nils-herrmann/clean_scirepeval huggingface dataset:
High speed and low power preset-able modified TSPC D flip-flop design and performance comparison with TSPC D flip-flop

Positron emission tomography (PET) is a nuclear functional imaging technique that produces a three-dimensional image of functional organs in the body. PET requires high resolution, fast and low power multichannel analog to digital converter (ADC). A typical multichannel ADC for PET scanner architecture consists of several blocks. Most of the blocks can be designed by using fast, low power D flip-flops. A preset-able true single phase clocked (TSPC) D flip-flop shows numerous glitches (noise) at the output due to unnecessary toggling at the intermediate nodes. Preset-able modified TSPC (MTSPC) D flip-flop have been proposed as an alternative solution to alleviate this problem. However, the MTSPC D flip-flop requires one extra PMOS to suspend toggling of the intermediate nodes. In this work, we designed a 7-bit preset-able gray code counter by using the proposed D flip-flop. This work involves UMC 180 nm CMOS technology for preset-able 7-bit gray code counter where we achieved 1 GHz maximum operation frequency with most significant bit (MSB) delay 0.96 ns, power consumption 244.2 μW (micro watt) and power delay product (PDP) 0.23 pJ (Pico joule) from 1.8 V power supply.
Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation

Histogram equalization (HE) is widely used for contrast enhancement. However, it tends to change the brightness of an image and hence, not suitable for consumer electronic products, where preserving the original brightness is essential to avoid annoying artifacts. Bi-histogram equalization (BBHE) has been proposed and analyzed mathematically that it can preserve the original brightness to a certain extend. However, there are still cases that are not handled well by BBHE, as they require higher degree of preservation. This paper proposes a generalization of BBHE referred to as recursive mean-separate histogram equalization (RMSHE) to provide not only better but also scalable brightness preservation. BBHE separates the input image's histogram into two based on its mean before equalizing them independently. While the separation is done only once in BBHE, this paper proposes to perform the separation recursively; separate each new histogram further based on their respective mean. It is analyzed mathematically that the output image's mean brightness will converge to the input image's mean brightness as the number of recursive mean separation increases. Besides, the recursive nature of RMSHE also allows scalable brightness preservation, which is very useful in consumer electronics. Simulation results show that the cases which are not handled well by HE, BBHE and dualistic sub image histogram equalization (DSIHE), have been properly enhanced by RMSHE.  Bi-histogram equalization; Dualistic sub-image; Histogram equalization; Recursive mean-separate; Scalable brightness preservation
Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs.

Face photo-sketch synthesis aims at generating a facial sketch (or photo) conditioned on a given photo (or sketch). It is of wide applications including digital entertainment and law enforcement. Despite the great progress achieved by existing methods, they mostly yield blurred effects and great deformation over various facial components. In order to tackle this challenge, we propose to use the facial composition information to help the synthesis of face sketch/photo. Specially, we propose a novel composition-aided generative adversarial network (CA-GAN) for face photo-sketch synthesis. First, we utilize paired inputs including a face photo/sketch and the corresponding pixel-wise face labels for generating the sketch/photo. Second, we propose an improved pixel loss, termed compositional loss, to focus training on hard-generated components and delicate facial structures. Moreover, we use stacked CA-GANs (SCA-GAN) to further rectify defects and add compelling details. Experimental results show that our method is capable of generating identity-preserving and visually comfortable sketches and photos over a wide range of challenging data. Besides, cross-dataset photo-sketch synthesis evaluations demonstrate that the proposed method is of considerable generalization ability.
A Survey of First-Order Probabilistic Models

There has been a long standing division in Artificial Intelligence between logical and probabilistic reasoning approaches. While probabilistic models can deal well with inherent uncertainty in many real-world domains, they operate on a mostly propositional level. Logic systems, on the other hand, can deal with much richer representations, especially first-order ones, but treat uncertainty only in limited ways. Therefore, an integration of these types of inference is highly desirable, and many approaches have been proposed, especially from the 1990s on. These solutions come from many different subfields and vary greatly in language, features and (when available at all) inference algorithms. Therefore their relation to each other is not always clear, as well as their semantics. In this survey, we present the main aspects of the solutions proposed and group them according to language, semantics and inference algorithm. In doing so, we draw relations between them and discuss particularly important choices and tradeoffs.
Improv Chat: Second Response Generation for Chatbot

Existing research on response generation for chatbot focuses on First Response Generation which aims to teach the chatbot to say the first response (e.g. a sentence) appropriate to the conversation context (e.g. the user’s query). In this paper, we introduce a new task Second Response Generation, termed as Improv chat, which aims to teach the chatbot to say the second response after saying the first response with respect the conversation context, so as to lighten the burden on the user to keep the conversation going. Specifically, we propose a general learning based framework and develop a retrieval based system which can generate the second responses with the users’ query and the chatbot’s first response as input. We present the approach to building the conversation corpus for Improv chat from public forums and social networks, as well as the neural networks based models for response matching and ranking. We include the preliminary experiments and results in this paper. This work could be further advanced with better deep matching models for retrieval base systems or generative models for generation based systems as well as extensive evaluations in real-life applications.
The beauty of capturing faces: Rating the quality of digital portraits

Digital portrait photographs are everywhere, and while the number of face pictures keeps growing, not much work has been done to on automatic portrait beauty assessment. In this paper, we design a specific framework to automatically evaluate the beauty of digital portraits. To this end, we procure a large dataset of face images annotated not only with aesthetic scores but also with information about the traits of the subject portrayed. We design a set of visual features based on portrait photography literature, and extensively analyze their relation with portrait beauty, exposing interesting findings about what makes a portrait beautiful. We find that the beauty of a portrait is linked to its artistic value, and independent from age, race and gender of the subject. We also show that a classifier trained with our features to separate beautiful portraits from non-beautiful portraits outperforms generic aesthetic classifiers.
Effective analysis, characterization, and detection of malicious web pages

The steady evolution of the Web has paved the way for miscreants to take advantage of vulnerabilities to embed malicious content into web pages. Up on a visit, malicious web pages steal sensitive data, redirect victims to other malicious targets, or cease control of victim's system to mount future attacks. Approaches to detect malicious web pages have been reactively effective at special classes of attacks like drive-by-downloads. However, the prevalence and complexity of attacks by malicious web pages is still worrisome. The main challenges in this problem domain are (1) fine-grained capturing and characterization of attack payloads (2) evolution of web page artifacts and (3) exibility and scalability of detection techniques with a fast-changing threat landscape. To this end, we proposed a holistic approach that leverages static analysis, dynamic analysis, machine learning, and evolutionary searching and optimization to effectively analyze and detect malicious web pages. We do so by: introducing novel features to capture fine-grained snapshot of malicious web pages, holistic characterization of malicious web pages, and application of evolutionary techniques to fine-tune learning-based detection models pertinent to evolution of attack payloads. In this paper, we present key intuition and details of our approach, results obtained so far, and future work.
APT attack behavior pattern mining using the FP-growth algorithm

There are continuous hacking and social issues regarding APT (Advanced Persistent Threat - APT) attacks and a number of antivirus businesses and researchers are making efforts to analyze such APT attacks in order to prevent or cope with APT attacks, some host PC security technologies such as firewalls and intrusion detection systems are used. Therefore, in this study, malignant behavior patterns were extracted by using an API of PE files. Moreover, the FP-Growth Algorithm to extract behavior information generated in the host PC in order to overcome the limitation of the previous signature-based intrusion detection systems. We will utilize this study as fundamental research about a system that extracts malignant behavior patterns within networks and APIs in the future.
Dynamic Conditional Random Fields for Joint Sentence Boundary and Punctuation Prediction

The use of dynamic conditional random fields (DCRF) has been shown to outperform linear-chain conditional random fields (LCRF) for punctuation prediction on conversational speech texts . In this paper, we combine lexical, prosodic, and modified n-gram score features into the DCRF framework for a joint sentence boundary and punctuation prediction task on TDT3 English broadcast news. We show that the joint prediction method outperforms the conventional two-stage method using LCRF or maximum entropy model (MaxEnt). We show the importance of various features using DCRF, LCRF, MaxEnt, and hidden-event n-gram model (HEN) respectively. In addition, we address the practical issue of feature explosion by introducing lexical pruning, which reduces model size and improves the F1-measure. We adopt incremental local training to overcome memory size limitation without incurring significant performance penalty. Our results show that adding prosodic and n-gram score features gives about 20% relative error reduction in all cases. Overall, DCRF gives the best accuracy, followed by LCRF, MaxEnt, and HEN.
Thinking forensics: Cognitive science for forensic practitioners.

Human factors and their implications for forensic science have attracted increasing levels of interest across criminal justice communities in recent years. Initial interest centred on cognitive biases, but has since expanded such that knowledge from psychology and cognitive science is slowly infiltrating forensic practices more broadly. This article highlights a series of important findings and insights of relevance to forensic practitioners. These include research on human perception, memory, context information, expertise, decision-making, communication, experience, verification, confidence, and feedback. The aim of this article is to sensitise forensic practitioners (and lawyers and judges) to a range of potentially significant issues, and encourage them to engage with research in these domains so that they may adapt procedures to improve performance, mitigate risks and reduce errors. Doing so will reduce the divide between forensic practitioners and research scientists as well as improve the value and utility of forensic science evidence.
A Personalized Graph-Based Document Ranking Model Using a Semantic User Profile

The overload of the information available on the web, held with the diversity of the user information needs and the ambiguity of their queries have led the researchers to develop personalized search tools that return only documents that meet the user profile representing his main interests and needs. We present in this paper a personalized document ranking model based on an extended graph-based distance measure that exploits a semantic user profile derived from a predefined web ontology (ODP). The measure is based on combining Minimum Common Supergraph (MCS) and Maximum Common Subgraph (mcs) between graphs representing respectively the document and the user profile. We extend this measure in order to take into account a semantic recovery between the document and the user profile through common concepts and cross links connecting the two graphs. Results show the effectiveness of our personalized graph-based ranking model compared to Yahoo search results.
Data Sharing in Data-Centric Multi-tenant Software as a Service

Cloud Computing include applications that are delivered as services using internet in the shape of infrastructure, systems or application software (SaaS) hosted at a remote computing facility. SaaS applications are deployed on a shared environment that can be accessed by its users from various client-end software by using Internet. Organizations using SaaS application do not have control over the infrastructure of the environment that is often built using multi-tenant system architecture. Usually data-centric multi-tenant applications provide a common UI for all the tenants or organizations and data of multiple tenants is saved in a single database. Core of these applications revolve around managing organizations data. For many reasons like mergers, joint marketing campaign, moving from pilot to production SaaS instance, we have to migrate data from one tenant to another. Data migration requires a very skilled and time consuming human effort and it results in data duplication. Therefore we propose a middleware for data sharing between different organizations, a design pattern for the implementation of the middleware, and extension to current multi-tenant database designs for SaaS applications which will help in eliminating human effort in data migration and avoiding data duplication. Our results shows that data can be shared between different tenants of a SaaS efficiently without making a significant change in existing architecture of SaaS.
An Exploration of Embeddings for Generalized Phrases

Deep learning embeddings have been successfully used for many natural language processing problems. Embeddings are mostly computed for word forms although lots of recent papers have extended this to other linguistic units like morphemes and word sequences. In this paper, we define the concept of generalized phrase that includes conventional linguistic phrases as well as skip-bigrams. We compute embeddings for generalized phrases and show in experimental evaluations on coreference resolution and paraphrase identification that such embeddings perform better than word form embeddings.
Deep Multiscale Convolutional Feature Learning for Weakly Supervised Localization of Chest Pathologies in X-ray Images

Localization of chest pathologies in chest X-ray images is a challenging task because of their varying sizes and appearances. We propose a novel weakly supervised method to localize chest pathologies using class aware deep multiscale feature learning. Our method leverages intermediate feature maps from CNN layers at different stages of a deep network during the training of a classification model using image level annotations of pathologies. During the training phase, a set of layer relevance weights are learned for each pathology class and the CNN is optimized to perform pathology classification by convex combination of feature maps from both shallow and deep layers using the learned weights. During the test phase, to localize the predicted pathology, the multiscale attention map is obtained by convex combination of class activation maps from each stage using the layer relevance weights learned during the training phase. We have validated our method using 112000 X-ray images and compared with the state-of-the-art localization methods. We experimentally demonstrate that the proposed weakly supervised method can improve the localization performance of small pathologies such as nodule and mass while giving comparable performance for bigger pathologies e.g., Cardiomegaly.
Development of GaN HEMT based High Power High Efficiency Distributed Power Amplifier for Military Applications

Implementing wide bandgap GaN HEMT device into broadband distributed power amplifier creates a tremendous opportunity for RF designers to develop high power high efficiency very broadband power amplifiers for military applications. Several prototypes of 10-40 W GaN based distributed power amplifiers, including MMIC distributed PA, are currently under the development at Rockwell Collins, Inc. In this paper, we will discuss the results of a 10 W distributed power amplifier with the maximum power output of more than 40 dBm and a power-added efficiency of 30-70% over the bandwidth of 20-2000 MHz.
Generative Single Image Reflection Separation

Single image reflection separation is an ill-posed problem since two scenes, a transmitted scene and a reflected scene, need to be inferred from a single observation. To make the problem tractable, in this work we assume that categories of two scenes are known. It allows us to address the problem by generating both scenes that belong to the categories while their contents are constrained to match with the observed image. A novel network architecture is proposed to render realistic images of both scenes based on adversarial learning. The network can be trained in a weakly supervised manner, i.e., it learns to separate an observed image without corresponding ground truth images of transmission and reflection scenes which are difficult to collect in practice. Experimental results on real and synthetic datasets demonstrate that the proposed algorithm performs favorably against existing methods.
Controlling electromagnetic fields.

Using the freedom of design that metamaterials provide, we show how electromagnetic fields can be redirected at will and propose a design strategy. The conserved fields-electric displacement field D, magnetic induction field B, and Poynting vector B-are all displaced in a consistent manner. A simple illustration is given of the cloaking of a proscribed volume of space to exclude completely all electromagnetic fields. Our work has relevance to exotic lens design and to the cloaking of objects from electromagnetic fields.
Perspectives of granular computing

Granular computing emerges as a new multi-disciplinary study and has received much attention in recent years. A conceptual framework is presented by extracting shared commonalities from many fields. The framework stresses multiple views and multiple levels of understanding in each view. It is argued that granular computing is more about a philosophical way of thinking and a practical methodology of problem solving. By effectively using levels of granularity, granular computing provides a systematic, natural way to analyze, understand, represent, and solve real world problems. With granular computing, one aims at structured thinking at the philosophical level, and structured problem solving at the practical level.
Semi-Supervised and Unsupervised Extreme Learning Machines

Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.
Applications of Educational Data Mining: A survey

Various educational oriented problems are resolved through Educational Data Mining, which is the most prevalent applications of data mining. One of the crucial goals of this paper is to study the most recent works carried out on EDM and analyze their merits and drawbacks. This paper also highlights the cumulative results of the various data mining practices and techniques applied in the surveyed articles, and thereby suggesting the researchers on the future directions on EDM. In addition, an experiment was also conducted to evaluate, certain classification and clustering algorithms to observe the most reliable algorithms for future researches.
An Implementation of ITIL Guidelines for IT Support Process in a Service Organization

—Service level management (SLM) is a challenge in a distributed systems environment because all processes should provide a consistent, reliable, and predictable service delivery. Early 1990s, most organizations established few service-level agreements (SLA) as the key performance indicators (KPI) but it was difficult to measure or monitor them in a distributed systems environment. The strength of Information Technology Infrastructure Library (ITIL) is the approach of integrating the SLM with the support processes at strategic, tactical, and operational levels. This paper focuses on implementing ITIL guidelines at an operational level for service desk, incidents, problems, and change management. The ITIL framework only provides guidelines, so a service organization needs to explore a methodology for evaluating existing service support processes and implementing ITIL guidelines for improvements. To this end, we investigate upon how to apply the ITIL framework for reengineering of IT service support process in an organization. The approach is actually implemented at a dentalcare service provider with ten dental clinics connected in Wide Area Network (WAN) and the data is collected into a central server in the main dental center. We first started with the process mapping, and then moved towards reengineering following ITIL guidelines, while collecting the results in key performance areas before and after the process reengineering. This paper used questionnaires, document reviews, archival records and observation techniques for collecting the data. The results demonstrated improvements in differing magnitudes. Some ststistical analysis such as mean and variance together with t-value distribution and null hypothesis were also developed to determine the quality of results. The paper is cautious about the limited scope of audience and the questionnaire which may compromise the results; however, the approach is useful in obtaining significant improvements when a company invests in integrating ITIL guidelines in to service support processes.