Reviewerly/exHarmony huggingface dataset:
Problem Solving Techniques Using Ant Colony Optimization in Computational Intelligence

Over the years, technology has played a crucial role in the advancement of the society. In achieving human like intelligence in machines, computational Intelligence plays an important role due to development of software algorithms imitating biological inspired intelligence behavior. In this paper we have briefed about that the evolution of computational intelligence over the years, starting from Turing Test to the present-day Swarm Intelligence. Also, the importance of CI and AI is highlighted by considering some major approaches like Fuzzy systems, Artificial Neural Networks, Evolutionary computation, Perception, Natural language processing, cognitive thinking. In this paper, the analysis of travelling salesman problem along with vehicle routing problem is presented using the approach of ant colony optimization. Finally, the applications of CI in the areas of space applications, driving cognition, and Bioinformatics are described.
Code Comprehension Confounders: A Study of Intelligence and Personality

Program comprehension is a cognitive psychological process. Accordingly, literature and intuition suggest that a developer's intelligence and personality have an impact on their performance in comprehending source code. Some researchers have made this suggestion in the past when discussing threats to validity of their study results. However, the lack of studies investigating the relationship of intelligence and personality to performance in code comprehension makes scientifically sound reasoning about their influence difficult. We conduct the first large-scale empirical evaluation, a correlational study with undergraduates, to investigate the correlation of intelligence and personality with performance in code comprehension, that is, in this context, with correctness in answering comprehension questions on code snippets. We found that personality traits are unlikely to impact code comprehension performance, at least not when their influence is considered in isolation. Conscientiousness, in combination with other factors, however, explains some of the variance in code comprehension performance. For intelligence, significant small to moderate positive effects on code comprehension performance were found for three of four factors measured, i.e., fluid intelligence, visual perception, and cognitive speed. Crystallized intelligence has a positive but statistically insignificant effect on code comprehension performance. According to our results, several intelligence facets as well as the personality trait conscientiousness are potential confounders that should not be neglected in code comprehension studies of individual performance and should be controlled for via an appropriate study design. We call for the conduct of further studies on the relationship between intelligence and personality with code comprehension, in part because code comprehension involves more facets than we can measure in a single study and because our regression model explains only a small portion of the variance in code comprehension performance.
On the Efficiency of Metaheuristic Optimization for Adaptive Image Steganography in the DFT Domain

Adaptive embedding is a popular area of image steganography. Adaptability means the use of cover image features in the embedding process. In many cases, the embedding adaptability is associated with choosing the best position of the message bits in the image. However, the total check of all possible options for the message fragment is redundant in most cases. This leads us to the optimization problem. In this paper, we investigate the applicability of some classical metaheuristic optimization algorithms for solving the problem of increasing the efficiency of adaptive embedding of information into the discrete Fourier transform phase spectrum. We consider three popular optimization algorithms: the genetic algorithm, the differential evolution algorithm, and the particle swarm optimization algorithm. The experimental results show that the particle swarm optimization algorithm can significantly increase the embedding capacity, while the imperceptibility also improves or remains at the same level, and the embedded information is extracted without any errors.
Curating Datasets for Visual Runway Detection

In Machine Learning systems, several factors impact the performance of a trained model. The most important ones include model architecture, the amount of training time, the dataset size and diversity. In the realm of safety-critical machine learning the used datasets need to reflect the environment in which the system is intended to operate, in order to minimize the generalization gap between trained and real-world inputs. Datasets should be thoroughly prepared and requirements on the properties and characteristics of the collected data need to be specified. In our work we present a case study in which generating a synthetic dataset is accomplished based on real-world flight data from the ADS-B system, containing thousands of approaches to several airports to identify real-world statistical distributions of relevant variables to vary within our dataset sampling space. We also investigate what the effects are of training a model on synthetic data to different extents, including training on translated image sets (using domain adaptation). Our results indicate airport location to be the most critical parameter to vary. We also conclude that all experiments did benefit in performance from pre-training on synthetic data rather than using only real data, however this did not hold true in general for domain adaptation-translated images.
Accelerating Low-Cost Edge-Based Real-Time Video Analytics using Task Scheduling

Reliable object detection is essential for smart surveillance systems, and video surveillance technology has been significantly improved thanks to Deep-Learning related techniques. The deployment of resource-constraint edge device algorithms based on Deep-Learning objection detection is an attractive approach with a range of challenging practical issues to solve, such as the enforcement of Deep-Learning model inference reliability on inexpensive, general-purpose edge devices. In this paper, we transform the state-of-the-art convolutional Single Single Shot Detector Object-Detection algorithm into a task optimization problem to investigate whether the overall system reliability can be further improved by using an under-studied technique called semi-partitioned rate-monotonic scheduling algorithm. Unlike current non-scheduling based methods, our scheduling approach and system configuration is empirically shown to achieve state-of-the-art results, such as enhancing Real-Time video inference performance from 0.5 frame-per-second (fps) to 2fps (i.e. four-fold increase) with a recorded mean 68% improvement in video fps deviation. Our findings are important as fluctuations in video fps, hereby termed system reliability, is known to affect the detection accuracy of object detection algorithms. Finally, we discuss the results and highlight several observations that have not been explicitly addressed in current literature.
Deep Learning in Time-Frequency Domain for Document Layout Analysis

Document layout analysis plays an important role in the area of Document Understanding. It is responsible for identifying and classifying the different components of digital documents. Currently, there is no universal algorithm that fits all types of digital documents. This work presents a novel approach for identifying tables, figures, isolated equations, and text regions in scientific papers using deep learning and computer vision techniques. Our proposed approach is a three-stage system: (i) obtaining the spectrograms of the horizontal and vertical intensity histograms of segmented regions of interest. (ii) labeling segmented regions of interest into text, table, and figures using a deep convolutional neural network classifier. (iii) identifying isolated equations in text regions using Bag of Visual Words (BOVW) with Zernike moments. We built a new dataset composed of 11007 papers to perform the experiments, using two common segmentation metrics to evaluate our model: (1) Adjusted Rand Index (ARI) and (2) Variation of Information (VI). The proposed document layout analysis system reached an overall accuracy of 96.2685%, outperforming prior art with a less computational cost.
Mineral deposit discovery order and three-part quantitative assessments

Larger oil pools tending to be discovered earlier in an exploration play suggests the same pattern might exist for mineral deposits and could be used in predicting sizes of undiscovered deposits in mineral assessments. The volume of individual petroleum pools is highly correlated with surface projection area of pools in basins. The gradual additions to individual oil pool reserves over time adds to the appearance of larger pools being discovered earlier. Comparisons of surface projected areas of mineral deposits to their tonnages showed significant positive relationships in all 10 deposit types analyzed, suggesting that larger deposits should be discovered earlier than small deposits. Analysis of deposits consistent with three-part mineral assessments identified 9 combinations of mineral deposit types in large regions each containing multiple geological permissive tracts showing negative and 1 positive relationships of deposit size with discovery date significant at the 1% level. Twenty other tests of regions containing multiple permissive settings had either negative or positive relationships, none significantly different from those that might occur by chance. The large regions are mostly based on political boundaries. These results suggest mineral deposit discovery order is not the same as observed in oil pool exploration. The widely employed three-part quantitative mineral resource assessments are an obvious choice to benefit from patterns of declining deposit sizes with order of discovery. The 30 tests of relationships of discovery dates to deposit sizes demonstrated here were performed with deposits consistent with those in three-part assessments, but the large areas are not consistent with permissive tracts used in these assessments because they also contain substantial non-permissive geology. In 100 permissive tracts assessed with three-part assessments of multiple deposit types located throughout the world, the median number of known well-explored deposits is 1 and 90 percent of tracts report less than 9 deposits. The number of well-explored deposits in three-part assessed tracts tends to be quite small, limiting any ability to recognize a discovery order versus size relationship. In a three-part assessment of undiscovered porphyry copper deposits of South America, only 7 of 26 delineated tracts contained more than 2 known deposits and only 1 had a significant negative relationship between tonnage of known deposits and year of discovery (p = 0.04). Most predicted undiscovered deposits in this tract were expected to be under extensive unexplored post-mineralization cover, meaning the general grade and tonnage model should be applied because the discovery order process starts over. Projection of deposit sizes based on discovery order would provide a biased estimate of the undiscovered deposit sizes in this case. Thus, although a discovery order versus size relationship could exist in three-part mineral assessments, only rarely might the pattern be useful to predict sizes of undiscovered deposits.
Seeds of SEED: R-SAW: New Side Channels Exploiting Read Asymmetry in MLC Phase Change Memories

Phase Change Memory (PCM) is a promising contender for future main memory solutions. While many architecture-level performance optimizations have been studied for PCM, the security implications of these designs are not well understood. This work demonstrates the first investigation of information leakage threats in PCM-based main memories. Notably, we find state-of-the-art read techniques leveraging access latency asymmetry in Multi-level Cell (MLC) PCM introduce new timing variations. To understand the severity of the vulnerability, we present R-SAW, a novel side channel attack that aims to exfiltrate secrets from a victim process via passively observing execution timings that are correlated with secret-dependent PCM accesses. We demonstrate the attack on a real-world cryptographic algorithm–AES encryption in OpenSSL. Our evaluation shows that R-SAW is able to completely recover the encryption keys. Furthermore, our experiments reveal that R-SAW exhibits superior noise resilience compared to the widely-studied cachebased side channels. Our work highlights the importance of understanding security in systems integrated with emerging memory technologies and motivates the need to architect secure-by-design PCM main memories in the future.
Enhancement of Electronic Voting Machine Performance Using Fingerprint and Face Recognition

This paper presents, the enhancement of Electronic Voting Machine (EVM) performance using fingerprint and face recognition. In democratic countries, the EVM plays a vital role in the election process. Even though it has more benefits; still the malpractices are continued due to manual verification of electoral documents for all elections in democratic countries. In this proposed method EVM with biometric fingerprint and face recognition devices are used to overcome this problem. To conquer the issues existing in the election process for voter identification the biometric fingerprint and web cameras are introduced in the EVMs. The people at the polling booth need to put the finger in the fingerprint equipment and face to be captured by the web camera provided to recognize them. The controller fetches the data from the input devices and compares it with the database to enable the polling device to cast their votes. In practice, due to some technical issue either in the input devices or from the voter side the election process will be continued by document verification. In the polling booths, the presiding officers are having the right to enable the polling device by pressing the enable button after document verification. The performance of the proposed method was validated with EVM successfully.
Adversarial Attacks on Multi-Level Fault Detection and Diagnosis Systems

Building automation systems are susceptible to malicious attacks, causing erroneous Fault Detection and Diagnosis (FDD). In this paper, we aim at examining the robustness of a Hierarchical Fault Detection and Diagnosis (HFDD) model, which uses multiple levels for detection and diagnosis, to adversarial perturbation attacks. We formulate convex programs to generate small perturbations targeting different levels of the HFDD model. We show that the HFDD model is harder to fool than the single level classifier and that attacking a certain level can be achieved with negligible effect on the higher level accuracy. We perform a case study of said attacks on the HFDD model using experimental data from faulty Air Handling Units. Performance is evaluated based on the reduction in classification accuracy, robustness of the higher level accuracy, and imperceptibility of the attack.
Zero-Shot Motion Pattern Recognition from 4D Point-Clouds

We address a timely and relevant problem in signal processing: The recognition of patterns from spatial data in motion through a zero-shot learning scenario. We introduce a neural network architecture based on Siamese networks to recognize unseen classes of motion patterns. The approach uses a graph-based technique to achieve permutation invariance and also encodes moving point clouds into a representation space in a computationally efficient way. We evaluated the model on an open dataset with twenty-one gestures. The model out-performes state-of-the-art architectures with a considerable margin in four different settings in terms of accuracy while reducing the computational complexity up to 60 times.
Classification of four qubit states and their stabilisers under SLOCC operations

We classify four qubit states under SLOCC operations, that is, we classify the orbits of the group $\mathrm{\mathop{SL}}(2,\mathbb{C})^4$ on the Hilbert space $\mathcal{H}_4 = (\mathbb{C}^2)^{\otimes 4}$. We approach the classification by realising this representation as a symmetric space of maximal rank. We first describe general methods for classifying the orbits of such a space. We then apply these methods to obtain the orbits in our special case, resulting in a complete and irredundant classification of $\mathrm{\mathop{SL}}(2,\mathbb{C})^4$-orbits on $\mathcal{H}_4$. It follows that an element of $(\mathbb{C}^2)^{\otimes 4}$ is conjugate to an element of precisely 87 classes of elements. Each of these classes either consists of one element or of a parametrised family of elements, and the elements in the same class all have equal stabiliser in $\mathrm{\mathop{SL}}(2,\mathbb{C})^4$. We also present a complete and irredundant classification of elements and stabilisers up to the action of ${\rm Sym}_4\ltimes\mathrm{\mathop{SL}}(2,\mathbb{C})^4$ where ${\rm Sym}_4$ permutes the four tensor factors of $(\mathbb{C}^2)^{\otimes 4}$.
Fuzzy Cognitive Maps

Fuzzy Cognitive Maps (FCMs) have gained popularity within the scientific community due to their capabilities in modeling and decision making for complex problems. In order to examine and estimate the FCM inference, we develop and the ‘fcm’ open-source package R package. Specifically, the fcm.infer function provides six inference rules and four threshold functions. This open-source ‘fcm’ package is available in CRAN and provides the opportunity for everyone to run different scenarios in their weighted matrices. Dedicated examples and visualizations are provided to show the proposed open-source FCM package for business modelling and decision making.
Coding™: Development Task Visualization for SW Code Comprehension

In a software development project, a developer tends to use the ‘diff’ view of the version control system (VCS) to understand development tasks such as fixing bugs, adding new features, and refactoring. However, the view only shows the difference of resources between the recent and previous versions in a commit, without providing any information about associated updates for completing a specific task. This causes a developer to spend a lot of time understanding development tasks, especially in the project where source code should be shared throughout team members. In order to handle this issue, we propose a novel tool Coding Time-Machine, in short Coding™, that automatically identifies and visualizes development tasks and their associated task elements (e.g., class and method). Coding™ extracts development tasks composed of task elements and causal relationships between them in a commit and facilitates one to compare the recent version of a code to the previous for each task. In addition, it allows one to navigate tasks of all commits in the code repository so that a developer feels like carrying out the time-travel of the coding activities in the software development project. For the evaluation, we measured the performance of tasks extracted from Coding™ for eight open-source Java projects, and obtained 0.87 of precision and 0.88 of recall. Also, we surveyed the usefulness of our tool for 20 participants, 80% of participants thought that showing tasks and their associated elements in a commit helps one to comprehend source code, and all participants responded that showing tasks in a chronicle way facilitates one to understand coding activities.
Flag codes: Distance vectors and cardinality bounds

Given Fq the finite field with q elements and an integer n⩾2, a flag is a sequence of nested subspaces of Fqn and a flag code is a nonempty set of flags. In this context, the distance between flags is the sum of the corresponding subspace distances. Hence, a given flag distance value might be obtained by many different combinations. To capture such a variability, in the paper at hand, we introduce the notion of distance vector as an algebraic object intrinsically associated to a flag code that encloses much more information than the distance parameter itself. Our study of the flag distance by using this new tool allows us to provide a fine description of the structure of flag codes as well as to derive bounds for their maximum possible size once the minimum distance and dimensions are fixed.
Spontaneous decay of artificial atoms in a three-qubit system

We study the evolution of qubits amplitudes in a one-dimensional chain consisting of three equidistantly spaced noninteracting qubits embedded in an open waveguide. The study is performed in the frame of single-excitation subspace, where the only qubit in the chain is initially excited. We show that the dynamics of qubits amplitudes crucially depend on the value of $kd$, where $k$ is the wave vector, $d$ is a distance between neighbor qubits. If $kd$ is equal to an integer multiple of $\pi$, then the qubits are excited to a stationary level. In this case, it is the dark states which prevent qubits from decaying to zero even though they do not contribute to the output spectrum of photon emission. For other values of $kd$ the excitations of qubits exhibit the damping oscillations which represent the vacuum Rabi oscillations in a three-qubit system. In this case, the output spectrum of photon radiation is determined by a subradiant state which has the lowest decay rate. We also investigated the case with the frequency of a central qubit being different from that of the edge qubits. In this case, the qibits decay rates can be controlled by the frequency detuning between the central and the edge qubits.
Revolutions Take Time

The 2018 paper titled “Common Patterns in Revolutionary Infrastructures and Data” has been cited frequently, since we compared the current discussions about research data management with the developments of large infrastructures in the past believing, similar to philosophers such as Luciano Floridi, that the creation of an interoperable data domain will also be a revolutionary step. We identified the FAIR principles and the FAIR Digital Objects as nuclei for achieving the necessary convergence without which such new infrastructures will not take up. In this follow-up paper, we are elaborating on some factors that indicate that it will still take much time until breakthroughs will be achieved which is mainly devoted to sociological and political reasons. Therefore, it is important to describe visions such as FDO as self-standing entities, the easy plug-in concept, and the built-in security more explicitly to give a long-range perspective and convince policymakers and decision-makers. We also looked at major funding programs which all follow different approaches and do not define a converging core yet. This can be seen as an indication that these funding programs have huge potentials and increase awareness about data management aspects, but that we are far from converging agreements which we finally will need to create a globally integrated data space in the future. Finally, we discuss the roles of some major stakeholders who are all relevant in the process of agreement finding. Most of them are bound by short-term project cycles and funding constraints, not giving them sufficient space to work on long-term convergence concepts and take risks. The great opportunity to get funds for projects improving approaches and technology with the inherent danger of promising too much and the need for continuous reporting and producing visible results after comparably short periods is like a vicious cycle without a possibility to break out. We can recall that coming to the Internet with TCP/IP as a convergence standard was dependent on years of DARPA funding. Building large revolutionary infrastructures seems to be dependent on decision-makers that dare to think strategically and test out promising concepts at a larger scale.