Reviewerly/exHarmony huggingface dataset:
An Approach to Extract and Compare Metadata of Human Activity Recognition (HAR) Data Sets

Currently, open data and data sets are emerging in human activity recognition (HAR) due to their importance in different application areas such as improving people's lives, enabling informed care decisions, real-world problem solutions, and strategies for choosing the best HAR approaches. There are challenges associated with curating and sharing open data and data sets due to the absence of metadata and complete descriptions of the shared data. By properly curating data sets it will be easier to recognise, obtain and reuse to help make progress in HAR research. In this paper, we propose a conceptual framework for understanding the open data set lifecycle as consisting of four phases of construction, sharing, finding, and using. Similarly, open issues and challenges are explored related to HAR data sets from the published literature. On this basis, an approach is presented to automatically extract metadata through web scraping of the HAR data sets and then perform a natural language processing (NLP) pipeline to detect the metadata of data sets. As a result of metadata retrieval, we show how comparisons can be performed under different scenarios which can help evaluate data set quality and identify areas for improvement in data set curation. This research work will assist the HAR research community in better understanding the open data set lifecycle and how data set quality can be improved.
Serverless Security Analysis for IoT Applications

In recent years, big data technologies have focused their efforts on promoting the use of IoT devices. Thanks to the latest advances, it is a consolidated reality today and the forecast of this phenomenon will continue to grow in the coming years. However, at the security level, new threats arise that must be taken into account when we try to connect our IoT devices with services that can expose our data. For example, from a security point of view, it would be interesting to analyze the actions and events that are sent to a serverless application taking an IoT device as its origin. In this paper we analyze the main security threats on IoT platforms and how some of these threats are related to security problems in serverless applications. First, we analyze the main threats we have in IoT devices and serverless applications, finally we analyze how IoT devices could lead to a security issue in serverless applications with which it is communicating, for example by producing a Denial of wallet due to events generated in an uncontrolled way.
Human-AI Collaboration to Promote Trust, Engagement and Adaptation in the Process of Pro-environmental and Health Behaviour Change

A necessary step in the digitalization of our environments is to include the users in the decision loop, following a more human-centric paradigm. Such an aproach will make their interactions with surrounding technology closer to them. Therefore, there is a recurrent need in contemporary technological solutions to create proposals to assist users in a way that is not exclusive to them and makes them feel integrated into the intelligent system. In fact, this is particularly relevant when the proposed technology or system aims to nudge users to form, shape, or change their daily behaviours. In essence, solutions designed for assisting users in that matter need to consider the inclusion of humans in the learning/decision loop and still the literature in the field is scarce. In this work, we identify and address three crucial human requirements that this technology has to integrate to promote a comfortable and long-term use of technology for the effective assistance of behaviour change: trust, engagement, and adaptation. Besides, we propose a collaborative workflow based on hybrid intelligent systems to cover the lack of human requirements and needs of traditional approaches. In essence, this work aims to shed light on how to promote closer collaboration between humans and intelligent agents for behaviour change under the principle that people should not be treated as mere users of technologies and services, but their behaviour should become one of the critical levers for designing and using technologies. That is, creating a closer interaction between these technologies and people.
A novel robust convolutional neural network for uniform resource locator classification from the view of cyber security

Summary Uniform resource locator (URL)‐based cyber‐attacks form a major part of security threats in cyberspace. Even though the experience and awareness of the end‐users help them protect themselves from these attacks, a software‐based solution is necessary for comprehensive protection. To this end, a novel robust URL classification model based on convolutional neural network is proposed in this study. The proposed model classifies given URLs into five classes, namely, () , () , () , () , and () . The proposed model was trained and evaluated on a gold standard URL dataset comprising of samples. According to the experimental result, the proposed model obtained an accuracy as high as which outperformed the state‐of‐the‐art. Based on the same architecture, we proposed another classifier, a binary classifier that detects malicious URLs without dealing with their types. This binary classifier obtained an accuracy as high as which outperformed the state‐of‐the‐art as well. The experimental result demonstrates the feasibility of the proposed solution.
Privacy-Oriented Transaction for Public Blockchain via Secret Sharing

Immutability and traceability are the main reasons for the popularity of blockchain. Nevertheless, its transparency also makes the transactions visible to all participants, which seriously violates the privacy of some dealers. In order to transfer accounts over blockchain, all verifiers should be empowered with the ability to confirm the transactions, leading to the conflict between extensive consensus and individual privacy. Orienting to privacy issues of UTXO (Unspent Transaction Output), this paper exploited the unconditional security of Shamir’s secret sharing to construct a logic-physical map for public chain, which enabled noninteractive transaction verification without privacy infringement. A comprehensive analysis indicated that the proposed scheme is secure under UC (Universal Composability) framework with practical computation and communication overheads.
Concept formation through multimodal integration using multimodal BERT and VQ-VAE

Humans form concepts from multimodal information obtained from multiple sensory organs and understand the environment and language by predicting unobserved information through these concepts. Robots can also make better predictions by forming concepts from multimodal information in a bottom-up manner. In contrast, BERT, a language model, acquires semantic representations of sentences applicable to various language comprehension tasks by learning relationships between words through mask prediction learning. We believe that BERT can be extended to multimodality and learn multimodal representations for world understanding by learning relationships among data across modalities. Therefore, we propose a multimodal learning model using BERT and VQ-VAEs with multimodal information as input and evaluate the model in terms of model predictability and concept formation. To verify the usefulness of the proposed model, experiments were conducted using a multimodal dataset of objects acquired by a robot. The experimental results showed that the proposed model has higher category classification accuracy and word prediction performance than multimodal latent Dirichlet allocation (MLDA), which is used as a concept formation model. An analysis of the representation of the model showed that clusters of object categories were formed within the model.
“This word no get concrete meaning oo”: Pragmatic markers in Nigerian online communication

This paper deals with Nigerians’ use of pragmatic markers in their computer-mediated communication. The investigated items originate from various languages but now seem to be used across ethnic boundaries in online interactions that take place in Nigerian Pidgin and Nigerian English. Relying on a large corpus from a Nigerian web forum, the study focuses on a set of common interjections and other textual and interpersonal pragmatic markers and their discourse-pragmatic functions. Their semantic flexibility allows these items to perform different functions depending on the context. I argue that they are vital in indicating emotional involvement and response and in contextualizing (Gumperz, 1982) the forum contributions. Furthermore, the particularities of employing, interpreting, and researching such items in text-based online communication are addressed. This paper contributes to a description of “pragmatic borrowing” (Andersen, 2014) in Nigeria, as well as to the rising field of pragmatics of computer-mediated communication.
Completely Anonymous Certificateless Multi-Receiver Signcryption Scheme with Sender Traceability

Certificateless multi-receiver signcryption scheme provides an efficient solution for one-to-many communication applications and avoids the key escrow problem in the existing identity-based multi-receiver signcryption schemes. Many certificateless multi-receiver signcryption schemes have been proposed with various secure properties, such as receiver anonymity and sender anonymity. However, among the existing certificateless multi-receiver signcryption schemes with sender anonymity, there is no scheme considers the sender tracing functionality. Sender anonymity is vulnerable and contradicts with non-repudiation. The anonymous sender may disavow the illegal message he has sent, which compromises the non-repudiation, so there is a high desirability to provide a sender tracing functionality which can be execute under certain conditions to balance the sender anonymity and non-repudiation. Motivated by these concerns, a new certificateless multi-receiver signcryption scheme is proposed, which achieves completely anonymity and provides the sender tracing functionality. In the proposed scheme, the key generation center can cooperate with any authorized receiver to trace the anonymous sender, but neither of them can do it alone. In this case, sender anonymity and non-repudiation reach a certain degree of balance.
Aggregation of attention and erasing for weakly supervised object localization

In weakly supervised object localization (WSOL), objects are localized using image-level labels only. Most previous WSOL methods erase the most discriminative region using a fixed threshold to cover the entire region of the object, or they use random selection between attention and erasing. However, these methods have several limitations. First, the previous erasing method does not guarantee the erasing of adjacent pixels in the most discriminative region. Because adjacent pixels have similar information, this property may decrease the effect of the erasing. Next, random selection does not ensure the consistent use of attention and erasing, and it can be less effective to utilize both of them. To overcome these limitations, we propose a new sequential block, namely, a spatial attention branch followed by a refinement branch. Initially, the spatial attention branch generates a feature map with enhanced spatial information. Subsequently, the refinement branch helps the network capture the entire area of the object using attention and erasing modules. The attention module enhances the positional information by considering the spatial relationships. The erasing module erases all values within the specified region using various erasing sizes to increase the erasing effect. Finally, to make full use of the advantages of attention and erasing, these modules are combined through element-wise addition. Using such approaches, the proposed method achieves a state-of-the-art localization performance on the CUB-200-2011 and ILSVRC 2012 datasets. Furthermore, the proposed block requires only lightweight trainable parameters, demonstrating the efficiency of the proposed approach.
Machine assistance for credit approval? Random wheel can recommend and explain

Approval of credit application is one of the censorious business decisions the bankers usually take on a regular basis. The growing number of new credit applications and the enormous amount of outstanding credit card bills during the recent pandemic make this even more challenging nowadays. Some of the previous studies suggest automating the credit approval process to mitigate this challenge. However, we believe that trustworthy machine assistance could be more beneficial as the effectiveness of complete automation may depend on the richness of the training dataset and model efficiency. A novel classifier, named random wheel, interestingly provides a more interpretable output. In this work, we have proposed an enhanced version of random wheel to facilitate a trustworthy recommendation for credit approval process. It not only produces more accurate and precise recommendations but also provides an interpretable confidence measure. Besides, it explains the machine recommendation for each credit application as well. The availability of recommendation confidence and explanation could bring more trust in the machine provided intelligence which may, in turn, enhance the efficiency of the credit approval process.
A Generalized Robot Navigation Analysis Platform (RoNAP) with Visual Results Using Multiple Navigation Algorithms

The robotic navigation task is to find a collision-free path among a mass of stationary or migratory obstacles. Various well-established algorithms have been applied to solve navigation tasks. It is necessary to test the performance of designed navigation algorithms in practice. However, it seems an extremely unwise choice to implement them in a real environment directly unless their performance is guaranteed to be acceptable. Otherwise, it takes time to test navigation algorithms because of a long training process, and imperfect performance may cause damage if the robot collides with obstacles. Hence, it is of key significance to develop a mobile robot analysis platform to simulate the real environment which has the ability to replicate the exact application scenario and be operated in a simple manner. This paper introduces a brand new analysis platform named robot navigation analysis platform (RoNAP), which is an open-source platform developed using the Python environment. A user-friendly interface supports its realization for the evaluation of various navigation algorithms. A variety of existing algorithms were able to achieve desired test results on this platform, indicating its feasibility and efficiency for navigation algorithm analysis.
Label confidence-based noise correction for crowdsourcing

In crowdsourcing scenarios, each instance obtains multiple noisy labels from different crowd workers and then gets its integrated label via a label aggregation method. In spite of the effectiveness of label aggregation methods, a certain level of noise still remains in the integrated labels. To solve this problem, several noise correction methods have been proposed in recent years. However, to our best knowledge, these methods seldom take the label confidence of each instance into account. Therefore, we propose a label confidence-based noise correction (LCNC) method. At first, LCNC calculates the label confidence of each instance using its multiple noisy labels to filter all instances and obtains an original clean set and noise set. Second, LCNC builds multiple random trees on the original clean set to recalculate the label confidence of each instance using its predicted labels generated by these random trees and then refilters all instances to obtain a new clean set and noise set. Finally, LCNC builds two heterogeneous classifiers on the new clean set to correct the noise instances in the new noise set according to a consensus voting strategy. The experimental results on 34 simulated and two real-world crowdsourced datasets show that LCNC significantly outperforms all the other state-of-the-art noise correction methods used for comparison.
Litho-structural mapping and structural evolution of the Bocaranga pluton, northwest Adamawa-Yadé domain, Central African Republic

The Bocaranga pluton, in the northern domain of the Central African Orogenic Belt (CAOB) in the Central African Republic (CAR), is located in the Adamawa-Yadé domain, near the Mbéré Shear Zone (MBSZ). This study provides new results of the satellite image processing, the detailed structural and petrographic studies of Bocaranga pluton which helps to understand the emplacement mechanism of this pluton. Image processing of the satellite data and field showed the occurrence and spatial distribution of (1) amphibole-pyroxenites, (2) garnet-biotite rich amphibolite, (3) migmatite, (4) hornblende-biotite and hornblende-garnet gneiss, (5) hornblende-biotite granite (HBG), (6) two micas granite (TMG), (7) fine-grained biotite granite (FBG), and (8) coarse-grained biotite granite (CBG) rock types. The mapping of regional lineaments was carried out by automatic and manual lineament extraction methods. NE-SW direction corresponds to the direction of S2 and S3 foliations, faults, mineral lineation, quartz vein, dyke (aplitic granitic or pegmatitic). NW-SE direction also corresponds to the direction of S1 foliation, F1 folds, faults, quartz veins, dykes (aplitic granitic or pegmatitic). In the absence of absolute geochronological data in study area, it has been possible to propose a polyphase structural evolution, deduced from the relative chronology of the Bocaranga granitoid. Three phases of deformation can be defined in this study. D1 phase is represented by early NW-SE trending foliation (S1) that has been progressively rotated parallel to S2 overprinting foliation occurring in gneiss, amphibolites and hornblende-biotite granite (HBG) intrusion as well. D2 phase of ductile sinistral sense from NE-SW to ENE-WSW is responsible for an emplacement of Bocaranga granitic pluton. The scarcity of structure orientation in the TMG and FBG (except the contact zones), would imply that these granites would have been crystallized in the absence of any stress. D3 is a phase of brittle deformation with three main directions corresponding to the main direction of the shear zones in the Northern domain of the CAR Pan-African fold belt. The mylonitic deformation observed in study area illustrates the intensity of the deformation. The dominance of ellipsoidal structures observed in Bocaranga area suggests that the shear mechanism is the main controlling structure of Bocaranga granite emplacement, which can be correlated with the emplacement of Tamkoro-Bossangoa granitoid in CAR and the Adamawa-Yadé domain in Cameroon, Chad and NE Brazil. These results imply a syntectonic emplacement of Bocaranga pluton during sinistral simple shear regime related to deformation along the Central Cameroon Shear Zone (CCSZ).
Generating news image captions with semantic discourse extraction and contrastive style-coherent learning

News image captioning is a popular and advanced task requiring the generation of image captions that reflect the relevant information contained in the news articles. The existing approaches usually align objects in images with corresponding news content and focus on entity-based approaches. However, their limitations are threefold: (a) semantic discourses of news contexts are largely unexplored, (b) the caption style has not yet been used, and (c) key entities are not ensured to be generated during caption generation. In this paper, we propose a novel framework for generating news image captions which are semantically informative, well-styled and entity-aware controllable. Specifically, the semantics of news articles are preserved by separating them into elementary discourse units (EDUs), which are fine-grained basic semantic units. Moreover, we introduce a Contrastive Style Reward to ensure that the style of captions and corresponding news content are coherent. Furthermore, we introduce a controllable mechanism to ensure that key entities are generated during the generation process. Extensive experiments on two large-scale news datasets demonstrate the effectiveness of the proposed framework.
A novel symmetric cryptographic method to design block complexity for data security

The growth and development of Cloud Computing from its inception till today has made a tremendous progress and the evident of the same is seen in every walk of life. However considering data security there is a big hiatus. It is consistently under threat besides many guards. This in turn has created a research gap to find out the secure guard for Data Security. Data security is primarily dependent on encryption algorithms to secure data in the CC environment. Thus the encryption algorithm plays a predominant role in Cloud Storage (CS) which is a place to store all the data of the users or data owners. Hence the usage of CS has become inevitable in almost all fields including education, industry, business and the usage of the same is grown in leaps and bounds particularly in the pandemic. The metamorphosis of the encryption algorithm (i.e. AES, Triple DES, and Blowfish) has a complex formation to secure the data. However complex the formation is; it fails to cope with the modern-day processer evaluation. The complexity level tends to be low considering the performance of the modern-day processer evaluation. Hence an alternate solution is demanded and this research paper has attempted to meet the demand by introducing the new multi-layered cryptographic algorithm named as UK to secure the CS data. This UK algorithm follows an entirely new transformation procedure to secure the data. It creates huge complexity in reasonable encryption time, even though brute force needs to be more complex ever. The key and Metadata are not stored in any place so that the CS service provider will not be able to do anything in this structure. In this case, the database service providers have the hold of the encrypted data of the user which cannot be accessed by any attacking techniques. Hence, the CS provider becomes the warehouse maintainer.