Reviewerly/exHarmony huggingface dataset:
Bingo: A Semi-Centralized Password Storage System

A lack of security best practices in modern password storage has led to a dramatic rise in the number of online data breaches, resulting in financial damages and lowered trust in online service providers. This work aims to explore the question of how leveraging decentralized storage paired with a centralized point of authentication may combat such attacks. A solution, “Bingo”, is presented, which implements browser side clients which store password shares for a centralized proxy server. Bingo is a fully formed system which allows for modern browsers to store and retrieve a dynamic number of anonymized password shares, which are used when authenticating users. Thus, Bingo is the first solution to prove that distributed password storage functions in the context of the modern web. Furthermore, Bingo is evaluated in both simulation and cloud in order to show that it achieves high rates of system liveness despite its dependence on its users being active at given intervals. In addition, a novel simulator is presented which allows future researchers to mock scheduled behavior of online users. This work concludes that with the rise in online activity, decentralization may play a role in increasing data security.
An Approach to Conceptualisation and Semantic Knowledge: Some Preliminary Observations

The paper below takes up the question of whether it is possible to transfer the notion of ‘semantic knowledge’—as a human process of making language generate and confer meanings—to machines, which have as one of their properties the capability of handling high amounts of information. This issue is presented in an extended introduction to the paper’s account of and solutions to this intricate problem. Thereafter, the theoretical notion of ‘knowledge’ is considered in its philosophical, and thereby scientific, context, and the basis of its modern import is pointed to being Immanuel Kant’s deliberations on a priori vs. a posteriori knowledge. The author’s solution to the predicament of modern ideas about knowledge is the proposed theory of Occurrence Logic, invented by the author, which abandons truth-values from valid reasoning, and this approach is briefly accounted for. It presupposes a theoretical model of human cognitive systems, and the author has such a model under development which, in the future, may be able to solve the question of what ‘semantic knowledge’ actually is. So far, the theoretical account in this paper points to the critical issue of whether natural language semantics can be grasped as words explaining words or must include the connection between words and objects in the world. The author is in favour of the last option. This leads to the question of the functions of the human brain as the organ connecting words with the outer world. The idea of the so-called ‘predictive brain’ is referred to as a possible solution to the brain/cognition issue, and the paper concludes with a suggestion that an emulation of the interaction between the mentioned cognitive systems may cast some new light on the field of Artificial Intelligence.
Nearest Neighbor Search with Compact Codes: A Decoder Perspective

Modern approaches for fast retrieval of similar vectors on billion-scaled datasets rely on compressed-domain approaches such as binary sketches or product quantization. These methods minimize a certain loss, typically the Mean Squared Error or other objective functions tailored to the retrieval problem. In this paper, we re-interpret popular methods such as binary hashing or product quantizers as auto-encoders, and point out that they implicitly make suboptimal assumptions on the form of the decoder. We design backward-compatible decoders that improve the reconstruction of the vectors from the same codes, which translates to a better performance in nearest neighbor search. Our method significantly improves over binary hashing methods and product quantization on popular benchmarks.
Performance evaluation of machine learning based voting classifier system for human activity recognition

In the last few decades, Human Activity Recognition (HAR) has been a centre of attraction in many research domains, and it is referred to as the potential of interpreting human body gestures through sensors and ascertaining the activity of a human being. The present work has proposed the voting classifier system for human activity recognition. For the voting classifier system, five machine learning classifiers are considered: Logistic Regression (LR), K-Nearest Neighbour (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM). These machine learning classifiers are ensembled by analyzing the best performers among them. The ensemble voting classifiers are proposed under two variations, i.e., hard voting and soft voting. The various combinations of voting classifiers are compared and evaluated. For experiments, the benchmark dataset of the UCI-HAR dataset is considered, and all the data files are combined into a single file to avoid bias. The dimensionality of the dataset is reduced by using Principal Component Analysis (PCA) from 561 features to 200 components. The results reveal that Voting Classifier-II (a combination of SVM, KNN, and LR) using soft voting outperformed other machine learning classifiers.
VAMPIRE: vectorized automated ML pre-processing and post-processing framework for edge applications

Abstract Machine learning techniques aim to mimic the human ability to automatically learn how to perform tasks through training examples. They have proven capable of tasks such as prediction, learning and adaptation based on experience and can be used in virtually any scientific application, ranging from biomedical, robotic, to business decision applications, and others. However, the lack of domain knowledge for a particular application can make feature extraction ineffective or even unattainable. Furthermore, even in the presence of pre-processed datasets, the iterative process of optimizing Machine Learning parameters, which do not translate from one domain to another, maybe difficult for inexperienced practitioners. To address these issues, we present in this paper a Vectorized Automated ML Pre-processIng and post-pRocEssing framework, approximately named ( VAMPIRE ), which implements feature extraction algorithms capable of converting large time-series recordings into datasets. Also, it introduces a new concept, the Activation Engine , which is attached to the output of a Multi Layer Perceptron and extracts the optimal threshold to apply binary classification. Moreover, a tree-based algorithm is used to achieve multi-class classification using the Activation Engine . Furthermore, the internet of things gives rise to new applications such as remote sensing and communications, so consequently applying Machine Learning to improve operation accuracy, latency, and reliability is beneficial in such systems. Therefore, all classifications in this paper were performed on the edge in order to reach high accuracy with limited resources. Moreover, forecasts were applied on three unrelated biomedical datasets, and on two other pre-processed urban and activity detection datasets. Features were extracted when required, and training and testing were performed on the Raspberry Pi remotely, where high accuracy and inference speed were achieved in every experiment. Additionally, the board remained competitive in terms of power consumption when compared with a laptop which was optimized using a Graphical Processing Unit.
The Auxiliary Role of College Music in Teaching in View of Artificial Intelligence

Music is a common art and it is a jewel of human civilisation. In the course of music’s development, the evaluation of music teaching is an inevitable step in the development of quality music. Universities are important places that abound with musical souls and their contribution to the development of music has been outstanding. But with the development of the times, university music has been hampered in the field of teaching and learning. As an important branch in the field of computer science and information technology, artificial intelligence technology contains many intersecting and comprehensive subject connotations, bringing brand-new elements to music education. It has also had an important impact on the development of music teaching. The article focuses in depth on the traditional process of music development in terms of the characteristics and ways of teaching music. Based on this, the article further explores the integration of artificial intelligence and music and analyses the role of emerging technologies as an aid to music from the perspective of the times. And this article uses emotion recognition as an evaluation index to explore the evaluation role of artificial intelligence technology in college music teaching, and improve the quality and efficiency of music teaching. The experimental results show that the teacher’s positive emotion rate based on image data is 57.8%, and the student’s positive emotion rate is 44.5%; the teacher’s positive emotion rate based on voice data is 53.3%, and the student’s positive emotion rate is 51.1%. The classroom emotion is negative at 7–13 minutes, the classroom emotion continues to be low at 28–40 minutes, and the teacher and student emotions are more positive at 13–28 minutes.
Graph-based Turkish text normalization and its impact on noisy text processing

User generated texts on the web are freely-available and lucrative sources of data for language technology researchers. Unfortunately, these texts are often dominated by informal writing styles and the language used in user generated content poses processing difficulties for natural language tools. Experienced performance drops and processing issues can be addressed either by adapting language tools to user generated content or by normalizing noisy texts before being processed. In this article, we propose a Turkish text normalizer that maps non-standard words to their appropriate standard forms using a graph-based methodology and a context-tailoring approach. Our normalizer benefits from both contextual and lexical similarities between normalization pairs as identified by a graph-based subnormalizer and a transformation-based subnormalizer. The performance of our normalizer is demonstrated on a tweet dataset in the most comprehensive intrinsic and extrinsic evaluations reported so far for Turkish. In this article, we present the first graph-based solution to Turkish text normalization with a novel context-tailoring approach, which advances the state-of-the-art results by outperforming other publicly available normalizers. For the first time in the literature, we measure the extent to which the accuracy of a Turkish language processing tool is affected by normalizing noisy texts before being processed. An analysis of these extrinsic evaluations that focus on more than one Turkish NLP task (i.e., part-of-speech tagger and dependency parser) reveals that Turkish language tools are not robust to noisy texts and a normalizer leads to remarkable performance improvements once used as a preprocessing tool in this morphologically-rich language.
An Introduction to Quantum Natural Language Processing (QNLP)

Quantum Natural Language Processing (QNLP) is an emerging field of Quantum AI where natural language is combined with quantum computing capabilities. The origins of this field come from an abstract mathematical concept called category theory especially monoidal categories which have a diagrammatic structure. These monoidal categories have been utilized to describe various quantum mechanical phenomena in a purely pictorial fashion. When a model of natural language is equivalent to a model which explains quantum theory it signifies that QNLP is quantum-native. This chapter provides a friendly introduction to QNLP by providing the necessary background details such as—diagrammatic quantum theory and ZX calculus and explains the compositional structure of Lambek’s pregroup grammar because of which QNLP models are now able to run on real quantum hardware.
Comparative Analysis of Teaching at Public Universities in Sinaloa during Confinement Due to COVID-19

The COVID-19 pandemic has affected educational institutions around the world. One partial solution for students and teachers to continue the academic process involved the use of software and hardware technologies via the internet. The main objective of this research was to analyze the actions carried out by computer science teachers (and teachers who taught related degrees) in Sinaloa, Mexico, during the COVID-19 confinement period, to determine if the working conditions were different at all educational institutions. Based on quantitative, descriptive-explanatory, correlational, field, and cross-sectional approaches to data collection-a survey was designed and sent to teachers who taught classes in computer science and related careers. The results showed that although teachers felt prepared in designing and implementing virtual courses (90.73%), 68.5% believed that virtual classes were not enough for students (i.e., regarding replacing the training being offered). Likewise, teachers observed that only 27.8% of their students showed real commitment to online classes. In the hypothesis test, a chi-squared value of 3.84 was obtained, with a significance (p-value) of 0.137. There was a probability of error of 13.7%; this is high, considering that the level of significance must be 0.05 (5%) or less. It was concluded that teachers must be permanently trained in the use of new digital technologies; in addition, they must continuously produce academic material and make it available to the educational community. It is necessary for universities to design plans for the regulated use of applications and devices for academic purposes, update study plans and programs, and train teachers and students beyond conventional education.
Hiding Needles in a Haystack: Towards Constructing Neural Networks that Evade Verification

Machine learning models are vulnerable to adversarial attacks, where a small, invisible, malicious perturbation of the input changes the predicted label. A large area of research is concerned with verification techniques that attempt to decide whether a given model has adversarial inputs close to a given benign input. Here, we show that current approaches to verification have a key vulnerability: we construct a model that is not robust but passes current verifiers. The idea is to insert artificial adversarial perturbations by adding a backdoor to a robust neural network model. In our construction, the adversarial input subspace that triggers the backdoor has a very small volume, and outside this subspace the gradient of the model is identical to that of the clean model. In other words, we seek to create a "needle in a haystack" search problem. For practical purposes, we also require that the adversarial samples be robust to JPEG compression. Large "needle in the haystack" problems are practically impossible to solve with any search algorithm. Formal verifiers can handle this in principle, but they do not scale up to real-world networks at the moment, and achieving this is a challenge because the verification problem is NP-complete. Our construction is based on training a hiding and a revealing network using deep steganography. Using the revealing network, we create a separate backdoor network and integrate it into the target network. We train our deep steganography networks over the CIFAR-10 dataset. We then evaluate our construction using state-of-the-art adversarial attacks and backdoor detectors over the CIFAR-10 and the ImageNet datasets. We made the code and models publicly available at https://github.com/szegedai/hiding-needles-in-a-haystack.
Load Balancing Optimization Based on Enhanced Genetic Algorithm in Cloud Computing

Abstract: Load imbalance tends to occur in cloud computingdue to dynamics and complexity. Load Balancing ensures that all the nodes and machines in the network does exactly same amount of work. To address the Load Balancing in the application of task scheduling, we put forward an approach based on improved Genetic multitask scheduling algorithm which has assigning function on multiple schedules. Then improved Genetic Algorithm is applied to re-assign the cloudlets to virtual machines and obtain minimum completion time. The aim of thisproject is to satisfy the demand for load balancing under cloud computing and to improve the resource utilization, compared totraditional algorithms.
Extracting Precedence Relations between Video Lectures in MOOCs

Nowadays, the high dropout rate has become a widespread phenomenon in various MOOC platforms. When learning a MOOC, many learners are reluctant to spend time learning from the first video lecture to the last one. If we can recommend a learning path based on learners' individual needs and ignore irrelevant video lectures in the MOOC, it will help them learn more efficiently. The premise of learning path recommendation is to understand the precedence relations between learning resources. In this paper, we propose a novel approach for extracting precedence relations between video lectures in a MOOC. According to "knowledge depth" of concepts, we extract the core concepts from the video captions accurately. Transformer-based models are used to discover concept prerequisite relations, which help us identify the precedence relations between video lectures in MOOCs. Experiments show that the proposed method outperforms the state-of-the-art methods.
Solution of design optimization problems via metaheuristic search methods

Metaheuristic algorithms inspired by natural phenomena are frequently used in solving optimization problems recently. Just as every problem has its characteristics, every algorithm has its unique structure. Therefore, problem-specific algorithm selection is an important issue. In addition, metaheuristic algorithms are very open to development. Therefore, improved/modified versions of algorithms are common. Working with benchmarking problems and engineering design problems is the best way to compare the performance and reliability of metaheuristic algorithms. In this study, the performances of firefly (FA), particle swarm optimization (PSO), bat algorithm (BA), ant colony optimization (ACO), glow worms (GSO), and hunting search (HuS) algorithms are compared. An enhanced version of the firefly algorithm (RFA) is also recommended and included in the comparison. A benchmark and five engineering design problems were selected for comparison purposes. All algorithms are set to twenty thousand iterations. The results show that the metaheuristic algorithm that gives the best results varies according to the nature of the problem. Moreover, although it does not change the ranking of the algorithm that gives the best result according to the problem, it shows that RFA gives better results in every problem than FA.
Stereo sampling moiré method for three-dimensional deformation mapping with a stereomicroscope

Three-dimensional (3D) deformation distribution measurement is of great interest in applications of materials evaluation. In this study, we propose a stereo sampling moiré method for full-field 3D deformation measurement based on a stereomicroscope. From the phase analysis on the left and right image planes as well as the relationship between displacements in the world and image coordinate systems, the 3D displacements and the in-plane strains of the specimen can be acquired. We also propose a method to calibrate the microscope convergence angle using the grid pitch variation. The validation experiment shows that the difference between the out-of-plane displacement measured by the proposed method and the movement of the sample stage is less than 0.2 µm. The microscopic 3D displacements and the in-plane strain distributions of a carbon fiber reinforced plastic specimen in a three-point bending test are investigated.
A Violent Video Detection Method Based on Image Semantic Segmentation

With the rapid growth of the network user base and the number of short videos, a large number of videos related to terrorism and violence have emerged in the Internet, which has brought great challenges to the governance of the network environment. At present, most short-video platforms still adopt manual-review and user-report mechanisms to filter videos related to terrorism and violence, which cannot adapt to the development trend of short-video business in terms of recognition accuracy and timeliness. In the single-mode recognition method of violent video, this paper mainly studies the scene recognition mode. Firstly, the U-Net network is improved with the SE-block module. After pretraining on the Cityscapes dataset, semantic segmentation of video frames is carried out. On this basis, semantic features of scenes are extracted using the VGG16 network loaded with ImageNet pretraining weights. SE-U-Net-VGG16 scene recognition model is constructed. The experimental results show that the prediction accuracy of SE-U-Net model is much higher than that of the FCN model and U-Net model. SE-U-Net model has significant advantages in the modal research of scene recognition.
Detection of Online Employment Scam Through Fake Jobs Using Random Forest Classifier

Abstract: To avoid fraudulent post for job in the internet, an automated tool using machine learning based classification techniques. Different classifiers are used for checking fraudulent post in the web and the results of those classifiers are compared for identifying the best employment scam detection model. It helps in detecting fake job posts form an enormous number of posts. Two major types of classifiers, such as single classifier and ensemble classifiers are considered for fraudulent job posts detection. However, experimental results indicate that ensemble classifiers are the best classification to detect scams over the single classifiers.
Radio Dabanga

Radio Dabanga is a radio station broadcasting for Sudan.However, their newsroom is located in Amsterdam because of Sudan's long history of repression of the free press.That means that two journalists are tasked with covering the journalistic needs of a population of 43 million citizens over 4000 miles away.How is that even possible?What are the information gathering and verification practices of these journalists?Can we design powerful tools that make this Herculean challenge even remotely manageable?