Miracle-dz/UniFAR-FTUs huggingface dataset:
A Mult-Task System for Detecting and Classifying Malware Signatures Using Random Forest Classifier

The rapid increase in the use of information technology has made cyber-attacks a major concern in the use of internet by users globally. These attacks are carried out in different forms, some are carried out as phishing, man in the middle, malicious applications and so on. In this study we will focus on malware attack. Malicious applications have been a major challenge in the use of applications on windows operating system. These malicious attacks are being carried out in different forms. Some of these attacks are trojan, ransom, keylogger etc. The need to detect and classifier these malicious attacks in windows operating system is an important task. So therefore, this paper presents a smart system for detecting and classifying eight categories of malware attack on windows operating system using random forest classifier. The system starts by collecting signatures of malware attack on windows from Virus Share, Virus Sign and Github respiratory. The collected malware signatures went through the following stages of preprocessing (First stage, Second Stage, and Third Stage). The first stage has to do with creating a pandas. Dataframe using the malware signatures. The second stage has to with data cleaning and the third stage has to do with data transformation. The result of the Random Forest Classifier shows a promising performance in terms of accuracy, precision, f1-score, and recall. The result shows that the Random Forest Classifier has an accuracy of about 100% for each of the matrix evaluation. Keywords- Malware signatures, Random Forest Classifier, Windows operating System, Matrix Evaluation
A Closer Look at the Isomerization of 5-androstene-3,17-dione to 4-androstene-3,17-dione in Ketosteroid Isomerase

We report a comprehensive investigation of the 5-androstene-3,17-dione to 4-androstene-3,17-dione isomerization in Ketosteroid Isomerase, gas phase and aqueous solution, applying as tools the Unified Reaction Valley Approach (URVA) and the Local Vibrational Mode Analysis. Conformational changes of the steroid rings are monitored via Cremer-Pople puckering coordinates. URVA identifies simultaneous breakage of the C[Formula: see text]–H bond and O–H bond formation with the catalytic acid, leading to an intermediate with the acid positioned over ring [Formula: see text] as the major chemical events of the first reaction step. Via a barrier-less shift, a second intermediate is formed with the acid being positioned over ring [Formula: see text]. Then, according to URVA, breakage of the intermediate O–H bond, the formation of the new C[Formula: see text]–H bond accompanied by a double bond shift in rings [Formula: see text] and [Formula: see text] forms the major chemical events of the second reaction step, which is facilitated by favorable ring puckering. Reactions in protein and gas phase have comparable activation enthalpies, whereas the barrier in aqueous solution is higher, confirming that the major task of the enzyme pocket is to shield the migrating hydrogen atom and the catalyzing acid from interactions with solvent molecules diluting the catalytic power. We do not find exceptional H-bonding with Asp99 and Tyr14, excluding their catalytic activity. There is no strong hydrogen bonding in the TS, which could account for lowering the activation barrier. Our study provides a clear picture of the isomerization process, which will also inspire similar investigations of other important enzymatic reactions.
Parking Search in the Physical World: Calculating the Search Time by Leveraging Physical and Graph Theoretical Methods

Parking plays a central role in transport policies and has wide-ranging consequences: While the average time spent searching for parking exceeds dozens of hours per driver every year in many Western cities, the associated cruising traffic generates major externalities, by emitting pollutants and contributing to congestion. However, the laws governing the parking search time remain opaque in many regards, which hinders any general understanding of the problem and its determinants. Here, we frame the problem of parking search in a very generic, but mathematically compact formulation that puts the focus on the role of the street network and the unequal attractiveness of parking spaces. This problem is solved in two independent ways, valid in any street network and for a wide range of drivers’ behaviours. Numerically, this is done by means of a computationally efficient and versatile agent-based model. Analytically, we leverage the machinery of Statistical Physics and Graph Theory to derive a generic mean-field relation giving the parking search time as a function of the occupancy of parking spaces; an expression for the latter is obtained in the stationary regime. We show that these theoretical results are applicable in toy networks as well as in complex, realistic cases such as the large-scale street network of the city of Lyon, France. Taken as a whole, these findings clarify the parameters that directly control the search time and provide transport engineers with a quantitative grasp of the parking problem. Besides, they establish formal connections between the parking issue in realistic settings and physical problems. Funding: This work was supported by IDEXLYON (IDEXLYON 2020–2021); Institut Rhonalpin des Systèmes Complexes (IXXI) (Vulnerabilite). Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023. 1206 .
Computers and communication : technologies for the management of international agricultural information

BEGINNING WITH THE HISTORICAL basis of today’s scientific information explosion, this article examines the changing paradigm of information retrieval. The shift from the point of view of the codifiers of information to that of the end users is brought into focus. Four key technologies are examined: telecommunications, optical media, data conversion technologies, and expert systems. Each is described technically and evaluated for its importance and potential applications. The conclusions highlight the ways in which libraries and information resource centers can employ these new technologies to increase their ability to respond to users and to the scientific community as a whole.
Direct Value Learning: Reinforcement Learning and Anti-Imitation

The value function, at the core of the Bellmanian Reinforcement Learning framework, associates to each state the discounted expected cumulative reward which can be gathered after visiting this state. Given an (optimal) value function, an (optimal) policy is most simply derived by "greedification", heading in each time step toward the neighbor state with maximal value. Following the Bellman equations, the value function can be built by (approximate) dynamic programming, albeit facing severe scalability limitations in large state and action spaces. An alternative, inspired from the Energy-based learning framework (LeCun et al. 2006), is investigated in this paper, searching for a pseudo-value function such that it induces the same local order on the state space as a (nearly) optimal value function. By construction, the greedification of such a pseudo-value induces the same policy as the value function itself. The presented Direct Value Learning (DiVa) approach proceeds by directly learning the pseudo-value, taking some inspiration from the Inverse Reinforcement Learning (IRL) approach. In IRL, expert demonstrations are used to infer the reward function. Quite the contrary, DiVa uses bad demonstrations to infer the pseudo-value. Bad demonstrations are notoriously easier to generate than expert ones; typically, applying a random policy on a good initial state (e.g., a bicycle in equilibrium) will on average lead to visit states with decreasing values (the bicycle ultimately falls down). DiVa thus uses bad demonstrations, generated from weak prior knowledge, to learn a pseudo-value along a standard learning-to-rank approach. The derived pseudo-value directly induces a policy in the model-based RL framework, when the transition function is known. In the model-free RL setting, the state pseudo-value is exploited using off-policy learning, to infer a state-action pseudo-value and induce a policy. The proposed DiVa approach and the use of bad demonstrations to achieve direct value learning is original to our best knowledge. The loss of optimality of the pseudo value-based policy is analyzed and it is shown that it is bounded under mild assumptions. Finally, the experimental validation of DiVa on the mountain car, the bicycle and the swing-up pendulum problems comparatively demonstrates the simplicity and the merits of the approach.
High-Resolution 400 GHz Submillimeter-Wave Quasi-Optical Radar Imaging System

A submillimeter-wave quasi-optical high-resolution imaging system, based on a frequency-modulated continuous wave radar sensor operating between 343GHz and 423GHz utilizing a bandwidth of 80GHz, is presented. The system setup in-tandem with the essential building blocks is described and the generation of high-resolution power- and elevation-profile images of various samples under test is presented. Experimental results show a lateral resolution limit in the order of wavelength.
Ultra-Low Power and Non-intrusive Wireless Monitoring for Smart Buildings

Wireless Sensor Networks (WSNs) offer a new solution for distributed monitoring, processing and communication. First of all, the stringent energy constraints to which sensing nodes are typically subjected. WSNs are often battery powered and placed where it is not possible to recharge or replace batteries. Energy can be harvested from the external environment but it is a limited resource that must be used efficiently. Energy efficiency is a key requirement for a credible WSNs design. From the power source's perspective, aggressive energy management techniques remain the most effective way to prolong the lifetime of a WSN. A new adaptive algorithm will be presented, which minimizes the consumption of wireless sensor nodes in sleep mode, when the power source has to be regulated using DC-DC converters. Another important aspect addressed is the time synchronisation in WSNs. WSNs are used for real-world applications where physical time plays an important role. An innovative low-overhead synchronisation approach will be presented, based on a Temperature Compensation Algorithm (TCA). The last aspect addressed is related to self-powered WSNs with Energy Harvesting (EH) solutions. Wireless sensor nodes with EH require some form of energy storage, which enables systems to continue operating during periods of insufficient environmental energy. However, the size of the energy storage strongly restricts the use of WSNs with EH in real-world applications. A new approach will be presented, which enables computation to be sustained during intermittent power supply. The discussed approaches will be used for real-world WSN applications. The first presented scenario is related to the experience gathered during an European Project (3ENCULT Project), regarding the design and implementation of an innovative network for monitoring heritage buildings. The second scenario is related to the experience with Telecom Italia, regarding the design of smart energy meters for monitoring the usage of household's appliances.
On the Investigation of Effective Factors on Electronic Structure Properties of Transition Metal Complexes: Robust Modeling Using GPR Approach

Materials discovery is usually done using high-throughput computational screening. The use of costly and complex direct density functional theory (DFT) simulation methods has been commonly used to determine subtle trends in spin-state ordering and inorganic bonding of inorganic materials and, in general, to predict the electronic structure properties of transition metal complexes. A Gaussian process regression (GPR) framework consisting of four kernel functions is introduced for spin-state splitting estimation through inorganic chemistry-appropriate empirical inputs. To this end, the present study reviewed an extensive range of data values from earlier works. According to statistical analysis, the GPR model showed very good performance. The coefficients of determination were calculated to be 0.986 for the exponential and Matern kernel functions, suggesting the highest predictive power of these methods. Moreover, the sensitivity of output to inputs was measured. Artificial intelligence (AI) helped accurately predict the target values through various input ranges.
Dual-User Haptic Teleoperation of Complementary Motions of a Redundant Wheeled Mobile Manipulator Considering Task Priority

With the increasing applications of wheeled mobile manipulators (WMMs), consisting of a mobile platform (MP) and a manipulator, in diverse fields, new challenges have arisen in achieving multiple tasks such as obstacle avoidance in a constrained environment during the end-effector (EE) operation. A WMM is usually redundant due to the combination of the MP and the manipulator, making multitask control possible via employing its null space. Dual-user/two-handed teleoperation of a WMM is desirable for tasks where it is important to simultaneously control the poses of both the MP and the EE. The existing teleoperation approaches for WMMs are mostly executed at the kinematic level, without considering the nonlinear rigid-body dynamics of the WMMs. In this article, a task-priority-based dual-user teleoperation framework for a WMM is implemented to perform tasks in a constrained environment. It can simultaneously manipulate the MP and the EE, the overground obstacles are avoided by telecontrolling the MP using the WMM’s null space. Any residual redundancy can be further employed for other tasks such as singularity avoidance. The stability of the entire teleoperation design is rigorously proved even with arbitrary time delays. Experiments with a dual-user teleoperation system, consisting of two local robots and an omnidirectional WMM, are conducted to verify the proposed approach’s feasibility and effectiveness.
Pattern-Based Prefetching with Adaptive Cache Management Inside of Solid-State Drives

This article proposes a pattern-based prefetching scheme with the support of adaptive cache management, at the flash translation layer of solid-state drives (SSDs). It works inside of SSDs and has features of OS dependence and uses transparency. Specifically, it first mines frequent block access patterns that reflect the correlation among the occurred I/O requests. Then, it compares the requests in the current time window with the identified patterns to direct prefetching data into the cache of SSDs. More importantly, to maximize the cache use efficiency, we build a mathematical model to adaptively determine the cache partition on the basis of I/O workload characteristics, for separately buffering the prefetched data and the written data. Experimental results show that our proposal can yield improvements on average read latency by 1.8%–36.5% without noticeably increasing the write latency, in contrast to conventional SSD-inside prefetching schemes.
Iridium- and Palladium-Based Catalysts in the Pharmaceutical Industry

Transition metal catalysts play a vital role in a wide range of industrial organic processes. The large-scale production of chemicals relying on catalyzed organic reactions represents a sustainable approach to supply society with end products for many daily life applications. Homogeneous (mainly for academic uses) and heterogeneous (crucial in industrial processes) metal-based catalysts have been developed for a plethora of organic reactions. The search for more sustainable strategies has led to the development of a countless number of metal-supported catalysts, nanosystems, and electrochemical and photochemical catalysts. In this work, although a vast number of transition metals can be used in this context, special attention is devoted to Ir- and Pd-based catalysts in the industrial manufacture of pharmaceutical drugs. Pd is by far the most widely used and versatile catalyst not only in academia but also in industry. Moreover, Ir-based complexes have emerged as attractive catalysts, particularly in asymmetric hydrogenation reactions. Ir- and Pd-based asymmetric reductions, aminations, cross-coupling reactions, and C–H activation are covered herein in the production of biologically active compounds or precursors; adaptation to bulk conditions is particularly highlighted.
Genomic Hatchery Introgression in Brown Trout (Salmo trutta L.): Development of a Diagnostic SNP Panel for Monitoring the Impacted Mediterranean Rivers

Brown trout (Salmo trutta L.) populations have been restocked during recent decades to satisfy angling demand and counterbalance the decline of wild populations. Millions of fertile brown trout individuals were released into Mediterranean and Atlantic rivers from hatcheries with homogeneous central European stocks. Consequently, many native gene pools have become endangered by introgressive hybridization with those hatchery stocks. Different genetic tools have been used to identify and evaluate the degree of introgression starting from pure native and restocking reference populations (e.g., LDH-C* locus, microsatellites). However, due to the high genetic structuring of brown trout, the definition of the "native pool" is hard to achieve. Additionally, although the LDH-C* locus is useful for determining the introgression degree at the population level, its consistency at individual level is far from being accurate, especially after several generations were since releases. Accordingly, the development of a more powerful and cost-effective tool is essential for an appropriate monitoring to recover brown-trout-native gene pools. Here, we used the 2b restriction site-associated DNA sequencing (2b-RADseq) and Stacks 2 with a reference genome to identify single-nucleotide polymorphisms (SNPs) diagnostic for hatchery-native fish discrimination in the Atlantic and Mediterranean drainages of the Iberian Peninsula. A final set of 20 SNPs was validated in a MassARRAY® System genotyping by contrasting data with the whole SNP dataset using samples with different degree of introgression from those previously recorded. Heterogeneous introgression impact was confirmed among and within river basins, and was the highest in the Mediterranean Slope. The SNP tool reported here should be assessed in a broader sample scenario in Southern Europe considering its potential for monitoring recovery plans.
Multiply-deserted areas: environmental racism and food, pharmacy, and greenspace access in the Urban South

ABSTRACT Unequal access to important resources like grocery stores, pharmacies, and parks in the urban built environment has been a significant social problem under study by social scientists. Drawing from work in urban and environmental justice studies that conceptualize racism as a structural factor that shapes environmental inequality, I assess spatial inequality in urban cities across the southern USA. Utilizing data from the U.S. Census, U.S. Department of Agriculture (USDA), the National Provider Identifier (NPI) registry, and county and state government websites, I examine the relevance of race and class to the existence of neighborhoods as single or multiple resource deserts, coined multiply-deserted areas (MDAs). Results indicate that predominantly Black neighborhoods are more than twice as likely to be resource deserts, even after adjusting for class. Additionally, predominantly Black neighborhoods are nearly three times as likely to have more intense, compounded resource scarcity than other neighborhoods. Moreover, results indicate a race and class interaction effect such that a predominantly Black neighborhood has increased odds of being a multiply-deserted area as median household income increases. The findings implicate yet another route through which racism shapes inequality and demonstrate a need to address racial differences in access to resources across socioeconomic status.
High Resolution Dynamical Analysis of Volatile Organic Compounds (VOC) Measurements During the BIO‐MAÏDO Field Campaign (Réunion Island, Indian Ocean)

This study presents a high‐resolution dynamical analysis of Volatile Organic Compound (VOC) concentrations measured from March 11 to April 4 2019 at Réunion Island during the BIO‐MAÏDO (Bio‐physicochemistry of tropical clouds at Maïdo: processes and impacts on secondary organic aerosols formation) campaign. We detail the dynamical and chemical processes that govern atmospheric VOC concentrations at two targeted sites of the western slope of Réunion Island: Petite France (PF), 950 m above sea level (a.s.l.) and Maïdo Observatory (MO), 2150 m a.s. l. A dynamical connection between PF and MO is found during four selected days: March 28 and 31, April 1 and 3. Trajectory calculations using the coupling of Meso‐Computing Advection‐interpolation of atmospheric parameters and Trajectory tool (CAT) (100 m horizontal resolution of Meso‐NH high‐resolution non‐hydrostatic model and the CAT trajectory model) Lagrangian transport model showed that air masses were dynamically linked between the two measurements sites for 19% of the time during the complete campaign. Trajectories from the Meso‐CAT model combined with the Corine Land Cover‐2018 register shows that backward‐trajectories are frequently located above biogenic area (mixed forest, 3%–46% of total number trajectory point) and cultures area (e.g., sugar cane plantation, 1%–17%). Regarding VOCs concentrations, air masses coming from downhill MO are associated with significant measured concentrations of isoprene, isoprene oxidation products and benzene. Averaged concentration daytime ratios of isoprene and isoprene oxidation products from PF to MO are 0.73 ± 1.01 and 0.26 ± 0.26 respectively illustrating a loss of these VOCs due to deposition, oxidation, or possibility dilution on clouds.