subtitles and stylizes keyframes into comic-style images. Then, we propose a novel automatic multi-page layout framework that can allocate the images across multiple pages and synthesize visually interesting layouts based on the rich semantics of the images (e.g., importance and inter-image relation). Finally, as opposed to using the same type of balloon as in previous works, we propose an emotion-aware balloon generation method to create different types of word balloons by analyzing the emotion of subtitles and audio. Our method is able to vary balloon shapes and word sizes in balloons in response to different emotions, leading to more enriched reading experience. Once the balloons are generated, they are placed adjacent to their corresponding speakers via speaker detection. Our results show that our method, without requiring any user inputs, can generate high-quality comic pages with visually rich layouts and balloons. Our user studies also demonstrate that users prefer our generated results over those by state-of-the-art comic generation systems.
Digital Reading and the Concept of Ebook: Metaphorical Analysis of Preservice Teachers’ Perceptions Regarding the Concept of Ebook

The aim of this study was to determine the metaphorical perceptions of preservice Turkish teachers (PTTs) regarding the concept of ebook. In accordance with this aim, answers to the questions of “What are the metaphors used by PTTs for the concept of e-book?” and “Under which categories could the metaphors used by PTTs for the concept of e-book be collected?” were sought. A total of 150 preservice teachers studying in the Faculty of Education, Turkish Education Department at Çukurova University, constituted the participants of the research designed according to the phenomenology design, one of the qualitative research methods. A semi-structured interview form was used as data collection tool. Data were analyzed using the content analysis technique. As a result of the study, it was determined that eight categories emerged from the metaphors produced by PTTs for ebook and that they mostly had positive meanings. These categories are, namely, facilitating, information source, accessible, portable, requirement, inadequate, attractive, and harmful. PTTs mostly produced metaphors for ebook in the facilitating category. It was followed by the information source and accessible categories. It was determined that some PTTs produced negative metaphors for ebooks, and these negative metaphors were collected under the inadequate and harmful categories.
Improving Critical Infrastructure Protection by Enhancing Software Acquisition Process Through Blockchain

Proliferating supply chain attacks indicate that today's practices are insufficient for ensuring security and increasingly represent the weakest link in cybersecurity of software-based operational technologies. By tracking every action of integrating procured software components into an existing system, from analyzing vendors’ security to auditing purchaser's supply chain, a more secure software supply chain can be provided. Hyperledger Fabric, as a permissioned blockchain network, provides immutable i.e. tamper-proof solution for tracking the information stored on the ledger, while allowing only preauthorized actors to participate in the network. An approach that is taken in this paper is to track the software components that are introduced in the system, whether they are commercial-off-the-shelf components or tailor-made components and track their security analysis by utilizing blockchain technology. By defining Organization in blockchain network which can participate in the supply chain management process, this paper demonstrates the benefits of utilizing Hyperledger Fabric for managing acquired software components that are introduced in critical infrastructure. Given the analyzed use cases and proposed architecture, it can be concluded that Hyperledger Fabric blockchain provides necessary trust in a multi-party environment which is the basis for more efficient auditing of the whole process. The trustworthy logs and easier auditing are the key enablers for a supply chain management process that can address the described needs.
Single Image Dehazing of Road Scenes Using Spatially Adaptive Atmospheric Point Spread Function

Image haze removal is essential in autonomous driving as the outdoor images captured during unfavorable weather conditions, such as haze or snow, are affected by poor visibility.Much research has been done to overcome image degradation such as low contrast and faded color due to haze.However, in the traditional model, a phenomenon is neglected that several particles simultaneously involved in light acquisition.To address this problem, we propose a novel single image dehazing method based on the spatially adaptive atmospheric point spread function (APSF).We developed a module that estimates the APSF to overcome the limitations of the spatially invariant APSF which used in existing dehazing algorithms.The key factor in the estimation is that road scenes with haze have different statistical characteristic from common hazy images in color and resolution.Furthermore, the APSF on the traffic signs or lights is estimated by generating superpixels to prevent halo artifacts around the sharp edges of the images.We adopted the total variation model as a regularization functional to reduce halo and unnatural artifacts that may occur during deconvolution.The haze-free images from the proposed method tested whether the proposed method can enhance the performance of vision algorithms for autonomous driving.The experimental results demonstrate that the proposed method outperforms state-of-the-art image dehazing methods enhancing the performance of the vision algorithms.Moreover, additional experiments demonstrated the effectiveness of the proposed method for quantitative and qualitative comparison with the state-of-the-art algorithms.
An RFID-Based Monitoring and Localization System for Dementia Patients

The fast increase in the percentage of elderly people over the past few years has prompted a major interest in developing monitoring systems to help in the well being of the elderly. More importantly, we are also witnessing an increase in the number of elderly needing assistance because of diverse chronic diseases. The elderly related dementia is among the most disabling diseases with dangerous consequences such as wandering into hazardous or insecure areas. This wandering, particularly in urban areas can be life threatening. Recently, with the rapid emergence of disruptive technologies like Internet of Things (IoT) and Radio Frequency Identification (RFID), it has become possible to build systems that combine IoT and the cloud for monitoring the elderly suffering from dementia or depression. This paper introduces an RFID-based novel cost-effective tracking system for a multi-person home. The proposed system can accurately track up to three elderly people within the room they are in. The proposed prototype solution is based on wearable anklets or bracelets with batteryless tags for localizing elderly wandering within the home using passive RFID. An Android Applet was developed for localizing the elderly and alerting caregivers of at-risk inhabitants of smart homes within and outside (at exit level) the residence area.
Savitzky–Golay filter energy features-based approach to face recognition using symbolic modeling

Face recognition is a well-researched domain however many issues for instance expression changes, illumination variations, and presence of occlusion in the face images seriously affect the performance of such systems. A recent survey shows that COVID-19 will also have a considerable and long-term impact on biometric face recognition systems. The work has presented two novel Savitzky-Golay differentiator (SGD) and gradient-based Savitzky-Golay differentiator (GSGD) feature extraction techniques to elevate issues related to face recognition systems. The SGD and GSGD feature descriptors are able to extract discriminative information present in different parts of the face image. In this paper, an efficient and robust person identification using symbolic data modeling approach and similarity analysis measure is devised and employed for feature representation and classification tasks to address the aforementioned issues of face recognition. Extensive experiments and comparisons of the proposed descriptors experimental results indicated that the proposed approaches can achieve optimal performance of 96-97, 92-96, 100, 84-93, and 87-96% on LFW, ORL, AR, IJB-A datasets, and newly devised VISA database, respectively.
SeBeST: Security Behavior Stage Model and Its Application to OS Update

To protect computers from various types of cyberattack, users are required to learn appropriate security behaviors. Different persuasion techniques to encourage users to take security behaviors are required according to user attitude toward security. In this paper, we first propose a Security Behavior Stage Model (SeBeST) which classifies users into five stages in terms of attitude toward security measurements; having security awareness and taking security behaviors. In addition, we focus on OS updating behaviors as an example of security behaviors and evaluated effective OS update messages for users in each stage. We create message dialogs which can promote user OS updating behaviors. We conduct two online surveys; we analyze the validity of SeBeST in Survey1 and then evaluate effective messages for each stage in Survey2. We find that SeBeST has high validity and appropriate messages for the users in each stage differ from one another.
Expressibility of OWL Axioms with Patterns

The high expressivity of the Web Ontology Language (OWL) makes it possible to describe complex relationships between classes, roles, and individuals in an ontology. However, this high expressivity can be an obstacle to correct usage and wide adoption. Past attempts to ameliorate this have included the development of specific, presumably human-friendly syntaxes, such as the Manchester syntax or graphical interfaces for OWL axioms, albeit with limited success. If modelers want to develop suitable OWL axioms it is important to make this as easy as possible.In this paper, we adopt an idea from the Protégé plug-in, OWLAx, which provides a simple, clickable interface to automatically input axioms of a limited number of types by following simple axiom patterns. In particular, each of these axiom patterns contains at most three classes or roles. We hypothesize that most of the axioms in existing ontologies could be expressed semantically in terms of simple patterns like these, which would mean that more complex patterns can be used very sparingly.Our findings, based on an analysis of 518 ontologies from six public ontology repositories, confirm this hypothesis: Over 90% of class axioms in the average ontology are indeed expressible with our simple patterns. We provide a detailed analysis of our findings.