Miracle-dz/UniFAR-FTUs huggingface dataset:
SoK: a taxonomy for anomaly detection in wireless sensor networks focused on node-level techniques

Wireless sensor networks play an important role in today's world: When measuring physical conditions, the quality of the sensor readings ultimately impacts the quality of various data analytical services. To maintain data correctness and quality, run-time measures such as anomaly detection techniques are gaining significance. In particular, the detection of threatening node anomalies caused by sensor node faults has become a crucial task. The detection of faulty sensor nodes is a non-trivial task because wireless sensor networks typically consist of low-cost embedded systems with strictly limited resources, especially regarding their energy budget. Thus, efficient and lightweight approaches that meet the requirements of sensor networks are required. In this SoK paper, we contribute with a novel taxonomy of anomaly detection approaches focused on wireless sensor networks and a meta-survey of related classification schemes. To the best of our knowledge, our taxonomy is a comprehensive super-set of all previously published taxonomies in this field. Based on the taxonomy, we present new insights in node-level anomaly detection approaches and the applicability of immune-inspired techniques, and we lay out related research challenges.
Education and Opportunity

After the Civil War, North Carolinians saw a need to expand opportunities for education, both at the K–12 level and, especially, in higher education. North Carolina State University was founded at this time, as was the first state college for women and the first of the state’s colleges for black students. In this chapter we’ll learn about who went to college in this new era, what they learned, and why.
Practical dependent type checking using twin types

People writing proofs or programs in dependently typed languages can omit some function arguments in order to decrease the code size and improve readability. Type checking such a program involves filling in each of these implicit arguments in a type-correct way. This is typically done using some form of unification. One approach to unification, taken by Agda, involves sometimes starting to unify terms before their types are known to be equal: in some cases one can make progress on unifying the terms, and then use information gleaned in this way to unify the types. This flexibility allows Agda to solve implicit arguments that are not found by several other systems. However, Agda's implementation is buggy: sometimes the solutions chosen are ill-typed, which can cause the type checker to crash. With Gundry and McBride's twin variable technique one can also start to unify terms before their types are known to be equal, and furthermore this technique is accompanied by correctness proofs. However, so far this technique has not been tested in practice as part of a full type checker. We have reformulated Gundry and McBride's technique without twin variables, using only twin types, with the aim of making the technique easier to implement in existing type checkers (in particular Agda). We have also introduced a type-agnostic syntactic equality rule that seems to be useful in practice. The reformulated technique has been tested in a type checker for a tiny variant of Agda. This type checker handles at least one example that Coq, Idris, Lean and Matita cannot handle, and does so in time and space comparable to that used by Agda. This suggests that the reformulated technique is usable in practice.
Clust-IT: clustering-based intrusion detection in IoT environments

Low-powered and resource-constrained devices are forming a greater part of our smart networks. For this reason, they have recently been the target of various cyber-attacks. However, these devices often cannot implement traditional intrusion detection systems (IDS), or they can not produce or store the audit trails needed for inspection. Therefore, it is often necessary to adapt existing IDS systems and malware detection approaches to cope with these constraints. We explore the application of unsupervised learning techniques, specifically clustering, to develop a novel IDS for networks composed of low-powered devices. We describe our solution, called Clust-IT (Clustering of IoT), to manage heterogeneous data collected from cooperative and distributed networks of connected devices and searching these data for indicators of compromise while remaining protocol agnostic. We outline a novel application of OPTICS to various available IoT datasets, composed of both packet and flow captures, to demonstrate the capabilities of the proposed techniques and evaluate their feasibility in developing an IoT IDS.
GeoPrune: Efficiently Matching Trips in Ride-sharing Through Geometric Properties

On-demand ride-sharing is rapidly growing. Matching trip requests to vehicles efficiently is critical for the service quality of ride-sharing. To match trip requests with vehicles, a prune-and-select scheme is commonly used. The pruning stage identifies feasible vehicles that can satisfy the trip constraints (e.g., trip time). The selection stage selects the optimal one(s) from the feasible vehicles. The pruning stage is crucial to lowering the complexity of the selection stage and to achieve efficient matching. We propose an effective and efficient pruning algorithm called GeoPrune. GeoPrune represents the time constraints of trip requests using circles and ellipses, which can be computed and updated efficiently. Experiments on real-world datasets show that GeoPrune reduces the number of vehicle candidates in nearly all cases by an order of magnitude and the update cost by two to three orders of magnitude compared to the state-of-the-art.
Bridge improvement cost estimation through both discrete and continuous features

Bridge improvement cost is inevitable as bridge being worn out. This work tries to employ new developed deep learning models to estimate bridge improvement cost, which helps ensuring bridge safety. To achieve the goal, five deep learning models are proposed with both discrete and continuous features, which are embedded and convoluted. The five models are verified through accuracy and loss. In the process, positive and negative features are mined by inspecting the performance of models after removing corresponding feature. The results show that the state code, route prefix, structure length, and structure kind are the top five positive features in affecting bridge improvement cost.
1RMA: Re-envisioning Remote Memory Access for Multi-tenant Datacenters

Remote Direct Memory Access (RDMA) plays a key role in supporting performance-hungry datacenter applications. However, existing RDMA technologies are ill-suited to multi-tenant datacenters, where applications run at massive scales, tenants require isolation and security, and the workload mix changes over time. Our experiences seeking to operationalize RDMA at scale indicate that these ills are rooted in standard RDMA's basic design attributes: connectionorientedness and complex policies baked into hardware. We describe a new approach to remote memory access -- One-Shot RMA (1RMA) -- suited to the constraints imposed by our multi-tenant datacenter settings. The 1RMA NIC is connection-free and fixed-function; it treats each RMA operation independently, assisting software by offering fine-grained delay measurements and fast failure notifications. 1RMA software provides operation pacing, congestion control, failure recovery, and inter-operation ordering, when needed. The NIC, deployed in our production datacenters, supports encryption at line rate (100Gbps and 100M ops/sec) with minimal performance/availability disruption for encryption key rotation.
Selectivity Estimation for Relation-Tree Joins

Estimating the join selectivity is a crucial problem in many aspects of query processing, such as query optimization and query refinement. Selectivity estimation has been extensively studied for the relational joins in SQL queries and structural joins in path-oriented queries. However, as leading databases have supported the multi-model data management on relational and tree-structured data together, a new problem has arisen: the existing estimation techniques mainly work for a single model but not for the heterogeneous situation due to the cross-model joins. A straightforward combination of existing estimators cannot provide a satisfactory estimation quality. This paper studies the problem of selectivity estimation for cross-model joins with relational and tree-structured data. Our estimator is based on the Kernel Density Estimation (KDE) model, which is a statistical approach using a data sample to approximate multivariate probability distribution. KDE has been successfully applied in relational databases to estimate the selectivity of range and join query. In this work, we propose an estimation method called location-value estimation (LVE) model based on KDE, which considers both value joins and structural joins in relational and tree-structured data. To boost the estimation efficiency in large data samples, we further propose the max-min approximation (MMA) and grid-based approximation (GBA) models to approximate the KDE contribution. Extensive experiments on four real and synthetic datasets demonstrate the effectiveness, efficiency, and scalability of our techniques.
SoK: engineering privacy-aware high-tech systems

The processing of personal data is becoming a key business factor, especially for high-tech system industries such as automotive and healthcare service providers. To protect such data, the European Union (EU) has introduced the General Data Protection Regulation (GDPR), with the aim to standardize and strengthen data protection policies across EU countries. The GDPR defines stringent requirements on the collection and processing of personal data and imposes severe fines and penalties on data controllers and processors for non-compliance. Although the GDPR is enforce since 2018, many public and private organizations are still struggling to fully comply with the regulation. A main reason for this is the lack of usable methodologies that can support developers in designing of GDPR-complaint high-tech systems. This paper examines the growing literature on methodologies for the design of privacy-aware systems, and identifies the main challenges to be addressed in order to facilitate developers in the design of such systems. In particular, we investigate to what extent existing methodologies (i) cover GDPR and privacy-by-design principles, (ii) address different levels of system design concerns, and (iii) have demonstrated their suitability for the purpose. Our literature study shows that the domain landscape appears to be heterogeneous and disconnected, as existing methodologies often focus only on subsets of the GDPR principles and/or on specific angles of system design. Based on our findings, we provide recommendations on the definition of comprehensive methodologies tailored to designing GDPR-compliant high-tech systems.
Rekayasa Media Pembelajaran Rantai Makanan Pada Hewan Menggunakan Augmented Reality

The food chain is the transfer of energy from the organism at a trophic level to the next trophic level in the event of eating and being eaten by a certain order. The food chain is conceptually structured in trophic levels. According to Kaufmann (2002), augmented reality technology can be utilized as an interesting learning media. One of them is to design and build educational applications by applying the Augmented Reality technology in mobile android-based application with the method SDLC (System Development Life Cycle). Making an application using Unity3D software and Vuforia Augmented Reality SDK in building, and Blender software in the manufacturing of three dimensional objects. In this application also included exercises in order to increase understanding of the user in understanding the food chain. Results of research applications that are tested to elementary school students MI AL-ISLAM Grobagan with students demonstrating and directly operate the application. Based on data from the questionnaire, the introduction of augmented reality applications instructional media food chain can attract and help students in studying the food chain with the test results 81% of students expressed a clear and attractive main menu and 87% of students said that the application is able to increase the desire to learn. Then, students completed the answer after running these applications with significant value. With incrase about 53% from 20% to 70%.
Turboboosting Visible Light Backscatter Communication

Visible light backscatter communication (VLBC) presents an emerging low power IoT connectivity solution with spatial reuse and interference immunity advantages over RF-based (backscatter) technologies. State-of-the-art VLBC systems employ COTS LCD shutter as optical modulator, whose slow response fundamentally throttles its data rate to sub-Kbps, and limits its deployment at scale for use cases where higher rate and/or low latency is a necessity. We design and implement RetroTurbo, a VLBC system dedicated for turboboosting data rate. At the heart of RetroTurbo design is a pair of novel modulation schemes, namely delayed superimposition modulation (DSM) and polarization-based QAM (PQAM), to push the rate limit by strategically coordinating the state of a liquid crystal modulator (LCM) pixel array in time and polarization domains. Specifically, DSM ensures we fully exploit the available SNR for high order modulation in the LCM-imposed nonlinear channel; PQAM is based on polarized light communication that creates a QAM design in polarization domain with flexible angular misalignment between two ends. A real-time near-optimal demodulation algorithm is designed to ensure system's robustness to heterogeneous signal distortion. Based on our prototyped system, RetroTurbo demonstrates 32x and 128x rate gain via experiments and emulation respectively in practical real-world indoor setting.
Experimentelle Ergebnisse über die Stabilität von Pockenviren unter Labor- und Umweltbedingungen

Summary Experimental results on the stability of poxviruses under laboratory and environmental conditions In a first series of investigations the resistance of five orthopox and one parapoxvirus to physical and chemical influences was evaluated under uniform test conditions. Virus suspended in cell culture medium with a small content of protein as well as cell-bound virus showed a largely uniform reaction. Differences in the stability with a practical importance could not be demonstrated. The principally moderate stability of cellfree poxvirus suspensions against inactivating agents was reconfirmed. In contrast, cell-bound virus showed considerably stronger resistance. Additionally, the stability of vaccinia virus, in a preparation similar to field virus, was examined under laboratory and environmental conditions. These experimental studies proved the empirical experience, that, under natural conditions, poxviruses are inactivated quite slowly. In a tissue-bound state and especially through drying, their infectivity is stabilized for weeks and months even under non convenient conditions. Zusammenfassung In einer ersten Reihe experimenteller Untersuchungen wurde die Widerstandsfahigkeit gegenuber verschiedenen physikalischen und chemischen Einflussen von 5 Orthopockenviren und einem Parapockenvirus, suspendiert in eiweisarmem Kulturmedium sowie gebunden in Kulturzellen, unter einheitlichen Versuchsbedingungen verglichen. Alle einbezogenen Virusarten verhielten sich sehr einheitlich, Unterschiede in der Tenazitat von praktischer Bedeutung liesen sich nicht erkennen. Dabei bestatigte sich die im Prinzip nur masige Stabilitat zellfrei suspendierter Pockenviren gegenuber Inaktivierungsmitteln. Durch den zellgebundenen Zustand jedoch erhohte sich ihre Widerstandsfahigkeit wesentlich. Anschliesend wurde nur das Vacciniavirus, in feldahnlichen Arten prapariert, auf seine Stabilitat unter Labor- und Umweltbedingungen gepruft. Experimentell liesen sich die uberwiegend empirischen Erfahrungen unter Beweis stellen, das Pockenviren unter naturlichen Bedingungen nur langsam inaktiviert werden und ihre Infektiositat insbesondere durch Trocknung und durch den meist gegebenen gewebegebundenen Zustand auch unter nicht forderlichen Umweltbedingungen wochenbis monatelang stabilisiert wird.
Data Science and Artificial Intelligence

The articles in this special section are dedicated to the application of artificial intelligence AI), machine learning (ML), and data analytics to address different problems of communication systems, presenting new trends, approaches, methods, frameworks, systems for efficiently managing and optimizing networks related operations. Even though AI/ML is considered a key technology for next generation networks, still many research challenges need to be solved before it could reach its full potential.
Prediction of the Kiwifruit Decline Syndrome in Diseased Orchards by Remote Sensing

Eight years after the first record in Italy, Kiwifruit Decline (KD), a destructive disease causing root rot, has already affected more than 25% of the area under kiwifruit cultivation in Italy. Diseased plants are characterised by severe decay of the fine roots and sudden wilting of the canopy, which is only visible after the season’s first period of heat (July–August). The swiftness of symptom appearance prevents correct timing and positioning for sampling of the disease, and is therefore a barrier to aetiological studies. The aim of this study is to test the feasibility of thermal and multispectral imaging for the detection of KD using an unsupervised classifier. Thus, RGB, multispectral and thermal data from a kiwifruit orchard, with healthy and diseased plants, were acquired simultaneously during two consecutive growing seasons (2017–2018) using an Unmanned Aerial Vehicle (UAV) platform. Data reduction was applied to the clipped areas of the multispectral and thermal data from the 2017 survey. Reduced data were then classified with two unsupervised algorithms, a K-means and a hierarchical method. The plant vigour (canopy size and presence/absence of wilted leaves) and the health shifts exhibited by asymptomatic plants between 2017 and 2018 were evaluated from RGB data via expert assessment and used as the ground truth for cluster interpretation. Multispectral data showed a high correlation with plant vigour, while temperature data demonstrated a good potential use in predicting health shifts, especially in highly vigorous plants that were asymptomatic in 2017 and became symptomatic in 2018. The accuracy of plant vigour assessment was above 73% when using multispectral data, while clustering of the temperature data allowed the prediction of disease outbreak one year in advance, with an accuracy of 71%. Based on our results, the unsupervised clustering of remote sensing data could be a reliable tool for the identification of sampling areas, and can greatly improve aetiological studies of this new disease in kiwifruit.
A Bio-inspired Quasi-resonant Compliant Backbone for Low Power Consumption Quadrupedal Locomotion

: Many quadrupeds are capable of highly power efﬁcient gaits thanks to their ﬂexible backbone. This is used during different stages of the gait in order to store and release elastic energy, also helping a smooth deceleration and a fast acceleration of the different parts of the body involved during running. In this work we present our current studies aimed to reproduce such phenomena for efﬁcient robot locomotion. In addition, we studied how to amplify such effect when the frequency of the oscillations is brought close to the natural resonant frequency of the compliant structure. We demonstrated that a ﬂexible artiﬁcial structure representing the backbone, muscle and tendons, driven to quasi-resonant oscillations is capable of dramatically reducing the power required to maintain oscillations. At the same time, these reach a bigger amplitude. Such effect will be used to design fast running and energy efﬁcient quadruped robots.
Big Picture on Privacy Enhancing Technologies in e-Health: A Holistic Personal Privacy Workflow

The collection and processing of personal data offers great opportunities for technological advances, but the accumulation of vast amounts of personal data also increases the risk of misuse for malicious intentions, especially in health care. Therefore, personal data are legally protected, e.g., by the European General Data Protection Regulation (GDPR), which states that individuals must be transparently informed and have the right to take control over the processing of their personal data. In real applications privacy policies are used to fulfill these requirements which can be negotiated via user interfaces. The literature proposes privacy languages as an electronic format for privacy policies while the users privacy preferences are represented by preference languages. However, this is only the beginning of the personal data life-cycle, which also includes the processing of personal data and its transfer to various stakeholders. In this work we define a personal privacy workflow, considering the negotiation of privacy policies, privacy-preserving processing and secondary use of personal data, in context of health care data processing to survey applicable Privacy Enhancing Technologies (PETs) to ensure the individuals’ privacy. Based on a broad literature review we identify open research questions for each step of the workflow.
Interactions With Histone H3 & Tools to Study Them

Histones are an integral part of chromatin and thereby influence its structure, dynamics, and functions. The effects of histone variants, posttranslational modifications, and binding proteins is therefore of great interest. From the moment that they are deposited on chromatin, nucleosomal histones undergo dynamic changes in function of the cell cycle, and as DNA is transcribed and replicated. In the process, histones are not only modified and bound by various proteins, but also shuffled, evicted, or replaced. Technologies and tools to study such dynamic events continue to evolve and better our understanding of chromatin and of histone proteins proper. Here, we provide an overview of H3.1 and H3.3 histone dynamics throughout the cell cycle, while highlighting some of the tools used to study their protein–protein interactions. We specifically discuss how histones are chaperoned, modified, and bound by various proteins at different stages of the cell cycle. Established and select emerging technologies that furthered (or have a high potential of furthering) our understanding of the dynamic histone–protein interactions are emphasized. This includes experimental tools to investigate spatiotemporal changes on chromatin, the role of histone chaperones, histone posttranslational modifications, and histone-binding effector proteins.