Reviewerly/exHarmony huggingface dataset:
CoPI: Enabling Probabilistic Conflict Prediction in Smart Space Through Context-awareness

In a smart space influenced by multiple parties, conflicts can arise when competing users try to control the same devices in different ways. Such conflicts usually require user negotiation to resolve and thus lower people's satisfaction and trust in the smart system. Finding a conflict is the first step to resolving it, and the timing when a conflict is identified impacts the options for resolution. Most existing approaches identify conflicts only at the time they occur, which offers little help to the users in resolving the conflicts, especially without them having to compromise. A better solution is to predict potential conflicts in advance so that the users can coordinate themselves to avoid conflict situations beforehand. In this paper, we propose a novel context-aware conflict prediction framework that addresses the research gaps identified in existing literature. We mine habit patterns from the user's previous interactions with smart devices in the various environments they occupy. These habits serve as inputs to our conflict prediction algorithm which takes the habits of pairs of users and outputs context situations in which those users have the potential to conflict. To support eventual flexible resolution, we use explicit models of the uncertainties of users' behaviors to associate each potential conflict scenario with a probability of that conflict occurring for these particular users. We evaluate our framework on real-world datasets to demonstrate the effectiveness of the proposed approach.
Reimagining a New Transformation of Digital Forensic Accounting: Strategic Analysis of the Use of Big Data Analytics in the Covid-19 Pandemic Era as an Opportunity for the Industries in Indonesia

The focus of this research is to find on how big data could be used in forensic accounting and be a solution in this COVID-19 (Corona Virus Disease 2019) era. Nowadays, industrial revolution 4.0 rises together with the sophisticated technologies that would give a big changes and impacted automation in lots of fields, especially accounting forensics. In one side, forensic accounting require technologies that can help in analytics processes to increase the quality of the information. Big data implementation in forensic accounting can help the system to analyze data in big amounts and create values ​​for the information as a result for forensic accounting process. Implementation of technologies could help companies to work at their best during this pandemic condition. The presence of technologies in industrial revolution 4.0
Argumentation frameworks with necessities and their relationship with logic programs

This paper presents a comprehensive study of argumentation frameworks with necessities (AFNs), a bipolar extension of Dung Abstract argumentation frameworks (AFs) where the support relation captures a positive interaction between arguments having the meaning of necessity: the acceptance of an argument may require the acceptance of other argument(s). The paper discusses new main acceptability semantics for AFNs and their characterization both by a direct approach and a labelling approach. It examines the relationship between AFNs and Dung AFs and shows the gain provided by the former in terms of concision. Finally, the paper shows how to represent an AFN as a normal logic program (LP) and vice versa and in both cases establishes a one-to-one correspondence between extensions under the main acceptability semantics (except for semi-stable semantics where the correspondence is not completely full) of an AFN and particular cases of 3-valued stable models of normal LPs.
Unmodulated fully-phase and shifted-phase encoded encryption key using Arnold transform and joint fractional Fourier transform correlator

We propose a nonlinear image encryption system using Arnold transform (ART) and a fully-phase encoded images as inputs to a nonzero-order joint transform correlator (JTC) in the fractional Fourier transform (FRFT) domain. Unlike other reported works, the fully-phase image along with two random-phase masks (RPMs) are introduced directly without linear phase modulation at the input plane of the JTC where the FRFT modulates this joint input image and produces the joint fractional power distribution (JFPD) at the output plane. It will be demonstrated that using modulating inputs in the optical setup makes the decryption scheme prone to error and leads to retrieving image failure. Further, a shifted-phase encoded key mask is applied in this proposal to achieve the non-zero order JFPD, which is adjusted by a nonlinear operation to guarantee a securely and high-quality retrieved ciphertext image. Furthermore, the proposed encryption scheme has four security keys: (i) two-random-phase masks, (ii) an ART frequency parameter, and (iii) a fractional order of the FRFT. It will be demonstrated that the decryption process is very sensitive to small variations of the ART frequency parameter, the fractional order of the FRFT, and the RPM. The cryptanalysis shows that the proposed encryption system is very invulnerable to Chosen plaintext (CPA), known plaintext (KPA), ciphertext only (COA) and brute force attacks. In addition, the robustness of the proposed scheme against noise and occlusion is presented using computer simulations.
On the use of Bayer Sensor Color Cameras in Digital Image Correlation

The digital image correlation (DIC) technique uses monochrome images, which are usually captured using monochrome cameras. Color cameras with Bayer filter patterns are available with higher specs and lower prices than monochrome cameras, and their images can be used in DIC after they are converted to monochrome. In this paper, in-plane rigid-body-translation experiments are used to compare the DIC strain measurement error for monochrome and color cameras under similar conditions. Furthermore, different algorithms for converting color images to monochrome are compared with the aim of reducing the DIC measurement error. The results show that monochrome images captured by color cameras cannot give the same level of accuracy as the images of monochrome cameras; however, DIC accuracy is dependent on the algorithm used for converting color-to-monochrome images. The results show that the interpolation methods used for demosaicing the color channels affect the DIC accuracy. For high contrast images, higher accuracy is achieved using low-order interpolation; while for low contrast images, higher-order interpolation gives better results. Interestingly, it is found that, for high contrast images, using the green channel image alone can give better DIC accuracy than the monochrome image that is obtained by combining the three channels.
Bent-based Realization of a Class of Ternary Plateaued Functions

A method is disclosed to generate a class of ternary plateaued functions, based on the concatenations of replicates of ternary bent functions. It is shown that increasing the number of replicates, plateaued functions on a large number of variables may be generated, the lower bound being the number of ternary bent functions. All generated ternary plateaued functions belong to a class named <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Omega$</tex> .
Gamification of the Educational Process. The Use of Decision Trees to Analyze the Influence of Computer Game Genres on the Preference of School Subjects

One of the tasks of today’s school education is to modernize the educational process by introducing teaching methods that increase its attractiveness. One of these methods is gamification, that is, the use of game forms of education to increase the effectiveness of the educational process. On the basis of a lot of data about attitude to educational subjects and interest in computer games, we have determined how attitude to the school subject and passion for a particular games genre are connected to increase raining efficiency. We created a questionnaire to conduct a survey on the preferences of subjects and attitudes to different genres of computer games, which we posted in Google Docs, as well as prepared a QR code to access the questionnaire; collected descriptive statistics for completed questionnaires; built decision trees using R language functions, showing the dependence of preferences to different school subjects on gender and age characteristics and attitudes to different genres of computer games.
ASSESSMENT OF SOLAR ENERGY POTENTIAL OF NIGERIA FOR SOLAR POWER GENERATION THROUGH NUMERICAL SOLAR IRRADIANCE MAPPING

Any thorough analysis of solar energy applications in the country requires a solar resources map. Many maps have been developed, but they only show distributions presented in colour intensities. Solar irradiance can only be estimated from such maps. Using on the spot experimental data for solar map production does not provide accurate maps because various climatic and environmental conditions such as dust haze and cloud cover affect the amount of solar energy that reaches the ground in various locations in Nigeria. This is particularly true during the harmattan period. Therefore data generated from such methods cannot be used to develop accurate numerical solar maps. Data for such applications should be collected from outer space and over a long time for various locations. The National Aeronautics and Space Administration (NASA) provides a resources base for such data. The data used in this study covered 25 years (1994-2018). The average daily global solar irradiance for various longitude-latitude nodes is calculated from such data. With these data available, it is possible to estimate the average daily global solar irradiance for any location provided the longitude and latitude is known. Irradiance values for 300 locations in the country are evaluated from details presented by the maps. To validate the map, experimental irradiance values for selected locations are compared with values extracted from the solar irradiance map. Calculated irradiance values are found to agree satisfactorily with experimental values. The maximum percentage difference between calculated and experimental values is 6.8%. This proved that the solar map developed is a satisfactory provides a satisfactory representation of solar data for the various locations and can be used for other locations where such data does not exist. KEYWORDS: Solar Energy, Solar Maps, Irradiance, Energy Patterns,
I can see Clearly Now: Developing a Camera-Based Automotive Rear-View Mirror Using a Human-Centred Philosophy

Driving is a visual task and as such it is essential that drivers have good vision out of their vehicles particularly on today’s busy roads. However, rear vision is not always optimum; rear passengers, luggage or the design of the car itself can all impair rear vision. A new camera-based rear view mirror is described in this paper. The system was developed by employing user-centred design methods; requirements for the system were established and a prototype was then tested dynamically with drivers’ feedback then used to improve and finalise the design.
FROM CHRONOLOGICAL NETWORKS TO BAYESIAN MODELS: CHRONOLOG AS A FRONT-END TO OXCAL

ABSTRACT We present a new method for creating an OxCal Bayesian model that bypasses the complex task of writing OxCal code. Our methodology employs the recent ChronoLog software as a graphical front-end for generating OxCal scripts. This approach enables archaeologists to create complex Bayesian models—including termini post and ante quem , duration bounds and synchronisms—with the help of a user-friendly interface. The target audience can be divided into beginners, who might struggle to create chronological models using OxCal directly, and experienced OxCal users, who should find that ChronoLog saves time when coding complex models. Three case-studies from recent publications are presented.
Exploring Cross-fusion and Curriculum Learning for Multi-modal Human Detection on Drones

In a number of applications ranging from warehouse management to people search and rescue, drones will need to evolve in the vicinity of human agents. In those situations, robust and fail-safe human detection by drones must be provided. However, human detection systems used on drones are currently based on single imaging cameras, beside a growing number of works investigating more robust detection schemes via sensor fusion. In the drone context, the fusion of standard RGB and event-based cameras has emerged, while in the automotive context, the fusion of RGB with radars has been proposed for up-most safety towards environmental conditions. In this paper, our aim is to debut the investigation of RGB, event-based camera and radar fusion. First, we acquire a novel dataset for the task of people detection in an indoor, industrial setting, by mounting the sensor fusion suite on a drone. Then, we propose a baseline convolutional neural network (CNN) architecture augmented with cross-fusion highways for sensor fusion and people detection. To train the network, we propose a novel multimodal curriculum learning procedure and demonstrate that our method (termed SAUL) greatly enhances the robustness of the system towards hard RGB failures ( on the peak F1 score) and provides a significant gain in detection performance ( on the peak F1 score) compared to the BlackIn procedure, previously proposed for cross-fusion network training. Finally, we report the performance of our system through precision-recall curve analysis and perform additional ablation studies to shed light on the key aspect of our system.
Axioms for a Logic of Consequential Counterfactuals

Abstract The basis of the paper is a logic of analytical consequential implication, CI.0, which is known to be equivalent to the well-known modal system KT thanks to the definition A → B = df A ⥽ B ∧ Ξ (Α, Β), Ξ (Α, Β) being a symbol for what is called here Equimodality Property: (□A ≡ □B) ∧ (◊A ≡ ◊B). Extending CI.0 (=KT) with axioms and rules for the so-called circumstantial operator symbolized by *, one obtains a system CI.0*Eq in whose language one can define an operator ↠ suitable to formalize context-dependent conditionals (so to counterfactual conditionals) via the definition (Def ↠) A ↠ B = df *A ⥽ B ∧ Ξ (Α, Β).. The central problem of the paper is to identify inside CI.0*Eq + Def ↠ a set of axioms yielding the fragment consisting of all and only theorems in which only truth-functional operators and the two operators → and ↠ occur. This system, here called CI.0, is introduced in §3. In view of the intended purpose, it is introduced a complete and tableau-decidable system KTw, which is an extension of KT with axioms for the so-called quasi-variables w(A), w(B)… Three translation functions among the three languages used in the paper are then introduced. The first, Tr, maps every wff of form *A into wffs of form w(A) ∧ A and translates theorems of CI.0*Eq into theorems of KTw. The second, t°, translates theorems of CI.0 into theorems of CI.0*Eq by applying Def↠. A third one, t, translates theorems of CI.0 into theorems of KTw. As a consequence, it follows that t°A = TrtA. In §4 it is proved that CI.0 is complete w.r.t. the class of CI.0-models of form &amp;lt;W, f, R,V &amp;gt; where f is a selection function and R an access relation. It is then proved (i) that CI.0 models may be converted into KTw-models; (ii) that the truth-value of a proposition in a world of a CI.0-model is preserved in the same world of the derived KTw-model; (iii) that A is a CI.0-thesis iff its translation tA is KTw-thesis. It follows that, if A is not a CI.0-thesis, tA is not a KTw-thesis, so also that TrtA is not a CI.0*Eq-thesis. Since t°A = TrtA and t°A is a function built on Def↠, this proves that every t°-translation of a CI.0-wff that is a CI.0*Eq-theorem is a CI.0-theorem. Since the converse proposition is proved in §2, their conjunction establishes the desired result. In the final section, it is proved that CI.0 is tableau-decidable, that ↠ is not a trivial operator and that ↠ enjoys the positive and negative properties required in §1 for context-dependent conditionals. Some final remarks suggest that studying the relations between CI.0 and systems of classical conditional logic may be a promising line of investigation.
On One Method for Assessing the Compulsory and Progress of Education among Preschoolers and Primary Schoolchildren in the Framework of Additional Education in the Study of Mathematics

This article discusses a method for assessing the knowledge of children of preschool and primary school age using mathematics as an example. This method allows diagnosing problems in children in the sections of the curriculum, which was compiled by a teacher of additional education for the academic year. This method characterizes each child individually, which allows teachers or parents to correct the map of his development as part of the development of the discipline.
An Analysis of Algebraic Codes over Lattice Valued Intuitionistic Fuzzy Type-3 <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>R</mi> </math>-Submodules

In the last few decades, the algebraic coding theory found widespread applications in various disciplines due to its rich fascinating mathematical structure. Linear codes, the basic codes in coding theory, are significant in data transmission. In this article, the authors' aim is to enlighten the reader about the role of linear codes in a fuzzy environment. Thus, the reader will be aware of linear codes over lattice valued intuitionistic fuzzy type-3 (LIF-3) R-submodule and α-intuitionistic fuzzy (α-IF) submodule. The proof that the level set of LIF-3 is contained in the level set of α-IF is given, and it is exclusively employed to define linear codes over α-IF submodule. Further, α-IF cyclic codes are presented along with their fundamental properties. Finally, an application based on genetic code is presented, and it is found that the technique of defining codes over α-IF submodule is entirely applicable in this scenario. More specifically, a mapping from the ℤ64 module to a lattice L (comprising 64 codons) is considered, and α-IF codes are defined along with the respective degrees.
Information economics as a tool of economics and development policy

dualism 1.2.3.Social dualism 2. eories of the development economics . . . . . . . . . . . . . . .Przemysław Deszczyński 2.1.e genesis and the concept of development economics 2.2.Classi cation of the theories of development economics 2.2.1.Development theories -the primacy of internal factors 2.2.2.Development theories -the primacy of external factors 2.2.3.Development theories -the primacy of external and internal factors 2.2.4.Development theories -the primacy of global factors 3. Conceptualization of the quanti cation of economic development . .
DiGAN: Directional Generative Adversarial Network for Object Transfiguration

The concept of cycle consistency in couple mapping has helped CycleGAN illustrate remarkable performance in the context of image-to-image translation. However, its limitations in object transfiguration have not been ideally solved yet. In order to alleviate previous problems of wrong transformation position, degeneration, and artifacts, this work presents a new approach called Directional Generative Adversarial Network (DiGAN) in the field of object transfiguration. The major contribution of this work is threefold. First, paired directional generators are designed for both intra-domain and inter-domain generations. Second, a segmentation network based on Mask R-CNN is introduced to build conditional inputs for both generators and discriminators. Third, a feature loss and a segmentation loss are added to optimize the model. Experimental results indicate that DiGAN surpasses CycleGAN and AttentionGAN by 17.2% and 60.9% higher on Inception Score, 15.5% and 2.05% lower on Fréchet Inception Distance, and 14.2% and 15.6% lower on VGG distance, respectively, in horse-to-zebra mapping.
The World Atlas of National Online Encyclopedias

The World Atlas of National Online Encyclopedias (WANOE) is a database of world's national and universal encyclopedias. There are information about online encyclopedias of Europe, America, Asia, Africa, Australia here. The WANOE published in 2022 as website with continent-related chapters. On the website, information about online encyclopedias presented in mapping way.
HybridVocab: Towards Multi-Modal Machine Translation via Multi-Aspect Alignment

Multi-modal machine translation (MMT) aims to augment the linguistic machine translation frameworks by incorporating aligned vision information. As the core research challenge for MMT, how to fuse the image information and further align it with the bilingual data remains critical. Existing works have either focused on a methodological alignment in the space of bilingual text or emphasized the combination of the one-sided text and given image. In this work, we entertain the possibility of a triplet alignment, among the source and target text together with the image instance. In particular, we propose Multi-aspect AlignmenT (MAT) model that augments the MMT tasks to three sub-tasks --- namely cross-language translation alignment, cross-modal captioning alignment and multi-modal hybrid alignment tasks. Core to this model consists of a hybrid vocabulary which compiles the visually depictable entity (nouns) occurrence on both sides of the text as well as the detected object labels appearing in the images. Through this sub-task, we postulate that MAT manages to further align the modalities by casting three instances into a shared domain, as compared against previously proposed methods. Extensive experiments and analyses demonstrate the superiority of our approaches, which achieve several state-of-the-art results on two benchmark datasets of the MMT task.
Multiple Biological Granularities Network for Person Re-Identification

The task of person re-identification is to retrieve images of a specific pedestrian among cross-camera person gallery captured in the wild. Previous approaches commonly concentrate on the whole person images and local pre-defined body parts, which are ineffective with diversity of person poses and occlusion. In order to alleviate the problem, researchers began to implement attention mechanisms to their model using local convolutions with limited fields. However, previous attention mechanisms focus on the local feature representations ignoring the exploration of global spatial relation knowledge. The global spatial relation knowledge contains clustering-like topological information which is helpful for overcoming the situation of diversity of person poses and occlusion. In this paper, we propose the Multiple Biological Granularities Network (MBGN) based on Global Spatial Relation Pixel Attention (GSRPA) taking the human body structure and global spatial relation pixels information into account. First, we design an adaptive adjustment algorithm (AABS) based on human body structure, which is complementary to our MBGN. Second, we propose a feature fusion strategy taking multiple biological granularities into account. Our strategy forces the model to learn diversity of person poses by balancing the local semantic human body parts and global spatial relations. Third, we propose the attention mechanism GSRPA. GSRPA enhances the weight of spatial relational pixels, which digs out the person topological information for overcoming occlusion problem. Extensive evaluations on the popular datasets Market-1501 and CUHK03 demonstrate the superiority of MBGN over the state-of-the-art methods.