Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Wireless Controlling and Monitoring System for Robotic Hand Using Zigbee Protocol

Nowadays, the robotic arm is fast becoming the most popular robotic form used in the industry among others. Therefore, the issues regarding remote monitoring and controlling system are very important, which measures different environmental parameters at a distance away from the room and sets various condition for a desired environment through a wireless communication system operated from a central room. Thus, it is crucial to create a programming system which can control the movement of each part of the industrial robot in order to ensure it functions properly. EDARM ED-7100 is one of the simplest models of the robotic arm, which has a manual controller to control the movement of the robotic arm. In order to improve this control system, a new controller system was redesigned in this work by using Zigbee. It is a communication protocol for safety and economic data communication in an industrial field, where the wired communication is either expensive or difficult under physical and experimental conditions, such as the worker cannot recognize the error through the manufacturing process. Hence, this paper introduced a system that used microcontroller (AT89S52) with wireless devices (Zigbee) and sensors to control the robotic hand (EDARM ED-7100) and to monitor the information regarding the robot's parameter using WiFi technology. A mathematical model was derived through an empirical method to specify the robot's configuration changes. In this work, the ability of controlling system had increased, as well as hardware, while the necessities of other similar equipment for data communication were minimized. In addition, it presents the comparison of two controlling systems: using the Zigbee and without using it. Based from the experiment it can be safely concluded that the robotic arm's movement had followed a linear function.
Equity, Access, and Mathematics Coursetaking Within Purposefully Created Small High Schools

An extensive body of research describes the stark social and academic disparities that characterize large, comprehensive public high schools. Advocates have argued that smaller high schools, with their necessarily more constrained curricula, have the potential to improve student academic outcomes and their equitable distribution. Using propensity score matching within a multilevel modeling framework, we explore this assertion in New York City, which has been at the center of small school reform. We find a small, positive impact of attending a new, small high school on progress through the math curriculum for low-achieving students, but no effect for average-achieving students, and a negative impact for higher achieving students. Despite any positive findings, the typical small school student still failed to complete Algebra II/Trigonometry—the lowest level course deemed “college preparatory” by the district.
Input Transformation Based Zero-Knowledge Argument System for Arbitrary Circuits with High Eﬃciency

We introduce a new eﬃcient transparent interactive zero-knowledge argument system that is based on the new input transformation concept which we will introduce in this paper. The core of this concept is a mechanism that converts input parameters into a format that can be processed directly by the circuit so that the circuit output can be veriﬁed through direct computation of the circuit or business logic per se. In our protocol, we convert circuit inputs in Pedersen commitment form to linear polynomials in integer form so the veriﬁers can use standard integer operations to compute and verify the circuit output. This direct computation mechanism replaces the constraint system often found in popular zero-knowledge protocols, and eliminates the need of using a front-end encoder to translate NP relation R to some zero-knowledge friendly representation ˆ R (such as the R1CS constraint system) before the relation can be converted to a proof system, making our protocol easy to implement and much easier to use compared to protocols using a constraint system. Our benchmark result shows our approach can signiﬁcantly improve veriﬁer runtime performance by more than one order of magnitude over the state of the art while keeping the prover runtime and communication cost competitive with that of the state of the art.
RETROSPECTIVE: Self-optimizing Memory Controllers: A Reinforcement Learning Approach

I. THE PATH TO REINFORCEMENT LEARNING Around the time we began working on this idea, the computer architecture community was not particularly familiar with machine learning (ML), and solutions to both offline (i.e., designor compile-time) and online (i.e., run-time) problems were largely based on ad hoc heuristics. Some proposals did employ ML offline, including genetic algorithms for automatic derivation of branch predictor designs [O-14] or artificial neural networks (ANNs) for static branch prediction [O-7]. 1 But when it came to online heuristics, ML-based solutions were virtually inexistent, with a notable exception in Jiménez and Lin’s perceptron-based branch predictor design [O-19]. A perceptron is essentially a very simple ANN, and thus a form supervised learning, a.k. a. “learning with a teacher. ” Supervised learning matches the branch prediction problem very well, as the outcome of every branch can be used to train the perceptron on what its prediction should have been for that branch. Jiménez and Lin’s work was elegant and showed that the perception could outperform the best-known ad hoc heuristics for that problem. At Cornell University, Engin Ipek had taken Rich Caruana’s course in ML and had become enamored with the technology and its possibilities. With collaborators Sally McKee, Bronis de Supinski, and Martin Schulz, Engin and Rich had successfully devised an offline methodology to dramatically cut down the exploration of a microprocessor’s design space, using ANNs to pick what configurations to simulate [5]. José Martı́nez was impressed by this work; however, he was more excited about the potential of inserting an ML engine right into the hardware that could beat the best-known online ad hoc heuristics for key architectural mechanisms, as Jiménez and Lin had done for branch prediction. Engin and José had been working together on the Core Fusion project [4] and wrestled with a problem that looked nothing like branch prediction: instruction steering. Instruction steering is a problem typical of clustered microarchitectures in general, and Core Fusion in particular: given a group of consecutively fetched instructions, which ones should be sent for execution to what core? Unlike branch prediction, steering decisions don’t have an obvious “correct answer,” even a posteriori, so supervised learning did not seem like a good fit. It is, in fact, a fairly complex problem because 1) each decision needs to balance multiple constraints (e.g., try to steer producers and consumers to the same core but, at the same time, try to balance the load across cores), and 2) the optimality of a decision depends in part on future unknowns (e.g., will an instruction have many data dependencies with others yet to be fetched, and if so, will we come to regret
Enhancing Blockage Detection and Handover on 60 GHz Networks with P4 Programmable Data Planes

Millimeter-Wave (mmWave) technology for 5G net-works is promising due to its capability of supporting high data rates and low-latency communications. Despite such benefits, its narrow beams make it highly susceptible to blockages. To overcome blockages, solutions rely on handover connections from the current (blocked) access point to an alternative (non-blocked) access point. With state-of-the-art techniques, handover decisions may take hundreds of milliseconds to a few seconds at best, thus disrupting connections, dropping packets, and substantially reducing throughput. This paper proposes a P4 Programmable Data Plane (PDP) assisted system that reduces the handover time between access points when a blockage occurs. The proposed scheme relies on the capability of a PDP to track flows in real-time and measure network metrics with nanosecond resolution. The PDP computes the packet Inter-Arrival Time (IAT) for each flow that is routed through a mm Wave access point. As a blockage occurs, the scheme is able to detect sudden increases in the packet IAT and handover flows to a non-blocked access point. Evaluation results show that the proposed approach can detect blockages faster than current solutions, thus mitigating the corresponding throughput degradation and packet losses. On average, the proposed approach can detect blockages in 20 milliseconds.
3-(4,5-Cell Growth Inhibition Study of Murraya paniculata (L.) Jack Leaves on Carcinoma Cells

We evaluated the cell viability activity of methanolic extract of Murraya paniculata leaves (MEMPL) on hepatoma, gastric and colorectal carcinoma cell lines. In gastric (AGS) and both the hepatoma (HepG2 and Huh7) cells, the MEMPL significantly reduced the cell viability in a time and concentration dependent manner whereas in colorectal carcinoma (HCT116) the extract showed less significant reduction in comparison to hepatoma control cells. Morphological analysis revealed multiple markers of apoptosis in cancer cells, including chromatin condensation, nuclear fragmentation, formation of apoptotic bodies, and damaged DNA bands. Treated cells showed significant mitochondrial membrane potential shift in the mitochondrial transmembrane potential activity involving the release of cytochrome C into the cytosol. The cell cycle phase arrest was observed in the G0/G1 phase change in the MEMPL treated hepatoma cells. These findings indicate that MEMPL exhibited anticancer activity in cancer cell lines.
Perbandingan Algoritma Data Mining Naïve Bayes dan Bayes Network untuk Mengidentifikasi Penyakit Tiroid

In data mining, known Classification model that can be used to identify thyroid disease, is Naive Bayes and Bayes Network methods. In this study, a model is made by using both algorithm. the data used are taken from the data of Patients with thyroid by using the tools KNIME. The model then compared to determine the best algorithm in determination of disease identification. To measure the performance of the two algorithms, it used methods of testing of cross validation and split percentage. The measurement results using confusion matrix and ROC curves. By using the confusion matrix, Bayes Network has higher accuracy with 98,491% compared with the Naive Bayes with 91,803%. By Using the ROC curve, Bayes Network also has higher accuracy with the ROC curve - negative (0.9337), ROC - hypertiroid (0.9933) and ROC - hypotiroid (0.9977). while Naive Bayes with ROC curve - negative (0.8760), ROC - hypertiroid (0.9789) and ROC - hypotiroid (0.9018). The method which has very good classification is sequentially bayes network and naive bayes based on assessment AUC between 0.90-1.00. thus Bayes Network algorithm can provide solutions the problems of identifying thyroid disease.
Budget-aware Static Scheduling of Stochastic Workflows with DIET

—Previous work has introduced a Cloud platform model and budget-aware static algorithms to schedule stochastic workﬂows on such platforms. In this paper, we compare the performance of these algorithms obtained via simulation and via execution on an actual platform, Grid’5000. We focus on DIET, a widely used workﬂow engine, and detail the extensions that were implemented to conduct the comparison. We also detail additional code that we made available in order to automate and to ease the reproducibility of such experiments.
Discovering the energy poverty profile: 9 most distinctive groups in 11 countries of Central and Eastern Europe

We are driven by several concerns. First, the energy poor should not be treated as a homogenous group. Households differ in terms of demographic characteristics, condition of a dwelling, and tenure status. We recognize it is a combination of factors that make them unique and vulnerable. Second, the policy action is the most effective when targeted on the support of a particular social group with well-known characteristics. The costs of eliminating energy poverty will vary depending on the size, composition prototype, etc. It is crucial to ensure the just distribution of welfare, which is not possible if policymakers are unaware of whom to help. Third, we believe the energy poor in the CEE countries must have some common traits. This is because energy poverty in these countries is mostly hidden; low-income households tend to save on energy costs, and use cheap solid fuels to heat and cook.
Représentations pour la détection d’anomalies : Application aux données vibratoires des moteurs d’avions

Les mesures de vibrations sont l’une des donnees les plus pertinentes pour detecter des anomalies sur les moteurs. Les vibrations sont acquises sur banc d’essai en phase d’acceleration et de deceleration pour assurer la fiabilite du moteur a la sortie de la chaine de production. Ces donnees temporelles sont converties en spectrogrammes pour permettre aux experts d’effectuer une analyse visuelle de ces donnees et de detecter les differentes signatures atypiques. Les sources vibratoires correspondent a des raies sur les spectrogrammes. Dans cette these, nous avons mis en place un outil d’aide a la decision automatique pour analyser les spectrogrammes et detecter tout type de signatures atypiques, ces signatures ne proviennent pas necessairement d’un endommagement du moteur. En premier lieu, nous avons construit une base de donnees numerique de spectrogrammes annotes. Il est important de noter que les signatures inusuelles sont variables en forme, intensite et position et se trouvent dans un faible nombre de donnees. Par consequent, pour detecter ces signatures, nous caracterisons les comportements normaux des spectrogrammes, de maniere analogue aux methodes de detection de nouveautes, en representant les patchs des spectrogrammes sur des dictionnaires comme les curvelets et la Non-negative matrix factorization (NMF), ainsi qu’en estimant la distribution de chaque point du spectrogramme a partir de donnees normales dependamment ou non de leur voisinage. La detection des points atypiques est realisee par comparaison des donnees tests au modele de normalite estime sur des donnees d’apprentissage normales. La detection des points atypiques permet la detection des signatures inusuelles composees par ces points.
Parameterized algorithms for connectivity, separation, and modiﬁcation problems in graphs

Graph problems are ubiquitous in computer science. Graphs are one of the most natural models that represent the networks in real-life world and have numerous applications in different disciplines. Computer scientists are perusing faster algorithms to solve graph problems, both in practice and in theory. On the other side, there are many graph problems which are resistant to efficient algorithms. NP-completeness theory provides some clue on these problems [29, 74, 54]. If a problem is shown to be in the class of NP-complete problems, any efficient or polynomial-time algorithms for this problem imply that every NP-complete problem admits polynomial-time algorithms. In other words, there are probably no efficient algorithms for this problem. Although NP-hardness imply strong restrictions of algorithms for problems, people are still interested in how fast a problem can be solved and where the limitations of algorithms are. Exact algorithms for NPhard problems focus mostly on reducing the exponential part of the running time as much as possible [49]. Approximation algorithms for NP-hard (optimization) problems aim to find efficient algorithms, classically polynomial-time algorithms at the cost of the optimality of the solution. Approximation algorithms try to find an approximate solution such that the distance between the approximate solution and the optimal solution is within a provable guarantee [114, 115]. Recently parameterized algorithms for NP-hard problems have received a lot of attention, which focus on both the input instance and the parameter. More formally, a parameterized problem is a language L ⊆ Σ∗×N, where Σ is a fixed finite alphabet. An input instance of a parameterized problem is of the form (x, k) ∈ Σ∗×N and k is called the parameter. If a parameterized problem can be solved in time bounded by f(k)|x|, where |x| is the size of the input instance, k is the parameter, f : N → N is a computable function, and c is a universal constant, then we say this problem is fixed-parameter tractable (FPT). If a parameterized problem can be solved in time bounded by f(k)|x|, where |x| is the size of the input instance, k is the parameter and f : N→ N is a computable function, then we say this problem can be solved in XP time. A parameterized problem admits a kernel of size g(k) for some computable function g if there is a polynomial-time procedure that reduces an arbitrary instance I of this problem with parameter k to an equivalent instance I ′ with size and parameter value bounded by g(k). We refer to the following books for a deeper introduction to parameterized algorithms [41, 48, 30, 51]. In this thesis, we study a few graph problems, mostly concerning connectivity and separation in graphs.
Properties of Slicing Conditions for Charged Black Holes

We consider an earlier analysis by Baumgarte and de Oliveira (2022) of static Bona-Massó slices of stationary, nonrotating, uncharged black holes, represented by Schwarzschild spacetimes, and generalize that approach to Reissner-Nordström (RN) spacetimes, representing stationary, nonrotating black holes that carry a nonzero charge. This charge is parametrized by the charge-to-mass ratio λ ≡ Q/M , where M is the black-hole mass and the charge Q may represent electrical charge or act as a placeholder for extensions of general relativity. We use a height-function approach to construct time-independent, spherically symmetric slices that satisfy a so-called Bona-Massó slicing condition. We compute quantities such as critical points and profiles of geometric quantities for several different versions of the Bona-Massó slicing condition. In some cases we do this analytically, while in others we use numerical root-finding to solve quartic equations. We conclude that in the extremal limit as λ→ 1, all slices that we consider approach a unique slice that is independent of the chosen Bona-Massó condition. We then study dynamical, i.e. time-dependent, Bona-Massó slices by analytically predicting the qualitative behavior of the central lapse, i.e. the lapse at the black-hole puncture, for a particular slice that Alcubierre (1997) proposed to mitigate gauge shocks. These shock-avoiding slices are a viable alternative to the very common so-called 1 + log slices but exhibit different behavior in dynamical simulations. We use a perturbation of the radial coordinate at the location of the puncture to recover approximately harmonic late-time oscillations of the central lapse that Baumgarte and Hilditch (2022) observed in numerical simulations.
Studies in the Economics of Transportation.

THE CAPABILITIES AND EFFICIENT OPERATION OF SEVERAL TYPES OF TRANSPORTATION SYSTEMS ARE STUDIED. FOR HIGHWAY TRAFFIC THE MAIN EMPHASIS IS ON CONGESTION AND ITS CONSEQUENCES. QUEUING MODELS ARE USED TO DETERMINE FLUCTUATIONS IN WAITING TIME FOR TWO CASES, THE FLOW OF CARS THROUGH AN INTERSECTION AND THE PASSING OF SLOWER CARS BY FASTER CARS ON A TWO-LANE HIGHWAY. THE RESULTS OF THESE STUDIES ARE INCORPORATED INTO A NON-LINEAR MODEL OF HIGHWAY TRAFFIC BEHAVIOR ON A ROAD NETWORK. THE CAPACITIES ARE EXPRESSED BY THE RELATIONSHIP BETWEEN TRAFFIC FLOWS ON ALL ROUTES AND THE COSTS ENCOUNTERED ON EACH AS A RESULT OF THESE FLOWS. HIGHWAY TRAFFIC DEMANDS ARE THE RESULT OF A GREAT MANY INDIVIDUAL DECISIONS ABOUT DESTINATION, ROUTES, AND PREFERRED SPEEDS. THE ANALOGY BETWEEN HIGHWAY TRAFFIC EQUILIBRIUM AND THE MARKET RESPONSE MODELS USED BY ECONOMISTS IS GIVEN. IF THE INDIVIDUALLY INCURRED COST OF TRANSPORTATION IS REGARDED AS THE "PRICE" IN THE TRANSPORTATION "MARKET," THE ECONOMIST'S DEMAND CURVE BECOMES APPLICABLE. FOR A SINGLE ROAD IT GIVES THE FLOW OF TRAFFIC DEMANDED FOR ANY GIVEN COST ENCOUNTERED ON THAT ROAD. GENERALLY, THE HIGHER THE COST, THE SMALLER THE FLOW. CONSIDERING A ROAD NETWORK THE DEMAND FUNCTION GIVES THE FLOWS ON EACH ROUTE IN RESPONSE TO A SET OF TRANSPORTATION COSTS ALONG THESE ROUTES. EQUILIBRIUM EXISTS IF THE FLOWS ON ALL ROADS RESULTING FROM THESE COSTS ARE THE SAME AS THE FLOWS THAT PRODUCE THESE SAME COSTS. THIS CONCEPT IS APPLIED TO A STUDY OF TRAFFIC EQUILIBRIUM AND USE OF THE ANALYSIS IN THE PREDICTION OF TRAFFIC FLOWS IS DISCUSSED. THE ANALYSIS OF DEMAND AND OF EQUILIBRIUM RECOGNIZES THAT FREEDOM OF CHOICE IS PART OF THE SERVICE RENDERED BY THE ROAD NETWORK. TOLLS FROM THE USERS OF CONGESTED ROADS AT RATES THAT WOULD MEASURE THE COST TO OTHERS CAUSED BY THE AVERAGE ROAD USER WOULD ENCOURAGE BETTER USE OF THE HIGHWAY SYSTEM. THERE ARE OBSERVATIONS ON HOW CLOSELY MAXIMUM EFFICIENCY CAN BE APPROACHED BY PROPER CHOICE OF RATES ON PRESENT TOLL ROADS AND BY OTHER WAYS OF PENALIZING ADDITIONS TO TRAFFIC CONGESTION. BECAUSE OF THE NONLINEAR RELATIONSHIP BETWEEN USE AND COST, SUCH PRICING DOES NOT NECESSARILY PRODUCE REVENUES EQUAL TO THE TOTAL COST OF OPERATING AND FINANCING THE INDIVIDUAL FACILITY. SOME OBSERVATIONS ON THE QUESTION OF CRITERIA FOR EXTENSIONS OR IMPROVEMENTS OF THE NETWORK TO RELIEVE CONGESTION ARE GIVEN. /AUTHOR/
Adaptive Artificial Time Delay Control for Robotic Systems

Artificial time delay controller was conceptualised for nonlinear systems to reduce dependency on precise system modelling unlike the conventional adaptive and robust control strategies. In this approach unknown dynamics is compensated by using input and state measurements collected at immediate past time instant (i.e., artificially delayed). The advantage of this kind of approach lies in its simplicity and ease of implementation. However, the applications of artificial time delay controllers in robotics, which are also robust against unknown statedependent uncertainty, are still missing at large. This thesis presents the study of this control approach toward two important classes of robotic systems, namely a fully actuated bipedal walking robot and an underactuated quadrotor system. In the first work, we explore the idea of a unified control design instead of multiple controllers for different walking phases in adaptive bipedal walking control (i.e. control taking care of unknown robot parameters) while bypassing computing constraint forces, since they often lead to complex designs. State-of-the art attempts to design a single controller for all walking phases either ignored or oversimplified the state-dependent constraint forces which may lead to conservative performance or even instability. This work proposes an innovative artificial time delay based adaptive control method, which covers the entire bipedal walking phase and provides robustness against state-dependent unmodelled dynamics such as constraint forces and external impulsive forces arising during walking. Studies using a high fidelity simulator under various forms of disturbances show the effectiveness of the proposed design over the state of the art. The second work focuses on quadrotors employed for applications such as payload delivery, inspection and search-and-rescue. Such operations pose considerable control challenges, especially when various (a priori unbounded) state-dependent unknown dynamics arises from payload variations, aerodynamic effects and from reaction forces while operating close to the ground or in a confined space. The existing adaptive control strategies for quadrotors, unfortunately, are suitable to handle unknown state-dependent uncertainties. We address such unsolved control challenge in this work via a novel adaptive artificial time delay controller. The effectiveness of this controller is validated using experimental results.