Reviewerly/exHarmony huggingface dataset:
Consistent affinity representation learning with dual low-rank constraints for multi-view subspace clustering

Multi-view clustering aims to achieve better accuracy of data clustering by leveraging complementary information embedded in multi-view data. How to learn a consistent clustering-friendly affinity representation matrix is a crucial issue. In this paper, we propose a consistent affinity representation learning method with dual low-rank constraints for multi-view subspace clustering. To be specific, for capturing the high-order correlations and global consensus among views, we collect the subspace representations of all views into a 3-order tensor, which is imposed with the tensor singular value decomposition (t-SVD) based tensor nuclear norm for achieving the low-rank recovery. Thus, we learn a consistent affinity matrix by fusing multiple subspace representations on the Grassmann manifold rather than handling them in the Euclidean space. In order to enhance the global cluster structure in the uniform subspace, the low-rank constraint is imposed on the consistent affinity matrix. Furthermore, the local geometric structure of the uniform subspace is encoded via graph regularization. The established model can be solved via the alternating direction method of multipliers algorithm (ADMM). Ultimately, the proposed method is experimentally validated to be superior to other state-of-the-art clustering algorithms.
High-throughput block cipher implementations with SIMD

The development of new technologies has put forward higher requirements for the performance and security of block ciphers. To meet the needs of these situations, Bitslicing is one of the block cipher implementation techniques which can provide high efficiency and resistance to cache-timing attacks. The S-box is usually the most time-consuming component for Bitsliced implementations. In this paper, we present techniques to optimize the S-box implementation and derive the most compact representations available for Bitslicing. Then we show that the efficiency of linear layers can be further improved by introducing a state-of-the-art technique. As a result, our implementations have significant advantages over previous implementations. The throughputs of our AES and SM4 implementations achieve 27068Mbps and 30026Mbps on i5-11335G7, both exceeding the throughput of AES-NI on the same platform.
Deep Learning-Based Classification of Spoken English Digits

Classification of isolated digits is the basic challenge for many speech classification systems. While a lot of work has been carried out on spoken languages, only limited research work on spoken English digit data has been reported in the literature. The paper proposes an intelligent-based system based on deep feedforward neural network (DFNN) with hyperparameter optimization techniques, an ensemble method; random forest (RF), and a regression method; gradient boosting (GB) for the classification of spoken digit data. The paper investigates different machine learning (ML) algorithms to determine the best method for the classification of spoken English digit data. The DFNN classifier outperformed the RF and GB classifiers on the public benchmark spoken English digit data and achieved 99.65% validation accuracy. The outcome of the proposed model performs better compared to existing models with only traditional classifiers.
Partial observability and management of ecological systems

The actual state of ecological systems is rarely known with certainty, but management actions must often be taken regardless of imperfect measurement (partial observability). Because of the difficulties in accounting for partial observability, it is usually treated in an ad hoc fashion, or simply ignored altogether. Yet incorporating partial observability into decision processes lends a realism that has the potential to improve ecological outcomes significantly. We review frameworks for dealing with partial observability, focusing specifically on dynamic ecological systems with Markovian transitions, i.e., transitions among system states that are influenced by the current system state and management action over time. Fully observable states are represented in an observable Markov decision process (MDP), whereas obscure or hidden states are represented in a partially observable process (POMDP). POMDPs can be seen as a natural extension of observable MDPs. Management under partial observability generalizes the situation for complete observability, by recognizing uncertainty about the system's state and incorporating sequential observations associated with, but not the same as, the states themselves. Decisions that otherwise would depend on the actual state must be based instead on state probability distributions ("belief states"). Partial observability requires adaptation of the entire decision process, including the use of belief states and Bayesian updates, valuation that includes expectations over observations, and optimal strategy that identifies actions for belief states over a continuous belief space. We compare MDPs and POMDPs and highlight POMDP applications to some common ecological problems. We clarify the structure and operations, approaches for finding solutions, and analytic challenges of POMDPs for practicing ecologists. Both observable and partially observable MDPs can use an inductive approach to identify optimal strategies and values, with a considerable increase in mathematical complexity with POMDPs. Better understanding of POMDPs can help decision makers manage imperfectly measured ecological systems more effectively.
Ethnopedological knowledge and scientific assessment of earthenware pottery-making soils of southern Botswana for natural resource management

Earthenware pottery making, a major occupation among rural dwellers in southern Botswana, has been one of the mainstay of their economy for centuries. Consequently, local potters have classified their pottery-making soils based on indigenous knowledge with no verifiable scientific support. This study explores information on the criteria used by indigenous potters in classifying pottery-making soils in southern Botswana. It also presents results on pedological parameters of the soils. Routine laboratory procedures were used to measure properties of two representative pedons. Three properties, namely colour, texture (clayeyness) and plasticity are the major factors used in classifying pottery-making soils in Botswana. Two soils were identified: seloko se se hibidu (red clay) and seloko se se sweu (white clay) as the soils used for pottery. With the exception of texture, data on scientific analysis are consistent with indigenous soil classification system. The soils had sandy clay loam texture with clay contents ranging from 25.3 to 31.8 %, while sand dominates with over 50 % composition in the two pedons. The soils have mixed clay mineralogical assemblage consisting of sepiolite, smectite, kaolinite, illite, mica and montmorillonite and vermiculite. The coefficient of linear extensibility (COLErod) of the soils are high to very high (0.10 – 0.18), implying the dorminance of 2:1 clay minerals. As the depth increased, the soil colour became more reddish (LT1; 10YR 5/3 > 10R 7/3 and LT2: 10YR 8/2 > 10R 8/2 > 10R 7/4 > 10R 7/4). Overall, the geochemical molecular ratios points to slow pedogenic processes including hydrolysis, leaching, salinization and calcification in the soils. Data on the pedological properties of the pottery-making soils would find enormous application in sustainable management of the soil resources.
An effective feature detection approach for image stitching of near-uniform scenes

The first step to accomplish panoramic or omnidirectional image stitching often rely on how well the feature detector extract sufficient number of matchable corresponding interest points between images. This step remains a challenging problem to many renowned feature detectors, especially when the image content is fairly near-uniform. The awful impacts are often reflected in obvious misaligned and projective distortion. To address this problem, we propose an effective feature detection method based on Log-Gabor feature transform. Instead of depending on image intensity for feature point detection, the proposed method utilizes log-Gabor function to construct a number of scaled and oriented log-Gabor scale spaces from which more distinctive and matchable features can be extracted effectively. In addition, we also introduce new evaluation metrics which are more relevant for evaluating the performance of image stitching. Our method has a better feature matching performance than other state-of-the-art feature extraction methods, particularly for near-uniform scenes.
Multi-style transfer and fusion of image’s regions based on attention mechanism and instance segmentation

Recently neural style transfer technology has made great progress in terms of generation effect and work efficiency, realizing multi-style fusion and fast stylization. However, most of existing methods rely on the additional semantic description network to maintain the semantic information of the original content image, which leads to high complexity of the stylization model; they usually process regional style transferring with manually labeled masks, resulting in unnatural connection between regions. To solve the above problems, we proposed an improved regional multi-style transfer scheme based on attention mechanism and instance segmentation. Firstly, a convolutional block attention module (CBAM) attention layer and a conditional instance normalization (CIN) layer are combined and imported into the stylization network so as to realize the multi-style transfer with preserving the original semantic information. Then, the YOLACT (you only look at coefficients) network and Poisson fusion algorithm are used for stylization design and synthesis of different regions. The experimental results indicate that the proposed method not only can make the generated artworks effectively maintain the original salience semantic and visual features of the content images, but also can realize the regional stylization with natural transition among the regions of the stylized works. Compared with existing methods, our method can better emphasize the theme of original contents and enrich the visual effects with more diverse styles.
Collaborative Autonomy: Human–Robot Interaction to the Test of Intelligent Help

A big challenge in human–robot interaction (HRI) is the design of autonomous robots that collaborate effectively with humans, exposing behaviors similar to those exhibited by humans when they interact with each other. Indeed, robots are part of daily life in multiple environments (i.e., cultural heritage sites, hospitals, offices, touristic scenarios and so on). In these contexts, robots have to coexist and interact with a wide spectrum of users not necessarily able or willing to adapt their interaction level to the kind requested by a machine: the users need to deal with artificial systems whose behaviors must be adapted as much as possible to the goals/needs of the users themselves, or more in general, to their mental states (beliefs, goals, plans and so on). In this paper, we introduce a cognitive architecture for adaptive and transparent human–robot interaction. The architecture allows a social robot to dynamically adjust its level of collaborative autonomy by restricting or expanding a delegated task on the basis of several context factors such as the mental states attributed to the human users involved in the interaction. This collaboration has to be based on different cognitive capabilities of the robot, i.e., the ability to build a user’s profile, to have a Theory of Mind of the user in terms of mental states attribution, to build a complex model of the context, intended both as a set of physical constraints and constraints due to the presence of other agents, with their own mental states. Based on the defined cognitive architecture and on the model of task delegation theorized by Castelfranchi and Falcone, the robot’s behavior is explainable by considering the abilities to attribute specific mental states to the user, the context in which it operates and its attitudes in adapting the level of autonomy to the user’s mental states and the context itself. The architecture has been implemented by exploiting the well known agent-oriented programming framework Jason. We provide the results of an HRI pilot study in which we recruited 26 real participants that have interacted with the humanoid robot Nao, widely used in HRI scenarios. The robot played the role of a museum assistant with the main goal to provide the user the most suitable museum exhibition to visit.
English-Chinese Machine Translation Based on Transfer Learning and Chinese-English Corpus

This paper proposes an English-Chinese machine translation research method based on transfer learning. First, it expounds the theory of neural machine translation and transfer learning and related technologies. Neural machine translation is discussed, the advantages and disadvantages of various models are introduced, and the transformer neural machine translation model framework is selected. For low-resource Chinese-English parallel corpus and Tibetan-Chinese parallel corpus, 30 million Chinese-English parallel corpora, 100,000 Chinese-English low-resource parallel corpora, and 100,000 Tibetan-Chinese parallel corpora were used to pretrain the transformer machine translation architecture. The decoders are all composed of 6 identical hidden layers, the initialization of the model parameters is done by the transformer uniform distribution, and the model training uses Adam as the optimizer. In the model transfer part, the parameters with the better effect of the pretrained model are transferred to the low-resource Chinese-English and Tibetan-Chinese machine translation model training, so as to achieve the purpose of knowledge transfer. The results show that the model transfer learning of low-resource Chinese-English parallel corpus improves the translation system's translation by 3.97 BLEU values compared with the translation system without transfer learning at 0.34 BLEU values. Model transfer learning on low-resource Tibetan-Chinese parallel corpus increases the BLEU value by 2.64 BLEU compared to the translation system without transfer learning. The neural machine translation system that uses BPE technology for preprocessing plus model transfer learning is compared to the translation system that only performs transfer learning and shows an improved 0.26 BLEU value.It is verified that the transfer learning method proposed in this paper has a certain improvement in the effect of low-resource Chinese-English and Tibetan-Chinese neural machine translation models.
Analysis of QoS aware energy‐efficient resource provisioning techniques in cloud computing

Summary By incorporating on‐demand resources, software and data for collaborative services through the Internet, the conventional Information Technology enterprise has been transformed by cloud computing. Based on the pay‐per‐use approach, Infrastructure, platform or Software resources and servers located across data centres are among the several types of resources offered to consumers in cloud computing. Data centres handle these resources. These resources are constantly provisioned to users based on their availability, demand, and quality requirements. Cloud computing systems are known as one of the largest utilisers of energy resources all over the world. Also, power consumption has become a crucial aspect as most cloud computing systems work on traditional nonrenewable resources of energy. In order to make data centres environment‐friendly, there is a need for optimal approaches to reduce energy consumption and their hazardous effects on the environment. To analyse different available strategies for building and maintaining an energy‐efficient cloud is the main objective of this paper. The paper will comprehensively review several energy‐efficient resource provisioning methods and provide a graphical comparative study of Quality of Service (QoS) Metrics in cloud computing. Moreover, the present study identifies the areas of study that need to be further improved to increase the energy efficiency of cloud computing systems.
Automatic Extraction of Medication Information from Cylindrically Distorted Pill Bottle Labels

Patient compliance with prescribed medication regimens is critical for maintaining health and managing disease and illness. To encourage patient compliance, multiple aids, like automatic pill dispensers, pill organizers, and various reminder applications, have been developed to help people adhere to their medication regimens. However, when utilizing these aids, the user or patient must manually enter their medication information and schedule. This process is time-consuming and often prone to error. For example, elderly patients may have difficulty reading medication information on the bottle due to decreased eyesight, leading them to enter medication information incorrectly. This study explored methods for extracting pertinent information from cylindrically distorted prescription drug labels using Machine Learning and Computer Vision techniques. This study found that Deep Convolutional Neural Networks (DCNN) performed better than other techniques in identifying label key points under different lighting conditions and various backgrounds. This method achieved a percentage of Correct Key points PCK @ 0.03 of 97%. These key points were then used to correct the cylindrical distortion. Next, the multiple dewarped label images were stitched together and processed by an Optical Character Recognition (OCR) engine. Pertinent information, such as patient name, drug name, drug strength, and directions of use, were extracted from the recognized text using Natural Language Processing (NLP) techniques. The system created in this study can be used to improve patient health and compliance by creating an accurate medication schedule.
“It’s Just a Lot of Prerequisites”: A User Perception and Usability Analysis of the German ID Card as a FIDO2 Authenticator

Two-factor authentication (2FA) overcomes the insecurity of passwords by adding a second factor to the authentication process. A variant of 2FA, which is even phishing-resistant unlike, e.g., SMS-based implementations, is offered by the FIDO2 protocol. In 2018 its compatibility with eID, the German electronic identification system, which is built into every German ID card, was published. Thus, users who own a German ID card may use it as a second factor to secure their online accounts.
Toward an Energy Data Platform Design: Challenges and Perspectives from the SYNERGY Big Data Platform and AI Analytics Marketplace

Abstract Today, the need for “end-to-end” coordination between the electricity sector stakeholders, not only in business terms but also in securely exchanging real-time data, is becoming a necessity to increase electricity networks’ stability and resilience while satisfying individual operational optimization objectives and business case targets of all stakeholders. To this end, the SYNERGY energy data platform builds on state-of-the-art data management, sharing, and analytics technologies, driven by the actual needs of the electricity data value chain. This paper will describe the layered SYNERGY Reference Architecture that consists of a Cloud Infrastructure, On-Premise Environments, and Energy Apps and discuss the main challenges and solutions adopted for (a) the design of custom pipelines for batch and streaming data collection and for data manipulation and analytics (based on baseline or pre-trained machine learning and deep learning algorithms) and (b) their scheduled, on-event, or real-time execution on the cloud, on-premise and in gateways, toward an energy data space. Particular focus will be laid on the design of the SYNERGY AI analytics marketplace that allows for trustful sharing of data assets (i.e., datasets, pipelines, trained AI models, analytics results) which belong to different stakeholders, through a multi-party smart contract mechanism powered by blockchain technologies.
Ontology-based Modelling and Reasoning for Forest Fire Emergencies in Resilient Societies

Every year, thousands of forest fires throughout the world cause disasters. One of the most critical challenges during a wildfire disaster is the effective management of heterogeneous information relative to the crisis to support human operators and authorities. Towards addressing this challenge, this paper presents an ontology-based framework for data representation and interlinking of wildfire events that are being used to foster advanced reasoning, situational awareness and interpretation for decision support. More specifically, we illustrate the capabilities of the ONTO-SAFE ontology to symbolically model contextual information in the domain, addressing application and user requirements promoting the creation of interoperable knowledge graphs. On top of the symbolic knowledge graphs, we define a rule-based framework for infusing expert knowledge in the form of constraints and rules to recognize patterns and situations of interest based on domain knowledge, assisting end-users in taking informed decisions and facilitating advanced decision-making.
Data Analysis in Digital Marketing using Machine learning and Artificial Intelligence Techniques, Ethical and Legal Dimensions, State of the Art.

Digital marketing has now been influenced by Artificial Intelligence to such an extent that optimized algorithms are able to predict consumer behavior, make the right offers to the right customers, and even evolve along with other new technologies such as blockchain. By adding value insights through data mining techniques, chatbots are now playing a key role in digital sales. Analyzing recent articles related to machine learning and Artificial Intelligence in digital marketing, this article presents a systematic review of recent progress to the task at hand. Apart from an examination of the legal and ethical context within which these techniques develop, we also investigated at the challenges Artificial Intelligence(AI) will present in the future of digital marketing.
Iterative Approach for Novel Entity Recognition of Foods in Social Media Messages

Entity recognition is the computational task of identifying words or phrases in natural language text that correspond to real-world objects of specific predefined types and has several text processing applications. However, current entity recognition methods are trained to recognize only a relatively small set of entity types. Extending an entity recognition method to a novel entity type requires a large labeled dataset of known mentions of the new entity type. As labeling natural language datasets is a time-consuming process, identifying novel entity types remains a challenging problem. This work extends the Snowball approach to enable recognition of novel entity types from unstructured text that is typical in social media. The approach uses a set of keywords known to be associated with a new entity type and a large unlabeled corpus of text that could contain mentions of the entities. The iterative approach starts with dataset messages that are most likely to contain the entities. Likelihood is based on the number of keywords that appear in a message. This approach is then applied to the problem of identifying food entities in messages on the Twitter network. The initial set of keywords is obtained from the FoodKeeper dataset, a dataset provided by the U.S. Food Safety and Inspection Service, and which contains information on a variety of foods. The motivation for this application is to build a system that can automatically respond to messages about food with relevant information about food safety and preparation in an effort to reduce food waste. We evaluated the precision and recall of the entity recognition method on a hand-labeled dataset of tweets. The system achieved a precision of 0.80 and a recall of 0.80 (f-score of 0.80) on this dataset.