Reviewerly/exHarmony huggingface dataset:
Course recommendation system using fuzzy logic approach

Course selection is a key for success in student’s academic path. In today’s education environment, various courses offered by different academic institutions required the students to explore the course outline manually. Most of them are lacking in knowledge, having dilemma and making blind selections to choose the right course. Therefore, it is essential to have a course recommendation to provide guidance to a student to choose the course related with their interest and skill. This paper proposed to develop a course recommendation system using fuzzy logic approach. The development methodology of this system involves several phases include requirements planning, user design and construction for prototyping, testing and cutover. This study used the fuzzy rules technique in order to calculate each associated student’s skill and interest level based on Mamdani fuzzy inference system method. Then, the rules will generate final outcome which recommend the suitable course path and provide the details to a user based on their course test. The result shows the functionality of this system has been achieved and works well. This study is significantly helping the students to choose their course based on the interest and skill.
Interactive Application Security Testing

The operation of e-commerce platform requires very high security. Interactive Application Security Test (IAST) is a new generation of vulnerability analysis technology first proposed by Synopsys Company in the United States. This technology can effectively solve the technical vulnerabilities of various websites represented by e-commerce platform. This technology combines static application security test (SAST) with dynamic application security test (DAST) by using a unique design context context association mechanism. Through this deep integration of interaction capabilities and differential comparison mechanism, a fast and highly automated vulnerability analysis capability can be built in running applications. IAST integrates the advantages of SAST and DAST technology, continuously monitors and identifies vulnerabilities in applications. Aspect-oriented programming technology enables differential comparison mechanism to perform dynamic security analysis in running programs, and extracts contextual content, data flow and flow control information from active applications to provide targeted information. Run the access capability of the actual data values at the code level. Therefore, precisely because of these abundant information, the differential comparison mechanism can identify more anomalies than other existing security tools, and achieve unprecedented accuracy. Through IAST technology, it can also confirm or eliminate whether the detected vulnerabilities can be used to attack, and determine the location of the vulnerabilities in the application code. This technology has been listed as one of the top ten information security technologies in 2014 by Gartner Information Technology Research and Consulting Company of the United States, and has a very broad application prospects.
CLER: Cross-task Learning with Expert Representation to Generalize Reading and Understanding

This paper describes our model for the reading comprehension task of the MRQA shared task. We propose CLER, which stands for Cross-task Learning with Expert Representation for the generalization of reading and understanding. To generalize its capabilities, the proposed model is composed of three key ideas: multi-task learning, mixture of experts, and ensemble. In-domain datasets are used to train and validate our model, and other out-of-domain datasets are used to validate the generalization of our model’s performances. In a submission run result, the proposed model achieved an average F1 score of 66.1 % in the out-of-domain setting, which is a 4.3 percentage point improvement over the official BERT baseline model.
EmoTag – Towards an Emotion-Based Analysis of Emojis

Despite being a fairly recent phenomenon, emojis have quickly become ubiquitous.Besides their extensive use in social media, they are now also invoked in customer surveys and feedback forms.Hence, there is a need for techniques to understand their sentiment and emotion.In this work, we provide a method to quantify the emotional association of basic emotions such as anger, fear, joy, and sadness for a set of emojis.We collect and process a unique corpus of 20 million emoji-centric tweets, such that we can capture rich emoji semantics using a comparably small dataset.We evaluate the induced emotion profiles of emojis with regard to their ability to predict word affect intensities as well as sentiment scores.
Detecting counterfeit pharmaceutical drugs

Purpose This paper aims to analyze drug counterfeiting, explains its risk factors and operating and legal environments reviews recent legal cases and develops a multi-stakeholder prevention strategy that includes forensic accounting methods. Design/methodology/approach This is a theoretical study based on legal case studies and the best forensic accounting strategies. Findings Pharmaceutical drug counterfeiting is a fast-growing fraud that so far has attracted little attention from forensic accountants. A recent estimate projects that criminals collect around $75bn annually in illicit sales from counterfeit drugs (Bairu, 2015). Pharmaceutical counterfeiting also leads to the loss of lives when criminals use lethal chemicals in the manufacturing of fake medicines (Liang, 2006a; Brown, 2005). Because the detection of drug counterfeiting is extremely difficult after fake medicines have been ingested by patients, the strategy developed in this paper is based on early discovery by using reliable tracking technologies and inventory management controls in the supply chain, conducting effective regulatory and legitimate customs inspections, and increasing consumer awareness of basic forensic accounting tools. Research limitations/implications This paper extends previous research by integrating various factors into a single multi-stakeholder prevention framework. Practical implications The paper presents a synthesized, comprehensive view of the drug fraud epidemic and analyzes concrete steps that can be taken to protect the pharmaceutical supply chain to reduce the loss of lives and monetary injuries. Originality/value No previous research has analyzed this issue from a multi-stakeholder point of view and used forensic accounting tools to complement a prevention strategy. The drug counterfeiting prevention strategy developed in this paper addresses the supply side, the regulatory enforcement side and the demand side.
A Tree-to-Sequence Model for Neural NLG in Task-Oriented Dialog

Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems. Sequence-to-sequence models on flat meaning representations (MR) have been dominant in this task, for example in the E2E NLG Challenge. Previous work has shown that a tree-structured MR can improve the model for better discourse-level structuring and sentence-level planning. In this work, we propose a tree-to-sequence model that uses a tree-LSTM encoder to leverage the tree structures in the input MR, and further enhance the decoding by a structure-enhanced attention mechanism. In addition, we explore combining these enhancements with constrained decoding to improve semantic correctness. Our experiments not only show significant improvements over standard seq2seq baselines, but also is more data-efficient and generalizes better to hard scenarios.
Speech Sanitizer: Speech Content Desensitization and Voice Anonymization

Voice input users’ speech recordings are being collected by service providers and shared with third parties, who may abuse users’ voiceprints, identify them by voice, and learn their sensitive speech content. In this work, we design <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Speech Sanitizer</i> to perturb users’ speech recordings so that the sanitized speech can be safely shared with third parties. First, we desensitize speech content by identifying sensitive words, localizing them in the audio using DTW-based keyword spotting, and substituting them with safe words. Both common and personalized sensitive words are identified and replaced. Then, we anonymize users’ voiceprints with a carefully designed voice conversion mechanism that is resistant to de-anonymization attacks. Meanwhile, we try to preserve the utility of the sanitized speech, measured by the accuracy of speech recognition performed on it. We implement Speech Sanitizer and present extensive experimental results that validate the effectiveness and efficiency of our algorithms. It is demonstrated that we are able to reduce the chance of a user's voice being identified from 50 people by 83.7 percent while keeping the drop of speech recognition accuracy within 19.1 percent. We can also easily relax the privacy level to improve speech recognition accuracy.
Privacy Risks of Sharing Data from Environmental Health Studies

BACKGROUND: Sharing research data uses resources effectively; enables large, diverse data sets; and supports rigor and reproducibility.However, sharing such data increases privacy risks for participants who may be re-identified by linking study data to outside data sets.These risks have been investigated for genetic and medical records but rarely for environmental data.OBJECTIVES: We evaluated how data in environmental health (EH) studies may be vulnerable to linkage and we investigated, in a case study, whether environmental measurements could contribute to inferring latent categories (e.g., geographic location), which increases privacy risks.METHODS: We identified 12 prominent EH studies, reviewed the data types collected, and evaluated the availability of outside data sets that overlap with study data.With data from the Household Exposure Study in California and Massachusetts and the Green Housing Study in Boston, Massachusetts, and Cincinnati, Ohio, we used k-means clustering and principal component analysis to investigate whether participants' region of residence could be inferred from measurements of chemicals in household air and dust.RESULTS: All 12 studies included at least two of five data types that overlap with outside data sets: geographic location (9 studies), medical data (9 studies), occupation (10 studies), housing characteristics (10 studies), and genetic data (7 studies).In our cluster analysis, participants' region of residence could be inferred with 80%-98% accuracy using environmental measurements with original laboratory reporting limits.DISCUSSION: EH studies frequently include data that are vulnerable to linkage with voter lists, tax and real estate data, professional licensing lists, and ancestry websites, and exposure measurements may be used to identify subgroup membership, increasing likelihood of linkage.Thus, unsupervised sharing of EH research data potentially raises substantial privacy risks.Empirical research can help characterize risks and evaluate technical solutions.Our findings reinforce the need for legal and policy protections to shield participants from potential harms of re-identification from data sharing.
VesARlius: An Augmented Reality System for Large-Group Co-located Anatomy Learning

Interactive educational environments are one of the prime applications for the use of Augmented Reality (AR). A large variety of such systems has been proposed in the past for various areas of education. However, in most cases the number of users these AR systems can support is limited. Only few systems have been developed that support a large number of co-located users to jointly collaborate in a dynamic and interactive learning environment. Multi-user AR collaboration presents a unique setting with distinct challenges and requirements for user interaction and information sharing. In this paper, we present VesARlius, a novel AR system for collaborative and interactive anatomy learning in a large group of co-located users. Our system employs a set of multi-user collaboration paradigms allowing users to engage in an interactive AR learning environment. We evaluated the collaborative features of our system in a user study with 16 medical students. Results demonstrate the potential of the VesARlius system to be used effectively for large-group AR anatomy learning. From our lessons learned, we provide a set of design guidelines for developing similar AR systems to enable large-group collaboration in other application domains.
Secure Multi-Party Computation on Blockchain: An Overview

Secure multi-party computation (SMPC) is a hot topic in the field of cryptography. It focuses on finishing computation tasks without revealing users’ inputs and outputs in decentralized scenarios. Although many researches have been conducted to perform SMPC protocols, it is hard to obtain fairness while most participants in SMPC are dishonest. Recently, the foundation of cryptocurrency, blockchain has attracted the attention of many scholars. Since blockchain’s ability to provide security and incentives, researchers start to make use of blockchain to provide fairness in SMPC protocols and increase efficiency. In this paper, we present a brief survey on how to use blockchain technology to perform SMPC protocol. We start by introducing the concept of secure computation and its security requirements. Then, we explain how we can utilize blockchain to provide fairness and present the basic model. We summarize state-of-the-art blockchain based SMPC applications and conclude this paper.
Crypto Accelerators for Power-Efficient and Real-Time on-Chip Implementation of Secure Algorithms

The demand for data exchange is ever growing. Internet of Things (IoT), industry 4.0, smart city and next-generation interconnected vehicles are some examples of scenarios in which a high volume of nodes share data across networks. Hence, the data protection plays a fundamental aspect to avoid disclosure or manipulation of sensitive information and disruption of services, particularly in safety critical applications. On the other hand, also the compute power at disposal of possible attackers and hackers is growing, and next-future post-quantum capabilities will require the usage of longer keys, certificates and digital signatures, to preserve the security level offered by cryptographic functions. This will affect not only the amount of exchange data, but also the computational resources to secure data, increasing processing time, latencies and power consumption, and lowering data rates. In this work, we investigate different implementation strategies to overcome such performance limitations. This work provides a comparison among pure software approach (both on 32b and 64b processors) and hardware-based solutions we developed for FPGA and ASIC System-on-Chip platforms, for the most common symmetric-key and public-key cryptographic algorithms. The proposed hardware accelerators feature one order of magnitude higher throughput (and lower latency) and more than two orders lower power consumption than their software counterparts. A highly configurable cryptographic suite is proposed that can be customized according to the application requirements and thus able to increase as much as possible the efficiency in terms of energy per enciphered bits per second.
Comparing Deep Neural Networks to Traditional Models for Sentiment Analysis in Turkish Language

Traditional bag-of-words (BOW) draws advantage from distributional theory to represent document. The drawback of BOW is high dimensionality. However, this disadvantage has been solved by various dimensionality reduction techniques such as principal component analysis (PCA) or singular value decomposition (SVD). On the other hand, neural network-based approaches do not suffer from dimensionality problem. They can represent documents or words with shorter vectors. Especially, recurrent neural network (RNN) architectures have gained big attractions for short sequence representation. In this study, we compared traditional representation (BOW) with RNN-based architecture in terms of capability of solving sentiment problem. Traditional methods represent text with BOW approach and produce one-hot encoding. Further well-known linear machine learning algorithms such as logistic regression and Naive Bayes classifier could learn the decisive boundary in the data points. On the other hand, RNN-based models take text as a sequence of words and transform the sequence using hidden and recurrent states. At the end, the transformation finally represents input text with dense and short vector. On top of it, a final neural layer maps this dense and short representation to a sentiment of a list. We discussed our findings by conducting several experiments in depth. We comprehensively compared traditional representation and deep learning models by using a sentiment benchmark dataset of five different topics such as books and kitchen in Turkish language.
Type IV codes over a non-unital ring

There is a special local ring [Formula: see text] of order [Formula: see text] without identity for the multiplication, defined by [Formula: see text] We study the algebraic structure of linear codes over that non-commutative local ring, in particular their residue and torsion codes. We introduce the notion of quasi self-dual codes over [Formula: see text] and Type IV codes, that is quasi self-dual codes whose all codewords have even Hamming weight. We study the weight enumerators of these codes by means of invariant theory, and classify them in short lengths.
User stories collection via interactive chatbot to support requirements gathering

Nowadays, software products have become an essential part of human life. To build software, developers must have a good understanding of the requirements of the software. However, software developers tend to jumpstart system construction without having a clear and detailed understanding of the requirements. The user story concept is one of the practices of the requirements elicitation. This paper aims to present the work conducted to develop an Android chatbot application to support the requirements elicitation activity in software engineering, making the work less time-consuming and structured even for users not accustomed to requirements engineering. The chatbot uses Nazief &amp; Adriani stemming algorithm to pre-process the natural language it receives from the users and artificial mark-up language (AIML) as the knowledge base to process the bot’s responses. A preliminary acceptance test based on the technology acceptance model results in an 83.03% score for users’ behavioral intention to use.
Multi-class Image Classification Using Deep Learning Algorithm

Abstract Classifying images is a complex problem in the field of computer vision. The deep learning algorithm is a computerized model simulates the human brain functions and operations. Training the deep learning model is a costly process in machine resources and time. Investigating the performance of the deep learning algorithm is mostly needed. The convolutional neural network (CNN) is most commonly used to build a structure of the deep learning models. In this paper convolutional neural network (CNN) model pre-trained on Image-Net is used for classification of images of the PASCAL VOC 2007 data-set. The transfer learning approach is used to improve the performance of the deep learning CNN model where classification works fairly well with the smallest amount of computation time and fewer machine resources. The behavior of the Deep learning CNN model is studied and the performance has been measured. The obtained results are compared with the obtained test results from the Super-vector coding of local image descriptors method, SVM method, and Region Ranking SVM method, which tested with the PASCAL VOC 2007 data-set. The final results evaluate the deep learning algorithm as a state-of-the-art method for an image classification task.
Estimating Rupture Dimensions of Three Major Earthquakes in Sichuan, China, for Early Warning and Rapid Loss Estimates

ABSTRACT Large earthquakes, such as Wenchuan in 2008, Mw 7.9, Sichuan, China, provide an opportunity for earthquake early warning (EEW), as many heavily shaken areas are far (∼50 km) from the epicenter and warning times could be sufficient (≥5 s) to take preventive action. On the other hand, earthquakes with magnitudes larger than ∼M 6.5 are challenging for EEW because source dimensions need to be defined to adequately estimate shaking. Finite-fault rupture detector (FinDer) is an approach to identify fault rupture extents from real-time seismic records. In this study, we playback local and regional onscale strong-motion waveforms of the 2008 Mw 7.9 Wenchuan, 2013 Mw 6.6 Lushan, and 2017 Mw 6.5 Jiuzhaigou earthquakes to study the performance of FinDer for the current layout of the China Strong Motion Network. Overall, the FinDer line-source models agree well with the observed spatial distribution of aftershocks and models determined from waveform inversion. However, because FinDer models are constructed to characterize seismic ground motions (as needed for EEW) instead of source parameters, the rupture length can be overestimated for events radiating high levels of high-frequency motions. If the strong-motion data used had been available in real time, 50%–80% of sites experiencing intensity modified Mercalli intensity IV–VII (light to very strong) and 30% experiencing VIII–IX (severe to violent) could have been issued a warning with 10 and 5 s, respectively, before the arrival of the S wave. We also show that loss estimates based on the FinDer line source are more accurate compared to point-source models. For the Wenchuan earthquake, for example, they predict a four to six times larger number of fatalities and injured, which is consistent with official reports. These losses could be provided 1/2∼3 hr faster than if they were based on more complex inversion rupture models.
MFA is A Necessary Chore!: Exploring User Mental Models of Multi-Factor Authentication Technologies

With technological advancements, traditional single-factor authentication methods, such as passwords, have become more vulnerable to cyber-threats.One potential solution, multi-factor authentication (MFA), enhances security with additional steps of verification.Yet, MFA has a slow adoption rate among users, and frequent data breaches continue to impact online and real-world services.Little research has investigated users' understanding and usage of MFA while specifically focusing on the their mental models and social behaviors in a work setting.We conducted semi-structured interviews with 28 individuals (11 experts, 17 non-experts), while focusing on their risk perceptions, MFA usage, and understanding of required technologies.We identified that experts treated MFA as a useful added layer of authentication, while non-experts did not perceive any additional benefits of using MFA.Both non-experts and experts expressed frustration with MFA usage, often referring to it as a 'chore.'Based on these findings, we make several actionable recommendations for improving the adoption, acceptability, and usability of MFA tools.