Miracle-dz/UniFAR-FTUs huggingface dataset:
Practitioner ’ s Section Challenges and opportunities in the sustainability of communication devices – an operator perspective exemplified by Deutsche Telekom AG

Considering that the communication devices (CD) segment only makes up just 7% of the yearly electrical and electronic equipment (EEE) waste, this means that there are no specific CD related sustainable measures, but just EEE generalized solutions. As such, this paper aims at exploring the critical factors for improving the sustainability as well as for the possible implementation of Circular Economy (CE) strategies for the CD segment along their entire supply chain. A wide range of factors pertaining to sustainability in the supply chain especially regarding the electrical and electronic equipment (EEE) market was gotten by means of an elaborate literature analysis. The factors are then compared to the present practices of the revenue leading telecommunications company in Europe, Deutsche Telekom AG (DTAG). From the literature review, we discovered a range of solutions a CD provider such as DTAG can use to improve the sustainability of its CD portfolio over the entire life cycle. These included ensuring that not only the communication company, but all the various suppliers follow sustainable practices such as Green Supply Chain Management or Closed Loop Supply Chain practices, and that they abide to the set of legislations and standardizations already in place for EEE such as REACH, RoHS, ISO 14004 and EPR. Good sustainable practice is also important, including the swapping of the present technologies for more sustainable ones and ensuring that SDGs are meet in the long run. Most of these methods and practices are already being implemented by the DTAG as part of their global strategy. In addition to an insight on the various concepts this industry should follow to ensure adequate implementation of CE, the study further provides suggestions as to strategies which could improve the sustainability of CDs.
Blog posts

CAN WE HAVE MASS-TOURISM WITHOUT DEGRADING THE ENVIRONMENT? 04 APRIL According to UNESCO, 30% of the world’s tourism is located in the Middle East and in 2030, this region is expected to welcome over 500 million tourists. Against this background, is it possible to have this expected mass-tourism without degrading the environment? This question was debated at the 6th Mediterranean Forest Week [1] in Lebanon. We all know that random and unorganized tourism has dangerous impacts on biodiversity and fragile natural areas. This type of negative tourism also degrades natural resources, pollutes sites and has physical influences such as deforestation. During the side-session on the preservation and valorization of Mediterranean forests from mass tourism impact, Jad Abou Arrage [2] gave a detailed report on the impacts of tourism on a Lebanese mountain trail. He noted that hikers play the role of guardians, since they’re the first ones to report pest outbreaks, forest fires, land deterioration and natural disasters. Now, more than ever before, we need to make a shift in the tourism industry, especially since mountains are trendy, popular and attract tourists. The advantages of tourism in terms of income, promotion, and preservation should be noted and highlighted. But
MOTION ANALYSIS OF 4 AXIS ROBOTIC ARM WITH INVERSE KINEMATICS FOR INDUSTRIAL APPLICATION

: This paper describes the deformation and stress induced for a particular payload of a robot. A 4 axis type industrial robotic arm has been considered for motion analysis. The modelling of articulated robotic hand has been created by 3D software SOLIDWORKS and the analysis have been performed by using ANSYS R15 software. In order to compensate the work the kinematic analysis also performed in a 2-D scale through computer simulation. This shows the 2D plot for the different combinations of joint angle within its work envelope. The forward and inverse kinematics analysis has been used to co-relate the desired location and orientation of end effector with joint variables. MATLAB simulation result has been compared with the theoretical analysis for various link positions and it has been found that both the results are in phase and have good agreement with each other.
On the Description Logic SHOIF Extended with Transitive Closure and (Restricted Forms of) Regular Role Expressions

We consider the description logic SHOIF extended with regular expressions on roles. Our main result is that satisﬁa-bility and ﬁnite satisﬁability are decidable in two fragments SHOIF 1 reg and SHOIF 2 reg , NE XP T IME -complete for the former and in 2NE XP T IME for the more expressive latter fragment. Both fragments are deﬁned by imposing restrictions on regular role expressions of the form α ∗ . SHOIF 1 reg encompasses the extension of SHOIF with transitive closure of roles and the modal logic of linear orders and successor, with converse. Consequently, these logics are also decidable and NE XP T IME -complete.
ENHANCING EVOLUTIONARY PROGRAMMING AND BYZANTINE FAULT TOLERANCE

Many cryptographers would agree that, had it no longer been for the look aside buffer, the deployment of kernels may in no way have took place. In truth, few electric engineers might disagree with the emulation of randomized algorithms. We pass over these algorithms for now. We concentrate our efforts on validating that RAID and cache coherence can synchronize to satisfy this venture
Factors of User Perceptions & Attitudes Influencing Self-service Interactive Kiosk usage: The Case of Lebanon’s Banking Sector

Banking operations are transforming from the traditional brick and mortar to more personalized kiosk experiences. Lebanese banks find significant advantages in the rollout of kiosk banking, form cost reduction to market penetration and image creation. On the other hand, consumer-banking behavior across different market profiles is shifting as people seek different means to interact with their bank, for faster processing and secure financial operations, away from the traditional branch setting. This paper looks into the consumer view of this new form of technology and identifies a few factors of interactive kiosk adoption. Findings contribute to our understanding of user perceptions and attitudes, as factors that influence self-service interactive kiosk banking in Lebanon.
From Markov chains to Markov decision processes

In this bachelor’s thesis we will try to build an understanding of Markov decision processes as an extension to ordinary discrete time Markov chains in an informal setting. The intended reader is assumed to have knowledge of basic probability theory. Throughout the text we use trivial examples of applying the theory, which we believe builds good intuition. As a last section we show how a basic Reinforcement learning algorithm, namely Q-learning, can be used to ﬁnd a solution of an MDP-problem.
Automatic Uncovering of Hidden Behaviors From Input Validation in Mobile Apps

Mobile applications (apps) have exploded in popularity, with billions of smartphone users using millions of apps available through markets such as the Google Play Store or the Apple App Store. While these apps have rich and useful functionality that is publicly exposed to end users, they also contain hidden behaviors that are not disclosed, such as backdoors and blacklists designed to block unwanted content. In this paper, we show that the input validation behavior—the way the mobile apps process and respond to data entered by users—can serve as a powerful tool for uncovering such hidden functionality. We therefore have developed a tool, InputScope, that automatically detects both the execution context of user input validation and also the content involved in the validation, to automatically expose the secrets of interest. We have tested InputScope with over 150,000 mobile apps, including popular apps from major app stores and preinstalled apps shipped with the phone, and found 12,706 mobile apps with backdoor secrets and 4,028 mobile apps containing blacklist secrets.
Integration of orientation and spatial frequency in a model of visual cortex

In the visual system complex scenes have to be integrated from simple local features into global and meaningful percepts. Contour integration, a basic process useful for figure-ground segregation and object recognition, is already well understood in terms of orientation alignment. However, there are other features playing a role in this process. Spatial frequency for example has a strong influence on contour visibility.
A 45nm CMOS Technology Exploring low power and Fast 4 bit Full Adder using XOR/XNOR Gates

Base paper Abstract: In this paper, novel circuits for XOR/XNOR and simultaneous XOR–XNOR functions are proposed. The proposed circuits are highly optimized in terms of the power consumption and delay, which are due to low output capacitance and low short-circuit power dissipation. We also propose six new hybrid 1-bit full-adder (FA) circuits based on the novel full-swing XOR–XNOR or XOR/XNOR gates. Each of the proposed circuits has its own merits in terms of speed, power consumption, power delay product (PDP), driving ability, and so on. To investigate the performance of the proposed designs, extensive HSPICE and Cadence Virtuoso simulations are performed. The simulation results, based on the 65-nm CMOS process technology model, indicate that the proposed designs have superior speed and power against other FA designs. A new transistor sizing method is presented to optimize the PDP of the circuits. In the proposed method, the numerical computation particle swarm optimization algorithm is used to achieve the desired value for optimum PDP with fewer iterations. The proposed circuits are investigated in terms of variations of the supply and threshold voltages, output capacitance, input noise immunity, and the size of transistors.
A Result Paper on Salient Object Detection using Edge Preservation and Multi-Scale Contextual Neural Network for identification

: Everywhere we go we take photographs, images, selfies etc for any social or private lifestyle of human being. That photos must be cleared, sharpened, filtered for this we have different techniques, application etc the one method is Salient Object Detection using Edge Preservation and Multi-Scale Contextual Neural Network . In this paper, we propose a novelty on edge preserving and multi-scale contextual neural networking for saliency. The full proposed framework architecture is mainly aims towards different address two ceiling of the existing CNN based methods. In recent years, s alient object detection, which aims to detect object that most attracts people’s attention throughout an image scenes along with whole foreground and background, has been widely exploited. The center of attraction and focus is to get maximum optimized result using the techniques like edge preserving, contextual networking, multi-scaling, detecting the most particular important object with feature extraction using SIFT algorithm. It has also been widely utilized for many computer vision tasks and digital image processing digital marketing, such as semantic segmentation, object tracking and image classification. These proposed framework achieves and optimizes the goals, targets both clear detection boundary of an input image and multi-scale contextual neural networks robustness simultaneously time being, and thus achieves an optimized performance results. Therefore, we get different and many more experimental results that’s sets higher benchmarks, result and applications using various datasets express that this designed proposed methods are to achieves through best and high leveled state-of-the-art performance optimally therefore our proposed system produce the best experimental result that demonstrates recognition, identification of rated object is higher than other method and also reach to high performance with optimal solution in an image .
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Due to the high costs associated with drilling deep wells with conventional rotary drilling, novel drilling technologies could be a key to implementing engineered geothermal technologies on a global scale. One such technology, flame jet thermal spallation drilling, uses high heat fluxes to rapidly heat the surface of rock, inducing thermal stresses that cause rock fragments, or “spalls,” to be ejected from the rock surface. To date, all field and laboratory tests investigating jet flame drilling have been performed in air-filled holes at near ambient pressures. In order to drill deep wells in practice with this technique, stable flames will need to be created in aqueous media over a range of pressures. As a first step, we have experimentally investigated a hydrogen-oxygen flame jet in water at a pressure of 100 bar (1500 psi) in a lab-scale apparatus that replicates conditions that would be found in a fluid filled borehole at a depth of about 1 km (3300 ft.). The heat flux from the jet flame to a brass block was determined by measuring the steady state temperature profile within the brass block. Estimated maximum heat fluxes on the order of 0.5 MW/m2 (44 BTU/ft2-s) were observed, which should be high enough to induce thermal rock spallation.
Measuring the Psychological Impacts of Prison-Based Dog Training Programs and In-Prison Outcomes for Inmates

As interest grows in programs that improve prison inmates’ behavior and psychosocial well-being, any such interventions must be rigorously examined and their underlying mechanisms for change must be understood. This pilot study examined the use of prison-based dog training programs across Washington State Department of Corrections facilities for their impacts on inmates’ infraction rates. The study also compared levels of empathy, self-efficacy, and anxiety between program participants and nonparticipants. Findings indicated that prison dog program participants’ infraction rates improved and that participants had lower levels of anxiety than nonparticipants.
Wage Premium of Communist Party Membership: Evidence from China

Social status and political connections could confer large economic benefits to an individual. Previous studies focused on China examine the relationship between Communist party membership and earnings and find a positive correlation. However, this correlation may be partly or totally spurious, thereby generating upwards-biased estimates of the importance of political party membership. Using data from three surveys spanning more than three decades, we estimate the causal effect of Chinese party membership on monthly earnings in in China. We find that, on average, membership in the Communist party of China increases monthly earnings and we find evidence that the wage premium has grown in recent years. We explore for potential mechanisms and we find suggestive evidence that improvements in one's social network, acquisition of job-related qualifications and improvement in one's social rank and life satisfaction likely play an important role. (JEL D31, J31, P2)
Template matching route classification

Abstract This paper details a route classification method for American football using a template matching scheme that is quick and does not require manual labeling. Pre-defined routes from a standard receiver route tree are aligned closely with game routes in order to determine the closest match. Based on a test game with manually labeled routes, the method achieves moderate success with an overall accuracy of 72% of the 232 routes labeled correctly.
Explainable Agents Through Social Cues: A Review

How to provide explanations has experienced a surge of interest in Human-Robot Interaction (HRI) over the last three years. In HRI this is known as explainability, expressivity, transparency or sometimes legibility, and the challenge for embodied agents is that they offer a unique array of modalities to communicate this information thanks to their embodiment. Responding to this surge of interest, we review the existing literature in explainability and organize it by (1) providing an overview of existing definitions, (2) showing how explainability is implemented and how it exploits different modalities, and (3) showing how the impact of explainability is measured. Additionally, we present a list of open questions and challenges that highlight areas that require further investigation by the community. This provides the interested scholar with an overview of the current state-of-the-art.
${\mathcal {H}_2}$ Performance of Series-Parallel Networks: A Compositional Perspective

We examine the <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_2$</tex-math></inline-formula> norm of matrix-weighted leader–follower consensus on series–parallel networks. By using an extension of electrical network theory on matrix-valued resistances, voltages, and currents, we show that the computation of the <inline-formula><tex-math notation="LaTeX">${\mathcal {H}_2}$</tex-math></inline-formula> norm can be performed efficiently by decomposing the network into atomic elements and composition rules. Finally, we examine the problem of efficiently adapting the matrix-valued edge weights to optimize the <inline-formula><tex-math notation="LaTeX">${\mathcal {H}_2}$</tex-math></inline-formula> norm of the network.
Hamiltonian structures for integrable hierarchies of Lagrangian PDEs

Many integrable hierarchies of differential equations allow a variational description, called a Lagrangian multiform or a pluri-Lagrangian structure. The fundamental object in this theory is not a Lagrange function but a differential $d$-form that is integrated over arbitrary $d$-dimensional submanifolds. All such action integrals must be stationary for a field to be a solution to the pluri-Lagrangian problem. In this paper we present a procedure to obtain Hamiltonian structures from the pluri-Lagrangian formulation of an integrable hierarchy of PDEs. As a prelude, we review a similar procedure for integrable ODEs. We show that exterior derivative of the Lagrangian $d$-form is closely related to the Poisson brackets between the corresponding Hamilton functions. In the ODE (Lagrangian 1-form) case we discuss as examples the Toda hierarchy and the Kepler problem. As examples for the PDE (Lagrangian 2-form) case we present the potential and Schwarzian Korteweg-de Vries hierarchies, as well as the Boussinesq hierarchy.
TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages

Abstract Confidently making progress on multilingual modeling requires challenging, trustworthy evaluations. We present TyDi QA—a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs. The languages of TyDi QA are diverse with regard to their typology—the set of linguistic features each language expresses—such that we expect models performing well on this set to generalize across a large number of the world’s languages. We present a quantitative analysis of the data quality and example-level qualitative linguistic analyses of observed language phenomena that would not be found in English-only corpora. To provide a realistic information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but don’t know the answer yet, and the data is collected directly in each language without the use of translation.
SIMBA: A Skyrmionic In-Memory Binary Neural Network Accelerator

Magnetic skyrmions are emerging as potential candidates for next-generation non-volatile memories. In this article, we propose an in-memory binary neural network (BNN) accelerator based on the non-volatile skyrmionic memory, which we call as Skyrmionic In-Memory BNN Accelerator (SIMBA). SIMBA consumes 26.7 mJ of energy and 2.7 ms of latency when running inference on a VGG-like BNN. In addition, SIMBA saves up to 97.07% in energy consumption with $3.73\times $ speedup compared with the other accelerators in the literature at similar inference accuracy. Furthermore, we demonstrate improvements in the performance of SIMBA by optimizing material parameters, such as saturation magnetization, anisotropic energy, and damping ratio. Finally, we show that the inference accuracy of BNNs is robust against the possible stochastic behavior of SIMBA (88.5%±1%).
Learning Predictive Representations for Deformable Objects Using Contrastive Estimation

Using visual model-based learning for deformable object manipulation is challenging due to difficulties in learning plannable visual representations along with complex dynamic models. In this work, we propose a new learning framework that jointly optimizes both the visual representation model and the dynamics model using contrastive estimation. Using simulation data collected by randomly perturbing deformable objects on a table, we learn latent dynamics models for these objects in an offline fashion. Then, using the learned models, we use simple model-based planning to solve challenging deformable object manipulation tasks such as spreading ropes and cloths. Experimentally, we show substantial improvements in performance over standard model-based learning techniques across our rope and cloth manipulation suite. Finally, we transfer our visual manipulation policies trained on data purely collected in simulation to a real PR2 robot through domain randomization.
Privacy-Cost Management in Smart Meters Using Deep Reinforcement Learning

Smart meters (SMs) play a pivotal rule in the smart grid by being able to report the electricity usage of consumers to the utility provider (UP) almost in real-time. However, this could leak sensitive information about the consumers to the UP or a third-party. Recent works have leveraged the availability of energy storage devices, e.g., a rechargeable battery (RB), in order to provide privacy to the consumers with minimal additional energy cost. In this paper, a privacy-cost management unit (PCMU) is proposed based on a model-free deep reinforcement learning algorithm, called deep double Q-learning (DDQL). Empirical results evaluated on actual SMs data are presented to compare DDQL with the state-of-the-art, i.e., classical Q-learning (CQL). Additionally, the performance of the method is investigated for two concrete cases where attackers aim to infer the actual demand load and the occupancy status of dwellings. Finally, an abstract information-theoretic characterization is provided.
Constraints on features in the inflationary potential from future Euclid data

With Planck cosmic microwave background observations, we established the spectral amplitude and tilt of the primordial power spectrum. Evidence of a red spectral tilt ($n_\mathrm{s}=0.96$) at $8\sigma$ provides strong support for the inflationary mechanism, especially the slow-roll of the effective scalar field in its nearly flat potential as the generator of scalar primordial perturbations. With the next generation of large-scale structure surveys, we expect to probe primordial physics beyond the overall shape and amplitude of the main, smooth and slowly-changing part of the inflaton potential. Using the specifications for the upcoming Euclid survey, we investigate to what extent we can constrain the inflation potential beyond its established slow-roll behaviour. We provide robust forecasts with Euclid and Planck mock data from nine fiducial power spectra that contain suppression and wiggles at different cosmological scales, using the Wiggly Whipped Inflation (WWI) framework to generate these features in the primordial spectrum. We include both Euclid cosmic shear and galaxy clustering, with a conservative cut-off for non-linear scales. Using Markov chain Monte Carlo simulations, we obtain an improvement in constraints in the WWI potential, as well an improvement for the background cosmology parameters. We find that apart from improving the constraints on the overall scale of the inflationary potential by 40-50 per cent, we can also identify oscillations in the primordial spectrum that are present within intermediate to small scales ($k\sim0.01-0.2\,\mathrm{Mpc^{-1}}$).