Miracle-dz/UniFAR-FTUs huggingface dataset:
MutantX-S: Scalable Malware Clustering Based on Static Features

The current lack of automatic and speedy labeling of a large number (thousands) of malware samples seen everyday delays the generation of malware signatures and has become a major challenge for anti-virus industries. In this paper, we design, implement and evaluate a novel, scalable framework, called MutantX-S, that can efficiently cluster a large number of samples into families based on programs' static features, i.e., code instruction sequences. MutantX-S is a unique combination of several novel techniques to address the practical challenges of malware clustering. Specifically, it exploits the instruction format of ×86 architecture and represents a program as a sequence of opcodes, facilitating the extraction of N-gram features. It also exploits the hashing trick recently developed in the machine learning community to reduce the dimensionality of extracted feature vectors, thus significantly lowering the memory requirement and computation costs. Our comprehensive evaluation on a MutantX-S prototype using a database of more than 130,000 malware samples has shown its ability to correctly cluster over 80% of samples within 2 hours, achieving a good balance between accuracy and scalability. Applying MutantX-S on malware samples created at different times, we also demonstrate that MutantX-S achieves high accuracy in predicting labels for previously unknown malware.
Employing Trainable String Similarity Metrics for Information Integration

The problem of identifying approximately duplicate objects in databases is an essential step for the information integration process. Most existing approaches have relied on generic or manually tuned distance metrics for estimating the similarity of potential duplicates. In this paper, we present a framework for improving duplicate detection using trainable measures of textual similarity. We propose to employ learnable text distance functions for each data field, and introduce an extended variant of learnable string edit distance based on an Expectation-Maximization (EM) training algorithm. Experimental results on a range of datasets show that this similarity metric is capable of adapting to the specific notions of similarity that are appropriate for different domains. Our overall system, MARLIN, utilizes support vector machines to combine multiple similarity metrics, which are shown to perform better than ensembles of decisions trees, which were employed for this task in previous work.
Rotor Voltage Dynamics in the Doubly Fed Induction Generator During Grid Faults

This paper presents a new control strategy for the rotor-side converter (RSC) of wind turbines (WTs) based on doubly fed induction generators (DFIG) that intends to improve its low-voltage ride through capability. The main objective of this work is to design an algorithm that would enable the system to control the initial overcurrents that appear in the generator during voltage sags, which can damage the RSC, without tripping it. As a difference with classical solutions, based on the installation of crowbar circuits, this operation mode permits to keep the inverter connected to the generator, something that would permit the injection of power to the grid during the fault, as the new grid codes demand. A theoretical study of the dynamical behavior of the rotor voltage is also developed, in order to show that the voltage at the rotor terminals required for the control strategy implementation remains under controllable limits. In order to validate the proposed control system simulation, results have been collected using PSCAD/EMTDC and experimental tests have been carried out in a scaled prototype.
Debugging at Full Speed

Debugging support for highly optimized execution environments is notoriously difficult to implement. The Truffle/Graal platform for implementing dynamic languages offers an opportunity to resolve the apparent trade-off between debugging and high performance. Truffle/Graal-implemented languages are expressed as abstract syntax tree (AST) interpreters. They enjoy competitive performance through platform support for type specialization, partial evaluation, and dynamic optimization/deoptimization. A prototype debugger for Ruby, implemented on this platform, demonstrates that basic debugging services can be implemented with modest effort and without significant impact on program performance. Prototyped functionality includes breakpoints, both simple and conditional, at lines and at local variable assignments. The debugger interacts with running programs by inserting additional nodes at strategic AST locations; these are semantically transparent by default, but when activated can observe and interrupt execution. By becoming in effect part of the executing program, these "wrapper" nodes are subject to full runtime optimization, and they incur zero runtime overhead when debugging actions are not activated. Conditions carry no overhead beyond evaluation of the expression, which is optimized in the same way as user code, greatly improving the prospects for capturing rarely manifested bugs. When a breakpoint interrupts program execution, the platform automatically restores the full execution state of the program (expressed as Java data structures), as if running in the unoptimized AST interpreter. This then allows full introspection of the execution data structures such as the AST and method activation frames when in the interactive debugger console. Our initial evaluation indicates that such support could be permanently enabled in production environments.
Wireless Networked Control Systems with Nonlinear Control and Novel Smith Predictor

This paper aims to the random and uncertain network delays, as well as controlled plant might be time-variant or nonlinear, a novel approach is proposed that novel Smith predictor combined with nonlinear control for wireless networked control systems (WNCS). This novel Smith predictor comes true to hide predictor models of the network delays into real network data transmission processes, therefore network delays don’t need to be measured, identified or estimated on-line. Based on IEEE 802.15. 4 (ZigBee), the results of simulation show validity of this control scheme, and indicate that system has better dynamic performance and anti-jamming ability.
A study of file sizes and functional lifetimes

The performance of a file system depends strongly on the characteristics of the files stored in it. This paper discusses the collection, analysis and interpretation of data pertaining to files in the computing environment of the Computer Science Department at Carnegie-Mellon University (CMU-CSD). The information gathered from this work will be used in a variety of ways: 1. As a data point in the body of information available on file systems. 2. As input to a simulation or analytic model of a file system for a local network, being designed and imlemented at CMU-CSD [1]. 3. As the basis of implementation decisions and parameters for the file system just mentioned. 4. As a step toward understanding how a user community creates, maintains and uses files.
A general, context-aware pedestrian trajectory prediction model

Autonomous driving on highways and freeways, as a feature, is already available in quite a few high-end commercial vehicles being sold today. Autonomous driving in urban environments, on the other hand, is still an active area of academic and industrial research [6], because of its relatively complex nature. Urban driving requires the self-driving vehicle to interact with not just other vehicles, but also other moving agents such as cyclists and pedestrians. Pedestrian trajectory prediction is challenging because of the relatively higher number of degrees of freedom in pedestrian movement and absence of uniform rules across different cities and different scenarios within a city. Furthermore, in scenarios such as intersections, context, such as pedestrian traffic lights, stop signs and sidewalk geometry, significantly influences pedestrian movement. The objective of this thesis is to present a general, context-aware, long term (order of few seconds) trajectory prediction model for pedestrians in urban intersections. To meet this objective, first, the Augmented Semi Nonnegative Sparse Coding (ASNSC) [13] framework, for trajectory prediction, is extended to embed context, and build the Context-aware Augmented Semi Nonnegative Sparse Coding (CASNSC) algorithm. For prediction in new, unseen intersections with different curbside geometries (orthogonal versus skewed), CASNSC is further extended to build the Transferable Augmented Semi Nonnegative Sparse Coding (TASNSC) algorithm. Urban intersections can at times vary significantly in the type of pedestrian behaviors encountered, even across intersections with similar geometries. For instance, faster, rule breaking students near a college campus versus slower pedestrians in a residential area. While TASNSC is capable of successfully transferring knowledge from one intersection to another, it lacks the ability to update its prediction model as, and when, new intersections are visited and novel behaviors are encountered. An online model, based on TASNSC, is also presented in this thesis to account for this particular limitation.
Single image focus editing

In this paper, we present a postprocessing approach to tackle the single image focus editing problem. In detail, the proposed method can accomplish the tasks of focus map estimation, image refocusing and defocusing. Given an image with a mixture of focused and defocused objects, we first detect the edges and then estimate the focus map based on the edge blurriness which is depicted explicitly with a well-parameterized model. The image refocusing problem is addressed in an elaborate blind deconvolution framework, where the image prior is modeled well by using both global and local constraints. Especially, we correct the defocused blurry edges to sharp ones with the aid of the parametric edge model and then render this cue as a novel local prior to ensure the sharpness of the refocused image. Experimental results demonstrate that the proposed approach performs well in producing different styles of realistic images from a single input by focus editing.
Rigid-Plating and Cortico-Cancellous Allograft Are Effective for 3-Level Anterior Cervical Discectomy and Fusion: Radiographic and Clinical Outcomes

Objective To determine the risk factors associated with radiographic changes and clinical outcomes following 3-level anterior cervical discectomy and fusion (ACDF) using rigidplate constructs and cortico-cancellous allograft. ACDF has demonstrated efficacy for treatment of multilevel degenerative cervical conditions, but current data exists in small heterogeneous forms. Methods A retrospective review included 98 patients with primary 3-level ACDF surgery at one institution from 2008 to 2013 with minimum 1-year follow-up. Cervical sagittal vertical axis (SVA), segmental height, fusion, and lordosis radiographs were measured preoperatively and at 2 postoperative periods. Results Rates of asymptomatic pseudarthroses and total reoperations were 18% and 4%, respectively. Results demonstrated immediate improvements in cervical lordosis (5.5°, p < 0.01) and segmental height (5.0-mm increase, p < 0.01) with little changes in the cervical SVA (3.2-mm increase, p < 0.01). The segmental height decreased from immediate postoperative period to final follow-up (1.7-mm decrease, p < 0.01). Older age was protective against radiolucent lines (p < 0.05). Patient-reported outcomes significantly improved following surgery (p < 0.01). Current smoking status and diagnosis of diabetes mellitus had no impact on radiographic or clinical outcomes. Risk factors were not identified for the 5 reoperations (4%). Conclusion Three-level ACDF with rigid-plating and cortico-cancellous allograft is an effective procedure for degenerative diseases of the cervical spine without the application of additional adjuncts or combined anteriorposterior cervical surgeries. Significant improvements in cervical lordosis, segmental height, and segmental alignment can be achieved with little change in cervical SVA and a low rate of reoperations over short-term follow-up. Similarly, patient-reported outcomes show significant improvements.
Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners

MOTIVATION Accurate prediction of protein contact maps is an important step in computational structural proteomics. Because contact maps provide a translation and rotation invariant topological representation of a protein, they can be used as a fundamental intermediary step in protein structure prediction. RESULTS We develop a new set of flexible machine learning architectures for the prediction of contact maps, as well as other information processing and pattern recognition tasks. The architectures can be viewed as recurrent neural network implemantations of a class of Bayesian networks we call generalized input-output HMMs (GIOHMMs). For the specific case of contact maps, contextual information is propagated laterally through four hidden planes, one for each cardinal corner. We show that these architectures can be trained from examples and yield contact map predictors that outperform previously reported methods. While several extensions and improvements are in progress, the current version can accurately predict 60.5% of contacts at a distance cutoff of 8 A and 45% of distant contacts at 10 A, for proteins of length up to 300.
Empowerment for continuous agent—environment systems

This article develops generalizations of empowerment to continuous states. Empowerment is a recently introduced information-theoretic quantity motivated by hypotheses about the efficiency of the sensorimotor loop in biological organisms, but also from considerations stemming from curiosity-driven learning. Empowerment measures, for agent—environment systems with stochastic transitions, how much influence an agent has on its environment, but only that influence that can be sensed by the agent sensors. It is an information-theoretic generalization of joint controllability (influence on environment) and observability (measurement by sensors) of the environment by the agent, both controllability and observability being usually defined in control theory as the dimensionality of the control/observation spaces. Earlier work has shown that empowerment has various interesting and relevant properties, for example, it allows us to identify salient states using only the dynamics, and it can act as intrinsic reward without requiring an external reward. However, in this previous work empowerment was limited to the case of small-scale and discrete domains and furthermore state transition probabilities were assumed to be known. The goal of this article is to extend empowerment to the significantly more important and relevant case of continuous vector-valued state spaces and initially unknown state transition probabilities. The continuous state space is addressed by Monte Carlo approximation; the unknown transitions are addressed by model learning and prediction for which we apply Gaussian processes regression with iterated forecasting. In a number of well-known continuous control tasks we examine the dynamics induced by empowerment and include an application to exploration and online model learning.
A Rational Design of Cu2O−SnO2 Core‐Shell Catalyst for Highly Selective CO2‐to‐CO Conversion

The electrochemical reduction of CO2 (CO2RR) is a versatile method that is capable of simultaneously reduce CO2 emission and produce valuable fuels and chemicals. However, its application is hindered by the lack of cost‐effective catalysts and significant overpotential requirement. In this work, we report a low‐cost and surfactant/capping agent free method to synthesize cubic Cu2O−SnO2 core‐shell electrocatalyst, whose thickness can be easily controlled by the content of tin precursor. The optimized Cu2O−SnO2 catalyst with a 5 nm‐thick shell achieved over 90 % faradaic efficiency towards CO at a low overpotential of 390 mV, which is comparable to some of the noble metal catalysts. The catalyst also exhibited good stability over 18 hours of test at −0.6 V vs. RHE in 0.5 M KHCO3 electrolyte. This work provides a widely applicable strategy for developing a low‐cost electrocatalyst for CO2 conversion.
Random Forest Oriented Fast QTBT Frame Partitioning

Block partition structure is a critical module in video coding scheme to achieve significant gap of compression performance. Under the exploration of future video coding standard by the Joint Video Exploration Team (JVET), named Versatile Video Coding (VVC), a new Quad Tree Binary Tree (QTBT) block partition structure has been introduced. In addition to the QT block partitioning defined by High Efficiency Video Coding (HEVC) standard, new horizontal and vertical BT partitions are enabled, which drastically increases the encoding time compared to HEVC. In this paper, we propose a fast QTBT partitioning scheme based on a Machine Learning approach. Complementary to techniques proposed in literature to reduce the complexity of HEVC Quad Tree (QT) partitioning, the propose solution uses Random Forest classifiers to determine for each block which partition modes between QT and BT is more likely to be selected. Using uncertainty zones of classifier decisions, the proposed complexity reduction technique is able to reduce in average by 30% the encoding time of JEM-v7.0 software in Random Access configuration with only 0.57% Bjøntegaard Delta Rate (BD-BR) increase.
ANALISIS EFEKTIVITAS DAN EFISIENSI PELAKSANAAN ANGGARAN BELANJA BADAN PERENCANAAN PEMBANGUNAN DAERAH (BAPPEDA) ACEH TENGAH

Penelitian ini dilakukan pada Badan Perencanaan Pembangunan Daerah Kabupaten Aceh Tengah (Bappeda Aceh Tengah), dengan objeknya adalah pelaksanaan anggaran belanja instansi tersebut selama tahun anggaran 2012-2016. Tujuan penelitian ini adalah untuk menganalisis bagaimana efektivitas dan efisiensi pelaksanaan anggaran belanja pada Bappeda Aceh Tengah. Penelitian ini menggunakan sumber data sekunder berupa Laporan Realisasi Anggaran Bappeda Aceh Tengah selama tahun anggaran 2012-2016. Penelitian ini merupakan penelitian kualitatif, dengan teknik analisis datanya bersifat deskriptif dan menggunakan perhitungan rasio keuangan, yaitu rasio efektivitas dan rasio efisiensi. Hasil penelitiannya menunjukkan bahwa efektivitas pelaksanaan anggaran belanja pada Bappeda Aceh Tengah selama tahun anggaran 2012-2016 umumnya sudah efektif, kecuali pada tahun 2013 yang berada pada kriteria cukup efektif yang diakibatkan oleh rendahnya realisasi belanja tidak langsung di tahun tersebut. Kemudian ditinjau dari sisi efisiensinya juga menunjukkan sudah efisien, karena realisasi belanja langsungnya dalam upaya mewujudkan program/kegiatan yang telah ditetapkan cenderung lebih rendah, dengan jumlahnya masih dibawah 50%
Exploring the customer satisfaction in a global context : The case of elite soccer league websites

This paper describes the use of Quality Function Deployment and Benchmarking technique to evaluate Sport Organizations Web sites, in the particular the case of Elite Soccer leagues. We used the 22 basic questions of WebQual 2.0 as the customer expectations; then, we evaluated the perceived quality and performance (customer satisfaction) of five professional soccer leagues websites. Using the dimensions of WebQual as the voice of the customers, we analyzed the impact of the website on the customers in order to evaluate the current online marketing strategy. Five international well known soccer teams from the most competitive and financially wealthy leagues are assessed using a survey. Data are collected from students using an on-line questionnaire and a Likert scale was used (1 to 5). Analyses of the results explain a rigorous and well planned marketing strategy in the professional leagues to keep the public informed and to increase the position of their brands. The findings and their implications for e-commerce and design of online marketing strategy are discussed and conclusions will be provided.
A survey on existing extractive techniques for query-based text summarization

Condensation of document information from the text according to the query is primarily a concerned matter due to the rapid growth of information. In fact, there is not usually enough time to scan through the contents and understand each document and make decision based on the queries. Hence, there is a great demand for query based summarization of text documents. Therefore, text summarization is considered to be a challenging task and a paramount research area these days. Moreover, query-based text summarization is a significant problem, which has innumerable applications. It becomes increasingly important to extensively study the different mechanisms that can provide an effective and a short summary. Therefore, in this paper, an effort has been made to go through the various extractive based approaches for query-based text summarization so that time and effort can be reduced to find a useful summary for the users as specified by their need.
MotifScorer: using a compendium of microarrays to identify regulatory motifs

UNLABELLED We describe MotifScorer, a program for systematic genome-wide identification of transcription sites. The program uses a compendium of gene expression microarrays and implements state-of-art partial least squares (PLSs) based regression and stepwise regression procedures. Candidate motifs from the upstream sequences of groups of co-regulated genes are identified and assigned a score using genomic background models and available motif finding tools. The use of a large library of expression data allows statistical comparative analysis of the specificity of motifs identified in different conditions. AVAILABILITY MotifScorer, which is written in Java and Matlab, manual and example files are available from the authors.
Toward power consumption in partitioned Wireless Sensors Networks routing technique

The increasing research in the areas of Wireless Sensors Networks (WSNs) have a positive impact in the world and have gained increasing attention from both the research community and actual users. A wireless sensor network compromises of a large number of diffuse independent sensors; these sensors is to be used to control an event range in scale from large national environmental pollution monitoring networks, to small scale body-worn medical monitoring systems. The sensor node consists of a wireless radio, processor and a variety of sensors. The sensors are operated with restricted capacity batteries, which of course have a lifetime, so to preserve power; the transmission/reception by the sensor radio should be decreased as much as possible. The potential contribution of this paper is to propose a theoretical model toward power consumption technique based on the idea of partitioning wireless network and we will use heinzelman equations using realistic variables for example, the initial power for each node, the distance between nodes, and the message size.