Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis on face recognition based on five different viewpoint of face images using MTCNN and FaceNet

Despite signiﬁcant recent achievements in the ﬁeld of face recognition, implementing proper face recognition system by training enough data is still a problem because not everyone has enough photos that we can use to train. The objective of this paper is to implement a proper face recognition system which can successfully recognize known and unknown person by feeding only ﬁve phase images for each person into training dataset. In this ﬁeld, accuracy and speed of identiﬁcation is the main issue. There are at least two reasons for the importance behind the research of face recognition which has recently received signiﬁcant attention, especially during the past several years. The ﬁrst is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. In this paper, we present a review of the most successful existing method FaceNet for face recognition technology and how we can use it successfully even though we don’t have enough data to train and to encourage researchers to embark on this topic. A brief on general information on this topic is also included to compose an overall review. This review is written by investigating past and ongoing studies done by other researchers related to the same subject.
A 20.5 TOPS Multicore SoC With DNN Accelerator and Image Signal Processor for Automotive Applications

Advanced driver-assistance systems (ADASs) that provide machine support to avoid critical accidents are already in wide use in vehicles in today’s market. SoCs for such systems have several requirements: 1) high computational performance to run several advanced algorithms at low latency; 2) low power consumption to permit running under the extreme heat and power conditions of real-world vehicles; and 3) high reliability to reduce the risk of serious accidents caused by faults. This article presents a novel SoC for ADAS applications, which resolves these difficult challenges. To achieve high performance with low power consumption, the SoC adopts the heterogeneous architecture, with ten processors, four DSPs, and eight types of accelerators, including the DNN accelerator and the image signal processor (ISP). To achieve higher reliability, the SoC implements several safety mechanisms, including system partitioning to prevent fault propagation, diagnostic features to detect faults, and a dedicated controller to operate runtime built-in self-test (BIST) of the ISP’s diagnostic features during the vertical blanking interval (VBLANK). The SoC is implemented in a 16-nm process, and its size is 94.52 mm2. Peak performance of 20.5 TOPS and low power consumption of 9.78 W are achieved.
Plant parts: processes, structures, or functions?

It is usually taken for granted that plants are composed of a series of discrete parts that can readily be compared both within a single plant and between closely or even distantly related organisms. The biological meaning of ‘part’ needs to be made more rigorous such that the naming of a part or the homologising of parts among organisms constitutes a scientific hypothesis that is testable, at least in principle. I explore three alternative approaches to defining parts. The parts-as-structures approach holds that parts (or “phenes”) are aspects of an organism that would not have been formed during development in the absence of causal genetic factors. A structural phene hypothesis is refuted by the lack of a hypothetical genetic deletion that would ablate just this part of the organism. The parts-as-functions approach focuses on pieces of the organism that could have been functionally different if one or more gene in the genome were mutated. A functional phene hypothesis is refuted by showing that all mutations affecting the phene have pleiotropic consequences. The part-as-process approach equates parts with developmental modules, making it especially helpful for addressing the concept of serial homology. I conclude that, depending on the context, parts are best understood sometimes as structures, sometimes as functions, and sometimes as processes, but in each case we need to develop rigorous concepts rather than falling back on human perception as the ultimate arbiter of part-ness.
Discriminating Unknown Software Using Distance Model

Crypto-ransomware is a class of malware that encrypt their victim’s data and only return the decryption key in exchange for a ransom. In a previous work, we have yet designed a solution able to detect any ciphering of files using statistical estimator. Once detected, a pop up requests the user to verify if that operation is allowed on not. To improve our tool, automation is needed. In this paper, an anomaly detection mechanism to determine if a suspected group of threads is an authorized cryptographic software or a malicious code is presented. The effectiveness of our solution to correctly distinguish between valid programs and ransomware is evaluated using a string analysis. The tf-idf metric is used to choose the most pertinent features. The distance of a candidate software with a vector representing the allowed cryptographic software is measured. If the distance exceeds a threshold, the suspected process is flagged as a ransomware. We have evaluated our approach with the samples provided by open databases and executed on our bare metal platform.
The development of textbooks for mobile application programming

Learning mobile programming has become more popular since advance in technology makes mobile phone more critical for every person. Meanwhile, the independence of student in learning the course is essential regarding the learning independence might make students more successful in learning. This study aims to develop and produce a book that will be used as a source in Mobile Programming Course. The four-step development model by Thiagarajan, Semmel, & Semmel was used as a development framework. The result shows that the book has passed all the development stage successfully which means that the book is ready to be used in the course.
Position error calibration of industrial robot based on binocular vision with reference length constraint

The position error on the end of the industrial robot is an important specification for evaluating its accuracy performance. In the ISO 9283 standard, the laser trackers and the binocular vision measurement methods are recommended to calibrate the positioning error. The calibration accuracy measured by using the laser tracker method is superior to that by using the binocular vision measurement method. Thus, this paper emphasized to study how to improve the calibration accuracy of the end position error by using the binocular vision measurement system. The high precision lengths as reference are introduced to combine with a conventional binocular vision system to make a new measurement system. The system errors of the binocular vision system can be corrected. In order to verify the correction effect, taking the industrial robot as an experiment example, the experimental results show that the absolute position error on the end of the industrial robot by using the binocular vision system with reference length constraint can reduce from 0.491 mm to 0.330 mm, and the repetitive positioning error reduced from 0.116 mm to 0.023 mm. The accuracy improved by about 33% and 80% respectively. Compared with the laser tracker measurement results under the same experimental conditions, the difference between the two are 0.34 μm and 7.80 μm. It can be considered that the corrected binocular vision calibration method can achieve the same accuracy with the laser tracker. It can be widely used for high-precision calibration of industrial robots as a means of balancing economics and precision.
Modelling environmental DNA data; Bayesian variable selection accounting for false positive and false negative errors

Environmental DNA is a survey tool with rapidly expanding applications for assessing the presence of a species at surveyed sites. Environmental DNA methodology is known to be prone to false negative and false positive errors at the data collection and laboratory analysis stages. Existing models for environmental DNA data require augmentation with additional sources of information to overcome identifiability issues of the likelihood function and do not account for environmental covariates that predict the probability of species presence or the probabilities of error. We present a novel Bayesian model for analysing environmental DNA data by proposing informative prior distributions for logistic regression coefficients that enable us to overcome parameter identifiability, while performing efficient Bayesian variable selection. Our methodology does not require the use of transdimensional algorithms and provides a general framework for performing Bayesian variable selection under informative prior distributions in logistic regression models.
Theory and implementation of scalable, retrodirective distributed arrays

A Distributed Multi-Input Multi-Output (DMIMO) system consists of many transceivers coordinating themselves into a “virtual antenna array” in order to emulate MIMO capabilities. In recent years, the field of research investigating DMIMO Communications has grown substantially. DMIMO systems o↵er all of the same benefits of standard MIMO systems on a larger scale because arrays are not limited by the physical constraint of placing many antennas on a single transceiver. This additional benefit does come at a cost, however. Since nodes are distributed and run from independent clock signals and with unknown geometry, each one must its own obtain channel state information (CSI) to the target nodes. In existing DMIMO architectures, array nodes depend on feedback from target nodes to properly synchronize. This means that target nodes must be cooperative and are responsible for the overhead calculating and transmitting CSI feedback to each node in the array. Within this work, we develop a set of techniques for Retrodirective Distributed Antenna Arrays. Retrodirective arrays have traditionally been used to direct a beam towards a target node, but the work in this thesis seeks to develop a more generalized definition of retrodirectivity. By our definition, a retrodirective array is one that acquires CSI to one or more intended targets simply by listening to the incoming transmissions of those targets; the array may subsequently use this information to do any number of typical MIMO tasks (i.e., beamforming, nullforming, spatial multiplexing, etc.). We explore two primary techniques: i) distributed beamforming and ii)
Performance analysis of a cognitive radio network with adaptive RF energy harvesting

ABSTRACT In this paper, we proposed an adaptive energy harvesting scheme and evaluated the performance of a dual hop cognitive radio network (CRN) under the proposed scheme. We considered a simultaneous wireless information and power transfer (SWIPT) protocol-based transmission of data from a secondary source (SS) to a secondary destination (SD) via a decode and forward (DF) relay. A time switching relay (TSR)-based energy harvesting is used to supply power to the DF relay. The secondary relay (SR) extracts energy from RF signal of SS to derive the transmit power at SR to a level of maximum allowable transmit power satisfying an interference constraint at the primary receiver (PR). To achieve a transmit power to a level of maximum allowable transmit power, the harvesting time duration or time fraction is dynamically adapted for each data frame. Outage probability with proposed adaptive harvesting scheme is derived analytically. A comparative study of the outage and throughput of our proposed adaptive harvesting scheme with a conventional fixed time based harvesting is presented. Finally, the proposed adaptive harvesting scheme is shown to outperform a fixed time-based harvesting scheme.
Mixing Deep Networks and Entangled Forests for the Semantic Segmentation of 3D Indoor Scenes

This work focuses on semantic segmentation over indoor 3D data, that is, to assign labels to every point in the point clouds representing working spaces: after researching the current state of the art, traditional approaches like random forests and deep neural networks based on PointNet are evaluated. The Superpoint Graph architecture and the 3D Entangled Forests algorithm are selected for mixing their features to try to enhance their performance.
A review of arabic text steganography: past and present

Steganography is a strategy for hiding secret information in a cover document in order to avoid attacker from predict about hidden information. Steganography exploit cover message, for instance text, audio, picture and video to hide the secret message. Before this, linguistic text steganographic techniques are implemented just for the English language. But nowadays different languages are used to hide the data like Arabic language. This language is still new in the steganography and still need practices for empowerment. This paper will present the text steganographic method for Arabic language, scholar paper within 5 year will be analyze and compared. The main objective of this paper is to give the comparative analysis in the Arabic steganography method that has been applied by previous researchers. Finally, the disadvantage and advantage of the method also will be presented in this paper.
The Complexity of Contracts

We initiate the study of computing (near-)optimal contracts in succinctly representable principal-agent settings. Here optimality means maximizing the principal's expected payoff over all incentive-compatible contracts-known in economics as “second-best” solutions. We also study a natural relaxation to approximately incentive-compatible contracts. We focus on principal-agent settings with succinctly described (and exponentially large) outcome spaces. We show that the computational complexity of computing a near-optimal contract depends fundamentally on the number of agent actions. For settings with a constant number of actions, we present a fully polynomial-time approximation scheme (FPTAS) for the separation oracle of the dual of the problem of minimizing the principal's payment to the agent, and use this subroutine to efficiently compute a δ-incentivecompatible (δ-IC) contract whose expected payoff matches or surpasses that of the optimal IC contract. With an arbitrary number of actions, we prove that the problem is hard to approximate within any constant c. This inapproximability result holds even for δ-IC contracts where δ is a sufficiently rapidly-decaying function of c. On the positive side, we show that simple linear δ-IC contracts with constant δ are sufficient to achieve a constant-factor approximation of the “first-best” (full-welfare-extracting) solution, and that such a contract can be computed in polynomial time.
Towards Efficient Secure Memory Systems with Oblivious RAM

When multiple users and applications share the resources on cloud servers, information may be leaked through hidden channels related to the memory. Encryption can help to protect data privacy. However, the physical address on the memory bus cannot be encrypted if there is no computation power on memory DIMM. The attacker may observe clear-text physical address access frequency and infer sensitive information in the program. To completely protect the system from address access pattern leakage, we need to use Oblivious RAM, which obfuscates the physical address by remapping it after each access. However, the ORAM access is still costly regarding bandwidth. In this dissertation, I focus on discussing and designing efficient and scalable secure memory systems with ORAM. Firstly, I studied the co-run interference between different applications on the modern computer servers. We found out that how to allocate shared resources between secure applications and other normal applications will determine the overall system performance. I proposed Cooperative-ORAM protocol, which achieves the goal of better resource allocation, utilization and same security guarantee as original ORAM design. Our design delivers an average of 20% overall performance improvement over the baseline Path ORAM design while providing a flexible resource tuning between different kinds of applications. In the next part, I address the problems when the application number further scales on the same server. The co-run interference and memory traffic will be more intense when we scale the number of applications on the server. Meanwhile, more applications mean that the demand for memory capacity is also increasing. I proposed the design of D-ORAM, which delegate the ORAM based secure engine on Buffer-on-Board(BoB), which is in between of the last level cache and main memory, to enable high-level privacy protection and low execution interference on cloud servers. By pushing the ORAM engine off-chip, most of the ORAM accesses will not need to be sent back to the processor side, which removes the excessive data movement overhead. Our evaluation shows that D-ORAM improves normal applications performance by 22.5% on average.
Abusive and Hate Speech Tweets Detection with Text Generation

Nalamothu, Abhishek. M.S., Department of Computer Science and Engineering, Wright State University, 2019. Abusive and Hate Speech Tweets Detection with Text Generation. According to a Pew Research study, 41% of Americans have personally experienced online harassment and two-thirds of Americans have witnessed harassment in 2017. Hence, online harassment detection is vital for securing and sustaining the popularity and viability of online social networks. Machine learning techniques play a crucial role in automatic harassment detection. One of the challenges of using supervised approaches is training data imbalance. Existing text generation techniques can help augment the training data, but they are still inadequate and ineffective. This research explores the role of domain-specific knowledge to complement the limited training data available for training a text generator. We conduct domain-specific text generation by combining inverse reinforcement learning (IRL) with domain-specific knowledge. Our approach includes two adversarial nets, a text generator and a Reward Approximator (RA). The objective of the text generator is to generate domain-specific text that is hard to discriminate from real-world domain-specific text. The objective of the reward approximator is to discriminate the generated domainspecific text from real-world text. During adversarial training, the generator and the RA play a mini-max game and try to arrive at a win-win state. Ultimately, augmenting diversified and semantically meaningful, generated domain-specific data to the existing dataset improves detection of domain-specific text. In addition to developing the Generative Adversarial Network-based framework, we also present a novel evaluation that uses variants of the BLEU metric to measure the diversity of generated text; uses perplexity and cosine similarity to measure the quality of the generated text. Experimental results show that the proposed framework outperforms a previous baseline (IRL without domain knowledge) on harassment (i.e., Abusive and Hate speech) tweet generation. Additionally, the generated tweets effectively augment the training data for online abusive and hate speech detection
P4VBox: Enabling P4-Based Switch Virtualization

Virtualization has become the powerhouse for several networking concepts, from local area networks (VLANs) to software switches, software-defined control plane, etc. Recent proposals like HyPer4, HyperVDP, and P4Visor brought the concept to the forwarding plane, by enabling emulation of several network contexts and/or composing several functions through a single program. In spite of the progress achieved, the real power of forwarding plane virtualization remains untapped. In this letter, we present P4VBox, a reconfigurable architecture for data plane virtualization. P4VBox provides parallel execution and hot-swapping of virtual switch instances, without requiring switch source code (for either emulation or program composition). We experimented P4VBox on a NetFPGA-SUME board with three virtual switches: a layer-2 switch, a simple router, and a firewall. Area occupation measurements evidence the feasibility of running up to 13 virtual switches in parallel. Compared to existing work, performance data show an improvement of up to two orders of magnitude for bandwidth and six orders for latency.
Using transfer learning technique for SAR automatic target recognition

In this paper, a deep learning approach for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is proposed. The novelty of the proposed framework stems from the fact that it is based on a transfer learning scheme, where a pre-trained Convolutional Neural Network (CNN) is employed to extract learned features in combination with a classical Support Vector Machine (SVM) for classification. The efficiency of the presented approach is validated on the MSTAR dataset, where ten target classes are used. A classification accuracy of 99.27% is achieved.
Novel bit-serial semi-systolic array structure for simultaneously computing field multiplication and squaring

This paper presents a novel bit-serial semi-systolic array structure to simultaneously execute modular multiplication and squaring operations in GF(2m). The architecture is explored by using a systematic methodology based on the proper choice of the scheduling and projection vectors applied to the algorithm dependency graph. The explored architecture has the advantage of sharing the data-path between the two operations, and hence it leads to saving more space compared to the case of using a separate data-path for each operation. Also, the simultaneous calculation of both operations significantly decreases the execution time required to perform modular exponentiation operation, as it mainly depends on these two core operations. Complexity analysis indicates that the developed bit-serial semi-systolic array structure outperforms the latest exiting competitor bit-serial systolic and non-systolic structures in terms of area-time (AT) by at least 24%. This makes the proposed structure more appropriate for use in resource-constrained cryptographic processors.