Reviewerly/exHarmony huggingface dataset:
On the Solution of the Travelling Salesman Problem for Nonlinear Salesman Dynamics using Symbolic Optimal Control

This paper proposes an algorithmic method to heuristically solve the famous Travelling Salesman Problem (TSP) when the salesman’s path evolves in continuous state space and discrete time but with otherwise arbitrary (nonlinear) dynamics. The presented method is based on the framework of Symbolic Control. In this way, our method returns a provably correct state-feedback controller for the underlying coverage specification, which is the TSP leaving out the requirement for optimality on the route. In addition, we utilize the LinKernighan-Helsgaun TSP solver to heuristically optimize the cost for the overall taken route. Two examples, an urban parcel delivery task and a UAV reconnaissance mission, greatly illustrate the powerfulness of the proposed heuristic.
Nested Session Types

Session types statically describe communication protocols between concurrent message-passing processes. Unfortunately, parametric polymorphism even in its restricted prenex form is not fully understood in the context of session types. In this article, we present the metatheory of session types extended with prenex polymorphism and, as a result, nested recursive datatypes. Remarkably, we prove that type equality is decidable by exhibiting a reduction to trace equivalence of deterministic first-order grammars. Recognizing the high theoretical complexity of the latter, we also propose a novel type equality algorithm and prove its soundness. We observe that the algorithm is surprisingly efficient and, despite its incompleteness, sufficient for all our examples. We have implemented our ideas by extending the Rast programming language with nested session types. We conclude with several examples illustrating the expressivity of our enhanced type system.
Combining solution reuse and bound tightening for efficient analysis of evolving systems

Software engineers have long employed formal verification to ensure the safety and validity of their system designs. As the system changes---often via predictable, domain-specific operations---their models must also change, requiring system designers to repeatedly execute the same formal verification on similar system models. State-of-the-art formal verification techniques can be expensive at scale, the cost of which is multiplied by repeated analysis. This paper presents a novel analysis technique---implemented in a tool called SoRBoT---which can automatically determine domain-specific optimizations that can dramatically reduce the cost of repeatedly analyzing evolving systems. Different from all prior approaches, which focus on either tightening the bounds for analysis or reusing all or part of prior solutions, SoRBoT's automated derivation of domain-specific optimizations combines the benefits of both solution reuse and bound tightening while avoiding the main pitfalls of each. We experimentally evaluate SoRBoT against state-of-the-art techniques for verifying evolving specifications, demonstrating that SoRBoT substantially exceeds the run-time performance of those state-of-the-art techniques while introducing only a negligible overhead, in contrast to the expensive additional computations required by the state-of-the-art verification techniques.
Hunting bugs with accelerated optimal graph vertex matching

Various techniques based on code similarity measurement have been proposed to detect bugs. Essentially, the code fragment can be regarded as a kind of graph. Performing code graph similarity comparison to identify the potential bugs is a natural choice. However, the logic of a bug often involves only a few statements in the code fragment, while others are bug-irrelevant. They can be considered as a kind of noise, and can heavily interfere with the code similarity measurement. In theory, performing optimal vertex matching can address the problem well, but the task is NP-complete and cannot be applied to a large-scale code base. In this paper, we propose a two-phase strategy to accelerate code graph vertex matching for detecting bugs. In the first phase, a vertex matching embedding model is trained and used to rapidly filter a limited number of candidate code graphs from the target code base, which are likely to have a high vertex matching degree with the seed, i.e., the known buggy code. As a result, the number of code graphs needed to be further analyzed is dramatically reduced. In the second phase, a high-order similarity embedding model based on graph convolutional neural network is built to efficiently get the approximately optimal vertex matching between the seed and candidates. On this basis, the code graph similarity is calculated to identify the potential buggy code. The proposed method is applied to five open source projects. In total, 31 unknown bugs were successfully detected and confirmed by developers. Comparative experiments demonstrate that our method can effectively mitigate the noise problem, and the detection efficiency can be improved dozens of times with the two-phase strategy.
A Conjecture of Zhi-Wei Sun on Determinants Over Finite Fields

In this paper, we study certain determinants over finite fields. Let $${\mathbb {F}}_\text {q}$$ be the finite field of q elements with q an odd prime power and $$q\equiv 2\pmod 3$$ . Let $$a_1,a_2,\cdots ,a_{\text {q}-1}$$ be all nonzero elements of $${\mathbb {F}}_\text {q}$$ and let $$T_\text {q}=\left[ \frac{1}{a_i^2-a_ia_j+a_j^2}\right] _{1\le i,j\le q-1}$$ be a matrix over $${\mathbb {F}}_\text {q}$$ . We obtain the explicit value of $$\det (T_\text {q})$$ . Also, as a consequence of our result, we confirm a conjecture posed by Zhi-Wei Sun.
Exploring collaborative caption editing to augment video-based learning

Captions play a major role in making educational videos accessible to all and are known to benefit a wide range of learners. However, many educational videos either do not have captions or have inaccurate captions. Prior work has shown the benefits of using crowdsourcing to obtain accurate captions in a cost-efficient way, though there is a lack of understanding of how learners edit captions of educational videos either individually or collaboratively. In this work, we conducted a user study where 58 learners (in a course of 387 learners) participated in the editing of captions in 89 lecture videos that were generated by Automatic Speech Recognition (ASR) technologies. For each video, different learners conducted two rounds of editing. Based on editing logs, we created a taxonomy of errors in educational video captions (e.g., Discipline-Specific, General, Equations). From the interviews, we identified individual and collaborative error editing strategies. We then further demonstrated the feasibility of applying machine learning models to assist learners in editing. Our work provides practical implications for advancing video-based learning and for educational video caption editing.
Application Research of Data Encryption Algorithm in Computer Security Management

In order to promote the research process of information security in the whole society, improve the safety factor of computer data communication, strengthen computer security management, the author proposes a computer data encryption strategy that combines the strong security of the 3DES encryption algorithm and the asymmetric encryption advantages of the RSA algorithm. Through the detailed analysis of DES encryption algorithm and 3DES encryption, creatively uses the RSA encryption algorithm to improve the single 3DES algorithm, consolidates the performance of the 3DES encryption algorithm to ensure data communication, and ensures the data integrity, better improve encryption performance. Experiments show that: The proposed encryption algorithm improves security performance by 10 times, and the response efficiency is only 1 ms away from other algorithms, compared with other algorithms, it has better encryption performance and is suitable for actual computer data communication scenarios. Conclusion: The encryption algorithm proposed by the author has achieved good results in terms of security performance and response efficiency, it is suitable for actual computer data security communication and can effectively improve computer security management.
Plug-and-play QKD architecture with a self-optical pulse train generator

The commercialization of quantum key distribution (QKD), which enables secure communication even in the era of quantum computers, has acquired significant interest. In particular, plug-and-play (PnP) QKD has garnered considerable attention owing to its advantage in system stabilization. However, a PnP QKD system has limitations on miniaturization owing to a bulky storage line (SL) of tens of kilometers. And, the secure key rate is relatively low because Bob transmits the signal pulses only at the dedicated time slots to circumvent backscattering noise. This study proposes a new method that can eliminate the SL by realizing an optical pulse train generator based on an optical cavity structure. Our method allows Alice to generate optical pulse trains herself by duplicating Bob's seed pulse and excludes the need for Bob's strong signal pulses that trigger backscattering noise as much as the conventional PnP QKD. Accordingly, our method can naturally overcome the miniaturization limitation and the slow secure key rate, as the storage line is no longer necessary. We conducted a proof-of-concept experiment using our method and achieved a key generation rate of 1.6×10-3 count/pulse and quantum bit error rate ≤ 5%.
Multiscale Dense U-Net: A Fast Correction Method for Thermal Drift Artifacts in Laboratory NanoCT Scans of Semi-Conductor Chips

The resolution of 3D structure reconstructed by laboratory nanoCT is often affected by changes in ambient temperature. Although correction methods based on projection alignment have been widely used, they are time-consuming and complex. Especially in piecewise samples (e.g., chips), the existing methods are semi-automatic because the projections lose attenuation information at some rotation angles. Herein, we propose a fast correction method that directly processes the reconstructed slices. Thus, the limitations of the existing methods are addressed. The method is named multiscale dense U-Net (MD-Unet), which is based on MIMO-Unet and achieves state-of-the-art artifacts correction performance in nanoCT. Experiments show that MD-Unet can significantly boost the correction performance (e.g., with three orders of magnitude improvement in correction speed compared with traditional methods), and MD-Unet+ improves 0.92 dB compared with MIMO-Unet in the chip dataset.
Leveraging attribute latent features for addressing new item cold-start issue

A recommender system employs an information filtering technology aiming to recommend items that are likely to be of interest to users, based on user behavior, rating feedback of items, or item characteristics. The cold-start problem occurs when the recommender system has difficulties in drawing any recommendations due to lack of information. In this paper, we focus on the item cold-start problem. When new items are added to the catalogue, they can easily be overlooked because no feedback information is accessible for recommending them to users, impeding the promotion of new products online. We propose a hybrid recommender system, called ALFNCF (Attribute Latent Features with Neural Collaborative Filtering), which combines the advantages of collaborative filtering, content-based filtering, and neural network technologies, to address the item cold-start issue. Like collaborative filtering, ALFNCF considers the interaction between users with similar behavior and items with similar feedback, while it also takes into account the influence of user’s and item’s attributes on individual behaviors. As in content-based filtering, attribute information is used in ALFNCF to establish links between new and old items. ALFNCF not only can retain the linear interactions between users and items, but also can learn the non-linear interactions by neural network units. Through the training on users’ past rating feedback information for old items, ALFNCF can predict the user’s ratings for new items. Experimental results show that our proposed method is effective and superior to other methods in the promotion of new items.
‘A Swarm of Sound’

This article explores the idea of audiovisual immersion through the portal of the virtual reality music video. Our focus falls on a close reading of Björk’s video, ‘Family’, which addresses questions of immersion in relation to user-experience, staging, and technological innovation. This article draws on the authors’ responses to the video by considering the implications of VR immersion in a new generation of music video productions. As part of the methodology on offer, a model for music analysis is devised for conceptualising virtual audiovisual space (VAVS) and the inextricable relationships between production and compositional design.
“Pictures are easier to remember than spellings!”: Designing and evaluating KidsPic — A graphical image-based authentication mechanism

Children encounter difficulties when they login to computers or websites because they have challenges remembering passwords. To improve children’s authentication, we conducted a series of formative studies with children (n=8, ages 6–11) to understand their authentication practices with respect to a traditional text-based password and a new graphical picture-based password called KidsPic. The results obtained from these initial investigations, a security analysis of these authentication mechanisms, and participatory design sessions with children (ages 6–11) inspired design enhancements to KidsPic. We subsequently conducted a study comparing KidsPic to a traditional text-based authentication mechanism (n=40, ages 6–11). The results and analysis indicate that children experience significantly more memorability challenges when using an alphanumeric authentication mechanism than with the graphical password KidsPic.
Digitization and data frames for card index records

We develop a methodology for converting card index archival records into usable data frames for statistical and textual analyses. Leveraging machine learning and natural-language processing tools from Amazon Web Services (AWS), we overcome hurdles associated with character recognition, inconsistent data reporting, column misalignment, and irregular naming. In this article, we detail the step-by-step conversion process and discuss remedies for common problems and edge cases, using historical records from the Reconstruction Finance Corporation.
Scenario generation and scenario quality using the cone of plausibility

The intelligence analysis domain is a critical area for futures work.Indeed, intelligence analysts' judgments of security threats are based on considerations of how futures may unfold, and as such play a vital role in informing policy-and decision-making.In this domain, futures are typically considered using qualitative scenario generation techniques such as the cone of plausibility (CoP).We empirically examined the quality of scenarios generated using this technique on five criteria: completeness, context (otherwise known as 'relevance/pertinence'), plausibility, coherence, and order effects (i.e., 'transparency').Participants were trained to use the CoP and then asked to generate scenarios that might follow within six months of the Turkish government banning Syrian refugees from entering the country.On average, participants generated three scenarios, and these could be characterized as baseline, best case, and worst case.All scenarios were significantly more likely to be of high quality on the 'coherence' criterion compared to the other criteria.Scenario quality was independent of scenario type.However, scenarios generated first were significantly more likely to be of high quality on the context and order effects criteria compared to those generated afterwards.We discuss the implications of these findings for the use of the CoP as well as other qualitative scenario generation techniques in futures studies.
Argumentation as a Method for Explainable AI : A Systematic Literature Review

Often, users do not know how artificial intelligence (AI) systems make decisions or obtain information, making it difficult for them to trust AI. This paper provides a systematic analysis of the advances in the use of argumentation theory to support explainability in dialogue systems.
Fourier Neural Operator for Image Classification

The present work seeks to analyze the performance of the Fourier Neural Operator (symbolized by FNO) as a convolution method for an image classification and how is its performance when compared to ResNet20 (benchmarking). The possible advantage of this technique is the success in pattern recognition through the wave-forms properties of Fourier analysis and due the power of synthesis that the Fourier Transform has. The FNO was very efficient to solve parametric partial differential equations like Navier-Stokes Equation. ResNet20 took 21 minutes and 51 seconds for training, while the FNO took 4:11:14 hours to complete a hundred of epochs. The convolution occurs in a competitive way in the FNO, being perfectly possible to be used in the image recognition processes, with accuracy, recall, precision and F-Score slightly better than ResNet20 and quite similar to other neural networks available in the literature. However, based on time consuming, the FNO is not indicated to image classification. It should be used for other purpose like solving partial derivative equations.
A proposal for Hand gesture control applied to the KUKA youBot using motion tracker sensors and machine learning algorithms

This paper presents a proposal for real-time hand gesture recognition for both dynamic and static gestures. For gesture detection, a dataset is obtained using capture sensors such as Leap Motion and Myo armband. The training process is managed by stages of data acquisition, preprocessing, feature extraction, and classification. In this context, in the classification stage, we determine the machine learning algorithms studies through mathematical model investigations. These algorithms allow to determine the real-time processing of hand gesture types and transfer them to a KUKA youBot for control. An application programming interface with ROS (Robot Operating System) is used for the communication process between the sensors and the robot. Finally, a hand gesture control environment is created after obtaining the training data set for the control of the KUKA youBot to test the remote operation with machine learning algorithms.
A Performance Review of Recurrent Neural Networks Long Short-Term Memory (LSTM)

Recurrent Neural Networks (RNN) coupled with Long Short-Term Memory (LSTM) is known for being a powerful connection model that is associated with time series and having retaining states. This paper performed a thorough investigation on RNN-LSTM model performance when applied on five (5) different datasets. Results of the varied experiment showed that implementing RNN-LSTM requires reasonable consideration on the number of dense layers in the model as it improves the overall performance accuracy. Among all the experiments performed on different datasets, working on MNIST datasets significantly reached peaked over a 99% accuracy rate, having (4) dense layers, (2) LSTM, and (3) dropout layers. It has also been found in this paper that the number of batch size and epochs have a minimal effect on the RNN-LSTM model performance as it greatly depends on the actual structure of the model layers.
Face Detection and its Features Extraction using Convolution Neural Network Model

Face emotion is an utmost efficient way of nonverbal communication and indication of sentimental status, mentality and intent. In this thesis, we focused on computer vision-based schemes to identify and recognize the faces and expressions, respectively. In this work a CNN based prototype is presented to estimate the age and to classify the gender of detected faces along with its emotions. The proposed model is implemented in three stages namely a hybrid feature extraction technique to identify the facial expressions by using SVM classifier. A combination of SIFT and Deep learning-based model is presented for identifying facial emotions. Whale optimization algorithm is applied to optimize the performance of face emotion recognition. This model gives a convolution neural network and introduces a novel technique for classification of facial emotions, age prediction and male-female identification. The data set for the analysis is considered from WideResNet architecture and Caffe models. The proposed model is tested on a static emotion identification benchmark dataset, and empirical investigation demonstrates that it improves emotion recognition significantly. An accuracy of 98.79% is reported and compared with the existing methods. The reported model is found to be suitable for many applications which involves human computer interaction with the adoption of emerging technologies AI and ML.
SQL Injection Detection and Prevention using Aho-Corasick Pattern Matching Algorithm

With growth of Internet and World Wide Web, the human dependency on websites and web applications has increased significantly in present days. Browsers and general web concepts are more familiar to most people to use than any other abstract computing interface. Users performing sensitive transactions online have paved a way for the attackers to spoof and tamper the transaction data. SQL Injection is one of the most frequent web attack methods used by attackers to steal confidential data from organisations. It is a code injection technique that allows hackers to inject malicious SQL statements into input fields, which is then executed by the underlying SQL database. Attackers can also use web application URLs for inserting malicious SQL queries. Although a significant amount of research has been done on SQLIA Detection and Prevention, SQL injection attacks are still prevalent and cannot be eliminated completely. In this paper, we have provided an overview of SQL Injection vulnerability, different forms of SQL Injection Attacks and threats posed by the attack. We propose a system to secure web applications using Hashing and Aho-Corasick pattern matching algorithm.
Exploring Multimodal Sentiment Analysis through Cartesian Product approach using BERT Embeddings and ResNet-50 encodings and comparing performance with pre-existing models

Multimodal Sentiment Analysis has become a popular interest of research in Machine Learning. It not only provides better results and context inference but also provides a good alternative for uni-modal analysis. The past few years have seen many new datasets for this task and state-of-the-art models dealing with the intermodular and intramodular dynamics of the task. This paper aims at exploring the pre-existing research on the topic of Multimodal Sentiment analysis and combining it with the idea of Sentence Embedding and Video encoding using state-of-the-art models, using the technique of classifying cartesian-product of extracted features, discussing experiments, and doing the qualitative analysis of the results.