Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrasound-guided musculoskeletal interventional procedures around the elbow, hand and wrist excluding carpal tunnel procedures

Abstract Ultrasound is a fast, reliable and radiation-free method for the assessment of a wide range of pathological conditions, as well as for the guidance of percutaneous interventional procedures around the elbow, hand and wrist. Intraarticular and periarticular interventional procedures can be easily performed under continuous ultrasound monitoring to ensure correct needle positioning and medication delivery to a specific target. The most common ultrasound-guided procedures performed around the elbow, wrist, and hand are described in this review, excluding carpal tunnel procedures. Specifically, elbow steroid injections are performed in patients with inflammatory disorders, while hyaluronic acid can be administered in case of osteoarthritis. Septic olecranon bursitis requires percutaneous drainage for diagnosis and appropriate treatment. Dry needling and injection of regenerative medications, such as blood derivatives, are among the treatment options for lateral epicondylosis. Steroid injections are performed to give symptom relief in patients with ulnar neuropathy at the elbow. Hand and wrist steroid injections are performed in case of osteoarthritis, subsequently followed by hyaluronic acid injections, and in inflammatory disorders. Wrist ganglia can be safely aspirated and injected with steroids under ultrasound guidance. De Quervain’s tenosynovitis and trigger finger are chronic tenosynovitides of the wrist and digits, respectively, which can be treated with steroid injections, subsequently followed by hyaluronic acid injections. In conclusion, proper knowledge of the musculoskeletal disorders around the elbow, hand and wrist, as well as US-guided treatment options and operator training, are prerequisites to achieve good outcomes.
Integrated “Generate, Make, and Test” for Formulated Products using Knowledge Graphs

In the multi-billion dollar formulated product industry, state of the art continues to rely heavily on experts during the “generate, make and test” steps of formulation design. We propose automation aids to each step with a knowledge graph of relevant information as the central artifact. The generate step usually focuses on coming up with new recipes for intended formulation. We propose to aid the experts who generally carry out this step manually by providing a recommendation system and a templating system on top of the knowledge graph. Using the former, the expert can create a recipe from scratch using historical formulations and related data. With the latter, the expert starts with a recipe template created by our system and substitutes the requisite constituents to form a recipe. In the current state of practice, the three steps mentioned above operate in a fragmented manner wherein observations from one step do not aid other steps in a streamlined manner. Instead of manually operated labs for the make and test steps, we assume automated or robotic labs and in-silico testing, respectively. Using two formulations, namely face cream and an exterior coating, we show how the knowledge graph may help integrate and streamline the communication between the generate, the make, and the test steps. Our initial exploration shows considerable promise.
Inhibitors and facilitators of corporate real estate dynamic alignment

Purpose This paper aims to identify the importance of individual variables in the corporate real estate (CRE) decision-making process. Design/methodology/approach Nine experts received a posed scenario of a changed business strategy requiring a CRE reduction in individual interviews. Based on their suggested response, a decision network was modelled for each expert using the causal network elicitation technique, incorporating the utilities for decision variables and importance weights for attributes and benefits. The decision model offers a graphical representation of decision-benefit links for the decisions CRE managers make in such a period of decline. Findings Perceived facilitators of CRE dynamic alignment were identified by calculating lift ratios on their perceived importance of the attributes they mentioned during the interviews as nodes in the network that link decisions to benefits. Facilitators included CRE metrics and workplace strategy, while capital expenditure and landlords inhibit alignment processes. The research provides more granular insight into the variables used in CRE decision-making and the factors that facilitate or inhibit the dynamic alignment process. Research limitations/implications The research set a specific scenario for the experts to consider. That could be regarded as small but there was clear evidence of saturation of expert knowledge. Additional face-to-face interviews with the experts may have generated further details on the thought processes of the experts. Practical implications The research provides more granular insight into the variables used in CRE decision-making and the factors that facilitate or inhibit the dynamic alignment process. Thereby providing CRE decision-makers with key elements for a decision model. Originality/value The research technique, causal network elicitation technique, uses semi-structured interviews to create decision networks, which is a technique that has not been widely applied to CRE research. The research provides a granular view of what are important inhibitors or facilitators of dynamic alignment of CRE to business strategy.
Detecting smartphone user habits using sequential pattern analysis

Recently, the study of smart phone user habits has become a highly focused topic due to the rapid growth of the smart phone market. Indeed, sequential pattern analysis methods were efficiently used for web-based user habit mining long time ago. However, by means of simulations, it has been observed that these methods might fail for smart phone-based user habit mining. In this paper, we propose a novel approach that leads to a considerably increased performance of the traditional sequential pattern analysis methods by reasonably cutting off each chronological sequence of user logs on a device into shorter ones, which represent the sequential user activities in various periods of a day.
Falling Into Place: Drop Assembly of Interlocking Puzzles

—This paper explores a system for assembling structures by dropping block components into place. During and after assembly, the blocks are held together by geometric interlock, so that fasteners or mortar are only needed to bind the ﬁnal block to one of its neighbors. Drop assembly is a promising strategy for assembly by swimming or ﬂying robots, as it may allow structures to be built without requiring close contact with the existing structure. The current paper explores a mathematical model of interlock, and presents a particular block design that allows interlock to be achieved using only gravity. Proof-of-concept demonstrations of the system are presented using a low-cost and relatively low-precision robot arm. The paper ﬁnally analyses some of the potential limitations of the approach, particularly including ﬂexing of the structure due to manufacturing tolerance limitations.
LA CONFIANCE DES CONSOMMATEURS EN LIGNE DANS L’E- COMMERCE ONLINE CONSUMER TRUST IN E-COMMERCE

With the growth of internet sales, new business opportunities have opened up in the field of social network services and e-commerce. It’s important for online sellers to understand the utility of online trust and its influence on consumers. In this article, we present an assessment of research on online consumer trust in e-commerce. Thus, as a first step, the specifics of online trust, and secondly, trust in the context of ecommerce and its determinants will be outlined. Finally, elements for reflection on its dynamics will be proposed.
IoT ready Eddy Current Testing Structural Health Monitor

Non-destructive testing is used to ensure and evaluate the quality and safety of a product, material or system while detecting faults and defects, with the advantage of not causing any damage. Eddy current testing is an electromagnetic and non-destructive method used to identify and assess surface breaking, or near-surface, defects on materials and structures, through detecting discontinuities in segments that conduct electricity. Structural Health Monitoring is one of the applications of non-destructive testing, being a set of systems that gives a continuous and periodic diagnostic of the structure and its parts during its lifetime. To achieve it, the method determines and analyzes all defects and flaws in the structure to be able to provide preventive support and avoid a collapse. To turn the structural health monitoring in real-time monitorization, a platform that supports all remote access to sensorial data and physical devices is being used, named Internet of Things. This work uses a designed electronic system that implements eddy current testing in a structural health monitoring system. The device operates in an autonomous and permanently-mounted and has the aim of reading multiple eddy current sensors, save the data collected and evaluate it. It also connects through a Bluetooth connection with a computer allowing real-time evaluation. The device is hardware ready for future communications using Narrowband-IoT Low-Power Wide Area Network type communication, and to report alarms previously deliberated directly to the cloud. Keywords— Non-Destructive Testing, Structural Health Monitoring, Eddy Current Testing and Internet of Things.
The stratigraphic record of continental breakup , offshore NW 1 Australia 2 3

15 Continental breakup involves a transition from rapid, fault-controlled syn-rift subsidence to 16 relatively slow, post-breakup subsidence induced lithospheric cooling. Yet the stratigraphic 17 record of many rifted margins contain syn-breakup unconformities, indicating episodes of 18 uplift and erosion interrupt this transition. This uplift has been linked to mantle upwelling, 19 depth-dependent extension, and/or isostatic rebound. Deciphering the breakup processes 20 recorded by these unconformities and their related rock record is difficult because associated 21 erosion commonly removes the strata that help constrain the onset and duration of uplift. We 22 examine three major breakup-related unconformities and intervening rock record in the 23 Lower Cretaceous succession of the Gascoyne and Cuvier margins, offshore NW Australia, 24 using seismic reflection and borehole data. These data show the breakup unconformities are 25 disconformable (non-erosive) in places and angular (erosive) in others. Our recalibration of 26 palynomorph ages from rocks underlying and overlying the unconformities shows: (i) the 27 lowermost unconformity developed between 134.98–133.74 Ma (Intra-Valanginian), 28 probably during the localisation of magma intrusion within continental crust and consequent 29 formation of continent-ocean transition zones (COTZ); (2) the middle unconformity formed 30 between ~134–133 Ma (Top Valanginian), possibly coincident with breakup of continental 31 crust and generation of new magmatic (but not oceanic) crust within the COTZs; and (iii) the 32 uppermost unconformity likely developed between ~132.5–131 Ma (i.e. Intra-Hauterivian), 33 coincident with full breakup of continental lithosphere and the onset of seafloor spreading. 34 During unconformity formation, uplift was focused along the continental rift flanks, likely 35 reflecting landward flow of lower crustal and/or lithospheric mantle from beneath areas of 36 localised extension towards the continent (i.e. depth-dependent extension). Our work 37 supports the growing consensus that the ‘breakup unconformity’ is not always a single 38 stratigraphic surface marking the onset of seafloor spreading; multiple unconformities may 39 form and reflect a complex history of uplift and subsidence during the development of 40 continent-ocean transition. 41
Improving Performance of ABAC Security Policies Validation using a Novel Clustering Approach

Cloud computing offers several services, such as storage, software, networking, and other computing services. Cloud storage is a boon for big data and big data owners. Although big data owners can easily avail cloud storage without spending much on infrastructure and software to manage their data, security is a big issue, and protecting the outsourced big data is challenging and ongoing research. Cloud service providers use the attribute-based access control model to detect malicious intruders and address the security requirements of today’s new computing technologies. Anomalies in security policies are removed to improve the efficiency of the access control model. This paper implements a novel clustering approach to cluster security policies. Our proposed approach uses a rule-specific cluster merging technique that compares the rule with the clusters where the probability of similarity is high. Hence this technique reduces the cost, time, and complexity of clustering. Rather than verifying all rules, detecting and removing anomalies in every cluster of rules improve the performance of the intrusion detection system. Our novel clustering approach is useful for the researchers and practitioners in the ABAC policy validation. Keywords—Anomalies; attribute-based access control model; big data; cloud storage; clustering; intrusion detection system; security policy
Representing Videos as Discriminative Sub-graphs for Action Recognition*

Human actions are typically of combinatorial structures or patterns, i.e., subjects, objects, plus spatio-temporal interactions in between. Discovering such structures is therefore a rewarding way to reason about the dynamics of interactions and recognize the actions. In this paper, we introduce a new design of sub-graphs to represent and encode the discriminative patterns of each action in the videos. Specifically, we present MUlti-scale Sub-graph LEarning (MUSLE) framework that novelly builds space-time graphs and clusters the graphs into compact sub-graphs on each scale with respect to the number of nodes. Technically, MUSLE produces 3D bounding boxes, i.e., tubelets, in each video clip, as graph nodes and takes dense connectivity as graph edges between tubelets. For each action category, we execute online clustering to decompose the graph into sub-graphs on each scale through learning Gaussian Mixture Layer and select the discriminative sub-graphs as action prototypes for recognition. Extensive experiments are conducted on both Something-Something V1 & V2 and Kinetics-400 datasets, and superior results are reported when comparing to state-of-the-art methods. More remarkably, our MUSLE achieves to-date the best reported accuracy of 65.0% on Something-Something V2 validation set.
Development of a smart node IoT for agriculture appliances

Nowadays, with the growth of population and climate changes, it is necessary to create more eﬃcient ways of farming. The use of information systems can reduce the waste in the agriculture process such water waste and the excessive use of fertilizers that stresses water resources and contribute to the erosion of soils. With new advancements in microelectronics and communication protocols, it is now possible to create ultra low power systems able to get data from sensors and transmit it to the Internet. New Low-Power Wide-Area Network protocols such as LoRaWAN and Sigfox allow each node to have a range of several kilometres. In addition, the use of low power regulators, with low quiescent currents, and a real time clock to generate interruptions, allow the system to achieve long periods of operation using a single battery charge. In this work an embedded system with a ultra low power sleep mode is proposed. Considering that physical variables in a farm present slow variations an embedded system used for precision agriculture can spend most of its time in sleep state. The ultra low power is achieved by disabling all the modules except the real time clock. In addition, the use of a DC-DC converter to power demanding modules allows optimal eﬃciency. The system was designed to be compliant with sensors that are used in precision agriculture.
The reduction in homeownership rates for the young By

Homeownership rates for younger households have been decreasing over time in the United States, the United Kingdom, and several major euro area economies such as Italy and Spain. Understanding the reasons behind these developments is key to study future developments of housing markets and housing prices, the evolution of the income and wealth distributions, and the design of policies. In this policy note I show that increases in labour income inequality and uncertainty are key drivers of the reduction in homeownership rates. Confronted with high house prices and a risky income, many households cannot or do not want to make a big, illiquid, and risky investment like buying a house. However, this also implies that they accumulate less wealth, and might be less able to insure themselves against future shocks. SUERF Policy Briefs No 118, June 2021
Dynamic Programming for Computing a Stackelberg Equilibrium in Finite Sequential Games with Partial Imperfect Information

There are two types of agents in Stackelberg games called leader and follower, which makes them asymmetrical. The leader has to commit to a strategy before the game begins. The follower observes the leader’s strategy before he commits to his own. This work aims to re-implement the existing algorithm for finding Stackelberg equilibria in a programming language Julia, and extend it for computing Stackelberg equilibria in games with partially imperfect information.
Towards Accurate Visual and Natural Language-Based Vehicle Retrieval Systems

In this work, we consider two tracks of the 2021 NVIDIA AI City Challenge, the City-Scale Multi-Camera Vehicle Re-identification and Natural language-based Vehicle Retrieval. For the vehicle re-identification task, we employ the state-of-art Excited Vehicle Re-Identification deep representation learning model coupled with best training practices and domain adaptation techniques to obtain robust embeddings. We further refine the re-identification results through a series of post-processing steps to remove camera and vehicle orientation bias that is inherent in the task of re-identification. We also take advantage of multiple observations of a vehicle using track-level information and finally obtain fine-grained retrieval results. For the task of Natural language-based vehicle retrieval we leverage the recently proposed Contrastive Language-Image Pre-training model and propose a simple yet effective text-based vehicle retrieval system. We compare our performance against the top submissions to the challenge and our systems are ranked 8th in the public leaderboard for both tracks.
Database Private Security Jurisprudence: A Case Study using Oracle

Oracle is one of the largest vendors and the best DBMS solution of Object Relational DBMS in the IT world. Oracle Database is one of the three market-leading database technologies, along with Microsoft SQL Server's Database and IBM's DB2. Hence in this paper, we have tried to answer the million-dollar question “What is user’s responsibility to harden the oracle database for its security? ” This paper gives practical guidelines for hardening the oracle database, so that attacker will be prevented to get access into the database. The practical lookout for protecting TNS, Accessing Remote Server and Prevention, Accessing Files on Remote Server, Fetching Environment Variables, Privileges and Authorizations, Access Control, writing security policy, Database Encryption, Oracle Data Mask, Standard built in Auditing and Fine Grained Auditing (FGA) is illustrated with SQL syntax and executed with suitable real life examples and its output is tested and verified. This structured method acts as Data Invictus wall for the attacker and protect user’s database.
Uncertainty-Aware Camera Pose Estimation from Points and Lines

Perspective-n-Point-and-Line (PnPL) algorithms aim at fast, accurate, and robust camera localization with respect to a 3D model from 2D-3D feature correspondences, being a major part of modern robotic and AR/VR systems. Current point-based pose estimation methods use only 2D feature detection uncertainties, and the line-based methods do not take uncertainties into account. In our setup, both 3D co-ordinates and 2D projections of the features are considered uncertain. We propose PnP(L) solvers based on EPnP [20] and DLS [14] for the uncertainty-aware pose estimation. We also modify motion-only bundle adjustment to take 3D uncertainties into account. We perform exhaustive synthetic and real experiments on two different visual odometry datasets. The new PnP(L) methods outperform the state-of-the-art on real data in isolation, showing an increase in mean translation accuracy by 18% on a representative subset of KITTI, while the new uncertain refinement improves pose accuracy for most of the solvers, e.g. decreasing mean translation error for the EPnP by 16% compared to the standard refinement on the same dataset. The code is available at https://alexandervakhitov.github. io/uncertain-pnp/.
Active Meta-Learning

This thesis outlines the methods used in machine learning to generate models which are effective on a variety of tasks. We begin with a quick overview of the field of machine learning, covering the topics necessary to understand more complex learning algorithms. We then discuss the field of transfer learning and the common strategies used to train models so that they can function in different domains and solve different tasks. We then continue with current research in the field of meta-learning, which aims to find optimal solutions to the problem of learning how to learn. We introduce modelagnostic meta-learning, or MAML, as an algorithm which addresses the difficulty of few-shot learning on a variety of tasks. We then consider PLATIPUS as a tool for reasoning about model uncertainty in meta-learned models. Using this uncertainty, we explore active learning as a means of selecting the optimal examples to train our models. Using PLATIPUS and active learning, we seek to address the problem of exploring the space of chemical reactions efficiently.
Timed Up and Go test: Reference data for Saudi preschool children.

Objective:To find reference values for the Timed Up and Go (TUG) test on typically developed Saudi preschool children aged from 3 to 5 years, and to determine whether differences in test scores are based on age or gender. Material and Methods: This pilot exploratory study involved determining TUG values of Saudi preschool-aged children with typical development. Differences in the mean and standard deviation of TUG values for each age group, gender, height, and Body Mass Index (BMI) were tested. Results: During the study period, 61 typically developed Saudi preschool children aged 4.37 ± 1.10 were tested, with 55.7% of the sample being male. TUG values ranged from 4.74 to 5.20 seconds across all age groups, with time taken tending to decrease for older children in the study group. No significant differences between males and females in TUG timing (P=0.81) were observed. Conclusion: This study provides reference data for the TUG test in typically developed Saudi preschoolers, which may benefit clinicians in assessing children with developmental delays and comparing them to age-matched norms.