Reviewerly/exHarmony huggingface dataset:
MPPM: A Mobile-Efficient Part Model for Object re-ID

Object re-identification (re-ID) is one of the core technologies in Multi-Object Tracking (MOT) that requires real-time decision-making. A Neural Processing Unit (NPU) is a low-power device that is dedicated to deploying neural network-based algorithms and has become one of the most important devices in today's mobile onboard systems. However, the current mainstream re-ID methods rarely consider the NPU characteristics, which makes it difficult for these methods to achieve both high onboard frame rates and high accuracies on an NPU. To address this problem, this paper focuses on designing a re-ID algorithm suitable for NPU deployment. The model of the object re-ID can be divided into two parts: the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">encoder</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">backbone</i> ) and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decoder</i> . In this article, a Mobile-efficient Pure Part Model (MPPM) is presented for re-ID task. First, for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">backbone</i> of re-ID, we propose an efficient structure GogglesNet, which is composed of traditional convolutions. GogglesNet performs well on the re-ID task and can be comparable to lightweight networks on ImageNet with regard to accuracy and is faster on NPU. We then revisit the architectures of Pure Part Model (PPM) in person re-ID, including PCB and MGN, and propose a mobile-efficient <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decoder</i> Dual Pattern Network (DPN) for re-ID. The proposed MPPM achieves comparable performance with MGN on five re-ID datasets Market-1501, DukeMTMC-reID, MSMT17, VeRi-776, and VehicleID, while the proposed parameter amount is only 10.2% of it, and the speed on NPU is more than eight times higher.
POSTER: A Transparent Remote Quantum Random Number Generator over a Quantum-Safe Link

We address the problem of sharing an expensive hardware-based high-entropy quantum random number generator (QRNG) among multiple users connected to it via the network. We demonstrate how to 1) divide the limited bandwidth of the QRNG device among multiple clients, 2) secure network communication between the QRNG and its users by applying quantum-safe algorithms, and 3) switch existing client-side applications to use randomness received from the remote QRNG without the need to recompile their code.
Visualizing learners’ level of understanding of lecture using digital text book system

This paper proposes a system to define and visualize learners’ level of understanding of lecture contents based on learning data obtained from the digital textbook system named SEA (Smart E-Textbook Application) with a quiz function. By feedbacking the visualization results to learners, learners can grasp information that should be reviewed which unit in the lecture. Thereby, the learners can improve their level of understanding of the lecture efficiently and effectively. An evaluation experiment was conducted to investigate whether our proposed system can enhance learning effectiveness and learners’ level of understanding of lecture. As a result, it found our proposed system can enhance learners’ level of understanding of lecture and learning effectiveness.
Analysing digital evidence towards enhancing healthcare security practice: The KID model

Due to emergency situations in healthcare, there is a requirement in electronic health records in various hospitals for emergency access known as "Break-the-glass" or self-authorization. This is in compliance with the availability aspect of the information security (IS) principles. This requirement therefore, provides broad access to healthcare staff. Meanwhile internal users of Electronic health records (EHR) system such as the healthcare staff can exploit their legitimate broad access right to breach privacy and hence, the need to analyse the digital evidence of their logs towards enhancing privacy and security.But, the lack of labelled data makes it challenging to efficiently assess the security practice of the users in a big data context. Therefore we developed a K-means iterative and discriminate model (KID model) towards identifying normal and abnormal security practice among healthcare staff with unlabelled logs of EHR of a typical hospital. The model was tested with real logs which captured all evidence of the healthcare staff’s login and logout transactions between 13 May 2014 and 17 April 2021. The data contains 933,508 sessions from 1,220 unique users in 2,623 unique days and these were anonymised.The model was evaluated to be reliable with the Silhouette approach and it was used to determine abnormal and normal security practices. Some staff logged in for over three years without logout which is a huge security gap. Multiple sources of login sessions of the system administrator’s account was also detected. Other abnormal clusters also showed unusual patterns of long session duration and some healthcare staff such as doctors and nurses were averagely using one terminal in a day to create login and logout sessions. Management could take measures to investigate into the cause of long session duration as that can be an opening for session hijacking. Also, the normal and abnormal behaviour which were detected in the study can be further assessed to be used for labelling data for supervised learning purposes.
Generating Trace Table for Java Programs

It is difficult for programming beginners to understand the behavior of object-oriented programs since an object-oriented program contains various dynamic behavior such as dynamic binding and polymorphism. Trace table is useful to facilitate understanding of object-oriented programs. Traditionally a trace table is used to facilitate understanding of a structured non-object-oriented program. We extend the notion of trace table to represent the dynamic behavior of object-oriented programs and propose a trace table generator for Java programs in this paper. The trace table is represented using an Excel file and is designed by extending the notion of the UML sequence diagram. Each worksheet of the trace table corresponds to an instance. Each column of the worksheet contains a value of the instance variables and local variables within the instance as well as message sending among the instances. Each row of the worksheet represents a sequence of steps at each method. The trace table generator is implemented using JavaParser and inserts code to the original collection of Java source codes. Various information to generate a trace table is extracted by executing the converted source code.
Dataset Construction and Classification Based on Pre-trained Models for Opinion Holder Detection

Nowadays, it is getting increased in the massive amount of internet users. People express subjective thinking (i.e., opinion) implicitly and explicitly on online platforms such as Facebook, Twitter, Amazon product reviews, etc. Opinion holders are the people or entities who express opinions implicitly and explicitly on the online platform. With the increasing trends of online opinion mass information, it is impossible to detect them manually. For this reason, an automatic approach for opinion holder detection is essential. Opinion holder detection is useful to detect specific person’s/entity’s concerns about a particular topic, product, or problem. Opinion holder detection consists of two steps: the presence of opinion holders in text and identification of opinion holders. In this paper, we focus on the first step, namely the presence of opinion holders in text. We handle this task as a binary classification problem: INSIDE or OUTSIDE. At first, we prepare a new English dataset for this task. Then, we apply two types of pre-trained models, BERT and DistilBERT, to the INSIDE/OUTSIDE classification task. BERT is a transformer-based pre-trained language model. DistilBERT is a small, fast, cheap, and light model based on knowledge distillation from the BERT architecture. As to the binary classification task, we employ a logistic regression model on the top layer of the pre-trained models. We compare the language models employed in our experiment in terms of the F1 score and accuracy. The experimental result shows that DistilBERT obtained superior performance among the models: 0.901 on the F1 score and 0.924 on the accuracy.
Personality Traits Estimation of Participants Based on Multimodal Information in Knowledge-Transfer-type Discussion

Although an evaluation index of participants’ communication skills in group dialog would be useful for improving each participant’s ability to interact in a group, conventional methods of assessing personality traits are required lots of labor- and time-consuming for participants when conducted frequently, so automation of these methods is preferred. In this study, we developed a method to estimate personality traits of each participant based on multimodal dialog information in knowledge-transfer-type discussions. To achieve it, we created a corpus of knowledge-transfer-type dialogs including participants’ multimodal information and personality assessments of the BigFive and Locus of Control and constructed statistical models to classify high and low degree of the personality traits based on participants’ multimodal information. The evaluation results showed that the model was able to estimate degree of each factor of the BigFive with accuracies in the range of 0.87-1.00, and to estimate degree of Locus of Control with accuracies in the range of 0.87-1.00. The most useful features to estimate personality traits were combinations of acoustic features, head movement features, turn-taking features, and linguistic features. It was also found that the personality traits of participants could be estimated with high accuracy even when using data from the first 5 minutes of the discussion session.
Detection of Flaming Participants Using Account Information and Stylistic Features of Posts

With the rapid development of SNS in recent years, the number of SNS users has increased rapidly, and people can easily communicate interactively with an unspecified large number of people. With these changes in the information society, a phenomenon known as "flaming", in which critical comments flood SNS, has become a frequent occurrence. In recent years, various studies on flaming have been conducted, but most of them are concerned with those who receive a large number of critical comments, not on those who write critical comments, called "flaming participants". In this study, we examine the characteristics of flaming participants on Twitter by using machine learning to classify them into two groups: flaming participants and normal users. For the classification features, we use account information, i.e., statistical data for each account, and stylistic features of the postings, i.e., (1, n)-grams of the part-of-speech tags of the postings. The experimental results show that these features are effective in detecting Twitter flaming participants. Furthermore, we found that flaming participants use more quote tweets than the normal user, and that there are word patterns that are characteristic of flaming participants.
Applications of Intelligent Environmental IoT Detection Tools in Environmental Education of College Students

A set of intelligent environmental IoT detection tools that apply the Internet of Things to environmental education, including "auxiliary tools for environmental IoT detection" and "environmental analysis system combined with artificial intelligence technology" is proposed in this study. Through various environment detectors at the underlying layer of the Internet of Things, the auxiliary tools for environmental IoT detection collect the data which need to be observed and introduce this set of tools into the living environment of college students. The environmental analysis system combined with artificial intelligence technology analyzes the data collected by the former and draws a visual chart, so that students can stay on top of the environmental change trend at each time point more clearly. Furthermore, by combining machine learning technology with artificial intelligence, this set of tools analyzes the collected data and makes predictions. When a poor environmental condition in the future is predicted, students will be informed of the causes and consequences to understand the connection between human beings and the environment.
Performance Analysis of Different Sentiment Polarity Dictionaries on Turkish Sentiment Detection

In places such as social media and news websites, the number of web-based textual materials those deal with today’s events and contain people’s emotions continues to increase inevitably day by day. All of these texts, with their own importance, affect our society in some way. For this reason, it is very important to automatically detect the sentiment polarities of these texts. In this study we developed several statistical-based semantic algorithms for Turkish sentiment analysis task. Furthermore, we conducted experiments on sentiment analysis in Turkish texts using different sentiment polarity dictionaries. We perform a number of experiments on some datasets which we collected from Twitter platform. In our experimental environment we also use two dictionaries to get the sentiment polarity score of Turkish terms and phrases: We built the first sentiment polarity dictionary by using a translator and this dictionary includes about 159,876 Turkish words. We built the second sentiment polarity dictionary by using GDELT (Global Data on Events, Languages and Tone) and this dictionary includes about 84,744 Turkish words. We also implement the state of the art baseline algorithms in order to compare the performance results. There are three important outcomes of this study: 1.) We built two publicly available sentiment polarity dictionaries for Turkish, 2.) We developed statistical-based novel semantic algorithms for Turkish sentiment analysis task, 3.) We report the observations of the effect of using different semantic-polarity dictionaries on Turkish sentiment polarity detection problem. Experiment results show that the algorithms we have developed are valuable because they give higher classification performance than the baseline algorithms on Turkish sentiment polarity detection task.
Quantum Machine Learning: Practical Cases

Machine learning (ML) is often listed among the most potential applications for quantum computing. This is in reality a puzzling choice: Nowadays machine learning algorithms are extremely powerful in practice, but still theoretically challenging to understand. Quantum computing, in contrast, does not give actual benchmarks on practical scales. However, theory remains the main tool we have to determine if it may become important for a task. Although it is far from reaching its full potential, nevertheless, it presents several techniques to efficiently deal with the problem which are tough to solve traditionally. In this perspective we deploy the Quanvolutional Neural Networks (QNNs) on a quantum computer to recognize handwritten digits, and compare its performance with that of its classical counterpart namely the Convolutional Neural Networks (CNNs). We used a quanvolutional layer to separate the pixel data and demonstrate the advantage of applying QNN on a quantum computer. Finally, we implement the Variational Quantum Classifier (VQC) and several classical classifiers built on the Iris dataset, in order to compare their accuracies. We employed amplitude encoding to map data into the large Hilbert space of a quantum computer. The results revealed that the QNN model and VQC had high accuracy and low loss value compared to their purely classical counterparts.
Semantic taxonomy enrichment to improve business text classification for dynamic environments

Taxonomies are widely used by various business organizations for document classification and organization. Business models built using taxonomies have the potential to reduce their efficiency with the arrival of new business ideas and concepts in the market over time. This happens because outdated business taxonomies get insufficient to fully capture the intended meaning of business documents to perform classification. Experts may need time to understand and engineering to place new data into the taxonomy to increase the classification accuracy of business documents. Here, the idea of automatic semantic enrichment of taxonomies came into consideration. This study introduces a taxonomy enrichment approach based on Natural Language Processing (NLP) technique, i.e. BERTopic, a Neural topic modeling using contextualized Bidirectional Encoder Representations from Transformers (BERT) to automatically augment a given business taxonomy with many additional concepts by leveraging a corpus of online news documents. The experiments show that augmenting topics from the text corpus into the taxonomy substantially increases the classification accuracy of business documents.
Deep Learning-Based Pronunciation Detection of Arabic Phonemes

Nowadays, detection of mispronunciations and providing feedback to users are provided by systems called computer-assisted language learning (CALL). In this study, we propose a model that detects the correct pronunciation of Arabic phonemes and supports Arabic language learning by blending it with deep and hybrid deep learning algorithms and mel-frequency cepstrum coefficients (MFCC) and Mel spectrogram feature extraction methods. In order to demonstrate the performance of the proposed model, 29 letters in the Arabic alphabet, 7 of which are hafiz, are voiced by a total of 10 different people. The amount of data has been increased by using the methods of adding noise, time shifting, time stretching, pitch shifting, and its performance on the model has been observed. Experiment results show that the combination of Mel-spectrogram and ConvLSTM exhibit the best results with 97.3% of accuracy when compared with the studies in the literature.
Natural Language Query and Control Interface for Database Using Afghan Language

The Afghan language, or Persian language, is one of the most widely used languages, with up to 110 million speakers worldwide. It is used in countries like Afghanistan, Azerbaijan, Iran, Iraq, Russia, Tajikistan, Turkmenistan, Uzbekistan, etc. The Persian language has insufficient resources and needs higher quality lexicon translation. The proposed Afghan Language Query and Control Interface (ALQCI) is based on NLIDB Approach. The Afghan language is used in this system to work with user-chosen data from a database. Retrieving information from the database requires knowledge of SQL Query Language or a well-designed interface. It is convenient for domain-expert users to retrieve data from the database. However, it may be challenging for non-expert users to access the database through SQL Query in the absence of a flexible user interface. This system helps overcome the challenge for those who speak Persian worldwide. The system may supplement or substitute less friendly user interfaces (UI). The system generates Structured Query Language (SQL) when a user inputs a spoken natural language query in the Persian language. The system works by allowing a user to enter a query in Persian, which the system then translates into a SQL query using the word mapping technique. The SQL is generated through semantic analysis. Afterwards, the generated SQL is used to extract knowledge using the Afghan language from a database.
Research on the Use of Task-Based Language Teaching Method in English Language Teaching Based on Big Data 5G IoT Scenario

The state implemented the compulsory education standard for full-time students in 2001, which clearly stipulates that English teachers should promote special language teaching methods, develop comprehensive language use skills, and propose teaching methods such as practice, cooperation, and communication. The task-based language teaching method has become a commonly used method in English teaching, and it is necessary to study the application of task-based language teaching method in English courses and try to apply the task-based language teaching method effectively. This study analyzes the needs of English teaching in the context of big data 5G IoT scenario, new curriculum reform, and theories related to English teaching methods in order to explore new methods of English teaching, verify its feasibility and necessity, and prepare reference materials for future grammar teaching research.
A Power Side-Channel Attack on the Reed-Muller Reed-Solomon Version of the HQC Cryptosystem

The code-based post-quantum algorithm Hamming Quasi-Cyclic (HQC) is a fourth round candidate in the NIST standardization project. Since their third round version the authors utilize a new combination of error correcting codes, namely a combination of a Reed-Muller and a Reed-Solomon code, which requires an adaption of published attacks. We identify that the power side-channel attack by Uneo et al. from CHES 2021 does not work in practice as they miss the fact that the implemented Reed-Muller decoder does not have a fixed decoding boundary. In this work we provide a novel attack strategy that again allows for a successful attack. Our attack does not rely on simulation to verify its success but is proven with high probability for the HQC parameter sets. In contrast to the timing side-channel attack by Guo et al. we are able to reduce the required attack queries by a factor of 12 and are able to eliminate the inherent uncertainty of their used timing oracle. We show practical attack results utilizing a power side-channel of the used Reed-Solomon decoder on an ARM Cortex-M4 microcontroller. In addition, we provide a discussion on how or whether our attack strategy is usable with the side-channel targets of mentioned related work. Finally, we use information set decoding to evaluate the remaining attack complexity for partially retrieved secret keys. This work again emphasizes the need for a side-channel secure implementation of all relevant building blocks of HQC.
Interleaved Prange: A New Generic Decoder for Interleaved Codes

Due to the recent challenges in post-quantum cryptography, several new approaches for code-based cryptography have been proposed. For example, a variant of the McEliece cryptosystem based on interleaved codes was proposed. In order to deem such new settings secure, we first need to understand and analyze the complexity of the underlying problem, in this case the problem of decoding a random interleaved code. A simple approach to decode such codes, would be to randomly choose a vector in the row span of the received matrix and run a classical information set decoding algorithm on this erroneous codeword. In this paper, we propose a new generic decoder for interleaved codes, which is an adaption of the classical idea of information set decoding by Prange and perfectly fits the interleaved setting. We then analyze the cost of the new algorithm and compare it to the other approaches.