nils-herrmann/clean_scirepeval huggingface dataset:
Hardware reverse engineering: Overview and open challenges

Hardware reverse engineering is a universal tool for both legitimate and illegitimate purposes. On the one hand, it supports confirmation of IP infringement and detection of circuit malicious manipulations, on the other hand it provides adversaries with crucial information to plagiarize designs, infringe on IP, or implant hardware Trojans into a target circuit. Although reverse engineering is commonplace in practice, the quantification of its complexity is an unsolved problem to date since both technical and human factors have to be accounted for. A sophisticated understanding of this complexity is crucial in order to provide a reasonable threat estimation and to develop sound countermeasures, i.e. obfuscation transformations of the target circuit, to mitigate risks for the modern IC landscape. The contribution of our work is threefold: first, we systematically study the current research branches related to hardware reverse engineering ranging from decapsulation to gate-level netlist analysis. Based on our overview, we formulate several open research questions to scientifically quantify reverse engineering, including technical and human factors. Second, we survey research on problem solving and on the acquisition of expertise and discuss its potential to quantify human factors in reverse engineering. Third, we propose novel directions for future interdisciplinary research encompassing both technical and psychological perspectives that hold the promise to holistically capture the complexity of hardware reverse engineering.
Preimages for Step-Reduced SHA-2

In this paper, we present preimage attacks on up to 43step SHA-256 (around 67% of the total 64 steps) and 46-step SHA-512 (around 57.5% of the total 80 steps), which significantly increases the number of attacked steps compared to the best previously published preimage attack working for 24 steps. The time complexities are 2, 2 for finding pseudo-preimages and 2, 2 compression function operations for full preimages. The memory requirements are modest, around 2 words for 43-step SHA-256 and 46-step SHA-512. The pseudo-preimage attack also applies to 43-step SHA-224 and SHA-384. Our attack is a meet-in-the-middle attack that uses a range of novel techniques to split the function into two independent parts that can be computed separately and then matched in a birthday-style phase.
Stability of photovoltaic and wind turbine grid-connected inverters for a large set of grid impedance values

The aim of this paper is to analyze the stability problems of grid connected inverters used in distributed generation. Complex controllers (e.g., multiple rotating dq-frames or resonant-based) are often required to compensate low frequency grid voltage background distortion and an LCL-filter is usually adopted for the high frequency one. The possible wide range of grid impedance values (distributed generation is suited for remote areas with radial distribution plants) challenge the stability and the effectiveness of the LCL-filter-based current controlled system. It has been found out and it will be demonstrated in this paper that the use of active damping helps to stabilise the system in respect to many different kinds of resonances. The use of active damping results in an easy plug-in feature of the generation system in a vast range of grid conditions and in a more flexible operation of the overall system able to manage sudden grid changes. In the paper, a vast measurement campaign made on a single-phase system and on a three-phase system used as scale prototypes for photovoltaic and wind turbines, respectively, validate the analysis.
An introduction to the Rasch measurement model: an example using the Hospital Anxiety and Depression Scale (HADS).

To demonstrate the use of Rasch analysis by assessing the appropriateness of utilizing the Hospital Anxiety and Depression Scale (HADS) total score (HADS-14) as a measure of psychological distress.



Cross-sectional, using Rasch analysis.



The HADS was administered to 296 patients attending an out-patient musculoskeletal rehabilitation program. Rasch analysis was conducted using RUMM2020 software to assess the overall fit of the model, the response scale used, individual item fit, differential item functioning (DIF) and person separation.



Rasch analysis supported the viability of the HADS-14 as a measure of psychological distress. It showed good person separation, little disordering of the thresholds and no evidence of DIE One anxiety item (item 11) showed some misfit to the model. The residuals patterned into the two subscales (anxiety and depression), but the person estimate derived from these two subscales was not statistically different to that derived from all items taken together, supporting the assumption of unidimensionality. A cut-point of 12 on the HADS-14 identified all cases that were classified as both anxious and depressed on the original individual HADS subscales.



The results of Rasch analysis support the use of the HADS-14 as a global measure of psychological distress. The study demonstrates the usefulness of Rasch analysis in assessing the psychometric properties of a scale and suggests that further use of this technique to assess the HADS-14 in other clinical groups is warranted.
MOBILE ROBOT TRAJECTORY TRACKING USING MODEL PREDICTIVE CONTROL

This work focus on the application of model-based predictiv e control (MPC) to the trajectory tracking problem of nonholonomic wheeled mobile robots (WMR). The main motivation of the use of MPC in this case relies on its ability in considering, in a straightforward way, control a nd state constraints that naturally arise in practical proble ms. Furthermore, MPC techniques consider an explicit performa nce criterion to be minimized during the computation of the cont r l law. The trajectory tracking problem is solved using two approaches: (1) nonlinear MPC and (2) linear MPC. Simulatio n results are provided in order to show the effectiveness of bo th schemes. Considerations regarding the computational effo rt of the MPC are developed with the purpose of analyzing the real-time implementation viability of the proposed techni ques.
As Time Goes By: Comprehensive Tagging of Textual Phrases with Temporal Scopes

Temporal expressions (TempEx’s for short) are increasingly important in search, question answering, information extraction, and more. Techniques for identifying and normalizing explicit temporal expressions work well, but are not designed for and cannot cope with textual phrases that denote named events, such as “Clinton’s term as secretary of state”. This paper addresses the problem of detecting such temponyms, inferring their temporal scopes, and mapping them to events in a knowledge base if present there. We present methods for this kind of temponym resolution, using an entityand TempEx-oriented document model and the Yago knowledge base for distant supervision. We develop a family of Integer Linear Programs for jointly inferring temponym mappings to the timeline and knowledge base. This enriches the document representation and also extends the knowledge base by obtaining new alias names for events. Experiments with three different corpora demonstrate the viability of our methods.
Understanding Organizational Level ERP Assimilation: A Multi-Case Study

Assimilation of ERP technology into organizational routine has received increased attention in the literature. The main objectives of this study are to examine the different levels of ERP assimilation in companies and to understand the primary drivers of organizational level ERP assimilation. Using a multi-case study method in this exploratory research, we conceptualize ERP assimilation at organizational level in a hierarchical model from supporting business process, to supporting operational decision marking, and to supporting business strategies. We then identify some primary drivers of organizational level ERP assimilation, including environment uncertainty, perceived usefulness, and internal managerial need. Finally, we identify some facilitating factors for organizational level ERP assimilation, including interaction with vendor, cautious implementation strategy, and strong IT champion. Theoretical contributions and managerial implications are discussed.
Web Mining Techniques in E-Commerce Applications

Today web is the best medium of communication in modern business. Many companies are redefining their business strategies to improve the business output. Business over internet provides the opportunity to customers and partners where their products and specific business can be found. Nowadays online business breaks the barrier of time and space as compared to the physical office. Big companies around the world are realizing that e-commerce is not just buying and selling over Internet, rather it improves the efficiency to compete with other giants in the market. For this purpose data mining sometimes called as knowledge discovery is used. Web mining is data mining technique that is applied to the WWW. There are vast quantities of information available over the Internet.
CapStones and ZebraWidgets: sensing stacks of building blocks, dials and sliders on capacitive touch screens

Recent research proposes augmenting capacitive touch pads with tangible objects, enabling a new generation of mobile applications enhanced with tangible objects, such as game pieces and tangible controllers. In this paper, we extend the concept to capacitive tangibles consisting of multiple parts, such as stackable gaming pieces and tangible widgets with moving parts. We achieve this using a system of wires and connectors inside each block that causes the capacitance of the bottom-most block to reflect the entire assembly. We demonstrate three types of tangibles, called CapStones, Zebra Dials and Zebra Sliders that work with current consumer hardware and investigate what designs may become possible as touchscreen hardware evolves.
Learning Structured Representation for Text Classification via Reinforcement Learning

Representation learning is a fundamental problem in natural language processing. This paper studies how to learn a structured representation for text classification. Unlike most existing representation models that either use no structure or rely on pre-specified structures, we propose a reinforcement learning (RL) method to learn sentence representation by discovering optimized structures automatically. We demonstrate two attempts to build structured representation: Information Distilled LSTM (ID-LSTM) and Hierarchically Structured LSTM (HS-LSTM). ID-LSTM selects only important, taskrelevant words, and HS-LSTM discovers phrase structures in a sentence. Structure discovery in the two representation models is formulated as a sequential decision problem: current decision of structure discovery affects following decisions, which can be addressed by policy gradient RL. Results show that our method can learn task-friendly representations by identifying important words or task-relevant structures without explicit structure annotations, and thus yields competitive performance.
Linear scan register allocation

We describe a new algorithm for fast global register allocation called linear scan. This algorithm is not based on graph coloring, but allocates registers to variables in a single linear-time scan of the variables' live ranges. The linear scan algorithm is considerably faster than algorithms based on graph coloring, is simple to implement, and results in code that is almost as efficient as that obtained using more complex and time-consuming register allocators based on graph coloring. The algorithm is of interest in applications where compile time is a concern, such as dynamic compilation systems, “just-in-time” compilers, and interactive development environments.
Evidential Deep Learning to Quantify Classification Uncertainty

Deterministic neural nets have been shown to learn effective predictors on a wide range of machine learning problems. However, as the standard approach is to train the network to minimize a prediction loss, the resultant model remains ignorant to its prediction confidence. Orthogonally to Bayesian neural nets that indirectly infer prediction uncertainty through weight uncertainties, we propose explicit modeling of the same using the theory of subjective logic. By placing a Dirichlet distribution on the class probabilities, we treat predictions of a neural net as subjective opinions and learn the function that collects the evidence leading to these opinions by a deterministic neural net from data. The resultant predictor for a multi-class classification problem is another Dirichlet distribution whose parameters are set by the continuous output of a neural net. We provide a preliminary analysis on how the peculiarities of our new loss function drive improved uncertainty estimation. We observe that our method achieves unprecedented success on detection of outof-distribution queries and endurance against adversarial perturbations.
BodyChat : Autonomous Communicative Behaviors in Avatars

Although avatars may resemble animated communicating interface agents, they have for the most part not profited from recent research into autonomous systems. In particular, even though avatars function within conversational environments (for example, chat or games), and even though they often resemble humans (with a head, hands, and a body) they are incapable of representing the kinds of knowledge that humans have about how to use the body during communication. Their appearance does not translate into increased communicative bandwidth. Face-to-face conversation among humans, however, does make extensive use of the visual channel for interaction management where many subtle and even involuntary cues are read from stance, gaze and gesture. We argue that the modeling and animation of such fundamental behavior is crucial for the credibility and effectiveness of the virtual interaction in chat. By treating the avatar as a communicative agent, we propose a method to automate the animation of important communicative behavior, deriving from work in context analysis and discourse theory. BodyChat is a system that allows users to communicate via text while their avatars automatically animate attention, salutations, turn taking, back-channel feedback and facial expression, as well as simple body functions such as the blinking of the eyes. 1. BEHAVIORS IN AVATARS One type of embodied agent that has received much airplay but little serious research attention in the agent community, is the avatar in a graphical chat. An avatar represents a user in a distributed virtual environment, but has until now not been autonomous. That is, it has not had knowledge to act in the absence of explicit control on the part of the user. In most current graphical chat systems the user is obliged to switch between controlling the avatar behavior and typing messages to other users. While the user is creating the message for her interlocutor, her avatar stands motionless or repeats a selected animation sequence. This fails to reflect the natural relationship between the body and the conversation that is taking place, potentially giving misleading or even conflicting visual
Negotiating cross-cultural issues at the end of life: "You got to go where he lives".

Culture fundamentally shapes how individuals make meaning out of illness, suffering, and dying. With increasing diversity in the United States, encounters between patients and physicians of different backgrounds are becoming more common. Thus the risk for cross-cultural misunderstandings surrounding care at the end of life is also increasing. Studies have shown cultural differences in attitudes toward truth telling, life-prolonging technology, and decision-making styles at the end of life. Using 2 case studies of patients, one of an African American couple in the southern United States and the other of a Chinese-American family in Hawaii, we outline some of the major issues involved in cross-cultural care and indicate how the patient, family, and clinician can navigate among differing cultural beliefs, values, and practices. Skilled use of cross-cultural understanding and communication techniques increases the likelihood that both the process and outcomes of care are satisfactory for all involved.
Deep Learning for Content-Based Image Retrieval: A Comprehensive Study

Learning effective feature representations and similarity measures are crucial to the retrieval performance of a content-based image retrieval (CBIR) system. Despite extensive research efforts for decades, it remains one of the most challenging open problems that considerably hinders the successes of real-world CBIR systems. The key challenge has been attributed to the well-known ``semantic gap'' issue that exists between low-level image pixels captured by machines and high-level semantic concepts perceived by human. Among various techniques, machine learning has been actively investigated as a possible direction to bridge the semantic gap in the long term. Inspired by recent successes of deep learning techniques for computer vision and other applications, in this paper, we attempt to address an open problem: if deep learning is a hope for bridging the semantic gap in CBIR and how much improvements in CBIR tasks can be achieved by exploring the state-of-the-art deep learning techniques for learning feature representations and similarity measures. Specifically, we investigate a framework of deep learning with application to CBIR tasks with an extensive set of empirical studies by examining a state-of-the-art deep learning method (Convolutional Neural Networks) for CBIR tasks under varied settings. From our empirical studies, we find some encouraging results and summarize some important insights for future research.
Boosting automatic event extraction from the literature using domain adaptation and coreference resolution

In recent years, several biomedical event extraction (EE) systems have been developed. However, the nature of the annotated training corpora, as well as the training process itself, can limit the performance levels of the trained EE systems. In particular, most event-annotated corpora do not deal adequately with coreference. This impacts on the trained systems' ability to recognize biomedical entities, thus affecting their performance in extracting events accurately. Additionally, the fact that most EE systems are trained on a single annotated corpus further restricts their coverage.



We have enhanced our existing EE system, EventMine, in two ways. First, we developed a new coreference resolution (CR) system and integrated it with EventMine. The standalone performance of our CR system in resolving anaphoric references to proteins is considerably higher than the best ranked system in the COREF subtask of the BioNLP'11 Shared Task. Secondly, the improved EventMine incorporates domain adaptation (DA) methods, which extend EE coverage by allowing several different annotated corpora to be used during training. Combined with a novel set of methods to increase the generality and efficiency of EventMine, the integration of both CR and DA have resulted in significant improvements in EE, ranging between 0.5% and 3.4% F-Score. The enhanced EventMine outperforms the highest ranked systems from the BioNLP'09 shared task, and from the GENIA and Infectious Diseases subtasks of the BioNLP'11 shared task.


AVAILABILITY
The improved version of EventMine, incorporating the CR system and DA methods, is available at: http://www.nactem.ac.uk/EventMine/.
Robust Sentiment Detection on Twitter from Biased and Noisy Data

In this paper, we propose an approach to automatically detect sentiments on Twitter messages (tweets) that explores some characteristics of how tweets are written and meta-information of the words that compose these messages. Moreover, we leverage sources of noisy labels as our training data. These noisy labels were provided by a few sentiment detection websites over twitter data. In our experiments, we show that since our features are able to capture a more abstract representation of tweets, our solution is more effective than previous ones and also more robust regarding biased and noisy data, which is the kind of data provided by these
A survey of multi-agent organizational paradigms

Many researchers have demonstrated that the organizational design employed by a system can have a significant, quantitative effect on its performance characteristics. A range of organizational strategies have emerged from this line of research, each with different strengths and weaknesses. In this article we present a survey of the major organizational paradigms used in multi-agent systems. These include hierarchies, holarchies, coalitions, teams, congregations, societies, federations, and matrix organizations. We will provide a description of each, discuss their costs and benefits, and provide examples of how they may be instantiated and maintained.
Shortest-path feasibility algorithms: An experimental evaluation

This is an experimental study of algorithms for the shortest-path feasibility problem: Given a directed weighted graph, find a negative cycle or present a short proof that none exists. We study previously known and new algorithms. Our testbed is more extensive than those previously used, including both static and incremental problems, as well as worst-case instances. We show that, while no single algorithm dominates, a small subset (including new algorithms) has very robust performance in practice. Our work advances the state of the art in the area.
Body Part Detection for Human Pose Estimation and Tracking

Accurate 3-D human body pose tracking from a monocular video stream is important for a number of applications. We describe a novel hierarchical approach for tracking human pose that uses edge-based features during the coarse stage and later other features for global optimization. At first, humans are detected by motion and tracked by fitting an ellipse in the image. Then, body components are found using edge features and used to estimate the 2D positions of the body joints accurately. This helps to bootstrap the estimation of 3D pose using a sampling-based search method in the last stage. We present experiment results with sequences of different realistic scenes to illustrate the performance of the method.
Learning Discriminative Space–Time Action Parts from Weakly Labelled Videos

Current state-of-the-art action classification methods aggregate space–time features globally, from the entire video clip under consideration. However, the features extracted may in part be due to irrelevant scene context, or movements shared amongst multiple action classes. This motivates learning with local discriminative parts, which can help localise which parts of the video are significant. Exploiting spatio-temporal structure in the video should also improve results, just as deformable part models have proven highly successful in object recognition. However, whereas objects have clear boundaries which means we can easily define a ground truth for initialisation, 3D space–time actions are inherently ambiguous and expensive to annotate in large datasets. Thus, it is desirable to adapt pictorial star models to action datasets without location annotation, and to features invariant to changes in pose such as bag-of-feature and Fisher vectors, rather than low-level HoG. Thus, we propose local deformable spatial bag-of-features in which local discriminative regions are split into a fixed grid of parts that are allowed to deform in both space and time at test-time. In our experimental evaluation we demonstrate that by using local space–time action parts in a weakly supervised setting, we are able to achieve state-of-the-art classification performance, whilst being able to localise actions even in the most challenging video datasets.