QSEE Trustzone Kernel Integer Overflow Vulnerability
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Your HTC One® VX User Guide 2 Contents Contents
Your HTC One® VX User guide 2 Contents Contents Unboxing HTC One VX 10 Back cover 11 SIM card 13 Storage card 14 Charging the battery 15 Switching the power on or off 15 Setting up your phone Setting up HTC One VX for the first time 17 Home screen 17 Getting contacts into HTC One VX 18 Transferring contacts from your old phone through Bluetooth 19 Getting photos, videos, and music on or off HTC One VX 19 Getting to know your settings 20 Updating the HTC One VX software 20 Your first week with your new phone Features you'll enjoy on HTC One VX 22 Touch gestures 24 Motion gestures 27 Sleep mode 29 Unlocking the screen 30 Making your first call 30 Sending your first text message 31 The HTC Sense keyboard 31 Notifications 31 Copying and sharing text 34 Capturing the HTC One VX screen 35 Switching between recently opened apps 35 Can't find the Menu button? 35 Checking battery usage 36 Camera Camera basics 37 Taking a photo 38 Recording video 38 Keeping the camera on standby 39 Taking continuous camera shots 39 3 Contents Camera scenes 40 Improving portrait shots 40 Taking a group shot 40 Taking a panoramic photo 40 Using HDR 41 Recording videos in slow motion 41 Improving video stability 41 Changing camera and video settings 42 Personalizing Making HTC One VX truly yours 43 Personalizing HTC One VX with scenes 43 Changing your wallpaper 44 Applying a new skin 45 Personalizing your Home screen with widgets 45 Adding apps and other shortcuts on your Home screen 46 Rearranging or removing widgets and icons on your Home screen 47 Personalizing -
Trusense: Information Leakage from Trustzone
TruSense: Information Leakage from TrustZone Ning Zhang∗, Kun Suny, Deborah Shandsz Wenjing Lou∗z, Y. Thomas Hou∗ ∗Virginia Polytechnic Institute and State University, VA yGeorge Mason University, Fairfax, VA zNational Science Foundation, Arlington, VA Abstract—With the emergence of Internet of Things, mobile Unlike software exploitations that target vulnerabilities in devices are generating more network traffic than ever. TrustZone the system, side-channel attacks target information leakage of is a hardware-enabled trusted execution environment for ARM a physical implementation via its interactions with the execu- processors. While TrustZone is effective in providing the much- needed memory isolation, we observe that it is possible to derive tion environment. Side-channel information can be obtained secret information from secure world using the cache contention, from different types of physical features, such as power [10], due to its high-performance cache sharing design. electromagnetic wave [11], acoustic [12] and time [13], [14], In this work, we propose TruSense to study the timing-based [15]. Among these side channels, the cache-based timing cache side-channel information leakage of TrustZone. TruSense attack is one of the most important areas of research [14], can be launched from not only the normal world operating system but also a non-privileged user application. Without access [15], [16], [8], [17], [18]. to virtual-to-physical address mapping in user applications, we Processor cache is one of the basic components in mod- devise a novel method that uses the expected channel statistics ern memory architecture to bridge the gap between the fast to allocate memory for cache probing. -
Kernel Methods for Knowledge Structures
Kernel Methods for Knowledge Structures Zur Erlangung des akademischen Grades eines Doktors der Wirtschaftswissenschaften (Dr. rer. pol.) von der Fakultät für Wirtschaftswissenschaften der Universität Karlsruhe (TH) genehmigte DISSERTATION von Dipl.-Inform.-Wirt. Stephan Bloehdorn Tag der mündlichen Prüfung: 10. Juli 2008 Referent: Prof. Dr. Rudi Studer Koreferent: Prof. Dr. Dr. Lars Schmidt-Thieme 2008 Karlsruhe Abstract Kernel methods constitute a new and popular field of research in the area of machine learning. Kernel-based machine learning algorithms abandon the explicit represen- tation of data items in the vector space in which the sought-after patterns are to be detected. Instead, they implicitly mimic the geometry of the feature space by means of the kernel function, a similarity function which maintains a geometric interpreta- tion as the inner product of two vectors. Knowledge structures and ontologies allow to formally model domain knowledge which can constitute valuable complementary information for pattern discovery. For kernel-based machine learning algorithms, a good way to make such prior knowledge about the problem domain available to a machine learning technique is to incorporate it into the kernel function. This thesis studies the design of such kernel functions. First, this thesis provides a theoretical analysis of popular similarity functions for entities in taxonomic knowledge structures in terms of their suitability as kernel func- tions. It shows that, in a general setting, many taxonomic similarity functions can not be guaranteed to yield valid kernel functions and discusses the alternatives. Secondly, the thesis addresses the design of expressive kernel functions for text mining applications. A first group of kernel functions, Semantic Smoothing Kernels (SSKs) retain the Vector Space Models (VSMs) representation of textual data as vec- tors of term weights but employ linguistic background knowledge resources to bias the original inner product in such a way that cross-term similarities adequately con- tribute to the kernel result. -
2.2 Kernel and Range of a Linear Transformation
2.2 Kernel and Range of a Linear Transformation Performance Criteria: 2. (c) Determine whether a given vector is in the kernel or range of a linear trans- formation. Describe the kernel and range of a linear transformation. (d) Determine whether a transformation is one-to-one; determine whether a transformation is onto. When working with transformations T : Rm → Rn in Math 341, you found that any linear transformation can be represented by multiplication by a matrix. At some point after that you were introduced to the concepts of the null space and column space of a matrix. In this section we present the analogous ideas for general vector spaces. Definition 2.4: Let V and W be vector spaces, and let T : V → W be a transformation. We will call V the domain of T , and W is the codomain of T . Definition 2.5: Let V and W be vector spaces, and let T : V → W be a linear transformation. • The set of all vectors v ∈ V for which T v = 0 is a subspace of V . It is called the kernel of T , And we will denote it by ker(T ). • The set of all vectors w ∈ W such that w = T v for some v ∈ V is called the range of T . It is a subspace of W , and is denoted ran(T ). It is worth making a few comments about the above: • The kernel and range “belong to” the transformation, not the vector spaces V and W . If we had another linear transformation S : V → W , it would most likely have a different kernel and range. -
23. Kernel, Rank, Range
23. Kernel, Rank, Range We now study linear transformations in more detail. First, we establish some important vocabulary. The range of a linear transformation f : V ! W is the set of vectors the linear transformation maps to. This set is also often called the image of f, written ran(f) = Im(f) = L(V ) = fL(v)jv 2 V g ⊂ W: The domain of a linear transformation is often called the pre-image of f. We can also talk about the pre-image of any subset of vectors U 2 W : L−1(U) = fv 2 V jL(v) 2 Ug ⊂ V: A linear transformation f is one-to-one if for any x 6= y 2 V , f(x) 6= f(y). In other words, different vector in V always map to different vectors in W . One-to-one transformations are also known as injective transformations. Notice that injectivity is a condition on the pre-image of f. A linear transformation f is onto if for every w 2 W , there exists an x 2 V such that f(x) = w. In other words, every vector in W is the image of some vector in V . An onto transformation is also known as an surjective transformation. Notice that surjectivity is a condition on the image of f. 1 Suppose L : V ! W is not injective. Then we can find v1 6= v2 such that Lv1 = Lv2. Then v1 − v2 6= 0, but L(v1 − v2) = 0: Definition Let L : V ! W be a linear transformation. The set of all vectors v such that Lv = 0W is called the kernel of L: ker L = fv 2 V jLv = 0g: 1 The notions of one-to-one and onto can be generalized to arbitrary functions on sets. -
On Semifields of Type
I I G ◭◭ ◮◮ ◭ ◮ On semifields of type (q2n,qn,q2,q2,q), n odd page 1 / 19 ∗ go back Giuseppe Marino Olga Polverino Rocco Tromebtti full screen Abstract 2n n 2 2 close A semifield of type (q , q , q , q , q) (with n > 1) is a finite semi- field of order q2n (q a prime power) with left nucleus of order qn, right 2 quit and middle nuclei both of order q and center of order q. Semifields of type (q6, q3, q2, q2, q) have been completely classified by the authors and N. L. Johnson in [10]. In this paper we determine, up to isotopy, the form n n of any semifield of type (q2 , q , q2, q2, q) when n is an odd integer, proving n−1 that there exist 2 non isotopic potential families of semifields of this type. Also, we provide, with the aid of the computer, new examples of semifields of type (q14, q7, q2, q2, q), when q = 2. Keywords: semifield, isotopy, linear set MSC 2000: 51A40, 51E20, 12K10 1. Introduction A finite semifield S is a finite algebraic structure satisfying all the axioms for a skew field except (possibly) associativity. The subsets Nl = {a ∈ S | (ab)c = a(bc), ∀b, c ∈ S} , Nm = {b ∈ S | (ab)c = a(bc), ∀a, c ∈ S} , Nr = {c ∈ S | (ab)c = a(bc), ∀a, b ∈ S} and K = {a ∈ Nl ∩ Nm ∩ Nr | ab = ba, ∀b ∈ S} are fields and are known, respectively, as the left nucleus, the middle nucleus, the right nucleus and the center of the semifield. -
Definition: a Semiring S Is Said to Be Semi-Isomorphic to the Semiring The
118 MA THEMA TICS: S. BO URNE PROC. N. A. S. that the dodecahedra are too small to accommodate the chlorine molecules. Moreover, the tetrakaidecahedra are barely long enough to hold the chlorine molecules along the polar axis; the cos2 a weighting of the density of the spherical shells representing the centers of the chlorine atoms.thus appears to be justified. On the basis of this structure for chlorine hydrate, the empirical formula is 6Cl2. 46H20, or C12. 72/3H20. This is in fair agreement with the gener- ally accepted formula C12. 8H20, for which Harris7 has recently provided further support. For molecules slightly smaller than chlorine, which could occupy the dodecahedra also, the predicted formula is M * 53/4H20. * Contribution No. 1652. 1 Davy, H., Phil. Trans. Roy. Soc., 101, 155 (1811). 2 Faraday, M., Quart. J. Sci., 15, 71 (1823). 3 Fiat Review of German Science, Vol. 26, Part IV. 4 v. Stackelberg, M., Gotzen, O., Pietuchovsky, J., Witscher, O., Fruhbuss, H., and Meinhold, W., Fortschr. Mineral., 26, 122 (1947); cf. Chem. Abs., 44, 9846 (1950). 6 Claussen, W. F., J. Chem. Phys., 19, 259, 662 (1951). 6 v. Stackelberg, M., and Muller, H. R., Ibid., 19, 1319 (1951). 7 In June, 1951, a brief description of the present structure was communicated by letter to Prof. W. H. Rodebush and Dr. W. F. Claussen. The structure was then in- dependently constructed by Dr. Claussen, who has published a note on it (J. Chem. Phys., 19, 1425 (1951)). Dr. Claussen has kindly informed us that the structure has also been discovered by H. -
The Kernel of a Derivation
Journal of Pure and Applied Algebra I34 (1993) 13-16 13 North-Holland The kernel of a derivation H.G.J. Derksen Department of Mathematics, CathoCk University Nijmegen, Nijmegen, The h’etherlands Communicated by C.A. Weibel Receircd 12 November 1991 Revised 22 April 1992 Derksen, H.G.J., The kernel of a deriv+ Jn, Journal of Pure and Applied Algebra 84 (1993) 13-i6. Let K be a field of characteristic 0. Nagata and Nowicki have shown that the kernel of a derivation on K[X, , . , X,,] is of F.&e type over K if n I 3. We construct a derivation of a polynomial ring in 32 variables which kernel is not of finite type over K. Furthermore we show that for every field extension L over K of finite transcendence degree, every intermediate field which is algebraically closed in 6. is the kernel of a K-derivation of L. 1. The counterexample In this section we construct a derivation on a polynomial ring in 32 variables, based on Nagata’s counterexample to the fourteenth problem of Hilbert. For the reader’s convenience we recari the f~ia~~z+h problem of iX~ert and Nagata’:: counterexample (cf. [l-3]). Hilbert’s fourteenth problem. Let L be a subfield of @(X,, . , X,, ) a Is the ring Lnc[x,,.. , X,] of finite type over @? Nagata’s counterexample. Let R = @[Xl, . , X,, Y, , . , YrJ, t = 1; Y2 - l = yy and vi =tXilYi for ~=1,2,. ,r. Choose for j=1,2,3 and i=1,2,. e ,I ele- ments aj i E @ algebraically independent over Q. -
Lecture 7.3: Ring Homomorphisms
Lecture 7.3: Ring homomorphisms Matthew Macauley Department of Mathematical Sciences Clemson University http://www.math.clemson.edu/~macaule/ Math 4120, Modern Algebra M. Macauley (Clemson) Lecture 7.3: Ring homomorphisms Math 4120, Modern algebra 1 / 10 Motivation (spoilers!) Many of the big ideas from group homomorphisms carry over to ring homomorphisms. Group theory The quotient group G=N exists iff N is a normal subgroup. A homomorphism is a structure-preserving map: f (x ∗ y) = f (x) ∗ f (y). The kernel of a homomorphism is a normal subgroup: Ker φ E G. For every normal subgroup N E G, there is a natural quotient homomorphism φ: G ! G=N, φ(g) = gN. There are four standard isomorphism theorems for groups. Ring theory The quotient ring R=I exists iff I is a two-sided ideal. A homomorphism is a structure-preserving map: f (x + y) = f (x) + f (y) and f (xy) = f (x)f (y). The kernel of a homomorphism is a two-sided ideal: Ker φ E R. For every two-sided ideal I E R, there is a natural quotient homomorphism φ: R ! R=I , φ(r) = r + I . There are four standard isomorphism theorems for rings. M. Macauley (Clemson) Lecture 7.3: Ring homomorphisms Math 4120, Modern algebra 2 / 10 Ring homomorphisms Definition A ring homomorphism is a function f : R ! S satisfying f (x + y) = f (x) + f (y) and f (xy) = f (x)f (y) for all x; y 2 R: A ring isomorphism is a homomorphism that is bijective. The kernel f : R ! S is the set Ker f := fx 2 R : f (x) = 0g. -
7. Quotient Groups III We Know That the Kernel of a Group Homomorphism Is a Normal Subgroup
7. Quotient groups III We know that the kernel of a group homomorphism is a normal subgroup. In fact the opposite is true, every normal subgroup is the kernel of a homomorphism: Theorem 7.1. If H is a normal subgroup of a group G then the map γ : G −! G=H given by γ(x) = xH; is a homomorphism with kernel H. Proof. Suppose that x and y 2 G. Then γ(xy) = xyH = xHyH = γ(x)γ(y): Therefore γ is a homomorphism. eH = H plays the role of the identity. The kernel is the inverse image of the identity. γ(x) = xH = H if and only if x 2 H. Therefore the kernel of γ is H. If we put all we know together we get: Theorem 7.2 (First isomorphism theorem). Let φ: G −! G0 be a group homomorphism with kernel K. Then φ[G] is a group and µ: G=H −! φ[G] given by µ(gH) = φ(g); is an isomorphism. If γ : G −! G=H is the map γ(g) = gH then φ(g) = µγ(g). The following triangle summarises the last statement: φ G - G0 - γ ? µ G=H Example 7.3. Determine the quotient group Z3 × Z7 Z3 × f0g Note that the quotient of an abelian group is always abelian. So by the fundamental theorem of finitely generated abelian groups the quotient is a product of abelian groups. 1 Consider the projection map onto the second factor Z7: π : Z3 × Z7 −! Z7 given by (a; b) −! b: This map is onto and the kernel is Z3 ×f0g. -
1 Semifields
1 Semifields Definition 1 A semifield P = (P; ⊕; ·): 1. (P; ·) is an abelian (multiplicative) group. 2. ⊕ is an auxiliary addition: commutative, associative, multiplication dis- tributes over ⊕. Exercise 1 Show that semi-field P is torsion-free as a multiplicative group. Why doesn't your argument prove a similar result about fields? Exercise 2 Show that if a semi-field contains a neutral element 0 for additive operation and 0 is multiplicatively absorbing 0a = a0 = 0 then this semi-field consists of one element Exercise 3 Give two examples of non injective homomorphisms of semi-fields Exercise 4 Explain why a concept of kernel is undefined for homorphisms of semi-fields. A semi-field T ropmin as a set coincides with Z. By definition a · b = a + b, T rop a ⊕ b = min(a; b). Similarly we define T ropmax. ∼ Exercise 5 Show that T ropmin = T ropmax Let Z[u1; : : : ; un]≥0 be the set of nonzero polynomials in u1; : : : ; un with non- negative coefficients. A free semi-field P(u1; : : : ; un) is by definition a set of equivalence classes of P expression Q , where P; Q 2 Z[u1; : : : ; un]≥0. P P 0 ∼ Q Q0 if there is P 00;Q00; a; a0 such that P 00 = aP = a0P 0;Q00 = aQ = a0Q0. 1 0 Exercise 6 Show that for any semi-field P and a collection v1; : : : ; vn there is a homomorphism 0 : P(u1; : : : ; un) ! P ; (ui) = vi Let k be a ring. Then k[P] is the group algebra of the multiplicative group of the semi-field P. -
Winnovative HTML to PDF Converter for .NET
Convert iTunes Movies to Android Tablets &Phones TunesKit for Mac For Windows Buy Resource Tutorial Support How to Convert iTunes Movies to Android Phones and Tablets Posted by Andy Miller on August 22, 2014 11:05:35 AM. iTunes as the world's biggest digital content store, also attracts many android fans to buy or rent movies, songs or iBooks here. However, the problems come up when they realized the DRM protection existing. All Apple movies are copy protected by Fairplay DRM protection. The consumers are not allowed to transfer their purchased or rented movies to any Non‐apple devices, of course including the Android phones and tablets. What to do if the android fans want to watch the iTunes movies to their Samsung Galaxy S5 or Galaxy Tab/Note, orHTC one M8, or even Amazon Kindle Fire? You know all these Android devices' screen is larger than iPhone. Crack iTunes DRM and Convert iTunes Movies to Android Devices To convert and transfer the iTunes purchased or rented movies to our android phones or tablets, we have to remove the DRM from iTunes movies at first. TunesKit iTunes DRM Media Converter is a great choice to help you accomplish your task. With TunesKit DRM Media Converter, we can easily get rid of the Fairplay DRM protection from both iTunes purchased and rented movies/TV shows. It converts iTunes DRMed M4V to common MP4 format which can be supported by any android phones and tablets well. The important reason we recommend it is that it can do this work very fast, nearly 20x faster than other similar application.