Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media

In the context of fake news, bias, and propaganda, we study two important but relatively under-explored problems: (i) trustworthiness estimation (on a 3-point scale) and (ii) political ideology detection (left/right bias on a 7-point scale) of entire news outlets, as opposed to evaluating individual articles. In particular, we propose a multi-task ordinal regression framework that models the two problems jointly. This is motivated by the observation that hyper-partisanship is often linked to low trustworthiness, e.g., appealing to emotions rather than sticking to the facts, while center media tend to be generally more impartial and trustworthy. We further use several auxiliary tasks, modeling centrality, hyper-partisanship, as well as left-vs.-right bias on a coarse-grained scale. The evaluation results show sizable performance gains by the joint models over models that target the problems in isolation.
Non-RLL DC-Balance based on a Pre-scrambled Polar Encoder for Beacon-based Visible Light Communication Systems

Current flicker mitigation (or DC-balance) solutions based on run-length limited (RLL) decoding algorithms are high in complexity, suffer from reduced code rates, or are limited in application to hard-decoding forward error correction (FEC) decoders. Fortunately, non-RLL DC-balance solutions can overcome the drawbacks of RLL-based algorithms, but they meet some difficulties in system latency, low code rate or inferior error-correction performance. Recently, non-RLL flicker mitigation solution based on Polar code has proved to be a most optimal approach due to its natural equal probabilities of short runs of 1's and 0's with high error-correction performance. However, we found that this solution can only maintain DC balance only when the data frame length is sufficiently long. Therefore, these solutions are not suitable for using in beacon-based visible light communication (VLC) systems, which usually transmit ID information in small-size data frames. In this paper, we introduce a flicker mitigation solution designed for beacon-based VLC systems that combines a simple pre-scrambler with a (256;158) non-systematic polar encoder.
A Comparative Study of Asynchronous Many-Tasking Runtimes: Cilk, Charm++, ParalleX and AM++

We evaluate and compare four contemporary and emerging runtimes for high-performance computing(HPC) applications: Cilk, Charm++, ParalleX and AM++. We compare along three bases: programming model, execution model and the implementation on an underlying machine model. The comparison study includes a survey of each runtime system's programming models, their corresponding execution models, their stated features, and performance and productivity goals. We first qualitatively compare these runtimes with programmability in mind. The differences in expressivity and programmability arising from their syntax and semantics are clearly enunciated through examples common to all runtimes. Then, the execution model of each runtime, which acts as a bridge between the programming model and the underlying machine model, is compared and contrasted to that of the others. We also evaluate four mature implementations of these runtimes, namely: Intel Cilk++, Charm++ 6.5. 1, AM++ and HPX, that embody the principles dictated by these models. With the emergence of the next generation of supercomputers, it is imperative for parallel programming models to evolve and address the integral challenges introduced by the increasing scale. Rather than picking a winner out of these four models under consideration, we end with a discussion on lessons learned, and how such a study is instructive in the evolution of parallel programming frameworks to address the said challenges.
Performance Improvement of Refractometric Sensors Through Hybrid Plasmonic–Fano Resonances

In this paper, we present a plasmonic refractometric sensor that works under normal incidence; allowing its integration on a fiber tip. The sensor's material and geometry exploit the large scattering cross section given by high contrast of the index of refraction subwavelength dielectric gratings. Our design generates a hybrid plasmonic–Fano resonance due to the interference between the surface plasmon resonance and the grating response. We optimize the sensor with a merit function that combines the quality parameter of the resonance and the field enhancement at the interaction volume where the plasmon propagates. Our device shows a high sensitivity (1000 nm/RIU) and a high figure of merit (775 RIU$^{-1}$). Degradation in performance is negligible through a wide dynamic range up to 0.7 RIU. These quantitative parameters overperform compared to similar plasmonic sensors.
H2B: Heartbeat-based Secret Key Generation Using Piezo Vibration Sensors

We present Heartbeats-2-Bits (H2B), which is a system for securely pairing wearable devices by generating a shared secret key from the skin vibrations caused by heartbeat. This work is motivated by potential power saving opportunity arising from the fact that heartbeat intervals can be detected energy-efficiently using inexpensive and power-efficient piezo sensors, which obviates the need to employ complex heartbeat monitors such as Electrocardiogram or Photoplethysmogram. Indeed, our experiments show that piezo sensors can measure heartbeat intervals on many different body locations including chest, wrist, waist, neck and ankle. Unfortunately, we also discover that the heartbeat interval signal captured by piezo vibration sensors has low Signal-to-Noise Ratio (SNR) because they are not designed as precision heartbeat monitors, which becomes the key challenge for H2B. To overcome this problem, we first apply a quantile function-based quantization method to fully extract the useful entropy from the noisy piezo measurements. We then propose a novel Compressive Sensing-based reconciliation method to correct the high bit mismatch rates between the two independently generated keys caused by low SNR. We prototype H2B using off-the-shelf piezo sensors and evaluate its performance on a dataset collected from different body positions of 23 participants. Our results show that H2B has a pairing success rate of 95.6%. We also analyze and demonstrate H2B's robustness against three types of attacks. Finally, our power measurements show that H2B is very power-efficient.
Plant Species Identification using Leaf Image Retrieval: A Study

Human beings along with other living beings and their ecological system are completely inter-dependent. In the past few decades, the technological development has affected the environment more radically than ever before. It has posed grave threats to the natural resources including habitat loss and degradation, over-exploitation of resources and change in climatic condition. Most of plant species are on the verge of extinction. In the present circumstances, it is essential to conserve ecological system. Plant identification is a crucial step towards ecosystem diversity conservation. It is time consuming and requires lots of efforts, specialized knowledge and in-depth training. Recent technological advancement in the field of imaging, data analysis, and plant morphology has improved the decision such as yield prediction, crop management, veterinary diet, improving climate and many more. This has also made it possible to develop plant species identification system. In this paper, we present a comprehensive study about plant species identification methods based on various feature extraction methods, classification and other challenges. Furthermore, the performance of two widely used classifiers viz. Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) on Flavia dataset is examined in terms of Precision, Recall and F-Score. It is observed that SVM performs better than PNN for plant species identification.
Azimuth Signal Multichannel Reconstruction and Channel Configuration Design for Geosynchronous Spaceborne–Airborne Bistatic SAR

In geosynchronous spaceborne–airborne bistatic synthetic aperture radar (GEO-BiSAR) system, the airborne platform achieves high-resolution imaging by passively receiving the signal from the interested scenario. In this paper, the Doppler characteristics of GEO-BiSAR and the individual contribution of the transmitter and the receiver are first analyzed. The airborne receiver is found to be the dominant contributor for the total Doppler bandwidth, which will lead to Doppler spectrum aliasing regarding the low pulse repetition frequency (PRF) adopted by the GEO-SAR. In order to suppress the Doppler ambiguity without adjusting the PRF of GEO-SAR, azimuth multichannel receiving technique is introduced to the airborne receiver. The multichannel transfer function is derived based on the method of series reversion and the spectrum reconstruction algorithm is then modified for multichannel GEO-BiSAR. Moreover, the reconstruction performance is closely related to the corresponding spacing between each channel (i.e., channel configuration). Therefore, the channel configuration design for GEO-BiSAR aims at optimizing the azimuth ambiguity-to-signal ratio with a satisfactory level of signal-to-noise ratio scaling factor by adjusting the channel configuration. The channel configuration design is modeled as a constrained single objective optimization problem (CSOP). Then, a channel configuration design method based on differential evolution and feasibility rule is proposed to solve the CSOP and obtain the channel configuration for the receiver with the optimal reconstruction performance. Finally, simulations results are presented to verify the effectiveness of the proposed method, and characteristics of channel configuration are analyzed in detail, which can be a practical guide for the implementation of multichannel GEO-BiSAR systems.
A Novel Hybrid Magnet Made of HTS Tape Stacks and Permanent Magnets for Motor Applications

High-temperature superconducting (HTS) tape stacks could be used in motors as a substitute for permanent magnets (PMs) to improve the power density. However, the magnetization difficulty and safety concern limit its wider application. A novel hybrid magnet made of HTS tape stacks and PMs is proposed in this paper and investigated in experimental and numerical modeling methods. A hybrid magnet sample is manufactured and tested using pulsed field magnetization (PFM) at 77.3 K. Then, a 3-D electromagnetic-thermal coupled model is built and verified to simulate the field performance at lower temperatures. After the optimization process of the thickness ratio, an optimal 5-mm hybrid magnet of 0.5-mm tape stack +4.5 mm PM is obtained. Compared with the PM, the optimal hybrid magnet could enhance the magnetic field by 114%. Compared with the pure tape stack of the same size, the hybrid magnet could be magnetized with relatively small pulsed field, could reduce the consumption of the HTS tapes, and could enhance the operation safety especially encountering a cooling system failure. The results offer a novel perspective to introduce the HTS material into the conventional motor applications and provide a possible reference to the future study of HTS motors.
Scaling Millimeter-Wave Networks to Dense Deployments and Dynamic Environments

Millimeter-wave (mmWave) communications have emerged as one of the most promising options to vastly increase wireless data rates due to the high bandwidth they offer. Given the high path loss at mmWave frequencies, such systems require directional antennas to achieve a good communication range. Thus, the communicating devices need to align the beam directions of their mmWave antennas. Due to the high penetration loss, the paths between the antennas also need to be free of blocking obstacles. This makes an efficient and reliable operation of mmWave networks in dynamic environments very challenging. At the same time, the directionality reduces interference and allows to scale these networks to much higher access point and device densities. In this paper, we discuss the above-mentioned challenges and present techniques that allow mmWave networks to scale to high-density deployments, to adapt to dynamic and mobile environments, and to consistently achieve high data rates. This includes learning the environment to find different propagation paths, reacting timely to channel impairments such as blockage, and integrating mmWave networks with networks operating at a lower frequency for robustness. A key ingredient to enable these forms of adaptivity is the use of location information. Such mechanisms then turn a collection of very-high-speed but brittle mmWave links into an efficient, low-latency, and reliable network.
Secure and Effective Reengineering Information System and Business Processes of Cross-Border Control between the Republic of Indonesia and the Republic Democratic of Timor-Leste

Technology disruption had boost every sector to adapt and to adjust an infrastructure and system according to the specific needs and business process which can improve an effective operation for the decision making processes and reporting systems within the workflow, social capability, data integrity, and open standard frameworks. Current administration process for capturing cross-border (traveler) in terrestrial immigration posts are used a conventional or handwriting; -passport information is collected on arrival or departure immigration cards or use of ‘ Pas Lintas Batas’ (PLB) special cards for the inhabitant was living alongside both countries border. It is quite difficult to organize cross-border cards for the archiving, filing system, retrieve data, and recapitulate numbers of cross borders for reporting purposes such as individual activities per cities and aims, daily, weekly, and monthly reports. The research will use waterfall with subprojects method within the mentioned modern framework. The aim of this research is to produce an effective and secure information system application prototype for immigration authorities, especially for terrestrial border control system.
Sentiment Analysis for Popular e-traveling Sites in Indonesia using Naive Bayes

Users of online ordering and/or purchase services on the marketplace often face difficulties in determining which objects or services are selected closest to the criteria of potential users. The rating or rating feature used by many marketplaces is sometimes not objective and does not match the content of reviews given by the reviewers. This results in a decrease in the level of user confidence in the ratings and ratings provided by the service. Therefore, the prospect will seek to obtain a thorough analysis, by reading and analyzing any reviews related to a particular product or service. The burden for users is the number of reviews that are not small and the use of different language styles. This Research proposes a method that can provide a rating value that is more in line with the content of the review with respect to the sentiments present in the review. The method developed utilizes a corpus built on the topic model of reviews on the site of the hotel service provider as well as articles relating to the hotel. The sentiment analysis was obtained by using the Naïve Bayesian classification method and the use of probabilistic value of the corpus. The results of the test show the success rate of methods in analyzing sentiment is 89%. The result of sentiment analysis is used as reference of calculation of rating value.
Unsupervised Hierarchical Land Classification Using Self-Organizing Feature Codebook for Decimeter-Resolution PolSAR

In this paper, we propose a hierarchical polarization feature generation using a self-organizing codebook to realize unsupervised land classification that fully utilizes the detailed polarization information contained in high-resolution polarimetric synthetic aperture radar (PolSAR) data. PolSAR has reached a decimeter-level high resolution. In general, conventional methods lower the resolution of the PolSAR data to 10–20 m in the real-space distance to classify observation regions into land classes such as farm, forest, and town. However, lowering resolution prevents us from discovering new land classes potentially enabled by the resolution enhancement. The hierarchical method we propose here not only classifies observation regions successfully into land classes such as farm, forest, and town that humans can naturally distinguish but also discovers new land subclasses findable only in high-resolution PolSAR data. We explain these two types of our achievements (classification/discovery) through experimental results for Japan Aerospace Exploration Agency’s polarimetric and interferometric airborne SAR-L2 data having decimeter resolution.
A Study of Short Channel Performance of Dual Material strained silicon Trigate (DM Strained-Si TG) SOI MOSFET

The current research highlights the analytical modeling for dual material strained silicon Trigate (DM Strained-Si TG) SOI MOSFET with additional work function engineered bottom gate. The device short channel behavior is investigated and compared with single material SOI trigate MOSFET equivalent to establish its superiority in improving the overall performance of the device and suppressing the limitations associated with device scaling. Verification of analytical data have been done using ATLAS simulated data.
All-Plasmonic IQ Modulator With a 36 μm Fiber-to-Fiber Pitch

A most compact all-metallic high-speed in-phase and quadrature modulator with a 36 μm fiber-to-fiber pitch operating at 200 Gbit/s is introduced. All components of the modulator are made of metal: The vertical crating couplers, the waveguides, polarization rotators, splitters, and phase shifters. As the modulator relies on substrate-independent processes, it can basically be put down on any smooth layer. The modulator is interconnected to a multicore fiber for optical input and output coupling. We demonstrate 100 GBd signal modulation with a quadrature phase-shift keying modulation format and a bit-error ratio below the hard-decision forward error correction. The metallic modulator shows the potential of plasmonics in offering highest speed and advanced modulations on a smallest possible footprint. The simple fabrication makes it attractive for low-cost applications such as short-haul and data-center networks.
A Proposed Model of Green Computing Adoption In Indonesian Higher Education

Green computing is one of the positive efforts in maintaining environmental sustainability. Green computing can be done by energy efficiency of Information and Communication Technology (ICT) devices, paper efficiency, and electronic waste reduction. This research has a final goal of producing a green computing policy for higher education in Indonesia. The policy is structured on the basis of a model of successful adoption of green computing. This paper provides suggestions about the model. The data collected to build the proposed model is through interviews from 26 academics from various campuses in Java Island, Bali Island, East Nusa Tenggara and Sumatra. Analysis of resource responses and previous research studies produced constructive constructs of models. In the proposed model, there are three things that play a role in the success of green computing adoption in college. Those three things are: government regulation, campus management style, and supporting tools both hardware and software.
A Complete Analytical Solution for the On and Off Dynamic Equations of a TaO Memristor

In this brief we provide a complete analytical model for the time evolution of the state of a real-world memristor under any dc stimulus and for all initial conditions. The analytical dc model is derived through the application of mathematical techniques to Strachan’s accurate mathematical description of a tantalum oxide nano-device from Hewlett Packard Labs. Under positive dc inputs the state equation of the Strachan model can be solved analytically, providing a closed-form expression for the device memory state response. However, to the best of our knowledge, the analytical integration of the state equation of the Strachan model under dc inputs of negative polarity is an unsolved mathematical problem. In order to bypass this issue, the state evolution function is first expanded in a series of Lagrange polynomials, which reproduces accurately the original model predictions on the device off-switching kinetics. The solution to the resulting state equation approximation may then be computed analytically by applying methods from the field of mathematics. Our full analytical model matches both qualitatively and quantitatively the tantalum oxide memristor response captured by the original differential algebraic equation set to typical stimuli of interest such as symmetric and asymmetric pulse excitations. It is further insensitive to the convergence issues that typically arise in the numerical integration of the original model, and may be easily integrated into software programs for circuit synthesis, providing designers with a reliable tool for exploratory studies on the capability of a certain circuit topology to satisfy given design specifications.
Investigating the Long-Term Spatial and Temporal Characteristics of Vegetative Drought in the Contiguous United States

Multipronged statistical approaches were applied to give spatiotemporal insight into the long-term patterns of vegetative drought over the contiguous United States (CONUS) from a weekly NOAA/AVHRR VHI product of 1981–2017. Based on the analysis results, the following characteristics have been found: First, the category of the most frequently occurring drought is the moderate drought (D1), and the drought frequency successively decreases from abnormally dry conditions (D0) to exceptional drought (D4). The frequencies of moderate drought (D1), severe drought (D2), and extreme drought (D3) in the West Mountains region are consistently higher than the frequency of the corresponding severity levels in other regions, while the frequency of exceptional drought (D4) in the Great Plains is the highest. The frequencies of three worst drought levels (D2, D3, and D4) in the East region are consistently the lowest in the CONUS. Second, there are gradual changes in the trends of vegetative drought from March to October and the change patterns are significantly different for newly identified four vegetation classes (evergreen forest, deciduous forest, grass, and crop). There is more increase in VHI values in March–May and less in June–October for deciduous forest and grass. Evergreen forest presents similar results except more in October. There are obviously more pixels with significant increase in VHI values in March, April and July, August for crop. Third, the trajectories of the centroids of vegetative drought have significantly moved southwestward in the past 37 years. With drought severity levels increasing, the centroid movement got larger and there were regular translations toward southwest.