Reviewerly/exHarmony huggingface dataset:
Exploring Risk Avoidance Practices Among On-Demand Cybercrime-as-Service Operations

Criminologists have recently explored online cybercrime-as-service markets where offenders offer hacking tools and services for a fee. So-called “stresser” or “booter” markets offering distributed reflective denial of service (DRDoS) attacks have been a recent target of international law enforcement action. Few have explored the ways in which these operations continued to function under increased enforcement and sanction. This study attempted to address this gap through a qualitative analysis of online advertisements from 44 booter and stresser operators. The findings revealed operators changed their preferred communications and payment platforms, as well as increased the attack methods available to customers. The implications of this analysis for our understanding of illicit online markets, and the decision-making processes of cybercriminals are discussed in detail.
Leva: Boosting Machine Learning Performance with Relational Embedding Data Augmentation

In this paper, we present Leva, an end-to-end system that boosts the performance of machine learning tasks over relational data. Leva builds a relational embedding by representing relational data as a graph and then using embedding methods to represent the graph as vectors. The embedding represents information from the entire database, including useful information for the downstream machine learning task. At the same time, some information in the graph will be erroneous, for example, corresponding to incorrect inclusion dependencies. However, we show that the supervision signal from the downstream task filters out information that is not useful. The result is a boost in ML performance. This result means that it is possible for analysts to avoid the time-consuming effort of collecting features across multiple relations-which requires solving a data discovery and integration problem-and instead rely on these techniques to train better-performing models. We demonstrate Leva's performance on different classification and regression datasets and compare it with multiple other baselines.
Human Activity Recognition in Maintenance Centers to Reduce Wasted Time

This paper proposed a system that extracts workers' poses from live cam and video clips using mode classification. In this paper, we tested two algorithms to detect worker activity. This system aims to detect and classify the positive and negative activities of the worker in car maintenance centers such as (changing the tire, changing the oil, using the phone, standing without work) by calculating the time of each activity to measure the worker's performance and determining the lost time correctly and properly. Two experiments were conducted, the first experiment was conducted to measure the performance of the dollar algorithm with different participants. The results showed that the 1 dollar recognizer achieved 94.2% accuracy when tested on 364 different videos. The second experiment was conducted to measure the accuracy of the system in recognizing real-time activities from the live camera. It was conducted on 5 participants in a controlled environment. The system achieved an accuracy rate of 93.3%.
An initial investigation of the effect of quality factors on Agile test case quality through experts’ review

A test case is a cornerstone of the testing process; hence, it is quintessential to ensure the quality of the test cases. However, test case design in the Agile testing process has limitations that affect its quality standards, leading to the failure of many software projects. Previous studies are limited in providing a clear guideline for assessing the test case quality. However, evaluating the quality of test cases will help software testing practitioners to understand some critical issues in designing test cases from various perspectives. Therefore, this paper aims to present the verified factors and criteria of test case quality by software testing experts in an Agile environment. The proposed factors and criteria were verified through expert review techniques, which included an online survey of professionals and those in domain and knowledge experts. Twenty-three industry practitioners, including developers, testers, and systems analysts, were used to identify the domain experts. In contrast, the knowledge experts were drawn from 13 academic areas of software engineering and testing. The results showed that the quality of test cases is paramount in Agile projects; therefore, the experts accept seven factors and 32 criteria with a few modifications and suggestions. The finding may assist the researchers in improving the proposed factors and criteria for validation purposes. Hence, may contribute to software testing practitioners as a guideline in constructing, designing, and assessing compelling test cases. Most significantly, the fixed factors and criteria may contribute to the body of knowledge in the software testing domain and industries.
AI and Deep Learning-driven Chatbots: A Comprehensive Analysis and Application Trends

Chatbots are the next big technological evolution in the realm of conversational assistants and voice assistants in the modern technology era. A chatbot, sometimes known as a bot, is a piece of code developed and built to respond effectively to users’ input, utilizing natural skills in understanding inquiries and delivering appropriate replies. Top industries and organizations are embracing new artificial intelligence-driven and deep learning-based interactive chatbots in various sectors, including banking, healthcare, finance, legal, telecommunications, retail, logistics, travel, auto, sports, entertainment, and media. This paper presents a comprehensive analysis of chatbots, their applications trends, and general chatbot architecture for response generation. Moreover, we compare various chatbots based on their features, technologies, languages, and application areas. Chatbots based on AI and deep learning are now becoming an indispensable part of interaction with machines for assistance and resolving customers’ queries.
MBHAN: Motif-Based Heterogeneous Graph Attention Network

Graph neural networks are graph-based deep learning technologies that have attracted significant attention from researchers because of their powerful performance. Heterogeneous graph-based graph neural networks focus on the heterogeneity of the nodes and links in a graph. This is more effective at preserving semantic knowledge when representing data interactions in real-world graph structures. Unfortunately, most heterogeneous graph neural networks tend to transform heterogeneous graphs into homogeneous graphs when using meta-paths for representation learning. This paper therefore presents a novel motif-based hierarchical heterogeneous graph attention network algorithm, MBHAN, that addresses this problem by incorporating a hierarchical dual attention mechanism at the node-level and motif-level. Node-level attention aims to learn the importance between a node and its neighboring nodes within its corresponding motif. Motif-level attention is capable of learning the importance of different motifs in the heterogeneous graph. In view of the different vector space features of different types of nodes in heterogeneous graphs, MBHAN also aggregates the features of different types of nodes, so that they can jointly participate in downstream tasks after passing through segregated independent shallow neural networks. MBHAN’s superior network representation learning capability has been validated by extensive experiments on two real-world datasets.
Using Emoji in Response to Customer Reservation Requests and Service Reviews

The use of paralinguistic cues—including emoji—in computer-mediated communication has become prevalent in recent years. Brands and service providers have also been using these cues in their communication strategies. And yet, research examining how such emoji use influences customers’ perception and behavior is still scarce. In two experiments (combined <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>N</mi> <mo>=</mo> <mn>401</mn> </math> ), we tested if using emoji to reply to a customer request (restaurant reservation, Study 1) or online review (hotel experience, Study 2) influenced perceptions of the brands. The emoji used by the brand was always congruent with the valence of the situation. Results from both studies revealed that the presence (vs. absence) of emoji influenced consumers’ perception of the brand/service at several levels. Specifically, the restaurant/hotel was perceived to have a more informal communication style, have a warmer service, and be more modern. In Study 1, we also observed that emoji use had a positive impact on competence perception and reservation intentions. Importantly, these effects of emoji use were not moderated by the valence of the situation. Taken together, our results showed that emoji can influence different perceptions about brands and services and determine how customers relate to brands.
Improved Multimedia Object Processing for the Internet of Vehicles

The combination of edge computing and deep learning helps make intelligent edge devices that can make several conditional decisions using comparatively secured and fast machine learning algorithms. An automated car that acts as the data-source node of an intelligent Internet of vehicles or IoV system is one of these examples. Our motivation is to obtain more accurate and rapid object detection using the intelligent cameras of a smart car. The competent supervision camera of the smart automobile model utilizes multimedia data for real-time automation in real-time threat detection. The corresponding comprehensive network combines cooperative multimedia data processing, Internet of Things (IoT) fact handling, validation, computation, precise detection, and decision making. These actions confront real-time delays during data offloading to the cloud and synchronizing with the other nodes. The proposed model follows a cooperative machine learning technique, distributes the computational load by slicing real-time object data among analogous intelligent Internet of Things nodes, and parallel vision processing between connective edge clusters. As a result, the system increases the computational rate and improves accuracy through responsible resource utilization and active-passive learning. We achieved low latency and higher accuracy for object identification through real-time multimedia data objectification.
ACT: Semi-supervised Domain-Adaptive Medical Image Segmentation with Asymmetric Co-training

Unsupervised domain adaptation (UDA) has been vastly explored to alleviate domain shifts between source and target domains, by applying a well-performed model in an unlabeled target domain via supervision of a labeled source domain. Recent literature, however, has indicated that the performance is still far from satisfactory in the presence of significant domain shifts. Nonetheless, delineating a few target samples is usually manageable and particularly worthwhile, due to the substantial performance gain. Inspired by this, we aim to develop semi-supervised domain adaptation (SSDA) for medical image segmentation, which is largely underexplored. We, thus, propose to exploit both labeled source and target domain data, in addition to unlabeled target data in a unified manner. Specifically, we present a novel asymmetric co-training (ACT) framework to integrate these subsets and avoid the domination of the source domain data. Following a divide-and-conquer strategy, we explicitly decouple the label supervisions in SSDA into two asymmetric sub-tasks, including semi-supervised learning (SSL) and UDA, and leverage different knowledge from two segmentors to take into account the distinction between the source and target label supervisions. The knowledge learned in the two modules is then adaptively integrated with ACT, by iteratively teaching each other, based on the confidence-aware pseudo-label. In addition, pseudo label noise is well-controlled with an exponential MixUp decay scheme for smooth propagation. Experiments on cross-modality brain tumor MRI segmentation tasks using the BraTS18 database showed, even with limited labeled target samples, ACT yielded marked improvements over UDA and state-of-the-art SSDA methods and approached an "upper bound" of supervised joint training.
HAP: An Efficient Hamming Space Index Based on Augmented Pigeonhole Principle

The emerging deep learning techniques prefer mapping complex data objects (e.g., images, documents) to compact binary vectors (i.e., hash codes) for efficient similarity search. In this paper, we study the problem of indexing large-scale binary databases to support fast Hamming distance-based similarity queries. Existing Hamming space indices usually divide long binary vectors into short disjoint pieces and apply the Pigeonhole Principle to prune unnecessary candidates. In our work, we relax the disjoint partition constraint by allowing dimension redundancy, which yields a tighter pruning bound named Augmented Pigeonhole Principle (APP). Intuitively, APP enables more optimization opportunities by capturing the correlation between database and query workloads. Based on APP, we propose HAP, an efficient Hamming space index framework to support both Hamming range queries and k-NN queries.
Temporal feature extraction based on CNN-BLSTM and temporal pooling for language identification

In this paper, a temporal feature extraction method based on convolutional neural network-bidirectional long-short term memory (CNN-BLSTM) and temporal pooling (TMPOOL) is proposed for language identification. First, the CNN-BLSTM model is employed as a front-end local feature extractor which learns temporal representation from acoustic features in both forward and backward direction. Then the temporal pooling unit, which is a non-linear support vector regression (SVR) machine, can efficiently learn the ordering relationship between the hidden states of BLSTM and its time indexes. At last, this ordering relationship is utilized as an utterance-level representation. Furthermore, we conducted the experiments on three tasks of the oriental language recognition (OLR-2019) challenge. Compared with other CNN (BLSTM) methods, the proposed method achieves comparable error reductions.
BAWS3TS: Browsing Authentication Web-Based Smartphone Using 3D Touchscreen Sensor

Modern mobile devices, especially smartphones, have developed rapidly and are widely adopted by people of all ages. Smartphones can assist users in various activities, i.e., from social networking to online shopping, but they have also become an attractive target for cybercriminals due to their stored personal data and sensitive information. Traditional authentication mechanisms such as the PIN suffer from limitations and drawbacks known in the security community; Thus, haptic behavioral authentication has been getting much attention lately. Intuitively, it was not easy to build a standalone authentication system based on free touches. However, in this work, we advocate that such authentication can consider users' actions within some mobile applications such as web browsers and then propose a multi-layer touch authentication system using a 3D touch sensor called BAWS3TS. The pressure force on the touch screen applies to many users, and the layer level, the pressure force, and the location of the touch can provide a comprehensive authentication option during the use of the browser, and the browser has within its design a certain level at a specific location within the processing of the 3D touch sensor. So, it has a unique pattern for the user on a particular smartphone browser. Three adult volunteers had a high accuracy rate of 98% as a consequence of the studies, according to their experience. We will increase the number of participants and conduct a study to determine the effect of future use of this technology.
From ideas to entrepreneurial opportunity: A study on AI

Abstract Artificial intelligence technology is applied in numerous fields. Several countries around the world have introduced national policies on artificial intelligence, and the competition for this emerging technology has become an important driving force for industrial development. In recent years, interdisciplinary research on artificial intelligence has made great progress. From the perspective of idea‐driven technology entrepreneurship, we discuss the concept of AI with other emerging technologies. For the early stage of the entrepreneurial process, from idea generation to entrepreneurial opportunity exploitation, we elaborate on the role played by AI. Finally, the future research prospects are proposed.
Real-Time Super-Resolution for Real-World Images on Mobile Devices

Image Super-Resolution (ISR), which aims at recovering High-Resolution (HR) images from the corresponding Low-Resolution (LR) counterparts. Although recent progress in ISR has been remarkable. However, they are way too computationally intensive to be deployed on edge devices, since most of the recent approaches are deep learning based. Besides, these methods always fail in real-world scenes, since most of them adopt a simple fixed “ideal” bicubic downsampling kernel from high-quality images to construct LR/HR training pairs which may lose track of frequency-related details. In this work, an approach for real-time ISR on mobile devices is presented, which is able to deal with a wide range of degradations in the real-world scenarios. Extensive experiments on traditional super-resolution datasets (Set5, Set14, BSD100, Urban100, Manga109, DIV2K) and real-world images with a variety of degradations demonstrate that our method outperforms the state-of-art methods, resulting in higher PSNR and SSIM, lower noise and better visual quality. Most importantly, our method achieves real-time performance on mobile or edge devices.
Differential-phase-shift quantum digital signature without disclosing measurement information

Abstract A novel quantum digital signature (QDS) scheme using differential-phase-shift signal is presented. A sender broadcasts a weak coherent pulse train with 0 or π phase to receivers, who measure its relative phases using delay interferometers with photon detectors and then employ the measurement results as authentication keys. The key distribution stage is completed with this signal transmission. Neither exchange of basis information between the sender and receivers nor exchange of a portion of a sifted key between the receivers with each other are conducted, unlike conventional QDS protocols. Therefore, our system is simpler than conventional ones. The security of the proposed scheme is discussed, and calculations evaluating system parameters to guarantee the QDS operation, such as the key length and authentication threshold, are presented.
MoodCubes: Immersive Spaces for Collecting, Discovering and Envisioning Inspiration Materials

In early stages of creative processes, practitioners externalize and combine inspirational materials, using strategies such as mood board creation to achieve a desired vision and aesthetic. Yet, collecting and combining materials can be difficult: (1) mood boards bias towards 2D images, neglecting audio, video, and 3D models; (2) alternative externalizations such as prototypes are best suited for later stages and can be time-consuming and tedious to create; and (3) online searches lead to disjointed sources between different websites and assets in the file system. To address these challenges, we created MoodCubes, a system for rapid creation and manipulation of multimedia content. When adding content, MoodCubes decomposes objects (e.g., extracting colour palettes), suggests new materials without the need to search (e.g., 3D models, images, lighting effects), and provides filters to change the scene's aesthetic. We studied eight creative professionals using MoodCubes, which suggested ways the system might advance existing design practices.
IoT Based Cold Chain Logistics with Blockchain for Food Monitoring Application

In the field of raw food and seafood transportation, the importance of real-time temperature control and monitoring cannot be overstated. The assurance of product safety throughout the transport will enhance the product’s trustworthiness which is one of the factors that customers consider while deciding whether to utilize the service. This paper presents the concept of the cold chain system using Internet of Things devices and blockchain technology. The device collects temperature and geolocation and sends it to Hyperledger Sawtooth, a key blockchain infrastructure that has been chosen. The idea behind the application is to allow consumers to track products through a supply chain and notify the driver when the container’s temperature is out of the range. In order to record the shipment log as a blockchain transaction, we connected the application, the devices, and the blockchain database together. This concept can guarantee the integrity, trust, and traceability of the data because no one has the authority to change, modify or delete any information.
An Improved ID-Based Data Storage Scheme for Fog-Enabled IoT Environments

In a fog-enabled IoT environment, a fog node is regarded as the proxy between end users and cloud servers to reduce the latency of data transmission, so as to fulfill the requirement of more real-time applications. A data storage scheme utilizing fog computing architecture allows a user to share cloud data with other users via the assistance of fog nodes. In particular, a fog node obtaining a re-encryption key of the data owner is able to convert a cloud ciphertext into the one which is decryptable by another designated user. In such a scheme, a proxy should not learn any information about the plaintext during the transmission and re-encryption processes. In 2020, an ID-based data storage scheme utilizing anonymous key generation in fog computing was proposed by some researchers. Although their protocol is provably secure in a proof model of random oracles, we will point out that there are some security flaws inherited in their protocol. On the basis of their work, we further present an improved variant, which not only eliminates their security weaknesses, but also preserves the functionalities of anonymous key generation and user revocation mechanism. Additionally, under the Decisional Bilinear Diffie–Hellman (DBDH) assumption, we demonstrate that our enhanced construction is also provably secure in the security notion of IND-PrID-CPA.
Human Intelligence Analysis through Perception of AI in Teaching and Learning

Instructional practices have undergone a drastic change as a result of the development of new educational technology. Artificial intelligence (AI) as a teaching and learning technology will be examined in this theoretical review study. To enhance the quality of teaching and learning, the use of artificial intelligence approaches is being studied. Artificial intelligence integration in educational institutions has been addressed, though. Students' assistance, teaching, learning, and administration are also addressed in the discussion of students' adoption of artificial intelligence. Artificial intelligence has the potential to revolutionize our social interactions and generate new teaching and learning methods that may be evaluated in a variety of contexts. New educational technology can help students and teachers better accomplish and manage their educational objectives. Artificial intelligence algorithms are used in a hybrid teaching mode in this work to examine students' attributes and introduce predictions of future learning success. The teaching process may be carried out in a more efficient manner using the hybrid mode. Educators and scientists alike will benefit from artificial intelligence algorithms that may be used to extract useful information from the vast amounts of data collected on human behavior.