Reviewerly/exHarmony huggingface dataset:
Equational Theorem Proving Modulo

Abstract Unlike other methods for theorem proving modulo with constrained clauses [12, 13], equational theorem proving modulo with constrained clauses along with its simplification techniques has not been well studied. We introduce a basic paramodulation calculus modulo equational theories E satisfying certain properties of E and present a new framework for equational theorem proving modulo E with constrained clauses. We propose an inference rule called Generalized E -Parallel for constrained clauses, which makes our inference system completely basic, meaning that we do not need to allow any paramodulation in the constraint part of a constrained clause for refutational completeness. We present a saturation procedure for constrained clauses based on relative reducibility and show that our inference system including our contraction rules is refutationally complete.
Blind oracular quantum computation

Abstract In the standard oracle model, an oracle efficiently evaluates an unknown classical function independent of the quantum algorithm itself. Quantum algorithms have a complex interrelationship to their oracles; for example the possibility of quantum speedup is affected by the manner by which oracles are implemented. Therefore, it is physically meaningful to separate oracles from their quantum algorithms, and we introduce one such separation here. We define the blind oracular quantum computation (BOQC) scheme, in which the oracle is a distinct node in a quantum network. Our work augments the client–server setting of quantum computing, in which a powerful quantum computer server is available on the network for discreet use by clients on the network with low quantum power. In BOQC, an oracle is another client that cooperates with the main client so that an oracular quantum algorithm is run on the server. The cooperation between the main client and the oracle takes place (almost) without communication. We prove BOQC to be blind : the server cannot learn anything about the clients’ computation. This proof is performed within the composable security definitions provided by the formalism of abstract cryptography. We enhance the BOQC scheme to be runnable with minimal physical qubits when run on a solid-state quantum network; we prove that this scheme, which we refer to as BOQCo (BOQC-optimized), possesses the same security as BOQC.
Ridge Regression Based on Glowworm Swarm Optimization Algorithm with t-Distribution Parameters

With the continuous development of machine learning and artificial intelligence (AI) related algorithms and methodologies, ridge regression, which is an important representative regression method in machine learning, can effectively overcome the problem of solutions with some items irreversible in multiple linear regression by adding L2 regularization term. Moreover, multiple linear regression have its own characteristics or constraints like cumbersome calculations, sparse, unstructured data, etc. In this paper, an ridge regression based on glowworm swarm optimization algorithm with t-distribution parameters has been proposed, which has a higher calculation efficiency in obtaining the optimal solution, and can effectively help the solution to avoid from falling into the local minimum trap.
PDMLP: Phishing Detection Using Multilayer Perceptron

A phishing website is a significant problem on the internet. It’s one of the Cyber-attack types where attackers try to obtain sensitive information such as username and password or credit card information. The recent growth in deploying a Detection phishing URL system on many websites has resulted in a massive amount of available data to predict phishing websites. In this paper, we purpose a new method to develop a phishing detection system called phishing detection based on a multilayer perceptron (PDMLP), which used on two types of datasets. The performance of these mechanisms evaluated in terms of Accuracy, Precision, Recall, and F-measure. Results showed that PDMLP provides better performance in comparison to KNN, SVM, C4.5 Decision Tree, RF, and RoF to classifiers.
Voice Transformation Using Two-Level Dynamic Warping and Neural Networks

Voice transformation, for example, from a male speaker to a female speaker, is achieved here using a two-level dynamic warping algorithm in conjunction with an artificial neural network. An outer warping process which temporally aligns blocks of speech (dynamic time warp, DTW) invokes an inner warping process, which spectrally aligns based on magnitude spectra (dynamic frequency warp, DFW). The mapping function produced by inner dynamic frequency warp is used to move spectral information from a source speaker to a target speaker. Artifacts arising from this amplitude spectral mapping are reduced by reconstructing phase information. Information obtained by this process is used to train an artificial neural network to produce spectral warping information based on spectral input data. The performance of the speech mapping compared using Mel-Cepstral Distortion (MCD) with previous voice transformation research, and it is shown to perform better than other methods, based on their reported MCD scores.
DKD-S: An Ontology-based Tool for Global Software Development

The Global Software Development has brought an opportunity to software companies to expand the business and market share, also given them the possibility of work with geographically dispersed teams, capitalizing on the advantages of this approach. However, this manner of developing software creates some new challenges, for instance, there is a lack of a formal and normalized model of a software project's data, and artifacts available in a coherent and easy access way for all members involved. The lack of this model makes it difficult for the project members to communicate, understand each other and what is specified on the project's artifacts. Then, this paper proposes a knowledge management tool that utilizes a domain-specific ontology for distributed development environments, intending to help distributed teams to overcome the challenges delivered by the distributed software development model, proposing techniques and best practices. The main result of this research is an ontology-based system to support the software development process with distributed teams.
Insider Perspectives of Human-Computer Interaction for Development Research: Opportunities and Challenges

Human-Computer Interaction (HCI) research has gained traction in Africa in recent years. Researchers and designers have exploited the opportunities created by advances in mobile technology and increasing access to internet services in the communities to develop and deploy user-centered digital solutions targeting African audience and for solving Africa's problems. However, only a few researches have investigated the potential opportunities and challenges that face the adoption of HCI projects and mobile technology among indigenous people, especially in Biafra land in Nigeria. Therefore, the goal of this paper is to present an insider perspective of our lived experiences in conducting HCI4D and mobile technology research, the methodological and practical challenges, and design opportunities for advancing social and culturally-sensitive HCI research and mobile application designs in the Global South.
Time Series Forecasting to Predict the Evolution of the Functional Profile of the Elderly Persons

There are many pathologies and capacity losses that progress with a similar evolution profile in certain groups of people. Health professionals are becoming increasingly knowledgeable in anticipating the development of these pathologies through preventive medicine. However, the increasing amount of data, coming from the collection of information from a larger number of patients, makes it difficult to analyse it manually. In the case of gerontology, it is difficult to classify in groups the evolution of the elderly for common pathologies in that age group. To be able to do this would make it possible to know in advance how a pathology or capacity loss will progress in an ageing person and to apply the corresponding preventive procedures. There are already works that try to improve the results of preventive medicine, but these are focused on analysing the current state of the patient and not their foreseeable future. In this article, time series forecasting by means of recurrent neural networks is used to analyse the evolution of the functional profile of ageing people as a time series. Based on the patterns contained in the patient’s time series and in the training of a model with data from previous patients, it is possible to determine the future evolution in patients with a similar history. To do this, functional profile data collected on an assessment platform developed by the authors of this article is used.
Quantifying Uncertainty for Predicting Renewable Energy Time Series Data Using Machine Learning

Recently, there has been growing interest in using machine learning based methods for forecasting renewable energy generation using time-series prediction. Such forecasting is important in order to optimize energy management systems in future micro-grids that will integrate a large amount of solar power generation. However, predicting solar power generation is difficult due to the uncertainty of the solar irradiance and weather phenomena. In this paper, we quantify the impact of uncertainty of machine learning based time-series predictors on the forecast accuracy of renewable energy generation using long-term time series data available from a real micro-grid in Sweden. We use clustering to build different ML forecasting models using LSTM and Facebook Prophet. We evaluate the accuracy impact of using interpolated weather and radiance information on both clustered and non-clustered models. Our evaluations show that clustering decreases the uncertainty by more than 50%. When using actual on-side weather information for the model training and interpolated data for the inference, the improvements in accuracy due to clustering are the highest, which makes our approach an interesting candidate for usage in real micro-grids.
Micro-Facial Movement Detection Using LBP-TOP Descriptors for Landmark Based Regions

In the paper the problem of the micro-facial movement detection is considered. The modern approaches to different stages of the micro-expression recognition are considered. A micro-facial movement detection pipeline based on the calculation of the LBP-TOP descriptors for the local 3D blocks is proposed. Two ways of constructing these blocks around the facial landmarks in the regions of the possible Action Units (AUs) is also proposed. The selection of the methods for each stage of the proposed pipeline is explained. The experiments carried out on the Spontaneous Actions and Micro-Movements (SAMM) dataset are described. The performance of the micro-facial movement detection from 74% to 81% is achieved depending on the type of the used classifier and the 3D block construction algorithm in the proposed pipeline.
Pictorial guide to canine and feline blood smears. Part 1: normal cells

Background : It is important for practitioner's to be able to examine blood smears as part of a patient's haematological assessment. In order to be able to recognise the presence of abnormal cells, it is essential that practitioners can first identify the morphological characteristics of normal blood cells. Aim of the article : This is the first of three articles on practical haematology. The aim of this article is to increase confidence in identifying normal red blood cells, white blood cells and platelets in cats and dogs. The following articles, to be published in subsequent issues, will focus on recognising common haematological abnormalities that can be seen during microscopic examination of blood smears.
Research and Development (R&amp;amp;D), Creativity and Innovation in Academic Libraries in Malawi: A Way To Rethink Library Development in the 21st Century

Noting the technological changes in the 21st century, academic libraries have been striving hard to remain competitive and relevant in their operations by providing transformative products, services and adopting best library practices to hype the whole process of service delivery. University libraries in the millennium age must meet the needs of a contemporary and pluralistic society with some sort of agility. In a technologically driven environment, library research and development, creativity and innovation are catchwords and they have been essential elements in restructuring library products and services for effective service delivery and best customer experience. This study explored the significance of R&D, creativity and innovation in 21st century librarianship at the University of Livingstonia (UNILIA). A descriptive survey was adopted for the study while a questionnaire was used as a data collection instrument. Findings revealed that at UNILIA libraries, innovative products and services mainly include Information and Communication and Technologies (ICT) based services (internet, emails, library 2.0. social media platforms); and electronic catalogues and portals. It was also discovered that with R&D, creativity and innovation, UNILIA libraries are able to adapt the rapidly technological changing environment, and improve existing products and services while at the same time exploiting new opportunities and trends in the library and information science. Major issues obstructing the pursuit of library R&D, creativity and innovation in UNILIA libraries include lack of research and innovation policies, lack of a well-established research and innovation center, unpredictable changes in the 21st century technological landscape, and university culture that doesn’t support research and innovation.
CSR 2021: The 1st International Workshop on Causality in Search and Recommendation

Most of the current machine learning approaches to IR---including search and recommendation tasks---are mostly designed based on the basic idea of matching, which work from the perceptual and similarity learning perspective. This include both the learning of features from data such as representation learning, and the learning of similarity matching functions from data such as neural function learning. Though many models have been widely used in practical ranking systems such as search and recommendation, their design philosophy limits the models to the correlative signals in data. However, advancing from correlative learning to causal learning in search and recommendation is an important problem, because causal modeling can help us to think outside of the observational data for representation learning and ranking. More specially, causal learning can bring benefits to the IR community on various dimensions, including but not limited to Explainable IR models, Unbiased IR models, Fairness-aware IR models, Robust IR models and Cognitive Reasoning IR models. This workshop focuses on the research and application of causal modeling in search, recommendation and a broader scope of IR tasks. The workshop will gather both researchers and practitioners in the field for discussions, idea communications, and research promotions. It will also generate insightful debates about the recent regulations on AI Ethics, to a broader community including but not limited to IR, machine learning, AI, Data Science, and beyond. Workshop homepage is available online at https://csr21.github.io/.
Understanding the Security of Deepfake Detection

Deepfakes pose growing challenges to the trust of information on the Internet. Thus, detecting deepfakes has attracted increasing attentions from both academia and industry. State-of-the-art deepfake detection methods consist of two key components, i.e., face extractor and face classifier, which extract the face region in an image and classify it to be real/fake, respectively. Existing studies mainly focused on improving the detection performance in non-adversarial settings, leaving security of deepfake detection in adversarial settings largely unexplored. In this work, we aim to bridge the gap. In particular, we perform a systematic measurement study to understand the security of the state-of-the-art deepfake detection methods in adversarial settings. We use two large-scale public deepfakes data sources including FaceForensics++ and Facebook Deepfake Detection Challenge, where the deepfakes are fake face images; and we train state-of-the-art deepfake detection methods. These detection methods can achieve 0.94–0.99 accuracies in non-adversarial settings on these datasets. However, our measurement results uncover multiple security limitations of the deepfake detection methods in adversarial settings. First, we find that an attacker can evade a face extractor, i.e., the face extractor fails to extract the correct face regions, via adding small Gaussian noise to its deepfake images. Second, we find that a face classifier trained using deepfakes generated by one method cannot detect deepfakes generated by another method, i.e., an attacker can evade detection via generating deepfakes using a new method. Third, we find that an attacker can leverage backdoor attacks developed by the adversarial machine learning community to evade a face classifier. Our results highlight that deepfake detection should consider the adversarial nature of the problem.
A Gated Fusion Network for Dynamic Saliency Prediction

Predicting saliency in videos is a challenging problem due to complex modeling of interactions between spatial and temporal information, especially when ever-changing, dynamic nature of videos is considered. Recently, researchers have proposed large-scale data sets and models that take advantage of deep learning as a way to understand what is important for video saliency. These approaches, however, learn to combine spatial and temporal features in a static manner and do not adapt themselves much to the changes in the video content. In this article, we introduce the gated fusion network for dynamic saliency (GFSalNet), the first deep saliency model capable of making predictions in a dynamic way via the gated fusion mechanism. Moreover, our model also exploits spatial and channelwise attention within a multiscale architecture that further allows for highly accurate predictions. We evaluate the proposed approach on a number of data sets, and our experimental analysis demonstrates that it outperforms or is highly competitive with the state of the art. Importantly, we show that it has a good generalization ability, and moreover, exploits temporal information more effectively via its adaptive fusion scheme.
Generalized automatic modulation recognition method based on distributed learning in the presence of data mismatch problem

Deep learning-based automatic modulation recognition (DL-AMR) methods are mainly based on centralized learning and decentralized learning. These methods have been developed for many applications in heterogeneous wireless networks (HWNs). However, the centralized DL-AMR methods involve high communications cost and computation cost and the decentralized DL-AMR methods are not robust since these methods are based on the assumption of uniform data distribution. In the practical HWNs, most sub-networks are independent and their heterogeneous datasets often do not match. In order to solve these problems, we propose a generalized AMR (GAMR) method based on distributed learning by considering the data mismatch scenario. First, each sub-network trains its own model by means of initialization model download from fusion center and formative modulated datasets. Second, sub-networks upload model weight of all sub-networks to the fusion center for re-training a generalized model, which will be immediately distributed to sub-networks for updating local model. Repeat these two steps until the global model converges successfully. Finally, simulation results are given to confirm advantages of the proposed GAMR method in different scenarios of HWNs.
Many-body anticommutator and its applications in the normalization of completely symmetric states with Majorana's stellar representations

Understanding the quantum properties of many-body states is important in the fields of both quantum information and condensed-matter physics. For this purpose, we generalize the basic concept of the anticommutator from two operators to the many-body case. Some key properties are given for the many-body anticommutators, and examples are provided for Pauli operators and density matrices. Using these results and techniques from the symmetry group, in a straightforward way, we give the normalization of the completely symmetric states with Majorana's stellar representations. Our developed method will help to pave the way in the study of many-body symmetric systems.
A survey on non-factoid question answering systems

Question Answering System (QAS) aims at providing the most appropriate answer to the user’s question asked in any natural language. It emerged as a future of web search that crawls structured and unstructured collections of documents to find answers. A lot of research has already been done on answering factoid questions and achieved promising accuracy, whereas non-factoid question answering is a challenging task and explores newer avenues for fact-finding and analysis. The research on handling different modules of non-factoid question answering has drastically increased from 2012 which evokes the need for summarizing and providing a comprehensive analysis of the work that has already been done in this field. The survey identifies different challenges faced in each module, how they have been addressed by the researchers with their advantages or disadvantages and further improvements. The survey is written with an objective to help the researchers understand the field with how it has been undertaken by different approaches with their impact on performance. The paper finally concludes by investigating research gaps and research objectives that are promising to get an insight into future research directions.
Impact of Long-Term Use of an Avatar to IVBO in the Social VR

The illusion of virtual body ownership (IVBO) gives us the feeling of perceiving a virtual body as our own body. It affects our behavior and the sense of presence. The recent emergence of lower-cost VR devices and social VR environments have created the ability for consumers to possess and manipulate avatars in an immersive virtual environment. In other words, IVBO can be induced even in consumer environments. However, apart from laboratory experiments, few studies have investigated IVBO in social VR, because it is necessary to have the same participants go to the laboratory and participate in experiments every day. Thus, we investigated how long-term use of an avatar impacts IVBO. Our experiment was conducted on social VR platform VRChat. Participants are instructed to pick up 30 objects during at the start of experiment. Then, they played VRChat up to 2 h. This task was lasted 30 day per user. Our results shown IVBO could be enhanced day by day. Specifically, IVBO can be significantly enhanced at day 14 regardless of the type of an avatar. However, this effect was not collated with avatar-identification.