Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis in Google Play Store: Algerian Reviews Case

In mobile application stores, users very often rely on the opinions of others before downloading an application and its reputation could depend entirely on them. This makes analysis of users' reviews very interesting for application owners to take future decisions. In this paper, we are interested in analyzing Algerian reviews on application store using sentiment analysis. To the best of our knowledge, this is the first study that explores the Algerian context where reviews have the particularity of being written using different languages (French, Arabic and Algerian Dialect) making them difficult to process. We analyzed these reviews according to two existing approaches: Automatic approach based on machine learning and Lexicon-based one. Evaluation of the proposed solution is conducted on more than 50 000 reviews collected from popular Algerian applications on Google play store. The obtained results are very promising, we achieved an accuracy of 80% using the lexicon-based approach and of 72% for SVM on Dialect reviews.
Face Recognition Based on Harris Detector and Convolutional Neural Networks

Facial recognition has always been a field of continuous development and research due to its usage in different areas such as security and robotics. It has gained even more popularity and interest by the researchers with the recent advancements in artificial intelligence and deep learning, which improved the robustness of facial recognition systems. In this paper, we focus on facial recognition using deep learning on small data sets with a limited number of individuals, for that we propose a local features based facial recognition approach that combines the robustness of feature extraction of CNN with the Harris corner detector. The experimental results of our proposed method surpassed the results of classical methods (LBP, Eigen Face, and Fisher Face) as well as recent works on Georgia Tech Face Database and AR Face Database and proved its efficiency and its robustness in different conditions including illumination variation, face pose variation, changes in facial expressions and face occlusions.
Fully Collision-Resistant Chameleon-Hashes from Simpler and Post-quantum Assumptions

Chameleon-hashes are collision-resistant hash-functions parametrized by a public key. If the corresponding secret key is known, arbitrary collisions for the hash can be found. Recently, Derler et al. (PKC '20) introduced the notion of fully collision-resistant chameleon-hashes. Full collision-resistance requires the intractability of finding collisions, even with full-adaptive access to a collision-finding oracle. Their construction combines simulation-sound extractable (SSE) NIZKs with perfectly correct IND-CPA secure public-key encryption (PKE) schemes. We show that, instead of perfectly correct PKE, non-interactive commitment schemes are sufficient. For the first time, this gives rise to efficient instantiations from plausible post-quantum assumptions and thus candidates of chameleon-hashes with strong collision-resistance guarantees and long-term security guarantees. On the more theoretical side, our results relax the requirement to not being dependent on public-key encryption.
Training Data Set Assessment for Decision-Making in a Multiagent Landmine Detection Platform

Real-world problems such as landmine detection require multiple sources of information to reduce the uncertainty of decision-making. A novel approach to solve these problems includes distributed systems, as presented in this work based on hardware and software multi-agent systems. To achieve a high rate of landmine detection, we evaluate the performance of a trained system over the distribution of samples between training and validation sets. Additionally, a general explanation of the data set is provided, presenting the samples gathered by a cooperative multi-agent system developed for detecting improvised explosive devices. The results show that input samples affect the performance of the output decisions, and a decision-making system can be less sensitive to sensor noise with intelligent systems obtained from a diverse and suitably organised training set.
Human-Centred Automated Reasoning for Regulatory Reporting via Knowledge-Driven Computing

The rise in both the complexity and volume of regulations in the regulatory landscape have contributed to an increase in the awareness of the level of automation necessary for becoming fully compliant. Nevertheless, the question of how exactly to become fully compliant by adhering to all necessary laws and regulations remains. This paper presents a human-centred, knowledge-driven approach to regulatory reporting. A given regulation is represented in a controlled natural language form including its metadata and bindings, with the assistance of subject matter experts. This representation of a semi-formal controlled natural language translates into a self-executable formal representation via a context-free grammar. A meta reasoner with the knowledge to execute the given self-executable formal representation while generating regulatory reports including explanations on derived results has been developed. Finally, the proposed approach has been implemented as a prototype and validated in the realm of financial regulation: Money Market Statistical Reporting Regulation.
A Taxonomy of Hypervisor Forensic Tools

Cloud computing models are deployed on a compute server whose hardware resources are virtualized to enable multiple virtual machines to run on a single physical system. Several types of virtualization such as bare metal and hosted virtualization are available along with virtualization modes such as full, paravirtualized, hardware-assisted and paravirtualized-hardware-assisted virtualization. Virtual machines are inaccessible from each other when the physical server hardware is abstracted in the full virtualization mode. Physical information such as hard disk drives and server memory are made available in a virtualized environment as a virtual hard disk, vCPU and guest operating system state. Hypervisor operations generate copious amounts of data that are of value in forensic investigations of virtualized cloud environments. This chapter presents a taxonomy of hypervisor forensic tools, which provides a searchable catalog for forensic practitioners to identify specific tools that fulfill their technical requirements. A case study involving a KVM hypervisor demonstrates the evidence that can be found in a virtual machine at the virtual machine manager and host system layers.
New Logical Perspectives on Monotonicity

Monotonicity-based inference is a fundamental notion in the logical semantics of natural language, and also in logic in general. Starting in generalized quantifier theory, we distinguish three senses of the notion, study their relations, and use these to connect monotonicity to logics of model change. At the end we return to natural language and consider monotonicity inference in linguistic settings with vocabulary for various forms of change. While we mostly raise issues in this paper, we do make a number of new observations backing up our distinctions.
Dynamic Self-Organising Swarm for Unsupervised Prototype Generation

Growing big data has posed a great challenge for machine learning algorithms. To cope with big data, the algorithm has to be both efficient and accurate. Although evolutionary computation has been successfully applied to many complex machine learning tasks, its ability to handle big data is limited. In this paper, we proposed a dynamic self-organising swarm algorithm to learn an effective set of prototypes for big high-dimensional datasets in an unsupervised manner. The novelties of this new algorithm are the energy-based fitness function, the adaptive topological neighbourhood, the growing/shrinking capability, and the efficient learning scheme. Experiments with well-known datasets show that the proposed algorithm can maintain a very compact set of prototypes and achieve competitive predictive performance as compared to other algorithms in the literature. The analyses also show that prototypes generated by the proposed algorithms have a stronger separatability compared to those from other prototype generation algorithms.
Machine Learning for Systems

The six papers in this special section focus on machine learning for computer systems. Specialized computer systems have driven the performance and capability of deep learning over the past decade.1 However, as machine learning models and systems improve, there is a growing opportunity to also use these models to improve how we design, architect, optimize, and automate computer systems and software. This is a challenging area, both from a learning and a systems perspective. Systems often impose tight size, latency, or reliability constraints on learning mechanisms that do not arise in other applications of machine learning, such as computer vision or natural language processing. From a learning perspective, systems is a challenging application, where input features are often large and sparse, action spaces are gigantic, and generalization is a key attribute.
Irregular Sampling of Regular Waveform Based Random Number Generator Exploiting Two Simultaneous Metastable Events of Tetrahedral Oscillators

An irregular sampling of regular waveform based random number generator(RNG) is proposed. The entropy source is constructed by using modified tetrahedral ring oscillators which exploits two metastable events in the circuit by switching the stable and unstable loops with a clock signal with help of additional inverters which can be turned on and off. The obtained circuit has higher entropy than the classical tetrahedral ring oscillator. The design is implemented using VHDL on Xilinx Zedboard Zynq Development Board as a proof of concept. The regular clock is adjusted to have close 50% duty cycle and a frequency at least 200 times of the irregular clock which has a center frequency of 70MHz and the rings are placed around the XOR gate in a circle to achieve higher entropy. The regular clock is sampled with the irregular clock and the sampled data is collected by using a UART module and TTL cable. The collected data sets are tested with National Institute of Standard and Technology (NIST) test suite to prove the randomness and they passed all the tests with success. The RNG has a bit rate of 273kb/s and it is implemented with 5 oscillators. The proposed architecture has a 2.73 times speed improvement and uses half the ring number to generate random number sequence without any post processing.
Fine-Grained Named Entity Recognition for Sinhala

For English, Named Entity Recognition (NER) is more or less a solved problem. However, for low-resourced and morphologically rich languages such as Sinhala, minimal research has been done. In this paper, we present a novel fine-grained Named Entity (NE) tag set and an NE annotated Sinhala corpus of 70k word tokens. We trained a custom NER model for Sinhala based on Conditional Random Fields (CRF). Despite the low-resourced setting, this NER model could achieve an micro-averaged F1 score of 84.8.
Enhancing Artificial Bee Colony Algorithm with Dynamic Best Neighbor-guided Search Strategy

Artificial bee colony (ABC) algorithm is a relatively new bio inspired optimization technique which has attracted a lot of attention for its competitive performance. However, in the basic ABC, the solution search equation performs well in exploration but poorly in exploitation, which may cause the problem of slow convergence rate. To tackle this issue, we propose a dynamic best neighbor-guided search strategy to enhance the performance of ABC. In the proposed strategy, a dynamic neighborhood with variable size is first constructed with respect to different evolution stage of the algorithm. After that, the best food source of the neighborhood is selected to guide search instead of only using the global best food source or some elite food sources, which aims to achieve a better balance between the exploration and exploitation. In addition, we design an improved global neighborhood search operator with better robustness to further enhance the performance of ABC. In the experiments, 50 different benchmark functions are used to verify our approach, including the CEC2013 benchmark test suite. The experimental results compared with other four recently well-established ABC variants show that our approach can significantly enhance the performance of ABC.
Learnersourcing Quality Assessment of Explanations for Peer Instruction

This study reports on the application of text mining and machine learning methods in the context of asynchronous peer instruction, with the objective of automatically identifying high quality student explanations. Our study compares the performance of state-of-the-art methods across different reference datasets and validation schemes. We demonstrate that when we extend the task of argument quality assessment along the dimensions of convincingness, from curated datasets, to data from a real learning environment, new challenges arise, and simpler vector space models can perform as well as a state-of-the-art neural approach.
Revisiting Success-Histories for Adaptive Differential Evolution

The introduction of success-history based adaptation in SHADE, a variant of JADE, resulted in a significant advance in the performance of adaptive differential evolution. Many variants of SHADE which use the same success history mechanism have been proposed, but the success history mechanism has remained poorly understood. We revisit use of the success history based adaptation, and show experimentally that the standard approach to sampling from the success history in SHADE may not be as vital to performance as previously assumed. We show that EnJADE, a simple, new variant of JADE which maintains an ensemble of control parameter distribution means, can outperform SHADE on the CEC14 benchmark suite. We also show the effectiveness of the new ensemble-based approach when combined with linear population reduction.
Baseband Modulation Classification using Incremental Learning

Automatic modulation classification (AMC) has been used in channel estimation to better understand the active users and signals. Traditional methods used complex and computationally expensive feature extraction and algorithms to perform AMC. More recently machine learning (ML) has been used to provide high accuracy AMC using methods such as convolutional neural network (CNN), residual neural network (ResNet), and long short-term memory (LSTM). These methods typically cannot integrate new modulation types to be classified without needing to be retrained with the entire dataset. One challenge that has not been addressed so far is the growing number of data sets and the limited capacity of computational systems to tackle this challenge. Incremental Learning (IL) algorithms introduce methods to train networks on new data, without forgetting knowledge learned from previous data. IL enables a network to learn new classes without requiring the entire dataset from previous classes. This work proposes an incremental learning classification model on signal constellation plots that is capable of adding classes incrementally to the model.
Learning to Segment Video Object With Accurate Boundaries

Video object segmentation has attracted considerable research interest these years. Top-performing video object segmentation methods mainly rely on fully convolutional neural networks which are specifically trained for predicting high-performance masks, resulting in a lack of preciseness in boundary details. This paper tackles the problem of predicting both mask-accurate and boundary-precise segmentation masks in videos. To solve this problem, we propose a simple and efficient network structure: the Mask-boundAry-Consistent Network ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MAC-Net</i> ). The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MAC-Net</i> is an end-to-end fully convolutional network, where both mask and boundaries are jointly optimized during training, enabling it to predict masks along with accurate boundaries. An inner-net boundary-computing module is incorporated in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MAC-Net</i> for producing spontaneously mask-consistent boundaries. We analyze the influence of parameter settings, network constructions of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MAC-Net</i> , and compare with state-of-the-art algorithms on three widely-adopted datasets. Experimental results show that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MAC-Net</i> achieves state-of-the-art performance, demonstrating the effectiveness of its mask-boundary-consistent network structure. We also propose that the boundary module in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MAC-Net</i> has high compatibility, and can be easily adapted to other segmentation-related techniques.
How to Handle Head Collisions in VR

In this paper a comparison of selected algorithms used to handle VR head collisions was presented. The following four methods were chosen and implemented: screen fade, delayed push-back, instant push-back, and teleportation. This paper examined what effects these methods have on VR sickness, the sense of presence, and the usability level.
Learning Regular Expressions for Interpretable Medical Text Classification Using a Pool-based Simulated Annealing Approach

In this paper, we propose a rule-based engine composed of high-quality and interpretable regular expressions for medical text classification. The regular expressions are autogenerated by a constructive heuristic method and optimized using a Pool-based Simulated Annealing (PSA) approach. Although existing Deep Neural Network (DNN) methods present high-quality performance in most Natural Language Processing (NLP) applications, the solutions are regarded as uninterpretable “black boxes” to humans. Therefore, rule-based methods are often introduced when interpretable solutions are needed, especially in the medical field. However, the construction of regular expressions can be extremely labor-intensive for large data sets. This research aims to reduce the manual efforts while maintaining high-quality solutions. The Pool-based Simulated Annealing method is proposed to automatically optimize the performance of machine-generated regular expressions without human interference. The proposed method is tested on real-life data provided by one of China's largest online medical platforms. Experimental results show that the proposed PSA method further improves the performance of initial machine-generated regular expressions compared with other meta-heuristics such as Genetic Programming. We also believe that the proposed method can serve as a vital complementary tool for the existing machine learning approaches in text classification applications when high levels of interpretability of the solutions are required.
An Improved EMD Parallel Steganography Algorithm

Abstract In Modification Direction Exploiting algorithm (EMD) exists binary information and multi-band data conversion redundancy problem, this paper proposed a new improved parallel algorithm. In this paper, we change the original EMD algorithm imbed function, and introduce the random safety parameter to improve the security of the algorithm. When 2n is equal to an integer power of 2, the algorithm does not have redundancy of data conversion problem, it can achieve the maximum theory embedding rate and the theory embedding efficiency. This algorithm utilizes multi-core parallel computing to improve operational efficiency. Therefore, the application of algorithm is more flexible and more efficient.
A Generative Graph Method to Solve the Travelling Salesman Problem

The Travelling Salesman Problem (TSP) is a challenging graph task in combinatorial optimization that requires reasoning about both local node neighborhoods and global graph structure. In this paper, we propose to use the novel Graph Learning Network (GLN), a generative approach, to approximately solve the TSP. GLN model learns directly the pattern of TSP instances as training dataset, encodes the graph properties, and merge the different node embeddings to output node-to-node an optimal tour directly or via graph search technique that validates the final tour. The preliminary results of the proposed novel approach proves its applicability to this challenging problem providing a low optimally gap with significant computation saving compared to the optimal solution.
A Symmetric grammar approach for designing segmentation models

Image segmentation is a relevant problem in computer vision present in multiple application domains. One of the most used methods for image segmentation is U-net, a type of convolutional network with additional constraints in its architecture. Studies regarding the U-net usually rely on well-known architectures, which leads to a narrow exploration of the possibilities, and possibly impacting the performance. Genetic Programming approaches have become increasingly popular for designing neural networks due to studies where the generated models were able to achieve results comparable to humans. These approaches can evolve the structure at different levels of abstraction, reducing the need for a specialist. In this paper, we propose the use of Grammatical Evolution for evolving U-net architectures. We propose a mirror grammar, which is capable of generating a variety of flexible U-nets that better explores the search space. We show that the proposed grammar can capture the complex constraints that define the U-nets and achieve comparable results in terms of accuracy, on a benchmark of segmentation problems of varying difficulty.
Internal documentation of library policies and practices with LibGuides: a survey and analysis

Accurate documentation is important in all library areas, especially as workflows change quickly to adapt to new technologies, policies, and practices. Many library faculty and staff use online tools and in 2019, the author conducted a survey to learn if and how colleagues use internal LibGuides to maintain accurate internal documentation. This article presents the survey results and analysis, covering both internal LibGuide use and current library documentation practices more generally.