Miracle-dz/UniFAR-FTUs huggingface dataset:
Uses of the novelty metrics proposed by Shah et al.: what emerges from the literature?

Abstract Several concepts and types of procedures for assessing novelty and related concepts exist in the literature. Among them, the two approaches originally proposed by Shah and colleagues are often considered by scholars. These metrics rely on well-defined novelty types and a specific concept of novelty; however, more than 20 years after the first publication, it is still not clear whether and to what extent these metrics are actually used, why they are used and how. Through a comprehensive review of the papers citing the main work of Shah, Vargas-Hernandez & Smith (2003a, 2003b) (the main study where the metrics are comprehensively described and applied), the present work aims to bridge this gap. The results highlight that only a few of the citing papers actually use the assessment approach proposed by Shah et al. and that a nonnegligible number uses a modified or adapted version of the original metrics. Furthermore, several criticalities in the application of the metrics have been uncovered, which are expected to provide relevant information for scholars involved in reliable and repeatable novelty assessments.
“I Could Be Unemployed the Rest of the Year”: Unprecedented Times and the Challenges of “Making More”

The COVID-19 pandemic has been unprecedented in many ways, but perhaps no more so than in the sudden expansion of—and increase in—unemployment assistance benefits. We ask how precarious workers, many of whom were “hustling” for money or engaged in creative fields, feel about making more on unemployment. How are they using the funds? We draw on remote interviews and online surveys with 199 gig and precarious workers in New York City during the first wave of the pandemic. We find that workers are ambivalent about unemployment assistance and concerned that a financial influx today portends a shortage tomorrow. This “specter of the unknown” affected workers’ use of their benefits. As a result, even though the Coronavirus Aid, Relief, and Economic Security Act was intended to mitigate the social and economic impact of the pandemic, these programs—despite being helpful—may have also contributed to precarious workers becoming even more certain of their insecurity.
iCareLoop: Closed-Loop Sensing and Intervention for Gerontological Social Isolation and Loneliness

Social isolation refers to a lack of social contact and interaction, while loneliness is the subjective feeling of being alone. These two mental health concerns are highly correlated and can significantly increase the risk of premature death in the elderly. In fact, this risk may even rival that of smoking, obesity, and physical inactivity. To address this issue, we propose iCareLoop, a closed-loop decision support system with clinician-in-the-loop actuation to mitigate gerontological social isolation and loneliness. By implementing targeted interventions, this system can help maintain the mental well-being of elderly individuals, which is particularly crucial given the social disruptions caused by the COVID-19 pandemic.
Resilient and Secure System on Chip with Rejuvenation in the Wake of Persistent Attacks

To cope with the ever increasing threats of dynamic and adaptive persistent attacks, Fault and Intrusion Tolerance (FIT) is being studied at the hardware level to increase critical systems resilience. Based on state-machine replication, FIT is known to be effective if replicas are compromised and fail independently. This requires different ways of diversification at the software and hardware levels. In this paper, we introduce the first FIT hardware-based rejuvenation framework, we call Samsara, that allows for creating new FIT replicas with computing cores of diverse architectures. This is made possible by taking advantage of the reconfiguration features of MPSoC with FPGAs. A persistent attack that analyzes and exploits the vulnerability of a core will not be effective as rejuvenation using a different core architecture can be done periodically. Samsara allows for both replacing and adding/removing new cores to adapt to varying levels of threat severity. We introduce this concept and discuss the feasibility using a preliminary design we propose. A more rigorous study and empirical evaluation are left for future work.
Joint Rebalancing and Charging for Shared Electric Micromobility Vehicles with Human-system Interaction

The use of shared electric micromobility vehicles, such as bikes and scooters, has become increasingly popular. It leads to the problem of management (i.e., rebalancing and charging). Existing approaches typically assume that all vehicles have an equal chance of being selected for a ride, which is not practical. To overcome this limitation, we propose a reinforcement-learning-based framework incorporating human-system interaction. We first predict the likelihood of each vehicle being selected, then integrate this prediction into the reinforcement learning framework. The aim is to create a more realistic simulation process to guide policy learning more effectively. Our experimental results demonstrate the effectiveness of incorporating human-system interaction.
An End-to-End Multi-Robot Framework for Weed Control in Agricultural Fields

Weed management is one of the major concerns for crop production in agricultural fields. In this work, we developed a novel multi-robot framework to control weeds in an agricultural field using two groups of mobile robots and a base station. Our framework consists of three modules: (i) the coverage module will take the field boundary as input and generate an optimal coverage path for a group of survey robots; (ii) the weed detection module will collect images of field locations along the coverage path, analyze the collected images, and return the weed locations; and (iii) the planning module will compute an optimal path and send commands to a group of sprayer robots to selectively spray herbicides at the weed locations. All three modules will run on the base station in a step-by-step manner.
Enviral: Fuzzing the Environment for Evasive Malware Analysis

Analyzing malicious behavior is vital to effectively safeguard computer systems against malware. However, contemporary malware frequently contains evasive behavior, which allows it to hide its malicious intent from analysis. More specifically, if the malware detects it is being executed in an analysis environment, it resorts to evasive routines that exhibit benign behavior. Manually deactivating evasive checks requires significant effort, and is therefore not a scalable technique with regards to the increasing amount of evasive malware. Unfortunately, the existing systems that automatically analyze evasive malware are impractical, computationally inefficient, or incomplete by design. In this paper, we introduce Enviral, an automatic evasive malware analysis framework that proposes a novel method to analyze evasive malware, combining the best elements of existing approaches. We achieve this by applying fuzzing techniques to repeatedly adapt the view of the execution environment, thereby iteratively defeating the evasive checks in the target application. We realize these adaptations by applying mutations to the outcomes of environment queries, which in turn leads to the exploration of multiple execution paths. Our experimental results demonstrate that Enviral can detect and overcome evasive behavior and thereby exposes previously hidden activity in malware. We evaluate our system against a similar framework, and conclude that Enviral can expose 39% more interesting hidden system call activity on average, and achieves productive explorations where previously unseen behavior is discovered in 67% more malware samples.
An Evaluation of DAOS for Simulation and Deep Learning HPC Workloads

Traditionally, distributed storage systems have relied upon the interfaces provided by OS kernels to interact with storage hardware. However, much research has shown that OSes impose serious overheads on every I/O operation, especially on high-performance storage and networking hardware (e.g., PMEM and 200GBe). Thus, distributed storage stacks are being re-designed to take advantage of this modern hardware by utilizing new hardware interfaces which bypass the kernel entirely. However, the impact of these optimizations have not been well-studied for real HPC workloads on real hardware. In this work, we provide a comprehensive evaluation of DAOS: a state-of-the-art distributed storage system which re-architects the storage stack from scratch for modern hardware. We compare DAOS against traditional storage stacks and demonstrate that by utilizing optimal interfaces to hardware, performance improvements of up to 6x can be observed in real scientific applications.
Light-Weight Synthesis of Security Logs for Evaluation of Anomaly Detection and Security Related Experiments

Recent decades saw the development of a plethora of approaches that aim to use artificial intelligence to detect anomalies and potential signs of compromise in a computer network. These approaches have commonly been trained and evaluated using only a small number of datasets, which were often criticised in literature. Developing new datasets for this purpose tends to be very resource consuming, as they usually rely on testbeds and network emulation. While this level of details is important for anomaly detection over network traffic, which inspects details of network packets, it is superfluous in cases when such algorithms work with logs of security controls, such as in SIEM systems and approaches for alert correlation. Moreover, evaluation over a testbed generated dataset may not be relevant for the target IT system. In this paper, we propose a light-weight method to enrich existing security control logs with carefully crafted synthetic records that would be produced in case of cyber attacks. This method does not require running a dedicated testbed or comparable specialized equipment. We prepare a set of attack records with emphasis on network scans, and perform experiments with real-world firewall logs and several common anomaly detection algorithms to demonstrate that the injected records are appropriately integrated into the original logs. In the end, we propose future experiments to properly validate the quality of the datasets produced using the proposed method.
Lethe: Secure Deletion by Addition

Modern data privacy regulations such as GDPR, CCPA, and CDPA stipulate that data pertaining to a user must be deleted without undue delay upon the user's request. Existing systems are not designed to comply with these regulations and can leave traces of deleted data for indeterminate periods of time, often as long as months. We developed Lethe to address these problems by providing fine-grained secure deletion on any system and any storage medium, provided that Lethe has access to a fixed, small amount of securely-deletable storage. Lethe achieves this using keyed hash forests (KHFs), extensions of keyed hash trees (KHTs), structured to serve as efficient representations of encryption key hierarchies. By using a KHF as a regulator for data access, Lethe provides its secure deletion not by removing the KHF, but by adding a new KHF that only grants access to still-valid data. Access to the previous KHF is lost, and the data it regulated securely deleted, through the secure deletion of the single key that protected the previous KHF.
Imaging Recommendations for Diagnosis, Staging, and Management of Cancer of the Thyroid, Parathyroid, and Salivary Glands

Abstract Thyroid cancer ranks as the leading endocrine malignancy in adults. The foundation for primary diagnosis of thyroid cancer is a high-resolution ultrasound (US) of the thyroid gland including US-guided fine-needle biopsy (FNB) of suspected thyroid nodules. Advanced cross-sectional imaging, including computed tomography (CT), magnetic resonance imaging, and positron emission tomography, can be useful in selected patients. The mainstay of treatment of thyroid cancer is surgery. It may be supplemented by radioactive iodine ablation/therapy in high-risk differentiated thyroid cancer. Radiology plays a crucial role in both diagnostic and posttreatment follow-up imaging. Primary hyperparathyroidism (PHPT) is the third most common endocrine disorder with single parathyroid adenoma being its most common cause. The radiologist's aim in parathyroid imaging is to provide the clinician with an illustrative picture of the neck, locating lesions with respect to landmarks. Imaging helps in the detection of solitary versus multiglandular disease, ectopic and supernumerary glands with precise localization. US, nuclear imaging, and four-dimensional CT are the most commonly used imaging modalities for the preoperative localization of the parathyroid disease. Salivary gland tumors account for approximately 0.5% of all neoplasms, the most common location being the parotid gland (70%). Imaging is crucial in salivary gland tumors by defining its location, detecting malignant features, assessing local extension and invasion, staging the tumors according to the tumor-node-metastasis classification, and assessing the feasibility of surgery.
Exploring Word Sense Distribution in Ukrainian with a Semantic Vector Space Model

The paper discusses a Semantic Vector Space Model targeted at revealing how Ukrainian word senses vary and relate to each other. One of the benefits of the proposed semantic model is that it considers second-order context of the words and, thus, has more potential to compare and distinguish word senses observed in a unique concordance line. Combined with visualization techniques, this model makes it possible for a lexicographer to explore the Ukrainian word senses distribution on a large-scale. The paper describes the first results of the research performed and the following steps of the initiative.
Grambank’s Typological Advances Support Computational Research on Diverse Languages

Of approximately 7,000 languages around the world, only a handful have abundant computational resources. Extending the reach of language technologies to diverse, less-resourced languages is important for tackling the challenges of digital equity and inclusion. Here we introduce the Grambank typological database as a resource to support such efforts. To date, work that uses typological data to extend computational research to less-resourced languages has relied on cross-linguistic morphosyntax datasets that are sparsely populated, use categorical coding that can be difficult to interpret, and introduce redundant information across features. Grambank presents similar information (e.g. word order, grammatical relation marking, constructions like interrogatives and negation), but is designed to avoid several disadvantages of legacy typological resources. Grambank’s 195 features encode basic information about morphology and syntax for 2,467 languages. 83% of these languages are annotated for at least 100 features. By implementing binary coding for most features and curating the dataset to avoid logical dependencies, Grambank presents information in a user-friendly format for computational applications. The scale, completeness, reliability, format, and documentation of Grambank make it a useful resource for linguistically-informed models, cross-lingual NLP, and research targeting less-resourced languages.
Quote Detection: A New Task and Dataset for NLP

Quotes are universally appealing. Humans recognize good quotes and save them for later reference. However, it may pose a challenge for machines. In this work, we build a new corpus of quotes and propose a new task, quote detection, as a type of span detection. We retrieve the quote set from Goodreads and collect the spans through a custom search on the Gutenberg Book Corpus. We measure unique vocabulary usage by a state-of-the-art language model and perform comparative statistical analysis against the Cornell Movie-Quotes Corpus. Furthermore, we run two types of baselines for quote detection: Conditional random field (CRF) and summarization with pointer-generator networks and Bidirectional and Auto-Regressive Transformers (BART). The results show that the neural sequence-to-sequence models perform substantially better than CRF. From the viewpoint of neural extractive summarization, quote detection seems easier than news summarization. Moreover, model fine-tuning on our corpus and the Cornell Movie-Quotes Corpus introduces incremental performance boosts.
Train Global, Tailor Local: Minimalist Multilingual Translation into Endangered Languages

In many humanitarian scenarios, translation into severely low resource languages often does not require a universal translation engine, but a dedicated text-specific translation engine. For example, healthcare records, hygienic procedures, government communication, emergency procedures and religious texts are all limited texts. While generic translation engines for all languages do not exist, translation of multilingually known limited texts into new, endangered languages may be possible and reduce human translation effort. We attempt to leverage translation resources from rich resource languages to efficiently produce best possible translation quality for well known texts, which is available in multiple languages, in a new, severely low resource language. We examine two approaches: 1. ) best selection of seed sentences to jump start translations in a new language in view of best generalization to the remainder of a larger targeted text(s), and 2. ) we adapt large general multilingual translation engines from many other languages to focus on a specific text in a new, unknown language. We find that adapting large pretrained multilingual models to the domain/text first and then to the severely low resource language works best. If we also select a best set of seed sentences, we can improve average chrF performance on new test languages from a baseline of 21.9 to 50.7, while reducing the number of seed sentences to only ∼1,000 in the new, unknown language.
Reporting of adverse events associated with spinal manipulation in randomised clinical trials: an updated systematic review

Objectives To describe if there has been a change in the reporting of adverse events associated with spinal manipulation in randomised clinical trials (RCTs) since 2016. Design A systematic literature review. Data sources Databases were searched from March 2016 to May 2022: MEDLINE (Ovid), Embase, CINAHL, ICL, PEDro and Cochrane Library. The following search terms and their derivatives were adapted for each platform: spinal manipulation; chiropractic; osteopathy; physiotherapy; naprapathy; medical manipulation and clinical trial. Methods Domains of interest (pertaining to adverse events) included: completeness and location of reporting; nomenclature and description; spinal location and practitioner delivering manipulation; methodological quality of the studies and details of the publishing journal. Frequencies and proportions of studies reporting on each of these domains were calculated. Univariable and multivariable logistic regression models were fitted to examine the effect of potential predictors on the likelihood of studies reporting on adverse events. Results There were 5399 records identified by the electronic searches, of which 154 (2.9%) were included in the analysis. Of these, 94 (61.0%) reported on adverse events with only 23.4% providing an explicit description of what constituted an adverse event. Reporting of adverse events in the abstract has increased (n=29, 30.9%) while reporting in the results section has decreased (n=83, 88.3%) over the past 6 years. Spinal manipulation was delivered to 7518 participants in the included studies. No serious adverse events were reported in any of these studies. Conclusions While the current level of reporting of adverse events associated with spinal manipulation in RCTs has increased since our 2016 publication on the same topic, the level remains low and inconsistent with established standards. As such, it is imperative for authors, journal editors and administrators of clinical trial registries to ensure there is more balanced reporting of both benefits and harms in RCTs involving spinal manipulation.
The Development of Technological Support Organizations as an Indicator of Management Efficiency

Abstract The article is devoted to analyzing the need for managers to spend money on developing technological support for organizations to achieve strategic goals. The impact of technologies in various sectors of organizations’ activity on the effectiveness of organizational systems is considered. The work examines the activity of Ukrainian profitable and non-profit organizations on the influence of the level of technologicalization of organizational processes on increasing the level of profit and the social effect of activity. The study considered the statistical data of the State Statistics Service of Ukraine for 2018, 2019, and 2021 according to the indicators Number of enterprises that have access to the internet and the number of employed who have access to the internet and Number of employed who have access to the internet. The given statistical data were quantitatively processed (summarized and compared). With the help of theoretical research methods (deduction and analysis) of the obtained results, it was found that the organization’s technological development helps to increase the organization’s profitability and the non-profit organization’s social effect. The practical value of the article lies in the visual representation of the dependence between the necessity of spending on the technological development of managers of profitable and non-profit organizations and the effectiveness of these organizations.
Sequence matching on holographicaly stored genetic strings

ACKNOWLEDGEMENTS I would like to thank Dr. Richard O. Gale for his encouragement and guidance and the idea for this topic, as well as for his patience with my erratic progress reports. I would also like to thank my wife, Jennifer, for her assistance editing and proof reading, and her constant support and encouragement. iv ABSTRACT In the field of computational biology one of the basic tasks is string matching. This provides the basis for both the sequencing and assembly of chromosomes but also the alignment of homologues strings in order to determine evolutionary and functional relationships. Over the last fifteen years the amount of data available has grown exponentially, but the ability to analyze this data has not kept pace. This paper examines the feasibility of leveraging the power of holographic storage and optical data processing to handle the data flood. The focus of the discussion is on algorithmic and data formatting issues.