Reviewerly/exHarmony huggingface dataset:
100 MHz Random Number Generator Design Using Interleaved Metastable NAND/NOR Latches*

This investigation demonstrates a wide bandwidth random number generator (RNG) based on interleaved NAND-/NOR-based SR (set-reset) latches. More specifically, the metastability of SR latches driven by the same input causing undefined output states is exploited. To achieve higher irregular sampling of the SR latches, not only NAND-based SR latches and NOR-based SR latches are interleaved integrated, their inputs are also randomly selected by another array of metastable SR latches. Namely, a 2-layer RNG architecture is realized to avoid locking phenomenon and enhance randomness. The proposed 2-layer RNG is realized using typical 40-nm CMOS process. All-PTV-corner (process, temperature, voltage) post-layout simulations validate that the proposed RNG passes long run test and mono-bit test given 100 MHz clock rate.
Improving the Quality of AES S-box by Modifications Irreducible Polynomial and Affine Matrix

S-box plays an important role in the process of secure data transmission over the internet. The proposed S-box uses the results of various modifications, i.e., a combination of an irreducible polynomial and an affine matrix. Irreducible polynomial modification is done by taking the 3 best irreducible polynomials based on previous research. Affine matrix modification is done by a Boolean operation XOR for each affine matrix element of the AES S-box. The combination results of these modifications are used to build several quality S-boxes. To determine the best S-box quality, nonlinearity, strict avalanche criterion, bit independence criterion-nonlinearity, and bit independence criterion-strict avalanche criterion testing were used. The test results show that the best quality S-box has scores of 112, 0.5015, 112, and 0.4997 for the nonlinearity, strict avalanche criterion, bit independence criterion-nonlinearity, and bit independence criterion-strict avalanche criterion, respectively. The test results are implemented using a predetermined mathematical formulation to obtain the S-box value. The results of the calculation show that the S-box value in this study is 16.0017. This result indicates that the proposed S-box has better quality than the S-boxes produced in previous studies.
Modelling for a 6DOF Anthropomorphic Manipulator

Anthropomorphic robotics is a novel challenging domain of study and research. To collaborate with people, the anthropomorphic robot is required not only a humanoid structure but a human-like feature regarding the motion. But most anthropomorphic robots use the system which is mature algorithms integrated or closed to customers. To avoid this, remodeling is necessary and important for theoretic studying as well as precise controlling which not only strengthens the understanding of robot structure but also makes the research more targeted. In this paper, a geometric-analytical mix approach is proposed to solve the inverse kinematics for a 6-DOF arm of Seed-Noid R7F robot due to its dissatisfactory of Pieper’s criteria, comparing with the general approach which doesn’t consider the payload changing, a dynamics model with the payload scenario is built to respond the payload sensibility. At last, the proposed solution is verified using MATLAB when the payload is from 2 kg to 3 kg separately.
Filter Combination Learning for Convolutional Neural Network

In this paper, we propose a method for representing convolution filters of a Convolutional Neural Network (CNN) model as linear combinations of small number of basis filters that are provided as input features. In our approach, the combination coefficients are searched (trained) with the given input basis filters (IBFs) to best generate the convolution filter parameters. Since all the convolution filters are generated by the linear combinations of the IBFs, the size of an CNN model can be compressed if the number of coefficients for the linear combinations is less than that of filter parameters. In our experiments, widely used well-known deep learning models such as VGG-16, ResNet-18 are utilized. About 70% of the learnable parameters in those models are reduced with the observation of 1-2% accuracy drops. Note that the aim of our approach is not for reducing the number of parameters and involved arithmetic operations. Our primary goal of work is to investigate the possibility of expressing filters with a small set of IBFs by linear combinations. The second goal is to compress a model by linear combinations and to make a benefit when the model needs to be distributed and stored (particularly downloaded to mobile devices through Wifi). The code for experiments are available at https://github.com/jjeamin/Filter_Generation_Network.
Fusion of Intrinsic &amp; Extrinsic Sentential Traits for Text Coherence Assessment

In this paper, we investigate the problem of text coherence modeling with a new perspective of learning better distributed sentence representation by incorporating document dependent and document independent features of a sentence. To this end, we propose a data-driven end-to-end novel neural coherence model that captures text coherence by exploiting the semantic and distributional aspects of the sentences in a document. The network is able to capture various document independent and document dependent features of a sentence which is imperative in assessing text coherence. Experiments on the standard Sentence Ordering task indicate that our proposed model shows significant performance gain of 1.7 percent in terms of accuracy score compared with the state-of-the-art baselines.
Deep Hashing Network Based on Split Channels for Hybrid-Source Remote Sensing Image Retrieval

Traditional remote sensing image retrieval (RSIR) methods are generally based on images from a specific single source. As different sources and huge volumes of remote sensing images have been easily available nowadays, RSIR is facing the challenge of retrieving remote sensing images with different spectral and spatial information from different sources. Benefited from compelling image feature extraction ability of deep neural networks and efficient computing power and effective retrieval ability of hashing, deep hashing networks has become prevalent for image retrieval researches. In this paper, a deep hashing network based on split channels is proposed for hybrid source RSIR called split-channels triplet deep hashing networks(SCTDHNs). It takes skillfully splitting channels as input, and is mainly composed of a hybrid source deep hashing subnetwork for cross source images retrieval and single-source deep hashing sub-network for a multi-spectral image retrieval, and each of them achieves high retrieval performance. Furthermore, a novel trick for loss function is proposed, called increased intervals between dissimilar pairs during training stage that dramatically improves the retrieval performance. Extensive experiments implement on dual-source remote sensing data set demonstrate that proposed method yields better performance than existing state-of-art hybrid source retrieval methods as far as is known.
Summary of Face Recognition under Low-Quality Conditions

In this paper, face recognition algorithms under three low-quality conditions, namely, complex illumination, low resolution and partial occlusion, are summarized, and relatively new face recognition algorithms under low-quality conditions are introduced and explained. Such as the algorithm of face recognition with occlusion based on partition, face recognition algorithm based on Iteratively Reweighted Robust Principal Component and face recognition with occlusion based on MEBML, illumination face recognition based on RPCA and Convolutional Neural Network, face recognition based on Illumination Normalization and Block-based Adaptive Local Ternary Pattern, low-resolution face recognition based on blocking CS-LBP and weighted PCA algorithm, pose Robust low-resolution face recognition via CKEDA and other face recognition algorithm under low-quality conditions. The existing problems and development trend of face recognition under low quality conditions are analyzed.
Dynamic Computational Diversity with Multi-Radix Logic and Memory

Today's computing systems are highly vulnerable to attacks, in large part because nearly all computers are part of a hardware and software monoculture of machines in its market, industry or sector. This is of special concern in mission-critical networked systems upon which our civil, industrial, and defense infrastructures increasingly rely. One approach to tackle this challenge is to endow these systems with dynamic computational diversity, wherein each processor assumes a sequence of unique variants, such that it executes only machine code encoded for a variant during the time interval of that variant's existence. The variants are drawn from a very large set, all adhering to a computational diversity architecture, which is based on an underlying instruction set architecture. Thus any population of machines belonging to a specific diversity architecture consists of a temporally dynamic set of essentially-unique variants. However, an underlying ISA enables development of a common development toolchain for the diversity architecture. Our approach is hardware-centric, relying on the rapidly developing microelectronics technologies of ternary computing, resistive RAM (ReRAM) memory, and physical unclonable functions. This paper describes our on-going work in dynamic computational diversity, which targets the principled design of a secure processor for embedded applications.
A Path Planning Method For Overhead Crane Based on Spatial Stratification

The automatic operation and obstacle avoidance for overhead crane in special environments is an urgent problem to be solved in the field of unmanned and intelligent factory. At present, the path planning problem for overhead crane is mostly based on 2D path planning method, and the research theory of spatial path planning is still immature. In this paper, a novel spatial path planning method is proposed for the lifting process of overhead crane, a factory space which containing obstacles is layered in the vertical direction, and the ant colony algorithm is used to calculate the path trajectory of each layer, then the optimal path can be determined through the evaluation indexes: running time, available energy and collision risk(represented by the turning point). Finally, the rationality and feasibility of the spatial path planning method proposed in this paper is verified by numerical examples.
An Ultra-low-power High-precision Dynamic Gesture Recognition Coprocessor Based On RISC-V Architecture

Contactless Dynamic gesture recognition (DGR), which has the extreme advantages of simple action, wide and strong application, faces challenges such as real-time, long-time work, low power, high complex algorithm, and more. In this article, we propose an ultra-low-power high-precision dynamic gesture recognition coprocessor based on the RISC-V architecture (UHDA). UHDA integrated on RISC-V ecological environment is configured and controlled by extended instruction set architecture (ISA). And UHDA has system feedback to adjust the background and human behavior. We adopt a low-complexity high-precision algorithm based on YCrCb and real-time online test the usual applied four gestures. The algorithm of detection gets an accuracy of 87%~100%. The final result shows that the recognition power of the UHDA with clock-gating in 28nm technology is 33.32 μw/frame. When UHDA runs at long-term standby mode, it saves more than 39.15% power than recognition power.
EmoEM: Emotional Expression in a Multi-turn Dialogue Model

Emotional intelligence is a crucial part for human-machine dialogue system. However, the existing research on dialogue mainly faces three problems: (1) focus on the content level of each response while ignoring the impact of emotional factors in the multi-turn dialogue; (2) lacking scalability and adaptability is that only the emotion categories specified by users are generated in a single-turn dialogue; (3) it is difficult to capture and perceive fine-grained emotions and the speaker's emotional state according to the emotional context. To address these problems, we propose an emotional expression model in multi-turn dialogue (EmoEM), which combines emotion-semantic graph with multitask learning mechanism, applying the dialogue generator based on seq2seq network and graph convolution network (GCN) to generate more natural and personalized emotional responses in a structured manner. Generally, EmoEM considers constructing emotion-semantic graph to describe explicit and implicit emotions dynamically. Then, the emotion-semantic graph is applied to the dialogue generator based on the seq2seq neural network, mainly to improve the semantic consistency and text quality in multi-turn dialogue. Moreover, multi-task learning mechanism is introduced to enhance the emotional expression of the text and obtain expected emotional responses. The experimental results show that EmoEM outperforms several baselines in BLEU, diversity and emotional expression.
A Programmable Quantum Router and Its Linear Optical Implementation

In this paper, we propose a linear optical implementation scheme of the quantum router. A programming unit is designed. The quantum router makes use of devices which are experimentally realized easily. The signal can be routed into a superposition of output modes through the routing operation, and the spatial degree of freedom of each output mode is related to the control parameter. In addition, the strategy of multistage routing is also exploited on the basis of this router model, and it can exponentially expand the number of output ports. The quantum router is deterministic, rather than probabilistic, no matter how many output ports the quantum router has.
Cluster Analysis of Passive DNS Features for Identifying Domain Shadowing Infrastructure

Illicitly-registered subdomains are often trusted by users since they typically inherit the trust and reputation of their parent (apex) domains. This trust can be leveraged by attackers to deliver malware, obtain credentials, or redirect users to exploit kits. Classifiers have been successfully used to identify shadowed domains; however, these approaches stop short of identifying associations among these domains that may correlate to a specific activity group or exploit kit. The identification of adversary infrastructure and the tools common to that infrastructure is helpful in developing possible mitigation strategies.This paper investigates how cluster analysis can be used to identify shadowed versus non-shadowed domains as well as group shadowed domains into meaningful clusters for tracking adversaries. Features are engineered primarily from PDNS data, with which a probabilistic model is built to identify sub-populations within the overall population of shadowed domains. Cluster labeling is then performed, using known exploit kit associations with the shadowed domains as a proxy for an activity group. Experimental results using PDNS information from three different exploit kit campaigns and a randomly selected set of non-shadowed domains show that non-shadowed domains generally cluster separately from shadowed. Furthermore, when only considering shadowed domains, the clusters largely consist of domains associated with a single exploit kit, illustrating that the features engineered from PDNS data are useful not only for identifying shadowed domains, but also for grouping infrastructure that is likely related.
Subspace-Dependent Adjacency Matrix Design via Discrete-Continuous optimization

We present a method for adjacency matrix design by applying both discrete and continuous optimization techniques, which are powered by energy minimization and gradient descent respectively. Most of the subspace clustering methods design the adjacency matrix by adopting a common calculation rule over the entire data set, which is under the assumption that a same noise model is shared between points. Therefore, these methods tend to fail when the points contain multi-source noises. To relax this limitation, we introduce a method that finds the neighborhoods by considering discrete-continuous optimization. Our method outperforms competitive methods on both synthetic and real-world data.
Experiments with Automatic and Semi-automatic Detection of Sparse Word Forms in Old Braj

This paper presents the work on automatic converb detection in Old Braj poetry from the 15–17 centuries. This is a part of research on non-finite verbal forms in early New Indo-Aryan (NIA) language corpora comprising data from Old Rajasthani, Awadhi, Braj, Dakkhini and Pahari [8]. The goal of the detection mechanism is to successfully identify a plaintext word as a converb or non-converb. Such mechanism facilitates further converb description and analysis, which is of great importance in research on historical syntax of NIA. In order to develop the automatic detector, a selection of state-of-art statistical classification mechanisms was used.
Constructing Neural Networks by Extending the Optimization Field

The performance of the neural network is not unilaterally increased with the training parameters of the neural network, and it is possible that the parameters of the training ground become more and the performance is degraded. This paper explains what the optimization field is and proposes a new way to solve this degradation problem. The new method is to built a neural network which includes the previous optimization field while adding parameters and extends the new optimization field. From this point, the paper explains the excellence of Inception, ResNet and DenseNet networks. Furthermore, this paper also proposes a new method for constructing neural networks that can improve the performance of neural networks with the increase of training parameters.
Experiencing autonomous futures: Engaged learning with next generation technology

Digital software has been proliferating in the classroom, frequently replacing students’ hands on experiences. This article reports on a study that tested how hands on experiences with physical emerging technology, namely personal robots, drones, cameras, and echo dots, may improve or impair student learning and enhance or diminish the classroom experience. This study examines the potential impact of hands on experiences on student knowledge, skills, and values regarding emerging technology in the context of a course that employed traditional learning, defined as theoretical applications of next generation technology, namely autonomous vehicles during the first half, and hands on experiences with physical emerging technologies related to domotics, during the second half. Using three diagnostics, tweets, reflections, and discussion posts, we suggest that hands on experiences allowed students to more easily identify potential challenges with, and reduced their fears regarding, emerging technology. Additionally, our findings suggest that traditional learning methods may decrease familiarity with most technologies, whereas hands on experiences increased familiarity. Hands on experiences also appear to reverse several other trends associated with traditional learning. Ultimately, given the proliferation of technologies in the modern world and the impacts of automation, hands on experiences may be even more valuable than expected to students as they enter the workforce.
Cloud Computing Technology: the choice to Enhance the Learning Environment for Automotive Vocational Teacher Education Students as Autonomous Learners

Abstract Automotive vocational teacher education is an education that has a purpose to prepare their students to be a teacher or an instructor in automotive vocational education. Therefore, they need to become autonomous learners to stay updated related to the development of automotive technology whenever they need it. This is very indispensable to set the learning environment to support their study activities to become autonomous learning. This study aims to determine the way the learning environment could be built to support their independent learning activities based on the students’ perspectives. This study is descriptive research that contains quantitative and qualitative data. The data was collected from final-year Automotive Engineering Education department students at Yogyakarta State University by giving an open questionnaire. The results of the study are (1) 40.54% of respondents choose the development of cloud computing technology, 13.51% and 35.14% of respondents choose the development of physical things and non-physical things respectively as the way to build a supportive learning environment, then the rest 10.81% of respondents said that the existed learning environment has already been good, (2) online collaboration learning, online connection with industrial practitioners, blended learning, online resource center, and online educational media are the examples at most of several ways and competencies that are needed to build a supportive learning environment from the development of cloud computing technology, (3) practical lesson infrastructure and teacher’s administration for teaching are needed from the development of physical things, (4) comfortable, pleasant, relax, and not serious situation, discipline, friendly lecturers, and an atmosphere as if being a vocational teacher are needed to be built so as to have supportive learning environment.
Localised FM Digital Audio Broadcasting using WiFi Mesh Networks

Digital Audio Broadcasting via FM radio continues to be popular, even today, owing to its accessibility, especially in rural areas where even common feature phones and affordable radio receivers are able to freely receive both, mainstream and local vernacular transmissions without any subscription charge. Additionally, it is established that broadcasts undertaken at a local level can play an instrumental role in facilitating cultural expression and aiding information delivery at a local level However, local broadcasts via the current Community Radio Stations (CRS) are not only expensive but also unsuitable for remote, isolated regions with their rugged terrains.The authors hereby propose a novel approach to help address these current problems through a distributed system for Digital Audio Broadcasting. Multiple FM transmitting nodes (using VHF II: 87.5-100 MHz) form a WiFi Mesh Network to receive digital audio signals to broadcast from 1 hub node. The components used are off-the-shelf, cost-effective, and modular. It is more cost-effective than current CRS systems for mid-scale local broadcasts, with essentially no data loss between the transmitter units in deployment. Additionally, unlike relays, an existing alternative, built-in redundancy in the system ensures reliability, and caching protects against minor network disturbances when recordings are being broadcast. This approach allows for flexible and efficient deployments with wide, precise and accurate coverage, notably remaining equally effective in areas with an uneven terrain, thereby expanding its potential applications.Experiments involving the system were conducted by varying the audio samples and the conditions, for which the evaluation metrics included the PSNR, PSD spectrograms, DFTs, signal strength, and data transmission rate to gauge the transmission quality, range and scalability. Results included a moderate data rate of 12 mbits/s at a 100 metres separation with obstacles from the transmitter, which is sufficient for the broadcasting and fast caching of high-fidelity audio.