Reviewerly/exHarmony huggingface dataset:
Functional Requirements Elicitation in IoT Systems: a follow-up study

As the Internet of Things (IoT) advances, specific views have been proposed for the entire software development cycle and also for Requirements Engineering (RE). The analysis of the use of RE techniques, tools, and models can contribute to obtain better results in this field. This paper presents a Systematic Mapping Study (SMS) to investigate techniques for Functional Requirements (FR) elicitation in IoT software systems, as well as gaps and limitations of current solutions. During the SMS, seventeen articles focused on FR in the IoT were found. The analysis was complemented with an input from the experience of practitioners who have dedicated to this topic, obtained through structured and semi-structured interviews. The results show that FR elicitation has started from the use of traditional techniques, but that these do not fully meet the specificities of the IoT. The majority of the models found are based on UML (Unified Modeling Language) and the most important techniques are based on scenarios. The tools that support these proposals are maturing or under development. In the conclusion, the study shows the advancements already achieved, as well as the challenges and opportunities that are still present.
Deep mixtures of unigrams for uncovering topics in textual data

Abstract Mixtures of unigrams are one of the simplest and most efficient tools for clustering textual data, as they assume that documents related to the same topic have similar distributions of terms, naturally described by multinomials. When the classification task is particularly challenging, such as when the document-term matrix is high-dimensional and extremely sparse, a more composite representation can provide better insight into the grouping structure. In this work, we developed a deep version of mixtures of unigrams for the unsupervised classification of very short documents with a large number of terms, by allowing for models with further deeper latent layers; the proposal is derived in a Bayesian framework. The behavior of the deep mixtures of unigrams is empirically compared with that of other traditional and state-of-the-art methods, namely k -means with cosine distance, k -means with Euclidean distance on data transformed according to semantic analysis, partition around medoids, mixture of Gaussians on semantic-based transformed data, hierarchical clustering according to Ward’s method with cosine dissimilarity, latent Dirichlet allocation, mixtures of unigrams estimated via the EM algorithm, spectral clustering and affinity propagation clustering. The performance is evaluated in terms of both correct classification rate and Adjusted Rand Index. Simulation studies and real data analysis prove that going deep in clustering such data highly improves the classification accuracy.
Intelligent Teaching Evaluation System Integrating Facial Expression and Behavior Recognition in Teaching Video

The student's listening status in the classroom is an important indicator to evaluate that if he takes an active participation in the classroom and study seriously. However, the main challenge in the teaching evaluation is that the teacher in class cannot timely, objectively and accurately evaluate each student's state of listening in accordance with the facial expression or behavior of the students. Along with the advance of deep learning algorithms, artificial intelligence technology is more and more widely applied in the field of education. Based on the above challenges, this paper proposes an intelligent teaching evaluation method that integrates student facial expressions and behaviors in teaching videos, designs and implements a deep learning based intelligent teaching evaluation system. We construct the face detection and recognition model based on deep convolutional neural network and triple loss function to realize the detection and recognition of face regions of students. And then the student facial expression recognition model and the student behavior recognition model based on the deep separable convolutional neural network are constructed. Finally, we propose a novel comprehensive teaching evaluation algorithm by fusion of the student facial expression and behavior, aiming at calculating the comprehensive evaluation value and obtain the corresponding evaluation level. Also, we construct the first teaching video database, student facial expression database and student behavior database for intelligent teaching evaluation. In this paper, the evaluation of students fully combines the students' specific facial expressions under certain behaviors in the classroom. Therefore, the final teaching assessment results are more comprehensive and accurate.
A shallow neural model for relation prediction

Knowledge graph completion refers to predicting missing triples. Most approaches achieve this goal by predicting entities, given an entity and a relation. We predict missing triples via the relation prediction. To this end, we frame the relation prediction problem as a multi-label classification problem and propose a shallow neural model (SHALLOM) that accurately infers missing relations from entities. Shallom is analogous to C-BOW as both approaches predict a central token (p) given surrounding tokens ((s, o)). Our experiments indicate that Shallom outperforms state-of-the-art approaches on the FB15K-237 and WN18RR with margins of up to 3% and 8% (absolute), respectively, while requiring a maximum training time of 8 minutes on these datasets. We ensure the reproducibility of our results by providing an open-source implementation including training and evaluation scripts at https://github.com/dice-group/Shallom.
Analysis &amp; Design of Convolution Operator for High Speed and High Accuracy Convolutional Neural Network-Based Inference Engines

Inference engines (IEs) based on convolutional neural networks (CNN) are memory intensive and computationally complex. The IEs require optimum data format for representing kernel weights and feature maps (FMs) to reduce the computational complexity of the convolution operator (CO). The proposed CO implements multiplications in floating-point and additions in fixed-point, considering the implementation aspects and loss-of-precision. Here, the optimal data format is decided through MATLAB-based range and precision analysis. To accomplish this image-based models such as AlexNet, VGG-16, and VGG-19 are considered with single-precision floating-point representation (SPFP) as reference representation. Analysis reveals that half-precision floating-point (HPFP) and 16-bit fixed-point (10-bit integer, 6-bit fraction) representations are required for kernel weights and feature maps respectively. A 16-bit Fix/Float 2&#x00D7;1 CO is designed. A trade-off analysis of the CO with proposed data format, 16-bit fixed-point, SPFP, and HPFP is performed. This CO has worst-case accuracy as close as 97 percent with SPFP. The proposed 2&#x00D7;1 CO is implemented with a multiplication operation processing unit (MOPU) in place of the shifter/barrel-shifter unit. ASIC implementation of CO requires a 22 percent lesser area, 17.98 percent lesser power than HPFP with 250MHz clock. Also, the speed of 750MOPS and hardware efficiency of 24.22TOPS/W are achieved.
Toward Real-Time Volcano Seismic Events’ Classification: A New Approach Using Mathematical Morphology and Similarity Criteria

This work proposes a new approach based on a suit combination of mathematical morphology and similarity criteria techniques to classify long-period and volcano-tectonic seismic events of the Cotopaxi volcano. The proposed method explores the seismic signal domain to compute a new feature space based on the edges map of the seismic events pattern represented in the gray-level spectrogram images, which is used to feed a set of similarity-based classifiers. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> -norm was selected as the best metric to be implemented by the proposed method. In terms of classification performance, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> -norm was statistically superior in the D1 data set (seismic events with overlapped signals of nonvolcanic origin) and similar in the D2 data set (events without overlapped signals) with respect to the other metrics, reaching accuracy mean scores of 93.34% and 96.88%, respectively. These results demonstrated that the computed edges map feature space is a better environment for separating both seismic events compared with the original gray-level space. Regarding the execution time, total time (TT) and time per-sample (TS) did not exceed 0.388 and 0.002 s during the training stage, respectively. During the testing stage, a TS of no more than 0.012 s was achieved. Finally, its execution time is faster, and the algorithm complexity is lower compared with the state-of-the-art methods, which makes it a practical and beneficial scheme to implement for real-time seismic events’ classification.
Novel Detection Technique For Framejacking Vulnerabilities In Web Applications

Web applications are providing the front end to the web users and service providers to easily facilitate the on demand access of web services through IP. So, web is repeatedly attracting the attackers to play with majority of web users from the remote end by exploiting its identity. Day by day attackers are exploiting the new web vulnerabilities at any stage of web environment including client side, server side or communication side. From the literature it has been identified that it is required to identify the newly emerging attack vectors and also require an easily updatable detection framework. So, in this paper firstly variants of frame jacking vulnerabilities and its severity have been explored. Secondly, a framework to identify the variants of frame jacking vulnerabilities is proposed. Thereafter, the proposed framework has been analyzed on different attack vectors generated and identified from the standard open source vulnerable projects. The log files generated at various stages of these vulnerable projects are scrutinized to test the accuracy of the developed framework as live dataset. It benefits to train proposed system for newly emerging attack vectors. Further, to perform the depth study, same framework has also been analyzed on existing available dataset. It fits the framework accurately on existing standards. It is observed from the validation of framework that the result of LogitBoost is more accurate on both the datasets rather than the other classification techniques including Naïve Bayes and J48.
FEANet: Foreground-edge-aware network with DenseASPOC for human parsing

Human parsing has drawn a lot of attention from the public due to its critical role in high-level computer vision applications. Recent works demonstrated the effectiveness of utilizing context module and additional information in improving the performance of human parsing. However, ambiguous objects, small scaling and occlusion problems are still the bottlenecks. In this paper, we propose a novel framework called - Foreground-Edge-Aware Network (FEANet) with DenseASPOC context module to further enhance the segmentation performance for human parsing. We claim that the fusion of foreground and edge information can effectively segment occluded regions by reducing the impact of pixels occupied by non-human object parts while persevering boundaries between each class. Moreover, we introduce the Dense Atrous Spatial Pyramid Object Context (DenseASPOC) module to address the problem of small and ambiguous objects by empowering feature extraction ability with solid spatial perception and semantic context information. We conducted comprehensive experiments on various human parsing benchmarks including both single-human and multi-human parsing. Both quantitative and qualitative results show that the proposed FEANet has superiority over the current methods. Moreover, detailed ablation studies report the effectiveness of the employment on each contribution.
Can It Clean Up Your Inbox? Evidence from South Korean Anti‐spam Legislation

Although spam email messages have been the primary source of cybercrime since the early Internet era, there is no quick fix to this problem. Governments have established anti‐spam legislation, but surprisingly, there has been no measurement of policy impact. This study aims to fill the gap by utilizing a quasi‐experimental setting in South Korea, where the anti‐spam policy was substantially amended in November 2014. A significant change was in the default setting, switching from an opt‐out to an opt‐in scheme, which required that commercial email senders obtain recipients' prior consent. Also, the law notably escalated the deterrent penalties for perpetrators. To empirically examine the policy effectiveness, we use a large‐scale data set of 5.61 billion spam emails originating from over 38,000 spammers in 226 countries during twenty months in 2014–2015. Our findings suggest that the amended policy adopting the opt‐in scheme decreased the volume of spam originating from Korea by 16.1%. The expected economic gain from the increased productivity of recipients is 7.649 million USD per year. This study contributes to the literature by highlighting that a well‐designed policy can lower cybersecurity incidents that threaten organizations, operations, and individuals. Our finding also provides important implications for policymakers and managers in designing effective policies with data‐driven evidence.
Achieving two-dimensional optical spectroscopy with temporal and spectral resolution using quantum entangled three photons

Recent advances in techniques for generating quantum light have stimulated research on novel spectroscopic measurements using quantum entangled photons. One such spectroscopy technique utilizes non-classical correlations among entangled photons to enable measurements with enhanced sensitivity and selectivity. Here, we investigate the spectroscopic measurement utilizing entangled three photons. In this measurement, time-resolved entangled photon spectroscopy with monochromatic pumping [A. Ishizaki, J. Chem. Phys. 153, 051102 (2020)] is integrated with the frequency-dispersed two-photon counting technique, which suppresses undesired accidental photon counts in the detector and thus allows one to separate the weak desired signal. This time-resolved frequency-dispersed two-photon counting signal, which is a function of two frequencies, is shown to provide the same information as that of coherent two-dimensional optical spectra. The spectral distribution of the phase-matching function works as a frequency filter to selectively resolve a specific region of the two-dimensional spectra, whereas the excited-state dynamics under investigation are temporally resolved in the time region longer than the entanglement time. The signal is not subject to Fourier limitations on the joint temporal and spectral resolution, and therefore, it is expected to be useful for investigating complex molecular systems in which multiple electronic states are present within a narrow energy range.
A Cross-Layer Trust-Based Consensus Protocol for Peer-to-Peer Energy Trading Using Fuzzy Logic

Peer-to-Peer (P2P) energy trading platforms are being actively designed, tested, and operated by engineers, power distribution companies, and prosumers. The assurance of the accountability of the conduct of different stakeholders through a robust trust management mechanism is imperative in such platforms. The usage of blockchain, as an underlying technology, can ensure numerous properties, such as immutability, transparency, and traceable execution of transactions, in addition to ensuring trust establishment among different entities of the system. Few blockchain-based decentralized energy trading platforms have been designed in the literature to build trust about the platform and among prosumers. However, none of these proposals have considered human-in-the-loop in the trust establishment process. Moreover, these solutions have considered trust only at a particular layer of the blockchain, such as at the application or consensus layer. To bridge this gap, this article presents a novel cross-layer trust-based consensus protocol that considers human-in-the-loop and employs fuzzy logic to address the issue of vagueness of trust values by offering human interpretable trust level. The experimental results demonstrate the efficiency and effectiveness of our proposed protocol in comparison to established consensus mechanisms. The analysis also shows the protocol is immune against selfish mining, 51% and Sybil attacks.
Let's Go There: Voice and Pointing Together in VR

Hand-tracking has been advertised as a natural means to engage with a virtual environment that also enhances the feeling of presence in and lowers the barriers to entry to virtual reality. We seek to explore combining hand-tracking with voice input (which is then processed with automatic speech recognition) for a novel multimodal experience. Thus, we created Let's Go There, which explores this joint-input method for four functions in virtual reality environments: positioning, object identification, information mapping, and disambiguation. This combination may serve as a more intuitive means for users to communicate and navigate in virtual environments. We expect there to be multiple potential applications of this multimodal form of interaction across numerous domains including training, education, teamwork, and games. Let's Go There, the system described in this paper, was first accepted at CUI 2020, however we also believe there is value in showcasing it at MobileHCI.
Automated Essay Scoring (AES); A Semantic Analysis Inspired Machine Learning Approach

With the advancements in Artificial Intelligence (AI), ‘Automated Essay Scoring’ (AES) systems have become more and more prevalent in recent years. This research proposes an extension to the Coh-Metrix algorithm AES, with a focus on feature lists. Technical features, such as, referential cohesion, lexical diversity, and syntactic complexity are evaluated. Furthermore, it proposes the use of four novel semantic measures, including estimating the topic overlap between an essay and its brief. A prototype implementation, using neural networks, is used to test the individual and comparative performance of the newly proposed AES system. The results show a considerable improvement on the results obtained in the existing research for the original Coh-Metrix algorithm; from an adjacent accuracy of 91%, to an adjacent accuracy of 97.5% (and a QWK of 0.822). This suggests that the new features and the proposed system have the potential to improve essay grading and would be a good area for further research
Atomic Bonded Cross-chain Debt

Inspired by the recent boom in decentralized finance (DeFi) and the unprecedented success of flash loan projects in this ecosystem, we introduce a decentralized debt derivative named atomic bonded cross-chain debt (ABCD) to bridge the gap between the growth of lending protocols on Ethereum and other UTXO-based blockchains specifically Bitcoin. We think of ABCD as the alphabet of interoperability for DeFi and as a credit infrastructure which unlike the current protocols is not limited by requiring either smart contracts, over-collateralization, or instantaneous payback.
Analysis Performance BCRYPT Algorithm to Improve Password Security from Brute Force

Abstract The Bcrypt algorithm is a hashing function created from the Blowfish Algorithm by two computer security researchers, Niels Provos and David Mazieres. This hashing function has several advantages, using the original random salt (the salt is the order in which it is added to the password to make it harder to bruteforce). Random salts also prevent lookup table creation. On this basis, the authors try to do a Brute Force experiment on plaintext that has been encrypted by the Bcrypt Algorithm based on 3 characters, namely alphabetic characters, numeric characters and mixed characters to see the security results of the Bcrypt Algorithm. From the results of tests conducted, the alphabetic character with a total of 4 characters can be returned to the original plaintext within 4 days while if the number of 5 characters cannot be found the original plaintext. Then the numeric characters with a total of 7 characters can be found in the original plaintext within 10 hours. Meanwhile, for mixed characters with a total of 7 characters, the original plaintext cannot be found within 5 days. The results of this study indicate that the security performance of the Bcrypt Algorithm is very good in warding off Brute Force attacks for mixed characters while the numeric and alphabetic characters are not good enough.
Using BFD1000 and Raspberry pi for Autonomous Vehicle

The paper focuses to build an autonomous car prototype monocular vision with the help of Raspberry Pi as a processing chip. The model will perform three basic tasks that include self-driving on track, detecting street sign and will help in obstacle detection and collision avoidance. The necessary data from the real world to the car is provided by camera. It uses a BFD1000 tracking sensor module that provides information to the system through raspberry pi to our system which is connected to computer with the same network. The collected data is then trained and analyzed and relevant information and details is alerted back to car for appropriate actions to be taken. The risk of human errors is thus avoided safely and intelligently as the car can reach the given destination. Algorithms like lane detection, obstacle detection and traffic sign detection are used to provide the important types of control to the car. Lane detection and obstacle detection is implemented using BFD1000 module ie., a tracking sensor for better performance and accuracy as compared to OpenCV while cascade object detection method is used for street sign detection.
Product of two groups integers modulo m,n and their factor groups using python

Abstract We all know that (Z m ×Z n ,+) is a group under addition modulo m and n. Recall that a cyclic subgroup is a subgroup that generated by one element in the group. We can get the group factor of all cyclic subgroups in a group (Z m ×Z n ,+) since any their cyclic subgroup is normal On the other hand, Python is a programming language that is multipurpose, easy to use, and also can run on various OS like Windows, Ubuntu, or Android. Moreover, the modulo operations on groups (Z m × Z n ,+) also can be calculated by Python, so we can use Python to get the factor groups off all their cyclic subgroups. In this paper, we determine the group factor in a group (Z m ×Z n ,+) of any cyclic subgroup using Python.
Sequential Gibbs Sampling Algorithm for Cognitive Diagnosis Models with Many Attributes

Cognitive diagnosis models (CDMs) are useful statistical tools to provide rich information relevant for intervention and learning. As a popular approach to estimate and make inference of CDMs, the Markov chain Monte Carlo (MCMC) algorithm is widely used in practice. However, when the number of attributes, K, is large, the existing MCMC algorithm may become time-consuming, due to the fact that O(2K) calculations are usually needed in the process of MCMC sampling to get the conditional distribution for each attribute profile. To overcome this computational issue, motivated by Culpepper and Hudson's earlier work in 2018, we propose a computationally efficient sequential Gibbs sampling method, which needs O(K) calculations to sample each attribute profile. We use simulation and real data examples to show the good finite-sample performance of the proposed sequential Gibbs sampling, and its advantage over existing methods.