Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychological Aspects of Pair Programming: A Mixed-methods Experimental Study

With the recent advent of artificially intelligent pairing partners in software engineering, it is interesting to renew the study of the psychology of pairing. Pair programming provides an attractive way of teaching software engineering to university students. Its study can also lead to a better understanding of the needs of professional software engineers in various programming roles and for the improvement of the concurrent pairing software. [Objective] This preliminary study aimed to gain quantitative and qualitative insights into pair programming, especially students’ attitudes towards its specific roles and what they require from the pairing partners. The research's goal is to use the findings to design further studies on pairing with artificial intelligence. [Method] Using a mixed-methods and experimental approach, we distinguished the effects of the pilot, navigator, and solo roles on (N = 35) students’ intrinsic motivation. Four experimental sessions produced a rich data corpus in two software engineering university classrooms. It was quantitatively investigated using the Shapiro-Wilk normality test and one-way analysis of variance (ANOVA) to confirm the relations and significance of variations in mean intrinsic motivation in different roles. Consequently, seven semi-structured interviews were conducted with the experiment's participants. The qualitative data excerpts were subjected to the thematic analysis method in an essentialist way. [Results] The systematic coding interview transcripts elucidated the research topic by producing seven themes for understanding the psychological aspects of pair programming and for its improvement in university classrooms. Statistical analysis of 612 self-reported intrinsic motivation inventories confirmed that students find programming in pilot-navigator roles more interesting and enjoyable than programming simultaneously. [Conclusion] The executed experimental settings are viable for inspecting the associations between students’ attitudes and the distributed cognition practice. The preliminary results illuminate the psychological aspects of the pilot-navigator roles and reveal many areas for improvement. The results also provide a strong basis for conducting further studies with the same design involving the big five personality and intrinsic motivation on using artificial intelligence in pairing and to allow comparison of those results with results of pairing with human partners.
Towards a User-centred Security Framework for Social Robots in Public Spaces

The use of social robots in public spaces is becoming increasingly popular due to their ability to provide personalized services to users. However, the convergence of different technologies and software applications has raised concerns regarding security requirements, standards, and regulations. Specifically, there are significant concerns about the evolving threat landscape for software applications in public settings, where social robots interact without supervision and are in direct contact with threat actors. During the development of social robots software, developers and practitioners need practical tools to continuously assess their products’ security profiles. This paper presents a preventive approach to the dynamic evolving security landscape of Social Robots in Public Spaces (SRPS) using design science research (DSR) methodology to develop a security framework. The study investigates security threats, vulnerabilities, and risks associated with SRPS software development and analyzes existing related frameworks to design a security framework for SRPS software developers. The research aims to provide insights into the security aspects of SRPS software application development processes and contribute to developing effective security frameworks to mitigate evolving risks and ensure secure operation and acceptance in public spaces.
Classification-based Static Collection Selection for Java: Effectiveness and Adaptability

Carefully selecting the right collection datastructure can significantly improve the performance of a Java program. Unfortunately, the performance impact of a certain collection selection can be hard to estimate. To assist developers, there exist tools that recommend collections to use based on static and/or dynamic information about a program. The majority of existing collection selection tools for Java pick their selections dynamically, which means that they must trade off sophistication in their selection algorithm against its run time overhead. For static collection selection, the Brainy tool has demonstrated that complex, machine-dependent models can produce substantial performance improvements, albeit only for C++ so far. In this paper, we port Brainy from C++ to Java, and evaluate its effectiveness for 5 benchmarks from the DaCapo benchmark suite. We compare it against the original program, but also to a ground truth, which we estimate using a variant of a brute-force approach to collection selection. Our results show that in four benchmarks out of five, our ground truth and the original program are similar. In one case, the ground truth shows an optimization yielding 20% speedup was available, but our port did not find this substantial optimization. We find that the port is more efficient but less effective than the ground truth, can easily adapt to new hardware architectures, and incorporate new datastructures with at most a few hours of human effort. We detail challenges that we encountered porting the Brainy approach to Java, and list a number of insights and directions for future research.
Test Suites Generation using UML Modelling and Heuristic Techniques: A Systematic Study

Software testing is not only crucial, but also a difficult stage in the system of software development life cycle. During the software testing phase, debuggers create test suites as input for the system being tested in order to evaluate the system's events. An eager role is played by creating test suites for identifying errors and enhancing the prominence of the product. This paper expands on a thorough analysis of generation of test suites processes used in many situations, including implementation of test suites utilizing a single UML diagram, a combinational UML diagram, and meta-heuristic methods. Together with this analysis, a selection of eminence literature from journals, symposia, workshops, and conferences is utilized. Issues with software testing have been identified by looking at the literature study that is based on methods used to develop test suites, coverage requirements, data foundation, testing level, and produced components. Deficits are acknowledged based on systematic study, and future efforts are advised. Through the use of inclusion-exclusion criteria, newly proposed coverage criteria, and test suite generating approaches, writers were able to identify the limitations of the work that was provided after examining the deliberative literature. The findings examined 74 significant studies in opposition to 6 research issues. By responding to the carefully considered study questions, the purpose has been satisfied. Following a review of the literature, it can be said that all kinds of generation techniques are frequently used in a range of testing scenarios, but that the period from 2010 to 2022 witnessed the greatest use of all techniques.
Detection of Brain Tumors Using Magnetic Resonance Images through the Application of an Innovative Convolution Neural Network Model

According to a report released by the WHO in February 2018, the mortality rate for people with brain or central nervous system cancer is highest in Asia. It is important that cancer screenings are conducted earlier to prevent these deaths. Due to the complexity of brain cancer diagnosis, it is very important that the development of effective and non-invasive tools for analyzing and predicting the grade of the disease is carried out. Currently, there are various imaging modalities that can be used to detect brain tumors, such as CT, MRI, and X-rays. Deep Learning is a type of artificial intelligence that imitates the brain's work. It can learn to recognize and interpret the voice, make decisions, and translate languages. It can also detect artifacts in data, and without human intervention, it can understand from unorganized information. A Convolutional Neural Network is a type of deep learning that is commonly used in optical representation analysis. Currently, there are systems that can detect brain tumors using small datasets. However, they only use image processing techniques and require a lot of computational resources. A new system that combines the three components of deep learning, namely image preprocessing, augmentation, and applying, is currently under development.
Exploring the UK Cyber Skills Gap through a mapping of active job listings to the Cyber Security Body of Knowledge (CyBOK)

Background: The UK cyber skills gap/shortage amplifies the broader impact of cyber-attacks, which inflict harms such as privacy and economic loss on wider society. The demand is greatest (and growing fastest) in cyber-enabled disciplines, such as software engineering. Objectives: In this paper, we create a term frequency-inverse document frequency representation of the Cyber Security Body of Knowledge (CyBOK). We then evaluate the potential of this representation by using it to automatically map job descriptions to the different areas of the CyBOK. Method: We generate two representations of the CyBOK. The representations are mapped to a corpus of 454 job descriptions using TF-IDF. Comparing the similarity scores across these mappings allows us to identify relevant knowledge areas/groups. Results: The results are preliminary, but suggest that the approach warrants further investigation. Certain job descriptions are mapped to certain knowledge areas/groups in a way that makes intuitive sense to the authors. However, there is a degree homogeneity to the scores returned for certain knowledge areas/groups. There are several threats to validity, most notably the low number of job descriptions that have been studied. Conclusions: Our work shows that it is possible to automatically map job descriptions to the CyBOK in a meaningful way. Further research is required to address threats and to explore alternative mapping approaches. The authors intend to undertake this research culminating with a Grey Literature Informed Model of Practice in Secure Software Engineering.
Enhanced Image Denoising by Using Minimum Patch Difference Based Technique and Image Gradients

Images are often corrupted with noise during acquisition, transmission and retrieval from storage media So efficient Image denoising algorithm is required for removal of noise from the noisy image . image denoising is one of the most vital challenges in the ground of image processing. In image denoising original image is estimated by removing noise from a noisy version of the image Images plays vital role in our daily life. Many algorithms have been proposed for removal of noise from the digital the image denoising methods are divided into two types: local and nonlocal. The methods that only use the spatial redundancy in local neighbourhoods are known as Local methods. The method that estimate pixel intensity from the complete image and shows the occurrence of similar patterns and features in an image are referred as Non-Local. This technique [3] estimates a noise-free pixel value as a weighted average of all other neighbourhood pixels in the image with similar neighbourhood. non-local means perform better at low level noise and second method is named as Non local euclidean median that improves the performance of NLM at high noise level. This paper propose a minimum patch difference based technique in which we use image gradients as a post processing operation to preserve the edges in an efficient manner. this technique is capable of removing noise at both levels i.e high level noise and low level noise. The simulated results preserve edges than available method. The proposed algorithm implemented over the MATLAB shows that noise removals can be done which is higher as compared to the existing algorithms. Keywords— Denoising, NLM, NLEM
Prominent elevation of extracellular matrix molecules in intracerebral hemorrhage

Background Intracerebral hemorrhage (ICH) is the predominant type of hemorrhagic stroke with high mortality and disability. In other neurological conditions, the deposition of extracellular matrix (ECM) molecules is a prominent obstacle for regenerative processes and an enhancer of neuroinflammation. Whether ECM molecules alter in composition after ICH, and which ECM members may inhibit repair, remain unknown in hemorrhagic stroke. Methods The collagenase-induced ICH mouse model and an autopsied human ICH specimen were investigated for expression of ECM members by immunofluorescence microscopy. Confocal image z-stacks were analyzed with Imaris 3D to assess the association of immune cells and ECM molecules. Sections from a mouse model of multiple sclerosis were used as disease controls. Tissue culture was employed to examine the roles of ECM members on oligodendrocyte precursor cells (OPCs). Results Amongst the lectican chondroitin sulphate proteoglycan (CSPG) members, neurocan but not aggrecan, versican-V1 and versican-V2 was prominently expressed in perihematomal tissue and lesion core compared to the contralateral area in murine ICH. Fibrinogen, fibronectin and heparan sulphate proteoglycan (HSPG) were also elevated after murine ICH while thrombospondin was not. Confocal microscopy with Imaris 3D rendering co-localized neurocan, fibrinogen, fibronectin and HSPG molecules to Iba1+ microglia/macrophages or GFAP+ astrocytes. Marked differentiation from the multiple sclerosis model was observed, the latter with high versican-V1 and negligible neurocan. In culture, purified neurocan inhibited adhesion and process outgrowth of OPCs, which are early steps in myelination in vivo. The prominent expression of neurocan in murine ICH was corroborated in human ICH sections. Conclusion ICH caused distinct alterations in ECM molecules. Amongst CSPG members, neurocan was selectively upregulated in both murine and human ICH. In tissue culture, neurocan impeded the properties of oligodendrocyte lineage cells. Alterations to the ECM in ICH may adversely affect reparative outcomes after stroke. What is already known on this topic – CSPGs are known to be elevated in multiple sclerosis and intraventricular hemorrhage, where they act as inhibitors of repair by hindering remyelination and axonal regeneration, as well as promoting neuroinflammation. However, there is currently no literature available regarding the role of CSPGs in ICH. What this study adds – Our findings demonstrate the elevation of specific ECM molecules, particularly neurocan, in murine and human ICH. These matrix molecules will likely affect subsequent reparative processes such as remyelination, as suggested by the result that purified neurocan impairs the process outgrowth and maturation of oligodendrocyte precursor cells. How this study might affect research, practice or policy – The targeting of ECM molecules represents a promising strategy to promote remyelination and control neuroinflammation, with the potential to improve prognosis following ICH.
Towards developing permanent memory system for artiﬁcial intelligence to acquire new knowledge after deployment

Artiﬁcial intelligence that accumulates knowledge and presents it to users is likely to become a good friend to humanity. It is thought to be useful for such artiﬁcial intelligence to have the ability to continuously accumulate knowledge while operating as artiﬁcial intelligence, rather than having to relearn when acquiring new knowledge. Artiﬁcial intelligence that can share past knowledge with users is thought to be a human-like existence. Some dialogue agents developed so far can partially use the memory of past conversations. Such memory is sometimes referred to as long-term memory. However, the ability of existing dialogue agents to continue accumulating knowledge and share that knowledge with users is likely not sufﬁcient at present. Within the framework of their current implementation, their memory capacity is at least ﬁnite. It is thought necessary to develop a method to improve this memory capacity, in order to realize memory to store any information in a medium of ﬁnite size. This might be a kind of research on data compression. Also, in the current method of developing dialogue agents by repetitively using human conversation data as training data, there is some doubt as to whether or not artiﬁcial intelligence can effectively utilize the memory they have acquired in the past. In this study, we demonstrate the impossibility. In contrast, this research proposes a method to store various information in a single ﬁnite vector data and retrieve it, utilizing a neural network with a structure similar to a recurrent neural network and encoder–decoder network, which we named mnemonist. This study only yielded very preliminary results, but it is fundamentally different from the conventional learning method of neural networks, and is a fundamental consideration for building artiﬁcial intelligence that can store any information in ﬁnite size data and freely utilize them in the future.
Plasticity-led evolution as an intrinsic property of developmental gene regulatory networks

The modern evolutionary synthesis seemingly fails to explain how a population can survive a large environmental change: the pre-existence of heritable variants adapted to the novel environment is too opportunistic, whereas the search for new adaptive mutations after the environmental change is so slow that the population may go extinct. Plasticity-led evolution, the initial environmental induction of a novel adaptive phenotype followed by genetic accommodation, has been proposed to solve this problem. However, the mechanism enabling plasticity-led evolution remains unclear. Here, we present computational models that exhibit behaviors compatible with plasticity-led evolution by extending the Wagner model of gene regulatory networks. The models show adaptive plastic response and the uncovering of cryptic mutations under large environmental changes, followed by genetic accommodation. Moreover, these behaviors are consistently observed over distinct novel environments. We further show that environmental cues, developmental processes, and hierarchical regulation cooperatively amplify the above behaviors and accelerate evolution. These observations suggest plasticity-led evolution is a universal property of complex developmental systems independent of particular mutations.
Thioredoxin regulates the redox state and the activity of the human tRNA ligase complex

The mammalian tRNA ligase complex (tRNA-LC) catalyzes the splicing of intron-containing pre-tRNAs in the nucleus and the splicing of XBP1 mRNA during the unfolded protein response (UPR) in the cytoplasm. We recently reported that the tRNA-LC co-evolved with PYROXD1, an essential oxidoreductase that protects the catalytic cysteine of RTCB, the catalytic subunit of the tRNA-LC, against aerobic oxidation. In this study we show that the oxidoreductase Thioredoxin (TRX) preserves the enzymatic activity of RTCB under otherwise inhibiting concentrations of oxidants. TRX physically interacts with oxidized RTCB, and reduces and re-activates RTCB through the action of its redox-active cysteine pair. We further show that TRX interacts with RTCB at late stages of UPR. Since the interaction requires oxidative conditions, our findings suggest that prolonged UPR generates reactive oxygen species. Thus, our results support a functional role for TRX in securing and repairing the active site of the tRNA-LC, thereby allowing pre-tRNA splicing and UPR to occur when cells encounter mild, but still inhibitory levels of reactive oxygen species.
Automated counting of Drosophila imaginal disc cell nuclei

Automated image quantification workflows have dramatically improved over the past decade, enriching image analysis and enhancing the ability to achieve statistical power. These analyses have proved especially useful for studies in organisms such as Drosophila melanogaster, where it is relatively simple to obtain high sample numbers for downstream analyses. However, the developing wing, an intensively utilized structure in developmental biology, has eluded efficient cell counting workflows due to its highly dense cellular population. Here, we present efficient automated cell counting workflows capable of quantifying cells in the developing wing. Our workflows can count the total number of cells or count cells in clones labeled with a fluorescent nuclear marker in imaginal discs. Moreover, by training a machine-learning algorithm we have developed a workflow capable of segmenting and counting twin-spot labeled nuclei, a challenging problem requiring distinguishing heterozygous and homozygous cells in a background of regionally varying intensity. Our workflows could potentially be applied to any tissue with high cellular density, as they are structure-agnostic, and only require a nuclear label to segment and count cells.
Sequences at gene segment termini inclusive of untranslated regions and partial open reading frames play a critical role in mammalian orthoreovirus S gene packaging

Mammalian orthoreovirus (MRV) is a prototypic member of the Spinareoviridae family and has ten double-stranded RNA segments. One copy of each segment must be faithfully packaged into the mature virion, and prior literature suggests that nucleotides (nts) at the terminal ends of each gene likely facilitate their packaging. However, little is known about the precise packaging sequences required or how the packaging process is coordinated. Using a novel approach, we have determined that 200 nts at each terminus, inclusive of untranslated regions (UTR) and parts of the open reading frame (ORF), are sufficient for packaging each S gene segment (S1-S4) individually and together into replicating virus. Further, we mapped the minimal sequences required for packaging the S1 gene segment to 25 5′ nts and 50 3′ nts. The S1 UTRs alone are not sufficient, but are necessary for packaging, as mutations of the 5′ or 3′ UTRs led to a complete loss of virus recovery. Using a second novel assay, we determined that 50 5′nts and 50 3′ nts of S1 are sufficient to package a non-viral gene segment into MRV. The 5′ and 3′ termini of the S1 gene are predicted to form a panhandle structure and specific mutations within the predicted stem of the panhandle region led to a significant decrease in viral recovery. Additionally, mutation of six nts that are conserved in the three major serotypes of MRV and are predicted to form an unpaired loop in the S1 3′UTR, led to a complete loss of viral recovery. Overall, our data provide strong experimental proof that MRV packaging signals lie at the terminal ends of the S gene segments and offer support that the sequence requirements for efficient packaging of the S1 segment include a predicted panhandle structure and specific sequences within an unpaired loop in the 3′ UTR.