Reviewerly/exHarmony huggingface dataset:
MTSS-GAN: Multivariate Time Series Simulation Generative Adversarial Networks

MTSS-GAN is a new generative adversarial network (GAN) developed to simulate diverse multivariate time series (MTS) data with finance applications in mind. The purpose of this synthesiser is two-fold, we both want to generate data that accurately represents the original data, while also having the flexibility to generate data with novel and unique relationships that could help with model testing and robustness checks. The method is inspired by stacked GANs originally designed for image generation. Stacked GANs have produced some of the best quality images, for that reason MTSS-GAN is expected to be a leading contender in multivariate time series generation.
Noise-tolerant, Reliable Active Classification with Comparison Queries

With the explosion of massive, widely available unlabeled data in the past years, finding label and time efficient, robust learning algorithms has become ever more important in theory and in practice. We study the paradigm of active learning, in which algorithms with access to large pools of data may adaptively choose what samples to label in the hope of exponentially increasing efficiency. By introducing comparisons, an additional type of query comparing two points, we provide the first time and query efficient algorithms for learning non-homogeneous linear separators robust to bounded (Massart) noise. We further provide algorithms for a generalization of the popular Tsybakov low noise condition, and show how comparisons provide a strong reliability guarantee that is often impractical or impossible with only labels - returning a classifier that makes no errors with high probability.
ID3 Learns Juntas for Smoothed Product Distributions

In recent years, there are many attempts to understand popular heuristics. An example of such a heuristic algorithm is the ID3 algorithm for learning decision trees. This algorithm is commonly used in practice, but there are very few theoretical works studying its behavior. In this paper, we analyze the ID3 algorithm, when the target function is a $k$-Junta, a function that depends on $k$ out of $n$ variables of the input. We prove that when $k = \log n$, the ID3 algorithm learns in polynomial time $k$-Juntas, in the smoothed analysis model of Kalai & Teng. That is, we show a learnability result when the observed distribution is a noisy variant of the original distribution.
Vibration-resistant interference microscope with assistant focusing for on-machine measurement of surface topography

The interference microscope is a powerful tool for surface topography measurement, but its high sensitivity to vibration hinders its application to on-machine use. To measure surface roughness on a machine for the ultra-precision machining, a vibration-resistant interference microscope (VRIM) with an assistant focusing function is developed. The basic principle of VRIM is an error-compensated phase-shifting interferometry. An iterative algorithm is presented to calculate the surface phase with the phase shift amounts as unknown variables, where the phase shift amounts are calculated and compensated with least-squares method. A narrow bandwidth illumination is employed to alleviate coherence envelop influence, and a simplified intensity model is established to decouple the variables. Assisting the microscope to find fringe quickly, the focusing is realized by introducing an off-axis thin beam to generate two spots, of which their relative position relates to the defocus. The focusing method is directional and determinant, and has a large range up to 0.3 mm. In the vibration disturbances of 0.2 μm and 0.4 μm amplitudes over 0 Hz to 20 Hz frequency region, the roughness accuracy and repeatability of measuring an ultra-precision machined surface are both up to the sub-nanometer level. The developed instrument is applied to a single-point diamond turning machine and achieves a sub-nanometer accuracy and repeatability.
Open Problem: Average-Case Hardness of Hypergraphic Planted Clique Detection

We note the significance of hypergraphic planted clique (HPC) detection in the investigation of computational hardness for a range of tensor problems. We ask if more evidence for the computational hardness of HPC detection can be developed. In particular, we conjecture if it is possible to establish the equivalence of the computational hardness between HPC and PC detection.
Computer Network Assisted Test of Spoken English

With the development of computer network technology, the means of foreign language teaching have changed. Computer aided spoken English test is a new test method, and there is a great advantage compared with the traditional ... | Find, read and cite all the research you need on Tech Science Press
Novel Alignment Approach of DNA Sequences

In recent decades, researchers have proposed a number of string matching algorithms, searching for instances of one model string in another string or body of text. Channel matching is usually deployed in plagiarism, text mining, network intrusion detection, form recognition, information security, application in bioinformatics and other areas of expertise. In this study, a new algorithm will be programmed, adapted and applied in the field of DNA sequence analysis based on the approach of the transition from discrete to continuous unlike most algorithms, which are based on the matching channels concept. Experimental results, revealed that our approach ensures an efficient pairing of DNA sequences.
Weakly-Supervised Semantic Segmentation via Sub-category Exploration

Existing weakly-supervised semantic segmentation methods using image-level annotations typically rely on initial responses to locate object regions. However, such response maps generated by the classification network usually focus on discriminative object parts, due to the fact that the network does not need the entire object for optimizing the objective function. To enforce the network to pay attention to other parts of an object, we propose a simple yet effective approach that introduces a self-supervised task by exploiting the sub-category information. Specifically, we perform clustering on image features to generate pseudo sub-categories labels within each annotated parent class, and construct a sub-category objective to assign the network to a more challenging task. By iteratively clustering image features, the training process does not limit itself to the most discriminative object parts, hence improving the quality of the response maps. We conduct extensive analysis to validate the proposed method and show that our approach performs favorably against the state-of-the-art approaches.
Trusted and Efficient Cross-Domain Access Control System Based on Blockchain

In a distributed system, cross-domain access control is an important mechanism to realize secure data sharing among multiple domains. Most of the existing cross-domain access control mechanisms are generally based on a single-server architecture, which has limitations in terms of security and reliability (the access decision may be incorrect) and completeness and confidentiality (the access records can be modified). Blockchain technology with decentralization, verifiability, and immutability properties can solve these problems. Motivated by these facts, in this article, we construct a trusted and efficient cross-domain access control system based on blockchain. Consequently, we integrate blockchain and role mapping technology to provide reliable and verifiable cross-domain access process. We use blockchain to record user roles, role mapping rules, access policies, and audit records, realizing user self-validation, and access nonreputation. Considering the low throughput of the blockchain, we design an efficient smart contract to make the access decision based on the access history of users. Finally, a performance evaluation of the system is presented to demonstrate the feasibility of the proposed system.
Fair Fit—A Load Balance Aware VM Placement Algorithm in Cloud Data Centers

Cloud computing is a kind of large-scale distributed computing. It has recently emerged as a new technology for providing services over the Internet. It has gained a lot of attention in individual, industry, and academia. Cloud computing dynamically allocates virtual resources as per the demands of users. The rapid increase of data computation and storage in cloud results in uneven distribution of workload on its heterogeneous resources, which may violate SLAs and degrades system performance. Distributing balanced workload over the available hosts is a key challenge in cloud computing environment. VM placement is the process by which it selects the most suitable physical machine (PM) in cloud data centers to deploy newly created virtual machine (VM) during runtime. In this paper, we study VM placement problem with the goal of balanced resource utilization in cloud data centers. Several algorithms have been proposed to find a solution to this problem. Most of the existing algorithms balance the cloud resources based on its utilization which results in unbalanced and inefficient cloud resource utilization. We propose an algorithm, which minimizes the imbalance of resource usage across multiple dimensions, reduces resource leak and wastage, and maximizes the resource utilization. Our algorithm finds a suitable host for incoming VM from categorized host list based on remaining resources using cosine similarity measure. Simulation results show major improvements over the existing algorithms like Binary Search Tree-based Worst Fit and Best Fit, Round Robin, and default Worst Fit.
Design of an Augmented Reality System for Immersive Learning of Digital Electronic

This article describes the development of two mobile applications for learning Digital Electronics. The first application is an interactive app for iOS where you can study the different digital circuits, and which will serve as the basis for the second: a game of questions in augmented reality.
On the left primeness of some polynomial matrices with applications to convolutional codes

Maximum distance profile (MDP) convolutional codes have the property that their column distances are as large as possible for given rate and degree. There exists a well-known criterion to check whether a code is MDP using the generator or the parity-check matrix of the code. In this paper, we show that under the assumption that [Formula: see text] divides [Formula: see text] or [Formula: see text] divides [Formula: see text], a polynomial matrix that fulfills the MDP criterion is actually always left prime. In particular, when [Formula: see text] divides [Formula: see text], this implies that each MDP convolutional code is noncatastrophic. Moreover, when [Formula: see text] and [Formula: see text] do not divide [Formula: see text], we show that the MDP criterion is in general not enough to ensure left primeness. In this case, with one more assumption, we still can guarantee the result.
Analyzing TF-IDF and Word Embedding for Implementing Automation in Job Interview Grading

Selecting the best talents from a large number of job applicants is challenging, especially for big companies that usually receive tens of thousands of applicants for every job opening. One of the most costly and time-consuming applicant selection stages is the interview process, since it usually performs face to face meetings and involves third parties to do the interviews and analyze the result. To this end, Human Capital Directorate at Telkom Indonesia adopts AI technology to automate some stages of job applicant selection to reduce manual process and third-party involvement. In this paper, we investigate appropriate feature extraction methods to automate job interview grading for reducing bias and human errors. TFIDF, one of the most popular feature extractions, is compared with word embedding to find the optimal method and parameters in classifying interview verbatims with ANN classifier. Based on the test results, the average accuracy for TFIDF outperforms word embedding by 85.22% against 74.88%, respectively. Therefore, for the case of job interview grading using our dataset, TF-IDF performs better to reduce the number of dimensions.
Temporal Network Analytics for Fraud Detection in the Banking Sector

A new methodology in temporal networks is presented for the use of fraud detection systems in the banking sector. Standard approaches of fraudulence monitoring mainly have the focus on the individual client data. Our approach will concentrate on the hidden data produced by the network of the transaction database. The methodology is based on a cycle detection method with the help of which important patterns can be identified as shown by the test on real data. Our solution is integrated into a financial fraud system of a bank; the experimental results are demonstrated by a real-world case study.
DNA Privacy: Analyzing Malicious DNA Sequences using Deep Neural Networks

Recent advances in next-generation sequencing technologies have led to the successful insertion of video information into DNA using synthesized oligonucleotides. Several attempts have been made to embed larger data into living organisms. This process of embedding messages is called steganography and it is used for hiding and watermarking data to protect intellectual property. In contrast, steganalysis is a group of algorithms that serves to detect hidden information from covert media. Various methods have been developed to detect messages embedded in conventional covert channels. However, conventional steganalysis algorithms are mostly limited to common covert media. Most common detection approaches, such as frequency analysis-based methods, often overlook important signals when directly applied to DNA steganography and are easily bypassed by recently developed steganography techniques. To address the limitations of conventional approaches, a sequence-learning-based malicious DNA sequence analysis method based on neural networks has been proposed. The proposed method learns intrinsic distributions and identifies distribution variations using a classification score to predict whether a sequence is to be a coding or non-coding sequence. Based on our experiments and results, we have developed a framework to safeguard security against DNA steganography.
Denoising, Edge Correction, and Enhancement of Breast Cancer Ultrasound Images

Mortality rate because of breast cancer diminishes to a large extent if the categorization of breast lesions as malignant or benign is done properly. But this process is quite complicated owing to erroneous detection of noisy pixels as false positives. It can be reduced by proper enhancement of cancer indicating features present in breast cancer ultrasound images. Therefore, the technique proposed for denoising, edge correction, and enhancement is pivoted around two drastic issues. The first issue is related to the blurring of important details because of improper noise suppression, whereas the second issue is associated with poor contrast between background tissues and masses in ultrasound images of breast cancer. In this paper, we propose an ensemble hybrid filter that restores the noisy ultrasound breast cancer images, corrects the edges of restored ultrasound images without getting degraded with additional Gaussian noise which degrades the ultrasound images during edge correction and finally enhances its visual quality. It uses Weiner filtering to remove a small amount of noise, fuzzy derivatives, and smoothing for edge preservation and intensification membership function along with contrast limited adaptive histogram equalization for enhancement. Experimental results are obtained to demonstrate the feasibility of the proposed approach. These results are also compared to traditional Weiner filter by numerical measures and visual inspection.
Not My Deepfake: Towards Plausible Deniability for Machine-Generated Media

Progress in generative modelling, especially generative adversarial networks, have made it possible to efficiently synthesize and alter media at scale. Malicious individuals now rely on these machine-generated media, or deepfakes, to manipulate social discourse. In order to ensure media authenticity, existing research is focused on deepfake detection. Yet, the very nature of frameworks used for generative modeling suggests that progress towards detecting deepfakes will enable more realistic deepfake generation. Therefore, it comes at no surprise that developers of generative models are under the scrutiny of stakeholders dealing with misinformation campaigns. As such, there is a clear need to develop tools that ensure the transparent use of generative modeling, while minimizing the harm caused by malicious applications. We propose a framework to provide developers of generative models with plausible deniability. We introduce two techniques to provide evidence that a model developer did not produce media that they are being accused of. The first optimizes over the source of entropy of each generative model to probabilistically attribute a deepfake to one of the models. The second involves cryptography to maintain a tamper-proof and publicly-broadcasted record of all legitimate uses of the model. We evaluate our approaches on the seminal example of face synthesis, demonstrating that our first approach achieves 97.62% attribution accuracy, and is less sensitive to perturbations and adversarial examples. In cases where a machine learning approach is unable to provide plausible deniability, we find that involving cryptography as done in our second approach is required. We also discuss the ethical implications of our work, and highlight that a more meaningful legislative framework is required for a more transparent and ethical use of generative modeling.
Diabetes Prediction using BPSO and Decision Tree Classifier

The diabetes mellitus causes different treatments and tests as well as the complications due to this disease. Even if there are prevention methods that can reduce the risk of having diabetes, sometimes it is impossible to avoid it as for type 1 diabetes. In these cases, the only solution is to be able to diagnose it very early and do everything possible to combat the complications. This paper presents a machine learning approachto help with the diagnosis using the classification of learning in the prediction of type 2 diabetes. Attributes selection is done using binary particle swarm optimization (BPSO). Selected attributes are classified by Decision Tree classifier.
Graph Convolutional Network for Visual Question Answering Based on Fine-grained Question Representation

Visual Question Answering (VQA) requires a fine-grained and simultaneous understanding of both the visual content of images and the textual content of questions. Recent research works mainly focus on the bilinear fusion methods and attention mechanism methods to explore the global features and local features, respectively. When an image exists multiple objects, the fine-grained question features can effectively identify the relationship between objects in an image. Therefore we propose a graph convolutional network based on fine-grained question representation (FQ-GCN). The object relation graph is firstly constructed. The fine-grained question features are used to explore the relations between objects in an image and prune unrelated edges between object nodes. Graph convolutional network is used to aggregate all neighborhood information of each object in an object graph. Experiments on the VQA 2.0 dataset show that the performance of our FQ-GCN model improves 2% ~ 3$% than the classical methods.
Pricing and Budget Allocation for IoT Blockchain With Edge Computing

Attracted by the inherent security and privacy protection of the blockchain, incorporating blockchain into Internet of Things (IoT) has been widely studied in these years. However, the mining process requires high computational power, which prevents IoT devices from directly participating in blockchain construction. For this reason, edge computing service is introduced to help build the IoT blockchain, where IoT devices could purchase computational resources from the edge servers. In this paper, we consider the case that IoT devices also have other tasks that need the help of edge servers, such as data analysis and data storage. The profits they can get from these tasks is closely related to the amounts of resources they purchased from the edge servers. In this scenario, IoT devices will allocate their limited budgets to purchase different resources from different edge servers, such that their profits can be maximized. Moreover, edge servers will set “best” prices such that they can get the biggest benefits. Accordingly, there raise a pricing and budget allocation problem between edge servers and IoT devices. We model the interaction between edge servers and IoT devices as a multi-leader multi-follower Stackelberg game, whose objective is to reach the Stackelberg Equilibrium (SE). We prove the existence and uniqueness of the SE point, and design efficient algorithms to reach the SE point. In the end, we verify our model and algorithms by performing extensive simulations, and the results show the correctness and effectiveness of our designs.
Intellectual Access to Digital Documents: Joining Proven Principles with New Technologies

This paper considers the relevance of Charles Ami Cutter’s principles of bibliographic access to the universe of Internet accessible digital objects and explores new methods for applying these principles in the context of new information technologies. The paper examines the value for retrieval of collocating authors’ names, identifying authors’ roles, collocating works and versions, and providing subject access through classification and controlled vocabularies for digital resources available through the World Wide Web. The authors identify emerging techniques and technologies that can be used in lieu of or as a supplement to traditional cataloging to achieve these functions in organizing access to Internet resources. [Article copies available from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.con]
Spatial Enhancement and Temporal Constraint for Weakly Supervised Action Localization

Weakly supervised temporal action localization (WSTAL) is a practical but challenging issue in video understanding. However, most existing methods have to activate background snippets or deactivate action snippets in cases of no boundary annotations, which inevitably affects the localization of action instances. In this letter, we propose a spatial enhancement and temporal constraint (SETC) model to address this problem from three aspects. Specifically, we first propose a spatial enhancement module to enhance the discrimination of the extracted features. Then we leverage the instance sparse constraint to restrain the drastic fluctuation class activation sequence (CAS). Finally, we use the confidence connectivity enhancement to connect the snippets that are broken up by mistake. Experiments on THUMOS'14 and ActivityNet datasets validate the efficacy of SETC against existing state-of-the-art WSTAL algorithms.