nils-herrmann/clean_scirepeval huggingface dataset:
Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review

The use of hand gestures provides an attractive alternative to cumbersome interface devices for human-computer interaction (HCI). In particular, visual interpretation of hand gestures can help in achieving the ease and naturalness desired for HCI. This has motivated a very active research area concerned with computer vision-based analysis and interpretation of hand gestures. We survey the literature on visual interpretation of hand gestures in the context of its role in HCI. This discussion is organized on the basis of the method used for modeling, analyzing, and recognizing gestures. Important differences in the gesture interpretation approaches arise depending on whether a 3D model of the human hand or an image appearance model of the human hand is used. 3D hand models offer a way of more elaborate modeling of hand gestures but lead to computational hurdles that have not been overcome given the real-time requirements of HCI. Appearance-based models lead to computationally efficient “purposive” approaches that work well under constrained situations but seem to lack the generality desirable for HCI. We also discuss implemented gestural systems as well as other potential applications of vision-based gesture recognition. Although the current progress is encouraging, further theoretical as well as computational advances are needed before gestures can be widely used for HCI. We discuss directions of future research in gesture recognition, including its integration with other natural modes of humancomputer interaction. Index Terms —Vision-based gesture recognition, gesture analysis, hand tracking, nonrigid motion analysis, human-computer
A Case Study of the Rustock Rootkit and Spam Bot

In this paper we present a case study of the steps leading up to the extraction of the spam bot payload found within a backdoor rootkit known as Backdoor.Rustock.B or Spam-Mailbot.c. Following the extraction of the spam module we focus our analysis on the steps necessary to decrypt the communications between the command and control server and infected hosts. Part of the discussion involves a method to extract the encryption key from within the malware binary and use that to decrypt the communications. The result is a better understanding of an advanced botnet communications scheme.
Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities

Recommender systems have been researched extensively over the past decades. Whereas several algorithms have been developed and deployed in various application domains, recent research effort s are increasingly oriented towards the user experience of recommender systems. This research goes beyond accuracy of recommendation algorithms and focuses on various human factors that affect acceptance of recommendations, such as user satisfaction, trust, transparency and sense of control. In this paper, we present an interactive visualization framework that combines recommendation with visualization techniques to support human-recommender interaction. Then, we analyze existing interactive recommender systems along the dimensions of our framework, including our work. Based on our survey results, we present future research challenges and opportunities.
Automatic Application Identification from Billions of Files

Understanding how to group a set of binary files into the piece of software they belong to is highly desirable for software profiling, malware detection, or enterprise audits, among many other applications. Unfortunately, it is also extremely challenging: there is absolutely no uniformity in the ways different applications rely on different files, in how binaries are signed, or in the versioning schemes used across different pieces of software. In this paper, we show that, by combining information gleaned from a large number of endpoints (millions of computers), we can accomplish large-scale application identification automatically and reliably. Our approach relies on collecting metadata on billions of files every day, summarizing it into much smaller "sketches", and performing approximate k-nearest neighbor clustering on non-metric space representations derived from these sketches. We design and implement our proposed system using Apache Spark, show that it can process billions of files in a matter of hours, and thus could be used for daily processing. We further show our system manages to successfully identify which files belong to which application with very high precision, and adequate recall.
Knowledge management issues in knowledge-intensive SMEs

The purpose of this paper is to present a study of knowledge management understanding and usage in small and medium knowledge-intensive enterprises.  The study has taken an interpretitivist approach, using two knowledge-intensive South Yorkshire (England) companies as case studies, both of which are characterised by the need to process and use knowledge on a daily basis in order to remain competitive. The case studies were analysed using qualitative research methodology, composed of interviews and concept mapping, thus deriving a characterisation of understandings, perceptions and requirements of SMEs in relation to knowledge management.  The study provides evidence that, while SMEs, including knowledge intensive ones, acknowledge that adequately capturing, storing, sharing and disseminating knowledge can lead to greater innovation and productivity, their managers are not prepared to invest the relatively high effort on long term knowledge management goals for which they have difficulty in establishing the added value. Thus, knowledge management activities within SMEs tend to happen in an informal way, rarely supported by purposely designed ICT systems.  This paper proposes that further studies in this field are required that focus on organisational and practical issues in order to close the gap between theoretical propositions and the reality of practice.  The study suggests that in order to implement an appropriate knowledge management strategy in SMEs cultural, behavioural, and organisational issues need to be tackled before even considering technical issues.  KM seems to have been successfully applied in large companies, but it is largely disregarded by small and medium sized enterprises (SMEs). This has been attributed primarily to a lack of a formal approach to the sharing, recording, transferring, auditing and exploiting of organisational knowledge, together with a lack of utilisation of available information technologies. This paper debates these concepts from a research findings point of view.
Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention

Classifying the semantic relationship between two entities in a sentence is termed as Relation Extraction (RE). RE from entity mentions is an important step in various Natural Language Processing tasks, such as, knowledge base construction, question-answering etc. Supervised methods have been successful on the relation extraction task [2, 18]. However, the extensive training data necessary for supervised learning is expensive to obtain and therefore restrictive in a Web-scale relation extraction task. To overcome this challenge,  proposed a Distant Supervision (DS) method for relation extraction to help automatically generate new training data by taking an intersection between a text corpora and knowledge base. However, the DS assumption is too strong, and may introduce noise such as false negative samples due to missing facts in knowledge base. In order to address this challenge, DS has been modeled as Multi-Instance Multi-Label (MIML) problem . More recently, neural models for DS have been proposed [17, 12]. In this paper, we define ‘instance’ as a sentence containing an entity-pair, and ‘instance set’ as a set of sentences containing the same entity-pair.
Philosophy and the practice of Bayesian statistics.

A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics. We argue that the most successful forms of Bayesian statistics do not actually support that particular philosophy but rather accord much better with sophisticated forms of hypothetico-deductivism. We examine the actual role played by prior distributions in Bayesian models, and the crucial aspects of model checking and model revision, which fall outside the scope of Bayesian confirmation theory. We draw on the literature on the consistency of Bayesian updating and also on our experience of applied work in social science. Clarity about these matters should benefit not just philosophy of science, but also statistical practice. At best, the inductivist view has encouraged researchers to fit and compare models without checking them; at worst, theorists have actively discouraged practitioners from performing model checking because it does not fit into their framework.
The Design , Implementation , and Evaluation of a Pointing Device For a Wearable Computer

U.S. Air Force special tactics operators at times use small wearable computers (SWCs) for mission objectives. The primary pointing device of a SWC is either a touchpad or trackpoint, which is embedded into the chassis of the SWC. In situations where the user cannot directly interact with these pointing devices, the utility of the SWC is decreased. We developed a pointing device called the G3 that can be used for SWCs used by operators. The device utilizes gyroscopic sensors attached to the user’s index finger to move the computer cursor according to the angular velocity of his finger. We showed that, as measured by Fitts’ law, the overall performance and accuracy of the G3 was better than that of the touchpad and trackpoint. These findings suggest that the G3 can adequately be used with SWCs. Additionally, we investigated the G3 ’s utility as a control device for operating micro remotely piloted aircrafts
Dermatologist-level classification of skin cancer with deep neural networks

Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
Deep reinforcement learning with successor features for navigation across similar environments

In this paper we consider the problem of robot navigation in simple maze-like environments where the robot has to rely on its onboard sensors to perform the navigation task. In particular, we are interested in solutions to this problem that do not require localization, mapping or planning. Additionally, we require that our solution can quickly adapt to new situations (e.g., changing navigation goals and environments). To meet these criteria we frame this problem as a sequence of related reinforcement learning tasks. We propose a successor-feature-based deep reinforcement learning algorithm that can learn to transfer knowledge from previously mastered navigation tasks to new problem instances. Our algorithm substantially decreases the required learning time after the first task instance has been solved, which makes it easily adaptable to changing environments. We validate our method in both simulated and real robot experiments with a Robotino and compare it to a set of baseline methods including classical planning-based navigation.
Reinforced Video Captioning with Entailment Rewards

Sequence-to-sequence models have shown promising improvements on the temporal task of video captioning, but they optimize word-level cross-entropy loss during training. First, using policy gradient and mixed-loss methods for reinforcement learning, we directly optimize sentence-level task-based metrics (as rewards), achieving significant improvements over the baseline, based on both automatic metrics and human evaluation on multiple datasets. Next, we propose a novel entailment-enhanced reward (CIDEnt) that corrects phrase-matching based metrics (such as CIDEr) to only allow for logically-implied partial matches and avoid contradictions, achieving further significant improvements over the CIDEr-reward model. Overall, our CIDEnt-reward model achieves the new state-of-the-art on the MSR-VTT dataset.
Deep Transfer Reinforcement Learning for Text Summarization

Deep neural networks are data hungry models and thus face difficulties when attempting to train on small text datasets. Transfer learning is a potential solution but their effectiveness in the text domain is not as explored as in areas such as image analysis. In this paper, we study the problem of transfer learning for text summarization and discuss why existing state-of-the-art models fail to generalize well on other (unseen) datasets. We propose a reinforcement learning framework based on a self-critic policy gradient approach which achieves good generalization and state-ofthe-art results on a variety of datasets. Through an extensive set of experiments, we also show the ability of our proposed framework to fine-tune the text summarization model using only a few training samples. To the best of our knowledge, this is the first work that studies transfer learning in text summarization and provides a generic solution that works well on unseen data.
Signaling in Equity Crowdfunding

This paper presents an empirical examination of which start-up signals will small investors to commit financial resources in an equity crowdfunding context. We examine the impact of firms’ financial roadmaps (e.g., preplanned exit strategies such as IPOs or acquisitions), external certification (awards, government grants and patents), internal governance (such as board structure), and risk factors (such as amount of equity offered and the presence of disclaimers) on fundraising success. Our data highlight the importance of financial roadmaps and risk factors, as well as internal governance, for successful equity crowdfunding. External certification, by contrast, has little or no impact on success. We also discuss the implications for successful policy design.
GRAM: Graph-based Attention Model for Healthcare Representation Learning

Deep learning methods exhibit promising performance for predictive modeling in healthcare, but two important challenges remain: - Data insufficiency: Often in healthcare predictive modeling, the sample size is insufficient for deep learning methods to achieve satisfactory results.  The representations learned by deep learning methods should align with medical knowledge.
 To address these challenges, we propose GRaph-based Attention Model (GRAM) that supplements electronic health records (EHR) with hierarchical information inherent to medical ontologies. Based on the data volume and the ontology structure, GRAM represents a medical concept as a combination of its ancestors in the ontology via an attention mechanism.
 We compared predictive performance (i.e. accuracy, data needs, interpretability) of GRAM to various methods including the recurrent neural network (RNN) in two sequential diagnoses prediction tasks and one heart failure prediction task. Compared to the basic RNN, GRAM achieved 10% higher accuracy for predicting diseases rarely observed in the training data and 3% improved area under the ROC curve for predicting heart failure using an order of magnitude less training data. Additionally, unlike other methods, the medical concept representations learned by GRAM are well aligned with the medical ontology. Finally, GRAM exhibits intuitive attention behaviors by adaptively generalizing to higher level concepts when facing data insufficiency at the lower level concepts.
Framewise phoneme classification with bidirectional LSTM and other neural network architectures

In this paper, we present bidirectional Long Short Term Memory (LSTM) networks, and a modified, full gradient version of the LSTM learning algorithm. We evaluate Bidirectional LSTM (BLSTM) and several other network architectures on the benchmark task of framewise phoneme classification, using the TIMIT database. Our main findings are that bidirectional networks outperform unidirectional ones, and Long Short Term Memory (LSTM) is much faster and also more accurate than both standard Recurrent Neural Nets (RNNs) and time-windowed Multilayer Perceptrons (MLPs). Our results support the view that contextual information is crucial to speech processing, and suggest that BLSTM is an effective architecture with which to exploit it.
Split and Match: Example-Based Adaptive Patch Sampling for Unsupervised Style Transfer

This paper presents a novel unsupervised method to transfer the style of an example image to a source image. The complex notion of image style is here considered as a local texture transfer, eventually coupled with a global color transfer. For the local texture transfer, we propose a new method based on an adaptive patch partition that captures the style of the example image and preserves the structure of the source image. More precisely, this example-based partition predicts how well a source patch matches an example patch. Results on various images show that our method outperforms the most recent techniques.
Storage Efficient Substring Searchable Symmetric Encryption

We address the problem of substring searchable encryption. A single user produces a big stream of data and later on wants to learn the positions in the string that some patterns occur. Although current techniques exploit auxiliary data structures to achieve efficient substring search on the server side, the cost at the user side may be prohibitive. We revisit the work of substring searchable encryption in order to reduce the storage cost of auxiliary data structures. Our solution entails a suffix array based index design, which allows optimal storage cost $O(n)$ with small hidden factor at the size of the string n. Moreover, we implemented our scheme and the state of the art protocol \citeChase to demonstrate the performance advantage of our solution with precise benchmark results.
Multimodal Learning with Deep Boltzmann Machines

A Deep Boltzmann Machine is described for learning a generative model of data that consists of multiple and diverse input modalities. The model can be used to extract a unified representation that fuses modalities together. We find that this representation is useful for classification and information retrieval tasks. The model works by learning a probability density over the space of multimodal inputs. It uses states of latent variables as representations of the input. The model can extract this representation even when some modalities are absent by sampling from the conditional distribution over them and filling them in. Our experimental results on bi-modal data consisting of images and text show that the Multimodal DBM can learn a good generative model of the joint space of image and text inputs that is useful for information retrieval from both unimodal and multimodal queries. We further demonstrate that this model significantly outperforms SVMs and LDA on discriminative tasks. Finally, we compare our model to other deep learning methods, including autoencoders and deep belief networks, and show that it achieves noticeable gains.
Cloud computing in academic institutions

IT plays important role in education. Cloud Computing is a new technology in the world of IT which emerged recently and began to spread rapidly. Cloud computing is an Internet-based computing services on-demand. The main goal of this paper is to highlight the implications of cloud computing in academic institutions. This paper presents the cloud computing paradigm, historical view, concept, layers, types and the characteristics. During reviewing of many instances of using cloud computing in academic institutions, this paper emphasizes that the adoption of cloud computing in academic institutions is useful and sometimes necessary to overcome many of challenges and barriers. The presentation shows that the cloud computing technology enhances the performance in academic institutions as well as cloud computing plays significant role to reduction of costs. As the adoption of cloud computing in academic institutions is still low, this paper provides the factors that affect the adoption of cloud computing in the academic institutions. Successful transition to cloud computing in academic institutions depends on well definition for transition strategies. This paper provides suggestion for transition strategies into cloud computing in academic institutions.
Fake News Detection on Social Media: A Data Mining Perspective

Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of \fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ine ective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.