Reviewerly/exHarmony huggingface dataset:
CSL: FPGA implementation of lightweight block cipher for power-constrained devices

The exploration of interconnected devices, embedded devices, sensors, and various network-connected devices helps to communicate each other and exchange communications. These devices overcome with security threats related to privacy and data exchange over billions of devices are interconnected. Lightweight block ciphers aim to provide a feasible solution for power-constrained devices which includes Radio-frequency identification (RFID) tags, ubiquitous computing, wireless sensor network, aggregation network and IoT. In this paper, we have implemented a lightweight block cipher compact, secure, and lightweight (CSL). It operates on 64-bit block size, and key size varies from 64-bit to 128-bit key for encryption and decryption. The hardware implementation of CSL algorithm was thrived using field programmable gate array (FPGA) architecture. A pipelined design of compact S-boxes implemented on Digilent Nexys 4 DDR Artix™-7 board. Our experimental results of CSL consumes 1145 LUTs (Lookup Tables) and has fewer memory requirements. CSL shows resistant against various cryptanalytic attacks.
Implementation of GSM Communication on Flood Monitoring Systems Based on Multiple Locations Visualization

Flooding is one of the national disaster tragedies that currently often occurs in various countries. Therefore the government encourages various government, private and community institutions to develop disaster mitigation systems. Disaster mitigation systems are short-term programs aimed at reducing disaster victims. Therefore, this paper aims to describe the results of research in building flood monitoring information systems by developing flood data visualization systems in many locations. The system as a whole consists of two systems namely a flood detection system as a client and a flood monitoring information system as a server. the two systems communicate in sending flood data using GSM services, namely SMS. Flood detection systems are designed using ultrasonic sensors, temperature sensors, rain sensors, Arduino Uno microcontrollers and GSM modules. While the flood monitoring information system server was designed using a GSM modem, web server application, mySQL database, PHP engine and Gammu. The results showed that the flood detection system can send flood data consisting of flood locations, water levels, temperatures and rain conditions to the flood monitoring information system using SMS. SMS data received can already be converted by a flood monitoring information system server to a flood visualization-based interface for many locations in real time.
Discovering social bots on Twitter: a thematic review

The onset of online social networks (OSN) like Twitter became a predominant platform for social expression and public relations. Twitter had 330 million monthly active users by the year 2019. With the gain in popularity, the ratio of virulent and automated accounts has also increased. It is estimated that 48 million of its functioning accounts are bots. Precisely, Twitter bots or Sybil accounts are kinds of automated web robot software that regulate activities like the tweet, retweet, like or follow via Twitter API. These bots misguide and delude genuine users by spreading spurious content. Hence, uncovering malicious bots from authentic users is obligatory to ensure a safe environment on Twitter. In this paper, the nature of Twitter bots and features of bot detection are discussed with their efficiency. Various bot detection approaches are classified based on the attributes and techniques used. Strong and weak aspects of distinct features and techniques are discussed. Key challenges and future research directions in detecting social bots are also presented. Special reference has been emphasised to contemporary emerging trends.
Peek Inside the Encrypted World: Autoencoder-Based Detection of DoH Resolvers

DNS-over-HTTPS (DoH), as a rising star to improve DNS security and privacy, has developed rapidly in recent years. It mixes with HTTP features, shares ports with other web services and provides API with URI templates. The unique characteristics of DoH, as well as its fast growth, bring both promising prospects and new risks, e.g. botnet communication, name abuse and data exfiltration. It is essential for network operators to learn about adoption and usage of DoH resolvers. Active scanning may be a possible way. However, it is considered to incur significantly additional overhead, which can be inefficient and aggressive. In this paper, we present DOHUNTER, a system for automati-cally discovering DoH resolvers. DOHUNTER: (i) picks DoH flow from miscellaneous HTTPS traffic, (ii)confirms DoH resolvers based on the detected DoH flow, (iii)mines other related DoH resolvers from the known ones. Our real-world experiments demonstrate the effectiveness of DOHUNTER in detecting DoH flow and finding DoH resolvers. Utilizing DOHUNTER, we witness an alarming increase in DoH adoption. Additionally, we also reveal oblivious growing trends of DoH, which may provide advice for both users and network operators.
In search of disruptive ideas: outlier detection techniques in crowdsourcing innovation platforms

The key challenge for data science in open innovation web systems is to find best ideas among thousands of community submissions. To date, this has been done with metrics reflecting enterprise needs or community preferences. This article proposes to look in a different direction: inspired by theoretical studies on disruptive innovation, we frame the problem of valuable ideas as those rarely taken up by masses or organisations yet having potential to change industries. Our aim is to find technological means for automatic detection of such innovations to aid decision making. Following past findings from business sciences on nature of disruptive innovations, the article presents a comparative study of multiple outlier detection algorithms applied to two real-world datasets containing textual descriptions of ideas for different industries. Obtained results demonstrate capability of outlier detection and show k-NN algorithm with TF-IDF and cosine distance to be the best candidate for the task.
A New Dataset and Efficient Baselines for Document-level Text Simplification in German

The task of document-level text simplification is very similar to summarization with the additional difficulty of reducing complexity.We introduce a newly collected data set of German texts, collected from the Swiss news magazine 20 Minuten ('20 Minutes') that consists of full articles paired with simplified summaries.Furthermore, we present experiments on ATS with the pretrained multilingual mBART and a modified version thereof that is more memoryfriendly, using both our new data set and existing simplification corpora.Our modifications of mBART let us train at a lower memory cost without much loss in performance, in fact, the smaller mBART even improves over the standard model in a setting with multiple simplification levels.
Ultrasound object detection using morphological region-based active contour: an application system

Ultrasound (US) is intensively employed as a screening tool for suspicious objects such as breast lesions and thyroid nodules. Avoiding the subjectivity radiologists and to overcome high variability of US interpretations among them, technological innovations in computer-aided diagnosis or CAD are massively developed. Automation and accuracy in object detection and segmentation techniques as the core of CAD are becoming prestigious knowledge creations in the current Industrial Revolution 4.0. In this paper, an application system of morphological region-based active contour called MoRbAC is presented to automatically detect the suspicious US objects. The proposed MoRbAC application was validated by applying it to detect breast lesions and thyroid nodules on 20 real US images. Quantitative measurements based on overlapping area compared to referred ground truth achieve an average accuracy of up to 98.58 ± 1.15% with a quite short mean execution time, i.e., 2.38 ± 0.89 seconds. This promising performance concludes the effectiveness and efficiency of MoRbAC as an empowered method for CAD.
Role of Artificial Intelligence in Providing Customer Services with Special Reference to SBI and HDFC Bank

Technology has changed the operating psychology of the businesses over the last couple of years. There is a growing need and demand to use Artificial Intelligence (AI) and the Indian banking Sector is gradually shifting itself towards using AI. Banks are exploring and implementing technology in various ways. Artificial Intelligence is getting better and smarter day by day. The adoption of AI in banking has grown a lot than other sectors. This is happened because banking sector still has lot of human intervention in the operations. Indian banking sector is exploring the ways to enhance customer services with the help of Artificial Intelligence. This paper is about how Artificial Intelligence (AI) is used by Indian banks with reference to SBI and HDFC Bank to improvise customer services in future.
Negative sampling and rule mining for explainable link prediction in knowledge graphs

Several KG embedding methods were proposed to learn low dimensional vector representations of entities and relations of a KG. Such representations facilitate the link prediction task, in the service of inference and KG completion. In this context, it is important to achieve both an efficient KG embedding and explainable predictions. During learning of efficient embeddings, sampling negative triples was highlighted as an important step as KGs only have observed positive triples. We propose an efficient simple negative sampling (SNS) method based on the assumption that the entities which are closer in the embedding space to the corrupted entity are able to provide high-quality negative triples. As for explainability, it actually constitutes a thriving research question especially when it comes to analyze KGs with their rich semantics rooted in description logics. Hence, we propose in this paper a new rule mining method on the basis of learned embeddings. We extensively evaluate our proposals through several experiments. We evaluate our SNS sampling method plugged to several KG embedding models through link prediction task performances on well-known datasets. Experimental results show that the SNS improves the prediction performance of KG embedding models, and outperforms the existing sampling methods. To assess the performance of our rule mining method with and without SNS, we mine and evaluate rules on three popular datasets. The extracted rules are evaluated as knowledge nuggets extracted from the KG and also as support for explainable link prediction. The overall results are good and open the way to many improvements and new perspectives.
Smart Voice and Gesture Controlled Wheel Chair

Recently, various smart devices are available on the market to assist physically impaired people. However, there are significant limitations in existing devices, such as the fact that they are expensive and cannot be purchased by the general public. Additionally, the existing wheelchairs are heavy and are difficult to manually push it from one place to another place. To overcome the aforementioned challenge, this research study has nronosed a smart voice and gesture controlled wheel chair. The wheel chair will be operated either with hand/finger/elbow/leg/head gesture or by voice command by employing a voice recognition module VR3 along with Arduino UNO. Followed by an extensive research study, this technology remains underutilized in the local market, either due to cost or due to hardware problems. The proposed prototype will be extremely beneficial to physically impaired people since they would be able to operate the wheelchair by just moving their fingers, hands, or other body parts. Depending on the direction of the acceleration or voice command like LEFT, RIGHT, FRONT, and BACK, the wheel chair directions can be changed. This can be controlled by using a microcontroller. This type of wheel chair will help in transferring the physically disabled person one place to another. The user can control the wheel chair by using either hand gesture or voice. From the results, it is evident that the proposed research study has successfully developed a cost-effective smart gesture and voice command controlled wheel chair.
Securing Certificate Management System Using Hyperledger Based Private Blockchain

Certificate digitalization has become a core part nowadays. It includes information about certificates where data is stored digitally. So far, most of the certificates are not digital which are difficult to maintain, and not highly secured. For these reasons, forgery of certificates and duplication of certificates are continuously happening. Blockchain gives us the opportunity of storing data that are highly secured and more efficient to use. Every transaction in this system is immutable which means it is quite impossible to change the data or hack the system. In this paper, a Hyperledger based low-cost and private blockchain is used for accomplishing the certificate system. This system restricts the access of users in some way but the owner is allowed to access all the functions of managing certificates. This privileges more security, privacy and authentication. Transactions have been made easier to be updated shortly and to insert the data into the blockchain. Deploying this blockchain-based certificate system will be beneficial for the digital world as it will resolve the issues of maintenance and indemnity.
Augmenting Knowledge Graphs for Better Link Prediction

Embedding methods have demonstrated robust performance on the task of link prediction in knowledge graphs, by mostly encoding entity relationships. Recent methods propose to enhance the loss function with a literal-aware term. In this paper, we propose KGA: a knowledge graph augmentation method that incorporates literals in an embedding model without modifying its loss function. KGA discretizes quantity and year values into bins, and chains these bins both horizontally, modeling neighboring values, and vertically, modeling multiple levels of granularity. KGA is scalable and can be used as a pre-processing step for any existing knowledge graph embedding model. Experiments on legacy benchmarks and a new large benchmark, DWD, show that augmenting the knowledge graph with quantities and years is beneficial for predicting both entities and numbers, as KGA outperforms the vanilla models and other relevant baselines. Our ablation studies confirm that both quantities and years contribute to KGA's performance, and that its performance depends on the discretization and binning settings. We make the code, models, and the DWD benchmark publicly available to facilitate reproducibility and future research.
Attention mechanism and adaptive convolution actuated fusion network for next POI recommendation

Nextpoint-of-interest (POI) recommendation has received widespread attention in recent years due to its superiority of recommending where users will go to next. However, there exist two limitations in many recommendation methods: (1) the hardness of modeling users' short-term preferences adaptively based on input sequence; (2) the efficient learning of the joint information between users' long- and short-term preferences. To this end, we propose an attention mechanism and adaptive convolution actuated fusion network (AMACF) innovatively, which optimizes forecast effectiveness of user preference. To better model some contextual information such as category, temporal, we utilize long- and short-term memory network to learn contextual features of POIs in historical check-ins and embed self-attention mechanism to capture users' preference in the long-term module. In the short-term module, for capturing the complicated interest, the adaptive convolution network (Ada-CN) is novelly proposed, which applies the attention mechanism to aggregate multiple parallel convolution kernels selectively and adjust to short-term preference according to users' continuously updated check-ins. Furthermore, an attention-based fusion mechanism is designed to combine long- and short-term preferences, where contributions to the next POI of different users are evaluated sufficiently. Experimental results on two real-world data sets indicates that AMACF outperforms baseline methods for next POI recommendation.
Towards more effective visualisations in climate services: good practices and recommendations

Abstract Visualisations are often the entry point to information that supports stakeholders’ decision- and policy-making processes. Visual displays can employ either static, dynamic or interactive formats as well as various types of representations and visual encodings, which differently affect the attention, recognition and working memory of users. Despite being well-suited for expert audiences, current climate data visualisations need to be further improved to make communication of climate information more inclusive for broader audiences, including people with disabilities. However, the lack of evidence-based guidelines and tools makes the creation of accessible visualisations challenging, potentially leading to misunderstanding and misuse of climate information by users. Taking stock of visualisation challenges identified in a workshop by climate service providers, we review good practices commonly applied by other visualisation-related disciplines strongly based on users’ needs that could be applied to the climate services context. We show how lessons learned in the fields of user experience, data visualisation, graphic design and psychology make useful recommendations for the development of more effective climate service visualisations. This includes applying a user-centred design approach, using interaction in a suitable way in visualisations, paying attention to information architecture or selecting the right type of representation and visual encoding. The recommendations proposed here can help climate service providers reduce users’ cognitive load and improve their overall experience when using a service. These recommendations can be useful for the development of the next generation of climate services, increasing their usability while ensuring that their visual components are inclusive and do not leave anyone behind.
Automated testing of image captioning systems

Image captioning (IC) systems, which automatically generate a text description of the salient objects in an image (real or synthetic), have seen great progress over the past few years due to the development of deep neural networks. IC plays an indispensable role in human society, for example, labeling massive photos for scientific studies and assisting visually-impaired people in perceiving the world. However, even the top-notch IC systems, such as Microsoft Azure Cognitive Services and IBM Image Caption Generator, may return incorrect results, leading to the omission of important objects, deep misunderstanding, and threats to personal safety.
Dual-Modal Transformer with Enhanced Inter- and Intra-Modality Interactions for Image Captioning

Image captioning is oriented towards describing an image with the best possible use of words that can provide a semantic, relatable meaning of the scenario inscribed. Different models can be used to accomplish this arduous task depending on the context and requirement of what needs to be achieved. An encoder–decoder model which uses the image feature vectors as an input to the encoder is often marked as one of the appropriate models to accomplish the captioning process. In the proposed work, a dual-modal transformer has been used which captures the intra- and inter-model interactions in a simultaneous manner within an attention block. The transformer architecture is quantitatively evaluated on a publicly available Microsoft Common Objects in Context (MS COCO) dataset yielding a Bilingual Evaluation Understudy (BLEU)-4 Score of 85.01. The efficacy of the model is evaluated on Flickr 8k, Flickr 30k datasets and MS COCO datasets and results for the same is compared and analysed with the state-of-the-art methods. The results shows that the proposed model outperformed when compared with conventional models, such as the encoder–decoder model and attention model.
Hierarchical Heterogeneous Graph Attention Network for Syntax-Aware Summarization

The task of summarization often requires a non-trivial understanding of the given text at the semantic level. In this work, we essentially incorporate the constituent structure into the single document summarization via the Graph Neural Networks to learn the semantic meaning of tokens. More specifically, we propose a novel hierarchical heterogeneous graph attention network over constituency-based parse trees for syntax-aware summarization. This approach reflects psychological findings that humans will pinpoint specific selection patterns to construct summaries hierarchically. Extensive experiments demonstrate that our model is effective for both the abstractive and extractive summarization tasks on five benchmark datasets from various domains. Moreover, further performance improvement can be obtained by virtue of state-of-the-art pre-trained models.
ADD: Frequency Attention and Multi-View Based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images

Despite significant advancements of deep learning-based forgery detectors for distinguishing manipulated deepfake images, most detection approaches suffer from moderate to significant performance degradation with low-quality compressed deepfake images. Because of the limited information in low-quality images, detecting low-quality deepfake remains an important challenge. In this work, we apply frequency domain learning and optimal transport theory in knowledge distillation (KD) to specifically improve the detection of low-quality compressed deepfake images. We explore transfer learning capability in KD to enable a student network to learn discriminative features from low-quality images effectively. In particular, we propose the Attention-based Deepfake detection Distiller (ADD), which consists of two novel distillations: 1) frequency attention distillation that effectively retrieves the removed high-frequency components in the student network, and 2) multi-view attention distillation that creates multiple attention vectors by slicing the teacher’s and student’s tensors under different views to transfer the teacher tensor’s distribution to the student more efficiently. Our extensive experimental results demonstrate that our approach outperforms state-of-the-art baselines in detecting low-quality compressed deepfake images.