Reviewerly/exHarmony huggingface dataset:
On the derivatives of set functions in matrix representation

This paper formalises the operations on capacities in matrix algebra framework. Various quantities that characterise the importance of the inputs and their dependencies are expressed through capacity derivatives, obtained through matrix–vector multiplication. Many relations between the set functions derivatives are established and found to be the consequences of the Divergence Theorem. New formulas for Shapley values and nonmodularity indices are found. The sums of the Shapley interaction indices are found to be related to lower order derivatives at the top and bottom elements of the respective power sets. The presented methods simplify many calculations and will facilitate efficient software implementations and applications of the capacity-based decision making methods.
LC-DECAL: Label Consistent Deep Collaborative Learning for Face Recognition

With the advent of deep learning architectures, the performance of face recognition has witnessed significant improvements. However, this has also necessitated the requirement of large labeled training database. While approaches exist to utilize labeled or unlabeled data from related domains, in this paper, we present a collaborative learning framework that utilizes the availability of both labeled and unlabeled data along with the presence of multiple experts, to improve the performance of face analysis related tasks. The proposed Label Consistent Deep Collaborative Learning (LC-DECAL) framework makes use of label consistency, transfer learning, ensemble learning, and co-training for training a deep neural network for the target domain. The efficacy of the proposed algorithm is demonstrated with two existing Convolutional Neural Network architectures, DenseNet and ResNet, via experiments on multiple face databases, namely YTF, PaSC Handheld, PaSC Control, CelebA, and LFW-a. Experimental results show that the proposed framework yields comparable results to state-of-the-art results on all the databases.
Evolution of Complex Coordinated Behavior

Cooperative tasks such as herding and hunting are common among higher animals in nature. A particularly complex example is that of mobbing by spotted hyenas. Through careful coordination, a large number of spotted hyenas can attack a group of lions and successfully steal a kill from them, even though lions are much bigger and stronger. This behavior is more complex than others that hyenas exhibit, and it appears to be heritable. How such behavioral advance can emerge in evolution is a fascinating question; it is difficult to study in nature, but computational simulations can provide insight. In simulation, hyenas initially evolved different levels of boldness, corresponding to simple behaviors such as solo attack, delayed attack, and delayed approach. These behaviors can be seen as stepping stones in constructing the more complex mobbing behavior in later generations. These results suggest a general stepping-stone-based mechanism through which complex coordinated behaviors can arise in humans and animals. This insight should prove useful in building cognitive architectures and team strategies for artificial agents in the future.
Implicit Multidimensional Projection of Local Subspaces

We propose a visualization method to understand the effect of multidimensional projection on local subspaces, using implicit function differentiation. Here, we understand the local subspace as the multidimensional local neighborhood of data points. Existing methods focus on the projection of multidimensional data points, and the neighborhood information is ignored. Our method is able to analyze the shape and directional information of the local subspace to gain more insights into the global structure of the data through the perception of local structures. Local subspaces are fitted by multidimensional ellipses that are spanned by basis vectors. An accurate and efficient vector transformation method is proposed based on analytical differentiation of multidimensional projections formulated as implicit functions. The results are visualized as glyphs and analyzed using a full set of specifically-designed interactions supported in our efficient web-based visualization tool. The usefulness of our method is demonstrated using various multi- and high-dimensional benchmark datasets. Our implicit differentiation vector transformation is evaluated through numerical comparisons; the overall method is evaluated through exploration examples and use cases.
Investigation of Faster-RCNN Inception Resnet V2 on Offline Kanji Handwriting Characters

In recent years detection and recognition of Offline handwriting character has being a major task in the computer vision sector, researchers are looking at developing deep learning models to avoid the traditional approaches which involves the tedious task of using the conventional methods for feature extraction and localization. However, state-of-the-art object detection models rely upon region proposal algorithms as a result, they settle for object location principles, such network reduces the time period of those detection network, exposing region proposal computation as a bottleneck. Faster-RCNN is a popular model used for recognition purpose in many recognition tasks, the goal of this paper is to serve as a guide for Multi-Classification on offline Handwriting Document using Pre-trained Faster-RCNN with inception resnet v2 feature Extractor. The result obtained from the experiments shows improved pre-trained models can be used in solving the research question concerning handwriting detection and recognition.
Fuzzy-Match Repair Guided by Quality Estimation

Computer-aided translation tools based on translation memories are widely used to assist professional translators. A translation memory (TM) consists of a set of translation units (TU) made up of source- and target-language segment pairs. For the translation of a new source segment s', these tools search the TM and retrieve the TUs (s,t) whose source segments are more similar to s'. The translator then chooses a TU and edit the target segment t to turn it into an adequate translation of s'. Fuzzy-match repair (FMR) techniques can be used to automatically modify the parts of t that need to be edited. We describe a language-independent FMR method that first uses machine translation to generate, given s' and (s,t), a set of candidate fuzzy-match repaired segments, and then chooses the best one by estimating their quality. An evaluation on three different language pairs shows that the selected candidate is a good approximation to the best (oracle) candidate produced and is closer to reference translations than machine-translated segments and unrepaired fuzzy matches ( t). In addition, a single quality estimation model trained on a mix of data from all the languages performs well on any of the languages used.
Color removal of Polyethylene Terephthalate waste as a raw material for carbon nanotube synthesis

The level consumption of Polyethylene Terephthalate (PET) as a packaging of colored beverage bottles occupies the fourth largest in the world. In Indonesia, PET consumption increased reaches 7% per year so that it can cause environmental impacts. This study aims to process the recycling PET waste by obtaining a simple potential process to remove the color from PET waste. The decolorized PET waste will be an alternative carbon source for Carbon Nanotube (CNT) synthesis. The colors of PET waste are blue and green bottles. The selected color removal agent is hydrogen peroxide (H2O2) because it is inexpensive reagent and has lower toxicity to environment. The colored PET waste and H2O2 will be heated in the oil bath system at temperature 110°C and pressure 1 atm. The result showed that color removal time for blue PET waste faster than the green PET waste, 72 and 115 minutes per 15 grams PET waste. The quality of color removal of blue PET waste is better than the green PET waste because it has a reflectance value closer to colorless PET waste, namely 80%. The CNT synthesis process from decolorized blue PET waste produce 8.58% yields. The average diameter of CNT crystals from its process is obtained at 37 nm. This shows that PET waste plastic which has been discolored can be used as a carbon source in CNT synthesis.
Sequential Estimation of States and Parameters of Nonlinear State Space Models Using Particle Filter and Natural Evolution Strategy

This paper proposes a new sequential estimation method for simultaneously estimating states and parameters of a state space model. Particle filter (PF) is known as a method that can estimate states in difficult sequential state estimation problems with nonlinearity and non-Gaussianity. PF updates an ensemble consisting of multiple particles representing states of a state space model in order to estimate the true state, based on observation, at each time step. However, when PF estimates not only states but also parameters of the state space model at the same time, it is observed that the estimation accuracy deteriorates. When estimating both states and parameters, PF utilizes particles representing states and particles. In order to overcome the problem of PF, we propose a new method that sequentially estimates states by PF and parameters by the separable natural evolution strategy (SNES). SNES is one of the most powerful black-box function optimization methods. In order to confirm the effectiveness of the proposed method, we compare the performance of the proposed method and that of PF using two nonlinear state space models, the Van der Pol model and the Lorenz model. In the Van der Pol model, the median MSE values of the state and the parameter of the proposed method were 0.003610 and 0.01468 and those of PF were 4.228 and 6.520, respectively. In the Lorenz model, the median MSE values of the state and the parameter of the proposed method were 0.002639 and 0.003479 and those of PF were 309.5 and 1.470, respectively. The smaller MSE is, the better the performance is.
Scalable Wildcarded Identity-Based Encryption with Full Security

Wildcarded identity-based encryption (WIBE) is an encryption system where one can encrypt messages to multiple users by specifying a pattern, which is a set of identity strings or wildcards. It is a useful primitive for practical applications where users are defined with multiple attributes (or affiliations), such as organization networks or IoT firmware updates. However, the ciphertext size in traditional WIBE schemes are linear to the number of wildcards in the pattern; since the ciphertext size determines the payload in network systems, it degrades the practicality when deployed in transmission-sensitive systems. In this paper, we represent scalable wildcarded identity-based encryption (SWIBE), which achieves a constant-size ciphertext regardless of the number of wildcards (or depth of patterns). the SWIBE scheme also allows the wildcard usage key derivation as well as encryption: a user with wildcarded pattern can delegate keys for the fixed pattern. Compared to the existing WIBE schemes, the SWIBE scheme is the first approach to yield constant-size ciphertext. Moreover, SWIBE also improves encryption time and decryption time while maintaining a key size of 2L, comparable to the key size of L in WIBE schemes (where L is a depth of the pattern). The experimental results show that the decryption time is 3 to 10 times faster than the existing WIBE schemes, and 650 times faster than the attribute-based encryption with constant-size ciphertext. For the security, we first propose the selective-CPA-secure SWIBE scheme in a prime order bilinear group and extend it to be selective-CCA-secure. Then we also propose a fully-secure SWIBE scheme which can overcome the selective security.
Towards the representational power of restricted Boltzmann machines

The restricted Boltzmann machine (RBM), which is a graphical model for binary random variables, has been proven to be a powerful tool in machine learning. However, theoretical foundations for understanding the approximation ability of RBMs are lacking. In this paper, we study the representational power of RBMs, with the focus lying on the sufficient number of hidden units of RBMs required to compute some classes of distributions of interest, with a fixed number of inputs. First, it is constructively shown how RBMs can approximate any distribution that depends on the scalar projection of the inputs onto a given vector up to arbitrary accuracy. Then, for any given distribution, we explore how it can be represented as the form that depends on the scalar projection of the inputs onto some vectors, and then study the properties of these vectors, from which a new proof for the universal approximation theorem of RBMs is deduced. Finally, we investigate the representational efficiency of RBMs by providing a description of all the distributions that can be efficiently computed by RBMs. More specifically, it is shown that a distribution can be computed by a polynomial-size RBM with polynomially bounded parameters, if and only if its mass can be computed by a two-layer feedforward network with threshold/ReLU activation functions, whose size and parameters are polynomially bounded.
Design and Implementation of Oil and Gas Information on Intelligent Search Engine Based on Knowledge Graph

Abstract With the deepening of oil and gas informatization construction, various types of oil and gas information increase geometrically. In response to the challenges brought by massive oil and gas information in the search process, this paper proposes an intelligent search engine for oil and gas information based on knowledge graph. Functional modules such as knowledge fusion, knowledge processing, semantic relevance judgment, topic classification, and search result set sorting display, using map service as an interactive interface, realize intelligent search of oil and gas information. Compared with traditional search engines, it has more advantages in the search of oil and gas information, and it is easier to understand the user’s search intent in the oil and gas field, effectively improving the quality of search.
Enhancing Deep Learning-Based Multi-label Text Classification with Capsule Network

Abstract Given a piece of text, multi-label text classification (MLTC) is designed to mark the most relevant one label or multiple labels for the text. Most of the existing MLTC models use convolutional neural network (CNN) as feature extractor, but CNN will lose information when dealing with MLTC task. In this paper, we explore the CNN combined with capsule network for MLTC. We use capsule network instead of pool layer in CNN to extract information related to classification results in high-dimensional features. We also explore the way of combining recurrent neural network (RNN) and CNN to model the characteristics of time and space for capsule network to complete classification. In two open MLTC datasets, our model achieves the better results as the baseline system, which shows the effectiveness of the combination of capsule network and CNN for MLTC.
Secure Path Balanced BDD-Based Pre-Charge Logic for Masking

This work presents a novel path balanced masked dual-rail pre-charge circuit design scheme based on binary decision diagrams (BDD), using one mask bit per output. It has been applied for achieving masked implementations of two common lightweight ciphers, namely Present and Lucifer. BDD based logic design is achieved using the CUDD tool and applying suitable variable reordering techniques. Path balancing has helped to counter the early propagation effect (EPE) to achieve a glitch free circuit design. Non-linear shift register has been used to generate the mask bits to avoid prediction of the mask bit, even if those should be occasionally uncovered. The design has been evaluated on many aspects such as glitch attack resistance, correlation / differential power analysis attack resistance, peak power variance, etc. and found to have satisfactory circuit performance while being highly robust to side channel attacks.
Show me your living room

The study reported here investigates AR based support for remote consultations, in which an on-site user is supported by a remote helper. In such situations, it is important for the remote helper (or, in our case, the consultant) to see the environment of the person asking for support in order to relate to it. Based on literature, we created and tested different mechanisms using a 2D video stream with a captured 2D/3D texturized virtual model of the room. In addition, we compared the often-used way of fixing the remote helper's view to the view of the on-site user with the possibility to move around freely in the 2D/3D model. The aim of the study was evaluating how to support an on-site user wearing an AR HMD. The study tested four conditions composed from these differences and with nine real furniture consultants. In the study, we compared four mechanisms in which the consultants were able to place furniture in the living room of a customer and advise the customer on their purchase. We found that there were hardly any differences in task load, social presence or perceived support between the four different conditions. However, participants had clear preferences for certain conditions and aspects of them. From our analysis, we provide recommendations for the design of mixed reality support for remote consultations.
Virtual Wall: Filtering Rootkit Attacks To Protect Linux Kernel Functions

Linux servers are being used in almost all clouds, datacenters and supercomputers today. Linux Kernel functions are facing a kind of malware attacks, known as rootkits with root-access capability. The rootkits appear as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">loadable kernel modules</i> (LKM) in today's Linux servers. These modules hide from other kernel objects, and can redirect the kernel control flow by tampering with the metadata needed in kernel service functions. The kernel rootkits are invisible to users after loading, which may bypass most security shields. Both spatial and temporal appearance of rootkits are randomly distributed, which makes it difficult to detect or removal. To deal with rootkit threats, we propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Virtual Wall</i> (VTW) approach to filtering out the rootkit-embedded LKMs by tracing the incurred kernel activities. This VTW is essentially a lightweight hypervisor built with rootkit detection and event tracing capabilities. Normally, the Linux runs in a guest mode. When a LKM execution violates the security policy set by the VTW, the OS control will switch to a host mode. The VTW at host mode enables the detection and tracing of rootkit events timely. In other words, potential rootkit attacks are detected, traced and classified to make meaningful filtering decisions. The whole detection and tracing process is based on memory access control and event injection mechanisms. Experimental results show that the VTW defense system is effective to detect and defend against kernel rootkits timely. The CPU overhead for executing VTW is less than 2 percent. Compared with other defense schemes (such as DIKernel, etc.), our vs is easier to implement with low performance degradation on Linux servers. We will demonstrate the advantages of VTW through its simplicity in implementation and potential performance gains. We will also compare our system with seven other rootkit defense systems.
Sampling from the complement of a polyhedron: An MCMC algorithm for data augmentation

We present an MCMC algorithm for sampling from the complement of a polyhedron. Our approach is based on the Shake-and-bake algorithm for sampling from the boundary of a set and provably covers the complement. We use this algorithm for data augmentation in a machine learning task of classifying a hidden feasible set in a data-driven optimization pipeline. Numerical results on simulated and MIPLIB instances demonstrate that our algorithm, along with a supervised learning technique, outperforms conventional unsupervised baselines.
Subclass Contrastive Loss for Injured Face Recognition

Deaths and injuries are common in road accidents, violence, and natural disaster. In such cases, one of the main tasks of responders is to retrieve the identity of the victims to reunite families and ensure proper identification of deceased/injured individuals. Apart from this, identification of unidentified dead bodies due to violence and accidents is crucial for the police investigation. In the absence of identification cards, current practices for this task include DNA profiling and dental profiling. Face is one of the most commonly used and widely accepted biometric modalities for recognition. However, face recognition is challenging in the presence of facial injuries such as swelling, bruises, blood clots, laceration, and avulsion which affect the features used in recognition. In this paper, for the first time, we address the problem of injured face recognition and propose a novel Subclass Contrastive Loss (SCL) for this task. A novel database, termed as Injured Face (IF) database, is also created to instigate research in this direction. Experimental analysis shows that the proposed loss function surpasses existing algorithm for injured face recognition.
DAML

Data aggregation based on machine learning (ML), in mobile edge computing, allows participants to send ephemeral parameter updates of local ML on their private data instead of the exact data to the untrusted aggregator. However, it still enables the untrusted aggregator to reconstruct participants’ private data, although parameter updates contain significantly less information than the private data. Existing work either incurs extremely high overhead or ignores malicious participants dropping out. The latest research deals with the dropouts with desirable cost, but it is vulnerable to malformed message attacks. To this end, we focus on the data aggregation based on ML in a practical setting where malicious participants may send malformed parameter updates to perturb the total parameter updates learned by the aggregator. Moreover, malicious participants may drop out and collude with other participants or the untrusted aggregator. In such a scenario, we propose a scheme named DAML , which to the best of our knowledge is the first attempt toward verifying participants’ submissions in data aggregation based on ML. The main idea is to validate participants’ submissions via SSVP, a novel secret-shared verification protocol, and then aggregate participants’ parameter updates using SDA, a secure data aggregation protocol. Simulation results demonstrate that DAML can protect participants’ data privacy with preferable overhead.