nils-herrmann/clean_scirepeval huggingface dataset:
A Heuristics-based Static Analysis Approach for Detecting Packed PE Binaries

Malware authors evade the signature based detection by packing the original malware using custom packers. In this paper, we present a static heuristics based approach for the detection of packed executables. We present 1) the PE heuristics considered for analysis and taxonomy of heuristics; 2) a method for computing the score using power distance based on weights and risks assigned to the defined heuristics; and 3) classification of packed executable based on the threshold obtained with the training data set, and the results achieved with the test data set. The experimental results show that our approach has a high detection rate of 99.82% with a low false positive rate of 2.22%. We also bring out difficulties in detecting packed DLL, CLR and Debug mode executables via header analysis.
IMAGE PROCESSING ALGORITHMS FOR ROBOTICS ON EMBEDDED SYSTEM

Vision is the most versatile and efficient sensory system. So, it is not surprising that images contribute an important role in human perception. This is analogous to machine vision such as shape recognition application which is an important field nowadays. This paper describes implementation of image processing on embedded platform and an embedded application, a robot capable of tracking an object in 3-dimensional environment. It is a real time operating system (RTOS) based embedded system which will run the Digital Image Processing Algorithms to extract the information from the images. The camera connected on USB bus is used to capture images on the ARM9 core running RTOS. Depending upon the information extracted, the locomotion is carried out. The camera is a simple CMOS USB-camera module which has a resolution about 0.3MP. Video4Linux API’s provided by kernel are used to capture the image, and then it is decoded, and the required object location is detected using image processing algorithms. The actuations are made so as to track the object. The embedded Linux kernel provides support for multitasking and ensures that the task is performed within the real time constraints. The OS makes system flexible for changes such as interfacing new devices, handling the file system and memory management for storage of data. KeywordsEmbedded Linux, ARM, Video4Linux, YUYV, Embedded C, Object detection, CMOS, USB, SOC, Kerne
Machine Learning in Adversarial Settings

Recent advances in machine learning have led to innovative applications and services that use computational structures to reason about complex phenomenon. Over the past several years, the security and machine-learning communities have developed novel techniques for constructing adversarial samples--malicious inputs crafted to mislead (and therefore corrupt the integrity of) systems built on computationally learned models. The authors consider the underlying causes of adversarial samples and the future countermeasures that might mitigate them.
Behavior Change Support Systems : The Next Frontier for Web Science

The new generation of the web has opened up opportunities for developing new kinds of information systems for influencing users. For instance, one of the most prominent areas for future healthcare improvement is the role of the web in fostering improved health and healthier lifestyles. The success or failure of web information systems seems to rely on their social features. For this reason, a better understanding of the techno-social aspects of web information systems and the way they influence people is needed. This article is conceptual and theory-creating by its nature. It introduces the concept of a behavior change support system and suggests it as a key construct for web science research. The behavior change support systems are characterized by their persuasive purpose.
A combined recognition and segmentation model for urban traffic scene understanding

The perception of traffic related objects in the vehicles environment is an essential prerequisite for future autonomous driving. Cameras are particularly suited for this task, as the traffic relevant information of a scene is inferable from its visual appearance. In traffic scene understanding, semantic segmentation denotes the task of generating and labeling regions in the image that correspond to specific object categories, such as cars or road area. In contrast, the task of scene recognition assigns a global label to an image, that reflects the overall category of the scene. This paper presents a deep neural network (DNN) capable of solving both problems in a computationally efficient manner. The architecture is designed to avoid redundant computations, as the task specific decoders share a common feature encoder stage. A novel Hadamard layer with element-wise weights efficiently exploits spatial priors for the segmentation task. Traffic scene segmentation is investigated in conjunction with road topology recognition based on the cityscapes dataset  augmented with manually labeled road topology ground truth data.
The Bunker Cache for spatio-value approximation

The cost of moving and storing data is still a fundamental concern for computer architects. Inefficient handling of data can be attributed to conventional architectures being oblivious to the nature of the values that these data bits carry. We observe the phenomenon of spatio-value similarity, where data elements that are approximately similar in value exhibit spatial regularity in memory. This is inherent to 1) the data values of real-world applications, and 2) the way we store data structures in memory. We propose the Bunker Cache, a design that maps similar data to the same cache storage location based solely on their memory address, sacrificing some application quality loss for greater efficiency. The Bunker Cache enables performance gains (ranging from 1.08x to 1.19x) via reduced cache misses and energy savings (ranging from 1.18x to 1.39x) via reduced off-chip memory accesses and lower cache storage requirements. The Bunker Cache requires only modest changes to cache indexing hardware, integrating easily into commodity systems.
Linguistic Knowledge and Transferability of Contextual Representations

Contextual word representations derived from large-scale neural language models are successful across a diverse set of NLP tasks, suggesting that they encode useful and transferable features of language. To shed light on the linguistic knowledge they capture, we study the representations produced by several recent pretrained contextualizers (variants of ELMo, the OpenAI transformer LM, and BERT) with a suite of sixteen diverse probing tasks. We find that linear models trained on top of frozen contextual representations are competitive with state-of-the-art task-specific models in many cases, but fail on tasks requiring fine-grained linguistic knowledge (e.g., conjunct identification). To investigate the transferability of contextual word representations, we quantify differences in the transferability of individual layers within contextualizers, especially between RNNs and transformers. For instance, higher layers of RNNs are more task-specific, while transformer layers do not exhibit the same monotonic trend. In addition, to better understand what makes contextual word representations transferable, we compare language model pretraining with eleven supervised pretraining tasks. For any given task, pretraining on a closely related task yields better performance than language model pretraining (which is better on average) when the pretraining dataset is fixed. However, language model pretraining on more data gives the best results.
Business Process Analytics

Business Process Management Systems are a rich source of events that document the execution of processes and activities within these systems. Business Process Analytics is the family of methods and tools that can be applied to these event streams in order to support decision-making in organizations. The analysis of process events can focus on the behavior of completed processes, evaluate currently running process instances, or focus on predicting the behavior of process instances in the future. This chapter provides an overview of the different methods and technologies that can be employed in each of these three areas of process analytics. We discuss the underlying format and types of process events as the common source of analytics information, present techniques for the aggregation and composition of these events, and outline methods that support backwardand forward-looking process analytics.
Concurrency by modularity: design patterns, a case in point

General purpose object-oriented programs typically aren't embarrassingly parallel. For these applications, finding enough concurrency remains a challenge in program design. To address this challenge, in the Panini project we are looking at reconciling concurrent program design goals with modular program design goals. The main idea is that if programmers improve the modularity of their programs they should get concurrency for free. In this work we describe one of our directions to reconcile these two goals by enhancing Gang-of-Four (GOF) object-oriented design patterns. GOF patterns are commonly used to improve the modularity of object-oriented software. These patterns describe strategies to decouple components in design space and specify how these components should interact. Our hypothesis is that if these patterns are enhanced to also decouple components in execution space applying them will concomitantly improve the design and potentially available concurrency in software systems. To evaluate our hypothesis we have studied all 23 GOF patterns. For 18 patterns out of 23, our hypothesis has held true. Another interesting preliminary result reported here is that for 17 out of these 18 studied patterns, concurrency and synchronization concerns were completely encapsulated in our concurrent design pattern framework.
Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender
Semantic Web Search based on Ontology Modeling using Protege Reasoner

The Semantic Web works on the existing Web which presents the meaning of information as well-defined vocabularies understood by the people. Semantic Search, at the same time, works on improving the accuracy of a search by understanding the intent of the search and providing contextually relevant results. The paper describes a semantic approach towards web search through a PHP application. The goal was to parse through a user’s browsing history and return semantically relevant web pages for the search query provided. The browser used for this purpose was Mozilla Firefox. The user’s history was stored in a MySQL database, which, in turn, was accessed using PHP. The ontology, created from the browsing history, was then parsed for the entered search query and the corresponding results were returned to the user providing a semantically organized and relevant output.
Working memory training: Improving intelligence – Changing brain activity

The main objectives of the study were: to investigate whether training on working memory (WM) could improve fluid intelligence, and to investigate the effects WM training had on neuroelectric (electroencephalography - EEG) and hemodynamic (near-infrared spectroscopy - NIRS) patterns of brain activity. In a parallel group experimental design, respondents of the working memory group after 30 h of training significantly increased performance on all tests of fluid intelligence. By contrast, respondents of the active control group (participating in a 30-h communication training course) showed no improvements in performance. The influence of WM training on patterns of neuroelectric brain activity was most pronounced in the theta and alpha bands. Theta and lower-1 alpha band synchronization was accompanied by increased lower-2 and upper alpha desynchronization. The hemodynamic patterns of brain activity after the training changed from higher right hemispheric activation to a balanced activity of both frontal areas. The neuroelectric as well as hemodynamic patterns of brain activity suggest that the training influenced WM maintenance functions as well as processes directed by the central executive. The changes in upper alpha band desynchronization could further indicate that processes related to long term memory were also influenced.
The Outer-connected Domination Number of Sierpiński-like Graphs

An outer-connected dominating set in a graph G = (V, E) is a set of vertices D ⊆ V satisfying the condition that, for each vertex v ∉ D, vertex v is adjacent to some vertex in D and the subgraph induced by V∖D is connected. The outer-connected dominating set problem is to find an outer-connected dominating set with the minimum number of vertices which is denoted by γ ~ c ( G ) $\tilde {\gamma }_{c}(G)$ . In this paper, we determine γ ~ c ( S ( n , k ) ) $\tilde {\gamma }_{c}(S(n,k))$ , γ ~ c ( S + ( n , k ) ) $\tilde {\gamma }_{c}(S^{+}(n,k))$ , γ ~ c ( S + + ( n , k ) ) $\tilde {\gamma }_{c}(S^{++}(n,k))$ , and γ ~ c ( S n ) $\tilde {\gamma }_{c}(S_{n})$ , where S(n, k), S +(n, k), S ++(n, k), and S n are Sierpi n ́ $\acute {\mathrm {n}}$ ski-like graphs.
Evaluating the robustness of repeated measures analyses: the case of small sample sizes and nonnormal data.

Repeated measures analyses of variance are the method of choice in many studies from experimental psychology and the neurosciences. Data from these fields are often characterized by small sample sizes, high numbers of factor levels of the within-subjects factor(s), and nonnormally distributed response variables such as response times. For a design with a single within-subjects factor, we investigated Type I error control in univariate tests with corrected degrees of freedom, the multivariate approach, and a mixed-model (multilevel) approach (SAS PROC MIXED) with Kenward-Roger's adjusted degrees of freedom. We simulated multivariate normal and nonnormal distributions with varied population variance-covariance structures (spherical and nonspherical), sample sizes (N), and numbers of factor levels (K). For normally distributed data, as expected, the univariate approach with Huynh-Feldt correction controlled the Type I error rate with only very few exceptions, even if samples sizes as low as three were combined with high numbers of factor levels. The multivariate approach also controlled the Type I error rate, but it requires N ≥ K. PROC MIXED often showed acceptable control of the Type I error rate for normal data, but it also produced several liberal or conservative results. For nonnormal data, all of the procedures showed clear deviations from the nominal Type I error rate in many conditions, even for sample sizes greater than 50. Thus, none of these approaches can be considered robust if the response variable is nonnormally distributed. The results indicate that both the variance heterogeneity and covariance heterogeneity of the population covariance matrices affect the error rates.
Fat cell adrenergic receptors and the control of white and brown fat cell function.

Five adrenoceptor subtypes are involved in the adrenergic regulation of white and brown fat cell function. The effects on cAMP production and cAMP-related cellular responses are mediated through the control of adenylyl cyclase activity by the stimulatory beta 1-, beta 2-, and beta 3-adrenergic receptors and the inhibitory alpha 2-adrenoceptors. Activation of alpha 1-adrenoceptors stimulates phosphoinositidase C activity leading to inositol 1,4,5-triphosphate and diacylglycerol formation with a consequent mobilization of intracellular Ca2+ stores and protein kinase C activation which trigger cell responsiveness. The balance between the various adrenoceptor subtypes is the point of regulation that determines the final effect of physiological amines on adipocytes in vitro and in vivo. Large species-specific differences exist in brown and white fat cell adrenoceptor distribution and in their relative importance in the control of the fat cell. Functional beta 3-adrenoceptors coexist with beta 1- and beta 2-adrenoceptors in a number of fat cells; they are weakly active in guinea pig, primate, and human fat cells. Physiological hormones and transmitters operate, in fact, through differential recruitment of all these multiple alpha- and beta-adrenoceptors on the basis of their relative affinity for the different subtypes. The affinity of the beta 3-adrenoceptor for catecholamines is less than that of the classical beta 1- and beta 2-adrenoceptors. Conversely, epinephrine and norepinephrine have a higher affinity for the alpha 2-adrenoceptors than for beta 1-, 2-, or 3-adrenoceptors. Antagonistic actions exist between alpha 2- and beta-adrenoceptor-mediated effects in white fat cells while positive cooperation has been revealed between alpha 1- and beta-adrenoceptors in brown fat cells. Homologous down-regulation of beta 1- and beta 2-adrenoceptors is observed after administration of physiological amines and beta-agonists. Conversely, beta 3- and alpha 2-adrenoceptors are much more resistant to agonist-induced desensitization and down-regulation. Heterologous regulation of beta-adrenoceptors was reported with glucocorticoids while sex-steroid hormones were shown to regulate alpha 2-adrenoceptor expression (androgens) and to alter adenylyl cyclase activity (estrogens).
ADONN: Adaptive Design of Optimized Deep Neural Networks for Embedded Systems

Nowadays, many modern applications, e.g. autonomous system, and cloud data services need to capture and process a big amount of raw data at runtime that ultimately necessitates a high-performance computing model. Deep Neural Network (DNN) has already revealed its learning capabilities in runtime data processing for modern applications. However, DNNs are becoming more deep sophisticated models for gaining higher accuracy which require a remarkable computing capacity. Considering high-performance cloud infrastructure as a supplier of required computational throughput is often not feasible. Instead, we intend to find a near-sensor processing solution which will lower the need for network bandwidth and increase privacy and power efficiency, as well as guaranteeing worst-case response-times. Toward this goal, we introduce ADONN framework, which aims to automatically design a highly robust DNN architecture for embedded devices as the closest processing unit to the sensors. ADONN adroitly searches the design space to find improved neural architectures. Our proposed framework takes advantage of a multi-objective evolutionary approach, which exploits a pruned design space inspired by a dense architecture. Unlike recent works that mainly have tried to generate highly accurate networks, ADONN also considers the network size factor as the second objective to build a highly optimized network fitting with limited computational resource budgets while delivers comparable accuracy level. In comparison with the best result on CIFAR-10 dataset, a generated network by ADONN presents up to 26.4 compression rate while loses only 4% accuracy. In addition, ADONN maps the generated DNN on the commodity programmable devices including ARM Processor, High-Performance CPU, GPU, and FPGA.
A root submergence technique for pontic site development in fixed dental prostheses in the maxillary anterior esthetic zone

This case report discusses the effect of a root submergence technique on preserving the periodontal tissue at the pontic site of fixed dental prostheses in the maxillary anterior aesthetic zone.



Teeth with less than ideal structural support for fixed retainer abutments were decoronated at the crestal bone level. After soft tissue closure, the final fixed dental prostheses were placed with the pontics over the submerged root area. Radiographic and clinical observations at the pontic sites were documented.



The submerged roots at the pontic sites preserved the surrounding periodontium without any periapical pathology. The gingival contour at the pontic site was maintained in harmony with those of the adjacent teeth, as well as the overall form of the arch.



The results of this clinical report indicate that a root submergence technique can be successfully applied in pontic site development with fixed dental prostheses, especially in the maxillary anterior esthetic zone.
Tactile Displays: Guidance for Their Design and Application

This article provides an overview of tactile displays. Its goal is to assist human factors practitioners in deciding when and how to employ the sense of touch for the purpose of information representation. The article also identifies important research needs in this area.



First attempts to utilize the sense of touch as a medium for communication date back to the late 1950s. For the next 35 years progress in this area was relatively slow, but recent years have seen a surge in the interest and development of tactile displays and the integration of tactile signals in multimodal interfaces. A thorough understanding of the properties of this sensory channel and its interaction with other modalities is needed to ensure the effective and robust use of tactile displays.



First, an overview of vibrotactile perception is provided. Next, the design of tactile displays is discussed with respect to available technologies. The potential benefit of including tactile cues in multimodal interfaces is discussed. Finally, research needs in the area of tactile information presentation are highlighted.



This review provides human factors researchers and interface designers with the requisite knowledge for creating effective tactile interfaces. It describes both potential benefits and limitations of this approach to information presentation.



The sense of touch represents a promising means of supporting communication and coordination in human-human and human-machine systems.



Tactile interfaces can support numerous functions, including spatial orientation and guidance, attention management, and sensory substitution, in a wide range of domains.