Miracle-dz/UniFAR-FTUs huggingface dataset:
ANALISIS AUDIT ENERGI SEBAGAI UPAYA PENINGKATAN EFISIENSI PENGGUNAAN ENERGI LISTRIK (APLIKASI PADA GEDUNG J16 DEPARTEMEN TEKNIK ELEKTRO UNIVERSITAS SUMATERA UTARA)

Audit energi adalah kegiatan untuk mengidentifikasi dimana dan berapa energi yang digunakan serta langkah-langkah apa yang dapat dilakukan dalam rangka konservasi energi pada suatu fasilitas pengguna energi. Pada tulisan ini, kegiatan audit energi dilakukan pada Gedung J16 Departemen Teknik Elektro Universitas Sumatera Utara. Dari hasil perhitungan diperoleh intensitas konsumsi energi listrik Gedung J16 Departemen Teknik Elektro USU untuk ruangan non-AC 1,77 kWh/m 2 /bulan termasuk dalam kriteria cenderung tidak efisien dan 8,84 kWh/m 2 /bulan untuk ruangan dengan AC termasuk dalam kriteria efisien. Berdasarkan penelitian yang telah dilakukan diketahui bahwa sebagian besar ruangan pada Gedung J16 Departemen Teknik Elektro Universitas Sumatera Utara memiliki temperatur ruangan ideal namun masih banyak ruangan memiliki intensitas cahaya dibawah nilai standar. Efisiensi energi listrik di Gedung J16 Departemen Teknik Elektro Universitas Sumatera Utara dapat ditingkatkan dengan menambah atau mengganti lampu di dalam ruangan yang intensitas cahayanya kurang, memperbaiki faktor daya juga menerapkan budaya hemat energi di lingkungan Departemen Teknik Elektro Universitas Sumatera Utara.
AI-Assisted Authentication: State of the Art, Taxonomy and Future Roadmap

Artificial Intelligence (AI) has found its applications in a variety of environments ranging from data science to cybersecurity. AI helps break through the limitations of traditional algorithms and provides more efficient and flexible methods for solving problems. In this paper, we focus on the applications of artificial intelligence in authentication, which is used in a wide range of scenarios including facial recognition to access buildings, keystroke dynamics to unlock smartphones. With the emerging AI-assisted authentication schemes, our comprehensive survey provides an overall understanding on a high level, which paves the way for future research in this area. In contrast to other relevant surveys, our research is the first of its kind to focus on the roles of AI in authentication.
Better Query Graph Selection for Knowledge Base Question Answering

This paper presents a novel approach based on semantic parsing to improve the performance of Knowledge Base Question Answering (KBQA). Specifically, we focus on how to select an optimal query graph from a candidate set so as to retrieve the answer from knowledge base (KB). In our approach, we first propose to linearize the query graph into a sequence, which is used to form a sequence pair with the question. It allows us to use mature sequence modeling, such as BERT, to encode the sequence pair. Then we use a ranking method to sort candidate query graphs. In contrast to the previous studies, our approach can efficiently model semantic interactions between the graph and the question as well as rank the candidate graphs from a global view. The experimental results show that our system achieves the top performance on ComplexQuestions and the second best performance on WebQuestions.
Energy-Efficient Dynamic Edge Computing with Electromagnetic Field Exposure Constraints

We present a dynamic resource allocation strategy for energy-efficient and Electromagnetic Field (EMF) exposure aware computation offloading at the wireless network edge. The goal is to maximize the overall system sum-rate of offloaded data, under stability (i.e. finite end-to-end delay), EMF exposure and system power constraints. The latter comprises end devices for uplink transmission and a Mobile Edge Host (MEH) for computation. Our proposed method, based on Lyapunov stochastic optimization, is able to achieve this goal with theoretical guarantees on asymptotic optimality, without any prior knowledge of wireless channel statistics. Although a complex long-term optimization problem is formulated, a per-slot optimization based on instantaneous realizations is derived. Moreover, the solution of the instantaneous problem is provided with closed form expressions and fast iterative procedures. Besides the theoretical analysis, numerical results assess the performance of the proposed strategy in striking the best trade-off between offloading sum-rate, power consumption, EMF exposure, and E2E delay. To the best of our knowledge, this is the first work addressing the problem of energy and exposure aware computation offloading.
A semi-analytical solution to the forward-reverse shock hydrodynamics of the gamma-ray burst afterglow

We extend the standard model of forward-reverse shock (FS-RS) for gamma-ray burst (GRB) afterglow to more general cases. On one hand, we derive the analytical solution to the hydrodynamics of the shocks in two limiting cases, i.e., an ultra-relativistic reverse shock case and a Newtonian reverse shock case. Based on the asymptotic solutions in these two limiting cases, we constitute a semi-analytical solution for the hydrodynamics of the shocks in the generic case, covering the mildly-relativistic reverse shock case. On the other hand, we derive the evolution of the system taking into account the condition of energy conservation which is not satisﬁed in the standard FS-RS model. A generic solution of semi-analytical expressions is also given. In both the extended standard FS-RS model (satisfying pressure balance condition) and the model satisfying energy conservation, we ﬁnd that the results in the ultra-relativistic reverse shock case and in the early stage of the Newtonian reverse shock case are diﬀerent from those in the standard FS-RS model by only a factor that close to one while the same initial conditions adopted. However, the asymptotic solutions in the limiting cases are not good approximations to those in the intermediate case. Our semi-analytical results agree well with the numerical results for a large range of model parameters, and hence can be easily employed to diagnose the physical quantities of the GRB shell and circumburst environment.
Hear No Evil:Encouraging construction workers to reduce their noise exposure

In construction, noise has traditionally been identified as a difficult hazard to address. However, the many associated costs have made noise exposure reduction an area of concern for comapnies. Ninety eight workers from a large construction organisation were interviewed about their knowledge and beliefs about noise reduction activities. Workers‟ participation in these activities and their attitudes to noise were examined in relation to constructs of the Health Belief Model. Opportunities to improve and encourage workers‟ motivation and participation are discussed. Introduction to Noise Exposure Costs and Consequences The direct cost of noise induced hearing loss (NIHL) as defined by compensation claims has been found to be considerable. In 2006/2007, in NSW Australia, hearing loss claims accounted for 56.1% of all occupational disease claims within the construction industry, equating to a direct cost of $AUS 7.6m (WorkCover, 2008). In reality, the actual cost of NIHL (including costs of ongoing rehabilitation, and indirect costs such as reduced work capacity and burden of disease costs incurred by individuals, families, and the community) is difficult to quantify. For this reason, the construction industry recognises noise as a serious workplace hazard. However, reducing noise exposure in construction has been acknowledged as a difficult goal to achieve (Berglund & Lindvall, 1995; Nietzel et al, 1999; EASHW, 2005; Colvin & Luxon, 2007). Worksites are generally expected to be an area of high noise and noise is often viewed as one of the lower level hazards on site. Thus workplaces need to carefully consider a range of about factors influencing workers‟ attitudes to noise reduction in order to improve their participation.
Edge-PRUNE: Flexible Distributed Deep Learning Inference

Collaborative deep learning inference between low-resource endpoint devices and edge servers has received significant research interest in the last few years. Such computation partitioning can help reducing endpoint device energy consumption and improve latency, but equally importantly also contributes to privacy-preserving of sensitive data. This paper describes Edge-PRUNE, a flexible but light-weight computation framework for distributing machine learning inference between edge servers and one or more client devices. Compared to previous approaches, Edge-PRUNE is based on a formal dataflow computing model, and is agnostic towards machine learning training frameworks, offering at the same time wide support for leveraging deep learning accelerators such as embedded GPUs. The experimental section of the paper demonstrates the use and performance of Edge-PRUNE by image classification and object tracking applications on two heterogeneous endpoint devices and an edge server, over wireless and physical connections. Endpoint device inference time for SSD-Mobilenet based object tracking, for example, is accelerated 5.8x by collaborative inference.
Electrified Autonomous Freight Benefit analysis on Fleet, Infrastructure and Grid Leveraging Grid-Electrified Mobility (GEM) Model

This paper analyzes the potential benefit of heavy-duty vehicle (HDV) electrification and automation on fleet cost, infrastructure cost, grid, and environmental impact. In this work, we extended the vehicle electrification benefit analysis tool: Grid-Electrified Mobility (GEM) model, which had primarily been used to study light-duty passenger vehicles (LDVs), to analyze the heavy-duty vehicle electrification. The extended model is derived for freight transportation and key results and findings on the impact of freight electrification and automation are presented and discussed.
CapOnImage: Context-driven Dense-Captioning on Image

Existing image captioning systems are dedicated to generating narrative captions for images, which are spatially detached from theimage in presentation. However, texts can also be used as decorations on the image to highlight the key points and increase theattractiveness of images. In this work, we introduce a new taskcalled captioning on image (CapOnImage), which aims to generatedense captions at different locations of the image based on contextual information. To fully exploit the surrounding visual context togenerate the most suitable caption for each location, we propose amulti-modal pre-training model with multi-level pre-training tasksthat progressively learn the correspondence between texts and image locations from easy to difficult. Since the model may generateredundant captions for nearby locations, we further enhance thelocation embedding with neighbor locations as context. For thisnew task, we also introduce a large-scale benchmark called CapOnImage2M, which contains 2.1 million product images, each with anaverage of 4.8 spatially localized captions. Compared with other image captioning model variants, our model achieves the best resultsin both captioning accuracy and diversity aspects.
The Rise of Buoyant Magnetic Structures through Convection with a Background Magnetic Field

Inspired by observations of sunspots embedded in active regions, it is often assumed that large-scale, strong magnetic flux emerges from the Sun’s deep interior in the form of arched, cylindrical structures, colloquially known as flux tubes. Here, we continue to examine the different dynamics encountered when these structures are considered as concentrations in a volume-filling magnetic field rather than as isolated entities in a field-free background. Via 2.5D numerical simulations, we consider the buoyant rise of magnetic flux concentrations from a radiative zone through an overshooting convection zone that self-consistently (via magnetic pumping) arranges a volume-filling large-scale background field. This work extends earlier papers that considered the evolution of such structures in a purely adiabatic stratification with an assumed form of the background field. This earlier work established the existence of a bias that created an increased likelihood of the successful rise for magnetic structures with one (relative) orientation of twist and a decreased likelihood for the other. When applied to the solar context, this bias is commensurate with the solar hemispherical helicity rules (SHHRs). This paper establishes the robustness of this selection mechanism in a model incorporating a more realistic background state, consisting of overshooting convection and a turbulently pumped mean magnetic field. Ultimately, convection only weakly influences the selection mechanism, since it is enacted at the initiation of the rise, at the edge of the overshoot zone. Convection does however add another layer of statistical fluctuations to the bias, which we investigate in order to explain variations in the SHHRs.
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning

Building generalizable goal-conditioned agents from rich observations is a key to reinforcement learning (RL) solving real world problems. Traditionally in goal-conditioned RL, an agent is provided with the exact goal they intend to reach. However, it is often not realistic to know the configuration of the goal before performing a task. A more scalable framework would allow us to provide the agent with an example of an analogous task, and have the agent then infer what the goal should be for its current state. We propose a new form of state abstraction called goal-conditioned bisimulation that captures functional equivariance, allowing for the reuse of skills to achieve new goals. We learn this representation using a metric form of this abstraction, and show its ability to generalize to new goals in simulation manipulation tasks. Further, we prove that this learned representation is sufficient not only for goal conditioned tasks, but is amenable to any downstream task described by a state-only reward function. Videos can be found at https://sites.google. com/view/gc-bisimulation.
Supervised Contrastive CSI Representation Learning for Massive MIMO Positioning

Similarity metric is crucial for massive MIMO positioning utilizing channel state information (CSI). In this letter, we propose a novel massive MIMO CSI similarity learning method via deep convolutional neural network (DCNN) and contrastive learning. A contrastive loss function is designed considering multiple positive and negative CSI samples drawn from a training dataset. The DCNN encoder is trained using the loss so that positive samples are mapped to points close to the anchor’s encoding, while encodings of negative samples are kept away from the anchor’s in the representation space. Evaluation results of fingerprint-based positioning on a real-world CSI dataset show that the learned similarity metric improves positioning accuracy significantly compared with other known state-of-the-art methods.
Echoes through Time: Evolution of the Italian COVID-19 Vaccination Debate

Italy was the first European country to be hit by COVID-19 in the early 2020, since then losing over 100,000 people to the disease. By the end of the vaccination campaign of 2021, 81% of the public received at least one dose. These dramatic developments were accompanied by a rigorous discussion around vaccination, both about its urgency and its possible negative effects. Twitter is one of the most popular social media platforms in the country, but pre-pandemic vaccination debate has been shown to be polarized and siloed into echo chambers. It is thus imperative to understand the nature of this discourse, with a specific focus on the vaccination hesitant individuals, whose healthcare decisions may affect their communities and the country at large. In this study we ask, how has the Italian discussion around vaccination changed during the COVID-19 pandemic, and have the unprecedented events of 2020-2021 been able to break the echo chamber around this topic? We use a Twitter dataset spanning September 2019 - November 2021 to examine the state of polarization around vaccination. We propose a hierarchical clustering approach to find the largest communities in the endorsement networks of different time periods, and manually illustrate that it produces communities of users sharing a stance. Examining the structure of these networks, as well as textual content of their interactions, we find the stark division between supporters and hesitant individuals to continue throughout the vaccination campaign. However, we find an increasing commonality in the topical focus of the vaccine supporters and vaccine hesitant, pointing to a possible common set of facts the two sides may agree on. Still, we discover a series of concerns voiced by the hesitant community, ranging from unfounded conspiracies (microchips in vaccines) to public health policy discussion (vaccine passport limitations). We recommend an ongoing surveillance of this debate, especially to uncover concerns around vaccination before the public health decisions and official messaging are made public.
GTNet: A Tree-Based Deep Graph Learning Architecture

We propose Graph Tree Networks (GTNets), a deep graph learning architecture with a new general message passing scheme that originates from the tree representation of graphs. In the tree representation, messages propagate upward from the leaf nodes to the root node, and each node preserves its initial information prior to receiving information from its child nodes (neighbors). We formulate a general propagation rule following the nature of message passing in the tree to update a node's feature by aggregating its initial feature and its neighbor nodes' updated features. Two graph representation learning models are proposed within this GTNet architecture - Graph Tree Attention Network (GTAN) and Graph Tree Convolution Network (GTCN), with experimentally demonstrated state-of-the-art performance on several popular benchmark datasets. Unlike the vanilla Graph Attention Network (GAT) and Graph Convolution Network (GCN) which have the"over-smoothing"issue, the proposed GTAN and GTCN models can go deep as demonstrated by comprehensive experiments and rigorous theoretical analysis.
Chiral hinge transport in disordered non-Hermitian second-order topological insulators

The generalized bulk-boundary correspondence predicts the existence of the chiral hinge states in three-dimensional second-order topological insulators (3DSOTIs), resulting in a quantized Hall effect in three dimensions. Chiral hinge states in Hermitian 3DSOTIs are characterized by the quantized transmission coefficients with zero fluctuations, even in the presence of disorders. Here, we show that chiral hinge transport in disordered non-Hermitian systems deviates from the paradigm of the Hermitian case. Our numerical calculations prove the robustness of hinge states of disordered non-Hermitian 3DSOTIs. The mean transmission coefficients may or may not equal the number of chiral hinge channels, depending on the Hermiticity of chiral hinge states, while the fluctuations of transmission coefficients are always non-zero. Such fluctuations are not due to the broken chirality of hinge states but the incoherent scatterings of non-Hermitian potentials. The physics revealed here should also be true for one-dimensional chiral channels in topological materials that support chiral boundary states, such as Chern insulators, three-dimensional anomalous Hall insulators, and Weyl semimetals.
Autonomous Vehicle Calibration via Linear Optimization

In navigation activities, kinematic parameters of a mobile vehicle play a significant role. Odometry is most commonly used for dead reckoning. However, the unrestricted accumulation of errors is a disadvantage using this method. As a result, it is necessary to calibrate odometry parameters to minimize the error accumulation. This paper presents a pipeline based on sequential least square programming to minimize the relative position displacement of an arbitrary landmark in consecutive time steps of a kinematic vehicle model by calibrating the parameters of applied model. Results showed that the developed pipeline produced accurate results with small datasets.
MemFHE: End-to-End Computing with Fully Homomorphic Encryption in Memory

The increasing amount of data and the growing complexity of problems has resulted in an ever-growing reliance on cloud computing. However, many applications, most notably in healthcare, finance or defense, demand security and privacy which today’s solutions cannot fully address. Fully homomorphic encryption (FHE) elevates the bar of today’s solutions by adding confidentiality of data during processing. It allows computation on fully encrypted data without the need for decryption, thus fully preserving privacy. To enable processing encrypted data at usable levels of classic security, e.g., 128-bit, the encryption procedure introduces noticeable data size expansion - the ciphertext is much bigger than the native aggregate of native data types. In this paper, we present MemFHE which is the first accelerator of both client and server for the latest Ring-GSW (Gentry, Sahai, and Waters [17]) based homomorphic encryption schemes using Processing In Memory (PIM). PIM alleviates the data movement issues with large FHE encrypted data, while providing in-situ execution and extensive parallelism needed for FHE’s polynomial operations. While the client-PIM can homomorphically encrypt and decrypt data, the server-PIM can process homomorphically encrypted data without decryption. MemFHE’s server-PIM is pipelined and is designed to provide flexible bootstrapping, allowing two encryption techniques and various FHE security-levels based on the application requirements. We evaluate MemFHE for various security-levels and compare it with state-of-the-art CPU implementations for Ring-GSW based FHE. MemFHE is up to 20k × (265 ×) faster than CPU (GPU) for FHE arithmetic operations and provides on average 2007 × higher throughput than [36] while implementing neural networks with FHE.
Stability in affine optimal control problems constrained by semilinear elliptic partial differential equations

This paper investigates stability properties of affine optimal control problems constrained by semilinear elliptic partial differential equations. This is done by studying the so called metric subregularity of the set-valued mapping associated with the system of first order necessary optimality conditions. Preliminary results concerning the differentiability of the functions involved are established, especially the so-called switching function. Using this ansatz, more general nonlinear perturbations are encompassed, and under weaker assumptions, than the ones previously considered in the literature on control constrained elliptic problems. Finally, the applicability of the results is illustrated with some error estimates for the Tikhonov regularization.
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation

Dialog response generation in open domain is an important research topic where the main challenge is to generate relevant and diverse responses. In this paper, we propose a new dialog pre-training framework called DialogVED, which introduces continuous latent variables into the enhanced encoder-decoder pre-training framework to increase the relevance and diversity of responses. With the help of a large dialog corpus (Reddit), we pre-train the model using the following 4 tasks, used in training language models (LMs) and Variational Autoencoders (VAEs) literature: 1) masked language model; 2) response generation; 3) bag-of-words prediction; and 4) KL divergence reduction. We also add additional parameters to model the turn structure in dialogs to improve the performance of the pre-trained model. We conduct experiments on PersonaChat, DailyDialog, and DSTC7-AVSD benchmarks for response generation. Experimental results show that our model achieves the new state-of-the-art results on all these datasets.