Miracle-dz/UniFAR-FTUs huggingface dataset:
Exponential Elimination for Bicartesian Closed Categorical Combinators

Categorical combinators offer a simpler alternative to typed lambda calculi for static analysis and implementation. Since categorical combinators are accompanied by a rich set of conversion rules which arise from categorical laws, they also offer a plethora of opportunities for program optimization. It is unclear, however, how such rules can be applied in a systematic manner to eliminate intermediate values such as exponentials, the categorical equivalent of higher-order functions, from a program built using combinators. Exponential elimination simplifies static analysis and enables a simple closure-free implementation of categorical combinators--reasons for which it has been sought after. In this paper, we prove exponential elimination for bicartesian closed categorical (BCC) combinators using normalization. We achieve this by showing that BCC terms can be normalized to normal forms which obey a weak subformula property. We implement normalization using Normalization by Evaluation, and also show that the generated normal forms are correct using logical relations.
Applying a Bayesian Stackelberg game to secure infrastructure system: from a complex network perspective

Protecting critical infrastructure is a challenging task for security agencies all around the world, which is exacerbated by terrorism. Many infrastructures have network characteristics, such as transportation and communication systems. Therefore, it is necessary to study them from the perspective of network science. Game theory provides a suitable theoretical tool for the study of attack-defense confrontation between rational agents. Sequential game, which is more in line with the actual situation, can better analyze the confrontation in infrastructure protection. Previous models which combine network science with game theory only consider the attacker as a single type. However, in the real world, accurate information about the type of the attacker is not always available for the defender. In this paper, we apply Bayesian Stackelberg game to model the attack-defense confrontation in the case of uncertain attacker type.
Research on Simultaneous localization and Mapping Scheme of Robot in Challenging Scenarios

Steam generator is the key equipment of nuclear power plant, and the cleanliness inspection of heat transfer tubes inside is especially important. A special-purpose evaporator inspection robot can effectively and safely inspect the heat transfer tubes. Location and mapping are of great significance to the operation of the special robot under complex steam generator conditions. Different usage scenarios are suitable for different mapping and positioning schemes. This paper introduces the scheme based on pure vision and the scheme of integrating inertial navigation unit. Compared with the scheme based on pure vision, applying the inertial measurement unit significantly increase the robustness of the V-SLAM system.
Rocket Powered Landing Guidance Using Proximal Policy Optimization

Rocket recovery requires advanced guidance algorithms to achieve pinpoint landing while satisfying multiple stringent constraints. In this paper, we design a guidance law based on reinforcement learning for the powered landing phase of vertical take-off and vertical landing reusable rocket. To this end, we apply the proximal policy optimization algorithm to develop a control policy that drives the rocket to land at a specified location. The policy parameterized using a neural network is updated by performing gradient ascent algorithm. After abundant amount of training, the learned policy is evaluated in a simulation of the rocket powered landing scenario considering aerodynamic drag, and the result demonstrates the ability of the proposed guidance method to successfully land the rocket from a random initial state.
Application of PSO on Electromagnetic Induction-Based Subsea Cable Detection

Subsea cables face more and more challenges due to the increasing marine economic activities and frequent natural disasters, such as anchorage, fishing, widening, earthquake and tsunami, which greatly affect the life of subsea cables and pipelines. Firstly, this paper reviews the situation of subsea cables and the resulting difficulties for localization. The tradition subsea detection methods, such as hydroacoustic and visual methods, are excluded from the subsea cable detection and localization due to the applicability of detection for large-sized and exposed subsea pipelines. Based on the structural composition of different subsea cables, the active electromagnetic sensing method are selected as the optimal approach to combined with particle swarm optimization (PSO). Secondly, based on the extensive application of the swarm intelligent optimization algorithm in practical engineering, this paper proposes a novel subsea cable localization (discrete cable location and burial depth) method by using an underactuated autonomous underwater vehicle (AUV) carrying with a tri-axial electromagnetic sensor. The electromagnetic sensor is used to capture the magnetic signal which are radiated from the AC electrified subsea cable. Based on the framework of PSO and electromagnetic signal sequences, the PSO localization method is designed. Furthermore, the electromagnetic sensing based PSO localization method is compared with cable-following detection and tracking method from the practical view. Finally, a principle numerical simulation is design to test the effectiveness of the proposed subsea cable localization method. In order to meet the practical demands, the PSO should be modified to improve the stability and convergence speed, the online path planning and platform motion control method should be developed.
Study on Differentially Expressed Genes in Breast Tissue

Nowadays, breast cancer has been considered as one of the most common malignant tumors among female diseases, and its incidence is increasing year by year. Genomic and transcriptome changes that affect cellular processes, such as cell proliferation, differentiation, apoptosis, and invasion, are commonly associated with the development of breast cancer. Epidemiological evidence has shown that the risk of breast cancer susceptibility varies among ethnic groups. In this paper, differentially expressed genes between human breast tumors and normal tissues are identified by studying Chinese, Malay and Indian ethnic groups and their paired tissues. The GO and KEGG methods were used for annotation and analysis of genes, and protein interaction networks were used to analyze protein interactions. The results of the study showed that there were 33 genes with significant differences in expression. The differentially expressed genes were CD36, LEP, LPL, ADIPOQ, PCK1, ACASL1, PPARG, PLIN1, ACACB and PPARG. In the protein interaction network, the interaction between AKR1C3 and AKR1C1 is the strongest. The study of differentially expressed genes and the search for proteins with the strongest interactions will contribute to the study of the molecular mechanism of the disease and the discovery of new drug targets, so as to improve the future treatment of breast cancer patients.
Adapting TCP for reconfigurable datacenter networks

Reconﬁgurable datacenter networks (RDCNs) augment traditional packet switches with high-bandwidth reconﬁgurable circuits. In these networks, high-bandwidth circuits are assigned to particular source-destination rack pairs based on a schedule. To make efﬁcient use of RDCNs, active TCP ﬂows between such pairs must quickly ramp up their sending rates when high-bandwidth circuits are made available. Past studies have shown that TCP performs well on RDCNs with millisecond-scale reconﬁguration delays, during which time the circuit network is ofﬂine. However, modern RDCNs can reconﬁgure in as little as 20 µ s, and maintain a particular con-ﬁguration for fewer than 10 RTTs. We show that existing TCP variants cannot ramp up quickly enough to work well on these modern RDCNs. We identify two methods to address this issue: First, an in-network solution that dynamically resizes top-of-rack switch virtual output queues to prebuffer packets; Second, an endpoint-based solution that increases the congestion window, cwnd , based on explicit circuit state feedback sent via the ECN-echo bit. To evaluate these techniques, we build an open-source RDCN emulator, Etalon, and show that a combination of dynamic queue resizing and explicit circuit state feedback increases circuit utilization by 1.91 × with an only 1.20 × increase in tail latency.
Enabling Robot Selective Trained Deep Neural Networks for Object Detection Through Intelligent Infrastructure

To save costs in logistics, handling steps are going to be automated by robots in the future. Due to the complex industrial conditions prevailing there, this is only possible with a sufficient degree of intelligence in the respective systems. Despite advances in artificially intelligent algorithms, the latest neural networks reveal significant weaknesses in performance and transferability to other applications. In order to enable a holistic autonomous material flow, the paper presents an infrastructure concept, which makes it possible to identify and train the most suitable networks robot-selectively with very limited effort. Using two practical examples, the functionality of the designed algorithms for the industrial implementation of a new use case as well as the updating and improvement of an existing system is finally outlined. It will be shown that with measures such as the automated collection of training data, the AI-supported labeling process, the intuitive validation of the trained networks via a mobile application and the automated retraining of robots already integrated, a further step can be taken towards holistically automated logistics process chains.
The Unreasonable Fairness of Maximum Nash Welfare

The maximum Nash welfare (MNW) solution—which selects an allocation that maximizes the product of utilities—is known to provide outstanding fairness guarantees when allocating divisible goods. And while it seems to lose its luster when applied to indivisible goods, we show that, in fact, the MNW solution is strikingly fair even in that setting. In particular, we prove that it selects allocations that are envy-free up to one good—a compelling notion that is quite elusive when coupled with economic efficiency. We also establish that the MNW solution provides a good approximation to another popular (yet possibly infeasible) fairness property, the maximin share guarantee, in theory and—even more so—in practice. While finding the MNW solution is computationally hard, we develop a nontrivial implementation and demonstrate that it scales well on real data. These results establish MNW as a compelling solution for allocating indivisible goods and underlie its deployment on a popular fair-division website.
Perforated Dielectric Resonator Antenna Operating on Higher Order Mode

A wideband, high gain perforated dielectric resonator antenna (DRA) is presented in this paper. Higher order TEδ15 mode is excited to enhance the gain, while perforations improve the bandwidth of the proposed antenna by lowering down the quality factor. The perforations are array of symmetrical square shaped slots that are uniformly drilled on the DRA. At 26 GHz, the proposed geometry exhibits a wide impedance bandwidth of 17.33% (24.5–27.8 GHz) and a maximum gain of 9.2 dBi. The simulated radiation efficiency is 94% for the complete bandwidth. ECCOCK HiK TEK material with dielectric constant of 10 is used to form dielectric resonator antenna. The DRA is excited by 50Ω microstrip line with aperture slot. The simulated results have been analyzed and discussed in terms of reflection coefficient, gain, efficiency and radiation patterns. The proposed antenna can be used for Internet of Things (IoT) applications such as Device-to-Device (D2D) Communication.
Poster Abstract: Secure Data Sharing by Means of Fragmentation, Encryption, and Dispersion

Outsourcing data to the cloud raise several security challenges. Among others, a user may fear that their data could be exposed during a data breach, misused by the storage provider, or acquired by an attacker observing communication channels. In this poster abstract, we introduce a conceptual multi-cloud architecture reinforcing the confidentiality of the information sharing. Not only it does protect against untrusted storage providers but also is a secure alternative to direct data sharing between users. Moreover, it simplifies the access revocation procedure when the data is shared between multiple users.
Monolithic $1\times4$ Reconfigurable Electro-Optic Tunable Interleaver in Lithium Niobate Thin Film

We propose and experimentally demonstrate a monolithically integrated <inline-formula> <tex-math notation="LaTeX">$1\times 4$ </tex-math></inline-formula> reconfigurable electro-optic tunable interleaver in X-cut lithium niobate thin film by cascading three asymmetrical Mach-Zehnder interferometers into two stages. Over a wide wavelength range from 1530 nm to 1605 nm, our fabricated device exhibits polarization-insensitive center wavelengths and channel spacing of ~50 GHz, slightly different switching voltages of 4.5 V and 6.7 V, and extinction ratios of 8–15 dB and 7–13 dB for the transverse electric (TE) and the transverse magnetic (TM) polarized input light, respectively.
Knowledge Map Completion Method Based on Metric Space and Relational Path

Knowledge Graph describes entities and their attributes and relationships in the objective world in a structured way. Aiming at the problem of knowledge imperfection widely existing in the knowledge map, a knowledge representation learning algorithm — PTranSparse, which is based on metric space and relational path, is proposed to complete the knowledge map. This method combines the ability of TranSparse model to process the heterogeneity and unbalance of entity and relationship and PTransE to make full use of the semantic information of relational path to improve the discrimination of knowledge representation learning. Based on the combination of the two models, the relationship types are considered, and the weights associated with the relationship types are added to differentiate the relationship types when entities are projected. Experiments show that, compared with the original models and the existing combination models and methods, this method can effectively improve the link prediction efficiency of the knowledge graph while solving the complex relation reasoning, and ensure a higher accuracy.
Cross-Domain Fault Diagnosis Using Knowledge Transfer Strategy: A Review

Data-driven fault diagnosis has been a hot topic in recent years with the development of machine learning techniques. However, the prerequisite that the training data and the test data should follow an identical distribution prevents the conventional data-driven diagnosis methods from being applied to the engineering diagnosis problems. To tackle this dilemma, cross-domain fault diagnosis using knowledge transfer strategy is becoming popular in the past five years. The diagnosis methods based on transfer learning aim to build models that can perform well on target tasks by leveraging knowledge from semantic related but distribution different source domains. This paper for the first time summarizes the state-of-art cross-domain fault diagnosis research works. The literatures are introduced from three different viewpoints: research motivations, cross-domain strategies, and application objects. In addition, the corresponding open-source fault datasets and several future directions are also presented. The survey provides readers a framework for better understanding and identifying the research status, challenges and future directions of cross-domain fault diagnosis.
Youtube traffic from the perspective of a developing country: the case of Uruguay

Understanding the interconnection of developing countries with the global Internet has both practical and theoretical relevance. On the one side, practitioners such as service providers and network managers would be particularly interested for both commercial and technical reasons, while on the other side, revealing general knowledge about the insights of such interconnection may be of interest for both academia and policy makers. In this study we concentrate in our country Uruguay. Studying the network topology is one of the most intuitive ways to start, but unfortunately this approach does not include traffic over the network or the behavior of users. According to the Cisco Visual Networking Index, the OTT (Over-The-Top) services are one of the biggest responsible for global traffic, with 75% of Internet traffic being video. Therefore, it is natural to expect that when studying where, when and how the OTT content comes from, we would shed light on how our country makes use of the Internet. Due to its relevance, we propose to study YouTube traffic. The content of YouTube (and OTT in general) is provided from CDNs (Content Delivery Networks), large distributed networks which store and distribute content opaque to external users. This makes the problem of studying the origin and traffic routes much more complex. There are several works that have tried to locate the servers and caches that provide content to a specific ISP, mostly combining traffic analysis techniques with diagnostic tools (such as ping and traceroute). The existing bibliography has explored these questions especially for actors in Europe, but little or nothing has been done in Latin America and Uruguay. We seek to reproduce (and hopefully improve) these results in our country and geographically locate (as far as possible) the different servers in which the content we consume is found, and as these change over time, which routes are used to reach our country, among other questions. On the other hand, once you have the information of the servers of origin, many questions arise naturally: is there any correlation between the proximity of the servers and the popular content in Uruguay? , and among the videos recommended to users? In this paper, we report experimental results revealing some interesting aspects of this YouTube's content distribution hierarchy and cache policies, looked from the perspective of an Uruguayan user.
A Novel Robust Finite-Time Trajectory Control With the High-Order Sliding Mode for Human–Robot Cooperation

Human-robot cooperation is the major challenges in robot manipulator control, as the controller has to couple the complicated motion of the human arm and the robot end-effectors. To improve the human-robot coordination, this paper proposed a novel robust finite-time trajectory control based on the nonsingular fast terminal sliding mode and the high-order sliding mode. The proposed method is able to quickly reach the global convergence and minimize motion errors. Based on the nonsingular fast terminal sliding surface, the proposed control method employed a super-twisting algorithm to eliminate the chattering issues to enhance the control robustness. Also, the simplified robust control term does not require the first derivative of the sliding variable. To validate the proposed controller, theoretical analysis and simulation were conducted and the results demonstrated the effectiveness of the proposed method.
DeepAttr: Inferring Demographic Attributes via Social Network Embedding

Online Social Networks (OSNs) (e.g., Facebook, Twitter, and Tecent QQ) are popular platforms for people to share information online. The providers of OSNs typically leverage demographic attributes of users in OSNs to perform business behaviors like personalized commodity recommendation, advertisement delivery, etc. Thus, understanding demographic attributes of users in OSNs is of great importance. However, some users refuse to reveal their demographic information due to privacy concerns. Therefore, inferring demographic attributes of users in OSNs with public information is an attractive topic for researchers. Most existing methods mainly focus on individual attribute inference without taking the relationship among attributes into account, which results in low precisions. In this paper, we propose a novel approach, called DeepAttr, to infer users’ multiple attributes simultaneously. DeepAttr leverages existing network embedding algorithm to learn the social embedding for each user. Meanwhile, it encodes multiple attributes into structured attribute vectors which are treated as class labels. The core component of DeepAttr is a multi-layer fully connected deep neural network which captures complex nonlinear mapping between the users’ social embeddings and the structured attribute vectors. Extensive experiments on a real-world dataset demonstrate that DeepAttr outperforms existing models in both single-attribute inference and multiple-attribute inference as to macro-Precision, macro-Recall, and macro-F1.
A Deep Learning Method for Fault Detection of Autonomous Vehicles

Fault detection is a crucial step for the safe operation of autonomous vehicles. Failure to detect faults can result in component failure leading to the breakdown of the car or even catastrophic accidents. In this paper, we propose a general fault detection method using deep learning techniques to learn patterns of faults reflected in the dynamic model of an autonomous vehicle. We have applied the proposed method to a remotely operated scaled multi-wheeled combat vehicle and evaluated the algorithm using normal and defective signals. The results show that the proposed deep learning method can accurately identify faults that are caused by mechanical problems or changes in system parameter which are reflected in the dynamic models. This general deep learning technique can be tailored to detect many defects or faults in the manufacturing and/or operation of autonomous vehicles.
Hyperspectral Image Classification via Matching Absorption Features

In this paper, we propose to extract spectral absorptions as the discriminative features to classify hyperspectral imagery. Different from previous researches that mainly take hyperspectral curves as high-dimensional inputs, we analyze hyperspectral data more from its physical and chemical origins. In the proposed approach, the discriminatory information, which is characterized by the observed materials’ constituents, is extracted as a group of absorption features. First, the original hyperspectral spectra are transformed to a normalized spectra, in which a modified continuum removal algorithm is adopted to highlight all spectral valleys. Next, a standard peak detection method is applied to the continuum-removed spectra, and all effective absorptions are found as the candidate features. Then, to obtain the most informative absorptions to classification, a novel mutual-information based feature selection method is used to search for the key absorption spectra. Finally, we put forward a matching algorithm to classify the absorption features using the multi-label learning. To testify the proposed method, both laboratory and remotely sensed hyperspectral data are used to evaluate the classification performance. Experimental results show that the proposed method achieves competitive classification accuracy against the state-of-the-art methods, but with an advantage of more compact feature representation.