Reviewerly/exHarmony huggingface dataset:
Alternating Fixpoint Operator for Hybrid MKNF Knowledge Bases as an Approximator of AFT

Abstract Approximation fixpoint theory (AFT) provides an algebraic framework for the study of fixpoints of operators on bilattices and has found its applications in characterizing semantics for various classes of logic programs and nonmonotonic languages. In this paper, we show one more application of this kind: the alternating fixpoint operator by Knorr et al. for the study of the well-founded semantics for hybrid minimal knowledge and negation as failure (MKNF) knowledge bases is in fact an approximator of AFT in disguise, which, thanks to the abstraction power of AFT, characterizes not only the well-founded semantics but also two-valued as well as three-valued semantics for hybrid MKNF knowledge bases. Furthermore, we show an improved approximator for these knowledge bases, of which the least stable fixpoint is information richer than the one formulated from Knorr et al.’s construction. This leads to an improved computation for the well-founded semantics. This work is built on an extension of AFT that supports consistent as well as inconsistent pairs in the induced product bilattice, to deal with inconsistencies that arise in the context of hybrid MKNF knowledge bases. This part of the work can be considered generalizing the original AFT from symmetric approximators to arbitrary approximators.
FRACTIONAL-VALUED MODAL LOGIC

Abstract This paper is dedicated to extending and adapting to modal logic the approach of fractional semantics to classical logic. This is a multi-valued semantics governed by pure proof-theoretic considerations, whose truth-values are the rational numbers in the closed interval $[0,1]$ . Focusing on the modal logic K , the proposed methodology relies on three key components: bilateral sequent calculus, invertibility of the logical rules, and stability (proof-invariance). We show that our semantic analysis of K affords an informational refinement with respect to the standard Kripkean semantics (a new proof of Dugundji’s theorem is a case in point) and it raises the prospect of a proof-theoretic semantics for modal logic.
A Survey on Multimodal Deep Learning for Image Synthesis

In recent years, deep learning technology has made breakthroughs in computer vision. After using large-scale data training, the deep neural network represented by GAN is significantly better than previous technologies in image generation, including generating more reasonable, higher-definition, more complex, and more accurate images. With the continuous development of datasets, models, and applications, the fusion of different modal information, including fusion of natural language, semantic layouts, tags, edge maps, and other different modal information, to generate images has become a new demand and challenge. There are related reviews on image generation and multimodal deep learning. However, there has not been a review dedicated to multimodal deep-learning image generation to discuss the current status, existing problems, and challenges of this task. Therefore, this review proposes a survey on multimodal deep learning image generation. It aims to provide readers with an application scenario for multimodal deep learning image generation. Also, it provides readers with new multimodal deep learning image generation technologies, the relevant datasets, evaluation metrics used, and some results comparison. Finally, this article describes some of the challenges and future topics of multimodal deep learning image generation.
Technologies and Issues of Cloud Computing

There are many different cloud computing companies on call on budget-friendly cost designs, for instance, registration and also lease located expense models. Solutions as an example, Remote Personal computer Session Multitude used for cloud desktop computer, Software as a Service (SaaS) utilized for renting program and also Platform as a Service (PaaS) used to rent out computing commercial infrastructure. The Storage as a Service (STaaS) is made use of to lease storage as well as Security as a Service (SeaaS) is made use of for cloud located security applications. This paper gives a comprehensive enhancement of technologies and issues of cloud computing.
Implantable and Wearable Sensors for Assistive Technologies

This article aims to introduce implantable and wearable sensors and state-of-the-art assistive technologies in which these sensors are used in this diverse and growing field. In the introduction, we discuss the motivation behind and the need for assistive technologies from the perspective of social and economic benefits that will impact the population at large, both nationally and beyond. The next section covers augmentative and assistive communication systems for voice/speech recognition and language disorders. Section three reviews sensory aids for visual and auditory impairments. The following section elaborates on seating and transportation systems that include smart chair, smart walker/cane, and smart bed. Section five addresses the systems for manipulating and controlling the environment, such as tongue driven systems and eye-trackers. Sections on prosthetics and invasive and non-invasive brain-computer interfaces are discussed next.
Machine Learning Approaches for the Traveling Salesman Problem: A Survey

Machine Learning techniques have been applied in many contexts with great success. In this survey, we focus on their applications in the Combinatorial Optimization (CO) domain, and in particular to the Traveling Salesman Problem (TSP). We propose an intuitive and simple mind map helpful to navigate through the wide existing literature, indicating the approaches we consider most promising. Different ML techniques introduced to solve the TSP are discussed and reviewed; and their differences and limitations are delved. Open problems for future research in this area are finally highlighted.
Attention Guided Relation Detection Approach for Video Visual Relation Detection

Video Visual Relation Detection (VidVRD) aims at detecting the relation instances between two observed objects in the form of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&lt; $</tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">subject-predicate-object</i> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> . Unlike image visual relation detection, due to the introduction of the time dimensions, the various predicates and spatial-temporal locations are both required to be tackled, making the task challenging. To balance these challenges, most existing works perform this task in two phases: first predicting relationships in segmented clips to capture the motions, and then associating them into the relation instances with proper locations in videos. These works detect different relationships by collecting the cues from multi-aspects, but treat them equally without distinction. Furthermore, due to the dynamic scenes and drifting problem in object tracking, the rigid spatial overlap used to determine the association in previous works is insufficient, which leads to missing associations. To address the problems, in this paper, we propose a novel attention guided relation detection approach for VidVRD. In order to model the distinction among different cues and strengthen the salient characteristics, we assign these cues the attention weights for relationship prediction and association decision-making. In addition, to comprehensively measure whether merging the relationships, we put forward a customized network to take both visual appearance and geometric location into account. Extensive experiment results on ImageNet-VidVRD dataset and VidOR dataset demonstrate the effectiveness of our proposed approach. And abundant ablation studies verify the component designed in the approach is essential.
Word-embedding-based query expansion: Incorporating Deep Averaging Networks in Arabic document retrieval

One of the main issues associated with search engines is the query–document vocabulary mismatch problem, a long-standing problem in Information Retrieval (IR). This problem occurs when a user query does not match the content of stored documents, and it affects most search tasks. Automatic query expansion (AQE) is one of the most common approaches used to address this problem. Various AQE techniques have been proposed; these mainly involve finding synonyms or related words for the query terms. Word embedding (WE) is one of the methods that are currently receiving significant attention. Most of the existing AQE techniques focus on expanding the individual query terms rather the entire query during the expansion process, and this can lead to query drift if poor expansion terms are selected. In this article, we introduce Deep Averaging Networks (DANs), an architecture that feeds the average of the WE vectors produced by the Word2Vec toolkit for the terms in a query through several linear neural network layers. This average vector is assumed to represent the meaning of the query as a whole and can be used to find expansion terms that are relevant to the complete query. We explore the potential of DANs for AQE in Arabic document retrieval. We experiment with using DANs for AQE in the classic probabilistic BM25 model as well as for two recent expansion strategies: Embedding-Based Query Expansion approach (EQE1) and Prospect-Guided Query Expansion Strategy (V2Q). Although DANs did not improve all outcomes when used in the BM25 model, it outperformed all baselines when incorporated into the EQE1 and V2Q expansion strategies.
Virtual Health Assistant

This paper illustrates the process of data collection, notation layout, and agreement results for achieving healthcare. The first method is to demonstrate an autonomous virtual health assistant to interact between a doctor and patients via chat. And the second method is called Attribute-based encryption which has been used to securely share personal health records (PHRs) in the cloud. The patient data is frequently collected through PHR from various sources including hospitals. Where the collected PHR files are securely stored in the cloud. By this method Encryption and Decryption process can be reduced also proves the security of the proposed scheme based on ciphertext encryption and attribute-based encryption.
Linearized trinomials with maximum kernel

Linearized polynomials have attracted a lot of attention because of their applications in both geometric and algebraic areas. Let q be a prime power, n be a positive integer and σ be a generator of Gal(Fqn:Fq). In this paper we provide closed formulas for the coefficients of a σ-trinomial f over Fqn which ensure that the dimension of the kernel of f equals its σ-degree, that is linearized polynomials with maximum kernel. As a consequence, we present explicit examples of linearized trinomials with maximum kernel and characterize those having σ-degree 3 and 4. Our techniques rely on the tools developed in [24]. Finally, we apply these results to investigate a class of rank metric codes introduced in [8], to construct quasi-subfield polynomials and cyclic subspace codes, obtaining new explicit constructions to the conjecture posed in [37].
Spatio-Temporal Split Learning

This paper proposes a novel split learning framework with multiple end-systems in order to realize privacy-preserving deep neural network computation. In conventional split learning frameworks, deep neural network computation is separated into multiple computing systems for hiding entire network architectures. In our proposed framework, multiple computing end-systems are sharing one centralized server in split learning computation, where the multiple end-systems are with input and first hidden layers and the centralized server is with the other hidden layers and output layer. This framework, which is called as spatio-temporal split learning, is spatially separated for gathering data from multiple end-systems and also temporally separated due to the nature of split learning. Our performance evaluation verifies that our proposed framework shows near-optimal accuracy while preserving data privacy.
Deep neural network for text anomaly detection in SIoT

Unsupervised textual anomaly detection, which discovers anomalies from unlabeled texts, is critical to improve the cybersecurity and interaction ability among the objects in the Social Internet of Things (SIoT). Recently, detecting anomalies by deep neural networks has become a popular trend. Specially, context vector data description (CVDD) method shows the promising performance. However, CVDD has two limitations: (1) it uses an one-class classification objective to constrain the sentence embeddings, which leads the learned embeddings to lose content information of text. (2) Scalar-based attention weights, which are used to extract sentence features, fail to focus on dimensional properties in a word. Learning the text contents and the dimensional properties is important for detection task in SIoT, which can help detector capture the difference between normal and anomaly texts. To overcome these limits, this paper proposes a textual anomaly detection network. First, an adversarial training strategy is designed to fight against the problem of missing content information. Second, a textual anomaly detection module with multiple dimensional transformation matrices is constructed to learn dimensional properties of words in diverse semantic subspaces. Experimental results on several textual datasets show that our proposed method outperforms CVDD and other strong baselines.
Revisiting the Importance of Open Access, Open Source, and Open Standards for Libraries

Over a decade and a half ago, I wrote an advocacy article entitled “The Importance of Open Access, Open Source, and Open Standards for Libraries.” I thought it would be interesting to revisit the article and see how much things have changed since that time. While open access, open source, and open standards continue to be important to libraries, looking back we can see that not everything is what I thought it would be.
Geopolitical Issues in Human Computer Interaction

This workshop will explore and discuss geopolitical issues in Human Computer Interaction (HCI) as a field of knowledge and practice. These issues are mainly seen at two levels: (1) on discourses surrounding motivations and value of HCI as a sociotechnical field, and (2) on discourses surrounding concepts of HCI diffusion, maturity and diversity as articulated by global and local knowledge networks. Since the beginning of HCI, discussions of democracy have been around. It may even be fair to say that the key notion of usability aims to support the citizens of a democratic society. Obviously, exactly how HCI should do this remains open for discussion. HCI has several roots deep in military needs from the world wars of the 20th century. It was also born out of the sociotechnical traditions with its emancipatory ambitions, aiming at creating conditions for supporting human agency that facilitates the realization of people's needs and potential. There's an inherent contradiction between these traditions. Thus, we're interested in exploring the following question: how to reconcile such diverse discourses as military power and emancipatory ambitions in a geopolitical analysis of HCI research and associated discourses? Moreover, the diffusion of HCI as field of knowledge and practice is dominated by political and post-colonial discourses that pervade local and global knowledge networks shaping what is considered useful and relevant research and practice. In this workshop we understand these issues as geopolitical in nature and aim to trace the cultural and sociotechnical dynamics that construct the field of HCI.
Internal and external spatial–temporal constraints for person reidentification

Spatial–temporal information is easy to achieve in a practical surveillance scene, but it is often neglected in most current person reidentification (ReID) methods. Employing spatial–temporal information as a constrain has been verified as beneficial for ReID. However, there is no effective modeling according to the pedestrian movement law. In this paper, we present a ReID framework with internal and external spatial–temporal constraints, termed as IESC-ReID. A novel residual spatial attention module is proposed to build a spatial–temporal constraint and increase the robustness to partial occlusions or camera viewpoint changes. A Laplace-based spatial–temporal constraint is also introduced to eliminate irrelevant gallery images, which are gathered by the internal learning network. IESC-ReID constrains the attention within the functioning range of the channel space, and utilizes additional spatial–temporal constrains to further constrain results. Intensive experiments show that these constraints consistently improve the performance. Extensive experimental results on numerous publicly available datasets show that the proposed method outperforms several state-of-the-art ReID algorithms. Our code is publicly available at https://github.com/jiaming-wang/IESC.
Empirical Evaluation of Agile Teamwork

During the fall 2020 we observed and tracked several student teams working remotely and independently to develop a non-trivial software product as the capstone project for a course of Software Engineering in our university. The teams used an integrated open-source development environment that we designed to be useful to support and measure Agile development efforts, storing all artifacts and logging productivity and interaction data. Moreover, teams were required to use the Essence visual language during the retrospectives in order to analyze and improve their Scrum-like process. The tools used by the teams were used to store and collect several process data, that post-mortem were also integrated by the answers given by the students to some questionnaires. This paper proposes an empirical evaluation of the process followed by the teams, using a teamwork quality model and an Agile maturity model. The two models highlight different facets of the teamwork. We have studied and compared the development and interaction activities of the teams, and found a correlation between the results of the two models.
Mobile user authentication-Turn it to unlock

Nowadays, with the prosperous development of smartphone, abundant applications in smartphone have sprung up. To secure privacy information of users from being leaked, user authentication become necessary and important. Traditional authentication scheme such as password are vulnerable to shoulder surfing attacks and smudge attacks. Physiological biometric authentication schemes usually have some limitations when people using. More importantly, once the physiological information leaked will cause serious consequences. In contrast, behavioral biometric authentication schemes are secure and convenient. In this paper, we propose a handy user authentication scheme by simply turning over the phone. The scheme uses embedded motion sensors to effectively capture the unique user behavior. We recruited 19 volunteers with about 760,000 turning samples collected over multiple weeks under sitting and standing postures. Eventually, the scheme we proposed is available to use and deploy in smartphone.
Obérisk: Cybersecurity Requirements Elicitation through Agile Remote or Face-to-Face Risk Management Brainstorming Sessions

Cyberattacks make the news daily. Systems must be appropriately secured. Cybersecurity risk analyses are more than ever necessary, but… traveling and gathering in a room to discuss the topic has become difficult due to the COVID, whilst having a cybersecurity expert working isolated with an electronic support tool is clearly not the solution. In this article, we describe and illustrate Obérisk, an agile, cross-disciplinary and Obeya-like approach to risk management that equally supports face-to-face or remote risk management brainstorming sessions. The approach has matured for the last three years by using it for training and a wide range of real industrial projects. The overall approach is detailed and illustrated on a naval use case, with extensive feedback from the end-users. We show that Obérisk is really time-efficient and effective at managing risks at the early stages of a project, whilst remaining extremely low-cost. As the project grows or when the system is deployed, it may eventually be necessary to shift to a more comprehensive commercial electronic support tool.
A Transcription Is All You Need: Learning to Align Through Attention

Historical ciphered manuscripts are a type of document where graphical symbols are used to encrypt their content instead of regular text. Nowadays, expert transcriptions can be found in libraries alongside the corresponding manuscript images. However, those transcriptions are not aligned, so these are barely usable for training deep learning-based recognition methods. To solve this issue, we propose a method to align each symbol in the transcript of an image with its visual representation by using an attention-based Sequence to Sequence (Seq2Seq) model. The core idea is that, by learning to recognise symbols sequence within a cipher line image, the model also identifies their position implicitly through an attention mechanism. Thus, the resulting symbol segmentation can be later used for training algorithms. The experimental evaluation shows that this method is promising, especially taking into account the small size of the cipher dataset.
Stereo Matching Algorithm Based on Two-Phase Adaptive Optimization of AD-Census and Gradient Fusion

Aiming at the bad performances of existing traditional local stereo matching methods in ill-posed regions, an improved AD-Census algorithm based on two-phase adaptive optimization and gradient fusion is proposed. During the cross arm construction phase, an adaptive nonlinear constraint of intensity difference between pixels is adopted to get the optimal arm length. In the cost computation phase, the absolute difference(AD) cost and Census transform(CT) cost of each pixel are weighted summed firstly. Then the result is fused with the gradient cost by adaptive weight, which is determined by the exponential function with the shortest arm length as the independent variable. Finally, the disparity map is obtained by cost aggregation, disparity selection and disparity refinement. The experimental results indicate that the average disparity error of all regions on Middlebury 2014 datasets is reduced by 31% compared with the original AD-Census algorithm, and the average disparity error of non-occlusion regions is reduced by 40%. The proposed algorithm performs better in textureless regions and disparity discontinuity regions, and it shows enough robustness for radiometric changes and noise.