Miracle-dz/UniFAR-FTUs huggingface dataset:
Adjustable Arbiter Physical Unclonable Function with Flexible Response Distribution

This paper proposes an adjustable arbiter PUF with flexible response distribution, which expands the application areas of PUFs. In comparison with existing works, it reduces the test time of reliable CRPs by about 3.3×104X, reduces the hardware area of ECC by at least 90%, and reduces the over-testing cases of reliable CRPs by 57%.
Halal Products on Twitter: Data Extraction and Sentiment Analysis Using Stack of Deep Learning Algorithms

Twitter is a leading platform among social media networks. It allows microblogging of up to 140 characters for a single post. Owing to this characteristic, it is popular among users. People tweet about various topics from daily life events to major incidents. Given the influence of this social media platform, the analysis of Twitter contents has become a research area as it gives us useful insights on a topic. Hence, this paper will describe how Twitter data are extracted, and the sentiment of the tweets on a particular topic is calculated. This paper focusses on tweets of two halal products, i.e., halal tourism and halal cosmetics. Twitter data (over a 10-year span) were extracted using the Twitter search function, and an algorithm was used to filter the data. Then, an experiment was conducted to calculate and analyze the tweets’ sentiment using deep learning algorithms. In addition, convolutional neural networks (CNN), long short-term memory (LSTM), and recurrent neural networks (RNN) were utilized to improve the accuracy and construct prediction models. Among the results, it was found that the Word2vec feature extraction method combined with a stack of the CNN and LSTM algorithms achieved the highest accuracy of 93.78%.
A New Gas Flow Gyroscope MEMS For Harsh Environment Applications

Gas flow MEMS gyroscope is a replacement technology for classic gyroscope because of its lack of mechanical part. This work reports the creation of a gas flow gyroscope partly integrated and designed to resist shock. Photolithographic and Etching classical processes have been used. Electrical circuit integration has been performed to measure the new sensor capacity. Stability has been tested to insure no deviation. The dependency of temperature differential to rotational speed is linear in accordance with the state of the art. The variation of speed flow shows a decrease of sensibility with a lower speed as predicted. The lack of information about the flows form, place and speed around the detectors induces an incorrect calculation of the measurement range.
Effect of Haptic Feedback on the Perceived Size of a Virtual Object

We investigate how different types of haptic feedback to hand affect the perceived size of a virtual object. Multiple haptic interfaces are designed to deliver different types of haptic feedback to hand. In the preliminary experiment, we investigated the effect of cutaneous feedback on the perceived size of the virtual object. The experimental results indicate that the availability of cutaneous feedback resulted in an insignificant effect on the perceived size of the virtual object. On the other hand, the availability of cutaneous feedback led the participants to exert significantly smaller grip force than when there is only force feedback. In the main experiment, we provided haptic feedback to the dorsum of the hand to modulate the hand kinesthesia at the moment of contact with the virtual object. For the reference stimuli, skin-stretch or vibrotactile feedback was provided to a participant’s hand along with cutaneous and force feedback to the fingertips. Reference stimuli were rendered only with cutaneous and force feedback to the fingers. The experimental results indicate a significant effect of the type of haptic feedback to the dorsum of a hand. The skin-stretch feedback led the participants to feel virtual objects with a size of 40 mm, larger than without the feedback. The vibrotactile feedback resulted in the perceived size of virtual objects with a size of 20 or 40 mm, smaller than without the vibrotactile feedback.
SSL-Net: Point-Cloud Generation Network With Self-Supervised Learning

Inferring the three-dimensional structure of objects from monocular images has far-reaching applications in the field of 3D perception. In this paper, we propose a self-supervised network (SSL-Net) to generate 3D point clouds from a single RGB image, unlike the existing work which requires multiple views of the same object to recover the full 3D geometry. To provide the extra self-supervisory signal, the generated 3D model is simultaneously rendered into an image and compared with the input image. In addition, a pose estimation network is integrated into the 3D point cloud generation network to eliminate the pose ambiguity of the input image, and the estimated pose is also used for rendering the 2D image with the same pose as input image from 3D point clouds. The extensive experiments on both real and synthetic datasets show that our method not only qualitatively generates point clouds with more details but also quantitatively outperforms the state-of-the-art in accuracy.
A multi-scale approach for the mechanical modeling of pressure sensors consisting of carbon nanotubes and a polymer

A multi-scale approach is used to model a composite of carbon nanotubes and a polymer mechanically. This composite is applicable as a pressure sensor in a large-area sensor matrix which can be used to prevent bedridden people from developing decubitus ulcer. Starting bottom-up, the carbon nanotubes are modeled mechanically, replaced by equivalent cylinders and embedded in a polymer. Following that, homogenization techniques are applied to determine the mechanical properties of the composite. The homogenization results are evaluated using finite element simulations.
An overview and perspectives on bidirectional intelligence: Lmser duality, double IA harmony, and causal computation

Advances on bidirectional intelligence are overviewed along three threads, with extensions and new perspectives. The first thread is about bidirectional learning architecture, exploring five dualities that enable Lmser six cognitive functions and provide new perspectives on which a lot of extensions and particularlly flexible Lmser are proposed. Interestingly, either or two of these dualities actually takes an important role in recent models such as U-net, ResNet, and DenseNet. The second thread is about bidirectional learning principles unified by best yIng-yAng ( IA ) harmony in BYY system. After getting insights on deep bidirectional learning from a bird-viewing on existing typical learning principles from one or both of the inward and outward directions, maximum likelihood, variational principle, and several other learning principles are summarised as exemplars of the BYY learning, with new perspectives on advanced topics. The third thread further proceeds to deep bidirectional intelligence, driven by long term dynamics ( LTD ) for parameter learning and short term dynamics ( STD ) for image thinking and rational thinking in harmony. Image thinking deals with information flow of continuously valued arrays and especially image sequence, as if thinking was displayed in the real world, exemplified by the flow from inward encoding / cognition to outward reconstruction / transformation performed in Lmser learning and BYY learning. In contrast, rational thinking handles symbolic strings or discretely valued vectors, performing uncertainty reasoning and problem solving. In particular, a general thesis is proposed for bidirectional intelligence, featured by BYY intelligence potential theory ( BYY-IPT ) and nine essential dualities in architecture, fundamentals, and implementation, respectively. Then, problems of combinatorial solving and uncertainty reasoning are investigated from this BYY IPT perspective. First, variants and extensions are suggested for AlphaGoZero like searching tasks, such as traveling salesman problem ( TSP ) and attributed graph matching ( AGM ) that are turned into Go like problems with help of a feature enrichment technique. Second, reasoning activities are summarized under guidance of BYY IPT from the aspects of constraint satisfaction, uncertainty propagation, and path or tree searching. Particularly, causal potential theory is proposed for discovering causal direction, with two roads developed for its implementation.
Efficient FPGA Emulation of Quantum Fourier Transform

Quantum computation shows its great advantage on exponential speed-up for some complex algorithms. Compared to software simulation with sequential execution, hardware emulation based on Field Programmable Gate Array (FPGA) can help to realize the intrinsic quantum parallelism. Quantum Fourier Transform (QFT) is the core of several quantum algorithms like Shor’s algorithm. This paper proposes a recursive decomposition of QFT-n, which can be emulated with a pipeline architecture based on Intel Stratix IV FPGA device. Although QFT-8 is much more complex than QFT-5, experimental results show that our QFT-8 architecture can reduces 91% DSP blocks and 76% combinational ALUTs at the cost of 11x registers compared with QFT-5 in [4] on average.
Impact of Matrix Factorization and Regularization Hyperparameter on a Recommender System for Movies

Recommendation system is developed to match consumers with product to meet their variety of special needs and tastes in order to enhance user satisfaction and loyalty. The popularity of personalized recommendation system has been increased in recent years and applied in several areas include movies, songs, books, news, friend recommendations on social media, travel products, and other products in general. Collaborative Filtering methods are widely used in recommendation systems. The collaborative filtering method is divided into neighborhood-based and model-based. In this study, we are implementing matrix factorization which is part of model-based that learns latent factor for each user and item and uses them to make rating predictions. The method will be trained using stochastic gradient descent and optimization of regularization hyperparameter. In the end, neighborhood-based collaborative filtering and matrix factorization with different values of regularization hyperparameter will be compared. Our result shows that matrix factorization method is better than item-based collaborative filtering method and even better with tuning the regularization hyperparameter by achieving lowest RMSE score. In this study, the used functions are available from Graphlab and using Movielens 100k data set for building the recommendation systems.
Transitioning from Block-Based to Text-Based Programming Languages

Block-based programming environments are becoming increasingly popular as introductory tools for teaching programming to children. These environments differ significantly from their text-based counterparts and have proven to be successful in motivating children and making it easy to start programming. However, several studies have recognised drawbacks of these tools which could potentially be detrimental when students transition to text-based languages. In this paper, the distinguishing features between block-based environments and text-based languages are discussed and the effectiveness of these features are explained. In considering the transition to text-based languages, this paper identifies two significant weaknesses to block-based programming and discusses suggestions for improvement.
Physical Orienteering Problem for Unmanned Aerial Vehicle Data Collection Planning in Environments With Obstacles

This letter concerns a variant of the orienteering problem (OP) that arises from multi-goal data collection scenarios where a robot with a limited travel budget is requested to visit given target locations in an environment with obstacles. We call the introduced OP variant the physical OP (POP). The POP sets out to determine a feasible, collision-free, path that maximizes collected reward from a subset of the target locations and does not exceed the given travel budget. The problem combines motion planning and combinatorial optimization to visit multiple target locations. The proposed solution to the POP is based on the variable neighborhood search (VNS) method combined with the asymptotically optimal sampling-based probabilistic roadmap (PRM*) method. The VNS-PRM* uses initial low-dense roadmap that is continuously expanded during the VNS-based POP optimization to shorten paths of the promising solutions and, thus, allows maximizing the sum of the collected rewards. The computational results support the feasibility of the proposed approach by a fast determination of high-quality solutions. Moreover, an experimental verification demonstrates the applicability of the proposed VNS-PRM* approach for data collection planning for an unmanned aerial vehicle in an urban-like environment with obstacles.
Distributed Robust Estimation with Dynamics Uncertainties and Random Communication Topologies

This paper discusses the design of distributed structures and gains for estimation of sensor network with nonlinear uncertain dynamics. Each sensor estimates the system state not only on its own but also on its neighbors’ information. Besides, these sensors communicate with their neighbors over a random communication topology, i.e., their measurements are transmitted randomly at every step. In addition, both the system uncertainties and its linearization errors are taken into consideration in this paper. It is strictly proved that an optimized upper bound of the estimation error covariance with respect to the proposed filtering method can be achieved.
"Why Am I Even Doing This?": The Experiences of Female Students in CS from an Insider Perspective

This project interprets, with a narrative approach, female students in CS and their individual experiences from their academic environment and motivation in their studies. Five students were interviewed and asked about their relationship to CS, stereotypes, confidence in CS and their academic experiences. As female students in CS ourselves, we have an in-depth understanding of the students' experiences. Therefore we were able to provide an insiders perspective through this project. Our analysis resulted in key characteristics that portray the students' individual experiences in their academic environment, experiences with stereotypes and underlying factors in their motivation. We could recognize common factors among the female students such as low confidence in CS, differences in treatment in their academic environment, difficulties regarding the stereotypes and the need for external support. This project gives insights of female students individual experiences in a CS environment and provides an understanding of the situation of female students in CS.
Optimizing Performance and Computing Resource Management of In-memory Big Data Analytics with Disaggregated Persistent Memory

The performance of modern Big Data frameworks, e.g. Spark, depends greatly on high-speed storage and shuffling, which impose a significant memory burden on production data centers. In many production situations, the persistence and shuffling intensive applications can suffer a major performance loss due to lack of memory. Thus, the common practice is usually to over-allocate the memory assigned to the data workers for production applications, which in turn reduces overall resource utilization. One efficient way to address the dilemma between the performance and cost efficiency of Big Data applications is through data center computing resource disaggregation. This paper proposes and implements a system that incorporates the Spark Big Data framework with a novel in-memory distributed file system to achieve memory disaggregation for data persistence and shuffling. We address the challenge of optimizing performance at affordable cost by co-designing the proposed in-memory distributed file system with large-volume DIMM-based persistent memory (PMEM) and RDMA technology. The disaggregation design allows each part of the system to be scaled independently, which is particularly suitable for cloud deployments. The proposed system is evaluated in a production-level cluster using real enterprise-level Spark production applications. The results of an empirical evaluation show that the system can achieve up to a 3.5- fold performance improvement for shuffle-intensive applications with the same amount of memory, compared to the default Spark setup. Moreover, by leveraging PMEM, we demonstrate that our system can effectively increase the memory capacity of the computing cluster with affordable cost, with a reasonable execution time overhead with respect to using local DRAM only.
Cross-Polarization Reduction of Linear-to-Circular Polarizing Reflective Surfaces

This letter proposes an efficient design process to reduce the cross polarization of linear-to-circular reflection polarizers illuminated by practical feeds. In particular, we propose a two-step process, where first a geometrical optimization of the unit cell is applied along one plane of the reflector, followed by a progressive rotation of the unit cell in the orthogonal plane. An offset flat polarizer comprised of dipole unit cells fed by a standard gain horn has been used to illustrate the process. Significant improvements in cross-polarization discrimination are demonstrated by simulation. Two breadboards have been manufactured, and their response is compared in order to demonstrate the validity of the design process. Good agreement with measurements is observed corroborating the proposed technique.
Memristive Logic in Crossbar Memory Arrays: Variability-Aware Design for Higher Reliability

The advent of the first TiO2-based memristor in 2008 revived the scientific interest both from academia and industry for this device technology and has so far led to several emerging applications including logic and in-memory computing. Several memristive logic families have been proposed in the current quest for energy-efficient future computing systems. However, the limited device endurance and variability (both cycle-to-cycle and device-to-device) are important parameters to be considered in the assessment of logic operations. In this work, we used an accurate physics-based model of a bipolar memristor (supporting parasitics of the device structure and variability of switching voltages and resistance states) and demonstrate that performance of memristor-based in-memory computations can de degraded owing to both variability and state drift impact, if such features are not properly considered in the design flow. Inspired on pseudo-NMOS ratioed logic and based upon a previous CMOS-like logic scheme, we propose a crossbar-compatible memristive ratioed logic style which is tolerant to device variability and does not affect device endurance as computations do not involve conditional switching of memristors. Using the Cadence Virtuoso suite, we compare this logic scheme with MAGIC and CNIMP approaches, focusing on the universal NOR gate and more complex logic functions.
Business Models and Roles for Mediating Services in a Truck Platooning System-of-Systems

Platooning of trucks on motorways has been proposed as a method of reducing fuel consumption. It requires communication between the trucks to control the distance between them. However, this system-of-systems (SoS) cannot only contain the trucks, but also needs to include mediating off-board services for match-making to find suitable constellations of trucks, and for creating platooning incentives by distributing the profit made equally among the constituent systems. This paper analyzes what actors in the SoS would be suitable to operate these services, and also business models that cover the cost for their implementation and operation. It concludes that the truck OEMs have a vital role in creating the mediating services, and that a suitable business model would be based on usage fees for the services rather than upfront payment for platooning equipment.
Beyond Load Balancing: Package-Aware Scheduling for Serverless Platforms

Fast deployment and execution of cloud functions in Function-as-a-Service (FaaS) platforms is critical, for example, for microservices architectures. However, functions that require large packages or libraries are bloated and start slowly. An optimization is to cache packages at the worker nodes instead of bundling them with the functions. However, existing FaaS schedulers are vanilla load balancers, agnostic of packages cached in response to prior function executions, and cannot properly reap the benefits of package caching. We study the case of package-aware scheduling and propose PASch, a novel scheduling algorithm that seeks package affinity during scheduling so that worker nodes can re-use execution environments with preloaded packages. PASch leverages consistent hashing and the power of 2 choices, while actively avoiding worker overload. We implement PASch in a new scheduler for the OpenLambda framework and evaluate it using simulations and real experiments. When using PASch instead of a least loaded balancer, tasks perceive an average speedup of 1.29x, and 80th percentile latency that is 23x faster. Furthermore, for the workload studied in this paper, PASch outperforms consistent hashing with bounded loads—a state-of-the-art load balancing algorithm—yielding a 1.3x average speedup, and a speedup of 1.5x at the 80th percentile.
Structural Robustness for Deep Learning Architectures

Deep Networks have been shown to provide state-of-the-art performance in many machine learning challenges. Unfortunately, they are susceptible to various types of noise, including adversarial attacks and corrupted inputs. In this work we introduce a formal definition of robustness which can be viewed as a localized Lipschitz constant of the network function, quantified in the domain of the data to be classified. We compare this notion of robustness to existing ones, and study its connections with methods in the literature. We evaluate this metric by performing experiments on various competitive vision datasets.
Knowledge Management Maturity Assessment in Air Drilling Associates using G-KMMM

Since the early 1990s, companies in the oil & gas industry have realized that their business operations are knowledge-based, where company performance can be derived from faster identification, an assessment of an opportunity, and the speed of an exploitation. The oil & gas industry is one of the leading industries in the application and development of knowledge management; this is caused by changes in market and technology from 1990s to the beginning of the 21st century. Utilizing knowledge management is a must to be able to compete with other oil and gas industry companies. Currently, Air Drilling Associates (ADA) as one of the companies in oil & gas industry already has implemented knowledge management system, but its benefits are far from the expectation. In order to position their efforts and initialize knowledge management, companies need framework to use as a template. The objective of this paper is to measure the knowledge management maturity of Air Drilling Associates and other suggestion related to knowledge management for improvement.
CountNet: End to End Deep Learning for Crowd Counting

We approach crowd counting problem as a complex end to end deep learning process that needs both a correct recognition and counting. This paper redefines the crowd counting process to be a counting process, rather than just a recognition process as previously defined. Xception Network is used in the CountNet and layered again with fully connected layers. The Xception Network pre-trained parameter is used as transfer learning to be trained again with the fully connected layers. CountNet then achieved a better crowd counting performance by training it with augmented dataset that robust to scale and slice variations.