Reviewerly/exHarmony huggingface dataset:
Towards Knowledgeable Supervised Lifelong Learning Systems

Learning a sequence of tasks is a long-standing challenge in machine learning. This setting applies to learning systems that observe examples of a range of tasks at different points in time. A learning system should become more knowledgeable as more related tasks are learned. Although the problem of learning sequentially was acknowledged for the first time decades ago, the research in this area has been rather limited. Research in transfer learning, multitask learning, metalearning and deep learning has studied some challenges of these kinds of systems. Recent research in lifelong machine learning and continual learning has revived interest in this problem. We propose Proficiente, a full framework for long-term learning systems. Proficiente relies on knowledge transferred between hypotheses learned with Support Vector Machines. The first component of the framework is focused on transferring forward selectively from a set of existing hypotheses or functions representing knowledge acquired during previous tasks to a new target task. A second component of Proficiente is focused on transferring backward, a novel ability of long-term learning systems that aim to exploit knowledge derived from recent tasks to encourage refinement of existing knowledge. We propose a method that transfers selectively from a task learned recently to existing hypotheses representing previous tasks. The method encourages retention of existing knowledge whilst refining. We analyse the theoretical properties of the proposed framework. Proficiente is accompanied by an agnostic metric that can be used to determine if a long-term learning system is becoming more knowledgeable. We evaluate Proficiente in both synthetic and real-world datasets, and demonstrate scenarios where knowledgeable supervised learning systems can be achieved by means of transfer.
RETRACTED: Research on decision-making strategy of soccer robot based on multi-agent reinforcement learning

This article studies a multi-agent reinforcement learning algorithm based on agent action prediction. In multi-agent system, the action of learning agent selection is inevitably affected by the act...
An Efficient Snapshot Strategy for Dynamic Graph Storage Systems to Support Historical Queries

Large-scale dynamic graphs typically involve big data. Recently a dynamic graph storage system is required to be capable of recreating any historical state to support historical queries. A typical storage solution supporting historical queries is called `snapshot plus log'. A snapshot records the whole data at a certain moment, while the log file is responsible for saving all the update operations. The historical state is then recreated from the nearest snapshot by redoing or undoing the related update operations saved in the log file. The challenge lies in how to minimize both the number of snapshots and that of redone and undone operations performed in historical state recreation. The traditional system stores snapshots at regular intervals. However, historical states do not share the same frequency of being requested. Therefore, the traditional strategy is very inefficient. This paper proposes a new strategy that determines the timestamps of the snapshots based on the distribution of the historical queries. First, the historical queries are clustered into a given number of groups according to the timestamps of the requested historical states, and the cluster centroids are calculated. Second, the snapshots are created according to the timestamps of the cluster centroids. Since the cluster centroids may change as time goes by, the above process is executed periodically. Experimental results show that with the same storage costs, the snapshot strategy proposed in this paper greatly improves the performance of recreating historical states, leading to at least 70.7% computation reduction in terms of the number of both redone and undone operations. Besides, with the same recreation performance guarantee, it brings nearly 78.9% storage reduction on average in terms of the number of snapshots.
Movie recommendation algorithm based on knowledge graph

The traditional collaborative filtering algorithm only uses the item-user scoring matrix, but does not consider the semantics of the item. The recommendation effect is often not ideal. The connotation knowledge of the movie and the interest preference of the user are added to the knowledge graph, wherein the connotation knowledge is represented by the relationship between the movie entity and its characteristic entity, and the user's interest preference is to use the like relationship and the like relationship between the movie entity and the user entity. Said. After adding the intension knowledge and the user's interest preferences in the knowledge graph, the complex and diverse relationship between the entities is represented in the form of a triple. The knowledge graph is used to represent the learning method, the movie knowledge graph is embedded in a low-dimensional semantic space, the movie entity is vectorized, and then the semantic similarity between the movie entities is calculated, similar to the item-based collaborative filtering. Sexual combination, the film's own connotation information and user preferences in the knowledge graph are integrated into the collaborative filtering for recommendation, which can not only make up for the problem of semantic sparsity, but also can solve the lack of user subjectivity of the knowledge graph because the knowledge graph contains the user's subjective interest preferences. In the knowledge graph, both the objectivity of the connotation knowledge of the film itself and the subjectivity of the user's interest are considered. Compared with the connotation knowledge of the film itself in the knowledge graph, user preferences and object-based items are added to the knowledge graph. The synergistic filtering fusion enhances the recommended effect.
Feature Extraction using Wavelet Transform and Euclidean Distance for speaker recognition system

The current speaker recognition system has many issues. i.e., channel mismatching, noises, emotions, idioms, etc. To build a reliable and robust speaker recognition system, it's essential to implement the precise feature extraction technique. A first major step in speaker recognition system is feature extraction from the speech signal for that speaker. The speaker model can be build using these features, So that one can extract correct and useful features. The traditional method for feature extraction is MFCC. Here, we have proposed a new technique to obtain the features. We are going to use the wavelet transform technique along with Euclidean Distance computational Technique, which gives multidimensional features for the speaker.
An Efficient High-Throughput LZ77-Based Decompressor in Reconfigurable Logic

Abstract To best leverage high-bandwidth storage and network technologies requires an improvement in the speed at which we can decompress data. We present a “refine and recycle” method applicable to LZ77-type decompressors that enables efficient high-bandwidth designs and present an implementation in reconfigurable logic. The method refines the write commands (for literal tokens) and read commands (for copy tokens) to a set of commands that target a single bank of block ram, and rather than performing all the dependency calculations saves logic by recycling (read) commands that return with an invalid result. A single “Snappy” decompressor implemented in reconfigurable logic leveraging this method is capable of processing multiple literal or copy tokens per cycle and achieves up to 7.2GB/s, which can keep pace with an NVMe device. The proposed method is about an order of magnitude faster and an order of magnitude more power efficient than a state-of-the-art single-core software implementation. The logic and block ram resources required by the decompressor are sufficiently low so that a set of these decompressors can be implemented on a single FPGA of reasonable size to keep up with the bandwidth provided by the most recent interface technologies.
A fractal model for pressure drop through a cigarette filter

A fractal model for pressure drop through a cigarette filter is suggested, the fractal dimensions of both a single fiber and the filter?s cross-sections are calculated, which are two main factors affecting the pressure drop. The two-scale transform is made to convert the fractal derivative model on a smaller scale to an approximate continuous model on a larger scale, so that the model can be easily solved. An optimal filter structure is suggested for minimal pressure drop.
Automatic detection of microcalcification based on morphological operations and structural similarity indices

In this paper, a new method for automatic detection of microcalcifications in digitized mammograms is proposed. Based on mathematical morphology theory to deal with the problem of low contrast between microcalcifications and their surrounding pixels, it uses various structuring elements of different sizes to reduce the sensibility to microcalcification diversity sizes. The obtained morphological results are converted to a suspicion map based on an image quality assessment metric called structural similarity index (SSIM). This continuous map is, then, locally analyzed using superpixels to automatically estimate threshold values and finally detect potential microcalcification areas. The proposed method was evaluated using the publicly-available INBreast dataset. Experimental results show the benefits gained in terms of improving microcalcification detection performances compared to state-of-the-art methods.
Efficient Pairwise Annotation of Argument Quality

We present an efficient annotation framework for argument quality, a feature difficult to be measured reliably as per previous work.A stochastic transitivity model is combined with an effective sampling strategy to infer highquality labels with low effort from crowdsourced pairwise judgments.The model's capabilities are showcased by compiling Webis-ArgQuality-20, an argument quality corpus that comprises scores for rhetorical, logical, dialectical, and overall quality inferred from a total of 41,859 pairwise judgments among 1,271 arguments.With up to 93% cost savings, our approach significantly outperforms existing annotation procedures.Furthermore, novel insight into argument quality is provided through statistical analysis, and a new aggregation method to infer overall quality from individual quality dimensions is proposed.
Gradient Temporal-Difference Learning with Regularized Corrections

It is still common to use Q-learning and temporal difference (TD) learning-even though they have divergence issues and sound Gradient TD alternatives exist-because divergence seems rare and they typically perform well. However, recent work with large neural network learning systems reveals that instability is more common than previously thought. Practitioners face a difficult dilemma: choose an easy to use and performant TD method, or a more complex algorithm that is more sound but harder to tune and all but unexplored with non-linear function approximation or control. In this paper, we introduce a new method called TD with Regularized Corrections (TDRC), that attempts to balance ease of use, soundness, and performance. It behaves as well as TD, when TD performs well, but is sound in cases where TD diverges. We empirically investigate TDRC across a range of problems, for both prediction and control, and for both linear and non-linear function approximation, and show, potentially for the first time, that gradient TD methods could be a better alternative to TD and Q-learning.
Structured Multi-Hashing for Model Compression

Despite the success of deep neural networks (DNNs), state-of-the-art models are too large to deploy on low-resource devices or common server configurations in which multiple models are held in memory. Model compression methods address this limitation by reducing the memory footprint, latency, or energy consumption of a model with minimal impact on accuracy. We focus on the task of reducing the number of learnable variables in the model. In this work we combine ideas from weight hashing and dimensionality reductions resulting in a simple and powerful structured multi-hashing method based on matrix products that allows direct control of model size of any deep network and is trained end-to-end. We demonstrate the strength of our approach by compressing models from the ResNet, EfficientNet, and MobileNet architecture families. Our method allows us to drastically decrease the number of variables while maintaining high accuracy. For instance, by applying our approach to EfficentNet-B4 (16M parameters) we reduce it to the size of B0 (5M parameters), while gaining over 3% in accuracy over B0 baseline. On the commonly used benchmark CIFAR10 we reduce the ResNet32 model by 75% with no loss in quality, and are able to do a 10x compression while still achieving above 90% accuracy.
Epipolar Transformer for Multi-view Human Pose Estimation

A common way to localize 3D human joints in a synchronized and calibrated multi-view setup is a two-step process: (1) apply a 2D detector separately on each view to localize joints in 2D, (2) robust triangulation on 2D detections from each view to acquire the 3D joint locations. However, in step 1, the 2D detector is constrained to solve challenging cases which could be better resolved in 3D, such as occlusions and oblique viewing angles, purely in 2D without leveraging any 3D information. Therefore, we propose the differentiable "epipolar transformer", which empowers the 2D detector to leverage 3D-aware features to improve 2D pose estimation. The intuition is: given a 2D location p in the reference view, we would like to first find its corresponding point p' in the source view, then combine the features at p' with the features at p, thus leading to a more 3D-aware feature at p. Inspired by stereo matching, the epipolar transformer leverages epipolar constraints and feature matching to approximate the features at p'. The key advantages of the epipolar transformer are: (1) it has minimal learnable parameters, (2) itcanbeeasilypluggedintoexistingnetworks,moreover (3) it is interpretable, i.e., we can analyze the location p' to understand whether matching over the epipolar line was successful. Experiments on Human3.6M [9] show that our approach has consistent improvements over the baselines. Specifically, in the condition where no external data is used, our Human3.6M model trained with ResNet-50 and image size 256 x 256 outperforms state-of-the-art by a large margin and achieves MPJPE 26.9 mm. Code is available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Smart number cruncher – a voice based calculator

Abstract ‘Smart Number cruncher-A voice based calculator’ is an electronic device which perform lengthy calculations. Traditional calculator uses key input to perform calculations, which is time consuming process. Present invention developed a new way of calculation. Smart number cruncher provides two modes of calculation, first mode is text mode and second mode is voice mode. Text mode performs mathematical calculation by typing numbers and symbols which is same as normal calculator. Voice mode takes voice input from user in the form of speech and performs mathematical calculation. Voice mode gives output in the form of voice only. This mode of calculation is easier than the regular text mode. Voice mode based calculation is useful for physically handicapped people. Smart number cruncher is user friendly since this system supports multiple regional languages. Smart number cruncher is based on various languages, initially user have to select the mode that is text mode or voice mode. On selection of voice mode user have to select language to perform mathematical calculation. Switching between regular text mode and voice mode, mathematical calculation, language selection all operations are done by micro-controller. All the operations done by voice mode are same as regular text mode internally. Proposed device is useful for the physically handicapped person as well as illiterate people. It saves lot of time wasted on lengthy calculations done by regular calculators. User doesn’t have to press any type of keys in the voice based calculator. Smart Number Cruncher is affordable to common people and easy to use.
Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN

Abstract Breast Cancer diagnosis is one of the most studied problems in the medical domain. Cancer diagnosis has been studied extensively, which instantiates the need for early prediction of cancer disease. To obtain advance prediction, health records are exploited and given as input to an automated system. The paper focuses on constructing an automated system by employing deep learning based recurrent neural network models. A stacked GRU-LSTM-BRNN is proposed in this paper that accepts health records of a patient for determining the possibility of being affected by breast cancer. The proposed model is compared against other baseline classifiers such as stacked simple-RNN model, stacked LSTM-RNN model, stacked GRU-RNN model. Comparative results obtained in this study indicate that the stacked GRU-LSTM-BRNN model yields better classification performance for predictions related to breast cancer disease.
Regularized Autoencoders via Relaxed Injective Probability Flow

Invertible flow-based generative models are an effective method for learning to generate samples, while allowing for tractable likelihood computation and inference. However, the invertibility requirement restricts models to have the same latent dimensionality as the inputs. This imposes significant architectural, memory, and computational costs, making them more challenging to scale than other classes of generative models such as Variational Autoencoders (VAEs). We propose a generative model based on probability flows that does away with the bijectivity requirement on the model and only assumes injectivity. This also provides another perspective on regularized autoencoders (RAEs), with our final objectives resembling RAEs with specific regularizers that are derived by lower bounding the probability flow objective. We empirically demonstrate the promise of the proposed model, improving over VAEs and AEs in terms of sample quality.
Keyphrase Generation for Scientific Articles Using GANs (Student Abstract)

In this paper, we present a keyphrase generation approach using conditional Generative Adversarial Networks (GAN). In our GAN model, the generator outputs a sequence of keyphrases based on the title and abstract of a scientific article. The discriminator learns to distinguish between machine-generated and human-curated keyphrases. We evaluate this approach on standard benchmark datasets. Our model achieves state-of-the-art performance in generation of abstractive keyphrases and is also comparable to the best performing extractive techniques. We also demonstrate that our method generates more diverse keyphrases and make our implementation publicly available1.
The Role of Cognition in Information Security

The main aim of this Lecture is to discuss the main role of the Cognitive science in many applications, and mainly within the (Information Security Field). The lecture has the main topics: ‐Future Parameters and Advanced Technologies. ‐Main Features towards Cognitive ‐The Internet of Things ‐Modeling Cognitive Security ‐Some important parameters ‐International Scientific support ‐Users - Customers ‐Decision makers ‐Academicals Side From 1990 up, the Future parameters: {Wireless, Smartness (Artificial Intelligence), Computing Techniques, Informatics, Convergence (in Technology), Virtually, Multidisciplinary, Electronic (commerce; government, Economy), Cyber, Software Defined, Chaos / Randomness, Nano, Cognition}. Also some concentration on wireless generation 1G up to 6G, SDN and CRN. And concentration on the new computing techniques and their important applications. Computational Type of Cognitive Science: Ubiquitous Computing, Social computing, Affective computing, Cognitive Computing, Soft Computing, Cloud Computing, Mobile Computing, - etc. The main aspect of the lecture is related to cognitive science, which is the interdisciplinary study of: {Mind and Intelligence, Philosophy, Psychology, Artificial intelligence, Neuroscience, Education linguistics, and Anthropology}. The Cognitive Neuroscience is an Academic Field concerned with the scientific study of the Biological processes. It addresses the questions of: how psychological/cognitive activities are affected or controlled by neural circuits in the brain. Cognitive Security is characterized by technology that is able to understand, reason and learn. A much greater scale of Key point's relevant security data is now accessible with cognitive systems. The new approach of the cognitive security based on the following important facts: ‐Use of cognitive systems to analyze security trends and distill huge volumes of structured and unstructured data into information, and then into actionable knowledge. ‐Use of security technologies, techniques and processes that support cognitive systems' having the highest level of context and accuracy. ‐Cognitive security is to add cognition by exploiting technologies such as: machine learning, knowledge representation, network control and management, etc., while solving security problems. The Cognitive cryptography: This New scientific area is marked by a new generation of information systems, focused on developing Intelligent Cryptographic Protocols and procedures utilizing cognitive information processing approaches. Such systems use: the Semantic Analysis of encrypted data to select the most appropriate method of its encryption. The Cognitive Cryptography based on: Cryptographers Personal Data, Semantic Meaning of Information, and Context of Procedure.
Experiment of Stereo Matching Algorithm Based on Binocular Vision

Abstract Binocular stereo matching is a hot topic in 3D reconstruction, target detection and target tracking. Through experiments, this paper analyzes and compares several classic binocular stereo matching algorithms, and finally selects SGBM with good comprehensive performance. Moreover, based on this, it gets accurate window size parameters through experiments, which provides a basis for the following 3D modeling.
A New Method of True Random Number Generation based on Galois Ring Oscillator with Event Sampling Architecture in FPGA

This paper proposes a new entropy extraction mechanism from clock jitter for the implementation of a true random number generator (TRNG) in a field programmable gate array (FPGA). Different with conventional clock sampling architecture, where a jittery signal is sampled by a regular clock signal, we use the jittery clock signal generated by Galois ring oscillator (GARO) to sample the regular clocks that are generated by normal phase locking loop (PLL) of FPGA. This sampling architecture is hereafter called as event sampling architecture. To guarantee every sampled bit has maximum entropy value, the regular clock signal is routed into a tapped delay line (TDL) forming a multi-phase clock signal before it is sampled for exclusive OR (XOR) operation. A prototype of such a TRNG is implemented in a Xilinx Artix-7 FPGA. The generation rate, the randomness of the bit sequence, as well as its robustness to ambient temperature are evaluated in detail. Comparing with the similar TRNG implementation but using the clock sampling architecture, the proposed TRNG can increase the generation bit rate by 1.33 times, while the logic resource consumption is greatly reduced (saving 33% of the look up tables and 81% of the flip-flops). A comparison with various prior TRNG designs in terms of category, logic resource usage, and generation rate demonstrate that the proposed method has the advantage of high rate of generation rate to logic resources. Therefore, it is more suitable for the generation of high-speed true random numbers, which is especially important for applications in current high-speed quantum key distribution systems.